Autonomous & Connected Vehicles Handbook 2019

Spectrum co-existence challenges in the fully connected car Page 10

Advanced circuit protection for connected autonomous vehicles Page 26

AUGUST 2019

AUTONOMOUS

& CONNECTED VEHICLES

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V2Clueless – Next generation of connected apps If you had been watching the Saturday Night Live TV show in mid January of 2012, you would have seen a skit called the Headz Up App. It was an ad for a fictional app aimed at people so distracted by texting on their smartphone that they were prone to walking cluelessly into traffic.

That idea got laughs seven years ago, but it looks as though advancing V2X technology may make it a reality. A press release from Purdue University about a system called Phade sounds a lot like Headz Up, but it is completely serious. Phade, devised by Purdue researchers, is a technology that lets ordinary public cameras be used to send personalized messages to people wandering around on the street with a smartphone.

The trick that makes Phade possible is the use of the smartphone’s pattern of motion on surveillance cameras as its address. A server taking in the camera video feed builds a data packet by linking a message to the address code it comes up with by watching people move around. When a smartphone gets a packet, it compares data from its own gyroscope and accelerometer to what the packet address says. If the two match, the smartphone automatically delivers the message to its owner.

You have to give the Purdue researchers credit for their ingenious way of sending messages without first learning the destination’s IP or MAC address, information ordinary data transmission protocols need before they can pass data back and forth. The researchers also built privacy into their scheme by keeping personal sensing data within the smartphone and by blurring out some of the motion details.

LELAND TESCHLER EXECUTIVE EDITOR

But they seem to be missing the boat when it comes to who would actually use Phade. The researchers talk about places such as museums where visitors can receive messages about the exhibits they’re viewing and stores such as Amazon Go, which uses phone technology to get rid of traditional checkout registers. They mention public safety uses in high-crime or high-accident areas to warn specific users about potential threats, but these almost seem like afterthoughts.

Clearly the researchers have never seen Saturday Night Live.

That the old SNL skit probably comes closer to a vignette of Phade’s future than what its creators envision. After all, Phade tackles a problem more compelling than uninformed museum visitors – autonomously driven vehicles that can’t be trusted to stop short of absent-minded pedestrians.

I have one piece of advice for the Purdue Researchers, who envision their system issuing relatively circumspect warnings to smartphone users, along the lines of, ‘Car coming, be careful.’ Instead, make the warnings as creative as the technology. For example, as the smartphone owner is about to step into the path of an oncoming car, the phone might blurt something like, ‘Keep walking. Laughing at people’s misfortunes cheers me up,’ or, ‘I was going to smack you in the face but I think that car bearing down on you is going to beat me to it,’ or, ‘Stumbling into traffic is the kind of pig-ignorant behavior I’d expect from a pinhead like you.’

Comments like these are just the thing to get the attention of people too absorbed in texting to see what’s in front of them.

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AUGUST 2019

CONTENTS AUTONOMOUS & CONNECTED VEHICLES

36 22 06

02 06

18

V2CLUELESS — NEXT GENERATION OF CONNECTED APPS HOW BIDIRECTIONAL CONVERTERS SPEED THE DESIGN OF DUAL-BATTERY AUTOMOTIVE SYSTEMS New standards for combined 48-V/12-V vehicles dictate stringent tests for overvoltages and currents that new converter ICs can help meet.

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SPECTRUM CO-EXISTENCE CHALLENGES IN THE FULLY CONNECTED CAR High-performance RF bandpass filters may hold the key to autonomous vehicle communication without interference.

14

VALIDATING CORRECT BEHAVIOR IN ADAS AND AV SYSTEMS Autonomous vehicles must operate in a world of uncertainties. Validation platforms are now capable of determining whether behavior is correct given complex contextual attributes.

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DATA INJECTION — TESTING AUTONOMOUS SENSORS THROUGH REALISTIC SIMULATION

26

New protocols for high connectivity need robust protection for reliability and safety.

HIGH-SPEED DATA MEANS 32 WHAT FOR CONNECTED VEHICLES Technologies behind multigigabit Ethernet and 5G could transform transportation.

36

22

Future connected vehicles are characterized by clusters of densely packed electronics. Thermoelectric modules can keep the temperature of these hot spots at manageable levels.

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WANT A NEW PERSPECTIVE ON BATTERY MANAGEMENT? BUY A PLUG-IN EV One facet of EV ownership: Thinking of your daily commute power consumption as equivalent to the sunlight hitting 20 ft 2 of your lawn.

39

SENSING WHEEL SPEED WITH GMR

42

BLUETOOTH AND THE ROAD TO A KEYLESS FUTURE

Proof that autonomous vehicles are safe to put on the road begins with rigorous models of the sensors that guide them.

KEEPING CONNECTED ELECTRONICS COOL

ADVANCED CIRCUIT PROTECTION FOR CONNECTED AUTONOMOUS VEHICLES

Giant magnetoresistance sensors are strong candidates for the high-accuracy wheel-speed sensing necessary in autonomous vehicle systems.

Efforts are underway to let smartphones double as car keys.

44

FLASH MEMORY KEEPS CARS CONNECTED A new kind of flash memory is optimized specifically to handle the hostile environment imposed on electronics in vehicular uses.

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AUTONOMOUS & CONNECTED VEHICLES

TONY ARMSTRONG | ANALOG DEVICES INC.

How bidirectional converters speed the design of dual-battery automotive systems New standards for combined 48-V/12-V vehicles dictate stringent tests for overvoltages and currents that new converter ICs can help meet.

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The 48/12-V automotive battery system is just around the corner. Most major automobile manufacturers have been perfecting such systems for past few years, and it is evident that their first implementations will be relatively near term.

The typical 48/12-V system consists of two separate branches of power. The traditional 12-V bus uses a conventional sealed lead-acid (SLA) battery to handle traditional systems such as infotainment, lighting, and windows. The new 48-V system will support heavier loads such as the starter/generator, A/C compressors, electric turbochargers, and regenerative braking. Vehicle electrical components will either run off the 48-V Li-ion battery or the 12-V SLA – but not both.

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BIDIRECTIONAL CONVERTERS Present-day 12 V vehicle system

Planned 48/12-V system

Jump-start

Jump-start

12V

12V

DC DC

48V

No human access Safety

S Starter

G 12V battery

Generator

Loads

The point of the 48-V system is to reduce weight in the wiring harness. The higher voltage allows use of less current for the same amount of power and thus makes it possible to employ lighter, lower-gauge wiring. The advantage becomes clear from the fact that high-end vehicles today can carry more than 4 km of wire.

The move to 48/12-V systems is a necessary and crucial step in the long and arduous journey to the fully autonomous passenger vehicle. Nevertheless, the transition doesn’t mean the 12-V battery is going away; there are far too many legacy 12-V systems in the installed vehicle base for this to happen anytime soon.

Nevertheless, use of 48/12-V systems introduce complexities. There must be two separate charging circuits for these individual batteries because of their respective chemistries.

Additionally, there must be a mechanism to safely move charge between them.

While dual batteries certainly complicate the design of the various vehicle electrical subsystems, there are advantages to be gained.

For one thing, having two batteries allows for redundancy should one fail. And according to some auto makers, a 48-V electrical system results in a 10-15% gain in fuel economy for internal combustion engine (ICE) vehicles, thus reducing CO2 emissions. Moreover, future vehicles that use a dual 48/12-V system will enable the integration of electrical booster technology that operates independently of the engine load, thereby improving acceleration.

Such compressors are already in the advanced stages of development and will reside between the induction system and the intercooler, using the 48-V rail to spin-up their turbos. eeworldonline.com | designworldonline.com

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M/G

S opt. starter

12V battery

Loads

Meanwhile, the proliferation of new electrical systems and connectivity within vehicles has brought the 12-V electrical system to its usable power limit. These changes, coupled with increased demands for power, have created the need for supplementing the 12-V SLA battery system with its 3-kW max output.

New automotive standards spell out some facets of how 48/12-V systems must function.

A newly proposed automotive standard, known as LV148, calls for a secondary 48-V bus that works with the existing 12-V system. In 2011, this standard was adopted by the Big Five German automakers – Audi, BMW, Daimler, Porsche, and Volkswagen – and spells out electrical requirements and test procedures for 48-V components. According to LV148, the 48-V rail is to include an integrated starter generator (ISG) or belt start generator, a 48-V lithium-ion battery, and a bidirectional dc/dc converter able to deliver 10 sec of kilowatt-level energy from the 48-V and 12-V batteries combined.

This technology is aimed at conventional ICE automobiles, as well as hybrid electric and mild hybrid vehicles.

INTEGRATING 48/12-V BATTERY SYSTEMS LV148 requires that the 12-V bus continuously power the ignition, lighting, infotainment and audio systems. It uses the 48-V bus to power bigger loads -- active chassis systems, A/C compressors, and so forth, and to participate in regenerative braking.

The addition of a 48-V supply network will impact other electrical components as well.

Electronic Control Units (ECUs), for example, will need to work at a higher voltage. This, in turn, 8 • 2019

opt. power loads

48V battery

Motor/ generator

The typical 12 and 48-V electrical system planned for emerging EVs and hybrids divides electrical loads into light and heavy-duty categories, with the conventional 12-V SLA battery handling infotainment and similar tasks while the 48-V battery powers big loads. The 48-V part of the system is designed so it is generally isolated from anyone but qualified auto technicians.

It should also be said that there is still some debate about exactly what loads will go on the 12-V rather than the 48-V part of the system. Nevertheless, it is clear from the 48/12V architecture that the bidirectional dc/dc converter that links the two electrical systems is a critical component key to the efficient operation of the entire vehicle.

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AUTONOMOUS & CONNECTED VEHICLES BOOST (48V AT 5A) V1 SUPPLY 24V TO 54V

4mΩ

6.8μH

DRVCC

SNS1P SNS1N TG BST

SNS2P SNS2N DS2

DS1

DG2 LT8228

BIAS

REPORT

DRVCC

The LT8228 configured in a simplified bidirectional battery backup system. The LT8228 mode of operation is externally controlled through the DRXN pin or automatically selected. In addition, the LT8228 has protection MOSFETs for the V1 and the V2 terminals. The protection MOSFETs provide negative voltage protection, isolation between the input and output terminals during an internal or external fault, reverse current protection and inrush current control. The LT8228 provides input and output current limit programming in buck and boost mode operation using four pins, ISET1P, ISET1N, ISET2P and ISET2N. The controller also provides independent input and output current monitoring using the IMON1 and IMON2 pins. Current limit programming and monitoring is functional for the entire input and output voltage range of 0 to 100 V. Additional features include a feedback voltage tolerance = ±0.5% over temperature; self-test, diagnostics and fault reporting; programmable fixed or synchronizable switching frequency from 80 kHz to 600kHz; and programmable softstart and dynamic current limit.

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μC VDD

FAULT

V2

8

V2 BATTERY 14V

V1D DG1 V1

SW BG

BUCK (14V AT 20A)

3mΩ

GND

will necessitate that manufacturers of dc/ dc converters introduce specialized ICs to enable this higher-power transfer.

In this regard, Analog Devices’ Power

by Linear (PbL) group designed and developed dc/dc converters able to transfer higher levels of energy with high efficiency, thus conserving energy while simultaneously minimizing thermal design aspects.

Specifically, 48/12-V systems call for a bidirectional step-down and step-up dc/dc converter that spans the two voltages. The converter plays a role in charging either of the batteries while simultaneously allowing both batteries to supply current to the same load if that’s what is required.

The first 48/12-V dual battery dc/dc converters to come off the drawing boards used different power components to step-up and step-down the voltage. However, ADI’s recently introduced LT8228 bidirectional dc/ dc controller uses the same external power components for the step-up conversion as for step-down conversion. This part is a 100-V bidirectional constant-current or constantvoltage synchronous buck or boost controller with independent compensation networks.

The LT8228 automatically determines the direction of power flow or the direction can be externally controlled. Input and output-protection MOSFETs protect 8 • 2019

DRXN

μC I/O

against negative voltages, control inrush currents, and provide isolation between terminals under fault conditions such as shorts in the switching MOSFETs. In stepdown mode, protection MOSFETs prevent reverse current. In step-up mode, the same MOSFETs regulate the output inrush current and use an adjustable timer circuit breaker to protect themselves.

The LT8228 implements bidirectional input and output current limiting as well as independent current monitoring. Masterless, fault-tolerant current-sharing allows LT8228 chips to work in parallel and to be added or subtracted while maintaining current sharing accuracy. Internal and external fault diagnostics and reporting are available via separate pins (FAULT and REPORT). Each LT8228 regulates to the average output current, thus eliminating the need for a master controller. When an individual LT8228 is disabled or in a fault condition, it stops contributing to the average bus, making the current sharing scheme fault tolerant. The LT8228 sits in a 38-lead TSSOP package.

The LT8228 controller provides a stepdown output voltage when in buck mode or a step-up output voltage when in boost mode.

The input and output voltage can be set as high as 100 V. In applications such as battery backup systems, the bidirectional feature eeworldonline.com | designworldonline.com

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BIDIRECTIONAL CONVERTERS Long-term overvoltage test It becomes clear why features such as dynamic current limiting and voltage protection are important in automotive bidirectional dc/dc converters by examining LV148, a standard adopted by the Big Five German automakers that spells out electrical requirements and test procedures for 48-V components. Among them are tests for transient overvoltage, longterm overvoltage, and overvoltage on regenerative components (graphs courtesy, AMETEK Compliance Test Solutions). These kinds of conditions, and many more along the same lines, are what the bidirectional dc/ dc converter in any 48/12-V system is likely to encounter.

allows the battery to be charged from either a higher or lower voltage supply. When the supply is unavailable, the battery boosts or bucks power back to the supply.

To optimize transient response, the LT8228 has two error amplifiers, one for boost mode, the other for buck mode with separate compensation pins. The controller operates in discontinuous conduction mode when reverse inductor current is detected for light load conditions.

The LT8228 brings a new level of performance, control and simplification to 48/12-V dual battery dc/dc automotive systems. During starting or when the vehicle needs additional power, the LT8228 allows both batteries to supply energy simultaneously to the same load. Thus this bidirectional converter helps designers easily configure the 12-V and 48-V battery systems which will be required for the fully autonomous vehicles of the near future.

REFERENCES Analog Devices | www.analog.com

LV 148

AB

B A

60 V 48 V

LV 124

a) B; b) B; c) C

tr = 100 ms

t0

tf = 100 ms

t2 = 1 s

17 V 13.5 V 0V

tr

10 ms

t1 60 mins

tf

10 ms

Overvoltage on regenerating components Funktionszustand A

LV 148

58 V 54 V 52 V

0V

t0 ≥1s

t1 ≤ 300 ms

Transient overvoltage test 70 V

LV 148

58 V 48 V

LV 124

Test 3a = 2.5 s (3x) 3b = 9 s (1000x) 100ms

18 V 17 V 16 V

40ms

600 ms

400 ms

600 ms Test 3a = 2 s (3x, Tmax) 3b = 2 s (3x, Tmin) 3c = 8 s (100x, TRT)

0V

to

tr

t1

tf

t2

tf

t3a, 3b, 3c

LT8228 data sheet | www.analog.com/lt8228

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AUTONOMOUS & CONNECTED VEHICLES

Spectrum co-existence challenges in the fully connected car ALI BAWANGAONWALA | QORVO, INC.

High-performance RF bandpass filters may hold the key to autonomous vehicle communication without interference.

Continuing advances in technology are making the autonomous vehicle a practical reality, and there is frequent discussion among technologists about Wi-Fi and 5G as enablers of the connected car.

Equally important, but less talked about, are the complexities of bringing these technologies together and making them work hand-in-hand without creating interference issues that impact safety and operation.

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The players in the autonomous vehicle industry must solve these challenges before the world can realize the potential of truly autonomous vehicles.

An autonomous vehicle is one capable of navigating itself from point A to point B without human intervention. This will take place through the sharing data, such as position and speed, with surrounding vehicles and infrastructures.

The data sharing will happen via Vehicleto-Everything (V2X) communication systems

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As depicted by this figure from Qualcomm, the 5G spectrum is divided into a sub-6-GHz region and a millimeter wave region.

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SPECTRUM CHALLENGES DSRC Operation in 5.9 GHz spectrum

C-V2X

Yes

Yes

Coexistence in 5.9 GHz

Yes (Adjacent channel with 3GPP technology)

Yes (Adjacent channel with .11p; co-channel coexistence from R-14 onwards)

Technology

Based on IEEE 802.11p

Based on mobile 3GPP

Safety only

Safety & enhanced

Limited (via aps only)

Yes

Support of low-latency direct communications

Yes

4ms with path 1ms with Rel-16

Security & privacy on V2V/V2I/V2P

Yes

Yes

Security & privacy on V2N

N/A

Yes

Evolution path

No

Yes

MIMO solution

No support standardized

Rx diversity for 2 antennas mandatory; Tx diversity for 2 antennas supported

Target use cases Support for network communications

that enhance driver awareness of potential hazards, improving collision avoidance and significantly reducing fatalities.

V2X is a wireless technology aimed at enabling data exchanges between a vehicle and its surroundings. It includes capabilities for Vehicle-to-Vehicle (V2V), Vehicle-toInfrastructure (V2I), Vehicle-to-Network (V2N) and Vehicle-to-Pedestrians (V2P) communications.

V2X is based on 5.9-GHz dedicated short-range communications, specifically defined for fast-moving objects and enabling establishment of a reliable radio link, even in non-line-of-sight conditions.

In addition to boosting safety, V2X will also enhance traffic efficiency by providing warnings for upcoming traffic congestion and proposing alternative routes. This supports eco-friendly driving with reduced CO2 emissions, greater transport efficiency and less need for vehicle maintenance.

DSRC VS. C-V2X V2X can either be DSRC (Dedicated ShortRange Communications) or C-V2X (CellularVehicle-to-Everything). Until a few years ago, DSRC, based on the IEEE 802.11p standard, was the only V2X technology available, with production in the U.S. and Japan beginning in eeworldonline.com | designworldonline.com

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2017. C-V2X, which utilizes cellular technology, was introduced more recently to create a direct communications link between vehicles.

Complicating the market situation as a whole is the fact that different countries and automakers are supporting one or the other approach. But while C-V2X and DSRC are different standards, they address the same problem using the same spectrum, and can co-exist.

A wide range of technologies play a role in providing full vehicular connectivity. Each technology has its own niche and must work with all the others in the autonomous car without degrading the performance of other technologies.

V2X (DSRC, C-V2X) for automotive safety: The automotive ecosystem will use V2X to communicate among vehicles, with roadside infrastructure, and with the overall environment to improve safety-consciousness and pave the way to autonomous driving.

• 4G/5G cloud connectivity for vehicle OEM services: 4G/5G connectivity could be used to remotely diagnose and monitor car operations, make over-the-air software updates, perform teleoperation, and redefine car ownership by operating a fleet of shared, autonomous vehicles.

8 • 2019

The similarities and differences between DSRC and C-V2X, as depicted by Qualcomm.

•

•

•

•

4G/5G cloud connectivity for in-vehicle experiences: Drivers and passengers could use this type of connectivity to enjoy new in-vehicle experiences, from augmented reality-based navigation, to rear-seat entertainment and music streaming services.

Wi-Fi for premium in-vehicle experiences and automotive dealer services: Drivers and passengers could enjoy many enhanced in-car Wi-Fi based experiences. For example, efficient Wi-Fi connectivity throughout the vehicle could support ultra-high definition (ultraHD) video streaming to multiple displays and enable screen mirroring from compatible devices and wireless backup cameras. Wi-Fi could also support automotive dealer services, enabling automatic check-in, diagnostic data transfer and software updates.

Bluetooth: Drivers and passengers could stream high-fidelity music via Bluetooth, as well as benefit from practical services such as using a smartphone as a key fob.

SDARS (Satellite Digital Audio Radio Services): With connectivity to satellitebased radio services, vehicle occupants are connected to their favorite radio broadcasts no matter where they are.

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AUTONOMOUS & CONNECTED VEHICLES

With an understanding of the various technologies involved and their respective missions, we can better examine their interoperability challenges, which will include compatibility with 5G and LTE. 5G is the fifth generation of cellular technology. It is designed to further boost data rates, reduce latency, and make wireless services more flexible. 5G also promises lower latency, which can improve the performance of business applications as well as other digital experiences such as online gaming, videoconferencing and selfdriving cars.

5G spectrum is classified as sub-6-GHz and millimeter wave.

Wi-Fi operates in 2.4 GHz, 5.2 GHz and 5.6-GHz spectrum.

2.4-GHz Wi-Fi must co-exist with the LTE B40 and B41 frequency bands. For this to work, radio designers must ensure they are using the right filter products that provide enough attenuation in adjacent bands to ensure good receiver sensitivity, or risk degrading the user experience.

5-GHz Wi-Fi enables higher data rates than 2.4 GHz because more channels can be bundled together in the 5-GHz band thanks to larger bandwidth. However, there are a few issues here.

The products using 2.4-GHz Wi-Fi must co-exist with LTE B40 and B41. The key to allowing the coexistence of products employing the two communication standards lies to a great degree in RF filters able to realize sharp skirts outside their pass bands.

For 5.2-GHz and 5.6-GHz Wi-Fi to co-exist, radio designers will need to ensure adequate out-of- band attenuation to get the full benefit of wider bands (i.e. data rates).

Another issue is 5.6-GHz Wi-Fi co-existence with V2X. Imagine a scenario where a passenger in the autonomous car is using a 5.6-GHz hot-spot. For reliable V2X operation (communication between the cars on the road), the V2X radio must ensure ‘zero desense to the receiver,’ which can only be realized with a choice of good filter products that provide enough out-of-band attenuation to 5.6-GHz Wi-Fi.

HIGH-PERFORMANCE FILTERING As automobiles evolve with enhanced features and added functions, the number of radios they carry is rising, up from the traditional two to three to as many as five. (i.e. V2X, 4G/5G, Wi-Fi, Bluetooth, SDARS).

To enable the best performance and a better user experience, some of these technologies must interact with each other and work together seamlessly. It’s clear from the discussion above that highly reliable co-existence is key to the success and widespread acceptance of autonomous vehicles.

(26 dBm class 2) 194 MHz

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26 dBm class 2

70 MHz

B38 TDD

B7 DL FDD

50 MHz

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It’s crowded up here — communication bands near Wi-fi

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SPECTRUM CHALLENGES 5 GHz automotive coexistence - V2X and Wi-Fi Wi-Fi 5.6 GHz Filter in Wi-Fi Module

Wi-Fi 5.2 GHz Filter in Wi-Fi Module

V2X (802.11p and C-V2X) 70 MHz V2X System Rx & Tx Filtering Required

V2X Coexistence Area

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Qorvo automotive V2X, Wi-Fi frontend modules, along with filter products and SDARS offerings, have been developed in close alignment with chipset and module suppliers – as well as carmakers. Through design and packaging, these solutions are delivering the accuracy, reliability and ruggedness essential to intelligent communication systems in the autonomous vehicle. Seamless co-existence of all the technologies on the connected car spectrum will ensure that our ever-mobile world is safer, more reliable and more enjoyable for all of us.

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Filter products are the key to enabling this kind of coexistence. Two of the parameters that characterize high-performance filter products are the resonator qualities, i.e. quality-factor (Q) and coupling-factor (k2). High Q is necessary to minimize insertion loss, while high k2 enables wider bandwidth.

Technology advances at the resonator level have brought low insertion loss and high selectivity performance with wider bandwidth filter products at frequencies up to 6 GHz. As an example of what’s possible in RF filtering today, consider that Qorvo’s filter products are designed using patented, Bulk-Acoustic-Wave (BAW) technology that is optimized to address complex selectivity requirements, from 1.5 GHz up to 6 GHz in standard footprints.

The Qorvo QPQ2200Q filter is the world’s first filter product designed to address coexistence of V2X with 5.6 GHz Wi-Fi for autonomous vehicles. Another example is the 2.4 GHz Wi-Fi coexist filter, QPQ2254Q, designed to enable coexistence with LTE B40 and B41.

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Products employing 5.6-GHz Wi-Fi will be able to co-exist with those communicating via V2X only through use of high-performance RF filters able to realize super-sharp skirts outside their pass bands.

REFERENCES Qorvo RF filters, https://www.qorvo.com/ products/filters-duplexers/rf-filters

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Validating correct behavior in ADAS and AV systems Autonomous vehicles must operate in a world of uncertainties.

Validation platforms are now capable of determining whether behavior is correct given complex contextual attributes.

There is an interesting dashcam video circulating in the autonomous vehicle (AV) development community of a Tesla-paid test driver coming up on what the driver calls the curve of death. He has gone through this particular curve numerous times with the autopilot feature turned on. In the video, the car starts out driving too close to the left-hand side of the curve. The Tesla is going so slowly that the driver worries aloud about the semitruck behind him. He’s concerned that the truck might try to pass him on the right though the vehicles

DAVID FRITZ MENTOR, A SIEMENS BUSINESS

are on what is supposed to be a single lane. As the Tesla goes through the curve, it is way off into what NASCAR calls the marbles, small bits of debris that come off tires and vehicles and accumulate near the outside wall of a track. Finally, the Tesla gets around the curve and the driver disengages the autopilot.

In AVs, actuation, sensing, embedded software, and SoCs are highly interdependent

AV functions have been divided into three separate levels: system level, vehicle level, and mobility level.

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ADAS AND AV SYSTEMS Decision making - complex HW/SW dependencies

The point of the video is that the actual state of AV technology is far behind what you might think from reading accounts in the press.

Clearly, we have a long way to go before AV technology is ready for prime time. Some of the reasons for this is the sheer complexity of the task. It involves sensing technology that incudes cameras, lidar and radar, and a complicated decision-making process that goes into managing vehicle steering, braking, and engine control.

The complexity is such that AV functions have been divided into three separate levels: system level, vehicle level, and mobility level.

The system level refers to the ICs and software that implements the decision making, the sensors, and the actuation mechanics for steering, engine control, and so forth. Today, the decision-making at the system level generally employs special-purpose SoCs that combine multiple CPU cores with Graphics Processing Units (GPUs), Digital Signal Processors (DSPs), Field Programmable Gate Array (FPGAs), Video Processing Units (VPUs), Image Processing Units (ISPs), Neural Processing Units (NPUs), and other kinds of computational accelerators.

Many automotive equipment and vehicle manufacturers are now developing or are contemplating developing custom SoCs for decision making rather than rely on commercially available ICs. The reasons include optimization for their platform, differentiation from other vendors, and to reduce dependence on traditional semiconductor vendors.

A case in point is Tesla, which says its own neural processing chipset performs 36.8 Tera Operations per Second (TOPS) while consuming 72 W. The overall performance of the Tesla custom chipset falls well short of the Nvidia Drive AGX Pegasus platform, a commercial device. But analysts figure Tesla’s approach saves 20% over the cost of the Nvidia platform.

Sitting one level above the system level is the vehicle level.

It covers inputs about the environment in which the vehicle finds itself such as nearby traffic. It also factors in knowledge about the vehicle performance and dynamics that the system level also uses for decision making.

Sitting above the vehicle level is the mobility level. It encompasses communication technologies that include V2V, 5G, V2X, and so forth, that are used to connect individual vehicles to infrastructure, other vehicles, and data sources. eeworldonline.com | designworldonline.com

Mentor — A&CV HB 08-19.indd 15

A sequence of events that might unfold as an AV driving down the road that hits a sizeable pothole. The decision-making processor creates multiple threads to deal with all the sensor data as its software tries to react to the event. When the software reacts, it causes the decision-making system to pull more power because there are cascading memory accesses.

In an extreme scenario, the increased power supply load may be such that supply voltage drops below a threshold.

The mobility system level, vehicle level, and system level functions are all highly interdependent. This interdependency means that developers can’t use conventional development techniques to devise an AV. In the old days, developers might first enumerate requirements, decompose the requirements into basic functions, do a few Matlab models, generate some C code, slap the code on a microcontroller, and call the project done. But complicated interdependencies prevent AVs from fitting nicely into this mold.

What’s changed in AV technology is not the requirements so much as how we implement or fulfill them. That’s where a lot of the emphasis on SoCs in decision making is starting to come into play.

It’s helpful to talk about specific examples to illustrate the interactions involved in decision making. Imagine your vehicle driving along at 130 km/hr. when the AV system sees what it thinks is a baby in the road. Any human would know that the object is probably not a baby, and they would probably want a closer look before making a decision.

But the AV system “knows” the best thing to do is to avoid hitting a baby. That’s it’s number-one priority. There is a car in the blind spot that the AV knows about, but it decides that statistically, the better decision is to change lanes to avoid hitting the baby because it cannot stop in time. Hopefully the car next to it will adjust and the AV avoids hitting the baby.

The scenario we’ve just outlined actually happened. Unfortunately, it did not have a happy ending. The car didn’t simply move over to miss the baby. It actually swerved, lost control, and hit a tree, causing injuries. And it turned out the baby in the road was actually a cat.

Several improvements to AV technology will be needed to head off bad outcomes like this one. One such improvement is to model vehicle dynamics at a higher level of precision. Consider another scenario where a car comes around the corner and its tires hit a slick spot, lose some traction, and cause an accident. This accident is avoidable if the modeling process is accurate enough to anticipate potential problems like a loss of traction.

More specifically, if you only look at an abstract virtual prototype of the car as it goes through a corner, it may seem like everything is good.

But a higher-fidelity model of the vehicle dynamics gives a more accurate picture of how the vehicle will behave. Models of vehicle dynamics with a 8 • 2019

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AUTONOMOUS & CONNECTED VEHICLES

high level of accuracy lead to a close calibration of the digital twin with the physical platform.

A typical way of calibrating the two is to run a set of scenarios through the digital twin, then mockup the same scenarios on a track.

Developers can have a high level of confidence in a digital twin that behaves identically to the real vehicle given those conditions.

A high-fidelity digital twin allows developers to run what are known as corner-case scenarios – scenarios involving a problem or situation that arises only outside of normal operating parameters, and specifically one that happens when multiple environmental variables or conditions are simultaneously at extreme levels, though each parameter is within its specified range.

Classic examples of corner cases include the experience of Waymo in Australia when its AVs didn’t behave properly because they didn’t know what a kangaroo was. Knowledge about kangaroos is important for cars in Sidney, less so for vehicles in San Francisco. On the other hand, it is probably more important for AVs traversing bridges in San Francisco to recognize when an earthquake is underway.

High-fidelity digital twins can be used to explore AV performance in corner cases, but it is useful to understand just what factors separate a “high fidelity” digital twin from a more generic version. Perhaps the clearest way of explaining the difference between the

two is through another example. Imagine an AV driving down the road that hits a sizeable pothole. The pothole shakes the AV sensors, causing several of them to register the presence of false objects. The decisionmaking processor creates multiple threads to deal with all the sensor data as its software tries to react to the event. When the software reacts, it causes the decision-making system to pull more power because there are cascading memory accesses. In an extreme scenario, the increased power supply load may be such that supply voltage drops below a threshold.

The drop in supply voltage causes bits to flip inside the memory cache and may cause the decision-making processor to reset. At that point, there may be a real possibility of the AV hitting someone.

A high-fidelity digital twin able to model this scenario can only do so by simulating events all the way down to the silicon level, literally running detailed simulations of chip operations. In contrast, it makes little sense to simulate the hitting of a pothole on PCs in a server farm. The average AV likely will generate between 2TB to 4TB of data an hour via its sensors and processors. All this data must be processed and then acted upon in real time. A data center, in comparison, would require a minimum of a dual-socket x86 server with multiple GPU or FPGA accelerators to process this much data in real time. The server farm approach might be

good for getting the software up and running but not for verifying the correct behavior of the system itself.

It is also worth commenting on some of the other nuances that distinguish high-fidelity digital twins. For example, the simulation must run the full software load – you can’t stub out parts of the software for expedience.

Ignoring one sensor for testing purposes changes the behavior of the whole system.

And high-fidelity models must include the electromechanical actuation functions and the system dynamics of all the electromechanical components themselves. This level of detail simply can’t take place on remote PCs. Once you have a digital twin with high fidelity, it becomes possible to use public databases -- now being compiled by governmental bodies and insurance companies -- as a source of verification scenarios. An example is Gidas, for German in-depth accident study. Gidas is one of the most intensive research projects for road traffic accident studies worldwide. It is a cooperative project between the Federal Highway Research Institute and the German Association for Research in Automobile Technology. Gidas analyzes an accident with about 3,600 parameters and includes information such as the condition of the wheels, the setting of the car in general, and the condition of the safety systems during the accident.

Testing for correctness Gidas, for German in-depth accident study, is one of the most intensive research projects for road traffic accident studies worldwide.

Gidas includes video data for about 30,000 accidents.

It’s possible to translate this video data into virtual scenarios which the digital twin can navigate. We may see the day when this sort of scenario test might serve as a prequalification for putting an AV on the road for final physical testing.

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ADAS AND AV SYSTEMS PAVE360 platform components A platform called PAVE360

by Siemens is a simulation program that allows semiconductor designers to develop and test highperformance virtual SoCs within a virtual car driving in a virtual world under controlled and repeatable virtual conditions.

More importantly Gidas includes video data for about 30,000 accidents. It’s possible to translate this video data into virtual scenarios which the digital twin can navigate. We may see the day when this sort of scenario test might serve as a prequalification for putting an AV on the road for final physical testing.

High-fidelity digital twins also open up the possibility of creating what is called scenario closure – coming up with scenarios that have yet to be imagined. It may be possible for intelligent machines to devise corner cases that humans are unlikely to think of and which could not be tested in a physical platform.

High-fidelity digital models can also model the passengers themselves, thus providing a simple criteria for passing one of the scenarios: Did you avoid the accident? If you did, did the occupants survive? This distinction is important because is easy to make a move in an AV that would actually injure passengers. The easiest decision might not be the best decision in terms of passenger survivability.

MODERN TOOLS Fortunately, tools are being developed to aid in the production digital twins with sufficient fidelity for AV testing. One example is a platform called PAVE360. The developer of PAVE360 is Siemens, which describes PAVE360 as a mixed-fidelity pre-silicon verification and validation environment. More specifically, PAVE360 is a simulation framework that allows semiconductor designers to develop and test high-performance virtual SoCs within a virtual car driving in a virtual world under controlled and repeatable virtual conditions.

The PAVE360 platform includes a number of components to make high-fidelity modeling possible. Some of the most important in this regard are Simcenter Prescan for scenario and physics-based sensor modeling; Veloce, Questa, and VirtuaLab for high-fidelity modeling of computational SoCs and ISO 26262 fault campaign execution; Simcenter Amesim for physics-based electromechanical modeling of vehicle dynamics; X-Step for 5G vehicle-to-vehicle (V2V) and vehicleto-infrastructure (V2I) modeling of smart cities; Catapult HLS for modeling and developing high-speed AI accelerators; Mentor Safe IC for automated ISO 26262 RTL analysis and synthesis; and Virtual Auto Network to connect virtual and physical systems through CAN, LIN, Flexray and Automotive Ethernet communications protocols.

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These tools let PAVE360 model high-performance SoCs capable of high bandwidth sensor fusion, real-time AI neural network processing, and automotive safety-certified control solutions in conjunction with the software and the rest of the vehicle platform.

While the main function of PAVE360 is to enable the design of SoCs, it also helps automotive systems designers simultaneously develop silicon, software, and electromechanical components using the FMI/ FMU, TLM2.0 and Simulink standards.

Another noteworthy aspect of PAVE360 is its extensive libraries which allow designers to develop or import models for silicon or electromechanical components ranging from sensors to ECUs. The models can be changed and tested during development of the individual components. Once the component designs are complete, high-fidelity models of each component are used for verification testing.

As you might suspect, PAVE360 is designed around the functional safety standards for automotive applications including ISO-26262, the functional safety standard for electrical and electronic systems, and ISO-21448, the safety standard for the intended functionality of the vehicle. This helps designers ensure the entire platform meets required safety standards before committing any portion of the platform to production.

All in all, correct behavior in AVs can only be defined in the context of the physical vehicle itself and the environment within which it is being driven. AV subsystems are integrated so a bad decision by one subsystem affects all the others. One subsystem can’t overcome mistakes made by others if they’ve been tested separately.

But a high-fidelity digital twin can use scenario databases or formal methodologies to show how the physical platform will behave in ways that are difficult to test on the road.

REFERENCES

Mentor, a Siemens Business | https://www.mentor.com/mentor-automotive/ TIRIAS Research sponsored whitepaper | https://www.tiriasresearch.com/ downloads/av-simulation-extends-to-silicon/ Autonocast podcast episode, David Fritz and Jim McGregor on developing chips for AVs | http://www.autonocast.com/blog/2019/5/22/144-david-fritzand-jim-mcgregor-on-chips-for-autonomous-vehicles

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Data injection — Testing autonomous sensors through realistic simulation Proof that autonomous vehicles are safe to put on the road begins with rigorous models of the sensors that guide them.

Onboard sensors are critical for autonomous vehicles to navigate the open road, but the real environment is full of obstacles. Everyday interferences such reflective surfaces, whiteout conditions, fog, rain, traffic congestion, and objects (i.e. pedestrians, parked vehicles, buildings, signs) clutter the atmosphere and can result in sensor misreads or false targets.

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HOLGER KRUMM | dSPACE GmbH

The sheer volume of testing necessary to evaluate every possible driving scenario makes it impractical to conduct real test drives on the road. The solution lays with the creation of virtual driving scenarios and sensor-realistic simulation, which can take place in the safety of the lab.

An efficient approach is to validate autonomous driving functions by using realistic, off-the-shelf simulation models (i.e. such as dSPACE Automotive

eeworldonline.com | designworldonline.com

8/8/19 8:10 AM

DATA INJECTION

Simulations of radar sensors produce data representing the presence of objects, their distance from the vehicle, and their relative speed and direction.

Simulation Models – (ASM) and a virtual testing platform. With a setup like this, entire test scenarios can be virtually reproduced, including the environment sensors (camera, radar, Lidar, etc.), the vehicle under test, traffic, roads, driving maneuvers, and the surrounding environment.

Sensor-realistic simulation is the most efficient way of verifying and validating the environment sensors onboard an autonomous vehicles. The basic premise behind sensor-realistic simulation is that sensor models mimic real sensors by generating the same kinds of signals. The sensor models use a geometrical approach to calculate distance, velocity, acceleration, horizontal and vertical angles to the nearest point of every detected object. The software models generate raw data from the sensors to simulate the environment (i.e. traffic objects, weather, lighting conditions, etc.) to mimic the feedback a real vehicle would receive.

Sensing Front camera Surround view Radar Lidar Ultrasonic OTA 1) stimulation of sensor front-end

Perception

Sensor simulation is often divided into five stages: sensing, perception, data fusion, application, and actuation.

Data fusion

Application

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Fused object list, free spaces, ...

Image processing Object detection and tracking 3D point cloud processing

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The sensor simulation process involves the following stages: Sensing – The simulated sensors receive a signal representative of one or more objects. The sensors detect the virtual targets as they would real objects. The sensors begin to capture real-time information such as distance, angular position, range and velocity.

Perception – Through imaging or signal processing, the sensors recognize the presence of the object(s).

Data fusion – The validation process begins as the raw data collected from the various sensors feeds to the CPU of the

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Actuation Steering Braking Powertrain chassis HMI

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Tools for sensor-realistic simulations Tools for sensor-realistic simulation have several key components: Models supporting virtual test drive scenarios, for example, driving maneuvers, roads, vehicles, traffic objects, sensors, etc. (i.e. dSPACE ASM) Software to support the 3-D animation and visualization of simulated test drive scenarios (i.e. dSPACE MotionDesk) An FPGA powerful enough to synchronously insert raw sensor data into sensor ECUs (i.e. dSPACE ESI Unit) A processing platform with a high-performance GPU to calculate environment and sensor models and generate raw data and target lists (i.e. dSPACE Sensor Simulation PC) A scalable PC-based platform that can support SIL test setups and highvolume cluster tests (i.e. dSPACE VEOS) A real-time system for HIL test setups that offers integration options for various sensors (i.e. dSPACE SCALEXIO) A radar test system or test bench that can perform over-the-air simulation of radar echoes (i.e. dSPACE DARTS)

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electronic control unit (ECU). Here, the information is combined and processed (this is also known as sensor fusion) in real time to create a target list (or point cloud) of objects, both static and moving.

Application - The object list is run through a perception algorithm where object classification, situation analysis, trajectory planning and decision-making activities take place. Based on the outcome, the ECU determines what actions the autonomous vehicle should take.

Actuation – The ECU sends output signals to the appropriate actuators to carry out the desired action.

For validation purposes, the sensor data gathered during the testing process must be recorded and stored in a timecorrelated manner (i.e. time stamped, tagged, synchronized) so it can be played back in the laboratory later.

It takes detailed and realistic models to address the high-complexity needs associated with autonomous sensor systems (i.e. decision algorithms, motion control algorithms). The more realistic the sensor model, the better the results.

Depending on the level of complexity, sensor models can be grouped into three general types: Ideal ground truth/ probabilistic; Phenomenological/physical; and Real/over-the-air (OTA).

Ideal ground truth/probabilistic sensor models are technology independent. They are primarily used for object-list-based injection (i.e. 3D and 2D sensors used to detect traffic lights, traffic signs, road objects, lanes, barriers, pedestrians, etc.). These kinds of models help check whether an object is detectable within a set range.

In a sensor simulation experiment, ground truth/probabilistic sensor models provide ideal data (ground-truth information), which can optionally be superimposed with probabilities of events (probabilistic effects).

For example, superimposition is used to simulate typical measurement noise of radar.

The simulation returns a list of classified objects (vehicles, pedestrians, cyclists, traffic signs etc.) as well as their coordinates and motion data (distance, relative speed, relative acceleration, relative azimuth and elevation angle).

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Ideal ground truth/probalistic sensor models are typically validated via softwarein-the-loop (SIL) simulations, which are faster than real time, and in hardware-inthe-loop (HIL) simulations, which take place in real time. They can also be deployed on cluster systems to play a part in conducting high volumes of tests.

Within the dSPACE tool chain, these sensors are part of the ASM tool suite (e.g.

ASM Ground Truth sensor models). They run on a CPU together with the vehicle, traffic and other relevant environment models. These models are easy to configure, and simulation always takes place synchronously.

Phenomenological/physical sensor models are physics-based. They are based on the measurement principles of the sensor (i.e. camera uptakes, radar wave propagation) and play a role simulating phenomena such as haze, glare effects or precipitation. They can generate raw data streams, 3-D point clouds, or target lists.

Because these models address physical effects, they can be quite complicated. Calculation typically takes place on a graphics processing unit (GPU).

These models also are typically validated in a SIL or HIL test setup.

Within the dSPACE tool chain, phenomenological/physical sensor models are visualized in MotionDesk and calculated on a dSPACE Sensor Simulation PC, which incorporates a high-performance GPU card and can facilitate deterministic, real-time sensor simulations with a high degree of realism.

Real/over-the-air sensor models are also physics-based models. They are used in tests with real physical signals and real sensor ECUs to analyze real-world sensor behavior.

Validation can take place by stimulating the whole sensor over-theair on a radar test system (i.e. dSPACE Automotive Radar Test Systems – DARTS).

This approach is useful for object detection scenarios. Alternatively, validation can take place on a complete radar test bench when there is a need to integrate other vehicle components (i.e. front bumper, chassis).

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8/8/19 12:47 PM

DATA INJECTION The hierarchy of AV sensor models Reality

Real Physical Phenomenological

Test bench Over the air (OTA) Sensor models ranging from ideal ground truth to real are used to validate autonomous sensor systems.

SensorSim Physics based model

Probabilistic

Ideal ground truth

ASM Generic and scalable

Sensor-realistic simulations rely on a bus system like CAN, CAN FD, FlexRay, LIN or Ethernet that carries signals and vehicle network traffic. So any autonomous vehicle simulations must include simulations of bus behavior, ranging from simple communication tests and rest bus simulation to complex integration tests.

Additionally, the sensor model must interface to the deviceunder-test to inject data for simulation testing. A high-performance FPGA can be used to feed raw sensor data, target lists and/or object lists into the sensor ECU in a synchronized manner. For example, the dSPACE Environment Sensor Interface (ESI) Unit was designed for this role. It receives raw sensor data, separates it according to the individual sensors, and then inserts the time-correlated data into a digital interface behind the respective sensor front end.

Other interfaces that are supportive of autonomous driving development include: FMI, XIL-API, OpenDrive, OpenCRG, OpenScenario or Open Sensor Interface. These interfaces give engineers the option of integrating valuable data from accident databases or traffic simulation tools for co-simulation activities.

With regard to cameras, the preferred practice today is to use over-the-air stimulation to feed raw image data directly into the camera’s image processing unit. As the camera sensor captures the image data stream, the animated scenery is displayed on a monitor and engineers can detect ranges and sensor outputs to the nearest point of an object (i.e. distance, relative velocity, vertical and horizontal angles, etc.), as well as sensor timings (i.e. cycles, initial offset, output delay time).

To validate camera-based sensors, simulations must account for different lens types and distortions – such as fish-eyeing, vignetting and chromatic aberrations. Additionally, test scenarios must factor in the use of multiple image sensors, as well as sensor characteristics (i.e. monochromatic representation, Bayer pattern, HDR, pixel errors, image noise, etc.). eeworldonline.com | designworldonline.com

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Complexity

The next generation of sensor simulation products is capable of producing highly realistic visualization using technologies such as 3-D remodeling, physics-based rendering, ray tracing and dynamic lighting. The detail of different terrains, environmental lighting (i.e. haze, shadows), lens flare, glare effects, dynamic materials (i.e. rain, snow, fog), and much more can have 3D photo-realistic qualities to help boost simulation accuracy.

Simulations of sensors such as Lidar and radar generally detect objects by computing the path of reflections for Lidar signals or how electromagnetic waves will propagate for radar. The procedure involves sending beams into a 3D scene and capturing their reflections, which allows for the integration of physical effects such as multipath propagation into the modeling. The result is a physically correct simulation of the propagation of radar waves or a nearinfrared laser beam.

Values for parameters such as reflection points, angles, distance, range, Doppler speed, diffuse scattering, multipath propagation, and so forth are collected and processed to calculate the vehicle’s distance from an object and to describe the surrounding environment (i.e. in the form of a point cloud). Gathered data is used to generate a target list that includes information on distance and the intensity of the reflected light (for Lidar sensors) or the frequency of an echo signal (for radar). The resulting sensor-realistic simulations help validate the behavior of the sensor path.

REFERENCES dSPACE Inc. | www.dspaceinc.com

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Keeping connected electronics cool

ANDREW DEREKA | LAIRD THERMAL SYSTEMS Future connected vehicles are characterized by clusters of densely packed electronics.

Thermoelectric modules can keep the temperature of these hot spots at manageable levels.

Advances in automotive electronics tend to come in small packages where dense circuitry squeezed into a pint-sized footprint generates lots of heat and thermal challenges for designers.

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The usual way of cooling electronics is to use convection heat transfer to a heat sink and then go to more drastic measures if necessary. But a heat sink alone will only cool to just above ambient temperatures. If the heat sink has a high thermal resistance, its hot-side temperature will be several degrees above ambient, a situation that happens frequently.

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COOLING ELECTRONICS

A greater temperature reduction can often be accomplished via spot cooling -- incorporating a Peltier cooler close to the electronics generating heat. The Peltier cooler, also known as a thermoelectric cooling module (TEM), can reduce the temperature of its cold side by as much as ~30°C below that of its hot side.

A Peltier cooler is comprised of p- and n- type semiconductor materials brought in contact to form a junction. Electrons flow when the device connects to a dc power source. At the cold junction, the electrons absorb energy (heat) and move from a low-energy state in the p-type semiconductor element to a higher energy state in the n-type semiconductor element. At the hot junction, energy is expelled to a heat exchanger (usually a heat sink) as electrons move from a high-energy level to a lower energy level. Reversing the direction of current flow reverses the direction of heat pumping.

Thus the TEM can provide both cooling and heating with a simple reversal of the current.

A point to note is that heat rejection at the hot side is critical – if the hot side saturates, heat will flow back into the Peltier cooler and heat it up.

This is where the Peltier cooler coefficient of performance (COP) applies. COP is a function of the material dimension, the temperature of the hot side and cold side, and the dc current to the module.

A given thermoelectric material has an optimum dc current for maximum COP if the hot-side and cold-side temperatures are fixed.

The design of a thermoelectric cooling system usually starts from a given temperature difference across the hot and cold sides of the module and the amount of cooling capacity necessary (in Watts). Cooling capacity varies with operating voltage and module current. TEMs can be configured to run on a variety of dc voltages through selection of a series or parallel configuration for the TEM’s internal construction. The heat sink’s thermal resistance is also a factor. The module selection process is often iterative and is aided by online calculation wizards.

Peltier coolers, like the HiTemp ET Series from Laird Thermal Systems, maintain a high coefficient of performance (COP) to allow for maximum heat rejection into the air environment even with poor heat sinking. Peltier coolers are available in a wide range of heat pumping capacities, form factors, and input voltages to support a wide range of applications.

An additional point to note is that Peltier coolers don’t outgas – they are basically comprised of ceramics. Designers need to avoid materials that can release any kind of gas that can form a coating over time near laser or imaging sensor optics, so Peltier coolers are an ideal solution for these applications.

Headlights increasingly use LEDs to get energy efficiency far exceeding that of incandescent bulbs, and future headlights may use laser-diode light sources rather than LEDs. Though laser diodes and LEDs are energy efficient, both they and their driving circuits generate heat in small spaces, making them candidates for TEM cooling.

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AUTONOMOUS & CONNECTED VEHICLES Ceramic substrate

Thermally conductive layer

TE material

However, a thermoelectric module is generally a more expensive option than passive cooling. The temperature sensing involved may necessitate a closed-loop feedback control, and circuits cooled to below the dew point may need considerations for condensation. But TEMs operated with a ProportionalIntegral-Derivative (PID) controller typically have operating lifetimes exceeding 70,000 hours. A thermoelectric based controller can drive the temperature of an enclosure to the preset temperature. PID control can also adjust the net power to the TEM to a precise degree, often within 0.5°C of a set point, allowing a fast and accurate response to heat load fluctuations.

Thermoelectric coolers operate by the Peltier effect. When a dc electric current flows through the device, heat transfers from one side of the device to the other, making one side cooler while the other heats up. The hot side is attached to a heat sink to help dissipate the heat.

In some applications, multiple coolers can be cascaded together to boost the cooling temperature differential.

It can be illustrative to examine applications that are now candidates for thermoelectric cooling. One is laser-based smart headlight technology. Smart headlights automatically adjust the direction of their light output. For example, they may direct the high beam away from oncoming traffic to avoid blinding the other driver or illuminate at an angle when the car turns to enhance the driver’s viewable area.

In a smart headlight, a single laser focuses on a grid of tiny oscillating mirrors that generate the headlight beam. This setup operates efficiently at temperatures up to 70°C, but the performance deteriorates if temperatures rises beyond this limit. The compact form factor of the headlight compartment combined with the heat generated from the engine and the outside environment may boost headlight enclosure temperatures to as high as 115°C. Use of Peltier coolers in the assembly can keep the temperature of the laser within its operational limits.

Another automotive application where spot cooling can be helpful is in high-end sensors. Some sensors now under development can capture high-

Head-up displays now on the drawing boards will be much bigger than the head-up displays of today, which typically project images in a space having a diagonal dimension below ten inches. The densely packed electronics necessary to project these larger displays is a candidate for TEM spot cooling.

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COOLING ELECTRONICS Thermoelectric coolers come in a variety of sizes and shapes. Some are designed specifically for high temperatures, incorporating construction techniques preventing copper diffusion which can degrade performance in standard-grade TEMs. Their ceramic material is alumina (Al2O3).

RELIABLE MOTION SOLUTIONS. resolution images using a portion of the spectrum outside that visible to the human eye. Due to thermal noise, the quality of the image resolution deteriorates as the temperature rises above 60°C. In automotive applications, the operating temperature can reach up to 85°C. Moreover, the heat rejection path for passive cooling of these devices tends to be inefficient. Active spot cooling using thermoelectrics can keep the imaging sensors cool in the presence of hot surroundings.

Head-up displays are other automotive application where thermoelectrics can be an ideal cooling solution. Advanced driver-assistance systems (ADAS) now on the drawing board make more extensive use of windshield projection technology than present-day technology, most of which has a projection area of just tens of square inches. Larger head-up displays dissipate more power and generate more heat. The compact nature of the projection electronics makes head-up displays candidates for active spot cooling using thermoelectric cooling.

HIGHLY COMPACT SMARTMOTOR™ SERVO SOLUTIONS FOR AUTONOMOUS VEHICLES. • Fully integrated designs • Most compact, power-dense solution on the market • Complete servo system

Learn how our easily deployable programming language gets your machine to market faster at animatics.com

REFERENCES Laird Thermal Systems | www.lairdthermal.com

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AUTONOMOUS & CONNECTED VEHICLES

Advanced circuit protection for connected autonomous vehicles New protocols for high connectivity need robust protection for reliability and safety.

Human ESD models 330 Ω

JAMES COLBY | LITTELFUSE, INC. At the turn of the century, automobiles hosted many electronic systems that were basically independent. Since then, the growth of connectivity and rise of artificial intelligence

330 pF Occupants inside passenger compartment

and machine learning have changed automotive electronics dramatically. Vehicles of all types are becoming complex interconnected communication centers, and autonomous vehicle functions are only increasing that level of sophistication.

New protocols are being deployed to boost connectivity while facilitating broadband-like automobile communications. V2X technology is designed to help vehicles to communicate with the road and each other to prevent collisions and optimize traffic flow. Automotive versions of Ethernet and HDBaseT are being implemented to boost the speed and efficiency of highspeed data transmission between key subsystems such as high-definition cameras, Lidar and radar sensors, and wireless connectivity features.

ISO 10695 basic test circuit 100M Ω

330 Ω / 2,000 Ω

150 pF/330 pF

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DUT

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2,000 Ω

150 pF

Outside person reaching into passenger compartment

ISO 10605 testing uses two different resistors, 2 kΩ and 330 Ω, to simulate different types of ESD events. The 2 kΩ resistor represents a human body discharging directly through the skin, while the 330 Ω resistor simulates a human body discharging through a metallic object. The test also takes place at two different capacitances: 150 pF and 330 pF. These values represent a human body inside and outside the vehicle respectively. The 330 pF and 330 Ω test is the highest energy/current of any of the ISO 10605 test parameters, and thus is the most widely used test standard. A typical ISO 10695 test starts with the test circuit switch open and charges the 150 pF/330 pF capacitor. Closing the switch causes the capacitor to discharge across the DUT. The 100 M resistor in the diagram represents the resistance of the ESD gun typically used for such tests. The parallel L-C represents parasitics.

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CIRCUIT PROTECTION Present-day HDBaseT auto apps

Better connectivity is making automobiles safer and more versatile, but also presents numerous technical challenges to the engineers designing it. The more advanced automotive chipsets must also become smaller and denser, making them more susceptible to electrostatic discharge (ESD). Design engineers must understand how to protect these chipsets to ensure their reliability.

Highly sensitive chipsets and demands for faster data require exceptional ESD protection for automotive modules that will utilize these new protocols. Low-capacitance, low-clamping ESD protection devices in compact packages can help make advanced automobile operation safe, reliable and efficient.

To provide the engineering community with a uniform and repeatable plan for ESD mitigation, the ISO 10605 document was created. The severity of the ISO 10605 ESD pulses is enough to ensure that chipsets will be hard-pressed to survive direct hits.

To create robust, reliable designs, engineers should consider ESD protection solutions early in the design process. In addition, engineers should review and understand the system-level ESD testing required for these modules.

ISO 10605 simulates the discharge of a human body inside or outside a vehicle. It specifically covers ESD in assembly, ESD caused by service staff, and ESD caused by passengers. ISO 10605 is partly based on IEC 61000-4-2, a standard for systemlevel ESD immunity. However, it has several key provisions specific to automotive uses. For one thing, ISO 10605 does not define a specific upper limit of stress voltage. The test voltages are generally in the range of 2 kV to 15 kV for direct-contact discharge, and 15 kV to 25 kV for discharge through an air gap.

Moreover, some automotive manufacturers have devised their own ESD stress levels -- some parts can have specifications as high as 30 kV contact, 30 kV air gap discharge.

Rear seat entertainment (RSE)

Head-up display Instrument cluster

Head unit display

Head unit

Typical HDBaseT ESD protection RJ-45 connector

Ethernet PHY

Functional block diagram

Automotive HDBaseT data lines might employ ESD protection in the form of the AQ2555NUTG, a low-capacitance, TVS Diode Array that also guards against CDE (cable discharge events), EFT (electrical fast transients), and lightning induced surges for highspeed, differential data lines. It’s packaged in a μDFN package (3.0 x 2.0 mm) and each component can protect up four channels or two differential pairs, up to 45 A (IEC 610004- 5 2nd edition,) and up to 30 kV ESD (IEC 61000-4-2). The “flow-through” design minimizes signal distortion, reduces voltage overshoot, and provides a simplified PCB design.

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AUTONOMOUS & CONNECTED VEHICLES Key systems for autonomous functions LIDAR Camera

Key systems that are necessary to create an Autonomous Vehicle. These systems will need to be tied together with low-latency, reliable data buses.

RADAR Onboard unit, empas

Wheel encoder

ISO 10605 testing uses two different resistors, 2 kΩ and 330 Ω, to simulate different types of ESD events. The 2-kΩ resistor represents a human body discharging directly through the skin, while the 330-Ω resistor simulates a human body discharging through a metallic object. The test also takes place at two different capacitances: 150 pF and 330 pF. These values represent a human body inside and outside the vehicle respectively.

The 330 pF and 330 Ω test is the highest energy/current of any of the ISO 10605 test parameters, and thus is the most widely used test standard.

Makers of ESD protection solutions design parts specifically to handle specialized automotive modules. It may be useful to consider a few examples.

Connected vehicles will carry Vehicle-to-vehicle (V2V) and Vehicle-to-infrastructure (V2I) communication modules that allow the vehicle to perform such tasks as making dynamic calculations based on the velocities and locations of other vehicles. Among the aims of the technology is to prevent vehicles from hitting other vehicles or pedestrians, to smooth traffic flows and speed the hunt for parking spots, and perhaps foster location-based advertising and promotion. The U.S. DoT thinks V2V communications could prevent up to 79% of vehicle crashes.

Typical ESD protection devices for V2X modules are designed to suppress fast-rising ESD transients up to 30 kV while adding virtually no capacitance to the circuit, important for maintaining signal integrity on the kind of high-speed comm lines that increasingly characterize automotive connectivity uses. These bi-directional surface-mount polymeric devices, going by the trade names PulseGuard or Xtreme-Guard, are designed to conduct quickly enough and at a voltage low enough to prevent damage in protected parts.

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GPS, 802.11p 4G/5G

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Ultrasonic sensors

Wheel encoder

Typical TVS diode array ESD protection for CAN bus SPLIT CANH R T/2 CAN BUS transceiver

CAN BUS

R T/2 CANL

Common mode choke (optional)

CG SM24CANB-02HTG (500W)

Bidirectional transient voltage suppression (TVS) diodes quickly conduct to prevent damage in protect ports.

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CIRCUIT PROTECTION

Automotive circuit protection solutions for advanced communication protocols (Example solutions from Littelfuse.)

Typical CAN and LIN bus communications

The CAN bus, with its 40 kb/sec to 1 Mb/sec baud rates, doesn’t have the bandwidth to handle state-of-the-art autonomous vehicle systems. Nevertheless, CAN and LIN bus systems will continue to play big roles in automotive electronic systems. CAN and LIN data lines can be protected from ESD and other overvoltage transients via devices such as the SM24CANB TVS Diode Array. The SM24CANB Series can absorb repetitive ESD strikes above the maximum level specified in the IEC 61000-4-2 international standard without performance degradation and safely dissipate 10A of 8/20 µsec surge current (IEC 61000-4-5 2nd Edition) with low clamping voltages.

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AUTONOMOUS & CONNECTED VEHICLES Typical V2V and V2I system architecture

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CIRCUIT PROTECTION

V2V and V2I on-board power and communication circuits need overcurrent, ESD and surge protection using Fuses, PPTCs, TVS Diodes and Diode Arrays, MLVs and Polymer ESD Suppressors.

V2X modules might employ ESD protection in the form of the AQ3045 back-to-back TVS diodes fabricated in a proprietary silicon avalanche technology to provide protection for electronic equipment that may experience destructive electrostatic discharges (ESD). These diodes can safely absorb repetitive ESD strikes up to the maximum level specified in IEC 610004-2 international standard (±30 kV contact discharge) without performance degradation. The back-to-back configuration provides symmetrical ESD protection for data lines when ac signals are present. (Example solutions from Littelfuse.)

Bidirection TVs diodes, functional block diagram 1

2 Pinout

Typical V2X antenna protection devices RF interface AQ3045-01ETG TVS diode array

BPF

LN A

AQ3045-01ETG AXGD10402

TVS diodes are also designed to work in high-speed Ethernet networks now being optimized for use in automotive environments.

High-speed Ethernet will allow multiple in-vehicle systems, such as infotainment and automated driver assistance, to simultaneously access high-bandwidth data throughput over a single, unshielded twisted pair cable.

Automotive Ethernet enables 100 and 1,000 Mbps communication while only using two communication lines. Similar to Ethernet, HDBaseT has a pathway to connect ADAS, telematics, A/V and display applications. Providing throughput speeds of up to 2 Gbps, HDBaseT can also move data up to 15 m (50 ft) with no loss of data integrity. The protocol can also tunnel multiple data types including audio/video, USB/PCIe, Ethernet, control signals, and even power on the same data pair.

Looking down the road, HDBaseT will provide higher throughputs to allow it to also be considered as a data backbone.

Expectations are it will eventually handle 4, 8, 12 and 16-Gbps eeworldonline.com | designworldonline.com

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AXGD10402 ESD suppressor

speeds. Highly sensitive chipsets and demands for faster data will require exceptional ESD protection. Low-capacitance, low-clamping ESD protection devices in compact packages will help make advanced automobile operation safe, reliable and efficient.

REFERENCES Littelfuse Inc. | automotive applications guide, https:// www.littelfuse.com/~/media/electronics/application_guides/ littelfuse_automotive_electronics_applications_guide.pdf.pdf

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HARSH PATEL | MOLEX

What high-speed data means for connected vehicles Technologies behind multigigabit Ethernet and 5G could transform transportation.

The connected vehicle segments are growing at a rapid pace. By 2020, industry analysts predict there will be 250 million connected vehicles and 10 million self-driving cars on the road, and 470 million connected/ autonomous vehicles by 2025.

As the number of connected cars rises, so too will the extent to which they produce, transmit and receive data. McKinsey & Company says the amount of data transmitted through a connected vehicle (and to and from the cloud) is approximately 25 GB of data/hr and predicts this figure will rise to nearly 500 GB of data/hr once vehicles are truly autonomous.

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An example of where automotive Ethernet is going: The Molex 10 Gb Ethernet Gateway with 25 Gbps+ capable Molex HSAutoGig interconnect solution. It supports the signal integrity margins needed for automotive sensors, cameras, gateways and switches that are essential components of tomorrow’s autonomous vehicles.

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HIGH-SPEED NETWORKING Big data on wheels

Data generated by connected cards compared to data usage of online activities (per hour) HB video streaming 869 MB 29 MB

Music streaming 25,000 MB

15 MB Web browsing 5 MB Turn-by-turn navigation

Analysts at Statistica prepared these graphs based on data from a McKinsey estimate that connected cars create up to 25 gigabytes of data per hour. That’s the equivalent of nearly 30 hours of HD video playback and more than a month’s worth of 24hour music streaming.

Vehicle data generated

Today’s in-vehicle networks actually use a combination of several different data networking protocols. Some of these have been around for decades. Among them are the controller area network (CAN), which handles powertrain and actuator functions; the local interconnect network (LIN), predominantly handling nontime-sensitive apps such as climate control, ambient lighting, seat adjustments, and so forth; media oriented system transport (MOST) for infotainment; and FlexRay for anti-lock braking, electronic power steering, and vehicle stability functions.

One result of using different protocols is that vehicle networks must incorporate gateways to transfer data within the infrastructure.

This practice is not optimum on several levels, one being that the wiring for each network adds weight to the vehicle. Wire harnesses are the third heaviest element of the typical vehicle (only the engine and chassis are heavier).

Automotive networks today also incorporate numerous electronic control units (ECUs), and the number of ECUs in the typical vehicle is also rising. It’s not uncommon for luxury models to carry 150 ECUs, and ordinary vehicles might have up to 90 ECUs. Automotive designers have concluded that data intensive applications emerging to support advanced driver assistance system (ADAS) can’t be handled with the kind of automotive networking technology in use today. The entire approach for in-vehicle networking must fundamentally change, both in terms of the topology and with regard to the underlying networking technology.

Today, the networking structure used in vehicles is moving toward a variant of what is called a domain-based architecture (each automaker has its own architecture, but the main concept is often similar). Where older vehicle networks had application-specific ECUs and application-specific bus systems, domain architectures are characterized by different domains for each key function: one ECU and network handling body control, one ECU/network for eeworldonline.com | designworldonline.com

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infotainment, one for telematics, one for powertrain, and so on. At least for now, often these domains still employ a mix of different network protocols (CAN, LIN and so forth).

As networks become more complex, this domain-based approach becomes less efficient. So there will be a migration away from the domain-based architectures toward a type called a zonal architecture.

A zonal architecture connects data from different traditional domains to the same ECU, based on the ECU’s location (zone) in the vehicle. This scheme greatly reduces the amount of wire harnessing partly because many functions now handled with discrete wiring will move to Ethernet technology.

Unlike other in-vehicle networking protocols, Ethernet has a well-defined development roadmap for reaching higher speeds. In contrast, traditional automotive protocols like CAN and LIN are at a point where planned applications exceed their capabilities with no clear upgrade path to solve the problem.

Expectations are that most data transfer within vehicles will be via Ethernet. Thus the plan is for a single homogeneous network throughout the vehicle. This in-vehicle network will be scalable such that it will be able to handle functions demanding higher speeds (10G for example) with ultra-low latency but also able to handle slower functions. Designers will select the Ethernet physical layer (PHY) for specific functions according to bandwidth demands. So data-rich image sensors such as radar and lidar might employ a 1 Gbps interface where low-data rate sensors might need only a 10 Mbps connection.

Zonal architectures will employ Ethernet switches to manage data for all the different domain activities. Different data domains will connect to local switches, and the Ethernet backbone would then aggregate the data. In this way the network can use the same core protocols to support the use of different speeds.

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AUTONOMOUS & CONNECTED VEHICLES Why 10G+ Traditional

Partial domain

EE architecture

Today, the networking structure used in vehicles is moving toward a variant of what is called a domainbased architecture. Domain architectures are characterized by different domains for each key function: one ECU and network handling body control, one ECU/network for infotainment, one for telematics, one for powertrain, and so on. As networks become more complex, this domain-based approach becomes less efficient. So there will be a migration away from the domain-based architectures toward a type called a zonal architecture.

Front right

Full domain

EE architecture

EE architecture

Passenger Zone ECU

Zone ECU

Switch

Switch

Central ECU

Zone ECU

Central ECU Zone ECU

Switch

Switch

Front left

Zone ECU

Zone ECU

Switch

Switch

Zone ECU

Zone ECU

Switch

Switch

Front left Tranisiton from domain to zonal architectures will require 10G+ links between the zonal ECUs

The trend toward vehicle networking has led to efforts aimed at standardizing automotive Ethernet. The automotive Ethernet specification currently in development is IEEE 802.3CH for high-speed automotive Ethernet applications (Multi-Gig Automotive Ethernet at 2.5G, 5G, and 10G) over 15 m with optional PoDL (power over data lines). The first version of this standard is due out by the end of this year.

IEEE has also chartered a study group under the 802.3 Ethernet working group for greater than 10Gbps automotive Ethernet.

This is a relatively recently development and it likely will be some time before any standard for 10 Gbps Ethernet emerges.

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Another trend affecting connected vehicles is the move to 5G. Major telecom carriers have begun activating fifthgeneration (5G) wireless networks in metropolitan areas. The advent of 5G wireless data could take intelligent and autonomous driving to the next level by enabling both faster data rates and secure vehicle-to-vehicle (V2V) and vehicle-toinfrastructure (V2X) connectivity. Slated for widespread adoption within a few years, 5G will require continued progress in the development of powerful network infrastructure and in-vehicle processing technologies to assure reliable signal speed with ultra-low latency bandwidth.

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To explain what 5G might mean for automotive connectivity, it might be useful to cite real examples of 5G capabilities that are now in the prototype phase. One such project is AutoAir which is being tested at the Millbrook Proving Ground in the UK. Researchers there set up base stations, antennas, and other hardware to produce scenarios that include maintaining a 1 Gbps connection to vehicles speeding at up to 160 mph.

Another 5G implementation called Invisible-to-Visible was described at CES 2019 by Nissan. It uses fast connectivity to see around corners and alert passengers to potential hazards such obstacles in the road or pedestrians obscured by vehicles. In a similar eeworldonline.com | designworldonline.com

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HIGH-SPEED NETWORKING Trends in automotive ethernet 1 port

<10 ports

2008

OBD

2013

2017

Low res camers

100BASE-TX

10-100 ports

2019

2021

2024

Connected car, IVI, TCU, gateway

100BASE-T1

1000BASE-T1

>100 ports

2025

2026+

ADAS & autonomous driving

2.5/5/10G BASE-T1

10G+

The trend toward vehicle networking has led to efforts aimed at standardizing automotive Ethernet. The automotive version is now called the IEEE 802.3CH standard for high-speed automotive Ethernet applications (Multi-Gig Automotive Ethernet at 2.5G, 5G, and 10G) over 15 m with optional PoDL (power over data lines). The first version of this standard is due out by the end of this year. It is a product of the Multi-Gig Automotive Ethernet Task Force (NGAUTO) which was established to define specific performance qualities and operation at 2.5 Gbps, 5 Gbps and 10 Gbps speeds.

vein, Ford Motor is working with Vodafone on a system that uses 5G to warn cars about approachwing emergency vehicles.

These high-speed automotive solutions demand a level of expertise and engineering that exceeds that of many other applications. Much of the design complexity comes from the push to realize better performance in connectors, cable assemblies and modules that have small footprints. It’s no longer enough for connection hardware to be characterized by simple parameters such as contact resistance and corrosion resistance. Hardware that handles high-speed data must address shielding against electromagnetic interference and must be characterized in terms of attenuation, return loss, mode conversion, crosstalk, impedance, and other issues that can impact signal transmission. Much goes into specifying connectors having the ability to reliably deliver highspeed, high-bandwidth and high-power signals to and from every sensor in a vehicle.

As a result, automakers are asking suppliers to create solutions for the short term that can be implemented with existing infrastructure but can evolve into solutions for the demands of tomorrow. Suppliers are working hard to meet—and expand—highspeed data capabilities to satisfy the requirements of automakers working toward higher levels of connectivity, including V2V, V2X, and full autonomous driving. eeworldonline.com | designworldonline.com

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REFERENCES Molex, connected vehicle page | www.molex.com/molex/ industry/automotive/automotive-connected-vehicle.jsp Gartner | www.gartner.com/, www.gartner.com/newsroom/ id/2970017 Business Insider Intelligence | www.businessinsider.com/report10-million-self-driving-cars-will-be-on-the-road-by-2020-2015-5-6 Boston Consulting Group | www.bcg.com/publications/2017/ automotive-making-autonomous-vehicles-a-reality.aspx Gartner Statista/McKinsey | www.statista.com/chart/8018/ connected-car-data-generation/

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AUTONOMOUS & CONNECTED VEHICLES

Want a new perspective on battery management?

Buy a plug-in EV One facet of EV ownership: Thinking of your daily commute power consumption as equivalent to the sunlight hitting 20 ft 2 of your lawn.

RUDYE MCGLOTHLIN SILICON LABS

Earlier this year I took my first step into the EV future by buying a plug-in hybrid electric vehicle (PHEV) – a Chevrolet Volt. A unique car in the hybrid space, the Volt has a relatively large battery, rated for 18.4 kWh. Competing PHEVs available in my region and at a similar price have batteries half the size or smaller. This battery capacity allows the Volt to run as a pure EV for more than 60 miles with the internal combustion engine never starting up.

This battery performance turns out to be perfect for my commute. My commute is about 18 miles each way. I am able to charge at my office, drive home, return the next morning, and still have a good bit of charge on the battery for driving to lunch or for any errands I need to make on the way.

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EV BATTERY MANAGEMENT

I don’t believe my situation is atypical, and the Volt satisfies my daily commuting needs perfectly. It is a shame that the model isn’t continuing in Chevrolet’s line-up.

Since driving the Volt, I’ve been surprised about how my thinking has changed about PHEVs. There’s no longer any point in monitoring my progress in miles-per-gallon. Instead, I’ve recognized that my home-and-back energy usage is approximately 9 kWh. However, recently my usage has reduced as schools have closed for the summer, and traffic has become less congested.

9 kWh is the amount of charge I need daily. It puts my energy usage in a much different context than my commute using a conventional car powered by an internal combustion engine. I have access to a Level 2 charger at the office, which is convenient for charging during the workday. It takes about three hours for the daily charge. If I must charge at home, where I only have a Level 1 charger, the Volt must be connected overnight.

Energy becomes time.

Energy comparisons have also become more straightforward. My daily commute is about 20% of our household energy usage. According to the solar energy potential map at the US Dept. of Energy website (energy.gov), approximately 20 ft2 of my lawn receives enough energy from sunlight in a day to power my commute. In broader terms of scale, my drive is about 7 nano-percent of the daily electricity production of my home state, Texas. It is the energy available from 2,300 AA alkaline batteries.

Battery range and charging speed are two of the biggest influences in EV market demand right now. Every driver must examine their habits and needs to decide whether they are susceptible to the ailment of “range anxiety.” Why don’t internal combustion engine drivers suffer from range anxiety? The answer is, of course, because the recharge stations for that power source are ubiquitous, and the process of filling the tank rarely takes more than 10 minutes. Thus, charging speed and availability are a preventative for the range anxiety ailment.

Technological innovations are in development that could speed charging and mitigate range anxiety. Some of these innovations are in areas like improved battery chemistry to boost power density or advanced charge/discharge algorithms that optimize the two active states of the battery.

Though I’m not regularly exposed to the engineering and discovery that happens in battery technology, I am exposed to emerging innovations in the semiconductor industry.

EV battery pack voltages and capacity are rising. Complex electronics are being developed for efficient maintenance, charging and use in the drivetrain. These developments depend eeworldonline.com | designworldonline.com

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on precise and accurate measurements of the battery pack voltages, currents, and temperature. The measurements must take place on high-voltage rails that have the potential of destroying the sensitive monitoring electronics.

The battery monitoring agent is one node that reports to the car’s central computer, providing important feedback to the operator. By providing up-to-date, accurate information about the state of the battery, the driver can anticipate the travel distance available with the energy remaining. Being able to make that prediction with confidence is a key part of eliminating range anxiety.

To be effective, that communication channel must be protected. On one side, the battery sits at 400 V, and in the future, it will sit at 800 V or higher. The other side has noisy switching, possibly coupling onto the bus, that would interfere with the sensitive measurements of the battery cells.

Noise and high voltage are common threads through the battery system. So, measurement components, analog-to-digital converters and specialized battery gauge ICs must be isolated when they connect across power domains. This isolation allows a lower-voltage control unit to measure the high-voltage system.

Likewise, the communications bus must be isolated from the battery control module to reduce the effect of the overall EV system noise coupling into the battery cell measurements.

Modern CMOS-based isolation technologies have been key for facilitating this isolation in EVs. Modern isolators, manufactured in standard semiconductor facilities, enable more integration than is possible with traditional optocoupler isolation.

More measurement functions, and even some calculation or control, can be included with the necessary isolation feature than before. Additionally, important parameters like transient noise immunity, signal propagation delay, power requirements, and product lifetimes improve to the level necessary for EV applications. The Si86xx digital isolators and Si8920 analog isolators from Silicon Labs are examples of modern isolators useful in EV battery applications.

The on-board charger (OBC) is another system in electric and plug-in hybrid electric vehicles that greatly benefits from semiconductor innovations. This is one area where my Volt feels behind the curve with respect to current technology. The OBC in the Volt operates at 3.3 kW and is the component setting the charge times. Advanced architectures, paired with advanced switch technologies, have pushed OBC capabilities up steadily.

Newer EV designs come equipped with onboard chargers that are 2x to 4x the capability of the OBC on my Volt. That directly corresponds to faster charging of larger batteries. Again, this development will help offset some consumer range anxiety with the promise of a faster recharge.

OBC power output levels directly depend on the efficiency of the power transfer architecture. Low efficiency means larger transformers and heatsinks, increasing the overall size and weight of the charger. New wide-bandgap switch technologies create 8 • 2019

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an opportunity to massively shrink the OBC and boost power density. These transistors switch at faster rates, which reduces the size of the magnetics, and they have lower power losses, reducing the need for bulky thermal management.

State-of-the-art power switches require innovative isolated-gate drive technology to function. Robust isolation enables the switches to go through the entire power conversion commutation that includes connection to a high-voltage bus. A fast gate drive, with considerable rejection of transient noise, is a requirement for taking advantage of the rapid-switching capabilities of wide-bandgap transistors. For example, the Silicon Labs Si827x and Si8261 isolated gate drivers enable high-power OBCs for the latest fast-charging EVs and PHEVs. My Volt has given me a new prospective on my energy use and how it relates to transportation. It is also exposing me to some of the concerns in the broader EV market and highlights how innovation from semiconductor suppliers can address them. More efficient use of the battery capacity depends on clean sensing and communication with computing

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Silicon Labs — A&CV HB 08-19.indd 38

The author’s Chevy Volt has a battery rated for 18.4 kWh and can run as a pure EV for more than 60 miles with the internal combustion engine never starting up. Pictured here is the powertrain and battery of the Volt’s replacement, the Bolt, a pure EV having an EPA-estimated 238-mile range on a full charge and carrying a 60-kWh battery. The Bolt can get up to 90 miles of range in 30 minutes when equipped with an optional dc Fast Charger.

Chevy says the Bolt can go 0-60 mph in 6.5 sec thanks to the 200 hp and 266 lb.-ft. (360 Nm) of torque its electric motor produces.

and control resources. Both depend on the latest isolation technology available. Charging power, directly related to the time needed to charge, is steadily rising due to availability of new transistor switches and the isolated gate drivers needed to control them.

All these power semiconductor innovations, as well as the automobile designs they enable, make me excited and optimistic about the future of EVs and PHEVs.

REFERENCES Silicon Labs | www.silabs.com

8 • 2019

eeworldonline.com | designworldonline.com

8/7/19 9:20 AM

GMR

Sensing wheel speed with GMR CHRISTINE GRAHAM | ALLEGRO MICROSYSTEMS

Giant magnetoresistance sensors are strong candidates for the high-accuracy wheel-speed sensing necessary in autonomous vehicle systems.

GMR and current sensing

The purpose of a braking system has evolved from simplistic stopping to critical functions in vehicle safety through highly accurate feedback to traction control (TCS) and advanced driver assistance systems (ADAS).

These advances have been made possible in part due to the technological developments in magnetic wheelspeed sensing.

Integrated circuits for magnetic sensing have evolved from simple 100-transistor designs to those containing hundreds of thousands of transistors. The Hall effect is a common transducer technology used in sensor designs.

The Hall transducer interfaces with processing circuitry to produce a switching square wave representing either speed or speed and direction for the braking control unit’s processing algorithms. Hall effect-based wheel-speed sensors have helped move braking system technologies from standard anti-lock braking (ABS) capabilities to traction control (i.e. anti-slippage) and full stability control (vehicle maneuverability).

The Hall effect, discovered in 1879 by Sir Edwin Hall, refers to the voltage produced when an electrical current flows through a conductive plate while it sits in a magnetic field. The resulting voltage is perpendicular to and proportional to the current and magnetic field applied. This Hall voltage is small in amplitude and susceptible to temperature variations. So it takes special signal processing to effectively integrate it into a sensor IC. Wheel speed sensors monolithically integrate Hall transducers on a single silicon substrate with signal conditioning circuitry. These plus compensating algorithms provide a reliable output square wave to the control unit. The Hall IC is either used in combination with a back-biasing magnet to sense a ferromagnetic target (such as an iron-based gear) or the IC directly senses a magnetic encoder ring (a.k.a. ring magnet), also known respectively as back biased (gear) or front biased (ring magnet). eeworldonline.com | designworldonline.com

Allegro — A&CV HB 08-19_v2.indd 39

A typical GMR sensor chip. The GMR resistors are connected in a Wheatstone bridge with half the elements positioned under one magnetic condition, the other half under another.

GMR and ring magnet applications Direction of rotation

Air gap

Branded face of package

A&B

Pin 8

C&D

Ring magnet

Pin 1

Element pitch Automotive speed sensing applications such as ABS may use a ring of magnetic material with alternating north and south magnetization. The GMR sensor may be placed under this material such that the plane of the die is horizontal. The spacing between the four Wheatstone bridge-connected GMR resistor elements creates a differential magnetic field sensed by these sets of elements based on where the ring magnet is in its rotational cycle.

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The fundamental principle of the GMR effect is based on electron spins. In a magnetoresistor, electron scattering rates rise or fall as a function of the interaction of the spin state of the electrons and the magnetic orientation of the medium where the electrons are traveling.

Electron scattering increases the mean free path of the electron flow, so the GMR transducer’s resistance changes in the presence of a magnetic field.

Hall-effect wheel speed sensors provide edge accuracy down to tenths of a degree with the air gap between the sensor and target typically in the 1 to 2-mm range. This amount of accuracy has been adequate for most of today’s braking systems, but not for driver assistance systems far beyond traction control that are quickly evolving.

From park-assist to collision avoidance, the wheel speed sensors provide valuable data back to the driver assistance system, allowing it to decide the best vehicle maneuver before the driver even sees the oncoming hazard. The growing desire to automate the driving process is defining the future of wheel speed sensing technology.

SIGNAL CONDITIONING Although the Hall effect meets current needs, driver assistance systems require higher accuracy at similar, or in some cases larger,

air gap distances between the sensor and the sensed target. Giant magnetoresistance (GMR) is a strong candidate to meet these enhanced requirements. Front-biased magnetic encoder rings are the most common target type in today’s light vehicle applications and the most common implementation for GMR. The GMR replaces the Hall effect as the sensing transducer. In principle, GMR and Hall are both magnetic sensors, but the two differ in fundamental operation and capability.

Although magnetoresistance is said to have been observed by Lord Kelvin in 1857, GMR specifically was discovered over a century after the Hall effect in 1988 by Albert Fert and Peter Grünberg, earning them a Nobel Prize in 2007. The fundamental principle of the GMR effect is based on electron spins. In a magnetoresistor, electron scattering rates rise or fall as a function of the interaction of the spin state of the electrons and the magnetic orientation of the medium

Hall vs GMR signals GMR signal 3 mm & 4.5 mm

Hall signal 3 mm & 4.5 mm

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GMR signals are magnitudes greater in amplitude than those from Hall sensors, with a much greater signal-to-noise ratio.

1/10 edge jitter with GMR vs hall

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GMR speed and direction output signals.

where the electrons are traveling. Electron scattering increases the mean free path of the electron flow, effectively altering the resistance of the medium. Simply put, the GMR transducer’s resistance changes in the presence of a magnetic field.

Earlier generations of MR technology limited sensing angle, linear range, and sensitivity. These limitations have been overcome in Allegro’s state-of-the-art patented GMR technology, providing greater range, and higher sensitivity. The sensing orientation allows the GMR sensor to be a drop-in replacement for Hall effect sensors.

Historically, the GMR was oriented 90° out of phase from The linear range of GMR the Hall effect sensor making it sensors typically span a difficult to swap the technologies. peak-to-peak magnetic Another issue seen in earlier MR signal amplitude in the designs is that of discontinuities in 100-gauss range. the signal at close air gaps. The

GMR

signal perturbation directly correlates to higher edge jitter, reducing the wheel speed sensor’s output accuracy. Nearly a decade has been spent overcoming the signal discontinuities in wheel speed sensors now available, such as Allegro’s A19250 and A19350 GMR wheel speed sensors. In addition to low jitter, these sensors offer large operating air gaps which extend far beyond the capability of Hall transducers.

Like Hall effect technology, GMR is monolithically integrated onto a single silicon substrate with signal conditioning circuitry. However, the resulting GMR signal is magnitudes higher than a Hall signal with much greater signal-to-noise and one-tenth the jitter. These qualities let GMR sensors sense objects at much bigger air gaps.

The magnetic design range for Hall-based sensors specifies a minimum operating field level of 20 gauss (2 mT) differential peakto-peak. In contrast, GMR-based sensors have a minimum operating field level of 5 gauss (0.5 mT) or lower differential peak-to-peak.

Comparing the accuracy, the Hall capability is tenths of a degree edge accuracy at 1.0 to 2.0 mm typical air gap, while the capability of GMR is hundredths of a degree of edge accuracy at typical air gaps of 3.0 mm or beyond.

GMR does require more stringent design-in conditions. One difference between the technologies is the capability of Hall to handle larger magnetic fields. This is a function of the sensitivity and a key part of the design process. GMR has a defined linear range with ideal peak-to-peak magnetic signal amplitude in the 100-gauss range.

GMR devices, like those employing the Hall effect, will operate outside of the magnetic design range without permanent damage.

However, depending on the signal processing algorithms, the performance may degrade. This degradation can arise with either technology, but GMR high sensitivity levels come at the cost of degraded performance at a higher magnetic field level. The magnetic field strength and temperatures at which GMR transducers can experience permanent damage are much higher than those expected in wheel speed applications, so they aren’t much of a concern. The primary design consideration is to optimize the magnetic circuit for superior performance with GMR technology.

All in all, state-of-the-art GMR technology has played an important role in realizing today’s highly accurate advanced braking systems. The vehicle response is so smooth the driver hardly notices it. GMR technology will play a similar role in semi and fully autonomous vehicles.

REFERENCES Saturated response Linear region

eeworldonline.com | designworldonline.com

Allegro — A&CV HB 08-19_v2.indd 41

Brian Cadugan, Allegro ICs Based on Giant Magnetoresistance (GMR) | https://www.allegromicro. com/en/insights-and-innovations/technical-documents/ giant-magnetoresistance-sensor-publications/allegroics-based-on-giant-magnetoresistance-gmr

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Bluetooth and the road to a keyless future From hands-free calling to tire pressure monitoring systems, Bluetooth has

Efforts are underway to let smartphones double as car keys.

revolutionized the way we interact with our vehicles and how the vehicle itself communicates with other technology systems and services. So, what’s next?

ERIK PETERS DIALOG SEMICONDUCTOR

Improved internet connectivity and other technological advancements have resulted in the rising popularity of car rental services such as Zipcar, Turo and Getaround, among others. Further enhancing the user experience for car rental services and owned vehicles alike will be the introduction of fully digital keys.

Powered by Bluetooth, digital keys will replace traditional key fobs, allowing individuals to use their mobile devices to unlock and lock their vehicles, adjust cabin temperature and more, with the added layer of security and transparency of real-time location tracking and instant connectivity that Bluetooth provides. As a result, accessing, starting and securing your car will be easier than ever before.

OEM backend TSM OEM proprietary

OEM proprietary

SE provider proprietary

Mobile UI TUI TEE

NFC

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SE provider

SE provider agent

Secure element (eSE, UICC)

8 • 2019

A diagram provides a high-level architecture highlighting the focus of the Car Connectivity Consortium including the interfaces to be standardized. Here, TSM stands for Trusted Service Manager.

It enables service providers (OEMs) to distribute and manage their contactless applications remotely by allowing access to the (embedded) secure element in smart devices. Mobile UI is an interface between OEM/TSM and the smart device. This is also known as the OEM application. Secure Element is secure storage on smart device. It can be in the form of an embedded Secure Element or UICC Secure Element. SE Provider is the owner of the SE which provides SE access to a TSM. An SE Provider Agent is an SE access interface for the SE Provider. It may be accessed by the SE provider via a proprietary interface/functions. TU is Trusted User Interface. It is usually part of the TEE, Trusted Execution Environment.

This is a secure application environment on the host application processor.

eeworldonline.com | designworldonline.com

8/9/19 10:31 AM

ELECTRONIC CAR KEYS

At one point or another, most of us have walked away from the car and forgot to lock it. With digital keys, this will no longer be a problem. With Bluetooth connectivity, the device will be able to measure how long the signal has traveled from the transmitter to the receiver. In doing so, multiple nodes or receivers on the car can determine your exact position in relation to the vehicle. This means that you will never have to worry about remembering to lock the door; you simply walk away from the car, and the key will lock it automatically once it senses you are out of range.

The same thinking can be applied in the reverse scenario. If you want to access a connected vehicle, you must first have the authenticated key, and then also be within a short distance from the vehicle to unlock it.

There is also an opportunity for the use of digital keys by rental car services. In fact, increases in connectivity and its cost effective “pay-per-use” model will continue to propel the global car rental market forward in the coming years. And as digital keys are passed between drivers, who has access to a car and when will be extremely important.

With digital keys, car rental companies and services can grant customers access to vehicles via their phones, and program these keys to expire when their rental period ends or when it has returned to a predetermined location. In addition to granting access, car sharing services will also be able to control the type of access each customer has. They can program the key to unlock the car for a lost item to be retrieved but block it from starting the engine, for example.

As technology advances, Bluetooth has the potential to control other features within the car as well. The key could be programmed with its owner’s preferences for features such as infotainment, music, in-cabin temperature, seat and mirror positioning, so that they can be adjusted automatically upon entering the vehicle.

The key could also send location-based personalized recommendations for shopping and food. In the event a key is lost or stolen, it can be installed on another device instead. eeworldonline.com | designworldonline.com

Dialog — A&CV HB 08-19_v2.indd 43

First implementations The first production vehicle to employ a Bluetooth (actually a BLE) digital key is Tesla’s Model 3. Model 3s can be set to unlock their doors and trunk automatically when the owner approaches, and starting the Model 3 is as simple as shifting out of Park.

Nevertheless, it could be a few years before other automakers widely adopt the virtual key, if the results of a Consumer Reports test of the Model 3 are any indication. There are still a few issues to be settled.

For example, CR testers found the Model 3’s virtual key doesn’t work unless the smartphone is turned on and the Tesla app is open and active. CR reports its testers occasionally found themselves unable to open the Model 3’s door when they had closed the app.

Tesla’s use of digital keys shows how the industry may handle scenarios such as how to open the car door when your smartphone battery dies. Tesla provides Model 3 owners with a backup keycard that can also be used when the car is valet parked. To lock or unlock the car, a driver must swipe the keycard along the pillar next to the driver’s seat.

Because the Model 3 doesn’t come with a traditional ignition switch or pushbutton start, drivers must also tap the key behind the front-seat cup holders to “start” the car.

The process sounds a bit kludgy. And it explains why many observers expect most cars with digital keys to still come with key fobs for the foreseeable future.

Experience with the Tesla implementation also indicates there are range issues to be worked out before

digital keys go mainstream. Traditional RF keyless entry fobs generally must be inside the vehicle before the car will start. CR testers found they could start a Model 3 when the phone with the digital key app was five feet outside the driver’s door. They were also able to drive the car away without the phone being inside.

A few other carmakers are beginning to roll out digital keys. The Lincoln Aviator now has an app that will unlock the SUV and stores driver profiles for comfort settings. The European version of the Mercedes-Benz E-Class and the European version of the 2019 Audi A6 both have phone-based digital keys, but both systems use near field communication (NFC) to operate.

There are also efforts underway to standardize aspects of digital key implementations. Last year, the Connected Car Consortium (CCC) released its Digital Key Release 1.0 specification which presents a method for users to transfer digital keys to their smart devices. CCC’s spec lets an owner create virtual keys with limited access rights for purposes such as speed governance or valet parking.

CCC is also working on a 2.0 specification due out this year. It will spell out a standard authentication protocol between the vehicle and smart device.

It is being developed in collaboration with CCC charter member companies that include Apple, Audi, BMW, General Motors, Hyundai, LG Electronics, Panasonic, Samsung, and Volkswagen, and core members Alps Electric, Continental Automotive GmbH, Denso, Gemalto, NXP, and Qualcomm Inc.

REFERENCES Dialog Semiconductor | www.dialog-semiconductor.com Car Connectivity Consortium | carconnectivity.org/digital-key/

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Flash memory keeps cars connected A new kind of flash memory is optimized specifically to handle the hostile environment imposed on electronics in vehicular uses.

You’d have to say the NAND flash memory had an inauspicious beginning. Invented in 1987 by Toshiba, the first NAND flash chip didn’t sell until 1995 when it finally found a spot in digital answering machines. Toshiba insiders say the company almost killed the project twice. When the technology finally made a commercial debut, it was in the form of a 4-Mbit part that went for a mere $10 per megabit.

Today, the same die area that held a 4-Mbit flash part in 1995 can hold 1.33 trillion bits, a factor of 333,000-times denser than the first part. And the price has dropped to two-hundredths of a cent per megabit, or about 20 cents per gigabit. Meanwhile, the NAND flash market has grown to something north of $60 billion as of last year thanks largely to applications ranging from digital cameras to cellphones. Meanwhile, industry analysts figure the NAND flash market is growing at a rate of about 40% annually and will continue to do so thanks to demand in uses such as data centers, servers, smartphones, tablets, and PCs.

LELAND TESCHLER | EXECUTIVE EDITOR

Interestingly, uses in connected vehicles don’t constitute much of the projected demand – yet. That’s about to change with the rise of ADAS technology and autonomous piloting functions. Analysts figure the average autonomous vehicle will generate data at the rate of 4 terabytes per day, and a lot of that data will be uploaded to the cloud.

Much of it will necessarily sit in flash memory chips residing both on vehicles and in data centers. The upshot of these trends is a boom time for flash chips that can handle the special needs of vehicles.

GOING FOR A RIDE Automakers are notorious for their penny pinching on components.

In the case of flash memory, they have an advantage in the cost area: smartphones are one of the biggest uses for flash chips, and there some 1.5 billion smartphones sold annually. Memory suppliers say that huge volume can help them keep down costs of flash chip destined for automotive use because there are a lot of commonalities between the memory chips deployed in both applications.

JEDEC UFS & MIPI unipro Block diagram and overview RST_n

UIC

Rx0 D+/Tx1 D+/Rx1 D+/-

MIPI unipro

MIPI MP HY

MIPI MP HY

MIPI unipro

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UIC

Tx0 D+/-

RMMI IF

44

UFS DEVICE

REF_CLK

UFS HOST

RMMI IF

8 • 2019

A diagram from the MIPI Alliance shows that in UFS v3.0, MIPI (Mobile Industry Processor Interface) M-PHY v4.1 and UniPro (for Unified Protocol) v1.8 form the UFS Interconnect Layer (UIC) that connects a UFS host with a UFS storage device. M-PHY is the physical layer and UniPro forms the link layer. These two layers communicate over RMMI (Reference M-PHY MODULE Interface) and can support two transmit and two receive lanes.

eeworldonline.com | designworldonline.com

8/8/19 8:32 AM

UFS vs e-MMC

FLASH MEMORY

How UFS flash memory improves over the older e-MMC standard. A point to note, though, is that UFS is not backward-compatible with e-MMC. Toshiba compiled the most notable advances in this table.

In-vehicle NAND market projection

Flash memory supplier Toshiba says the typical vehicle will use 700 GB of flash storage eleven years from now, with fully autonomous vehicles using as much as three terabytes each. If the predictions are correct, they imply a 67% annual growth rate for flash in the coming years.

Boot time comparison UFS vs e-MMC

Flash memory supplier Toshiba estimates that the boot time of UFS flash chips can be 69% faster than that of the older e-MMC devices. The shorter boot time is particularly important for vehicles where owners expect systems to be up and running as soon as the car comes alive.

eeworldonline.com | designworldonline.com

Toshiba — A&CV HB 08-19_v2.indd 45

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Quick review: Basics of flash, NAND flash, and NOR flash Flash memory stores information in an array of memory cells made from floating-gate transistors. Each memory cell resembles a standard metaloxide-semiconductor field-effect transistor (MOSFET) except that the transistor has two gates instead of one. The floating gate (FG) and the control gate (CG) control the current flowing between the source and drain. The CG resembles the gate in ordinary MOS transistors.

The FG is completely insulated by an oxide layer and sits between the CG and the MOSFET channel. The electrical isolation of the FG traps any electrons placed on it. A charge placed on the FG screens the electric field from the CG, boosting the threshold voltage (VT1) of the cell. This means the CG must see a higher voltage to make the channel conductive.

(This higher voltage, often written VT2 in texts, almost always comes from an internal charge pump. Flash chips today need only a single supply voltage.)

Applying a voltage between the threshold voltages (VT1 and VT2) to the CG is all that’s needed to read a value from the transistor. When the channel conducts at this intermediate voltage, it means the FG is uncharged (a charged FG would prevent conduction because

the intermediate voltage is less than VT2).

This means the gate stores a logical “1.”

When the channel doesn’t conduct at the intermediate voltage, the FG is charged, so the gate stores a logical “0.”

Thus current flowing or not flowing through the transistor when the intermediate voltage is asserted on the CG indicates the presence of a logical “0” or “1.” Flash memories with multilevel cells, which store multiple bits per cell, sense the amount of current flow rather than simply its presence or absence to figure out the level of charge on the FG. Today, there are two main types of flash, NOR and NAND. In NOR flash, each cell has one end connected directly to ground. The other end connects directly to a bit line. The NOR flash moniker comes because the arrangement acts like a NOR gate: when one of the word lines (connected to the cell CG) is brought high, the corresponding storage transistor pulls the output bit line low.

NAND flash also uses floating-gate transistors, but they connect in a way that resembles a NAND gate: several transistors connect in series, and the bit line goes low only if all the word lines are pulled high (above the transistor VT).

Simple NAND flash structure GroundWord select transistor line 0

Word line 1

Word line 2

Word line 3

Word line 4

Word line 5

Word line 6

Bit line

Bit lineWord select line 7 transistor

NAND flash memory structure is characterized by serial-linked groups of memory cells that add an extra level of addressing not present in NOR flash memory.

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Simple flash memory cell cross section Control gate 3.3 V

0V

SOURCE

Floating gate

DRAIN

3.3 V

A simple flash memory cell comprised of a MOSFET having two gates, the control gate and the floating gate.

These groups then connect via additional transistors to a NOR-style bit line array in the same way that single transistors are linked in NOR flash.

A NOR flash might address memory by page and then word. NAND flash might address it by page, word and bit. Bit-level addressing suits bit-serial applications (such as hard disk emulation), which access only one bit at a time. Executein-place applications, on the other hand, require every bit in a word to be accessed simultaneously. This requires word-level addressing. In any case, both bit and word addressing modes are possible with either NOR or NAND flash.

Though it contains additional transistors, NAND flash has fewer ground wires and bit lines that allows a denser layout and greater storage capacity per chip than is available with NOR flash.

In addition, NAND flash can typically contain a certain number of faults and still work (NOR flash is generally expected to be fault-free). Because of the series connection and removal of word-line contacts, a large grid of NAND flash memory cells will occupy perhaps only 60% of the area of equivalent NOR cells.

eeworldonline.com | designworldonline.com

8/8/19 12:48 PM

The automotive use case Camera driven bandwidth

FLASH MEMORY

Car camera bandwidth depending on number of cameras, resolution and frame rate

BANDWITH (B/S)

Data from the MIPI Alliance shows how the interface speed of UFS flash can accommodate applications involving multiple cameras as used in ADAS and autonomous vehicles, allowing either more cameras or faster framerates and higher image resolution. Expectations are that the UFS standard will continue to evolve to handle ever-more-demanding uses.

NUMBER OF CAMERAS

Today, the main flash memory used in vehicles follows a Jedec (Joint Electron Device Engineering Council) standard called e-MMC, short for embedded MultiMedia Controller. It simply refers to a package consisting of both flash memory and a flash memory controller integrated on the same silicon die. This format has been the mainstay flash memory for mid to high-end cell phones.

But the e-MMC format is showing its age. The main problem is that e-MMC employs and eight-bit parallel interface.

This type of connection has a limited interface speed that can’t match the demanding mobile and mobile-influenced applications now coming down the pike.

The Universal Flash Storage (UFS) Jedec format is designed to remedy problems with e-MMC. A key difference is that UFS uses a serial differential-signaling serial interface rather than a parallel interface.

Additionally, the parallel eight-bit interface of eMMC forced read and write operations to be sequential, or half-duplex.

The new UFS standard has dedicated read and write paths to permit simultaneous reads and writes. The serial interface and full-duplex data transfers let UFS realize two to four times the peak bandwidth of eMMC and do so with more power efficiency.

The peak bandwidth of UFS defined so far reaches 11.6 Gbps over two lanes. eeworldonline.com | designworldonline.com

Toshiba — A&CV HB 08-19_v2.indd 47

UFS also incorporates a feature called Command Queue (CQ), similar to that of recent e-MMC memories, which makes use of the multi-tasking features of mobile operating systems and multi-core CPUs. This feature allows the parallel execution of multiple read and write commands, significantly boosting command processing speeds.

These improvements allow the latest version of the UFS standard to boost sequential read speeds by 40%, sequential write speeds by 20%, and the number of input/output operations per second for random reads by 73% compared to what’s possible with e-MMC devices. Overall, UFS improves system boot times by 15% and application loading times by 30% compared to that of e-MMC. And UFS incorporates features that are particularly useful in automotive settings.

Among them are thermal control where a UFS device exceeding 105°C notifies the host processor so it can take corrective action. Thanks to a feature called extended diagnosis, the UFS controller monitors operational parameters such as read/write cycles and temperature, then reports the its status to the host processor.

Industry analysts also say automotive uses impose demands on flash chips that other consumer applications don’t. Perhaps the most obvious of these is an extended temperature range, but flash suppliers will 8 • 2019

likely field versions of their products with other qualifications necessary for automotive use. These qualifications include adherence to a PPAP (Production Part Approval Process), an industry guideline detailing the specific reports and documentation necessary to gain part approval in the automotive industry. And because vehicles have a much longer expected life than other consumer products, flash suppliers expect to provide lengthy periods of support and longer-than-usual product change notifications (PCNs).

All in all, UFS is starting to migrate from high-end phones to mainstream mobile markets, and in the near future will find a prominent spot in automotive applications.

REFERENCES Toshiba Memory America Inc. business.toshiba-memory.com

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AD INDEX

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Keystone Electronics Corp...............................................3

Anritsu........................................................................... BC

Memory Protection Devices.............................................1

Coilcraft............................................................................2

Moog Animatics.............................................................25

Digi-Key............................................................. Cover, IFC

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Publisher Mike Emich memich@wtwhmedia.com 508.446.1823 @wtwh_memich Managing Director Scott McCafferty smccafferty@wtwhmedia.com 310.279.3844 @SMMcCafferty

@jpowers_media

EVP Marshall Matheson

Courtney Nagle cseel@wtwhmedia.com

mmatheson@wtwhmedia.com 805.895.3609 @mmatheson

440.523.1685

Mike Francesconi mfrancesconi@wtwhmedia.com

2014 Winner

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Ad Index — A&CV HB 08-19.indd 48

2011 - 2019

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8/8/19 11:48 AM

SENSE Is it cold in here or did your boss just walk in?

We have a sensor for that.

Shop Allied. It makes sense.

1.800.433.5700

Š Allied Electronics & Automation, 2019

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alliedelec.com 6/6/19 2:39 PM 8/2/19 3:22 PM

Automotive Radar Testing Simplified

OVERCOMING RF MEASUREMENT CHALLENGES OF FMCW RADAR With the critical nature of connectivity in the modern automobile, ensuring that key wireless technologies operate as expected has become increasingly important.

™

Frequency modulated continuous wave (FMCW) radar is revolutionizing object detection technology, especially in automotive where FMCW chirps may be transmitted in almost every direction for safety features like adaptive cruise control, blind spot monitoring and collision prevention.

RF measurement of this complex modulation scheme can be complicated, expensive, and time consuming. Anritsu’s MS2760A ultraportable handheld spectrum analyzer is an excellent tool for basic FMCW radar test, combining useful measurements with unmatched size and convenience.

Download our App Note Today: Automotive Radar Testing with Anritsu’s Spectrum Master™ MS2760A

https://info.goanritsu.com/AugustEEWAuto

1-800-ANRITSU www.anritsu.com © 2019 Anritsu Company

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