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AUTONOMOUS & CONNECTED VEHICLES

Connecting the car is the easy part AT

the annual conference of the Society of Automotive Engineers, no small number of technical papers cover issues surrounding driver interactions with features on the car dashboard. Touchscreens in particular are a hot topic. You can find SAE papers with impressive-sounding titles such as, Touch Interactive Display Systems: Human Factors Considerations, System Design and Performance Guidelines; Impact of In-Vehicle Touchscreen Size on Visual Demand and Usability, and InVehicle Touchscreen Concepts Revisited: Approaches and Possibilities. Despite all this research, the evidence is that touchscreens in vehicles are problematic. Consider an informal study conducted by Jacky Li, a product designer at Toronto-based Connected Lab. Engineers there observed 21 subjects “driving” a simulator equipped with touchscreens mimicking those in modern vehicles. It is obvious, says Li, that touchscreens require more hand-eye coordination than traditional buttons and dials. The lack of tactile feedback from touchscreens force drivers to look to see where they’re pressing. But Li says engineers were shocked to find that even when participants weren’t performing touchscreen-related tasks, their eyes were still drawn away from the road and towards the screen. “They would routinely glance over to see if there was anything new to look at,” he reports. Thus Li’s results indicate that touchscreens seem to have the same effect on driver attention as textingwhile-driving. This can’t be good. It also indicates that keeping drivers out of

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harm’s way when using connected car features may be a bigger problem than connecting the car. Another design professional who is no fan of touchscreens in vehicles is Amber Case, a research fellow at M.I.T.’s Media Lab. Case has also delivered a TED talk on cyborg anthropology. “The really good touch displays were pioneered by Apple. That company spent a long time trying to get the interface right and innovating through the iPod. The problem automakers have (in perfecting touchscreens) is the five-to-ten-year product development timeline,” she says. The result: Better touchscreen designs don’t get into production until years after they’ve been conceived. More specifically, Case sees a number of problems plaguing automakers and touchscreens. “I don’t think they’re being tested on the road or in the right sub-optimal conditions,” she says. She also suspects that aesthetics are trumping function. “There are aesthetic expectations people agree on before they actually agree on what a thing should be. The aesthetic expectations of (touchscreens) are that they look new, therefore they must be good.” Case suspects a herd mentality among automakers when it comes to touchscreens. “When it comes to product features, marketers may say, ‘If we don't include this we'll look backwards, and we won't sell as many cars.’ Finally, she believes there may be a disconnect between top management at auto companies and people who

8 • 2018

know better. “If an automaker comes to a professional product design firm and says they want a touchscreen, that design firm isn’t going to tell them ‘Touchscreens are bad,’ regardless of what they really think, because then they won’t get the job. The client will go to whomever gives them the thing they want,” she says. And what about all those SAE papers on the nuances of touchscreen use? “There’s often a disconnect when people who know better tell their managers, ‘Hey, we really want to do a six-month road test.’ The people that know the problems well are usually at the bottom of the company,” Case says. “They might not have the clout within the company to get more testing or throw out bad ideas.”

Leland Teschler Executive Editor

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AUTO N O M O US & CO N N EC TE D VE H I C LE S AUGUST 2018

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02 Connecting the car is the easy part 06 Teaching cars to see Neural network software can be difficult to debug. One reason: it functions only vaguely in the way it has been explained to the general public. 10 Million-mile drives on computers that don't move Simulation is the only way to get the kind of confidence necessary to field autonomous vehicle functions. 13

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Intertial measurement units will keep self-driving cars on track IMUs are just as important as cameras and radar when it comes to operating autonomous vehicles reliably. Zero-to-sixty in 48 volts Versatile automotive 48-V electrical systems will reduce CO2 emissions and make practical super-efficient electrical subsystems.

20 Connected vehicles will make their connections through gallium nitride The high performance of many autonomous automotive features will liekly depend on the use of super-fast GaN semiconductors.

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24 Galvanic isolation for electric vehicle systems High voltages and high currents circulating in EVs make the fundamentals of electrical isolation techniques an important topic. 28 How to design a functionally safe vehicle display It pays to know about the ASIL B compliance measures needed for automotive subsystems. 34 How telematics is evolving with the connected car The embedded systems that control vehicle tracking grow more sophisticated by the day. 38 Are power systems up to the task of running self-driving cars? Automotive electronics depends on power supplies that can handle abuse like load dumps. New ICs are optimized for these extreme environments and can meet stringent vehicle safety standards. 42 When tire pressure monitoring gets smart Bluetooth-enabled tire monitors will check inflation pressures and temperatures with much more precision than available through yellow lights on a dash panel. 44 Testing for time-sensitive networking Connected vehicles depend on networks that won't bog down when handling real-time data. Specialied TSN tests ensure messages arrive on time and intact.

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AUTONOMOUS & CONNECTED VEHICLES

Teaching cars to see

Leland Teschler Executive Editor

Neural network software can be difficult to debug. One reason:

A simple neural network

It functions only vaguely in the

HIDDEN LAYER

way it has been explained to INPUT LAYER

the general public. OUTPUT LAYER

Neural networks are often explained as layers of neurons connected by weights. A positive weight adds the value to the neuron to the ensuing layer, while negative weights subtract its value. So the value of a given neuron is modified by the weighted sum of those behind it. This activity is referred as a linear combination. An activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be -1 and 1. The point to note is that actual computing hardware that implements neural networks doesn’t look like layers with connections. Neuron values as well as weighting values all are comprised of memory locations. Summing, propagation of values through the network, and activation functions all are implemented in software.

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CRACK

open an autonomous-driving vehicle and you will find neural networks taught to recognize objects such as pedestrians, road signs, and other vehicles. Recent accidents involving autonomous and semi-autonomous vehicles have brought up questions about these deep learning networks. One factor that sometimes puzzles outsiders is how to debug a neural net that gets the wrong answer about images it sees. The topic can bewilder non-practitioners because, to human eyes, the data a neural net stores may bear no obvious resemblance to images it knows about. It can be useful to review how neural nets used in AV technology actually recognize images because there are widespread misconceptions about the recognition process. Artificial neural networks are usually depicted as columns of artificial “neurons” – usually dubbed a layer -connected to other columns of neurons. There is an input layer of neurons and an output layer, as well as several intervening or hidden layers between the input and output layers. Input neurons connect to each of the neurons in the next hidden layer. Neurons in a hidden layer, in turn, connect to the neurons both in the layer before and after it. Finally, neurons of the output layer receive connections from the last hidden layer and produce an output pattern. The connections between neurons are weighted. That is, the value of an input neuron is multiplied by a specific weighting factor and added to the neuron in the inner layer to which it connects. Weights can be positive and negative, and the higher the weight, the more the influence. If the sum of the inputs to a given neuron exceeds a certain threshold value, the neuron fires and triggers inputs to the neurons to which it connects.

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m

TEACHING CARS TO SEE

Neural nets learn things by comparing the output the network produces to what it was supposed to produce. The difference in values is used to change the weights of the connections between neurons in the network, starting at the output neurons going back through progressive layers of hidden neurons. The process is called backpropagation. The idea is that eventually, backpropagation reduces the difference between actual and intended outputs to a point where they coincide. Most explanations of neural nets use the model of columns of neurons and connections between them. In actuality, the physical realization of neural nets looks nothing like this model. Neural nets run on ordinary processors. The “neurons” are just locations in a memory. So are the connections between the neurons and the weights they represent. An algorithm computes the neuron values from the weights. For the sake of computation speed, neural nets may employ computer architectures that are highly parallel, so values get calculated as quickly as possible. But these architectures look nothing like the columns of neurons and connections that characterize neural net explanations. Neural network technology has progressed to the point where opensource software for constructing neural

A real neural algorithm

Mathematician Grant Sanderson has produced a series of YouTube videos under the moniker 3blue1brown that explain the working of neural networks. This image from one of his videos shows the value of one internal neuron in a neural network designed to recognize typed letters and numbers, after being shown a “3.” The point to note is that the neuron data looks random rather than discerning any part of the number 3. This random nature will characterize any neuron inside a neural network. It is only through the combination of internal layers that recognizable features emerge.

nets has become available. Some of the more well-known packages include Neuroph, a Java neural network framework; Weka, a collection of machine learning algorithms from the University of Waikato in New Zealand; Eclipse Deeplearning4j, a deep-learning library written for Java and Scala; and Caffe, a deep learning framework developed by Berkeley AI research. However, it is unlikely that any autonomous systems use open-source frameworks for their neural nets. The more typical approach is probably similar to that employed by Affectiva, an M.I.T. Media Lab spin-off that fields a neural net for measuring the emotional and cognitive states of vehicle driver and passengers, based on inputs from a camera and microphones. As explained by Affectiva product manager Abdo Mahmoud, “We started our neural net from scratch because we want to run a deep neural net using extremely low computational power. Open-source neural nets usually come from academia where computational power isn’t a constraint”. The purpose of Affectiva’s neural net is to gage the mood and demeanor of both the driver and passengers in real time. Outputs of the neural net would then be used to detect distracted drivers or bored passengers. The result might be an audible warning for the driver or suggestions to passengers for video or music from a virtual assistant. The Affectiva neural net typically runs on the car’s infotainment processor and is written in C++. Mahmoud says the program designers used numerous methods to minimize the errors that the

Here’s the general outline for how a real neural net makes decisions. This diagram is from a patent taken out by Affectiva covering its automotive AI system which does a real-time measure of nuanced emotional and cognitive states based on observations of the car driver’s face and voice. The scheme shown just computes an anxiety score, but the system runs through similar procedures to gage distraction, anger, and other emotions.

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program might make in gaging moods. Their main technique was to measure performance across a wide variety of use cases. Program designers included situations where occupants sported head scarfs, sun glasses, facial hair, and other impediments obscuring their facial expressions. Designers also conducted specific experiments to elicit emotions and measure the program’s performance on those use cases. Affectiva designers don’t use just one image to categorize emotions. The system builds confidence by considering a sequence of images. A good example is in differentiating a person speaking from one yawning. The system’s confidence in telling one from another grows with succeeding images. Thus the Affectiva neural net doesn’t respond to momentby-moment changes in occupant expressions, Mahmoud explains. By responding only to significant emotional changes in facial expressions, the system greatly reduces false positives. WHAT NEURAL NETS REALLY SEE When authors try to explain how neural nets recognize images, they often do so by taking a simple example, such as recognizing the letter “O.” They then suggest that the neural net separates the letter into sections such as loops, edges, and straight lines – each divined by one of the inner layers of neurons -- then deduces the letter O from the position of these features. Unfortunately, this example is strictly an analogy to what transpires when a neural net examines an object. In reality, the weight matrix for each neuron in the net would most likely look like random noise with no recognizable features evident. The reason for the random-looking nature of the weight matrix is that it is only in combination with other inner-layer neurons that features useful for recognition emerge. Examining single inner neurons doesn’t provide any insight into what features have been discerned. The inability to judge what happens at a given neuron is problematic when it comes to debugging. It’s not possible to debug a neural net simply by looking at the weight matrix of its individual neurons. This is why there is concern about neural networks that fail to notice important details like a pedestrian crossing in front of an autonomous vehicle.

This dash cam footage of a real driver was used as a test case for Affectiva’s Automotive AI system which employs a neural net to gage driver facial expressions and voice quality as a means of warning when a driver might not be paying full attention to the road. In this case the software has noted that the driver is a male, and it has caught him in the midst of a big yawn.

In fact, the debugging process for a neural network tends to probe general attributes of the network rather than starting with a specific problem and tracking back to find the source. For example, many troubleshooting procedures for neural networks include checking the training data for errors, checking the neural network’s behavior with a small subset of the original training data, removing internal layers to see what happens, using a different distribution of weights for the initial model, checking whether the summation of errors changes a lot over long periods, and detecting whether activation functions have saturated and prevented the network from further learning. The point to note about this process is that the debugging procedure is largely the same for any number of situations where the network gives a wrong output. It is not specific to a particular wrong output. And if the network suddenly begins generating the correct output after it’s been debugged, it may not be possible to understand exactly what change brought the desired output. REFE R E N CE S

Affectiva www.affectiva.com Caffe neural network framework www.caffe.berkeleyvision.org Eclipse Deeplearning4j www.deeplearning4j.org Neuroph, a Java neural network framework www.neuroph.sourceforge.net/features.html This is what road rage looks like to Affectiva’s Automotive AI system. The neural-net based software can distinguish between a yawn and a mouth open to utter an epithet.

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Weka www.s.waikato.ac.nz/ml/weka

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AUTONOMOUS & CONNECTED VEHICLES

Million-mile drives on computers that don’t move

Karsten Krügel Sven Flake dSPACE GmbH

Simulation is the only way to get the kind of confidence necessary to field autonomous vehicle functions.

HOW

do you ensure that an autonomous vehicle will perform safely when the driver’s responsibility has essentially been removed? The necessity for reliable operation under this scenario is raising major challenges within the engineering community on the best methods to validate autonomous driving functions. In the absence of the driver, the autonomous vehicle takes over key tasks. It decides how to react in an open situation and how and when it must alert the driver to take back control of the car. The vehicle’s ability to recognize the environment and predict probable changes over the next few seconds is vital. The autonomous vehicle not only must monitor open situations closely, it must also be predictive. It must be able to sense the environment as precisely as possible. In determining if

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a vehicle is performing as expected in these kinds of situations, a good place to begin is with modeling … not just of a single sensor with a single task, but of the entire vehicle and its surrounding environment. This is a key step to starting the validation process. The number of tests necessary to validate the functions of an autonomous vehicle is massive. Thus, it’s unrealistic to try and accomplish all these tests in the field. A more commonsense approach is to perform virtual simulation testing in the lab. Virtual simulation testing can include model-in-the-loop (MIL), software-in-the-loop (SIL) and hardware-in-the-loop (HIL) methods, as well as test benches enhanced by simulation. With simulation, a virtual test drive is created using models … models of the vehicle, the onboard sensors, the road, the

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m

MILLION-MILE DRIVES

One method of speeding the completion of numerous simulation scenarios is via a cluster of PC based-simulation computers where virtual test simulations can run in parallel. The PC cluster is typically controlled by one central unit that schedules the execution of test case simulations. This technique can complete simulations equivalent to driving thousands of miles over the course of a day.

surrounding traffic, the environment, traffic signs, pedestrians, intersections, GPS, etc. These models enable simulations to run in real time. With the aid of off-the-shelf simulation models, you can get a jump start on building virtual test drive scenarios. As an example, the dSPACE automotive simulation model (ASM) tool suite supports numerous application areas such as combustion engines, vehicle dynamics, electric components, hybrid drivetrains, sensors, the traffic environment and road networks. Engineers can use it to model a whole virtual vehicle and its surrounding environment. In this virtual world, the test vehicle can be equipped with multiple sensors for object detection and recognition. Autonomous vehicles carry numerous sensors that serve different purposes. Some are designed to sense the internal state of the car. Others-usually cameras, radar, Lidar and ultrasound-are designed to see the environment. In a process known as sensor fusion, the data captured and processed from various sensors are combined to create a more complete and accurate picture. Aside from the sensors, connectivity also plays a key role in sensing the environment and should be included in the model. An understanding of how the vehicle communicates with other traffic participants (i.e. via sensor stimulation, over-the-air, GNSS, V2X, GPS, data in the cloud, etc.) helps to establish the electronic horizon. Once a model scene is built, the data that is gathered from different types of sensors and connectivity activities can be integrated into the model to evaluate various driving scenarios or virtual test drives. These findings help to decide actions and determine how those actions should be carried out. With a robust model in place, countless virtual test drive scenarios (i.e. oncoming traffic, stop and go, pedestrians) can be simulated with real-time controllers online, in a HIL environment, and offline, on a PC at your desk. HIL testing When it comes to testing the driving functions of an autonomous vehicle, developers needn’t wait for a finished vehicle prototype to begin testing. The preferred technique is to forgo the real world and get a jump start on testing in the virtual world via simulation.

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The functions of an autonomous vehicle are under the control of electronic control units (ECUs). So in the absence of a prototype vehicle, it is still possible to begin early validation of autonomous driving functions by testing the real code in the ECUs. The ECUs connect to an HIL system to enable a real-time simulation of the test drive scenario. A test automation tool (i.e. AutomationDesk) executes the simulation. A measurement and calibration tool (i.e. ControlDesk) performs diagnostics on ECUs. Measurement and calibration tools like ControlDesk are also used to visualize the human interface and custom layouts. Additionally, a 3D online animation tool (i.e. MotionDesk) is recommended to visualize the overall test drive scenario. Finally, a real-time script should be run on the HIL system to track important events directly on the system. A test management tool (i.e. SYNECT) can manage all the test data. Typical tests run in an HIL simulation environment include release tests for camera and radar applications, the testing of image processing ECUs, testing of camera-based systems, multisensory systems, V2X apps, GNSS-based driving

8 • 2018

functions, and tests of over-the-air radarin-the-loop simulations. Virtual validation If only a few ECU prototypes are available or none at all are available,̶ it is still possible to move forward with testing autonomous driving functions using virtual validation. With virtual validation, the behavior of a function (e.g. automatic parking) is reproduced using a mathematical model (preferably, these models should be Simulink-based, open and fully parametizeable). This makes simulation possible … the only difference is that the simulation takes place in an SIL test environment instead of an HIL environment. In the virtual validation test scenario, the HIL system is replaced with SIL PCbased simulation (i.e. VEOS). The same models that would run on a HIL system can also run on an SIL PC-based system, but virtual ECUs are connected instead of the real ECU. Virtual ECUs are created from the software components that represent the ECU. They can be directly integrated into the SIL simulation test scenario. Simulink

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models and third-party models based on the Functional Mock-Up Interface (FMI) standard can also be integrated. For PC-based simulations, the ECU code that is generated should be compiled for a Microsoft Windows platform. Transitioning back and forth from HIL to SIL PC-based simulation is easy because the same models can be shared, and the same tools can be used for measuring, test automation and model parameterization. Reacting in an unpredictable world The world is unpredictable, and unexpected situations will arise. Autonomous driving functions can’t be validated with only a set of pre-defined test drives. Thought must go into how to create test drives that reflect an unpredictable world. It’s also insufficient to just generate test drives randomly. Developers must also think about how to create good test situations and many of them. For example, it will take several versions of test drives to develop a test case to determine how a vehicle will react when it meets traffic. Developers must consider how the vehicle will react when other participants follow traffic rules and what will happen when they don’t. Adding these kinds of varying details can quickly lead to an explosive number of test scenarios. It takes a considerable number of test drives to evaluate the numerous autonomous

It takes a massive number of test cases to verify autonomous driving features function correctly. Typical test cases include those involving pedestrians in various scenarios and other vehicles that don’t behave the way the system might expect.

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driving function scenarios. Tests must take place both during the development phase and for the final release, so efficient, time-saving methods are extremely valuable. Organizations can be more resourceful and speed testing activities by utilizing existing test cases and performing PC cluster simulation. Every test scenario to be simulated needn’t be created manually. Thousands of test cases can be imported using resources such as OpenDRIVE, OpenCRG, OpenSCENARIO, OpenStreetMap, Open Simulation Interface (OSI), Simulation of Urban Mobility (SUMO), VISSIM, HERE, TomTom, your own GPS data, etc. Accident databases (i.e. GIDAS) are another go-to source for information. These kinds of tools support the configuration of simulation models so they can realistically emulate defined scenarios during simulation. As test cases begin to pile up, a great way to complete numerous simulation scenarios automatically is on a cluster of PC based-simulation computers (e.g. VEOS). With this setup, virtual test simulations can run in parallel. Additionally, test system setups can easily be scaled up or down as needed. The PC cluster is typically controlled by one central unit that schedules the execution of test case simulations. The number of tests run via PC cluster simulation is equivalent to driving thousands of miles over the course of a day. Whether using an SIL or an HIL environment, the testing of autonomous driving functions revolves around the interpretation of sensor fusion algorithms. Sensor and vehicle network data gets recorded and played back time-synchronously for testing purposes. To support highperformance sensor data processing, the test environment should be equipped to capture, synchronize, process and merge data from various sensors, such as cameras, radars, Lidars and GNSS receivers.

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It should also handle multiple, highbandwidth data streams, calculate application algorithms, interface with communication networks (i.e. CAN/ CAN FD, LIN, Ethernet), and connect to actuators or human machine interfaces (HMIs). Data tagging programs, such as RTMaps, are available to provide time stamping, tagging, recording, synchronizing, and playback options to enable sensor data processing and ensure that all data is time-correlated. The high number of tests necessary to validate autonomous driving functions makes data management and traceability a necessity. Test data should be centrally managed through a facility that ensures consistent data versions and full traceability. Data management tools are available to support test planning, execution, evaluation and reporting. dSPACE SYNECT is a data management and collaboration software tool with a special focus on model-based development and ECU testing. Autonomous vehicles are made up of a set of connected ECUs with embedded software. For convenience, the software architecture should be configured to support software updates over the air. Currently, most software architectures for ECUs are designed to be parameterizable, but they are still static, ideally designed using the AUTOSAR Classic Platform. The software architecture needs to support software updates on the go without changing the overall architecture. The recent AUTOSAR Adaptive Platform implements this idea of flexible embedded software architectures. Likewise, the ECU architecture will need to support powerful ECUs responsible for the intelligence of the car. Less powerful ECUs can be used for more basic functions.

R E FE R E N CE S dSPACE GmbH www.dspace.com/en/pub/home.cfm

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Inertial measurement units will keep self-driving cars on track Mike Horton Aceinna Inc.

IMUs are just as important as cameras and radar when it comes to operating autonomous vehicles reliably.

AN

inertial measurement unit (IMU) is a device that directly measures a vehicle’s three linear acceleration components and three rotational rate components (and thus its six degrees of freedom). An IMU is unique among the sensors typically found in an autonomous vehicle (AV) because an IMU needs no connection to or knowledge of the external world. This environment independence makes the IMU a core technology for both safety and sensor-fusion. The typical IMU for autonomous vehicle use includes a three-axis accelerometer and three-axis rate sensor. Those that are 9-DOF (degrees of freedom) units include a three-axis magnetometer. But a true IMU is just 6-DOF. A magnetometer is not particularly useful in automotive apps due to a vehicle’s local magnetic field and fields of nearby cars and trucks. A self-driving car requires many different sensing technologies. For example, it typically needs Lidar to create a precise 3D image of the local surroundings, radar in a different part of the spectrum for ranging targets, cameras to read signs and detect colors, high-definition maps for localization, and more. Unlike the IMU, each of these technologies interacts with the external

If an autonomous vehicle wanders in its lane, it will appear to be driven by a bad driver. And wandering out of a lane during a turn could easily result in an accident. The IMU is a key dynamic sensor to steer the vehicle dynamically, maintaining better than 30-cm accuracy for short periods when other sensors go offline. The IMU is also used in algorithms that can cross-compare position/location and then assign a certainty to the overall localization estimate. Without the IMU, it may be impossible to know when the location error from a Lidar has degraded.

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AUTONOMOUS & CONNECTED VEHICLES

In 2006, a typical IMU measured 12 cm on each side. Today, latest IMUs from Aceinna sit in a 9 cm 3 module and have 1/100th the cost of units made 12 years ago. The IMU380ZA IMU is engineered for precision agriculture applications, with a MTBF greater than 50,000 hours and low power consumption. It sits in a compact package weighing less than 17 gm. Aceinna has shipped over 50,000 inertial systems for use in heavy machinery and has deployed inertial navigation units in over 600 types of aircraft.

environment to send data back to the software stack for localization, perception, and control. The IMU helps provide “localization” data i.e., information about where the car is. Software implementing driving functions then combines this information with map and “perception stack” data that tell the car about objects and features around it. The perception stack is part of what’s called the AV stack. The AV Stack is basically the brains behind autonomous vehicles. It is a collection of hardware and software components consolidated into a platform that can handle end-toend vehicle automation. The AV stack includes perception, data fusion, cloud/ OTA, localization, behavior (a.k.a. driving policy), control and safety. The system engineer must consider every scenario and always have a backup plan. Failure Mode Effects Analysis (FMEA) formalizes this requirement into design requirements for risk mitigation. FMEA will ask questions like, what happens if the Lidar, radar, and cameras all fail simultaneously? An IMU can deadreckon for a short period of time, meaning it can briefly determine full position and attitude independently. IMUs used for AV applications typically have uncertainties of much less than 1 mG (10-3 G) for their accelerometers and much less than 10 deg/hr for their rate sensors. In terms of position, this means the IMU can track for 10 sec to about 30 cm or better. An IMU alone can slow the vehicle in a controlled way and bring it to a stop, providing the best practical outcome in a bad situation. While this may seem like a contrived requirement, it turns out to be fundamental to a mature safety approach. An accurate IMU can also determine and track attitude precisely. When driving,

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the direction or heading of the vehicle is as crucial as its position. Driving in a slightly wrong direction even briefly may put the vehicle in the wrong lane. Dynamic control of the vehicle requires sensors with dynamic response. An IMU does a nice job of tracking dynamic attitude and position changes accurately. Its fully environment-independent nature lets an IMU track position even in tricky scenarios such as slipping and skidding where tires lose traction. A precise attitude measurement is often useful as an input for other algorithms. While Lidar and cameras can be useful in determining attitude, GPS is often almost useless. Finally, a stable independent attitude reference has value in calibration and alignment. It turns out humans who are not distracted or drunk are typically not bad at driving. A typical driver can hold position in a lane to better than 10 cm. This is actually quite tight. If an autonomous vehicle wanders in its lane, it will appear to be driven by a bad driver. And wandering out of a lane during a turn could easily result in an accident. The IMU is a key dynamic sensor to steer the vehicle dynamically, maintaining better than 30-cm accuracy for short periods when other sensors go offline.

8 • 2018

The IMU is also used in algorithms that can cross-compare position/location and then assign a certainty to the overall localization estimate. Without the IMU, it may be impossible to know when the location error from a Lidar has degraded. Tesla is famous for its “No Lidar Required” autopilot technology. In systems lacking Lidar, a good IMU is even more critical because camera-based localization of the vehicle will experience more frequent periods of low-accuracy depending on the external lighting and what is in the camera scene. Camera-based localization uses SIFT (scale invariant feature transform) tracking in the captured images to compute attitude. Briefly, SIFT recognizes an object in a new image by individually comparing each of its features to reference images in a database and matching features based on the Euclidean distance (ordinary straight line) of their feature vectors.) If the camera is not stereo (often the case), inertial data from the IMU itself is also a core part of the math to compute the position and attitude. The combination of high-accuracy Lidar and high-definition maps is at the core of the most advanced Level 4 selfdriving approaches such as those tested by Cruise and Waymo. In these systems,

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INTERTIAL MEASUREMENT

IMU exploded view

The typical IMU for autonomous vehicle use includes a three-axis accelerometer and three-axis rate sensor. 9-DOF units include a three-axis magnetometer.

CPU

PCBA

3-AXIS GYRO

3-AXIS ACCEL

3-AXIS MAGNETOMETER

Lidar scans are matched to the HD map in real-time using convolutional signal processing techniques. Based on the match, the system estimates the precise location of the vehicle and its attitude. This process is computationally expensive. While we all like to believe the cost of computing is vanishingly small, it simply is not that cheap on a vehicle. The more accurately the algorithm knows its initial position and attitude, the less computation necessary to compute the best match. In addition, use of IMU data minimizes the risk of the algorithm getting stuck in a local minimum of HD map data. In today’s production vehicles, GPS systems use low-cost single-frequency receivers. This makes the GPS accuracy essentially useless for vehicle automation. However, low-cost multi-frequency GPS is on the way from several silicon suppliers. Additionally, network-based correction schemes such as RTK and PPP can provide GPS fixes to centimeter-level accuracy under ideal conditions. But bridges, trees, and buildings can degrade the accuracy of these techniques. GPS reliability improves through use of high-accuracy IMUs at a low-level in the position scheme. In that regard, navigation systems similar to those long used in aircraft and ships will likely find use in AVs. GPS/INS (inertial navigation systems) techniques use GPS satellite signals to correct or calibrate data from an inertial navigation system. The INS uses a

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computer, accelerometers, gyroscopes, and occasionally magnetometers to continuously calculate by dead reckoning the position, the orientation, and the velocity of the vehicle. INS tend to drift with time and must calculate angular position by integration of the angular rate from the gyros, also incurring some error that the GPS data corrects. It turns out that production automobiles already have anywhere from one-third of an IMU to a full IMU on board. Vehicle stability systems rely heavily on a Z-axis gyro and lateral X-Y accelerometers. Roll-over detection relies on a gyro mounted with its sensitive axis in the direction of travel. These sensors have been part of vehicle safety systems for over a decade. The only problem is that the sensor accuracy is typically too low to be of use for AV purposes. So there is an argument for upgrading the vehicle to a high-accuracy IMU which can help it drive autonomously. The R E FE R E N CE S main barrier for this idea has been cost. At Aceinna, we are using proprietary manufacturing techniques to get the cost Aceinna Inc. down. This has let us go into high-volume www.aceinna.com production of IMUs for self-driving tractors. Thus it is natural to think AVs could be the next area to benefit from these versatile sensors.

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AUTONOMOUS & CONNECTED VEHICLES

Zero-to-sixty in 48 volts Joseph Pulomena EPCOS, a TDK Group Company

Versatile automotive 48-V electrical systems will reduce CO 2 emissions and make practical superefficient electrical subsystems.

NEARLY

five years ago, the U.S. government issued rules requiring automakers to hit a fleet average of 54.5 mpg for new cars and trucks – nearly doubling average fuel economy. Electric vehicles and hybrid technology have sped the quest for high MPG while the development of so-called micro hybrid and mild hybrid systems based on 48-V power has also shifted into high gear. As a result, the long-used 12-V automobile electrical system is reaching the end of the road. Use of 48-V technology offers several advantages for both vehicle manufacturers and drivers. Not only does it reduce environmental impact, it also improves engine performance and, most

importantly, fuel consumption. It does so by means that include facilitating the use of powerful electric turbochargers, lighter wiring harnesses, efficient electric A/C units, and start/stop engine operation. Moreover, the sheer volume of electrical loads in vehicles is exponentially rising. Technologies including telematics, auxiliary electrical heating systems, complex drivetrain management, antilock braking systems (ABS), electronic stability program (ESP), and dozens of other systems consume high levels of energy. Today’s alternators keep up with this demand through use of a powerful buckboost converter. A little perspective on vehicle electrical systems is useful. In 1955, 12-V

charging systems were introduced and replaced older systems based on 6-V technology. Between the 1955 and the 1980s, standard alternator output was typically below 0.5 kW, although some high efficiency alternators could produce as much as 0.7 kW. But today’s vehicles require an alternator output of 3.5 kW – seven times more. Specifically, the real output power of the alternator is 14 V x 250 A = 3.5 kW. But the alternator has mechanical and electrical losses, mainly from friction in the bearings and brushes, as well as losses in the diodes and alternator windings. Thus the alternator has a maximum efficiency of about 70%. This means the engine must bring 3.5 kW / 0.7 = 5 kW of power to

Getting from 48 V to 12 V

Architecture of a combined 12/48-V on-board power supply

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ZERO-TO-SIXTY

Simplified buck-boost schematic

Buck-boost converter circuit diagram. The design of a buck-boost converter resembles that of a buck converter and boost converter, except it is a single circuit. Not shown is a control unit that senses the level of input voltage and takes appropriate action on the circuit based on that voltage.

the alternator. The additional power necessary reduces fuel efficiency and, subsequently, boosts CO2 emissions. Enter 48-V technology, which allows a number features that reduce both CO2 emissions and overall fuel consumption that are impractical in 12-V systems. These features include the support of micro-hybrid and mild-hybrid systems, as well as high-performance energy recuperation at >5 kW, extended start-stop functions such as sailing or coasting; and electrification of systems such as turbochargers and power steering units. The 48-V system is more of an extension than a replacement of the existing 12-V architecture. It allows the system to handle more powerful loads through the use of a bi-directional buck-boost converter that controls both a 12-V level and the 48-V level. Think of this as version 2.5. In this version, a standard lead-chemistry battery is used for the 12-V level and a lithium-ion battery handles the 48-V level. The generator also outputs 48 V, helping to realize more efficient operation. New best practices specify that double-layer capacitors be connected in parallel for improved electrical storage. The central component of the combined 12 /48-V system is the buck-boost converter which controls a bidirectional energy flow between the two voltage levels. Most buck-boost converters are designed for outputs of between 2 and 5 kW. The buck-boost converter (also known as a step-down/ step-up converter) normally operates as a buck converter so the power generated at 48-V is stepped down to power

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12-V features. Output at 48 V utilizes the boost mode. Serially-connected systems with six or eight phases are typically used to minimize voltage and ripple currents. The buck-boost converter must operate in harsh automotive environments, so it is built with highquality, reliable switching transistors, power inductors, and storage capacitors. For example, designs calling for storing significant amounts of power in inductors and smoothing chokes generally specify ceramic SMD components. Power chokes should feature a third soldering pad, in addition to the two solder pads for the winding, for better mechanical stability on the PCB. If a SMD inductor is not possible, inductors with plated thru-hole terminations can also be used. All components should be designed to operate in temperatures ranging from -40 to +150°C. Other key components in buck-boost converters include robust aluminum electrolytic capacitors for storage and smoothing chokes. The capacitors should also operate up to 150°C. These capacitors should be specifically designed for the stringent demands of automotive electronics. Examples include the EPCOS B41689 and B41789 series from TDK. These aluminum electrolytic capacitors are characterized by their extremely high vibration strength of up to 60 g and a soldering star design. Some capacitors come with cathode plate contacts on both ends to enable optimized mounting with low ESL values.

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AUTONOMOUS & CONNECTED VEHICLES

The fast response of E-turbos

The effect of quick-responding electric turbochargers on engine efficiency is evident from graphs of cylinder pressure vs. engine speed.

Capacitors should also feature low ESR values to accommodate higher ripple currents and incur lower losses. The capacitors should be rated at voltages of 25 V, 40 V (for 12-V systems) and 63 V (for 48-V systems). These voltages let them serve in on-board power systems at both voltage levels. The capacitance range extends from 360 to 4,500 °F. ELECTRIC TURBOCHARGERS Another benefit of 48-V technology is that it makes e-turbochargers practical, with a resulting improvement in engine efficiency. Until recently, conventional turbochargers were driven by exhaust gases, so they performed better as engine speed rose. One drawback to this mode of operation is a slight delay between the time they are actuated and when they kick on. This is known as turbo lag. A 48-V system removes this lag through use of an electrically operated charger. The turbocharger responds instantly and also works at lower speeds, boosting overall efficiency both in urban traffic conditions and on the highway. Even better, combining a conventional turbocharger with an electric turbocharger further steps-up charging pressure so the electrical charger can be switched off at high engine speeds, saving power and improving overall efficiency. Not only does 48-V technology improve engine performance and efficiency, it is also attractive to drivers who want to reduce fuel consumption. It creates a truly wellequipped automobile that can go from zero to 60 in 48 V.

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REF EREN CES

EPCOS, a TDK Group Company www.epcos.com

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PUSH PERFORMANCE TO THE TOP

PCB Terminal Blocks for Power Electronics • • • •

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AUTONOMOUS & CONNECTED VEHICLES

Connected vehicles will make their connections through gallium nitride The high performance of many autonomous automotive features will likely depend on the use of super-fast GaN semiconductors. Alex Lidow Efficient Power Conversion (EPC)

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WITH

the rise of autonomous cars and electric propulsion as driving forces in automotive applications, a huge new market for power devices based on gallium nitride grown on a silicon substrate (GaN-on-Si) is emerging. IHS Markit estimates that 12 million cars will be autonomous by 2035. According to Bloomberg New Energy Finance, Marklines, 32 million cars will have electric propulsion. Both trends are putting performance demands on power semiconductors. These demands are coming at a time when silicon is reaching its performance limits, thus further opening opportunities for GaN technology devices. Over the past eight years GaN power devices have been in mass production. And there have emerged several large automotive applications where GaN has significant advantages over the aging silicon MOSFET: LiDAR (Light Detection and Ranging), radar, 48-to-12-V dc-dc conversion, ultra-high-quality infotainment, highintensity headlamps, and on-board wireless power charging. One of the first automotive applications for GaN transistors and ICs was in LiDAR. Use of this object detection and distance measuring technology was prompted by the need to gain critical information as quickly as possible about

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CONNECTED VEHICLES Simplified block diagram — 360˚ LiDAR

A simplified block diagram of LiDAR employing a spinning disk with solid-state stacked laser system. Laser light reflects from targets back to a receiver. Sophisticated signal processing circuitry constructs a digital point-cloud image from the returns. LiDAR sensors using GaN FETs quickly create an accurate digital point cloud. Image, courtesy of Velodyne.

a self-driving car’s surroundings. Velodyne was the first LiDAR company to recognize the contribution that the superior switching speed of GaN can make to an autonomous vehicle’s LiDAR system. By triggering high-current laser pulses that are extremely narrow, it is possible to accurately measure the time-of-flight for emitted photons, and there will be enough photons reflected to see objects at long distances. The more frequently a LiDAR system can fire its laser, the more information obtained about the surroundings. The combination of high current, narrow and frequent pulses makes it possible to rapidly resolve objects with an accuracy of a few millimeters at distances of a few hundred meters. Velodyne implements LiDAR using a spinning disk carrying several solid-state lasers stacked parallel to the axis of rotation. The resulting laser beams create a fast and accurate digital point cloud identifying the surroundings of a self-driving car. The laser optical pulse has two main parameters: pulse width and energy. These two factors have a large effect on the distance resolution and the range, respectively. The pulse width of the transmitted optical signal has a great influence on the LiDAR system. Given that the light pulse must travel to the target, be reflected and travel back, the time td between pulse transmission and reception for a target at distance d,

towards narrower pulses, the pulsed current must rise to maintain sufficient pulse energy. GaN FET high-speed devices can create highcurrent pulses with extremely short pulse widths. This is one reason Velodyne chose eGaN FETs from Efficient Power Conversion as the triggering devices for firing the laser. LiDAR is just one of the technologies developed for autonomous vehicles. The large amount of data generated by the array of LiDAR sensors used in vehicle navigation and control, combined with data provided by radar, cameras and other information-gathering devices on the car, has fostered a new market for high-performance graphic processors. These processors integrate multiple sensor inputs, digest their meaning, and decide what commands to send to the self-driving actuators. Fast processing is a key attribute. So companies such as NVIDIA Corp. and Intel’s Mobileye have developed ultra-fast multicore processors. These processors can gather, integrate and make sense of all the inputs from multiple radar, LiDAR, camera, and ultrasonic sensors quickly enough to safely navigate our roads and highways. High-performance processors An EPC2202 AEC-Q101 for autonomous vehicle navigation qualified FET is used to are extremely power hungry. The

generate a 1.8 nsec pulse (yellow trace) at a peak current of 26 A. The optical receiver pulse signal is shown as the blue trace.

td = 2d/c where c is the speed of light in air. Various signal processing techniques can improve the resolution for a given pulse width. But it is clear a shorter pulse gives better inherent precision, and pulses on the order of a few nanoseconds are desirable for humanscale resolution. Pulse energy determines the range of the LiDAR. As demand for better resolution drives designs

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AUTONOMOUS & CONNECTED VEHICLES

48/12-V DC-DC converter

Comparing GaN and Si efficiencies

On the level of a simplified block diagram, a 48-to12 V dc-dc power converter looks something like this.

need for power places a heavy burden on traditional automotive 12-V electrical distribution buses. Consequently, automakers increasingly turn to 48-V distribution buses to provide enough power. Use of a 48-V bus reduces current levels and wire sizes by a factor of four. The advantages of 48 V are even more evident when considering all the new power-hungry electronically-driven functions and features on the latest cars. Among them are electric start-stop, electric steering, electric suspension, electric turbo-supercharging, and variable-speed air conditioning. These new functions and features generally need 48 V – 12 V dc-dc converters. GaN power transistors help these power supplies operate efficiently. They take power at 48 V and convert it to 12 V to run legacy systems and battery packs. IHS Automotive estimates that 11 million cars will have 48-V systems in 2025. GaN FETs and ICs have the highest power density and are the most efficient way to reduce 48 V to 12 V. GaN devices are many times smaller than equivalent silicon power MOSFETs and many times faster. The resulting electronics can have high efficiency while being smaller and less expensive. eGaN FETs from EPC are also competitive with silicon when it comes to volume pricing. And for the automotive market, GaN technology has promoted wide-spread adoption by passing AEC-Q101 qualification testing. In terms of reliability, eGaN technology has been mass produced for over eight years, accumulating billions of hours of successful field experience in automotive applications. EPC and other GaN product suppliers, such as Transphorm and GaN Systems, have products undergoing AEC-Q101 qualification testing. Two of these products, EPC2202 and EPC2203, are EPC discrete transistors that have completed qualification tests. The devices are in wafer-level chip-scale packaging (WLCS) with 80 VDS ratings. They are harbingers of future eGaN discrete transistors and integrated for the harsh automotive environment. The EPC2202 is an 80-V, 16-mΩ enhancement-mode FET with a pulsed current rating of 75 A in a 2.1x0.6-mm chip-scale

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Efficiency plots illustrate the performance of the EPC9130, a 700-W 48-to-12-V dc-dc converter based on EPC2045 eGaN FETs. It has higher power density and higher efficiency than the best silicon-based converters. The eGaN FET-based converter also has the least expensive bill of materials.

Comparing efficiency and power density

package. The EPC2203 is an 80-V, 73-mΩ part with a pulsed current rating of 18 A in a 0.9-mm2 chip-scale package. These eGaN FETs are many times smaller and realize switching speeds 10 to 100 times faster than their silicon MOSFET counterparts. Both products are designed for a wide range of emerging automotive applications. In addition to LiDAR and dc-dc conversion, they also include high-intensity headlights and ultrahigh-fidelity infotainment systems. To complete AEC-Q101 testing, these eGaN FETs had to undergo rigorous environmental and bias-stress tests that include

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CONNECTED VEHICLES

The 80-V EPC2202 device measures 2.1x1.6 mm and has a pulsed current rating of 75 A. The 80 V EPC2203 device measures 0.9x0.9 mm and has a pulsed current rating of 18 A. Both have passed AEC-Q101 testing.

humidity with bias (H3TRB), high-temperature reverse bias (HTRB), high-temperature gate bias (HTGB), temperature cycling (TC), as well as several other tests. Of note is the fact that these wafer level chip-scale (WLCS) devices passed the same testing standards created for conventional packaged parts. Thus chip-scale packaging does not compromise on ruggedness or reliability. These parts are produced in facilities certified to the Automotive Quality Management System Standard IATF (for International Automotive Task Force) 16949. Throughout 2018 additional 80-V parts will undergo certification, expanding the range of performance to higher currents. The stage is set, the future will see an expanding adoption of GaN devices for many traditional and emerging automotive applications.

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REFEREN CE S EPC, www.epc-co.com/epc S. Taylor, “Why We’ll Soon Be Living in A Class D World,” Audiophilereview.com, 17 Sept 2016, https://audiophilereview.com/cd-dac-digital/why-well-soon-be-living-in-a-class-d-world.html

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Bob Chabot, “48 V Systems Gain Ground,” Motor Magazine Newsletter, July 5, 2016, www.motor.com/newsletters/2016/20160705/!ID_48V_Systems. html A. Lidow, J. Strydom, M. de Rooij, D. Reusch, GaN Transistors for Efficient Power Conversion, Second Edition, Wiley, 2014. R. Cortland, “Gallium Nitride Power Transistors Priced Cheaper Than Silicon,” IEEE Spectrum, 8 May 2015, spectrum.ieee.org/tech-talk/semiconductors/design/gallium-nitride-transistors-priced-cheaper-than-silicon

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Galvanic isolation for electric vehicle systems Battery charging in electronic vehicles

Block diagram of a typical on-board charger system, including galvanic isolation components.

High voltages and high currents circulating in EVs make the fundamentals of electrical isolation techniques an important topic. Ross Sabolcik Silicon Labs

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AS

automotive designs move toward electrification, high-wattage power electronics become critical to new electronic drivetrain and battery systems. In these applications, digital controllers safely interface with the high-voltage systems of modern electric vehicles thanks to galvanic isolation. Galvanic isolation is critical to the operation of these circuits, so it can be useful to review the basics of electrical isolation and how modern automotive electronics implement it. Galvanic isolation refers to a means of preventing current flow between two parts of an electrical system. The key trait of galvanic isolation, compared to ohmic isolation, is that it has no direct conduction path between the two circuits. Put another way, the output power circuit is both electrically and physically isolated from the input power circuit. But

8 • 2018

galvanic isolation still allows energy or information to be exchanged between the two sections by other means. There are two main reasons galvanic isolation may be necessary. The first is that the electrical grounds of the two electrical systems may be at different potentials. In the absence of galvanic isolation, there can be ground loop current flowing between two units sharing a ground conductor. Groundloop currents constitute electrical noise that can interfere with the operations of either circuit. Moreover, if the difference in ground potentials is sufficiently large, the resulting ground-loop current can pose a safety issue. Thus the second reason for galvanic isolation is safe operation. Safety is the primary reason galvanic isolation is mandated in automotive electronics. EVs and mild hybrid

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GALVANIC ISOLATION

electric vehicles (HEVs) are characterized by high-voltage circuitry carrying lethal currents. The high-voltage sections are under the control of digital electronics employing milliamp-level currents. Galvanic isolation, compared to other types of isolation, is a more robust way of preventing faults in power stages from damaging the control electronics that operates them.

vehicle from voltages that can be well over 300 V. High-voltage subsystems, such as the OBC, are typically controlled over a CAN bus, which likewise must be isolated. The low-voltage controllers in an EV communicate with high-voltage subsystems over connections that are often noisy because of proximity to high currents and electrical switching. In addition, low-voltage controllers must stay isolated from the high-voltage power transistors they control while also measuring the currents or voltages in other high-voltage sections of the system. Systems outside the EV, such as electric charging piles, have similar system requirements and needs for isolation. Several types of isolation technology can be used in electric vehicles including isolation transformers, optocouplers, capacitor-based semiconductor isolators, and transformer-based semiconductor isolators. Isolation transformers use magnetic fields to communicate across the isolation barrier with dielectric insulation between the windings and the magnetic core providing the isolation barrier. Optocouplers use an LED and optodetector to communicate across

High-voltage circuits in EVs It is useful to review the high-power circuits found in EVs and HEVs. Both kinds of vehicles generally employ 48-V systems and batteries with high energy storage density and the ability to charge in minutes instead of hours. In addition, the battery management and associated power conversion system must be small and light weight, and they must “sip” battery current. Modern EV/HEV designs use modular components in the drive train and energy storage/conversion systems. EV/HEV battery management systems typically include five major circuit assemblies: On-board charger (OBC): LithiumIon batteries get charged by an onboard charger consisting of an ac-todc converter with power factor correction that is supervised by a Galvanic isolation in an EV battery management system. Battery management system (BMS): The BMS monitors How solid-state galvanic isolation can and handles the charging and work in a communication interface for discharging of battery cells to a battery management system. The ensure high efficiency and safety. high-voltage domain is the side with the Specifically, the BMS controls the battery pack. The low-voltage domain charging, state of health, depth is the side with the CAN transceiver. of discharge, and conditioning of This example focuses on the CAN bus interface. Real-life systems generally individual battery cells. incorporate additional isolation DC/DC converter: The dc/ between the microcontroller and the dc converter connects the highbattery pack. voltage battery to the internal 12-V dc network, which also powers accessories and provides bias to the local switching converters. Main Inverter: The main inverter drives the electric motor and is also used for regenerative braking and returning unused energy to the battery. High-performance isolation protects digital controllers on the

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the isolation barrier. The airgap between the LED and photodetector is typically not wide enough to support the isolation voltages required. As a result, a dielectric tape is inserted between the devices in an optocoupler to increase the isolation rating. Semiconductor-based isolation uses either a differential capacitor pair or a MEMS-based transformer as the isolation component. In these devices a signal is modulated across the barrier to pass information. In capacitor-based isolators, silicon dioxide is typically used as the dielectric. A polyimide layer is used in transformer-based systems. An isolation channel consists of a transmitter and receiver separated by this semiconductorbased isolation barrier. Modulation can be based on either an on-off keyed RF carrier or an edge-based detection scheme. The receiver contains a demodulator that decodes the input state according to its RF energy content. The RF on/off keying scheme offers superior noise immunity compared to edge-based schemes but with the tradeoff of higher power consumption. Compared to optocouplers, semiconductor-based

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AUTONOMOUS & CONNECTED VEHICLES

isolators have numerous advantages including longer lifetimes, significantly better stability over temperature and aging, faster switching, and much higher noise immunity. Semiconductor-based isolation is particularly helpful as automotive suppliers target wide bandgap power transistors based on gallium nitride (GaN) or silicon carbide (SiC). GaN or SiC systems often use higher switching speeds to reduce the size of the system magnetics, a practice which can result in significantly higher electrical noise. Semiconductor isolation can deal with these higher speeds and noisier environments. The shrinking the size and rising power density of automotive electronics will drive operating temperatures higher, which can stress optocouplers and reduce their performance. Semiconductor-based isolation has significantly better reliability over these higher temperature ranges, making them a good choice for EV designs. Inside the OBC and BMS It may be helpful to review applications in EV and HEV electronics where galvanic isolation based on RF techniques can be helpful. One area is in the OBC system responsible for converting a standard ac charging source into a dc voltage that charges the vehicle battery pack. In addition, the OBC performs other key functions such as voltage monitoring and protection. The OBC system takes the ac input source, converts it to a high-voltage dc bus voltage using a full-wave rectifier and provides power factor correction (PFC). The resulting dc signal is chopped into a switched square wave that drives a transformer to create the required output dc voltage. The chopping of the input signal takes place using isolated-gate drivers such as Silicon Labs’ Si8239x device. The output voltage can be filtered to the final dc voltage using sync field effect transistors (FETs) under the control of

The isolation components frequently used to allow communication and control in EV systems.

Typical digital isolation in an EV

How various digital isolation devices might work in a simplified traction motor control system.

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GALVANIC ISOLATION

Typical EV subsystems

The typical subsystems that make up an EV.

isolated-gate drivers. The output voltage can be monitored to provide closed-loop feedback to the system controller using isolated analog sensors such as Silicon Labs’ Si892x device. The entire system can be monitored and controlled through an isolated CAN bus. The CAN bus is isolated with digital isolators with integrated dc/ dc power converters such as Silicon Labs’ Si86xx and Si88xx isolators. A review of a simplified BMS system also highlights the importance of signal and power isolation. In most EV subsystems, the CAN bus is isolated from the high voltages in that subsystem through digital isolation. Modern digital isolation requires a power supply on both sides of the isolator (the high-voltage domain and the low-voltage domain). This power supply can also be used to power other devices attached to the isolator such as a CAN bus transceiver. Isolation in traction motor systems Several critical isolated components work in the traction motor drive system. The traction motor in most electric vehicles will be an ac induction motor. To drive the motor, the traction motor controller must synthesize a variable ac waveform from the highvoltage dc rail from the battery pack. These systems require isolated drivers between the motor controller and the power transistors. The isolation allows the low-voltage controller to safely switch the high-power transistors to create the ac

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waveform. In addition, there is likely an isolated CAN bus in the motor control system and some method to sense the current being driven to the motor for monitoring and controlling speed and torque. Automotive electronics must meet more stringent testing and quality standards than those for industrial devices. Most automotive customers require the more stringent AECQ-100 qualification, ISO/TS16949 audit compliance, extended operating temperatures ranges (-40 to +125°C) and extremely low defect rates. These enhanced requirements mean automotive electronics suppliers must take additional steps to ensure their components measure up. Additional quality controls at the wafer fab, device packaging and final assembly are required. True automotivegrade devices must be supported by quality systems and documentation such as the Part Production Approval Process (PPAP), International Material Data Systems (IMDS) and China Automotive Material Data Systems (CAMDS). All in all, ever-increasing power densities in EV subsystems create demanding thermal and electrical noise conditions. Semiconductor-based isolation offers significant advantages over legacy optocoupler solutions. Automotive customers demand wider operating temperatures, higher quality, and more stringent documentation and systems than industrial customers. Electronics suppliers that can meet all these demands are poised to ride the coming EV wave.

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R E FE R E N CE S Silicon Labs www.silabs.com

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How to design a functionally safe vehicle display Automotive-grade ICs that meet ASIL B criteria support functional safety demands of future vehicle displays.

It pays to know about the ASIL B compliance measures needed for automotive subsystems. Szukang Hsien Automotive Business Unit Maxim Integrated

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TAKE

the wheel of a new car today and you’ll be greeted with the bright glow of screens filled with useful data: how fast you’re going, what’s playing on the radio, a playlist of your songs, a phone book of your contacts, a map highlighting your current route, the fuel efficiency of your vehicle, whether there’s another car in your blind spot. Vehicle displays now show critical information like speed and blind spot views, but they also must be functionally safe. Much like television screens, the displays inside cars are getting bigger and sharper. Analysts project strong growth in the greater-than eight-inch display market, with 12.3-in. displays gaining more traction

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for fully digital instrument clusters. By 2023, 37-in. in-vehicle screens could be common. In the coming years, resolutions of 4K (on the order of 4,000 horizontal pixels) and eventually 8K will be the norm. Future automotive displays will boast capabilities like local dimming, which improves the contrast ratio to make colors more crisp and vivid. For instance, a black screen would be truly black, making an instrument cluster easier to read. There will also be more displays inside each car; in fact, we can already find up to 10 displays in a modern vehicle: the instrument cluster, the center information display (CID) (1-2), smart back mirror, side mirror replacement (2), heads-up display (HUD), rear-seat mount on the head

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FUNCTIONALLY SAFE VEHICLE DISPLAY

ASIL metrics ASIL B

ASIL C

ASIL D

Single-Point Fault Metric

≥90%

≥97%

≥99%

Latent-Point Fault Metric

≥60%

≥80%

≥90%

Common ASIL metrics for single-point fault (a fault that leads directly to a safety violation) and for latent-point fault (a multiple-point fault that is not detected by a safety mechanism nor the user).

support (2), and rear-seat mount on the roof. Highlighting the infusion of display technology in cars, analysis from the market research firm IHS Markit points out that cameras and displays are increasingly serving as replacements for traditional side mirrors, improving fuel efficiency and safety. “Aerodynamic improvements and enhanced visibility are the primary reasons behind emerging mirror replacement applications, while designers will welcome newfound freedom after having explored novel exterior mirrors in concept vehicles for decades. Now that the regulatory environment is taking shape to support this concept, production applications will soon follow,” says IHS Markit. The firm projects that by 2025, nearly half a million side-view camera display systems will replace side mirrors each year in new vehicles. These automotive displays are providing safetycritical information via advanced driver assistance systems (ADAS), so they must comply with functional safety standards. ISO 26262 is an international standard for functional safety of automotive electronic/electrical systems. A key part of the standard is the Automotive Safety Integrity Level (ASIL), which classifies the inherent safety risk in an automotive system. There are four ASIL levels, with ASIL D requiring the most safety-critical process and testing, based on severity (of injuries), exposure (probability), and controllability. Typical automotive systems requiring ASIL D compliance include windshield wipers, electric power steering, side-view cameras (mirror replacements), airbag deployment, braking, and engine management. Automotive displays typically require ASIL B compliance. Within the instrument cluster display are various blocks that should meet functional safety standards. As examples, consider how two of them, thin film transistor (TFT) bias for power management and the light-emitting diode (LED) backlighting driver, can be designed for ASIL B compliance. TFT bias typically consists of these AVDD and NAVDD voltages for the TFT source driver, VGON and VGOFF voltages for the TFT gate driver and, in some cases, VCOM voltage for the liquid-crystal display

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(LCD) backplane. I2C and a fault pin are used to communicate with the host microcontroller unit (MCU). To achieve ASIL B compliance, the TFT bias block should ideally have the following features: I2C (the data signal and the clock signal) and the fault pin to perform setting adjustments and diagnostics on each rail; undervoltage and overvoltage on each rail; internal resistors with fixed or adjustable voltage through I2C (external resistors mean more points of failure, so designers typically avoid them); redundant reference; and open enable, which provides additional redundancy. When the enable is open, the chip will look at another pin to determine whether it is on or off. There are three TFT bias fault scenarios to be aware of: VCOM voltage goes out of range; VGON voltage is in an overvoltage situation; and fail-safe operation with open enable pin In the first two scenarios involving VCOM and VGON voltages, the fault pin will alert the MCU of the scenario. The MCU will then read the register to validate the situation and use I2C to adjust the voltages accordingly. In the last scenario, when the enable pin is open while FEN is still high, the output voltages will fall back to the default settings. The MCU can adjust the voltage via I2C. Now, consider the LED backlighting driver. Here, the input typically connects directly to the car battery, which has voltage protection when the output is short. The output can either be a boost or single-ended primary-inductor converter (SEPIC), depending on the number of LEDs per string. I2C and a fault pin are needed to communicate with the MCU.

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AUTONOMOUS & CONNECTED VEHICLES TFT bias ASIL B features

For ASIL B compliance, the LED driver would need these features: I2C (the data signal and the clock signal) and fault pin to perform setting adjustments and diagnostics on each rail; open or short LED per-string detection; output voltage measurement; LED current measurement per string; internal resistors with fixed output or adjustable output through I2C; open enable; and a redundant reference to monitor the output. As with the TFT bias application, there are also fault scenarios to address with LED drivers: If string 1 has an LED open. In this case, the fault pin will alert the MCU, and the MCU will read the l2C register to know which LED string has an open. The MCU will then pump more current to the other strings to reach the same brightness.

Typical TFT bias application

A typical TFT bias application might use a bias IC connected as shown. The bias chip itself must incorporate ASIL B features that include the handling of various fault scenarios.

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AUTONOMOUS & CONNECTED VEHICLES

LED driver ASIL B features

A typical schematic for an LED driver circuit and the corresponding IC that would implement the necessary ASIL B features.

If string 2 has an LED short. As with the previous scenario, the fault pin will alert the MCU, and the MCU will read the I2C register to know which LED string has the short. To save power, the LED driver will shut down the string with the short. The MCU will then pump more current to the other strings to produce the same brightness.

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If boost output voltage is low. The fault pin again alerts the MCU, and the MCU reads the I2C register to know that the boost voltage is low. Usually, the out capacitor is short to ground, or the LED is short to ground. A front protection device, such as PGATE, will be open. In this scenario, only important messages like speed or engine temperature will be displayed on the dashboard. Fail-safe operation with open enable pin. When the enable pin is open while FEN is still high, the

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LED driver current falls back to the default settings. The MCU can adjust the current via I2C. Many TFT bias ICs don’t have a communication mechanism. But an example of one that does is made by Maxim. Among the TFT bias products, for instance, is the MAX20067 TFT-LCD bias IC with VCOM buffer, level shifter, and I2C interface. This PMIC provides the industry’s first integrated power solution

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FUNCTIONALLY SAFE VEHICLE DISPLAY

LED driver in typical application circuit

for TFT-LCD with synchronous boost, gate shading, and I2C. Its I2C interface offers settings control as well as diagnostics and monitoring. It also has spread-spectrum modulation in the AVDD boost converters, which reduces peak interference and optimizes electromagnetic interference (EMI) performance. Among the backlighting products are the MAX20446 six-channel, I2C LED driver with highREFEREN CES voltage dc-dc controller and battery disconnect. The chip’s integrated current-mode switching controller drives a dc-dc converter that HIS Markit camera and display mirror market study provides the voltage needed for multiple news.ihsmarkit.com/press-release/automotive/new-camstrings of high brightness (HB) LEDs. I2C offers era-and-display-mirrors-enhance-vehicle-safety-and-fuel-efsettings control as well as diagnostics and ficiency-c monitoring to support ASIL B requirements. Spread spectrum with phase shifting and Maxim MAX20067 data sheet hybrid dimming enhance EMI performance. www.maximintegrated.com/en/products/power/disThe IC also offers a high dimming ratio to play-power-control/MAX20067.html handle bright sunlight as well as pitch-dark environments. Maxim MAX20446 data sheet www.maximintegrated.com/en/products/power/disAll in all, needle-based speedometers, play-power-control/MAX20446.htmlera-and-display-mirfuel gauges, and the like are quickly becoming rors-enhance-vehicle-safety-and-fuel-efficiency-c things of the past as vehicle dashboards go digital. As the auto industry moves toward Level 5 fully autonomous vehicles, functional safety of key vehicle components, such as these digital displays, will be essential. For the underlying display technologies, functional safety is marked by compliance with the ASIL B standard. Automotive-grade ICs that are designed to meet ASIL B criteria can help streamline the design cycle for reliable, high-performing display applications designed to support driver, passenger, and pedestrian safety.

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How telematics is evolving with the connected car

Key telematics components in modern vehicles include a smart telematics gateway that manages data from numerous on-board systems such as those for vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) transmissions, eCall facilities for emergencies, and several antennae for the frequency bands involved.

The embedded systems that control vehicle tracking grow more sophisticated by the day. Hope Bovenzi Texas Instruments Inc.

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THE

phrase “car of the future” often invokes thoughts about autonomous driving. But it is fair to also consider what it will be like as a passenger once our driving habits change. With free hands and relaxed feet, the daily commute will be transformed from a tedious grind to an opportunity for work, video chat or stream content guilt-free. This is just one vision for the future of telematics in the connected car, and the possibilities are limitless. It is useful to review what we know about telematics today to get a glimpse of what’s in store for the future. In the early 2000’s, eCall burst onto the scene offering safety and emergency assistance, and navigation to help drivers

8 • 2018

know where they’re going and provided welcome silence from backseat drivers. (We can all be thankful for this.) The basic idea of eCall is to install a device in all vehicles that will automatically dial for help in the event of a serious road accident, and wirelessly send airbag deployment and impact sensor information, as well as location coordinates to local emergency agencies. This technology was mainly driven by the European Commission E112 in Europe, the European Regions Airline Association (ERA) Global Navigation Satellite System (GLONASS) in Russia, and E911 in the U.S. On March 31, 2018, the European Union (EU) made it mandatory for all new cars to include eCall hardware. What started

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HOW TELEMATICS IS EVOLVING

Emergency calling at the architectural level

A typical telematics control unit with emergency calling functions.

as a basic cellphone and navigation device now seamlessly integrates over-the-air updates, predicting driving behaviors and communicating with other vehicles. The market research firm Strategy Analytics predicts sales of telematics electronic control units (ECU) will grow at a 7.8% compound annual growth rate (CAGR) between 2017 and 2025. Expectations are that other infotainment platforms may plateau or see a sales deceleration over the same time frame. What’s worth noting about this prediction is that it only considers OEM and aftermarket telematics ECUs. It doesn’t consider the evolving trends in the telematics market such as vehicle-to-X (V2X), onboard diagnostics (OBD) port dongles and fleet management systems, or smart telematics gateways. It’s important to identify new emerging trends in the telematics space. Today, the market is fragmented, and in many instances, it’s hard to know what comes first, the chicken or the egg (in the telematics space, the legislation or the infrastructure). A secure telematics system is essential. The trend is to create a single point of entry into the telematics ecosystem: a gateway from the cloud to the car and an infrastructure network connecting all car subsystems. Telematics control units (TCUs) will evolve into smart telematics gateways with more integration. With the revenue potential for infotainment systems and connected cars

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– not to mention the thrill of the challenge – technology providers will continually raise the bar for themselves and the industry as a whole. In the telematics space, every OEM and Tier 1 has their own variation of hardware. There are thousands of ways to build a TCU. However, all OEMs and Tier Ones face similar hardware design issues; whether it’s designing a backup battery, power supply, audio or a processor, there are many common design challenges in a telematics system. These challenges can vary depending on where the system resides in the vehicle. Consider the typical TCU with integrated eCall system. Requirements (if any) for such a system vary depending on region. The EU’s mandate requires an eCall system that can operate during and after a crash, automatically and without the car battery; withstand extreme temperatures (even -20 or -40°C), connect an eight-to-ten-minute phone call over a 10-year lifetime; place an emergency services call on the cellular network for 60 minutes; and comply with International Organization for Standardization (ISO) 26262 Automotive Safety Integrity Level (ASIL) A standards. BACKUP BATTERY The backup battery is a good place to start when designing a TCU. EU requirements dictate a backup battery must support audio power anywhere from 6 to 20 W and peak currents from a Global System for Mobile Communications (GSM) module of around 2 A (nominally 350 mA). The backup battery defines the rest of the system. Variables include the battery chemistry (common types include lithium-ion, lithium-ion phosphate, and nickel-metal hydride), the number of cells, and the current capabilities.

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AUTONOMOUS & CONNECTED VEHICLES Charging: Simple but large

Power-path variations in connected vehicles can depend on the charging scheme used. Each of the two variations shown here accomplishes the same task with the same number of components but with different configurations. The configuration is dictated by the choice of backup battery and battery charger. The first variation is inexpensive and simple but is physically larger and employs multiple boost regulators. The second variation uses a lithium-ion battery that requires more protection but fewer cells. Both are feasible options, but cost, size and reliability all play a factor in picking one over the other.

Charging: Svelt but more complex

Where the battery resides within the power path also defines the qualities of the charger or lowdropout regulator and whether there must be a boost regulator. The choice of power regulators presents additional challenges. As with any automotive application, offvehicle battery power must sustain harsh temperatures, wide input voltages, and it must include measures for electromagnetic interference (EMI) mitigation. Telematics systems may reside in areas of a car (windshield, passenger compartment, trunk, engine) that see high temperatures, so integrated circuit junction temperatures can rise to as high as 150°C. Good board thermals and high-efficiency power circuits are a must to keep temperatures within tolerable levels. Input voltages based on OEM load dump, reverse polarity and cold-cranking conditions vary, but typically start at 4.5 V with peaks up to 42 V. None of the switching regulators can interfere with AM and FM bands of the car radio, so the switching frequency must be either around 400 kHz (below the AM band) or 2.1 MHz (above the AM band and below the FM band). A switching regulator operating at appropriate frequencies, inclusion of dithering/spreadspectrum techniques, and optimized layouts are all key practices ensuring good EMI performance. Similarly, requirements for audio power can vary widely. Some designers may choose a low-power system on the order of 4 to 6 W while other systems can be as high as 20 W. Besides monitoring power consumption and how it varies, speaker diagnostics and protection are critical for audio. There must be guards against open and shorted output loads and output-to-power and ground shorts, and audio circuits must incorporate typical automotive protection against short-circuits and load dumps, as well as temperature protection and monitoring.

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There is sometimes confusion about what separates a stand-alone eCall system from a TCU. The key differentiator is the data communication outside of the unit. In other words, if it is communicating data, it’s a TCU; if it doesn’t communicate data and is essentially a cellphone for the car, it’s an eCall system. Of course, a TCU can have eCall functions, but an eCall system can’t have a TCU. The functions that telematic systems provide have increasingly been integrated into one or two SoCs. Consequently, this concentration of circuitry has forced a drastic rise in the data rates of connections to head units or central gateways. Gone are the days when just CAN, LIN or even USB data communication could handle the load. These schemes have been replaced by 10/100Mbps and even 1 GMbps pipes. All in all, it’s hard to anticipate trends in telematics because the technology in this area changes rapidly. Developments today range from simple aftermarket on-board diagnostic dongles to sophisticated V2X systems. There are plenty of design challenges, security concerns, infrastructure needs, and legislative dictates to keep design engineers busy. REFEREN CES Infotainment and cluster products, Texas Instruments, www.ti.com/applications/automotive/infotainment-cluster/overview.html

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AUTONOMOUS & CONNECTED VEHICLES

Are power systems up to the task of running selfdriving cars? Tony Armstrong Analog Devices Inc.

Automotive electronics depends on power supplies that can handle abuse like load dumps. New ICs are optimized for these extreme environments and can meet stringent vehicle safety standards.

THE A simplified LT8650S application schematic. This chip delivers 5 V at 4 A and 3.3 V at 4 A, operating at 2 MHz. The LT8650S utilizes internal top and bottom high efficiency power switches with the boost diode, oscillator, control and logic circuitry integrated into one die. Low ripple Burst Mode operation maintains high efficiency at low output currents while keeping output ripple below 10 mV p-p. The LT8650S resides in a small thermally enhanced 4x6-mm 32-pin LGA package.

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auto industry expects the transition to autonomous vehicles via both an evolutionary path, where existing cars get there little by little (analogous to Tesla’s autopilot feature), and via a revolutionary path characterized by vehicles that are totally self-driving (like the ones Google is working on). In the next few years, more advanced driver assistance features will be synchronized to navigation and GPS systems. Companies like Google will gather and accumulate data about every situation a self-driving vehicle might encounter. And mapping companies will intensify the 3D mapping of major cities. Fully autonomous cars will clearly host numerous electronic systems. The more obvious ones include advanced driver assistance systems (ADAS), automated driving computers, autonomous parking assist, blind spot monitoring, intelligent cruise control, night

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POWER SYSTEMS

Sychronous buck converter EMI

Radiated EMI Performance of the LT86505S.

vision, lidar sensing, and more. All these systems require a variety of different voltage rails and current levels; however, they generally require power directly from the automobile battery or alternator. In some instances, they may be powered from a post-regulated rail derived from one of the main sources. This is usually the case for the core voltages of VLSI digital ICs such as FPGAs and GPUs which can need operating voltages below one volt at currents ranging from a couple of amps to tens of amps. System designers must ensure that the ADAS systems comply with the noise immunity standards pertaining to vehicles. In the automotive environment, switching regulators replace linear regulators in areas where low heat dissipation and efficiency are valued. The switching regulator is typically the first active component on the input power bus line, so it has a significant impact on the EMI performance of the complete converter circuit. In the case of conducted emissions, the noise is localized to a specific terminal or connector in the design. Compliance with conducted emissions requirements can often be assured relatively early in the development process with a good layout or filter design. However, it can be more difficult to minimize radiated emissions

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because every trace on the board is potentially an antenna and every copper plane is a resonator. Even with careful design, power supply designers never really know how bad the radiated emissions will be being until the system gets tested. And radiated emissions testing cannot formally take place until the design is essentially complete. Filters are often used to reduce EMI by attenuating the strength at a certain frequency or over a range of frequencies. A portion of this energy that travels through space (radiated) is attenuated by adding metallic and magnetic shields. The part that rides on PCB traces (conducted) is tamed by adding ferrite beads and other filters. EMI cannot be eliminated but can be attenuated to a level that is acceptable by other communication and digital components. Moreover, several regulatory bodies enforce standards to ensure compliance.

Modern input filter components in surface mount technology have better performance than through-hole parts. However, this improvement is outpaced by the increase in operating switching frequencies of switching regulators. Higher efficiency, low minimum on- and off-times result in higher harmonic content due to the faster switch transitions. For every doubling in switching frequency, the EMI becomes 6 dB worse while all other parameters, such as switch capacity and transition times, remain constant. The wideband EMI behaves like a first-order high-pass with 20-dB higher emissions if the switching frequency rises by a factor of ten. Savvy PCB designers will make the hot loops small and use shielding ground layers as close to the active layer as possible. Nevertheless, device pin-outs, package construction, thermal design requirements and package sizes needed for adequate energy storage in decoupling components dictate a minimum hot-loop size. To further complicate matters, in typical planar printed circuit boards, the magnetic or transformer style coupling between traces above 30 MHz will diminish all filter efforts because the higher the harmonic frequencies are, the more effective

Simplified LT8645S application schematic. The chip delivers 5 V at 8 A while operating at 2MHz

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AUTONOMOUS & CONNECTED VEHICLES

Monolithic synchronous buck converter EMI Radiated EMI Performance of the LT8645S.

unwanted magnetic coupling becomes. EMI cannot be eliminated but can be attenuated to a level that is acceptable. The usual approach is with tuned filters, metallic and magnetic shields, and ferrite beads. These components, of course, add cost. These factors are among the reasons IC makers have devised special components aimed at producing power rails on connected vehicles. An example is the LT8650S – capable of working with high input voltages, it is a dual-output monolithic synchronous buck converter that also has low EMI/EMC emissions. Its 3-to-42-V input voltage range makes it a candidate for automotive applications, including ADAS. One of the requirements for handling ADAS is an ability to regulate through cold-crank and stop-start scenarios with minimum input voltages as low as 3 V and in the presence of load dump transients in excess of 40 V. This chip’s dual-channel design consists of two high-voltage 4-A channels, delivering voltages as low as 0.8 V, thus giving it the ability to drive the lowest-voltage microprocessor cores currently available. Its synchronous rectification topology delivers up to 94.4% efficiency at a switching frequency of 2 MHz, while Burst Mode operation keeps quiescent current under 6.2 µA (both channels on) in no-load standby conditions making it a good candidate for always-on systems. As a quick review, Burst Mode technology allows a switchmode power supply to operate efficiently even when lightly loaded. It does so by turning off non-essential circuitry when the output is in regulation. Meanwhile, a comparator actively monitors the output so the control circuitry can quickly turn on when the output begins to droop. As the load current rises, the converter will automatically transition between Burst Mode to the lowernoise PWM mode of operation. Conversely, the converter will automatically transition from PWM to Burst Mode operation when the load drops. The LT8650S switching frequency can be programmed from 300 kHz to 3 MHz and synchronized throughout this range. Its 40-nsec minimum on-time enables 16 ViN to 2.0 VOUT step-

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down conversions on the high voltage channels with a 2-MHz switching frequency. This chip has what’s called a Silent Switcher 2 architecture designed to minimize EMI. It does so through, among other things, use of two internal input capacitors as well as internal barium strontium titanite (BST) and INTVCC (voltage powering the IC gate driver and control circuit) capacitors to minimize the area of the hot loops. Other features designed to minimize EMI include well-controlled switching edges and an internal construction with an integral ground plane and the use of copper pillars in lieu of bond wires. Moreover, the EMI/EMC performance is not sensitive to board layout. The LT8650S can easily pass the automotive CISPR25, Class 5 peak EMI limits with a 2 MHz switching frequency over its entire load range. Spread spectrum frequency modulation is also available to lower EMI levels further. Another example of a chip intended for automotive use is the LT8645S. It targets applications needing a wider input range. It is a monolithic synchronous buck converter that also has low EMI emissions. Its 3.4 to 65-V input voltage range makes it a good candidate for both automotive and truck applications which must regulate through cold-crank and stop-start scenarios with minimum input voltages as low as 3.4 V and load dump transients exceeding 60 V. It is a single-channel design, delivering an 8-A output at 5 V. Its synchronous rectification topology delivers up to 94% efficiency at a switching frequency of 2 MHz, while Burst Mode operation keeps quiescent current under 2.5 µA in no-load standby conditions, making it work well for always-on systems. The LT8645S switching frequency can be programmed from 200 kHz to 2.2 MHz and synchronized throughout this range. Like the LT8650S, it incorporates features that minimize EMI and can easily pass the automotive CISPR25, Class 5 peak EMI limits over its entire load range. Spread-spectrum frequency modulation is also available to further lower EMI levels. The LT8645S utilizes internal top and bottom high-efficiency power switches with the necessary boost diode, oscillator, control and logic circuitry integrated into a single die. Low ripple Burst Mode operation maintains high efficiency at low output currents while keeping output ripple below 10 mV p-p. Finally, the LT8645S sits in a small thermally enhanced 4x6-mm 32Pin LQFN package. In the future, voltage and current levels needed for automotive circuits are likely to change; nevertheless, R E FE R E N CE S the requirements for low EMI/EMC emissions will not go away nor will the hostile environment in which they Analog Devices Inc. must operate. But, in the not too www.analog.com distant future, we will be able to sit back, relax and enjoy our car taking us for a ride.

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8/8/18 10:13 AM


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AUTONOMOUS & CONNECTED VEHICLES

When tire pressure monitoring gets smart Addy Mulders Dialog Semiconductor

Bluetooth-enabled tire monitors will check inflation pressures and temperatures with much more precision than available through yellow lights on a dash panel.

THE

words “connected cars” often bring to mind self-driving autonomous vehicles. But connected cars will offer a variety of capabilities, not the least of which is tire pressure monitoring systems (TPMS). As its name suggests, TPMS provides real-time data on vehicle tire pressure, so no one need be surprised by a flat tire. Beyond that, though, TPMS also offers critical information on tire temperature –temperatures that are too high can indicate an excess of friction caused by misalignment of the wheels, which contributes to early tire wear and less safe driving. Newer, tire-mounted models are able to also gauge acceleration and, in some cases, the direction of wheel spin. Additionally, TPMS sensors provide insight into their own battery voltage levels, indicating when they should be replaced. TPMS can only do so much on its, own, though. And, for all the benefits this system provides,

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its capabilities and applications for the connected car are greatly expanded when integrated with Bluetooth low energy (BLE) connectivity. BASICS OF TPMS TPMS became mandatory in the U.S. and EU in 2007 and 2014 respectively. There are two types of TPMS, direct (dTPMS) and indirect (iTPMS). Indirect TPMS do not use physical pressure sensors. Instead, they monitor air pressure primarily by measuring the rotational speeds of individual wheels. This works because under-inflated tires have a slightly smaller diameter (and hence higher angular velocity) than those correctly inflated. These differences are measurable through the wheel speed sensors of ABS/ ESC systems.

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m

TIRE PRESSURE MONITORING

Simplified block diagram: DA 14585 BLE SoC A main difficulty of iTPMS is that it cannot measure absolute pressure values. When the tires are inflated to the correct pressure, the press of a reset button calibrates all ensuing measurements relative to these initial readings. Also, iTPMS are sensitive to the influences of different tire styles, road surfaces, and driving speeds. In contrast to iTPMS, direct TPMS reports absolute tire pressures. It does so through use of pressure sensors typically located on the inside of each tire rim. Some units also measure and report tire temperatures. Many TPMS display realtime tire pressures both when the vehicle is moving and when parked. Most dTPMS are battery powered. Some sensors employ a wireless power system, based on electromagnetic induction, resembling that used in RFID tag reading. This makes the sensor more light weight. The batteries powering dTPMS aren’t replaceable – when the battery runs down, the whole sensor is replaced. Also, sensitive electronics within the dTPMS wear out with time and can be influenced by pressure and vibration. TPMS sensors in low-profile wheels also tend to wear out more quickly, especially if they experience poor road conditions. Today, TPMS most commonly uses 315 MHz in the U.S. for transmitting tire information to the receiver in the vehicle. But the frequency used varies from region to region, so there is no single worldwide platform to support it. In rolling mode, sensors transmit on average about every 60 to 180 seconds. While parked or in stationary mode, depending on the manufacturer, sensors may transmit periodically or when sensors detect a pressure change. In the event of rapid air loss, most sensors go into an alert mode that transmits a warning. Unfortunately, direct TPMS radio signal technology is prone to interference. Most TPMS sensors are activated with a low-frequency signal at 125 kHz. Reception of this LF signal forces the sensor to transmit. Several widely used pieces of test equipment found in auto shops emit signals near these frequencies. Signals coming from other autos sometimes can also actuate transmission as well.

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The DA14585 is the smallest, lowest power and most integrated Bluetooth SoC. It can serve as a means of adding Bluetooth low energy not only to TPMS, but also to products like remote controls, proximity tags, beacons, connected medical devices and smart home nodes. It supports all Bluetooth developments up to and including Bluetooth 5 and Bluetooth low energy Mesh.

Such difficulties have led several manufacturers to base TPMS on Bluetooth Low Energy (BLE) communication standards. BLE both provides a single worldwide platform – cutting down on development, qualification and logistical costs – while allowing over-the-air firmware updates and other general maintenance that would otherwise not be possible. In short, BLE ensures that TPMS sensors are constantly kept up-to-date, secured, and optimized. Additionally, BLE offers more data bandwidth that opens the door for new potential use cases. For example, it may facilitate the outfitting of other sensors throughout the vehicle, such as tire deformation sensors that can more closely monitor the condition of the tires. Current OEM TPMS chips only offer connections to the car body electronics and will flash dashboard warnings to the driver when tire pressure readings drop to concerning levels. But a BLE-enabled TPMS sensor can connect directly to the driver’s smartphone for a more direct line of communication. This feature may be redundant for drivers whose dashboards already display TPMS warnings, but it can be helpful for drivers whose dashboards lack this warning. The transportation sector, in particular, can benefit from BLE-enabled TPMS. Transportation companies optimize their vehicles’ tire pressure to minimize tire wear and fuel consumption. Eighteenwheelers must connect to various trailers built by equally various manufacturers. A standard TPMS retrofit system that spans the dashboards of all trucks and trailers is still a long way off, but a TPMS that connects directly to the driver’s phone or tablet helps to fill that niche.

8 • 2018

BLE-enabled TPMS can do much more than just monitoring tire wear and pressure. For example, consider capabilities enabled by use of the SmartBond DA14585, the smallest, lowestpower, and most integrated Bluetooth 5 SoC. The chip’s BLE connectivity provides a range of additional potential applications for TPMS, including keyless entry, onboard diagnostics (OBD) telematics and various sensor applications and remotecontrol functions. TPMS sensors integrated with the SmartBond DA14585 and its BLE connectivity support can improve the tracking, monitoring, and maintenance of these vehicles. Moreover, they can do so in a way that lowers manufacturing costs and provides engineers with new avenues for smart automotive applications.

REF EREN CES SmartBond DA14585 SoC https://www.dialog-semiconductor.com/ products/da14585

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AUTONOMOUS & CONNECTED VEHICLES

Testing for time-sensitive networking Jeff Warra Spirent

Connected vehicles depend on networks that won’t bog down when handling real-time data. Specialized TSN tests ensure messages arrive on time and intact.

ŸŸ

ŸŸ ŸŸ

ETHERNET

has been the backbone of IT infrastructure for more than 40 years, but it was never designed to be a determinist network. That’s why, over time, Ethernet standards have evolved to address low-latency applications. More recently, we’ve seen Audio/Video Bridging (AVB) change to Time-Sensitive Networking (TSN). Standard bodies, including the Institute of Electrical and Electronics Engineering (IEEE), and the Internet Engineering Task Force (IETF) are constantly improving the quality of standards for TSN, which define mechanisms to guarantee data transport with bounded low latency and low delay variation for the timesensitive transmission of data over ethernet networks. The target is to guarantee that application end-to-end latency requirements are not exceeded. Most of the standards address the transmission of deterministic and consistent transmission latency to provide zero congestion loss typically required for applications such as steaming audio and video, industrial controls, and autonomous driving control and feedback in real-time.

ŸŸ ŸŸ ŸŸ

ŸŸ

frames for transmission), Frame Preemption (temporarily suspending transmission of non-critical frames), Scheduled Traffic flows, Cyclic Queueing and Forwarding (basically, a transmission selection algorithm for calculating deterministic delays through a bridged network). Asynchronous Traffic Shaping (basically, mechanisms for a bridge to handle frame queues deterministically that don’t depend on clock synchronization) Ultra-reliable networks Frame Replication and Elimination (so bridges and end stations can replicate frames for redundant transmission and ID duplicates), Path Control, Per-Stream Filtering and Policing (which lets a bridge count frames, filter and police traffic based on the particular data stream to which the frame belongs, and specifies a synchronized cyclic time schedule) Time sync reliability across domains Dedicated resources and API Enhancements to Stream Reservation Protocol (basically, checks before making a connection to see if current resources can handle the connection) Link-local Reservation protocol (for reserving network addresses valid only within a specific network segment)

Today, there’s a virtual alphabet soup of TSN standards and extensions for timing and traffic shaping. It’s a lot for designers

Cost of software defects

TSN looks to put in place: ŸŸ Timing and Synchronization ŸŸ Bounded low latency ŸŸ Credit-based shaper (basically, a means of selecting audio/video

A simple cost breakdown of where and when bugs get found and fixed. Reference: Project Cost Management – Sharan Kalwani – IEEE SEM

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m

TIME-SENSITIVE NETWORKING

Creation of a realistic car Ethernet environment

In tests of TSN functions, Spirent software might mimic the actions of vehicle systems that include sensors and driver displays.

to consider as they develop devices, and the best way to navigate this complexity is to use tools that test each of the standards. Here are just some of the various TSN standards Spirent is testing today: ŸŸ IEEE 802.1AS-Rev - Enhanced Generic Precise Timing Protocol: Adds support for Performance, Redundancy, Aggregation. ŸŸ IEEE 802.1Qbv - Time Aware Shaper: Achieves the theoretical lowest possible latency in engineered networks. ŸŸ IEEE 802.1Qbu & IEEE 802.3br – Packet Pre-emption: Reduces latency of time-sensitive streams in non-engineered networks. ŸŸ IEEE 802.1CB - Frame Replication & Elimination: Supports zero switch over time when a link fails, or frames are dropped (aka: Seamless Redundancy). ŸŸ IEEE 802.1Qcc - Enhanced Stream Reservation Protocol: Adds support class configurations, shaper and replication. ŸŸ IEEE 802.1Qci - Per Stream Filtering & Policing: Assigns flows to policer. ŸŸ IEEE 802.1Qch - Cyclic Queuing & Forwarding: Supports known latencies, no central controller needed, limits hops. ŸŸ IEEE 802.1Qcr - Asynchronous Traffic Shaping: Supports zero congestion loss for asynchronous traffic, and deterministic latency without using network topology information. ADVANCED TESTING DRIVES QUALITY Packet-switched networks are being used to move the world’s data, from planes, trains and automobiles to industrial motor controllers, autonomous vehicles and the Internet of Things (IoT). The demand to create systems-of-systems has never been greater and introduces new challenges for product and network development engineers. The convergence of those domains is amongst us here with TSN. Engineers need to gain expertise and knowledge on new technologies to properly evaluate architecture designs to implement these highly sophisticated, time-aware networks. New techniques for testing are needed to ensure smooth and reliable signal flows inside a network for safety-critical applications. Designing a quality product on time and under budget can be difficult if not impossible these days, and the key to success in this

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challenge is to partner with vendors that have performed the same or similar feats. It goes without saying: Finding product defects in early design stages save time and cost for a project. But it might seem problematic to find defects without all the other members in the network being present. The way to address this dilemma is by testing a product against standards for its application area. An example for automotive would be either AUTOSAR, OPEN Alliance or Avnu test suites that have been developed to help ensure conformance to industry standards. TSN specifications are defining ways to enable zero congestion loss of time-critical data flows. But loss due to congestion is only half the issue, as network designers and architects also must measure worst-case end-to-end latency within the network. Latency measurements must also consider primary and redundant flows during fault recovery conditions. It is important to measure normal vs redundant data path flows in a live network as this parameter is extremely critical to industrial motor and autonomous vehicle control applications. Using the Spirent emulated devices, engineers can quickly check the Best Clock Master Algorithm (BCMA) inside real devices on the network to ensure they recover from faulted conditions. Once the emulated device timing is validated, the Spirent end-to-end latency measurement can be observed and characterized under various switch load conditions to validate against application requirements and to ensure safe and reliable data flows can be realized. Testing the network with a mix of real devices and emulated devices using “generalized Precision Time Protocol” (gPTP) functions will enable designers to find out what scenarios can help them optimize and check network traffic to ensure no loss of time-critical data flows. (Briefly, gPTP provides a way of synchronizing network devices without forcing each of them to have a super-precise clock. It can provide accuracy to 1 µsec. across seven hops.) This checking process employs purpose-built equipment that contains metrics for counters and timers.

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AUTONOMOUS & CONNECTED VEHICLES

STORE-AND-FORWARD VS. CUT-THROUGH Consider Ohm, Volt and Amperes the fundamental components of electronics. As resistance impedes current flow, so similarly does a microprocessor (MPU) real-time scheduler affect the processing of data. If you’re constantly flushing your RAM, your capacity to handle larger data flows becomes a capacity issue (analogous to over/under voltage). Signal flow as it pertains to signal latency (analogous to Amperes) can reduce your overall signal update rates if your software stack is too large or not optimized. When developing a device, a switch or endpoint, designers should take into consideration the number of packets a CPU will ultimately have to deal with. One of the critical parameters is frame per second (FPS) and the device’s ability to keep up with all of the ethernet traffic routing to ensure deterministic latency when forwarding packets. Depending on the ethernet switching mode – Store-and-forward or cut-through switching – the effects on the microprocessor, RAM and software stack will have drastic impacts on the performance of products with regard to signal latency, jitter and capacity or bandwidth. (As a quick review, store-and-forward techniques send information to an intermediate station (which could be a microprocessor). This station occupies microprocessor cycles to work with the data while routing it to a final destination. It tends to find use where a packet must be reviewed by the processor which can result in transmission delays and poor performance of the ethernet link. Cut-through switching involves techniques to begin the transmit process before the whole frame has been received, as a way to help streamlne data and to reduce latency.)

RAM, CPU, and software stack functions can be viewed as being analogous to voltage, resistance, and current flow in electrical systems.

I NSI D E g PT P Time-sensitive networking came out of technical standards collectively known as audio video bridging or AVB. The specification for timing and synchronization for time-sensitive applications (gPTP) is IEEE 802.1AS. To understand the basics of gPTP and how it synchronizes a network, it is helpful to consider a simple example. Consider the case where a master clock wants to transfer time across a network to a slave. The master transmits a sync message and perhaps a follow up sync message which carries to the slave the time the message was generated. Suppose the message leaves the master at some time T1. It arrives at the slave sometime later at T2. For synchronization, the slave needs to know how long it took the message to get through the network. In this case T2 is equal to T1 plus the message transit time. We need to know the network transit time to synchronize the two nodes. We’ll consider one way to determine transit time called peer-to-peer. The time it takes a message to traverse the wires between devices, plus the time messages spend in devices, is calculated through something called a peer delay request response mechanism. There is a special field in synchronization messages called a synchronization correction field. That correction field accumulates as the message goes through the network. By the time the message arrives at the final destination the correction field contains the total transit time through the network. Network synchronization also involves time stamps, and it’s important that frequencies of the various clocks in the network are synchronized

together so the time stamps are consistent. gPTP has a means for ensuring this synchronization inexpensively. Each device on the network has a local clock that runs at some rate that is used for time stamping. Traditional IEEE 1588 time-sensitive networks generally use a physical synchronization technique to do this – phase-locked loops, and so forth. But 802.1AS uses logical rather than physical synchronization. The technique examines the frequency difference between the clocks of successive devices and transfers the information from one device to the next to determine the frequency offset. The technique uses what’s called neighbor rate ratio (NRR) and cumulative scaled rate offset (CSRO) mechanisms. The CSRO is generated by accumulating NRR values across the chain of devices through which a message passes in the network. Timestamps are then adjusted using the CSRO. NRR is a measure of the relative frequency offset between the previous device in the chain and the current device, determined by comparing message delay times between a requester and responder. It is calculated continuously because it can change depending on temperature and other physical factors. The benefits of using a logical synchronization method like this include an ability to synchronize a network containing Ethernet devices with low-grade oscillators (±100 ppm) as is typical in off-the-shelf equipment. gPTP networks also can stabilize quickly, usually within two seconds for a seven-hop network.

To wit: ŸŸ Memory (volt analogy) – All the processing in the world will not solve a RAM issue, and constantly swapping RAM hinders the best stacks. ŸŸ SW Stack (ampere analogy) - All the RAM in the world will not solve a stack issue. ŸŸ Computer Process (ohm analogy) - Overtaxed processor schedulers hinder the best stacks. As you are deploying a time-aware network, take into consideration timing errors. The two main contributors to timing errors are the accuracy of the correction field and the peer delay calculations for each member in the network. Do they reflect the actual delay experienced by the sync messages? Possible errors may include variable errors, quantization errors in time stamps, synchronization/rounding errors in local DUT clocks, phase noise in oscillators, fixed errors, asymmetric delays in the physical layer, i.e., time stamp type and point; and cable lengths between forward and reverse paths.

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TIME-SENSITIVE NETWORKING

Tests of a time-aware bridge in a TSN that uses gPTP protocol might introduce delays to verify the capacity of the bridge to deal with message transmission problems.

In a typical device being tested, the application turnaround time can and will impact the R E F E R E NC E S downstream path delay calculation when internal DUT hardware and software Spirent resources are shared www.spirent.com between the application functions and network communications. Having a Spirent test bed in place that can emulate, measure and impair gPTP networked devices using industry-proven techniques and products to help develop robust products with accurate timing.

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T ES T I N G g PT P N ET W O RK D EV I CES Tests using “generalized Precision Time Protocol” (gPTP) functionality lets designers optimize and check network traffic to ensure no loss of timecritical data flows. These tests use purpose-built equipment that contains metrics for counters and timers. Typical gPTP test equipment tracks over 40 measurements in real-time for each received stream, including: • Advanced sequencing: In-order, lost, reordered, late and duplicate • Latency: Avg, min, max and short-term avg; first/last frame arrival timestamp • Latency modes: LILO, LIFO and FIFO • Data integrity: Generate Errors: IP checksum, TCP/UDP checksum, frame CRC, embedded CRC and PRBS bit errors • Histograms: Jitter, Inter-arrival, Latency, Sequence • Spirent also automatically calculates the following IEEE802.1as gPTP clock information: • IEEE802.1as Clock Results that include States (Clock Identity, State, Clock Accuracy), Timers (Current, Min, Max, Avg Mean Path Delay), Counter (TX / RX Announce, TX/RX Sync, TX / RX Follow up, TX / RX Peer Delay Request, TX / RX Peer Delay Response, TX / RX Peer Delay Follow up) • IEEE802.1as Time Properties Results: States (gPTP Time Scale, Current UTC offset Valid, Leap59, Leap61; Time/Frequency Traceable, Time Source), Counter (Current UTC offset). • IEEE802.1as Clock Sync Results: Timers (Time of Day), Counters (Current offset, Positive/Negative offset Peak and deviation; Current, Min, Max, Average Mean Path Delay; Average offset plus/minus deviation; Step Removed, Minimum Pdelay Request Interval; Peer Mean Path Delay; Sync/Follow-up/Pdelay Correction Field Response and follow-up; Invalid Timestamp count) • IEEE802.1as Parent Clock Info Results: States (Parent Stats, Step Mode), Timers (Observed Parent offset scaled log Variance; Observed Parent Clock Phase Change Rate; Grandmaster Identity; Grandmaster Clock Class, accuracy, offset Variance; Grandmaster Priority 1, 2) • IEEE802.1as Message Rate Results: Counters (Announce Rate: Tx / Rx Min, Max Average Packets per second; Sync Rate: RX Min, Max, Average Packets per second; Follow up Rate: RX Min, Max, Average Packets per second; Peer Delay Request Rate: RX Min, Max, Average; Peer Delay Response Rate: RX Min, Max, Average; Peer Delay Response Follow up Rate: RX Min, Max, Average) • IEEE802.1 State Summary Results: Counters (Faulty, Disabled Count, Listening Count; Pre-Master Count, Master / Slave Count; Passive, Uncalibrated Count, 802.1as up / down)

8 • 2018

DESIGN WORLD — EE NETWORK

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Autonomous & Connected Vehicles August 2018  

Autonomous & Connected Vehicles August 2018 Galvanic Isolation for electric vehicle systems How to design a functionally safe vehicle displa...

Autonomous & Connected Vehicles August 2018  

Autonomous & Connected Vehicles August 2018 Galvanic Isolation for electric vehicle systems How to design a functionally safe vehicle displa...