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EEWeb Issue 81

January 15, 2013

Stephan Zizala

Sr. Director of Industrial and Multimarket Microcontrollers, Infineon TECHNICAL ARTICLE

Why Can't Johnny Design? Part 3

Electrical Engineering Community




Stephan Zizala INFINEON Interview with Stephan Zizala - Sr. Director of Industrial and Multimarket Microcontrollers

Featured Products


Short Discharge Time as a Charateristic of Battery Technology


BY DAVIDE ANDREA WITH ELITHION How to select cells for a power battery by using the short discharge time required to discharge a full cell through a short circuit.


Why Can’t Johnny Design? Part 3: Doing the Math BY TOM LEE WITH QUANSER Why more and more engineering students hate math and how educators can get them to come around to this essential field.

Product Overview: Rigol DS-4054 Digital Oscilloscope


RTZ - Return to Zero Comic








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EEWeb PULSE How did you get into engineering? I would say I got into engineering with the first computers that became available in the 80s. I was one of those who spent afternoons working with those first personal computers. This is where my enthusiasm for technology started. After high school, I chose electrical engineering as a major at the University of Munich, where I received my degree in 1997. Then, I worked at an EDA company in Silicon Valley, where as an engineer I got really enthusiastic about electronic design automation. I decided I wanted to go deeper and got a PhD in analog design automation. I was awarded a doctorate degree in 2001 from the Technical University of Munich. During this time, I was already working as a staff member for an analog mixed-signal development team at Infineon. It wasn’t very tough to decide that Infineon would be a perfect employer for me, so I joined after completing my PhD. Since then, I have had the opportunity to step into quite a number of positions, including product marketing and application engineering and now heading a business segment for industrial microcontrollers. The big step for me was to get out of a technical role into a strategic role starting with marketing and technical concepts. Why don’t you tell us a little bit more about your current role at Infineon? Infineon’s microcontroller activity is mainly focused on automotive and industrial applications, and I am responsible for the industrial business segment. Within my responsibility, we define the company’s strategy for industrial microcontrollers, define products


and bring them to market, and support our customers to make them successful with our portfolio. This is what I’ve done since 2005. When I started in this position, we had a pretty substantial industrial microcontroller business, mainly in Europe, based on our proprietary architectures. The big change was to plan a transition to Infineon’s first ARM-Cortex M-based microcontroller family, XMC4000, which we are currently bringing to the market. We introduced the first MCUs in this family in January 2012 and we are on track and building momentum with customers. My main responsibility is to execute on our roadmap to deliver a broad

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and very competitive ARM-Cortexbased industrial MCU portfolio featuring significant differentiation. Do you anticipate your ARMCortex solution to be more targeted to the industrial space or the automotive space? The XMC4000 microcontroller family is purely targeted for industrial applications. For our automotive business, we fully focus on a portfolio built around Infineon’s TriCore architecture. More than 100 million TriCore-based MCUs have been shipped. We just recently announced the latest generation


“Within my responsibility, we define the company’s strategy for industrial microcontrollers, define products and bring them to market, and support our customers to make them successful with our portfolio.”

of this architecture for automotive microcontrollers, a product family called AURIX™. The main point that sets the XMC4000 family apart is that we focused on three main target applications: industrial drives, renewable energy and factory automation. For these three segments we did everything to optimize the product performance and deliver a robust set of peripherals. For the first segment, we clearly needed to provide the best realtime control capability you can achieve. Therefore, we decided on a completely new time structure,

where we could bring our 20 years of experience with electrical motors to optimize the MCU for PWM generation. One example is that we can control three-level inverters with the XMC4000 family, which is not possible with other microcontrollers on the market. From a peripherals viewpoint, to do this in the best possible way, we included up to four 12-bit A to Ds. This means you can do four conversions exactly in parallel. And each of those ADC modules has 8 separate channels. The third thing we did addresses inverter control, which is an overlap from motor control to power conversion that needs extremely high real-time performance. This means that you

don’t have the time to use the CPU to handle all of the interruptions that might happen. Instead you need to have a hardware architecture and peripherals that you can optimize. We achieved this with a connection matrix that lets you use software to optimize the system. You could see this as a very simple FPGA, where you can recombine certain parts of the digital logic via software. Those are the highlights from the peripheral side of motor control. Looking at power conversion applications, if you think of microinverters or high-end digital power supplies, one key characteristic is that the efficiency gets better as Visit


EEWeb PULSE accuracy of your PWM gets better. For those variants that address the power conversion market in the renewable energy space, we introduced a PWM unit with the resolution down to 150 picoseconds, which is also not available in any other microcontroller family on the market. We thought this was one thing that our customers will appreciate because they will have a very powerful 32-bit microcontroller core plus hardware peripherals for the exact target application. For factory automation, in addition to powerful peripherals we have error correction in the flash memory,

HXC4000 Hexagon Board by Infineon

“Infineon is convinced that the highest value to the customer is in products that allow an exact fit to the target applications.” which relates to safety issues, and an operating temperature range up to 125 °C. No other Cortex M4 MCU has this temperature qualification which is particularly helpful if the MCU needs to be integrated into a device without external cooling.

Infineon’s DAVE 3rd Generation icon


With your motor control solution, do you have IP available or tools to help with motor control software development? Yes we do. We started quite a long

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time ago with developing motor control algorithms for our legacy MCU families. Over time, especially in cooperation with our colleagues from our power business groups, we developed a really solid understanding of what’s going on in motor control designs. The approach we chose is a bit different from the usual example codes and libraries that most of our competitors offer. At the same time that we introduced the XMC4000 family, we also introduced a completely new version of our development

INTERVIEW environment, DAVE. The new DAVE 3 is two things. On one hand it’s a completely free-of-charge development environment with everything you need from editing to compiling to debugging. It’s everything in one package, you just download it, install it and it works. On the other hand, DAVE 3 introduces a new way to program in this class of processor. This is a componentbased programming approach. So, in a graphical user interface, you can configure and combine certain library elements like A to D conversion and PWM generation and you can connect the signal from the software components to each other. This allows a developer to combine different software components—we call them “DAVE Apps”—without touching the source code. Once you have done this, you can map this to the XMC4000 hardware. The tool suggests which physical PWM unit a certain part of the algorithm should use. It also suggests the pin-out— e.g. it says that a specific A to D conversion use pin 12, or whatever. When this is done and you like what you see, you press another button and DAVE 3 automatically generates the C-code,

which you can then compile either with the integrated free tool chain or with any other tool chain you want to use. The big advantage of this, over the traditional approach, is that it’s much easier to combine and configure different algorithm parts compared to editing the source code in a code file. I think this is the most important part. It allows development teams to gain control in a world where software development efforts are exploding. DAVE 3 introduces something that allows a higher level of automation in programming, but also allows the developer to dig in to the last bit of the source code at any given time. What are some of Infineon’s goals? Infineon is convinced that the highest value to the customer is in products that allow an exact fit to the target applications. We are not bringing out general purpose controllers that - among many others - can also address industrial applications. Rather, we developed a controller that is tailor-made for industrial applications. Then we added configurability so that it can

be tailored more exactly to the target applications. This is an essential part of our strategy. The other, as I mentioned, is that software development must be made easier. We are convinced that just providing software examples for sample libraries is a good start, but it’s not really the big step the industry needs. If you look at studies, it shows that software development is the biggest challenge for our customers. So, introducing the component-based programming approach with DAVE 3 and the DAVE Apps is, for us, a cornerstone of our strategy to serve this need. What you are going to see, looking forward, is an open environment. DAVE 3 can be plugged into other tool chains, and other tool chains can be plugged into DAVE. Already now there are DAVE Apps for operating systems that provide a wrapper around the 3rd party software to integrate it in DAVE. We see this growing, so we want to prepare DAVE 3 in a way that 3rd-party DAVE Apps can be implemented by our customers in their own development in the longterm.

For more information about Infineon and their products, visit their website at:



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Short disch

as a characteris of battery techn


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harge time

stic nology Davide Andrea Engineer


hen selecting cells for a power battery, it is useful to have the ability to quickly compare various cell technologies, and calculate the resulting pack resistance and efficiency, independently of capacity and voltage. This article proposes a way of doing so, using the “short discharge time,� the theoretical time required to discharge a full cell (or battery) through a short circuit. This constant is a characteristic of each battery cell technology, regardless of capacity or voltage.




Characterization From specifications Given a cell’s or battery’s DC resistance , capacity and voltage, the short discharge time is:

short_discharge_time [s] = 3600 * capacity [Ah] * resistance [Ω] / voltage [V]

For example, the specifications of a 26650 size, LiFePO4 cell from A123 are: 3.3 V, 2.3 Ah, 10 mΩ. Therefore, the short discharge time of those cells (and of batteries built from those cells, regardless of the arrangement) is: short_discharge_time [s] = 3600 * 2.3 [Ah] * 10 m [Ω] / 3.3 [V] = 25 s

Graphically Unfortunately, very few manufacturers specify true DC resistance. Many specify impedance at 1 kHz instead, which easy to measure, but is useless to the user, and is quite unrelated to DC resistance. In that case, if discharge curves are available, they may be used to derive the short discharge time. These graphs plot the cell voltage versus SOC at various specific currents (such as 0.5 C, 1C, 2C, 5 C...). From such a set of curves, pick two points at 50 % SOC (for example, at 0.5 C and 2 C). Note the difference in the cell voltage at those two points; that’s the delta-voltage, in Volts. Take the difference of the two specific currents; that’s the delta-specific-current, in 1/ hours. Then, use those values to calculate the short discharge time:

3.2 3.0 2.8




2.4 2.2 2.0

short_discharge_time [h] = capacity [Ah] * resistance [Ω] / voltage [V]

In practice, the short discharge time of actual cells ranges from 0.004 to 0.06 hours (15 to 220 s). Therefore, seconds is a more practical measure of short discharge time than hours:



Cell Voltage (V)

The short discharge time of a battery technology can be derived from specification sheets or empirically.





SOC [%]

The delta voltage is 3.28 – 2.98 = 0.3 [V]; the delta specific current is 5 – 1 = 4 [1/h]; the cell voltage is 3.3 V. Then, the short discharge time of this cell (and of cells of any size using the same technology, and of batteries using these cells) is: short_discharge_time [s] = 3600 * 0.3 [V] / 4 [1/h] / 3.3 [V] = 82 s

Empirically Having access to an actual cell, one can derive the short discharge time empirically; - Fully charge the cell - Discharge the cell to empty, in the span of 1 hour, while integrating the current - The cell capacity is the final value of the integral - Charge the cell to 50 % - Measure the cell’s open circuit voltage - Apply a load to the cell to draw approximately 1 C of current - Measure the load current - Wait for the cell voltage to settle and measure the loaded cell voltage - Calculate the cell resistance = (Open circuit voltage – loaded cell voltage) / load current

short_discharge_time [s] = 3600 * Δ voltage [V] / Δ specific_ current [1/h] / cell_voltage [V]

At this point, the cell voltage, cell capacity and cell resistance are known, and therefore the cell’s short discharge time can be calculated:

For example, we can pick two points in the discharge curve for a LiFePO4 cell seen to the right.

short_discharge_time [s] = 3600 * capacity [Ah] * resistance [Ω] / voltage [V]


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Sample short discharge times


Using the methods above, the short discharge time of various cell technologies was calculated, and is listed below.

The short discharge time can be used to rapidly select cell technology for power applications, and to rapidly calculate battery resistance and round trip efficiency.

Technology Model

Short Discharge Time [s]

Lead Acid

Panasonic VRLA


Xtreme Power


EnerSys Cyclon



Seet table below





Panasonic HHR


Sanyo Cadnica



Cell selection

This table compares the short discharge time of various cell and battery chemistries. You will note that all chemistries offer some cells that have a low resistance; and that Li-ion covers the entire range, from the best to the worst. More specifically, this graph compares the short discharge time of a select few Li-ion cell technologies.

Having the short discharge time of various cell technologies, one may immediately select the one that will result in the most efficient battery, by selecting the one with the lowest short discharge time. Of the cells analyzed in this study, the Kokam SLPB-H5 series LiPo cells have the best short discharge time, and should be selected to manufacture batteries (of a given capacity and voltage) with the lowest resistance. Obviously, battery resistance is not the only criterion used in cell selection; cost, energy, weight and volume are also important. Specifically, if considering cost, one may select Enerdel cells over Kokam cells: Enerdel 16 Ah cells are not as expensive as Kokam SLPB-H5 cells, and provide a better value, in the sense that a battery using $ 1000 worth of Enerdel cells, will have a lower

Worse - Better

Kokam - SLPB....H5, 4, 4.5 & 5 Ah Enerdel PHEV 16 Ah LiPo Kokam - SLPB98188216P 30 Ah A123 - M1 26650 grade A K2 - LFP26650P Amperex 35 Ah Valence - IFR26650-Powercell Enerdel EV 17.5 Ah LiPo Amperex 66 Ah Boston Power Swing 5300 K2 - LFP26650E RealForce prismatic A123 - 20 Ah pouch RealForce pouch Gold Peak - E10, 10 Ah GBS LiFePO4 CALB - SE LiFePO4 Sinopoly - SP LiFePO4 Thundersky / Winston LiFeYPO4 Headway - HW LiFePO4 Winston Li-Sulphur 0










Short Discharge Time [s]



EEWeb PULSE resistance that a battery of the same voltage using $ 1000 worth of Kokam cells (it will also have a higher capacity).

Battery resistance calculation

This table lists the efficiency for various discharge times. In it, we see that, if a battery is discharged 10 times slower than its short discharge time, the efficiency is 90 %; if 100 times, 99 %.

Having the short discharge time of the cell technology used in a battery, one may rapidly calculate the nominal internal resistance of that battery.

Relative Discharge Time






Given the battery voltage and capacity:

































resistance [Ω] = short_discharge_time [s] * voltage [V] / capacity [Ah] / 3600

Or, given the voltage and energy: resistance [Ω] = short_discharge_time [s] * (voltage [V])^2 / energy [Wh] / 3600

Or, given the energy and capacity: resistance [Ω] = short_discharge_time [s] * energy [Wh] / (capacity [Ah])^2 / 3600

For example, using a cell technology that has a short discharge time of 36 seconds, given the battery voltage and capacity: 10 V, 100 Ah -> resistance = 36 * 10 / 100 / 3600 = 1 mΩ 100 V, 100 Ah -> resistance = 36 * 100 / 100 / 3600 = 10 mΩ 10 V, 10 Ah -> resistance = 36 * 10 / 10 / 3600 = 10 mΩ 100 V, 10 Ah -> resistance = 36 * 100 / 10 / 3600 = 100 mΩ

Efficiency calculation Given the short discharge time of a battery, the efficiency is easily derived. efficiency [%] = 100 * (1 – short_discharge_time [s] / actual_ discharge_time [s] )


The following graph is of the data in the previous table: 100% 90% 80% 70%


Which makes sense: as the voltage increases, having more cells in series results in a higher resistance; conversely, as the capacity increases, having more cells in parallel results a lower resistance.

60% 50% 40% 30% 20% 10% 0%

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Relative Discharge Time



63.10 100.00

Standardizing power density Today, power density is the preferred measure of a cell’s or battery’s technology ability to provide power for a given volume or mass. Volumetric power density is measured in Wh / liter, and gravimetric power density is measured in Wh / kg.


the industry may pick a point to measure power density. Using a point that is a particular multiple of the short discharge time would be convenient, as it would make that point independent of voltage and capacity. Possible candidates for such factors include: - e (~2.718), where the efficiency is 1-e^-1 (~63.21%)

Lack of a standard

- 10, where the efficiency is 90 %

Energy density and short discharge time are well defined physical characteristics. On the contrary, power density is a nebulous measure: who is to say how much power a cell can deliver? Is it the continuous power that will not result in a damaging temperature rise? Is it the peak power that the conductors can handle? Is it the maximum power delivered when the load has the same resistance as the battery (at 50 % efficiency)? It is up to the manufacturer to define what the power that a cell technology can deliver: on one side, a conservative manufacturer may define a low value, for the sake of improving the cycle life of the cell; on the other side, and aggressive manufacturer may define a high value, for the sake of impressing the market. That may be the reason why so few manufacturers specify power density.

- 100, where the efficiency is 99 %.

Proposed standard The industry would be well served if power density were specified at a standard point. Just as the industry picked C/20 or C/1 as the current used to measure capacity,

The factor of e is mathematically elegant, while a factor of 10 is more easily described. Ultimately, we chose a factor of e because it results in values that are more in line with the practice of the few cell manufacturers who do specify specific power (A123 among them).

Sample power densities An analysis of power density of the same 20 Li-ion cell technologies was performed, at a current that will discharge the cell in e times the short discharge time. This analysis reveals that the short discharge time is a close indicator of power density. This graph lists the cell technologies in order of short discharge time, the same order as the previous graph. You will note that the decrease of power density is nearly monotonic (with the notable exception of Boston Power), indicating a close relationship between short discharge time and power density.

Worse - Better

Kokam - SLPB....H5, 4, 4.5 & 5 Ah Enerdel PHEV 16 Ah LiPo Kokam - SLPB98188216P 30 Ah A123 - M1 26650 grade A K2 - LFP26650P Amperex 35 Ah Valence - IFR26650-Powercell Enerdel EV 17.5 Ah LiPo Amperex 66 Ah Boston Power Swing 5300 K2 - LFP26650E RealForce prismatic A123 - 20 Ah pouch RealForce pouch Gold Peak - E10, 10 Ah GBS LiFePO4 CALB - SE LiFePO4 Sinopoly - SP LiFePO4 Thundersky / Winston LiFeYPO4 Headway - HW LiFePO4 Winston Li-Sulphur

W/I W/kg 0



7500 10000 12500 15000 17500 20000 22500

Power Density Visit



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W/l W/kg

Power Density





0 0







Inverse Short Discharge Time [1/h] A scatter plot of power density versus inverse short discharge time makes that point even clearer.

Current limits Short discharge time is not a replacement for the current limits specified by the manufacturer. For one thing, short discharge time is a theoretical time: you certainly to not want to discharge a cell into a short circuit! For another thing, short discharge time does not consider any limitations on the cell current that are imposed by the chemistry and by the interconnections. Therefore, when selecting a cell for a given application, short discharge time must be used in combination with the manufacturer’s specified current limits.

Energy density vs. short discharge time Classically, we use Ragone charts to correlate the energy density and the power density of a cell or battery technology. Given the impreciseness of power density, let us create a new type of chart, replacing power density with inverse short circuit time.

About the Author Davide Andrea is the author of the book “Battery Management Systems for Large Lithium-Ion Battery Packs.” He is also the designer of the Lithiumate and Lithiumotive BMS for Elithion. He also designed the Prius and Ford Escape Hybrid PHEV conversions for Hybrids Plus.






Boston Power


Energy Density

350 300

K2 E

A123 26650 Kokam SLPB-H5 K2 P


Gold Peak Headway A123 pouch


Winston Sulphur


Enerdel 16 AH

150 100

Sinopoly/ CALB/ Winston/ Thundersky




0 0










Short Discharge Time [s] Visit


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Tom Lee

Chief Education Officer Quanser

Why Can’t Johnny Design?

Part 3:

Doing the Math It has been almost 25 years since I received my Bachelor’s degree in engineering from the University of Waterloo. My classmates and I are now a little older, a little heavier, a lot greyer, and hopefully a little wiser. On those occasions when I do meet up with my friends, the conversation inevitably diverts to recollections of our experiences as students. Among the tales of dorm parties, all-night cram sessions, impossible exams, and unintelligible TAs, as sure as Newton’s Second Law, the topic of math comes up. Typically it comes up in context of those aspects of the college experience that people hated.


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This, of course, is an overly broad generalization but I have observed that more often than not, graduates cite their math courses as having been some of their least favorite or least useful for their careers. In the US, the Accreditation Board for Engineering and Technology (ABET), requires that an accredited program must instill in its graduates, “an ability to apply knowledge of mathematics, science, and engineering.” This criterion is the first among the eleven key criteria demanded by ABET for accreditation. In the US and Canada, the undergraduate engineering student takes, nominally, three calculus courses, an algebra course, likely a statistics and probability course, and as electives, partial differential equations, numerical methods, and countless courses where more than half of the course is essentially mathematical. So here is the essential conflict: the system believes that math is king and students think that math sucks. In 2008, I wrote about a new discussion group that emerged in the early days of Facebook’s history. The group was called, Every Time I Walk Into Math Class a Little Part of Me Dies, and its avatar had in bold letters “Math Sucks.” This group grew very quickly to over 12,000 members who shared a common interest – the fear, loathing, and ultimately hatred of math. Of course, being hated is not the same as being useless. I hate turnips but I am fully aware of its nutritional value. Having said that, some part of these sentiments is shared with those who are more experienced and knowledgeable. The comments that I have received from readers of my previous articles often cite the disconnect between the theoretical emphasis of conventional engineering education and what they believe are the core skills of the successful modern engineer. Often the criticisms seem to center on the notion that real life is not an idealized closed linear system and a broad range of technical and human skills are necessary. Calculus as a pump not a filter In 1987, the Mathematical Association of America (MAA) released a landmark proceedings entitled Calculus for a New Century: A Pump, Not a Filter (you can get an online pdf here). The analogy was that for too long, institutions have been using the math courses as some metric of student ability for engineering and the physical sciences – those who do well in math must be the smartest and therefore will likely become the best engineers. You can debate whether such “boot camp” approaches are good or bad but this is missing the point. Math does have a contributing role to engineering


practice except, in my opinion, the system has yet to figure out how best to present the case. In other words, math can be a pump to enable greater performance among students. Fundamentally, the principal role of mathematics, and the reason why it appears first on the list of ABET requirements is its ability to serve as models of physical and other systems of interest to engineers. In engineering, students are immersed in calculus because rates of change (derivatives) are very good at mapping to changes in physical, measurable variables such as energy, position, concentrations, etc. The topic of differential equations ultimately coalesce the foundational techniques of limits, differentiation, and integration to formulate, solve, and apply differential equations in a modeling context. Model-based Design in industry From a practical, and industrial perspective, the theoretical tools of modeling are supposed to offer the engineer an ability to predict possible behavior of designs and provide guiding information for sorting through large complex sets of possible parameter values. In an ideal engineering workflow, mathematical modeling, analysis, and with the advent of powerful modern computing tools, simulation, precedes development of prototypes and testing to ensure that as much iteration is done in a virtual or theoretical

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Figure 1 shows a common representation of the process. In this case, the overall process is for the development of a complex automotive control system (e.g. active suspension, ABS, fuel injection, etc.). The “V” shape outlines the two broad groupings of phases connected by a step called Hardware in the Loop (HIL) testing. The left arm of the V is the sub-process where a sound mathematical foundation is critical. The right arm of the V is concerned with how effectively the theoretical outcomes of the left side is tuned and optimized for the final application and the real world. HIL testing is the systematic evaluation of subsystems where some configuration of models and actual hardware and connected to assess design performance with successively more realistic hardware configurations.

environment where revision and iteration is significantly cheaper and safer. The net result is supposed to be overall reduced time in the development and refinement of designs (i.e. cost savings) and greater performance (i.e. better designs). In complex systems such as cars and aircraft, the notion of building complete prototype vehicles based on best guesses on the designs of subsystems is no longer a feasible workflow as design timelines shrink and competitive pressures increase to unprecedented levels. Such industries have been steadily refining the techniques of model-based design.

When model based design works, the preparatory work, largely framed by mathematics, allows you to predetermine likely parameter values and potential hazards so that testing on hardware starts from much better initial estimates and the theoretical predictions can also guide how refinements and tuning can be done. Consequently, you can achieve optimal designs faster. All major automotive companies as well as others engaged in advanced engineering (e.g. aerospace, bio-medical, robotics, high-precision machinery, etc.) have signed on to this technique for the simple reason that stakes are getting very high and engineers are seeking greater sophistication in their methods to deal with increasing complexity. In 2009, Toyota initiated the very well-publicized recall of 5.2 million vehicles due to reported unexpected acceleration. In the end, the cause was deemed to be a purely mechanical problem of sticky pedals or poorly placed mats. The night-mare

“Fundamentally, the principal role of mathematics, and the reason why it appears first on the list of ABET requirements is its ability to serve as models of physical and other systems of interest to engineers.” Visit


EEWeb PULSE scenario for Toyota was, however, that it was a problem with the control electronics for the powertrain – e.g. the engine control unit (ECU) improperly injects more fuel at the wrong time due to a software bug. At the time of the recall, the company deployed significant resources to quickly determine the source of the problem, but the specter of a potential controller bug was driving a quick and sweeping response from Toyota and in the end triggered a very expensive recall. The fix of a controller program bug may seem simple – i.e. fix the bug and reprogram the controller – but the actual problem could also be systemic reflecting a deficiency in their modeling, controller design, programming, and countless other soft variables that influence a complex programming activity. In the end, from a business perspective, a company like Toyota, which built its market position on quality and engineering excellence, cannot afford to have its reputation tarnished by suspicions of buggy or non-robust processes. Implications for engineering education This context of this article is math. The example of model-based design is not intended to make the point that math can somehow fix all of these complex problems. The main point is that tools such as math and modeling can no longer be considered some isolated set of techniques that is the exclusive domain

of universities. Global business drivers are demanding a much broader range of solutions to be brought to bear on a complex design problem. The sophistication of modern engineering is not so much these specific solutions and techniques but the way they have connected and integrated into contemporary processes as in the one example of model-based design. My company Quanser, in the business of developing hands-on lab systems that better reflect modern engineering workflows is currently working to undergraduate lab exercises, with a particular focus on control engineering. The greatest challenge we face is reconciling the traditional compartmentalization of the curriculum with the more integrated nature of engineering processes today. Within the context of control engineering, as early as 2000, key leaders in the academic and industrial control community began prescribing remedies for resolving the educational disconnects. Richard Murray of Caltech struck a panel of global authorities in control and summarized their recommendations in Future Directions in Control in an Information-Rich World (IEEE Control Systems Magazine, April 2003). “The community must continue to unify and compact the knowledge base by integrating materials and frameworks from the past 40 years … It is also important that these courses emphasize the principles of control rather than simply providing tools that can be used in a given domain” (Murray, Åström, Boyd, Brockett, Stein 2003). This emphasis on the connectedness of the theoretical and the practical application define the methodology that we infuse into new lab concepts. In Part 2 of this series, the Quanser Driver Simulation (QDS) was discussed. This approach marries control theory with a visual automotive driving simulation within the framework of a HIL control loop. The educational framework that supports this concept is very much in response to the type of recommendations that Murray and others have


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“The sophistication of modern engineering is not so much these specific solutions and techniques but the way they have connected and integrated into contemporary processes as in the one example of model-based design.” offered. Figure 2, illustrates the ideal integrative process that QDS aims for. Role of mathematical software The various laws of physics tell us that if we are to add stuff to a closed system then something else needs to give. So the practical question of what has to give in terms of the traditional theoretical curriculum in response to new concepts and techniques within a finite course time limit is critical. In the context of the math, one key part of the answer is the emergence of software that has been adopted by industry and has also begun influencing education. The first group is composed of general systemlevel modeling tools. Examples include Simulink® (based on the math language MATLAB®) from The Mathworks, and LabVIEW® from National Instruments. Both are general platforms where you can define complex system models of physical systems via direct math or logical programming within a block diagram framework. Furthermore, they offer a broad range of analytical tools for control analysis and design, and ultimately HIL control implementation via a series of embedded system programming tools. Both of these platforms are in wide deployment in industry though they have particular respective advantages. They have also become part of the fundamental toolset that most engineering education programs introduce to students either to aid in the efficiency of certain math calculations or to better reflect industrial practice. The second group includes general-purpose symbolic math tools including Maple™ from Maplesoft, and

Mathematica® from Wolfram Research. These differ from systems like MATLAB in that they are able to directly represent and compute with algebraic equations without resorting to floating point values. These programs can directly differentiate the function x2+sin(a x) symbolically to 2x+a cos(a x) instead of using a numerical algorithm producing a table of floating point numbers. Such packages have had limited adoption in industry but have had sweeping adoption within the core math courses in universities. In fact, the calculus reform of the 1990’s was intimately related to the refinement and proliferation of these packages because of their ability to preserve underlying mathematical meaning but remove the nit picky detail of the actual steps of algebra. A third, very interesting group has now emerged that seems to show the potential for integrating the general system approach to modeling with theoretical rigor all within the context of a practical, industry-oriented engineering workflow. Loosely grouped as “physical modeling” systems, packages such as Maplesoft’s MapleSim™(derived from Maple) and Dassault System’s Dymola® (based on the open modeling language Modelica) offer a particular form of block diagram approach to modeling that is more suitable for expressing complex systems with a variety of component types, with the mathematical model equations remaining in tact for more advanced analysis. Furthermore, these tools are compatible with industry-standard software such as Simulink and LabVIEW allowing them to bring a more mathematical and rigorous approach to practical control system development and HIL testing. This class of systems have begun deploying in automotive and




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other sectors in key engineering communities such as Japan where engineers are betting that a more thorough modeling framework may deliver more comprehensive testing and preventing catastrophic errors in control system design. In education, however, this combination offers a more interesting opportunity. Conceptually it now provides a toolchain where the mathematical approach that so dominates our classrooms and textbooks have a welldefined and accepted place in industrial practice. Although symbolic math tools have been in place for over two decades in the math curriculum, it still struggled to find meaning within the engineering application courses encountered in the later years. At companies like Quanser, this is welcome news as a rigorous treatment of the modeling phase of design has always played prominently within our own R&D workflow. Many of Quanser’s most strategic products such as the quadrotor UAV and the haptics devices contain highly complex mechanical dynamics that require mathematical techniques to refine the required controllers. This modern generation of analytical tools can not only make our workflow more effective but these same techniques can then be mirrored within the lab exercises that we develop based on these devices and be introduced in a meaningful way into academic applications. What’s next? In all, universities and industries have taken modest steps at best in reconciling the disconnect between traditional curriculum and modern practice. Though it is clear that the technology pieces are likely robust enough for use by students, the inertia of centuries of academic tradition is difficult to overcome even with the best of ideas and tools. Recently, I was invited by the American Society of Engineering Education to serve on a committee to develop recommendations for a more comprehensive strategy for closing the industry-university gap. In Part one of this series, I wrote about the efforts being undertaken in Korea and China to modernize the curriculum. Although all of these initiatives are tackling a much larger set of problems than just math, math continues to stand out as that thing that everyone probably believes is a good thing but we have not figured out how best to present it in a motivating and relevant way. But the discussions and debates have at least started and the recent developments of innovative tools and techniques are beginning to put the pieces of the grand puzzle together.


But something tells me that there may be deeper and even cultural issues to all of this. Math-phobia, at least, in North America, is a chronic problem. I often cringe when a well-educated, articulate person openly boasts in public that he or she stinks at math. When was the last time you saw someone proudly proclaim that he was illiterate? As with any human endeavor, there are those who are better at math than others. But there are those who write poetry better than others, or can build a cabinet better than others, or can cook paella better than others, or can read Latin better than others, or can hit baseballs better than others. Ultimately, the specific techniques of calculus and algebra is not beyond the capacities of the vast majority of our engineering students if we can make the need for engineering math proficiency as obvious as making a great paella. I am hoping that concerted efforts in observing and listening to industry, as well as collectively pausing to smell the paella … that is, to reflect on the empowering and creative nature of mathematics and modeling and move away from the rigid compartmentalized framework of the traditional curriculum are a few more pieces of the puzzle.

Part 4 of this series will cover yet another difficult educational challenge – motivating younger students.

About the Author Dr. Lee has been an active contributor in the global engineering and control systems community for over twenty years. As Chief Education Officer at Quanser, a leader in realtime control and mechatronics solutions for education, research, and industry, Dr. Lee develops and implements the company’s strategy for enriching and increasing the educational effectiveness of technology in the modern engineering education context. Prior to his appointment at Quanser, Dr. Lee was Vice President of Applications Engineering at Maplesoft, creators of the renowned Maple mathematical software system. In that capacity, he helped the company transform the mathematical technology to a complete engineering modeling and simulation solution. He also serves as an Adjunct Professor of Systems Design Engineering at the University of Waterloo, noted for its leadership in engineering, computer science, and mathematics. Dr. Lee earned his Ph.D. in Mechanical Engineering at the University of Waterloo, and his M.A.Sc. and B.A.Sc. in Systems Design Engineering at the University of Waterloo. He has published numerous papers and is a frequent invited speaker in the areas of engineering education, engineering modeling and simulation, and engineering computation. Visit



Overview of the

RIGOL DS-4054 Digital Oscilloscope EEWeb Tech Lab


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Features & Price: - 500Mhz - 4GSa/s - 4 Channels - 140 Mpts of Memory - 9 Inch Screen - Connectivity: USB & Ethernet, LXI, VGA Output Port - $5899.00

Layout The first thing you’ll notice when you pull the Rigol out of the box is its nice, big screen. You’ll also notice that all of the normal functions are easy to find. The horizontal adjustment knob is on the top right front of the oscilloscope, and the vertical adjustment knob is below and to the left. You can access the channel settings with the channel soft keys under the “Vertical” menu. The settings will appear on the right hand side of the display screen, and can be controlled and selected using the corresponding soft keys.

Adding Measurements Adding measurements to the display is quite easy. If you were to add a 1 Mhz square wave to the display, and wanted to measure the peak-to-peak voltage, you could use the soft keys on the left side of the display screen to navigate the menu and measure the peak-topeak voltage. You can also use these soft keys to click over to the horizontal measurements and measure the frequency of the wave. The menu buttons are all readily accessible on the left hand side of the oscilloscope, which makes the process quick and easy.


triggering modes that can be used. You can set up all sorts of different triggering functionality by navigating the “Trigger” menu (located on the far right side of the oscilloscope) using the soft keys on the right of the display screen. From the Trigger menu you can choose a traditional edge type trigger or communication type triggers, and even set up your own custom type triggers by using the pattern function. Using the pattern function will allow you to set different codes for different patterns; you’ll see the pattern displayed near the bottom of the display screen. If the communication type you are using isn’t already built into the oscilloscope, you can still set up a triggering pattern based on that communication type. The Rigol DS-4054 also comes with a recording feature, which allows you to record your signal and play it back. You can control your recording using the “stop,” “play,” and “record” soft keys, and monitor the signal using the display screen. This feature allows you to go back and review all the different things that are going on in your recording.

Conclusion This Rigol DS-4054 Digital Oscilloscope has easy and accessible measurement functions, has a nice display, and is a great value for its price.

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Custom Measurements To add custom measurements, simply access the cursor menu under the “Cursor” soft key, which can be found on the right hand side of the oscilloscope. Turn the cursors on to “manual,” and you can then adjust them any way you like using the “Intensity” knob.

Other Features: Trigger Modes, Custom Triggers, and Recording Function The Rigol DS-4054 has some features which are unique for its price range. One of those features is the various

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