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Li-Ion cell formation is a critical step in the manufacturing process that turns a freshly assembled cell into something capable of storing an electrical charge. Before the forming process the Li-ion cell is merely an assembly of its components; mainly an anode, a cathode, a separator, terminals, electrolyte, and packaging that holds it all together. Then, during forming, a series of charge-rest-discharge-rest cycles are applied in a specific way to make it capable of storing a charge. Subtle differences in the forming process and equipment can either enhance or be a detriment to the performance and quality of the cell. Keysight recognized this in the development of their BT2200 Charge-Discharge Platform, making certain to provide the flexibility that allows cell manufacturers to fully optimize their cell forming process.

 

There is a lot more going on in the forming process than just putting an electrical charge into the cell. Forming is the process of growing a passivation layer on the anode of the cell. This passivation layer is known as the solid electrolyte interface, or SEI, layer, and is a vital part of what makes a Li-Ion cell work.

 

Proper SEI layer formation is essential to the performance and life of the cell. When a cell is properly formed, only some of the electrolyte and lithium is consumed during formation in creating the SEI layer. The formed SEI layer then serves as a barrier between the anode and electrolyte, inhibiting further SEI layer formation along with more consumption of the electrolyte and lithium. Ideally it needs to be as thin and uniform as practical without any porosity or other defects. Defects lead to ongoing SEI formation, diminishing the cell’s performance and capacity over time.

 

It generally turns out that to get a good SEI layer to form, the cell must be formed very slowly, typically at a C-rate on the order of 0.1C. This translates to charging the cell over a 10-hour period.  The cell is then allowed to rest, and then it gets discharged at low rate and allowed to rest again. The cell will go typically through two or more charge-rest-discharge-rest cycles, as the SEI layer is completely formed. Both the amp-hours of charge put into the cell and then discharged back out are carefully measured for each cycle. The difference in the two values indicates how much charge was used in forming the SEI layer for each cycle. Depending on several factors specific to the cell design, 10 to 20% of the charge is consumed during forming. The manufacturer knows what to expect and if the amount of charge loss is different, it is a clear indicator something is not right!

 

It’s not hard to see, given the cells are being charged, rested, discharged, and rested again for several cycles at such low C-rates, that the forming process may take several days to complete. This represents a significant amount of the process time, work-in-progress, investment in equipment, and ongoing cost of electricity in manufacturing. As such, it is a top priority for the manufacture to optimize the forming process to minimize the time it takes while still producing high quality cells, to be competitive.

 

As Li-Ion cell manufacturers gain more experience optimizing their overall forming process, they are finding more advanced capabilities over the charging and discharging to be an essential part of this. To assist the manufacturers in this regard, Keysight has recently introduced the BT2200 Charge-Discharge Platform. This modular platform consists of the BT2202A and BT2203A 8-slot Charge-Discharge Chassis and BT2203A 32-channel Charge-Discharge Modules, providing up to 256 channels per set up. The BT2200 is shown in Figure 1.

 

BT2200 Charge Discharge Platform 

Figure 1: The Keysight BT2200 Charge-Discharge Platform for Li-Ion cell forming

 

Keysight has worked with several manufacturers to identify and incorporate key features into the B2200 to enhance and optimize the cell formation process. Examples include:

  • Energy-efficient regenerative platform that achieves up to 90% efficiency during charging. Energy regenerated during discharge can be used by other cells or put back onto the AC line. This reduces the amount of net energy consumed, a large expense in cell forming.
  • Provides up to 256 channels in one compact unit, reducing floor space needed for the forming area, reducing manufacturing floor space and overhead.
  • Each channel provides up to 6 volts and 6.25 amps. Up to 32 channels can be paralleled into a single channel providing up to 200 amps in a single channel. This provides flexibility and “future-proofing” to accommodate a wide range of cell capacities within the manufacturing process.
  • Voltage and current are sampled with high accuracy every second, providing a way to better measure and discern subtle differences that might occur during the forming process.
  • Similarly, high accuracy voltage and current programming controls provide tight process control during forming.
  • 256 sequence steps are provided for configuring complex, dynamic charge and discharge forming profiles. This allows the user to optimize the forming process to yield a higher quality SEI layer in less time.
  • An extensive number of parameters are monitored in real time, providing greater options for making conditional logical decisions during the forming process. This provides greater flexibility to dynamically make changes that can further enhance the forming process.

 

If Li-Ion cell manufacturing is going to be an important part of your responsibilities, you should find it beneficial to learn more about Keysight’s solutions here. To learn more, click on the following link“The BT2200 Charge-Discharge Platform”to take you to its product page where you will find a lot of relevant and useful information on this solution.

In an earlier posting “The Value of Measuring Self-Discharge on Lithium Ion Cells” (click here to review) I explained what measuring self-discharge on LiIon cells is all about. I also introduced two new solutions for measuring self-discharge, from Keysight. These solutions use a proprietary approach that directly measures the self-discharge current of the cells in the matter of hours or less, instead of traditional approach of monitoring the cells’ open circuit voltage loss over weeks of time, to infer what the self-discharge is.

 

The first solution introduced is the BT2191A Self-Discharge Measurement System, shown in Figure 1. It is ready-to-use single-channel solution for evaluating and analyzing self-discharge current on Li-Ion cells. It is primarily intended for R&D engineers who are evaluating new Li-Ion cell designs and impact of design changes during development. A necessary and integral part of the BT2191A is its BT2192A Self-Discharge Measurement System Software. The BT2192A is integral because it integrates several pieces of standard test equipment into a specialized solution. A big advantage of the BT2192A software is it saves time and effort by providing a very convenient way to set up, capture, display, analyze, store, and recall self-discharge measurement results, without having to resort to any programming.

 

 BT2191A Self-Discharge Measurement System

Figure 1: BT2191A Self-Discharge Measurement System

 

The second solution introduced is the BT2152A Self-Discharge Analyzer, shown in Figure 2. The BT2152A is a one-box 32-channel solution for testing self-discharge on a much larger number of Li-Ion cells in manufacturing. The BT2152A was designed from the ground up specifically for this purpose, so separate software comparable to the BT2192A is not required to make the BT2152A a working solution for self-discharge measurement. Typically, several BT2152A’s are incorporated into a test system and code for automated self-discharge testing in manufacturing is developed by the manufacturer to be compatible with their existing systems. To ease this programming task the BT2152A Self-Discharge Analyzer has a simplified command set specifically for making self-discharge measurements. This greatly reduces the effort required for developing dedicated test code for automating self-discharge testing.

 

 BT2152A Self-Discharge Analyzer

Figure 2: BT2152A Self-Discharge Analyzer

 

However, one situation where it would be especially advantageous to have ready-to-run software is performing initial evaluations on the BT2152A for self-discharge on Li-Ion cells. To address this, Keysight has recently introduced the BT2155A Self-Discharge Analysis Software. Now a test engineer can immediately start conducting evaluations using the BT2152A, without having to spend time up-front to create a test program. The BT2155A’s graphical user interface is shown in Figure 3.

 

 BT2155A Self-Discharge Analysis Software

Figure 3: BT2155A Self-Discharge Analysis Software for the BT2152A Self-Discharge Analyzer

 

Just a few the many features of the BT2155A user interface shown in Figure 3 include:

  1. Access to several pull-up menus to set up instrument addresses, data formatting preferences, external fixture wiring calibration, and more.
  2. Setup test parameters.
  3. Setup test parameters specific to each of the individual test channels.
  4. Test execution messages window, including information, warnings, and errors.
  5. Temperature of cells over duration of test measurement logging.
  6. Voltages and self-discharge currents of cells over duration of test measurement logging.

 

In this example in Figure 3, after about 4.5 hours’ test/settling time, eight 2.4 Ah Li-Ion cells were found to range from 8 to 12 micro amperes of self-discharge current. This is much faster than the weeks of time needed for the traditional inferred approach of measuring a significant amount of cell open circuit voltage loss due to self-discharge.

 

If evaluating self-discharge of Li-Ion cells is relevant to your work, the BT2155A software should be of interest to you. You now have a turn-key solution for evaluating self-discharge of large number of Li-Ion cells when it is paired with the BT2152A Self-Discharge Analyzer. Check out the Keysight home pages for the new BT2155A Self-Discharge Analysis Software (click here to access) and BT2152A Self-Discharge Analyzer (click here to access) to learn more about self-discharge measurements and these solutions. There you will find application notes, data sheets, evaluation guides, and more to help you out.

Hybrid electric vehicles (HEV) and  electric vehicles (EV) have multiple architectural variations.  Figure 1 shows a simplified block diagram of a couple of these architectures.  For the strong (or parallel) hybrid and the pure EV (no engine), a high voltage (HV) bus supplied by a large battery, drives the electric powertrain.  Power levels of the inverter and motor/generator range from ~ 60 kW up to and over 180 kW.  Along with the large Li-Ion battery, a significant investment is required to develop these architectures.  Most of the components are bidirectional, allowing for power to go from the battery to the inverter, which turns the motor and moves the vehicle (traction drive).  When decelerating, the momentum of the vehicle turns the generator, which drives power back through the inverter and charges the battery (regenerative braking). 

HEV/EV Power Train Architectures

Figure 1: HEV/EV Power Train Architectures

 

In the mild hybrid (MH), the motor/generator, inverter and battery are also bidirectional.  They are not large enough to drive the vehicle by themselves (as in the HEV or EV), but instead are used to supplement the engine power during acceleration and recharge the battery during deceleration.  The voltage level for MHs is typically 48 V, keeping the bus structure under the 60 V safety rating for HV.  The DC:DC converter is also bidirectional for MHs.  In addition to charging the 12 V battery from the 48 V bus, the DC:DC converter also needs to convert power from the 12 V bus to the 48 V bus.  The main application is to pre-charge the 48 V bus (i.e. input capacitance of the inverter), before contactors connect the 48 V battery to this bus.  The pre-charge equalizes the voltage of the battery and the input to the inverter, minimizing arcing across the contactor. So how does one test the bidirectional nature of HEV/EV power converters?

 

Bidirectional power flow requires test solutions that are capable of sourcing and sinking power to the power converter, for example simulating the Li-Ion battery when testing the DC side of the traction inverter or the DC:DC converter.  This is traditionally accomplished by connecting a power supply and an electronic load in parallel.  However, external circuitry (i.e. diode to stop current flow into the power supply) and cumbersome ‘two-instrument’ programming typically doesn’t allow for smooth signal transitions between sourcing and sinking power, reflecting an inaccurate simulation of the operating conditions. Reference ‘Conquering the Multi Kilowatt Source/Sink Test Challenge – Application Note’ (https://literature.cdn.keysight.com/litweb/pdf/5991-2873EN.pdf?id=2373832) to learn more about this test challenge.

 

Electronic loads typically dissipate the power transferred to them from the DC:DC converter.  But this dissipated power (heat) can start to add up, especially for test applications where multiple DC:DC converters are tested in parallel.  Because of the need to remove the heat from the electronic loads, they are often large with significant forced air (via fans) or potentially even water circulation to cool them.  Therefore, it is necessary to increase test system size and HVAC requirements to remove the heat from the facility. 

 

To address these challenges, multiple vendors are introducing integrated source/sink solutions in a single product.  The products can seamlessly move from sourcing current (Quadrant I) to sinking current (Quadrant II) without external circuits or synchronized programming of a separate power supply and electronic load (see Figure 2). The integration enables a smooth output waveform that correctly simulates the bidirectional DC:DC converter’s transition between opposite directions of power flow.

Source/Sink Power System

 

 

 

 

 

 

Figure 2 – Source/Sink Power System

 

When the power system sources power to the DC:DC converter, most of the power (depending on the efficiency) is passed through the converter to the automotive load.  When the power system sinks power from the DC:DC converter, the power must be absorbed by the power system.  At the 5kW power level and above, there are source/sink power systems and electronic loads that regenerate (or return) the power to the AC mains (see Figure 2).  This technique is not 100% efficient, but most designs allow ~ 90% of the power to be delivered back to the grid.  This leaves only 10% of the power (~500W in the case of a 5 kW product) to be dissipated as heat.  The result is a dramatic reduction in size of the products and the HVAC cost to remove heat from the test system environment.

 

The important thing to note for regenerative solutions is the fidelity of the power being returned to the AC mains.  If you work in manufacturing, any distortion in the power returned to the AC mains will be amplified by the number of test systems in the facility.  ‘Dirty Power’ can create intermittent problems in your facility, requiring isolation transformers for each test system to help mitigate the problem created by poor regeneration.  It is best to check with the product vendor to confirm low distortion power is returned to the AC mains (see Figure 3).    

THD & PF measurements on power

Figure 3: THD & PF measurements on power returned to AC mains from Keysight’s RP7900A Regenerative Power System.  Measurements made with PA2203A IntegraVision Power Analyzer

 

Keysight Technologies provides many solutions for the HEV/EV market, including DC:DC converter design and test.  For more information on the RP7900A RPS (Figure 4) go to www.keysight.com/find/ev1003a.

 

With its recent acquisition of Scienlab, Keysight Technologies has a comprehensive test and measurement portfolio to serve the HEV/EV market.  For more solutions, please visit: www.scienlab.com.

RP7900A Regenerative Power System

 

Figure 4: RP7900A Regenerative Power System

 

About Keysight Automotive & Energy Solutions (AES)

 

The automotive and energy industries are synergistically paving the way for a future built on digital transformation and the electrification of everything. Engineers like you are pioneering this electrical revolution through the development of smarter, safer, and more efficient technologies; whether driving the latest advancements in communications for the Connected Car, creating new power electronics designs to facilitate renewable energy integration and vehicle electrification, or pressing forth in the quest to economize next-generation battery energy storage systems (BESS).

 

Keysight AES is committed to addressing the biggest design and test challenges faced by engineers in the automotive and energy industries. With fully-integrated solutions combining leading-edge hardware and ultra-sophisticated software, Keysight AES removes technological barriers and streamlines innovation, helping to bring your breakthroughs to market faster, cheaper, and easier. These range from powerful solar array simulator platforms for rapid optimization of modern photovoltaic (PV) system designs, to highly-efficient regenerative hybrid-electric/electric vehicle (HEV/EV) test systems for putting onboard power converters through their paces, and revolutionary battery performance characterization and high-volume Li-Ion cell production solutions for unprecedented savings of time and space; not forgetting advanced power circuit simulator tools to ensure seamless integration of new, state-of-the-art wide bandgap (WBG) devices.

 

Follow the Keysight AES blog to stay tuned-in on the latest insights from thought leaders in design and test for automotive and energy applications.

In my previous post, I explained why measuring the self-discharge on Li-Ion cells is vitally important to both R&D and manufacturing. The downside of measuring self-discharge however, when using the traditional method of measuring the cell’s open circuit voltage (OCV) loss is the amount of time required, typically weeks.

 

I also shared that Keysight has introduced two new solutions, the BT2191A Self-Discharge Measurement System and the BT 2152A Self-Discharge Analyzer. These solutions utilize a new and different approach for measuring self-discharge of cells. Instead of relying on OCV loss over time (an indirect indicator of self-discharge) they instead directly measure the cell’s self-discharge current. We refer to this as the potentiostatic method for measuring self-discharge, as the test voltage is held constant for the duration of the testing. To review the details of all of this, click on the following title of my previous post “The Value of Measuring Self-Discharge on Li-Ion cells” to learn more!

 

The potentiostatic method for directly measuring the self-discharge current of cells takes typically hours or less to settle on the result, as illustrated in Figure 1. And as shared in the previous post, it takes even less time to discern cells having excess self-discharge from good ones.

 

BT2191A Li-Ion cell self-discharge measurement

Figure 1: Measurement of a Li-Ion cell’s self-discharge current using the BT2191A

 

There are challenges to using the potentiostatic method as well, to get meaningful results quickly. The most prominent challenge is the need for maintaining an extremely stable test voltage for the duration of the testing. The Keysight solutions address this in innovative ways. Another major challenge is having and maintaining the cell under test in a similarly stable state, which can be achieved by taking the appropriate steps and measures to do. Briefly this includes:

  • Providing adequate rest time after subjecting the cell to any charging or discharging.
  • Take appropriate steps to maintain the cell at an adequately constant temperature.
  • Avoid temperature differences that can lead to transient thermo-electric voltage effects.
  • Take advantage of having the cell at a state of charge (SoC) that leads to the most favorable result. 

 

We have just released a new application note that goes into much greater details about measuring the self-discharge on Li-Ion cells, including:

  • How the open circuit voltage method works.
  • How the potentiostatic method works.
  • Fundamentals on the technology incorporated into the Keysight solutions.
  • Details on having and maintaining the cell in a stable state for applying the potentiostatic method.

 

So if Li-Ion cell and battery performance test are important to your work, you should find this new application note informative and helpful. To learn more about Li-Ion cell self-discharge, measurement approaches, and test solutions you can access this new application note by clicking on its title here: “Evaluate Self-Discharge of Lithium Ion Cells in a Fraction of the Time Traditionally Required”.

You should find it very informative!

 

About Keysight Automotive & Energy Solutions (AES)

 

The automotive and energy industries are synergistically paving the way for a future built on digital transformation and the electrification of everything. Engineers like you are pioneering this electrical revolution through the development of smarter, safer, and more efficient technologies; whether driving the latest advancements in communications for the Connected Car, creating new power electronics designs to facilitate renewable energy integration and vehicle electrification, or pressing forth in the quest to economize next-generation battery energy storage systems (BESS).

 

Keysight AES is committed to addressing the biggest design and test challenges faced by engineers in the automotive and energy industries. With fully-integrated solutions combining leading-edge hardware and ultra-sophisticated software, Keysight AES removes technological barriers and streamlines innovation, helping to bring your breakthroughs to market faster, cheaper, and easier. These range from powerful solar array simulator platforms for rapid optimization of modern photovoltaic (PV) system designs, to highly-efficient regenerative hybrid-electric/electric vehicle (HEV/EV) test systems for putting onboard power converters through their paces, and revolutionary battery performance characterization and high-volume Li-Ion cell production solutions for unprecedented savings of time and space; not forgetting advanced power circuit simulator tools to ensure seamless integration of new, state-of-the-art wide bandgap (WBG) devices.

 

Follow the Keysight AES blog to stay tuned-in on the latest insights from thought leaders in design and test for automotive and energy applications.

Have you ever wanted to be more efficient? Not just turning the lights off when you leave a room to conserve electricity or xeriscaping your yard to save on water, but have you ever wanted to optimize everything around you in order to increase the effectiveness of tasks in everyday life?

 

I think about this all the time… in fact, it’s been somewhat of an obsession of mine for as long as I can remember. It’s the reason I cringe when people use search engines without strategically placed Boolean operators. It’s also why, in college, I designed and built a piezoelectric floorboard that turns the simple act of walking into an electricity generation system. And it’s the reason that today, I find myself daydreaming of a Tesla Roadster in my garage as its battery charges from the electricity generated by my solar roof, powering my home whenever a patch of clouds rolls overhead and harmoniously shaking hands with my utility provider whenever they need assistance in meeting peak demands (for a small fee, of course). With a buzz of my smart phone, I’ll know my Roadster is fully primed to hit the pavement and turn some heads while providing a complete summary of just how many gallons of gas and kilowatt-hours of electricity I won’t be purchasing this week thanks to its integrated home energy management system (HEMS).

 

I’ve always been fascinated by the notion of optimization and the role it plays in technology and innovation. This might be why I’ve become so captivated by the energy industry. Our world is currently undergoing the most impactful and expansive technological revolution since the internet, and it involves reimagining the concept of energy infrastructure as well as renovating arguably the largest and most complex machine known to mankind; the North American electric power grid. Unless you’ve been living under a rock for the past decade, you’ve probably heard the phrase “smart grid” at some point in time. You’ve probably also found yourself thinking, ‘just what the heck is this “smart grid” thing?’ It’s a valid question, and one that is best answered by the underlying motive of today’s power and energy pioneers hellbent on maximizing the value of every last electron.

 

With this in mind, it makes sense that efficiency stands firmly in the spotlight of advanced power system research and development. But to fully understand the roles efficiency plays in the smart grid, it is important to think broadly about all the different factors contributing to the overall effectiveness of a power system. As technologists, it feels natural to strictly associate the word “efficiency” with the classical definition of “output-over-input.” However, when it comes to advanced power systems facilitative of the smart grid, the term “efficiency” has much greater meaning. An efficient power system is one that maximizes supply, optimizes transmission and distribution, manages consumption, mitigates faults and disturbances, promotes resiliency, guarantees safety, and ensures consistent availability.

 

Exemplifying this notion in practice are modern power conversion systems (PCS) such as advanced photovoltaic (PV) inverters, which are beginning to play an expanded role as the heart and brain of PV systems. The PV inverter acts as a controllable gateway for power flow. Through constant monitoring of system performance, digital communication with other entities, and embedded intelligence for autonomous decision-making, PV inverters can modify their behavior in response to their surroundings in order to increase the efficiency of the system as a whole. This can range from adapting its input characteristics to maximize power generation based on the intermittent supply from a PV array, to changing its output to rectify instabilities elsewhere the greater power network, or even optimizing power flow between devices according to commands from an external energy management system (EMS) designed to reduce the cost of electricity.

 

The very essence of the smart grid is that devices like power converters should be able to optimize not only their own functionality, but also the functionality of all the devices around them. It is not just about using less of a resource to get the same job done, but even more so about using a group of resources more intelligently to do the same job better. Therein lies the fundamental objective of the smart grid; to improve the overall efficiency of operation of a power network through optimizing the control and coordination of its various electronic devices in real-time. However, implementing such levels of interoperability is not an easy task, and the burden of creating, economizing, and deploying these advanced power systems falls on the shoulders of industry innovators. Test and measurement science is critical for developers of next-generation power products in which advanced functions focused on increasing efficiency must be validated under a wide range of volatile operating conditions and disparate system/device configurations. The ability to rapidly test these products’ performance and interoperability within a simulated power system environment is essential to accelerating the development and integration of smart grid technologies.

 

This is the first of a series of blogs discussing the different roles efficiency plays in the various technologies driving the formation of the smart grid. More importantly, we’ll be diving into the technological challenges engineers and innovators are facing as they work to develop the power systems of tomorrow and optimize the end-to-end efficiency of our future energy infrastructure; from generation-to-consumption and every step along the way.

 

While the likelihood of waking up to a fully-electric sports car in my garage seems farfetched to say the least, it is exciting to know that the technology in my Tesla daydream is inching closer to becoming a reality for everyone with each passing day.

 

About Keysight Automotive & Energy Solutions (AES)

 

The automotive and energy industries are synergistically paving the way for a future built on digital transformation and the electrification of everything. Engineers like you are pioneering this electrical revolution through the development of smarter, safer, and more efficient technologies; whether driving the latest advancements in communications for the Connected Car, creating new power electronics designs to facilitate renewable energy integration and vehicle electrification, or pressing forth in the quest to economize next-generation battery energy storage systems (BESS).

 

Keysight AES is committed to addressing the biggest design and test challenges faced by engineers in the automotive and energy industries. With fully-integrated solutions combining leading-edge hardware and ultra-sophisticated software, Keysight AES removes technological barriers and streamlines innovation, helping to bring your breakthroughs to market faster, cheaper, and easier. These range from powerful solar array simulator platforms for rapid optimization of modern photovoltaic (PV) system designs, to highly-efficient regenerative hybrid-electric/electric vehicle (HEV/EV) test systems for putting onboard power converters through their paces, and revolutionary battery performance characterization and high-volume Li-Ion cell production solutions for unprecedented savings of time and space; not forgetting advanced power circuit simulator tools to ensure seamless integration of new, state-of-the-art wide bandgap (WBG) devices.

 

Follow the Keysight AES blog to stay tuned-in on the latest insights from thought leaders in design and test for automotive and energy applications.

Ideally electrical cells would be able to hold their electrical charge without any losses for all time. However, all electrical cells have some amount of self-discharge. Self-discharge is the loss of charge over time while sitting unused and not connected to anything. Self-discharge is a normal attribute of all cells. It results from chemical reactions taking place within a cell. Primary (non-rechargeable) cells have extremely low self-discharge which can give them a shelf life of more than a decade. Secondary (rechargeable) cells have greater self-discharge. Rechargeable lithium ion cells, after a larger initial loss in the first month after being charged, typically lose around 1% of their charge per month thereafter.

 

Additional self-discharge can result from leakage current paths existing within the cell. Particulate contaminates and dendrite growths produce internal “micro-shorts”, creating such leakage current paths. These are not normal attributes of cells and as they can lead to catastrophic failure of the cell detecting them early on is a top concern to lithium ion cell designers and manufacturers.

 

To detect higher-than-normal self-discharge in lithium-ion cells, developers and manufacturers have traditionally relied on measuring the drop of a cell’s open-circuit voltage (OCV) over a period of several weeks or longer to get good validation. Having to wait this long during development results in lost opportunities by being late to the market with new designs. This problem is further compounded if self-discharge testing must be repeated. In manufacturing, having to store large quantities of cells for a long time to screen them for self-discharge presents major expense, logistics, and safety problems to contend with. Clearly, ways of reducing the time to evaluate the self-discharge of cells is highly valued.

 

Measuring a cell’s self-discharge current provides an alternate means to directly determine a cell’s self-discharge rate. Cells exhibiting excessively high self-discharge can be identified and isolated in a small portion of the time required by the traditional OCV approach, greatly reducing the associated expenses, difficulties and potential hazard. The challenge of performing direct self-discharge current measurement is suitable equipment possessing required stability and resolution has not existed to make it practical.

 

Keysight has recently introduced two new solutions that directly measure the self-discharge current on cells, greatly reducing the time required to evaluate cells for self-discharge, compared to the traditional approach of measuring a cell’s OCV loss over time. The first of these solutions is the BT2191A Self-Discharge Measurement (SDM) System, shown in Figure 1. The BT2191A allows engineers to reduce design cycle time and optimize self-discharge performance of new cell designs.

 

BT2191A Self-Discharge Measurement System 

Figure 1: Keysight BT2191A SDM System

 

The second new solution is the BT2152A Self-Discharge Analyzer, shown in Figure 2. The BT2152A measures self-discharge current on many cells at the same time, greatly reducing the time required to discern bad cells from good cells for self-discharge in manufacturing. An example of this is shown in Figure 3. The BT2152A can provide significant reductions in work-in-progress (WIP) in manufacturing, along with associated reductions in expenses, logistics issues, and safety problems incurred with carrying the WIP for that time.

 

 BT2152A Self-Discharge Analyzer

Figure 2: Keysight BT2152A Self-Discharge Analyzer

 

BT2152A measurement example 

Figure 3: BT2152A measurement discerning a bad cell from good cells

 

So, if you are involved with the design or manufacturing of Lithium Ion cells then cell self-discharge should be a top concern for you. To learn more about BT2191A Self-Discharge Measurement System the following link will take you to its home page: <BT2191A home page>   To learn more about BT2152A Self-Discharge Measurement Analyzer the following link will take you to its home page: <BT2152A home page> Here you will find additional information providing greater details about self-discharge measurements on cells and about these two new solutions!

 

About Keysight Automotive & Energy Solutions (AES)

The automotive and energy industries are synergistically paving the way for a future built on digital transformation and the electrification of everything. Engineers like you are pioneering this electrical revolution through the development of smarter, safer, and more efficient technologies; whether driving the latest advancements in communications for the Connected Car, creating new power electronics designs to facilitate renewable energy integration and vehicle electrification, or pressing forth in the quest to economize next-generation battery energy storage systems (BESS).

Keysight AES is committed to addressing the biggest design and test challenges faced by engineers in the automotive and energy industries. With fully-integrated solutions combining leading-edge hardware and ultra-sophisticated software, Keysight AES removes technological barriers and streamlines innovation, helping to bring your breakthroughs to market faster, cheaper, and easier. These range from powerful solar array simulator platforms for rapid optimization of modern photovoltaic (PV) system designs, to highly-efficient regenerative hybrid-electric/electric vehicle (HEV/EV) test systems for putting onboard power converters through their paces, and revolutionary battery performance characterization and high-volume Li-Ion cell production solutions for unprecedented savings of time and space; not forgetting advanced power circuit simulator tools to ensure seamless integration of new, state-of-the-art wide bandgap (WBG) devices.

Follow the Keysight AES blog to stay tuned-in on the latest insights from thought leaders in design and test for automotive and energy applications.

Hello everyone,

 

This is the first in what should be a series of blog posts on photovoltaic (PV) simulators, also known as solar array simulators (SAS).  In this introductory blog post, the topics covered will be:

  • Why you would choose a PV Simulator for your application
  • How a PV simulator differs from a standard DC Power Supply
  • What solutions Keysight offers for testing solar products

There will be future blog posts and application notes that will go more in depth on PV simulators including how they generate their output and how to program them.  Be on the lookout for those!

 

Why Choose a PV Simulator?

The first topic that we are going to discuss is why you would want to use a PV Simulator instead of an actual PV Array. The short answer is: a PV Simulator is a whole lot more practical than a PV Array.

 

The longer answer is that a PV Array will be large, very expensive, and the output power is uncontrollable in that the output power will depend on variable environmental conditions such as temperature and sun exposure (also known as irradiance). To put the size into perspective, a 15 Kw PV array would contain 50 300 W solar panels (300 W is a common size for a solar panel). This would take up almost 1000 ft2!

 

 A PV simulator is a much more flexible solution: it is much more efficiently sized, less expensive, has a programmable output, and it is backed by a warranty in case anything happens to it.  You can program the output to simulate any weather condition that you’d like to test.  For instance, you can program it to output a characteristic that simulates 50% cloud coverage over your array.  If you were using your actual PV array you would need to wait until the weather conditions were perfect to do this.  A PV Simulator also is made to be a test instrument. In the case of the Keysight PV Simulator: it is efficiently sized to be installed into a standard test rack (up to 15 kW in 3 rack units) and it has a full suite of SCPI (Standard Commands for Programmable Instruments) to control and measure its output. 

 

What makes a PV Simulator different from a standard DC power supply?   

The second thing that we want to discuss is why would you use a PV simulator instead of a standard DC power supply.  The short answer for this topic is that a PV simulator’s output is a bit different than the output of a standard power supply.

 

A standard DC power supply typically comes with one of two output characteristics: rectangular or auto-ranging.

 

                                         

Figure 1 - Rectangular Characteristic                                       Figure 2 - Autoranging Characteristic

 

A rectangular output characteristic is what you would see on most standard power supplies.  There is a rated voltage (VRATED) and a rated current (IRATED).  This is illustrated in Figure 1.  You calculate the maximum power (PMAX) by multiplying VRATED and IRATED.  The power supply can output any voltage and current combination as long as it is within the specified limits.  In Figure 1, that would be anywhere under the rectangle formed by the limits (hence the name).

 

Less commonly, there are DC power supplies with an autoranging output characteristic.  The difference is that the limits are determined by a PMAX that is not the product of VRATED and IRATED.  It is probably easiest to look at an example.  Let’s take a look at the Keysight N6752A DC power supply module.  It is rated for 50 V (V1 in fig. 2) and 10 A (I2).  If PMAX was equal to VRATED multiplied by IRATED, this would make the N6752A a 500 W power supply but it is rated for 100 W.  The N6752A is capable of outputting any voltage or current combination equal to 100 W from 50 V, 2 A (V1, I1) to 10 V, 10 A (V2, I2).  Autoranging power supplies are very flexible since you do not need to worry about having multiple power supplies to cover different voltage and current combinations for the same power levels. 

 

A PV Simulator actually has another type of output characteristic, commonly referred to as the I-V curve.

 

Figure 3 – PV Array I-V curve

 

The output curve in Figure 3 represents the output characteristic of a solar array more accurately than either of the other two output characteristics. The first thing to notice is that the y axis is current in this figure, and not voltage as it is in the other two graphs.   The second thing to notice is that the shape is different from the other two characteristics.  This shape is representative of the natural output characteristic of a PV array. 

 

There are two ways that you can generate an I-V curve with a PV Array Simulator. The first way is referred to as SAS mode.  In this mode, the user inputs four parameters that are shown in Figure 3:  the open circuit voltage (VOC), the maximum power voltage (VMP), the short circuit current (ISC), and the maximum power current (IMP).   PV Simulator firmware then uses these parameters to generate the curve based on a mathematical model.  This is the easiest way to generate a curve since you only need to enter four parameters.  The tradeoff is that you are limited to the mathematical model that the instrument is using. 

 

The second way, a more complex method that generates PV curves, is referred to as table mode.  In table mode, the user sends a list of voltage and current pairs to the unit.   The PV Simulator firmware parses all the entered points and generates a PV curve.  The PV Simulator firmware will interpolate between the points to draw a complete curve.  In fact, you can input a table with as little as three points!  This is a much more flexible approach than SAS mode but it is also more difficult to do.  There are rules that must be followed in order to generate the curve and if any rules are broken, you will get an error.  The programming is also more complicated because you need to enter tables of values. Table mode is very important though since it lets you generate any table you want to. SAS mode limits you to the particular mathematical model that the PV Simulator follows.    

 

Most of the power supplies that Keysight sells are optimized to work in constant voltage (CV) mode.  Keysight’s power supplies do work in constant current (CC) mode as well. However, they are optimized to be voltage sources rather than current sources. One of the biggest differences from our standard DC power supplies is that our PV Simulators are optimized to work in CC mode since PV arrays are often modeled in circuitry as a CC device (unlike something like a battery that is a CV device). 

 

What Does Keysight Offer?

There are two families of PV Simulators available from Keysight. 

 

The first is the E4360A family of modular Solar Array Simulators.

 

Figure 4 - The Keysight E4360A Solar Array Simulator

 

The second family released is the N8900APV family

Figure 5 - The Keysight N8900APV PV Simulator

 

This is great for high power terrestrial applications such as PV string inverters.  We also have a software package to make controlling them a bit easier.  The N8900APV family also doubles as an autoranging DC power supply. 

 

As you can see, Keysight offers quite a few different ways to test devices that are powered by PV Arrays.  Please feel free to contact us with any questions that you have about PV Simulators and stay tuned to this blog for some more posts highlighting some of the great features of the PV simulator.

 

About Keysight Automotive & Energy Solutions (AES)

The automotive and energy industries are synergistically paving the way for a future built on digital transformation and the electrification of everything. Engineers like you are pioneering this electrical revolution through the development of smarter, safer, and more efficient technologies; whether driving the latest advancements in communications for the Connected Car, creating new power electronics designs to facilitate renewable energy integration and vehicle electrification, or pressing forth in the quest to economize next-generation battery energy storage systems (BESS).

Keysight AES is committed to addressing the biggest design and test challenges faced by engineers in the automotive and energy industries. With fully-integrated solutions combining leading-edge hardware and ultra-sophisticated software, Keysight AES removes technological barriers and streamlines innovation, helping to bring your breakthroughs to market faster, cheaper, and easier. These range from powerful solar array simulator platforms for rapid optimization of modern photovoltaic (PV) system designs, to highly-efficient regenerative hybrid-electric/electric vehicle (HEV/EV) test systems for putting onboard power converters through their paces, and revolutionary battery performance characterization and high-volume Li-Ion cell production solutions for unprecedented savings of time and space; not forgetting advanced power circuit simulator tools to ensure seamless integration of new, state-of-the-art wide bandgap (WBG) devices.

Follow the Keysight AES blog to stay tuned-in on the latest insights from thought leaders in design and test for automotive and energy applications.

In my previous post, I shared the exciting developments around autonomous driving and its potential benefits to save more lives, by removing human errors which have been blamed for more than 90% of fatal driving accidents.

 

Now let’s switch gear to more detailed discussions of the enabling technologies of autonomous driving.

 

In this post, we will focus on sensing technologies – these are among the most important enabling technologies of autonomous driving. We will look at three major sensing technologies – namely radar (Radio Detection and Ranging), LiDAR (Light Detection and Ranging), and camera.  This is an exciting area with developers working hard to finetune sensitivity and response-abilities of all sorts of automotive sensors.

 

From the technology point of view, radar technology has been widely used mostly in the aerospace and defense industry before and during World War II, so it’s not brand new technology. Even for automotive radar research, the first projects happened in the 1970’s – that’s a long time ago.

 

However, widespread application of radar technologies in the automotive consumer market is fast growing these days. It started with high-end passenger cars, but these days, more and more trucks and low-end passenger cars have automotive radar sensors mostly for better safety as well as more convenience (eg: Stop & Go at the traffic jam).

 

Automotive radar detects distance (range) and motion, including velocity and angle. Radar systems have several benefits. They work in almost every condition, use reflected radio waves to detect the obstacle behind other obstacles, and have fewer signal processing requirements. On the other hand, there are some limitations. For instance, radar cannot interpret what the obstacles are (eg: human, dog, another car, paper box, or even if it’s a huge heavy refrigerator dropped on the highway). It simply detects the presence of an object without providing information what it is. This is a key reason why radar may not be able to provide enough data characterization to enable a full autonomous driving system due to limited information about the detected obstacles. 

 

LiDAR provides much more intelligent 3D mapping using laser light. LiDAR scans 360 degrees of the environment around the autonomous driving car with more than a 100 meter-range. Some LiDAR systems provide as many as 64 channels, and over a million points of scans per seconds, enabling the car to have enough information of surrounding situations so it can execute a decision on how to react to the environment.

 

The cons of LiDAR-based sensing technology are that the sensors are still very expensive although some leading LiDAR companies are announcing more economic versions of LiDAR sensors. It also generates huge amounts of data, which requires tremendous signal processing power and data management sub-systems.

 

Camera technology aims for recognition and image classification. Compared to radar and LiDAR, it is a cheaper sensing technology, although signal processing cost is still not cheap. It can provide vision-based imaging data, hence with some level of signal processing, the camera can read traffic signs such as limited maximum speed, school zone notice, and more. The limitation of camera-based sensing technology is that cameras are affected by weather and other environments. For example, there was a widely reported tragic accident whereby the vehicle was using camera-based semi-autonomous driving when the camera sensor failed to recognize a white truck crossing the road due to the reflections from the truck.

 

 

As each sensing technology has pros and cons, the industry currently can’t just depend on a single sensing technology for autonomous driving research. Most leading players of the autonomous driving industry use all three, or at least two of the technologies discussed here, to make sure their autonomous driving system gets enough data from all around the vehicle.

 

However, radar technology plays more roles in Advanced Driver Assistance System (ADAS), which is already currently a tangible way to save more lives by using technology to mitigate human driver errors. It is a good bridging platform before fully autonomous driving cars come into real-life. Applications of ADAS include radar-based emergency braking systems, forward collision warning, blind spot detection, rear collision warning system, adaptive cruise control, and many more applications that help enable safer driving.  

 

Currently, four key areas of radar technology development are driving numerous automotive applications:

  1. Higher frequencies, including 24, 77 and 79 GHz
  2. Wider bandwidths of 1, 2 and 4 GHz
  3. Accurate power control, which helps ensure the sensors transmit and receive radar signals to the objects with minimized interference from other vehicles
  4. In-vehicle Ethernet enabling fast, accurate, and reliable in-vehicle communication of the large amounts of data captured by the radar sensor.

 

The trend is also for automotive radar technologies to use higher frequencies with wider bandwidths. For example, more and more short range radars will use 79 GHz instead of 24 GHz. This is because of better resolution from wider bandwidth, enabling objects to be more clearly detected and differentiated. Higher frequency at 79 GHz can guarantee wider bandwidth and encounter fewer spectrum occupancy or regulatory issues as agencies worldwide continue to work on harmonizing frequency allocation for vehicular radars in the frequency range 77 GHz to 81 GHz.  

 

As per the example given in my previous post and illustrated below, signals with better resolution from 4 GHz bandwidth (on the right side) clearly differentiated two obstacles compared to the left one using only 1 GHz. It is critical for the radar to detect both obstacles to avoid any risky situation. In addition, higher frequency makes smaller and lighter sensors possible, which helps car designers achieve more compact designs and better gas mileage.     

 

Engineers working on autonomous driving and sensing technologies contribute to making our roads much safer while saving more lives. They are real super heroes working behind the scene to help save more than 90% of the 1.2 million people killed in car accidents every year. These engineers save more people than Superman, Batman, or Wonder Woman, and their ‘weapons of choice” are accurate test and measurement solutions – critical tools to make sure their life-saving projects work just perfect. 

 

automotive application

 

Follow theAutomotive and Energy Solutions blog today and gain insight from our solution experts as they share their experiences, opinions and measurement tips on Connected Car, Automotive Radar, electric vehicle and more.

 

About Keysight Automotive & Energy Solutions (AES)

The automotive and energy industries are synergistically paving the way for a future built on digital transformation and the electrification of everything. Engineers like you are pioneering this electrical revolution through the development of smarter, safer, and more efficient technologies; whether driving the latest advancements in communications for the Connected Car, creating new power electronics designs to facilitate renewable energy integration and vehicle electrification, or pressing forth in the quest to economize next-generation battery energy storage systems (BESS).

Keysight AES is committed to addressing the biggest design and test challenges faced by engineers in the automotive and energy industries. With fully-integrated solutions combining leading-edge hardware and ultra-sophisticated software, Keysight AES removes technological barriers and streamlines innovation, helping to bring your breakthroughs to market faster, cheaper, and easier. These range from powerful solar array simulator platforms for rapid optimization of modern photovoltaic (PV) system designs, to highly-efficient regenerative hybrid-electric/electric vehicle (HEV/EV) test systems for putting onboard power converters through their paces, and revolutionary battery performance characterization and high-volume Li-Ion cell production solutions for unprecedented savings of time and space; not forgetting advanced power circuit simulator tools to ensure seamless integration of new, state-of-the-art wide bandgap (WBG) devices.

Follow the Keysight AES blog to stay tuned-in on the latest insights from thought leaders in design and test for automotive and energy applications.

When we think about an RC time constant, also known as  (tau) the first thing that usually comes to mind is its relevance to filters. But when it comes to super capacitors it has a related, but somewhat different connotation. Let me explain.

 

One of the things we learn about early on in electrical engineering about the RC time constant is its relevance to the corner or cutoff frequency of first-order low pass RC filter, as depicted in Figure 1.

 

 RC low pass filter

Figure 1: First order low pass RC filter

 

The cutoff frequency, fc, is the point where the AC signal amplitude is down by 3 dB, as shown in Figure 2. Correspondingly the power is down by 6 dB, which is also referred to as the half-power point. 

 

RC low pass filter response

Figure 2: First order low pass RC filter response

 

Here the cutoff frequency is related to the RC time constant by the expression:

 RC low pass filter response expression

 However, another aspect to consider about an RC time constant is its relevance to the time it takes for the capacitor to be charged and discharged.  This is significant for power and energy applications using capacitors for energy storage. The capacitor’s voltage response for charging is given by the expression:

 Capacitor voltage response expression

 For  (one time constant) the capacitor is charged up to 63.2% of its final voltage. Similarly, when discharging, the capacitor is discharged to 36.8% of its final voltage.

 

So, how is this of significance with a super capacitor? The fastest limiting case for charging and discharging the capacitor is when any external resistance is set to zero. Then the only limiting resistance becomes the internal equivalent series resistance (ESR) of the capacitor. The RC time constant of a capacitor is based on the R being the ESR. For conventional capacitors, the RC time constant will typically be on the order of microseconds to tens of microseconds. However, super capacitors, with capacitance in Farads to hundreds of Farads (or greater) and ESR of milliohms (or less) have RC time constants on the order of a second.  The fastest they can be charged and discharged for power applications is on the order of seconds or slower. This may sound slow compared to more conventional capacitors but this is not what you really want to compare them to. Because of their extremely large capacitance they are useful for energy storage applications. And when you compare them against other means of storing reasonable amounts of energy, like cells and batteries for example, they are then extremely fast. This makes them ideal for an application needing to store and/or deliver quick surges of energy, such as is the case of regenerative braking or high power RF amplifiers that transmit in bursts.

 

In comparison to electrical cells, super capacitors are considered to have high power density but low energy density, while cells and batteries have low power density but high energy density. Super capacitors and cells are at times connected in parallel, as they can nicely complement one another in then being able to provide high peak power and a lot of energy over time.

 

So now when you see the RC time constant, or tau, being used about super capacitors, you now know it’s meant as a figure of merit for how quickly they can be charged and discharged!

 

About Keysight Automotive & Energy Solutions (AES)

The automotive and energy industries are synergistically paving the way for a future built on digital transformation and the electrification of everything. Engineers like you are pioneering this electrical revolution through the development of smarter, safer, and more efficient technologies; whether driving the latest advancements in communications for the Connected Car, creating new power electronics designs to facilitate renewable energy integration and vehicle electrification, or pressing forth in the quest to economize next-generation battery energy storage systems (BESS).

Keysight AES is committed to addressing the biggest design and test challenges faced by engineers in the automotive and energy industries. With fully-integrated solutions combining leading-edge hardware and ultra-sophisticated software, Keysight AES removes technological barriers and streamlines innovation, helping to bring your breakthroughs to market faster, cheaper, and easier. These range from powerful solar array simulator platforms for rapid optimization of modern photovoltaic (PV) system designs, to highly-efficient regenerative hybrid-electric/electric vehicle (HEV/EV) test systems for putting onboard power converters through their paces, and revolutionary battery performance characterization and high-volume Li-Ion cell production solutions for unprecedented savings of time and space; not forgetting advanced power circuit simulator tools to ensure seamless integration of new, state-of-the-art wide bandgap (WBG) devices.

Follow the Keysight AES blog to stay tuned-in on the latest insights from thought leaders in design and test for automotive and energy applications.

In my earlier post, I shared the exciting emergence of new possibilities as 5G and automotive technologies merge.

Among the use cases, what interests me most is how to reduce latency in network communications. Currently, the latency on a 4G network varies from 30 - 100 ms, half of that of a 3G network. Developers are now working towards 1 ms latency for 5G – and that is critical to realize the ubiquitous mobile network for highly automated or autonomous vehicles.

 

So why the need for speed? Low latency doesn’t mean we are speeding up our lives – it’s about minimizing the transit time between end-points in a communications system.  

 

There are any number of use cases where improved latency will benefit future commuters using autonomous vehicles. These include:

  • How autonomous vehicles operate and react to potentially dangerous situations such as maneuvers at junctions or merging lanes.
  • Collision prevention in the event of a traffic light system failure.
  • High-density platooning for space and fuel economy.

 

As an example, my car is driving along and the car in front of me suddenly puts on the breaks for some reason – whether there is a stalled truck in front of it, an accident ahead or roadworks.

 

My car will get a message from the car in front of me, or even from two or more cars ahead that can automatically trigger the brakes on my car or take evasive action – fully automatically and safely. This can only be achieved if the time taken for the ‘safety message’ from the cars ahead get to my cars on-board systems in time to take the evasive action! Ultra-low latency will ensure the time-critical information is transferred well in advance of dangerous situations.

 

In another scenario, current detection technologies depend on cameras or radar and work better for detection within a line or radius of detection. To enable better accident-prevention capabilities, if there is something around the corner, the on-board detection system in my car must be able to broadcast those hidden potential dangers to me. Lower latency means minimizing the time for sending that critical message from one car to the other, and triggering a cohesive series of automated safety measures, like putting on the brakes hard, pre-tensioning the seat belts, to avoid, or prepare for an imminent crash.

 

Human errors have been documented to account for some 90 percent of all fatal accidents, but each time there is a serious or fatal crash involving a self-driving car, public trust in this technology suffers and the automotive industry are aware of this. That is why consumer confidence in this technology is paramount, and every device and connection must be tested thoroughly before being released to market.

 

The experienced human driver relies so much on familiarity, habits and instincts without consciously thinking about how to drive, but there is a lot going on in our heads! The car of the not-too-distant future will need to be able to replicate and improve on our driving ability at the same time as perform efficient navigation from A to B.

 

The key challenges for automotive engineers and designers is to design in enough capability and performance to carry out all of these tasks safely, in comfort and at acceptable speeds (consumers would not be willing to have an ultra-cautious vehicle that will travel in relative safety but can only travel at walking speed!)… all within time and cost budgets.

 

Processing speed, power and data volume are increasing all the time, with new design and test requirements.

 

Data from sensors, V2X systems, road conditions and navigations all must be processed concurrently with minimum latency, with on-board systems and artificial intelligence to process the information and predict the possibilities moving forward.

 

Keysight’s task is to focus on improving automotive wireless connectivity, be it cellular connection or DSRC using 802.11p standards. For 5G cellular V2X, Keysight is working with stakeholders to identify how best to test each channel, measure each connection’s accessibility, latency, message decodes, and overall performance. This is where we can contribute our experience to test and evaluate use cases with our rich experience in the following connected car and radar test technologies:  

 

If you have any insights to share with our readers on your best practices for latency reduction use cases, I welcome you to share your comments here.

 

About Keysight Automotive & Energy Solutions (AES)

The automotive and energy industries are synergistically paving the way for a future built on digital transformation and the electrification of everything. Engineers like you are pioneering this electrical revolution through the development of smarter, safer, and more efficient technologies; whether driving the latest advancements in communications for the Connected Car, creating new power electronics designs to facilitate renewable energy integration and vehicle electrification, or pressing forth in the quest to economize next-generation battery energy storage systems (BESS).

Keysight AES is committed to addressing the biggest design and test challenges faced by engineers in the automotive and energy industries. With fully-integrated solutions combining leading-edge hardware and ultra-sophisticated software, Keysight AES removes technological barriers and streamlines innovation, helping to bring your breakthroughs to market faster, cheaper, and easier. These range from powerful solar array simulator platforms for rapid optimization of modern photovoltaic (PV) system designs, to highly-efficient regenerative hybrid-electric/electric vehicle (HEV/EV) test systems for putting onboard power converters through their paces, and revolutionary battery performance characterization and high-volume Li-Ion cell production solutions for unprecedented savings of time and space; not forgetting advanced power circuit simulator tools to ensure seamless integration of new, state-of-the-art wide bandgap (WBG) devices.

Follow the Keysight AES blog to stay tuned-in on the latest insights from thought leaders in design and test for automotive and energy applications.

There are a variety of electrical disturbance tests for conducting validation tests on automotive electronic devices defined by ISO-7637-2 and ISO-16750-2, and a variety of other comparable standards. Based on the latest revisions of these two standards, ISO-7637-2 incorporates disturbances mostly with very high speed rise or fall times of nanoseconds to microseconds, while ISO-16750-2 incorporates electrical disturbances having relatively slow rise and fall times, on the order of a 1 millisecond, in comparison. From my previous posting I had illustrated how several the electrical disturbances defined in ISO-16750-2 can be quickly and easily implemented.

 

The ISO-7637-2 disturbances having fast rise and fall times are primarily a result of voltage spikes created by switching inductive devices on and off, or electrical devices creating a stream of voltage spikes while active. One exception is test pulse 2b of ISO-7637-2 section 5.6.2, which addresses the electrical disturbance created by an electrical motor, like that of a blower motor within the heating and air conditioning system. When the motor is running and then the ignition is switched off, the motor will change over from consuming power to generating a relatively slow voltage pulse back onto the electrical system, until all the energy from its spinning mass is dissipated. Test pulse 2b is depicted in Figure 1.

 

 

Figure 1: Test pulse 2b of ISO-7637 section 5.6.2

 

For this particular test pulse the standard recommends using an arbitrary waveform generator driving a DC power supply/amplifier with an analog control input. This is sensible given its complex shape, consisting of a step drop followed by the motor regeneration energy pulse. To simplify the set-up here, a Keysight N7951A 20V, 50A, 1KW Advance Power System power supply was chosen, as it already has arbitrary waveform generation capabilities built in, negating the need for the separate arbitrary waveform generator. The N7900A series APS is depicted in Figure 2.

 

 

Figure 2: N7900A series Advanced Power System, 1KW and 2KW models

 

While the step portion of test pulse 2b is easy to define and generate, how does one define the motor regeneration pulse? There are a number of possible approaches:

  • A piecewise linear model can be constructed to approximate the shape.
  • Alternately, software tools are available that can generate a data file of points from a graphical image of the waveform.
  • Finally, there is a mathematical expression that defines this waveform, referred to as a double exponential, which can be utilized once it is understood how to do so. 

A double exponential is basically the difference of two exponentials having different time constants, as shown in the expression:

 

UDE = UA(e-K1t – e-K2t)

 

Where UA is the electrical system (alternator) voltage, K1 is the slow time constant related to td, the duration of the test pulse, and K2 is the fast time constant related to tr, the rise time of the test pulse.

 

The trick of making use of this mathematical expression is to figure out how to relate the constants in the expression to the test pulse values shown in Figure 1. It turns out that this is relatively straight forward for this application due to the large relative difference between test pulse 2b’s rise and duration times. The time constant for the slow exponential related to the duration time can be defined as:

 

K1 = (2.303/td) Where td is the duration time (for the 100% to 10% transition)

 

 

While the time constant for the fast exponential related to the rise time can be defined as:

 

K2 = (2.197/tr) Where tr is the rise time (for the 10% to 90% transition)

 

The important thing here is that this is valid for when td >> tr. As the ratio of the two times lessens then there is more interaction between the two exponentials, requiring some compensation be made, primarily adjusting for some loss in amplitude. The resulting exponential and double exponential waveforms are shown in Figure 3 using the double exponential expression, based on using the rise and duration times, and amplitude value given in Figure 1 for test pulse 2b.

 

 

Figure 3: Exponential and double exponential waveforms for implementing ISO 7637-2 test pulse 2b

 

To actually generate test pulse 2b, the arbitrary waveform generation and editing capabilities in the Keysight 14585A software were used to put together a sequence consisting of a voltage step followed by the double exponential we just mathematically defined. The 14585A is a companion software package used to set up and run the ARB, and then retrieve back, display and analyze measurements from the N7900A series Advanced Power System. The resulting test pulse waveform was run, captured and displayed in the 14585A’s scope mode, shown in Figure 4.

 

 

Figure 4: Test pulse 2b of ISO 7637-2 generated and captured using 14585A software

 

In closing, test pulse 2b automotive electrical disturbance in the ISO 7637-2 standard can be easily generated based on the mathematical expression for a double exponential waveform. It just a matter of understanding  the relation between the test pulse’s rise and duration times, and the double exponential waveform expression’s time constants, as we have shown here!

 

About Keysight Automotive & Energy Solutions (AES)

The automotive and energy industries are synergistically paving the way for a future built on digital transformation and the electrification of everything. Engineers like you are pioneering this electrical revolution through the development of smarter, safer, and more efficient technologies; whether driving the latest advancements in communications for the Connected Car, creating new power electronics designs to facilitate renewable energy integration and vehicle electrification, or pressing forth in the quest to economize next-generation battery energy storage systems (BESS).

Keysight AES is committed to addressing the biggest design and test challenges faced by engineers in the automotive and energy industries. With fully-integrated solutions combining leading-edge hardware and ultra-sophisticated software, Keysight AES removes technological barriers and streamlines innovation, helping to bring your breakthroughs to market faster, cheaper, and easier. These range from powerful solar array simulator platforms for rapid optimization of modern photovoltaic (PV) system designs, to highly-efficient regenerative hybrid-electric/electric vehicle (HEV/EV) test systems for putting onboard power converters through their paces, and revolutionary battery performance characterization and high-volume Li-Ion cell production solutions for unprecedented savings of time and space; not forgetting advanced power circuit simulator tools to ensure seamless integration of new, state-of-the-art wide bandgap (WBG) devices.

Follow the Keysight AES blog to stay tuned-in on the latest insights from thought leaders in design and test for automotive and energy applications.

Earlier this year, I had the honor of being selected to represent our company for the 5G Automotive Association (5GAA). Having spent a good part of my 30-year career with HP/Agilent/Keysight in the mobile wireless industry, I must admit, this is a very exciting opportunity to be linked directly into the evolution of cellular technology and how it will shape the future of automotive connectivity, autonomous vehicles and enhanced safety.

 

I attended the 5GAA’s first F2F meeting in Barcelona in February with 120 members representing different industries and organizations from many different countries. A friend jokingly likened it to being invited to an unexpected marriage of two celebrities.

 

For the combined 5GAA technology, there is one side consisting of the Network Operators with their up and coming technology star, 5G, promising superfast broadband speeds, ubiquitous connectivity, seamless multiple applications and superb performance. On the other side there is the automotive industry, which is already hogging the limelight, igniting the imagination of engineers worldwide designing the next slew of Connected Car experiences, from ADAS to self-driving and even flying cars!

 

In this 5G-AA ‘marriage,’ clan members all have an active hand in shaping the new ‘baby’ which is described by the 5GAA Mission Statement –

 

Develop, test and promote communications solutions, initiate their standardization and accelerate their commercial availability and global market penetration to address society’s connected mobility and road safety needs with applications such as autonomous driving, ubiquitous access to services and integration into smart city and intelligent transportation.

 

5GAA has set up five working groups (WGs) and have adopted 3GPP procedures to deliver on the above Mission Statement :

  • WG1 - Determines use cases & technical requirements. An area of focus is addressing latency issues in cellular-V2X communication.
  • WG2 – Once WG1 outlines the use cases and technical requirements, this group will define the system architecture and solutions, such as what network architecture is required to achieve 1 ms end-to-end latency for cellular-V2X.
  • WG3 – Next comes evaluation, testbeds and pilots, and the challenge of figuring out how to test these architectures or devices, to make sure the device performance meets requirements.
  • WG4 – Members in this work group focus on defining the standards and spectra associated with the Connected Car ecosystem, and interoperability with other platforms such as 3GPP.
  • WG5 – Technology is only as viable as its adoption by people. So WG5 focuses on business models, go-to market, and how to maximize the benefits of Cellular- V2X (V2V, V2C, V2I, V2N) to promote safety and enhanced driving experience.

 

From a Keysight point of view, there is a lot of experience to offer across the workgroups and we will be able to make significant contributions to the organization. For example, we can leverage our aerospace & defense and communication experiences as 5G moves us into mmW frequencies with challenging performance targets for reliability and latency. We also have expertise in developing and testing new radio architectures, modems, wireless interfaces and antenna systems, all of which are going to be re-engineered for 5G and even subsequent releases of 4G LTE/LTE-A will require additional testing and verification.

 

Keysight is in an excellent position to contribute and even accelerate the vision of 5GAA to deliver on its Mission Statement. It's a chance to change the world and make it a safer place. How much more exciting can being a part of these new technologies and their advancements get? 

 

In my next blog, I will address one of my pet topics, latency and its impact on safety. Till then, thank you for visiting my blog and here’s a message about 5GAA that I gave at MWC in Barcelona : https://youtu.be/7H1vFgAEhSs

 

Follow the Automotive and Energy Solutions blog today and gain insight from our solution experts as they share their experiences, opinions and measurement tips on Connected Car, Automotive Radar, electric vehicle and more.

 

About Keysight’s Automotive & Energy Solutions

 

The world is seeing a rapid convergence of automotive and energy technologies for safer, better energy efficient, and more convenient driving. Engineers like you are the drivers, designing the latest automotive Ethernet, radar, 802.11p and 4G/5G applications in the Connected Car, or pressing forth in the quest for greater energy efficiency in tapping solar power, conversion and storage for vehicle electrification including Electric Vehicle (EV) and Hybrid EV.

 

Keysight is committed to help bring your vehicle electrification innovations to market faster with our design and test solutions in this energy efficiency ecosystem. These range from powerful solar array simulator solutions to maximize your PV efficiency, to EV test for vehicle electrification innovations, and time and space-saving revolutionary Li-Ion cell and battery performance test solutions; not forgetting power circuit simulator tools to ensure the power devices behind all these innovations work seamlessly.

 

Stay tuned for the latest technology thought leadership in the automotive and energy efficiency space by following our blog.

ISO 16750 is an international standard for environmental testing of the electrical and electronic equipment in road vehicles. ISO 16750-2 (i.e. part 2) addresses electrical loads. Basically, this describes a series of tests depicting subjecting automotive electrical and electronic equipment to variety of standardized electrical disturbances that exist within the electrical system of an automobile. These disturbances are a variety of transient events, some due to normal operation while others are caused by fault conditions within the electrical system. Either way the electrical and electronic equipment must be able to withstand these disturbances.

 

The electrical disturbance tests in ISO 16750-2 tend to be complex waveforms in most cases having relatively low speed rise and fall times of about a millisecond or slower, created by events such as the crank starting of the engine, for example. In comparison, the standardized tests in ISO 7637-2 tend to be electrical disturbances that are less complex but having much faster rise and fall times, down to nanosecond levels in some cases. This is because ISO 7637-2 tests are typically based on events such as switching inductive loads which results in very fast inductive voltage spikes.

 

There are several challenges with conducting the ISO 16750-2 tests. One challenge is recreating the complexity of many of the electrical disturbances. This calls for the solution to have an arbitrary waveform generator (ARB) as the basis for the signal generation and then a suitable power stage to bring the signal up to appropriate voltage, current, and power levels needed to both power the device under test (DUT) as well as drive the levels considerably higher in many cases, to apply the overload disturbance described by the test standard. Yet mores challenges are good ways to easily create, save, recall, and modify these electrical disturbances as required without having to resort to extensive programming to do so.

 

It turns out the Keysight N7900A Advance Power System (APS) is very well suited for is performing a variety of automotive electrical disturbances. This is due to its higher power output of 1 or 2 KW depending on model, provide greater power when paralleled, relatively fast output slew rates, and ability to store and run 64,000-point ARB waveforms. The N7900A APS family pictured in Figure 1.

 

Keysight N7900A Advanced Power System family

Figure 1: Keysight N7900A Advanced Power System, 1 and 2 KW family

 

Complementing the N7900A APS family is the 14585A Control and Analysis software, with enables the user to create and manage complex waveforms and disturbances that can be run on the N7900A APS without needing to perform any programming. The 14585A has a comprehensive library of ARB waveforms, lets you import and edit ARB files (for example, you capture an actual crank waveform profile with an oscilloscope), as well as create a mathematical expression for an ARB waveform. On top of that individual ARB waveforms can be tied together to create larger, much more complex ARB sequences. This is excellent for creating a variety of automotive electrical disturbances. Together they form an excellent solution for implementing and running many of the electrical disturbances defined by ISO 16750-2.

 

To see how well I could do on implementing several electrical disturbances defined in ISO 16750-2, I figured I would start with something easy and work my way up to more challenging ones from there. The first one was “4.5.1 Momentary drop in supply voltage”. This simulates a 0.1 second drop due to an electrical load suddenly short-circuiting followed by its fuse blowing open. In this case I used the pre-defined pulse ARB waveform from the 14585A library, set up in the 14545A ARB configuration screen as shown in Figure 2.

  

 Drop out test set up

Figure 2: Setting up ISO 16750-2 4.5.1 Momentary Drop in Supply Voltage in 14585A Software

 

The standard calls for under 10 milliseconds fall and rise times. The N7951A 20 volt APS provided about 0.4 millisecond fall and rise times and I was able to also use the slew control to set it slower if I desired.  Alternately I could have used ramp ARBs and enter the ramp times there. The resulting momentary drop was captured in the 14585A’s oscilloscope mode of operation, shown in Figure 3. This solution lets you verify your waveform is what you were expecting to get with the digitizing readback built into the N7900A, instead of having to connect up a separate oscilloscope.

  

 Drop out test result

Figure 3: Capturing ISO 16750-2 4.5.1 Momentary Drop in Supply Voltage in 14585A Software

 

Next I decided to see how well I could do with implementing “4.5.2 Reset behavior at voltage drop”. This consists of a series of 5 second-long voltage drops spaced 10 seconds apart, increasing by an additional 5% drop in amplitude each time. This tests the DUT to see at what voltage drop level it takes to cause the DUT to reset due to low voltage. For this I linked 20 voltage drop ARB waveforms together in a longer sequence, in the 14585A software. Due to the longer duration, the results of running this ARB sequence were instead captured in the 14585A’s data logging measurement mode, shown in Figure 4.

 

Reset test result 

Figure 4: Capturing ISO 16750-2 4.5.2 Reset Behavior at Voltage Drop in 14585A Software

 

OK, I think I am up for a bigger challenge, and the ISO 16750-2 ”4.5.3 starting profile” looked to be just right to take on. This is a combination of a series of voltage ramps slewing from milliseconds to 10’s of milliseconds at the beginning and end with a seconds-long period of a sine wave superimposed on DC embedded in the middle, to emulate the actual steady-state cranking portion.  As there are multiple versions of this starting profile, I selected one with an extended cranking period, as I figured that one would be the more challenging for fast details to be reproduce accurately, due to more memory being dedicated to the extended cranking period. I implemented this in the 14585A ARB generation screen, using a combination of two ramps, a sine wave, and another ramp, as shown in Figure 5.

  

 Start test set up

Figure 5: Setting up ISO 16750-2 4.5.3 Starting Profile in 14585A Software

 

I captured the results of the ISO 16750-2 ”4.5.3 starting profile” I created in the 14585A’s oscilloscope measurement mode, which is shown in Figure 6.

  

 Start test result

Figure 6: Capturing ISO 16750-2 4.5.3 Starting Profile in 14585A Software

 

Overall it appears to be good in Figure 6. The cranking sine wave superimposed on the DC is as it should be. I then expanded the time scale on the captured waveform to check to see if the fast slewing ramps at the beginning and end were also as expected, the beginning transient portion of the profile shown in Figure 7.

  

 Start test result detail

Figure 7: Capturing ISO 16750-2 4.5.3 Starting Profile in 14585A Software, beginning details

 

I was really pleased to see the timing of these milliseconds-long events were spot-on even when being just a small part of a seconds-long total ARB sequence. And because the ARB sequences are constructed with high level models it is an easy matter to make changes as well as quickly construct new or non-standard disturbances. The 14585A software took the challenge out of me trying to manually program these complex arbitrary automotive electrical disturbances.  While I like taking on challenges, with how quick and easy the 14585A software made this task become, in this case I didn’t mind it haven taken most of the challenge out of the task one bit!

 

About Keysight’s Automotive & Energy Solutions

 

The world is seeing a rapid convergence of automotive and energy technologies for safer, better energy efficient, and more convenient driving. Engineers like you are the drivers, designing the latest automotive Ethernet, radar, 802.11p and 4G/5G applications in the Connected Car, or pressing forth in the quest for greater energy efficiency in tapping solar power, conversion and storage for vehicle electrification including Electric Vehicle (EV) and Hybrid EV.

 

Keysight is committed to help bring your vehicle electrification innovations to market faster with our design and test solutions in this energy efficiency ecosystem. These range from powerful solar array simulator solutions to maximize your PV efficiency, to EV test for vehicle electrification innovations, and time and space-saving revolutionary Li-Ion cell and battery performance test solutions; not forgetting power circuit simulator tools to ensure the power devices behind all these innovations work seamlessly.

 

Stay tuned for the latest technology thought leadership in the automotive and energy efficiency space by following our blog.

JungikSuh

That Flying Car Dream

Posted by JungikSuh Employee Mar 28, 2017

As a sci-fi buff when I was a kid, I watched Bruce Willis in the movie The Fifth Element as he navigated his flying yellow cab in 23rd century New York City. I was not only fascinated, but the idea of flying a car fueled my imagination of being able to do that in my lifetime.

 

Well, some 20-years since the movie’s release, that fantasy doesn’t seem so far-fetched in the world which I work in now – as I deal with the latest technology used to design and test advanced automotive electronic technologies including Connected Car, advanced driver assist system, and self-navigating vehicles of the future.

 

Already, companies like AeroMobil are toying with flying cars, which can take off in as short as an 800 ft-meter runway; the car reportedly can hit a top speed of 160 kmph and soar the skies at about 320 kmph.

 

While I wait for the first car pilots to pave the way before I consider putting a down payment for my flying car, I am really excited about the possibilities that autonomous driving and, eventually, self-driving cars have to offer in the pretty foreseeable future.

 

Studies have already indicated that autonomous driving cars can prevent 9 out of every 10 road accidents we are encountering. As the most recent and future technologies like automotive wireless connectivity including 802.11p DSRC, LTE-V at 3GPP Release 14, and soon to come 5G communications, radar sensing, LIDAR, camera, in-vehicle Ethernet, and navigation continue to improve safety and bridge the potential gaps caused by human errors, the automotive world is likely to see greater adoption of self-driving cars. Artificial Intelligence (AI) with deep learning algorithms play more and more roles to improve the autonomous driving system. Currently, AI is “learning” how to improve its driving skills in the same way humans learn how to drive. AI is capable of making a decision whether to just ignore an empty paper box on the road or push a hard break to avoid a crash with a big refrigerator on the highway. Some people still say they don’t want to rely on a computer system or machine to drive their car, but commuters around the world hardly realize many of their rail rides every day are in self-driving trains. Even big jet airliners are controlled by auto-pilot systems, not by human pilots for most of their routes.    

 

That same human-free transportation mode has already made its way into many preliminary programs with self-driving vehicles in specific zones reserved for self-driving vehicles – there is not so much a concern that these self-driving cars cannot deliver their safety promises, but more so, for designers to zoom in on a myriad of potential risks caused by aggressive human drivers.  

 

I know many of us are not quite ready to get to the stage of sipping morning coffee or enjoying happy hour beer and watching a movie while our cars drive us to work or back home, or sending kids off to school in our self-driving vehicle and fetching them back from their soccer practices without parents’ supervision. But wouldn’t it be wonderful if you could enjoy your time while your self-driving car takes your kid to soccer practice rather than just sitting in the car at a parking lot of a soccer field?

 

Just musing over the endless possibilities, that sci-fi dreamer in me would like to design a light-weight driverless pizza delivery pod on wheels – making piping hot pizzas on its way to the customers as it self-navigates, and deftly avoids a tipsy driver, or slows down way ahead of a neighborhood with kids on skateboards. Even in the event that an excited dog might cross the car’s path at the very last minute, my self-sacrificing pod is designed to fall apart, with its super light-weight fiberglass materials causing no harm to any living creature. In fact, the furry friend might even enjoy the bolognaise sauce on the pizza.

 

These are exciting times, and I am glad I am in the thick of it, working alongside some of today’s best engineers dedicated to creating a safer autonomous and self-driving automotive ecosystem with technologies for environment-friendly electric vehicles, superb powertrain, body and security features, advanced driver assistance systems, and even nanoscale automotive research.

 

My next posting will include more details of the enabling technologies for autonomous driving. Here is a teaser of my next posting: Why does radar technology require a wider bandwidth for autonomous driving development? As you can see from the figure below, a 1 GHz bandwidth radar signal can’t clearly separate two objects on the road. However, a 4 GHz wide bandwidth radar signal shows two separated objects. It is definitely clear that engineers who develop a safer system should use wider bandwidth radar systems.

 

Follow the Automotive and Energy Solutions blog today and gain insight from our solution experts as they share their experiences, opinions and measurement tips on Connected Car, Automotive Radar, electric vehicle and more. 

In part 1 (click here to review) of this two-part series on power and energy, I delved into the specifics of what energy is about. To continue on this; what then exactly is power?

 

We learned in part 1 how energy can be increased or extracted from a system by work applied to or derived from that system. Work performed changed the energy level in that system. But how long a period was that work performed over? Perhaps it was performed over a minute, a day or a year? Power is a measure of the rate of which work is performed and energy added or removed from a system.

 

Average power = work performed / interval of time

 

When we hear the word power the thing that might come to the mind most often is the horsepower one’s car has (OK, let me preface that with the mind of most auto enthusiasts!).  While most commonly used to refer to mechanical systems, horsepower is still power, just the same as the electrical power we get from the electrical outlets in our homes, which is also power.

 

Back in the early days of heat engines James Watt developed the term horsepower as means to compare his steam engines to the rate of work a horse could produce.  Mechanical work is the measure of a force (pounds) moved through a distance (feet). A horse was judged to be able to move 550 foot-pounds in one second, or produce 550 foot-pounds per second of power

 

Electrical power is also a measure of work performed per unit of time. In this case however it is moving an electrical charge of one coulomb against a potential of one volt in one second. Note also that one ampere equals one coulomb per second. One unit of electrical power equals one watt (in honor of James Watt!).  To summarize:

               

P (watts) = Q (coulombs) * V (volts) / t (seconds) = I (amps) * V (volts)

 

Recall in part 1 how energy was measured in watt-seconds and kilowatt-hours. Divide by time interval it is used over and it becomes power in watts and kilowatts! So how are mechanical and electrical power related? Well when electrical motors came onto the scene it was necessary to relate the work they could do to that of heat engines which were rated in horsepower, where one horsepower is equal to 550 foot-pounds/ second. It was determined that a 100% efficient motor required 746 watts of electrical power to produce one horsepower of mechanical power. Note that this horsepower rating is based on English mechanical terms. The measure of horsepower based on metric terms ends up being slightly different; about 735 watts instead.

 

So you can just as easily state the power consumption of your electrical appliances in terms of horsepower as you can watts. Conversely, you can also state the power-generating rating of your automobile’s engine in watts (or kilowatts) instead of horsepower, and this is actually more often practiced nowadays, as the measure of a watt is recognized world-wide, while horsepower is not.

Some may wonder why I am discussing these various terms and how they interrelate. Having a good understanding of these basics is necessary in the area of energy and power conversion, and indeed even more complex problems can usually be understood by breaking them down to the basics.

 

About Keysight’s Automotive & Energy Solutions

 

The world is seeing a rapid convergence of automotive and energy technologies for safer, better energy efficient, and more convenient driving. Engineers like you are the drivers, designing the latest automotive Ethernet, radar, 802.11p and 4G/5G applications in the Connected Car, or pressing forth in the quest for greater energy efficiency in tapping solar power, conversion and storage for vehicle electrification including Electric Vehicle (EV) and Hybrid EV.

 

Keysight is committed to help bring your vehicle electrification innovations to market faster with our design and test solutions in this energy efficiency ecosystem. These range from powerful solar array simulator solutions to maximize your PV efficiency, to EV test for vehicle electrification innovations, and time and space-saving revolutionary Li-Ion cell and battery performance test solutions; not forgetting power circuit simulator tools to ensure the power devices behind all these innovations work seamlessly.

 

Stay tuned for the latest technology thought leadership in the automotive and energy efficiency space by following our blog.