Test challenges and trends for battery management
Battery cell stacks and battery management systems (BMS) are already in use everywhere from power tools, vacuum cleaner robots and drones to micromobility applications like eScooters and eBikes. Less flashy things like uninterruptible power supplies (UPS) and renewable energy storage require large numbers of battery cells. Each battery stack must be monitored to ensure that it is charged and discharged safely, and to measure the overall health of the battery. With rechargeable batteries, there are some challenges that require testing for very accurate voltage values. In addition, batteries are tested in a stack, which requires precision measurements at high common mode voltages. The trend is to add more cells to battery stacks to achieve higher voltage systems.
How battery management systems work
A BMS device typically consists of a series of cell sensing probes (12 to 24 or more channels) that feed into a front-end analog-to-digital converter (ADC). This ADC measures cell voltage and provides precise measurement of individual battery cells. There is an additional pair of cell balancing pins per cell that also have ADC inputs. These pins are used to equalize the voltages between cells in the stack. The remaining pins are power supply pins and analog and digital control lines. Each cell stack in a battery requires a BMS, so in some cases there may be six to twelve or more BMS devices in a single electric vehicle, not counting devices that serve redundancy. These devices are typically powered from the lower and upper rails in each cell module, so that each BMS device floats on top of the BMS or cell module below it. This means that all of these devices require digitally isolated, daisy-chained communication with each other and with a main controller, usually a microcontroller unit (MCU).
Electric vehicles will drive BMS advances
The largest future market for battery systems, which is the focus of this article, is automotive applications. These include (see Figure 1) fully battery electric vehicles (BEVs, 400 volts and above) and internal combustion engines (ICE) with start-stop technology (typically 48-volt systems) and both mild-hybrid (48-volt battery-powered) and hybrid EVs. In 2022, less than 5% of new vehicles sold were electric, but many automakers expect the share of electric vehicles to increase to 50% by 2030. With this in mind, electric vehicle technology is one of the fastest growing markets for many semiconductor manufacturers.
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In order to drive the adoption of electric vehicles, several key requirements must be met. The first of these is the need for a higher quality BMS that impacts range. With a more accurate battery management system, consumers can achieve greater range from the same set of batteries. For example, if the BMS can measure the state of charge with an accuracy of 1% or more, the battery can be charged closer to its maximum capacity. Think of it like a guard band - with a 5% margin of error, the battery should only be used between 15% and 85% of its capacity. When the BMS is more accurate, the usable charge of the battery does not need to be monitored as much. So if you go from 5% error to 1% error, you can use 8% more of the stored charge, resulting in more miles per charge. See Figure 2 for more details.
Figure 2: Battery charge limits
Second, a precise state of charge (SOC) allows for better battery utilization while maintaining battery safety (avoiding catastrophic failures) and increased range prediction accuracy in terms of safety and reliability. Higher battery utilization and efficiency also enables smaller, lighter battery packs, which reduces vehicle costs.
There are a couple of trends driving changes in electric vehicle batteries. The first, as noted above, is better accuracy, which translates directly into higher mileage between charges and more moderate cell aging. A more accurate "fuel gage" also increases driver safety and confidence.
The second major trend is that battery stacks have increasingly higher voltages, requiring a greater number of cells in a stack, which in turn requires more front-end channels ( ADC ) and cell balancing pins per BMS device. Common battery cells today operate at about 400 volts, with some high-performance electric vehicles already using 800-volt systems. These levels are expected to increase to 1000+ volts in a few years, resulting in faster charging that will help EVs achieve recharge times closer to the time required to refuel an internal combustion engine. These capabilities represent a competitive advantage for semiconductor manufacturers, who can now add value to a battery pack.
However, higher overall voltages mean that more batteries must be packed into the electric car. Currently, manufacturers assemble batteries into modules and then assemble those modules into battery packs. This modularization requires more connections, which drives up the cost and weight of battery packs. The cell-to-pack architecture is a novel approach that places the battery cells directly into the battery pack and avoids the module carrier model. Moving to a cell-to-pack architecture means manufacturers can either fit the same number of cells into a smaller, lighter assembly or fit more cells into the available space. This can lead to more cells in a stack, which in turn leads to more front-end ADC channels on the BMS device.
These trends in battery management systems present new challenges for companies that manufacture automated test equipment (ATE). The first challenge is improved BMS accuracy. When measuring the discharge curve of a battery, most of the usable range is along a very narrow curve. The full Li-ion state-of-charge range (SOC) of 100-0% extends from ~4.3 V (fully charged) down to 2.2 V (discharged). Looking at the full Li-ion range, it seems a simple task to measure the change (~2.1 V voltage range or 21 mV / 1% SOC change).
ATE Testing Challenges
A typical Li-ion discharge ranges from 80-20% or 90-10% of full battery power. In the 80-20% range, the SOC voltage is quite flat at 3.75-3.65 V (~100 mV total or 1.7 mV / 1% SOC change). This explains why BMS vendors aim for measurement accuracy of 100uV or 50uV in the 5-V range. Figure 3 below shows a typical Li-ion discharge voltage curve.
This sub-100uV accuracy level translates directly to the forcing accuracy of the ATE stimulus channel that feeds into the ADC of each cell in a BMS device. While this should be easy to achieve with a low supply voltage, in some test cases this accuracy must be maintained over a range of 5-6V. It is also important to provide this stimulus as a very low noise, very low drift stimulus so that a fixed, known voltage biases the ADC input.
The second challenge is that the BMS device may consist of 16 to 24 or more cells that must be biased with common mode voltages to mimic a stack of battery cells. In some cases, this can exceed 120 volts for some of the top cell pins. This creates a need for dense, high-precision, low-noise floating voltage/current sources (VIs) from ATE. Due to the test nature of these devices, it is usually necessary to multiplex a high-precision measurement system onto all cell pins and the corresponding discharge pins of the device. This may require a large number of relays on the device interface board (DIB) if the ATE system does not have sufficient internal matrices, resulting in the need for a very large application area on the DIB. In most cases, semiconductor manufacturers want to test as many devices as possible in parallel to reduce test costs. For BMS devices, this is also important due to the extremely high volumes that BEVs are expected to require in the near future. The ideal ATE system should have all the matrices and multiplexing already built into the system to allow for the maximum number of sites.
Another challenge is daisy-chain communication between individual BMS devices and the MCU. All BMS devices send data over the bus. Since this bus is located inside the battery cell modules or cell pack, it can create a very noisy environment. This also happens with stacked voltages, and this requires an isolated interface. This isolated communication usually runs asynchronously and requires sufficient capacity of the digital instrument to handle the unique pattern generation. Some key features needed in this case at ATE are format and period switching on the fly, and the ability of the digital instrument to read asynchronous data.
Batteries and BMS devices continue to evolve to meet the demands of the automotive market. These improvements require new test methods. From 1000+ voltage systems to new battery chemistries, there will be many challenges in a short time. The explosive growth in electric vehicles will require new test equipment capabilities with short production cycles, so ATE will need to exceed BMS requirements and provide cost-effective solutions for a high number of sites.
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