Imagine eliminating 100 wires and 200 electrical connections from every electric vehicle (EV). As we know, the impact on cost, weight, labor, packaging space, and the resulting critical reliability may change the driving experience of electric vehicles.
There are multiple ways to achieve these goals by making different levels of modifications to the battery management system (BMS), depending on the level of improvement required and the degree of openness to BMS changes. Industry innovators are developing new technologies that can eliminate 100 wires. They can also collect cell data for preventive maintenance and warranty analysis, detect unmatched cells before irreversible damage to the entire battery pack, and determine the internal Temperature and collect information to accurately calculate the state by simply measuring the charge (SOC). With this new technology, in addition to improving reliability and safety, it can also increase the range and overall performance of electric vehicles. Baseline system
Battery Sensing Electronics (CSE) is usually located on a separate printed circuit board assembly (PCBA) close to each battery module. The CSE includes a multi-cell battery module integrated circuit (BMIC) on each PCBA. The CSE is connected to the battery connection board (CCB) through a wire harness. Nowadays, some automakers and Tier 1 suppliers are installing CSE PCBA on or near the module to minimize the length of the wire harness.
Others may place a harness "pigtail" on the CCB, thereby eliminating half of the harness connectors (instead of the baseline case). However, in both cases, there are usually about 100 voltage detection lines.
There are several ways to improve the baseline system. The following four concepts provide completely different methods to eliminate 100 voltage sensing lines in an EV. A solution can provide lower cost without compromising performance. Although the cost of another solution has increased, and the cost of two of these solutions has increased, it provides the next logical step in terms of battery data tracking and performance.
Improved solution #1: Integrate cell sensing electronics into the cell connection board
Achieve significant returns with minimal modifications. Eliminate 100 voltage sensing lines without changing battery sensing electronics, BMS changes, or circuit cost increase. The solution involves moving the entire CSE PCBA onto the CCB itself, and directly soldering the CSE PCBA with a multi-cell battery module integrated circuit (BMIC) to the CCB to form an embedded battery sensing circuit (eCSC), thereby eliminating approximately 100 analog voltage sensing wires and 30 analog temperature sensing wires from the battery pack.
Moving the entire CSE PCBA to the CCB itself can eliminate the cost of analog sensing wires and connectors, but without the need to change the BMS hardware or software. Simply put, it is packaging efficiency. This option also helps meet the long-term safety challenges of electric vehicles. Eliminating the voltage sensing wires carrying analog signals eliminates the possibility of high-voltage electrical short circuits with these wires. In a baseline CSE system with analog voltage sensing lines, most of the voltage sensing lines passing between CSE and CCB are in a high voltage state. The voltage of the last wire in the battery series is approximately 400 V. The size of the voltage sensing wire cannot carry a large amount of current, so if it is short-circuited to ground, the result is an accidental fuse and a potential fire hazard. Since the analog voltage sensing signal from each battery is converted into a digital signal by the multi-battery ASIC, there are only two to four digital sensing lines per module. The digital signal does not have the same safety hazard as the high-voltage analog voltage sensing line, because the digital signal is electrically isolated.
Significant returns are achieved through additional changes and some additional costs. Eliminate 100 voltage sensing lines and change the unit sensing electronics and BMS. There is some increased circuit cost.
The world's major chip suppliers are preparing to launch a wireless system on chip (SOC) to ensure the safe and reliable communication of important data on the battery in the car. This wireless communication must meet the automotive safety integrity level D requirements (ASIL D) required by ISO 26262. This method has resulted in the battery pack being reduced by approximately 100 analog voltage lines and 30 analog temperature sensing lines, but it needs to be modified with CSE PCBA and BMS hardware. Implementing this solution has both challenges and compromises, such as the cost of SOC chips. Considerations such as the ability to ensure a high signal-to-noise ratio in high-current noisy environments and a clear wireless signal path to the BMS are also critical. However, the benefits include complete flexibility in module placement (within wireless range) and galvanic isolation.
Similar to Improved Solution #1, the elimination of analog voltage sensing lines eliminates the possibility of high-voltage electrical short circuits. However, in this case, the elimination of the analog sensing line is achieved by using wireless technology.
Improved solution #3: Electrochemical Impedance Spectroscopy (EIS)
Get greater returns and get detailed battery data, and add additional changes and costs. Eliminate 100 voltage sensing wires and make necessary changes to battery sensing electronics and BMS. Further increase the circuit cost.
Several manufacturers have developed new battery monitoring IC (BMIC) chips, measurement algorithms, and software that can use electrochemical impedance spectroscopy (EIS) to measure the impedance of each battery. Unlike other systems that may only be able to determine the battery resistance when charging at a charging station, this impedance measurement can be performed while driving the vehicle. Some of these measurements can be taken during acceleration (discharge), constant speed (discharge), and deceleration (regenerative braking, charging), while others are best taken when the vehicle is stationary. Factors such as battery temperature, SOC, and state of health (SOH) can
Unparalleled degradation of the battery caused by BMS and by production changes, uneven heating or cooling of the battery, internal short circuit or other problems. Unparalleled battery degradation is critical, because the viability of lithium-ion battery packs depends on the worst battery. This data can also be used to prepare car battery packs for secondary use. The data of each battery can be recorded from the cradle to the grave, so it is possible to understand the history of each battery in depth, and provide sustainable development benefits for battery manufacturers, module manufacturers, original equipment manufacturers and consumers.
In order to use EIS measurements, cell characteristics need to be correlated with EIS data. For example, the internal battery temperature dependency needs to be measured and calibrated before putting it into the battery pack. In the calibration process, the phase shift of the AC disturbance of the impedance spectrum is calibrated to form the relationship between the battery temperature and the phase shift. A fitting function is then used to correlate the phase shift with the temperature. Similarly, the SOC of each battery can be correlated with the data of the shunt from the result of impedance measurement (the amplitude of the AC voltage). This information is very valuable for improving the user experience of OEMs and vehicle users. Some advantages are listed below:
Similar to the improved solution #1, the analog voltage sensing signal from each battery is converted into a digital signal by ASIC, eliminating 100 analog voltage sensing lines and 30 analog temperature sensing lines, so only two to four are needed Digital voltage and temperature sensing lines can be the entire package. In this case, each unit has an EIS ASIC. Then, the digital voltage detection signal from each ASIC can be daisy-chained between the battery and the battery and between the module and the module, thereby providing two to four voltage detection lines for the entire battery pack. Digital signals do not have the same safety hazards as high-voltage analog voltage sensing lines because they are electrically isolated.
A further benefit of EIS unit data is the ability to perceive physical changes that usually occur before safety events (such as thermal runaway). As mentioned above, EIS data can accurately and quickly determine the temperature inside the battery. This is in contrast to traditional systems that sense bus temperature, which is thermally far away from the actual battery temperature. In addition, EIS battery data can identify battery swelling, which is a physical event that usually occurs before battery exhaust and potential vehicle fire hazards.
These data are not only essential to improve vehicle safety, but such information may be the only reliable way to meet the new United Nations (UN) General Technical Regulation GTR20, which has been adopted as the national standard for electric vehicle batteries in China (GB 38031)- 12), sometimes called the "five-minute rule". In this legislation, BMS needs to notify vehicle occupants five minutes before a potential fire occurs due to battery problems. Similar legislation is being considered in other parts of the world, such as GTR20 and EVS.
As with any system, there are always advantages and disadvantages. The disadvantages of the EIS system are the cost of the ASIC (one for each parallel battery pack), changes in the hardware and software of the BMS system, and the use of EIS to characterize the battery in detail before building the battery pack. However, the aforementioned advantages may greatly outweigh the additional cost and development effort, especially when security is considered. The cost of parts and development can easily be demonstrated by improving the safety of passengers and reducing the risk of potential recalls or safety issues for the company.
Improved solution #4: Electrochemical impedance spectroscopy (EIS) with big data analysis
The greatest return is achieved through further changes and higher costs. Eliminate more than 100 voltage sensing lines, and perform predictive analysis by changing the unit sensing electronics and BMS. Continuously collect cell data to create a data lake, and then use machine learning/artificial intelligence for further analysis.
Like the improved solution #3, the digital signal from EIS ASIC eliminates 100 analog voltage sensing lines and 30 analog temperature sensing lines, so only two to four digital voltage and temperature sensing lines are needed for the entire battery pack . However, in this method, EIS data is used for predictive analysis. By transmitting the data of each vehicle to the data lake, advanced algorithms and machine learning/artificial intelligence (AI) can have stronger processing power and data storage capacity than vehicle-mounted BMS to further process the data. Compared to traditional systems based on beginning of life (BOL) cell data, these insights can improve the safety and driving experience of every vehicle and all vehicles used in a fleet or sold by original equipment manufacturers over time. Becomes less accurate. EIS data sets can be evaluated using historical indicators and compared with all similar vehicles on the road to determine trends and use AI prediction methods to predict system behavior. Therefore, data analysis can inform the BMS to provide warnings to vehicle occupants a few minutes, days, or possibly months before a security incident occurs, easily meeting the five-minute requirement. In addition to the general use of EIS data, the extended benefits of using data analysis and AI include:
Smarter systems for smarter future cars
Leading original equipment manufacturers and automotive suppliers are collaborating to develop advanced electric vehicles with higher reliability, safety, performance and range. The elimination of 100 wires from each electric car shows the potential for improvement that can be used when evaluating future vehicle efficiency technologies. As vehicles continue to evolve to meet the needs of high-demand consumers, safety and performance advantages are the most important, and vehicle systems must also develop as demonstrated by the above four different methods to promote cost savings, reliability, and reliability from concept to production. Safety and performance, and finally, to the showroom floor. Simplified packaging and wiring, advanced ASIC sensors, BMS control, big data analysis, and machine learning/artificial intelligence and other collaborations in a wide range of fields can make intelligence, interconnection, automation, sharing, and the overall performance of electric vehicles continue to improve in the future.
Michael Ciaccio serves as Executive Director of Gentherm Advanced Electrification Technology. Ferdinand Minnerrath is a project engineer at Gentherm.