Imagine eliminating 100 wires and 200 electrical connections from every electric vehicle (EV). The impact to cost, weight, labor, packaging space and resulting critical reliability could transform the EV driving experience as we know it.
There are several ways to achieve these goals with varying levels of modification to the battery management system (BMS), dependent upon the level of improvement desired and openness to BMS changes.Industry innovators are developing new technologies that can eliminate 100 wires and also collect cell data for preventive maintenance and warranty analysis, detect mismatched cells before irreversible damage to the entire battery pack, determine the temperature inside the cell and gather information for accurately calculating the State of Charge (SOC) with a simple measurement.With this new technology, range and overall performance of an EV can be improved in addition to creating greater reliability and safety.Baseline System
Cell sensing electronics (CSE) are typically positioned on a separate printed circuit board assembly (PCBA) near each battery module. The CSE contain multi-cell battery module integrated circuits (BMIC) on each PCBA. The CSE is connected to the cell connecting board (CCB) with a wire harness. Today, some automakers and Tier One suppliers are mounting the CSE PCBA on or near the module, minimizing the wire harness length.
Others may put a wire harness “pigtail” on the CCB, eliminating half of the wire harness connectors (alternate baseline case). However, in both cases there are typically about 100 voltage sense wires.
There are several ways to improve on the baseline system. The four concepts below offer distinctly different approaches to eliminating 100 voltage sense wires in EVs. One solution offers lower cost without compromising performance. While another solution has a somewhat increased cost and two of the solutions have increased cost but offer the next logical step in battery data tracking and performance.
Improved Solution #1: Cell Sensing Electronic Integration into Cell Connecting Board
Achieve significant payback with the fewest modifications. Eliminate 100 voltage sense wires with no cell sensing electronics changes, BMS changes or circuit cost increases.This solution involves moving the entire CSE PCBA onto the CCB itself, forming an embedded cell sensing circuit (eCSC) by soldering the CSE PCBA with multi-cell battery module integrated circuits (BMICs) directly onto the CCB, thus eliminating about 100 analog voltage sense wires and 30 analog temperature sense wires from the battery pack.
Moving the entire CSE PCBA onto the CCB itself eliminates the cost of the analog sense wires and connectors but requires no changes to the BMS hardware or software. It is simply packaging efficiency. This option also helps combat a long-term safety challenge for EVs. The elimination of voltage sense wires carrying analog signals removes the potential to create high voltage electrical shorts with those wires. In the baseline CSE system with analog voltage sense wires, most of the voltage sense wires that travel between the CSE and CCB are at high voltage. The last wire in the cell series is at about 400 V. The voltage sense wires are not sized to carry a lot of current, so if it shorts to ground the result is an unintended fuse and a potential fire hazard. Since the analog voltage sense signal from each cell is converted to a digital signal by the multi-cell ASIC, the result is just two to four digital sense wires leaving each module. The digital signal does not present the same safety hazard as the high voltage analog voltage sense wires because the digital signals are galvanically isolated.
Improved Solution #2: Wireless System-On-Chip
Achieve significant payback with additional changes and some added cost. Eliminate 100 voltage sense wires with changes to cell sensing electronics and BMS. Some increased circuit cost incurred.
Major global chip suppliers are preparing to introduce wireless systems-on-chip (SOC) that ensure a safe and reliable communication of battery vital data inside the vehicle. This wireless communication must be achieved with compliance to demanding automotive safety integrity Level D requirements (ASIL D) according to ISO 26262. This method results in elimination of about 100 analog voltage wires and 30 analog temperature sense wires for the pack, but requires modification to the CSE PCBA, the BMS hardware. There are both challenges and compromises to implementing this solution, such as cost of the SOC chips. Considerations such as the ability to ensure a high signal-to-noise ratio in a noisy environment of high current and a clear wireless signal path to the BMS are critical, as well. However, benefits include complete flexibility of placement of modules (within wireless range) and galvanic isolation.
Similar to Improved Solution #1, elimination of analog voltage sense wires eliminates the potential to create high voltage electrical shorts. However, in this case, elimination of the analog sense wires is achieved by using wireless technology.
Improved Solution #3: Electrochemical Impedance Spectroscopy (EIS)
Achieve much bigger payback and gain detailed battery data with additional changes and cost. Eliminate 100 voltage sense wires with required changes to cell sensing electronics and BMS. Further increased circuit cost incurred.
Multiple manufacturers have developed new battery monitoring IC (BMIC) chips, measurement algorithms and software that can measure the impedance of each cell using electrochemical impedance spectroscopy (EIS). Unlike other systems that may only be able to determine cell resistance at top of charge while at a charging station, this measurement of impedance can be taken while driving the vehicle. Some of these measurements can be taken during acceleration (discharge), constant speed (discharge) and deceleration (regenerative braking, charge), while others are best taken when the vehicle is at rest. Factors such as cell temperature, SOC and State of Health (SOH) can
be determined by the BMS along with the unmatched degradation of cells caused by variations in production, nonuniform heating or cooling of cells, internal shorts, or other issues. Unmatched cell degradation is critical because lithium-ion battery packs are only as viable as the worst cell. This data can also be used to prepare automotive battery packs for second life use. The data from each cell can be logged from cradle to grave so there is insight into the history of each cell, providing a sustainability benefit to the cell manufacturer, module manufacturer, OEM and consumers.
In order to make use of the EIS measurements, the cell properties need to be correlated to the EIS data. For example, correlation of internal cell temperature requires measurement and calibration before it is placed in the battery pack. During calibration, the phase shift of the impedance spectroscopy AC perturbation is calibrated to form a relationship between cell temperature and the phase shift. A fitting function is then used to correlate the phase shift into temperature. Similarly, the SOC of each cell can be correlated from the results of the impedance measurement (amplitude of the AC voltage) along with data from a shunt. This information is incredibly valuable in improving user experience for both the OEM and vehicle user. Some of the advantages are listed below:
Advantages to the OEM:
Advantages to the consumer:
Similar to Improved Solution #1, the analog voltage sense signal from each cell is converted to a digital signal by an ASIC eliminating 100 analog voltage sense wires and 30 analog temperature sense wires, resulting in just two to four digital voltage and temperature sense wires for the entire pack. In this case, there is an EIS ASIC for each cell. The digital voltage sense signal from each ASIC can then be daisy chained from cell to cell and module to module to result in just two to four voltage sense wires for the entire pack. The digital signal does not present the same safety hazard as the high voltage analog voltage sense wires as they are galvanically isolated.
Further benefits to the EIS cell data are the ability to sense physical changes that typically precede safety events such as thermal runaway. As stated above, the EIS data can accurately and quickly determine the temperature inside of the cell. This is in contrast to traditional systems that sense temperature at the busbars, thermally far away from the actual cell temperature. Additionally, the EIS cell data can identify swelling of the cells, which is the physical event that typically happens before cell venting and potential vehicle fire hazard occurs.
Not only is this data critical to improving vehicle safety, this type of information may be the only reliable way to meet the new United Nations (UN) General Technical Regulation GTR20 that is already adopted in China as the National Standard for EV Batteries (GB 38031-12), sometimes called “The five-minute rule.” In this legislation, the BMS is required to inform vehicle occupants five minutes before a potential fire could occur due to battery issues. Similar legislation such as GTR20 and EVS is in consideration in other parts of the world.
As with any system, there are always advantages and disadvantages. The disadvantage of the EIS system is the cost of the ASICs (one per parallel cell grouping), the changes to the hardware and software of the BMS system and the detailed characterization of the cell with EIS prior to building the packs. However, the advantages described above could heavily outweigh the additional cost and development work, especially when safety is considered. The parts and development costs are easily justified by the improved safety for passengers, as well as the lowered risk of a potential recall or safety issue for corporations.
Improved Solution #4: Electrochemical Impedance Spectroscopy (EIS) with Big Data Analytics
Achieve biggest payback with further changes and higher cost. Eliminate over 100 voltage sense wires and gain predictive analysis with changes to cell sensing electronics and BMS. Cell data is continuously collected to create a data lake which is further analyzed using machine learning/AI.
As with Improved Solution #3, a digital signal from the EIS ASIC eliminates 100 analog voltage sense wires and 30 analog temperature sense wires, resulting in just two to four digital voltage and temperature sense wires for the entire pack. However, in this approach, the EIS data is used for predictive analysis. By transmitting the data of each vehicle to a data lake, advanced algorithms and machine learning/artificial intelligence (AI) can further process the data with more processing power and data storage than the onboard BMS. These insights can improve the safety and driving experience of each individual vehicle, as well as all vehicles used in a fleet or sold by an OEM compared to traditional systems that are based on Beginning of Life (BOL) cell data which become less accurate over time. The EIS data sets can be evaluated with historical metrics and compared with all similar vehicles on the road to determine trends and forecast system behavior with AI predictive methods. As a result, the data analytics can inform the BMS to provide warning to the vehicle occupants minutes, days or potentially months before a safety event occurs, meeting the five-minute requirement with ease. The extended benefits of using data analytics and AI in addition to the general use of EIS data, include:
Data Analytics/AI Benefits:
Smarter Systems for Smarter Future Vehicles
Leading OEMs and automotive suppliers are working together to develop advanced EVs with improved reliability, safety, performance and range. The elimination of 100 wires from every EV shows the magnitude of potential improvements available when evaluating technologies for future vehicle efficiency. As vehicles advance to meet the needs of high-demand consumers, with safety and performance benefits paramount, vehicle systems must also evolve as demonstrated in the four distinct approaches above to drive cost-savings, reliability, safety and performance from concept to production, and ultimately, to the show room floor. Collaboration of a wide range of areas, such as simplified packaging and wiring, advanced ASIC sensors, BMS controls, big data analytics and machine learning/AI, can enable ever-improving overall performance for the intelligent, connected, automated, shared and electric mobility future.
Michael Ciaccio serves as executive director of advanced electrification technology at Gentherm. Ferdinand Minnerrath is a project engineer at Gentherm.
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