Analysis of the core technology of electric vehicle power battery BMS
What is the core technology of BMS?
Recently, I saw a publicity card of a domestic enterprise. Because of the underlying software such as AUTOSAR's software architecture, it claimed to “completely master BMS software and hardware technologyâ€, “reach the world advanced levelâ€, and “use multiple equalization control capabilitiesâ€. Very attractive to the eye. Are these things the core technology of BMS?
Generally, a BMS system usually includes a detection module and an operation control module.
Detection refers to measuring the voltage, current and temperature of the cell and the voltage of the battery pack, and then transmitting these signals to the arithmetic module for processing and issuing commands. So the arithmetic control module is the brain of the BMS. Control modules typically include hardware, basic software, runtime environment (RTE), and application software. The most central part is the application software. The environment developed with Simulink is generally divided into two parts: battery state estimation algorithms and fault diagnosis and protection. State estimation includes SOC (State Of Charge), SOP (State Of Power), SOH (State of Health), and equalization and thermal management.
Battery state estimation is usually estimated by SOC, SOP, and SOH. SOC (charge state) is simply how much power is left in the battery; SOC is the most important parameter in BMS, because everything else is based on SOC, so its accuracy and robustness (also called error correction) Ability) is extremely important. If there is no precise SOC, adding more protection functions will not make the BMS work properly, because the battery will often be protected and the battery life will not be extended.
In addition, the accuracy of SOC estimation is also very important. The higher the accuracy, the higher the cruising range for batteries of the same capacity. Therefore, high-precision SOC estimation can effectively reduce the battery cost required. For example, Chrysler's Fiat 500e BEV can always discharge SOC = 5%. It became the electric car with the longest cruising range at that time.
The figure below is an example of algorithm robustness. The battery is a lithium iron phosphate battery. Its SOCvs OCV curve varies by only 2-3 mV in the SOC from 70% to 95%. The measurement error of the voltage sensor is 3-4mV. In this case, we intentionally let the initial SOC have a 20% error and see if the algorithm can correct the 20% error. If there is no error correction function, the SOC will follow the curve of the SOCI. The SOC output by the algorithm is the CombinedSOC, which is the solid blue line in the figure. CalculatedSOC is the true SOC that is pushed back based on the final verification result.
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