5. Conclusion
Massive data storage in BMS can provide statistical and individual working conditions of battery packs which support the further development of EVs. The optimal recording frequency in practice should be as low as possible on the condition that little signal distortion happens. In this paper, we present a novel method which uses a multi-frequency recording technology according to the signal dynamics. Firstly, dynamic properties of signals in BMS are discussed. Temperature, SOC, the current and voltages during stationary charging are low frequency signals. While the current and voltages during dynamic charging and discharging are high frequency signals. The multi-frequency recording technology according to the signal dynamics is then proposed. For low frequency signals, a flexible recording frequency method is applied which only records signals when their values are changed. Compared to the fixed recording frequency, the recorded signal length is drastically shortened with the additional benefit of even lower signal distortion. For dynamic signals, the most dynamic period is successfully found using DWT. The mean absolute derivative method also veri- fies the most dynamic period from the engineering point of view. The most dynamic period is further analyzed by FFT. By comparing two recording signal indicators for four different recording frequencies, we conclude that recording at 1 Hz is not qualified during the dynamic period in our system due to the high dynamic performance of the vehicle. Only by increasing the recording frequency to at least 2 Hz, can the accuracy of the recorded cell voltage achieve the level the same as the measurement accuracy in engineering.
