Recording frequency optimization for massive battery data storage in battery management systems

Massive data storage is an advanced function in a fully functional battery management system (BMS). Reducing the recording signal length undoubtedly saves the precious memory space for BMS. And it also reduces the network and computation loads. However, it leads to a side effect that the trend of signal distortion is enhanced. The optimal recording frequency in practice should be as low as possible on the condition that little signal distortion happens. This paper presents a novel method which uses a multi-frequency recording technology that cooperates two approaches according to the signal dynamics. A flexible recording frequency method is applied for stationary signals which only records signals when their values are changed. While for dynamic signals, the most dynamic period is found using discrete wavelet transformation (DWT) and further analyzed by fast Fourier transformation (FFT). By comparing two recording signal indicators for four different recording frequencies, we conclude that recording at 1 Hz is not qualified for the cell voltage and current during the dynamic period in our system due to the high dynamic performance of the vehicle. In the demonstrated 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. And we also verify that when the recording frequency is reduced to the optimal frequency compared to the high frequency recorded original signals, the accuracy of the SOC estimation is not influenced. (C) 2016 Elsevier Ltd. All rights reserved.