دانلود رایگان مقاله ثبت بهینه سازی فرکانس برای ذخیره سازی داده های عظیم باتری

Abstract

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.

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.