Prediction of Battery Remaining Useful Life Based on Wavelet Packet Energy Entropy
Chen Lin1,2, Chen Jing1, Wang Huimin1, Wei Haiyan1, Pan Haihong1
1. School of Mechanical Engineering Guangxi University Nanning 530004 China; 2. Guangxi Key Laboratory of Electrochemical Energy Materials Collaborative Innovation Center of Renewable Energy Materials Guangxi University Nanning 530004 China
Abstract：Accurate prediction of battery remaining useful life (RUL) is one of the key technologies of battery management systems. The traditional methods depend heavily on the battery capacity and the capacity data is difficult to obtain directly. Therefore, the wavelet packet energy entropy (WPEE) extracted from the battery discharge voltage was proposed to replace the capacity charactering the battery degradation. Then the extracted WPEE was used to construct a fractional grey model (FGM), and the model was applied to be fused with the adaptive unscented particle filter (AUPF) for realizing battery RUL prediction. The experimental results show both the battery discharge voltage WPEE and battery capacity can be used as the degradation characterization indicator under the proposed FGM-AUPF algorithm framework to achieve battery RUL prediction. And the relative error of the battery discharge voltage WPEE prediction results is no more than 5.96%.
陈琳, 陈静, 王惠民, 韦海燕, 潘海鸿. 基于小波包能量熵的电池剩余寿命预测[J]. 电工技术学报, 2020, 35(8): 1827-1835.
Chen Lin, Chen Jing, Wang Huimin, Wei Haiyan, Pan Haihong. Prediction of Battery Remaining Useful Life Based on Wavelet Packet Energy Entropy. Transactions of China Electrotechnical Society, 2020, 35(8): 1827-1835.
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2020, Vol. 35 
