基于小波包能量熵的电池剩余寿命预测

doi:10.19595/j.cnki.1000-6753.tces.190094

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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%.

Key words： Battery remaining useful life wavelet packet energy entropy fractional grey model unscented particle filter

Received: 22 January 2019 Published: 24 April 2020

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TM911

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	Chen Lin
	Chen Jing
	Wang Huimin
	Wei Haiyan
	Pan Haihong

Cite this article:

Chen Lin,Chen Jing,Wang Huimin等. Prediction of Battery Remaining Useful Life Based on Wavelet Packet Energy Entropy[J]. Transactions of China Electrotechnical Society, 2020, 35(8): 1827-1835.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.190094 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/I8/1827

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