基于数据预处理和长短期记忆神经网络的锂离子电池寿命预测

doi:10.19595/j.cnki.1000-6753.tces.210860

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Abstract The remaining useful life (RUL) of lithium-ion battery can evaluate the reliability of battery, which is an important parameter of battery health management. Accurate prediction of RUL of battery can effectively improve the safety of equipment and reduce the working risk. In this paper, a RUL prediction framework combined with the adaptive data preprocessing method and long-term and short-term memory neural network (LSTM) was proposed. Selecting capacity as the health factor, in the data preprocessing stage, the adaptive double exponential model smoothing method was used to reduce the negative effect of capacity recovery and the adaptive white noise integrated empirical mode decomposition (CEEMDAN) is used to suppress the noise. In the model constructing stage, the LSTM model was built for RUL prediction by training the preprocessed data. The NASA and CALCE open source data were selected to verify the performance of the proposed method. The experimental results show that it has good robustness and can provide RUL prediction results with high precision.

Key words： Lithium-ion battery remaining useful life adaptive bi-exponential model smooth method the complete ensemble empirical mode decomposition with adaptive noise long short-term memory network

Received: 14 June 2021

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TM912

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	Articles by authors
	Huang Kai
	Ding Heng
	Guo Yongfang
	Tian Haijian

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Huang Kai,Ding Heng,Guo Yongfang等. Prediction of Remaining Useful Life of Lithium-Ion Battery Based on Adaptive Data Preprocessing and Long Short-Term Memory Network[J]. Transactions of China Electrotechnical Society, 2022, 37(15): 3753-3766.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.210860 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/I15/3753

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