锂离子电池在不同区间下的衰退影响因素分析及任意区间的老化趋势预测

doi:10.19595/j.cnki.1000-6753.tces.200071

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Abstract The accurate estimation of the state of health（SOH）of lithium-ion batteries is very important for the development of controlling strategies and operating maintenance. Considering the influence of charge-discharge interval and voltage phase transition process on battery aging, in this paper, 11 cycle life and performance tests in different state of charge (SOC) intervals were designed for 2.75Ah 18650 energy Lithium-ion battery. According to the experimental results, the mechanism of SOC width, constant voltage charging process, average SOC and charging phase transition process on battery aging were analyzed. Based on the aging mechanism and experimental results of batteries, the characteristic parameters which quantify the influence of partial SOC intervals on aging were extracted. The SOH prediction model based on recurrent neural network with long-short term memory network (LSTM RNN) was established to study the long-term dependence of battery aging on cycle numbers and characteristic parameters. The accuracy and reliability of the model were analyzed by the maximum error, the average absolute error, the root mean square error and the variance. The results show that the cycle life model proposed in this paper can predict the capacity degradation trend of any SOC interval and save testing time and cost.

Key words： Lithium-ion battery SOC intervals aging prediction recurrent neural network

Received: 16 January 2020

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TM911

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	Articles by authors
	Sun Bingxiang
	Ren Pengbo
	Chen Yuzhe
	Cui Zhengtao
	Jiang Jiuchun

Cite this article:

Sun Bingxiang,Ren Pengbo,Chen Yuzhe等. Analysis of Influencing Factors of Degradation under Different Interval Stress and Prediction of Aging Trend in Any Interval for Lithium-Ion Battery[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 666-674.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.200071 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I3/666

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