梯次利用锂电池健康状态预测

doi:10.19595/j.cnki.1000-6753.tces.170107

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Abstract Lithium-ion battery retired from electric vehicles may be considered expanding their useful life for second use if their functional components are effective. The retired battery has been separated from battery management system and they are offline. Their residual capacity and performance are different from each other, so it is necessary to reevaluate their performance. In this paper, a suitable test profile for performance evaluation of retired battery was designed which is to identify ohm resistance based on the first-order RC model, and the health feature extraction method was proposed based on the identifiable I-ARX model. The three health indicators (HIs), average internal resistance, minimum internal resistance and resistance-SOC curve, were obtained from health test data. Based on the three HIs, battery health models were established and their effectiveness has been validated by simulation. Furthermore, an evaluation method using multi-model fusion technology was proposed to solve the issue by BP neural network, which is to enhance the accuracy of SOH prediction for the retired lithium-ion batteries, and its effectiveness was verified by experimental data.

Key words： Second-use lithium-ion battery state of health prediction health indicator health model multi-model fusion technology

Received: 24 January 2017 Published: 08 May 2018

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TM912

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	Sun Dong
	Xu Shuang

Cite this article:

Sun Dong,Xu Shuang. State of Health Prediction of Second-Use Lithium-Ion Battery[J]. Transactions of China Electrotechnical Society, 2018, 33(9): 2121-2029.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.170107 OR https://dgjsxb.ces-transaction.com/EN/Y2018/V33/I9/2121

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