基于放电过程信息的锂电池剩余寿命预测

张翾; 冯海林

doi:10.3969/j.issn.1000-1158.2022.11.16

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计量学报 ›› 2022, Vol. 43 ›› Issue (11) : 1492-1500. DOI: 10.3969/j.issn.1000-1158.2022.11.16

电磁学计量

基于放电过程信息的锂电池剩余寿命预测

张翾,冯海林

作者信息 +

Remaining Life Prediction of Lithium Battery Based on Discharge Process Information

ZHANG Xuan,FENG Hai-lin

Author information +

文章历史 +

摘要

容量或内阻是衡量锂离子电池健康状态的重要指标,但在锂电池实际运行中,其容量和内阻很难实时获取。为此,提出了基于放电过程信息获取新健康指标的方法,并对锂电池的剩余寿命进行预测。主要研究了锂电池放电过程中电压变化的规律,提出两种可在线测量的新健康指标,并通过Box-Cox变换修正了新健康指标的准确性。比较分析表明,所提取的健康指标与容量之间存在着强相关性,在某种程度上可以解决锂电池容量难以在线测量的问题。此外还基于新健康指标建立了锂电池退化过程模型,并利用相关向量机算法进行锂电池的剩余寿命预测。实验结果表明,在寿命预测性能上相关向量机算法优于其他算法,并且预测时间越晚,预测结果就越准确,所提取的健康指标也能够很好地描述锂电池的退化过程,并在剩余寿命预测结果上表现优越。

Abstract

Capacity or internal resistance is an important indicator to measure the health status of lithium-ion batteries. But in the actual operation of lithium-ion batteries, the battery capacity and internal resistance are difficult to obtain in real time. So, a method to obtain new health indicators based on the discharge process information is proposed and the remaining useful life of lithium-ion batteries is predicted. The law of voltage change during the discharging process of lithium-ion batteries is studied. On the above basis, two new health indicators which can be measured online are proposed. The accuracy of the new health indicators is corrected by Box-Cox transformation. The results show that there is a strong correlation between the extracted health indicators and the capacity, which can solve the problem that the battery capacity is difficult to measure online to some extent. In addition, the battery degradation process model based on new health indicators is established, and the remaining useful life of lithium-ion batteries is predicted by the relevance vector machine. The experimental results show that the relevance vector machine algorithm is better than other algorithms in life prediction performance. And the later the prediction time, the more accurate the prediction results. The extracted health indicators can describe the degradation process of lithium-ion batteries well and predict the remaining useful life accurately.

导出引用

张翾,冯海林. 基于放电过程信息的锂电池剩余寿命预测[J]. 计量学报. 2022, 43(11): 1492-1500 https://doi.org/10.3969/j.issn.1000-1158.2022.11.16

ZHANG Xuan,FENG Hai-lin. Remaining Life Prediction of Lithium Battery Based on Discharge Process Information[J]. Acta Metrologica Sinica. 2022, 43(11): 1492-1500 https://doi.org/10.3969/j.issn.1000-1158.2022.11.16

中图分类号： TB971

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