基于容量增量曲线峰值区间特征参数的锂离子电池健康状态估算

doi:10.19595/j.cnki.1000-6753.tces.L90355

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摘要当前,学术界广泛采用容量增量（IC）曲线上的特征参数（FPs）估算锂离子电池的健康状态（SOH）。该方法通常利用整个IC峰区域的FPs实现SOH估算,而不同IC峰区域下的FPs对SOH估算有较大差异。为了提高电池SOH估算的准确性,该文采用锂离子电池在IC曲线的峰值区间（△V_reg,通常为峰下的部分荷电状态区间）提取FPs的SOH估算方法,基于高斯过程（GP）回归建立SOH估算模型。通过三个峰值区间下的SOH估算结果,发现SOH对不同△V_reg的FPs敏感程度不同。该文进一步利用NASA提供的5号、6号、7号和18号电池数据,对11组峰值区间△V_reg在[23.1%,100%]内的SOH估算结果进行分析,结果表明,6号、7号和18号电池的峰值区间△V_reg分别选取[53.4%,88.1%]、[50.4%,92.3%]和[42.3%,100%]时,其估算的方均根误差小于2%,这说明SOH对上述峰值区间范围更敏感。利用该文的方法量化SOH对峰值特征参数的敏感区间,在敏感区间内的SOH估算有较高的准确性。

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	杨胜杰
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	朱国荣

关键词 ：锂离子电池, 峰值区间, 电池健康状态, 容量增量

Abstract：At present, researchers have widely used feature parameters (FPs) of incremental capacity (IC) curve to estimate the state of health (SOH) of lithium ion batteries. The FPs are commonly extracted from a whole peak in the IC curve. The method fails to consider the effect of the FPs extracted from different ranges of the peak on the accuracy of estimated SOH. In order to provide an accurate SOH estimation, we select the FPs from the peak region(△V_reg,a state of charge range of a peak). Then SOH estimation is achieved by setting up the relationship between the SOH and the FPs based on Gaussian process (GP) regression. Results show that the accuracy of SOH estimation is sensitive to the different FPs, according to the estimated SOH under the three △V_reg. Furthermore, the comparison of the eleven △V_regof FPs that the data come from the NASA No.5, 6, 7 and 18 batteries between 23.1% and 100% is studied. It is found that the estimated SOH root mean square error is less than 2% when the △V_reg of No.6,7 and 18 batteries are in the regions of [53.4%,88.1%],[50.4%,92.3%] and [42.3%,100%], respectively. It is indicated that that SOH estimation is more sensitive to the above peak region. This method gives an approach to achieve the high precision of SOH estimation because we prove that the SOH estimation is sensitive to △V_reg.

Key words： Lithium-ion battery peak region state of health incremental capacity analysis

收稿日期: 2020-07-10

PACS:

TM912

基金资助:国家自然科学基金资助项目（51777146, 51977163）

通讯作者: 康健强,男,1976年生,副教授,硕士生导师,研究方向为动力及储能电池应用技术。E-mail：kjqiang@whut.edu.cn

作者简介: 杨胜杰,男,1996年生,硕士研究生,研究方向为电动汽车动力电池管理系统。E-mail：yangsj_77@whut.edu.cn

引用本文:

杨胜杰, 罗冰洋, 王菁, 康健强, 朱国荣. 基于容量增量曲线峰值区间特征参数的锂离子电池健康状态估算[J]. 电工技术学报, 2021, 36(11): 2277-2287. Yang Shengjie, Luo Bingyang, Wang Jing, Kang Jianqiang, Zhu Guorong. State of Health Estimation for Lithium-Ion Batteries Based on Peak Region Feature Parameters of Incremental Capacity Curve. Transactions of China Electrotechnical Society, 2021, 36(11): 2277-2287.

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