静态EIS多频特征点结合WOA-BP神经网络估算锂离子电池SOH

IF 5.2 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Zitong Gao , Yuhong Jin , Yuan Zhang , Ziheng Zhang , Siquan Li , Jingbing Liu , Hao Wang
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引用次数: 0

摘要

电化学阻抗谱(EIS)已成为评价锂离子电池(LIBs)健康状态(SOH)必不可少的非破坏性技术。然而,从原始或衍生的EIS数据中提取有效和直接的健康指标(HIs)对于SOH估算的实际应用至关重要。在本研究中,我们基于Nyquist图探索了商用电池在各种SOH下的静态EIS多频特征点作为HI。随后,将提取的HIs输入鲸鱼优化算法反向传播(WOA-BP)神经网络,以实现准确的电池SOH估计。利用相同条件下循环使用的4个电池样品对所建立的模型进行了验证,该模型的均方根误差范围在0.23% ~ 0.43%之间。值得注意的是,即使在我们的模型中使用未经训练的数据,它也可以在实际验证中获得一个值得称赞的均方根误差。此外,与其他主流算法相比,我们的WOA-BP模型具有更高的精度,即使没有历史EIS数据,也显示出巨大的应用潜力。结果表明,与利用全阻抗谱作为HI相比,选择特征频率点可以大大减少测试时间,为实际应用中的电池SOH估计提供了一种有效的策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Static EIS multi-frequency feature points combined with WOA-BP neural network for Li-ion battery SOH estimation

Static EIS multi-frequency feature points combined with WOA-BP neural network for Li-ion battery SOH estimation
Electrochemical impedance spectroscopy (EIS) has been established as an essential and non-destructive technique for estimating the state of health (SOH) of lithium-ion batteries (LIBs). However, the extraction of effective and straightforward health indicators (HIs) from either original or derived EIS data is critical for practical applications in SOH estimation. In this study, we explore static EIS multi-frequency feature points as HI based on Nyquist plots at a various SOH for commercially available batteries. Subsequently, the extracted HIs are fed into a whale optimization algorithm back propagation (WOA-BP) neural network to achieve the accurate battery SOH estimation. The developed model is validated using four battery samples cycled at the same condition, which can achieve a low root-mean-square error range from 0.23 % to 0.43 %. Notably, even when employing the untrained data with our model, it can achieve a commendable root-mean-square error during practical validation. Moreover, our WOA-BP model exhibits superior accuracy compared to other mainstream algorithms and shows the significant application potential even without historic EIS data. The results indicate that selecting characteristic frequency points can greatly reduce testing time compared to utilizing full impedance spectroscopy as HI, presenting an effective strategy for battery SOH estimation in real-world applications.
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来源期刊
Measurement
Measurement 工程技术-工程:综合
CiteScore
10.20
自引率
12.50%
发文量
1589
审稿时长
12.1 months
期刊介绍: Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material, whose ultimate goal is an advancement in the state of the art of: measurement and metrology fundamentals, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures and methodologies for plants and industrial processes, performance analysis of systems, processes and algorithms, mathematical models for measurement-oriented purposes, distributed measurement systems in a connected world.
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