Zitong Gao , Yuhong Jin , Yuan Zhang , Ziheng Zhang , Siquan Li , Jingbing Liu , Hao Wang
{"title":"静态EIS多频特征点结合WOA-BP神经网络估算锂离子电池SOH","authors":"Zitong Gao , Yuhong Jin , Yuan Zhang , Ziheng Zhang , Siquan Li , Jingbing Liu , Hao Wang","doi":"10.1016/j.measurement.2025.117780","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117780"},"PeriodicalIF":5.2000,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Static EIS multi-frequency feature points combined with WOA-BP neural network for Li-ion battery SOH estimation\",\"authors\":\"Zitong Gao , Yuhong Jin , Yuan Zhang , Ziheng Zhang , Siquan Li , Jingbing Liu , Hao Wang\",\"doi\":\"10.1016/j.measurement.2025.117780\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":18349,\"journal\":{\"name\":\"Measurement\",\"volume\":\"253 \",\"pages\":\"Article 117780\"},\"PeriodicalIF\":5.2000,\"publicationDate\":\"2025-05-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Measurement\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S026322412501139X\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Measurement","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S026322412501139X","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
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.
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.