Online generation of full-frequency electrochemical impedance spectra for Lithium-ion batteries using early-stage partial relaxation voltage curve

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation Pub Date : 2025-09-13 DOI:10.1016/j.etran.2025.100482

Jiajun Zhu , Xin Lai , Zhicheng Zhu , Penghui Ke , Yuejiu Zheng , Xiaopeng Tang , Xiang Li , Ye Yuan , Haoyu Chong , Chenhui Yan , Ying Wang , Yanke Lin , Xiaolei Zhou , Yingjie Chen

{"title":"Online generation of full-frequency electrochemical impedance spectra for Lithium-ion batteries using early-stage partial relaxation voltage curve","authors":"Jiajun Zhu , Xin Lai , Zhicheng Zhu , Penghui Ke , Yuejiu Zheng , Xiaopeng Tang , Xiang Li , Ye Yuan , Haoyu Chong , Chenhui Yan , Ying Wang , Yanke Lin , Xiaolei Zhou , Yingjie Chen","doi":"10.1016/j.etran.2025.100482","DOIUrl":null,"url":null,"abstract":"<div><div>Electrochemical impedance spectroscopy (EIS) serves as a powerful non-destructive tool for lithium-ion battery state assessment, yet its real-time application faces significant challenges including expensive hardware requirements, time-consuming measurements, and stringent data quality demands. This study develops a hardware-free online electrochemical impedance spectroscopy using only relaxation voltage, achieved through a physics-informed neural network (PINN) that predicts full-frequency EIS from early-stage partial relaxation curves. The proposed approach exhibits remarkable insensitivity to battery state of charge and state of health, as validated by a comprehensive dataset containing over 300 impedance spectra from four batteries under various aging conditions. Experimental results demonstrate accurate EIS prediction with relative errors (RE) below 5.6 % and mean absolute errors (MAE) below 1.12 mΩ when using complete relaxation curves. Crucially, the method maintains reliability under practical constraints, achieving maximum RE of 6.1 % and MAE of 1.29 mΩ even with limited sampling data and shortened relaxation curves. By enabling online full-frequency EIS acquisition through relaxation voltage signals without hardware requirements, this work establishes a new paradigm for real-time battery diagnostics, providing valuable insights for state estimation and fault detection in battery management systems.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100482"},"PeriodicalIF":17.0000,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Etransportation","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S259011682500089X","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}

引用次数: 0

Abstract

Electrochemical impedance spectroscopy (EIS) serves as a powerful non-destructive tool for lithium-ion battery state assessment, yet its real-time application faces significant challenges including expensive hardware requirements, time-consuming measurements, and stringent data quality demands. This study develops a hardware-free online electrochemical impedance spectroscopy using only relaxation voltage, achieved through a physics-informed neural network (PINN) that predicts full-frequency EIS from early-stage partial relaxation curves. The proposed approach exhibits remarkable insensitivity to battery state of charge and state of health, as validated by a comprehensive dataset containing over 300 impedance spectra from four batteries under various aging conditions. Experimental results demonstrate accurate EIS prediction with relative errors (RE) below 5.6 % and mean absolute errors (MAE) below 1.12 mΩ when using complete relaxation curves. Crucially, the method maintains reliability under practical constraints, achieving maximum RE of 6.1 % and MAE of 1.29 mΩ even with limited sampling data and shortened relaxation curves. By enabling online full-frequency EIS acquisition through relaxation voltage signals without hardware requirements, this work establishes a new paradigm for real-time battery diagnostics, providing valuable insights for state estimation and fault detection in battery management systems.

查看原文本刊更多论文

利用早期部分弛豫电压曲线在线生成锂离子电池全频率电化学阻抗谱

电化学阻抗谱（EIS）是锂离子电池状态评估的一种强大的非破坏性工具，但其实时应用面临着巨大的挑战，包括昂贵的硬件要求、耗时的测量和严格的数据质量要求。本研究开发了一种仅使用弛豫电压的无硬件在线电化学阻抗谱，通过物理信息神经网络（PINN）实现，该网络可以从早期部分弛豫曲线预测全频率EIS。通过包含4个电池在不同老化条件下的300多个阻抗谱的综合数据集验证了该方法对电池充电状态和健康状态的不敏感性。实验结果表明，使用完全松弛曲线预测EIS的相对误差（RE）小于5.6%，平均绝对误差（MAE）小于1.12 mΩ。至关重要的是，该方法在实际约束下保持了可靠性，即使在有限的采样数据和缩短的松弛曲线下，也实现了最大的6.1%的RE和1.29 mΩ的MAE。通过在没有硬件要求的情况下通过松弛电压信号实现在线全频率EIS采集，这项工作为实时电池诊断建立了一个新的范例，为电池管理系统的状态估计和故障检测提供了有价值的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Etransportation Engineering-Automotive Engineering

CiteScore

19.80

自引率

12.60%

发文量

审稿时长

39 days

期刊介绍： eTransportation is a scholarly journal that aims to advance knowledge in the field of electric transportation. It focuses on all modes of transportation that utilize electricity as their primary source of energy, including electric vehicles, trains, ships, and aircraft. The journal covers all stages of research, development, and testing of new technologies, systems, and devices related to electrical transportation. The journal welcomes the use of simulation and analysis tools at the system, transport, or device level. Its primary emphasis is on the study of the electrical and electronic aspects of transportation systems. However, it also considers research on mechanical parts or subsystems of vehicles if there is a clear interaction with electrical or electronic equipment. Please note that this journal excludes other aspects such as sociological, political, regulatory, or environmental factors from its scope.