Sebastian Pohlmann , Ali Mashayekh , Johannes Buberger , Julian Estaller , Andreas Wiedenmann , Manuel Kuder , Antje Neve , Thomas Weyh
{"title":"利用阻抗光谱对锂离子电池老化过程进行相关性分析和特征提取","authors":"Sebastian Pohlmann , Ali Mashayekh , Johannes Buberger , Julian Estaller , Andreas Wiedenmann , Manuel Kuder , Antje Neve , Thomas Weyh","doi":"10.1016/j.est.2024.114715","DOIUrl":null,"url":null,"abstract":"<div><div>To optimize the operation of batteries and to accelerate the transition to a renewable energy supply and sustainable transportation, it is crucial to determine the condition of Lithium-Ion Batteries at all time. A non-invasive tool for determining the State-of-Health of a battery cell is the electrochemical impedance spectroscopy, in which the impedance is calculated as a function of the excitation frequency. This paper presents a detailed correlation analysis between impedance and State-of-Health and, therefore, the dependency between capacity and impedance over the life cycle of a battery. After extensive testing series with 48 cells resulting in 25,344 impedance spectra, the highest correlations between the impedance and the State-of-Health could be obtained at a State-of-Charge of 10%. Further, features are extracted from the impedance based on their correlation to estimate the State-of-Health. To validate the results of the correlation analysis, a support vector regression and a multi-layer perceptron are trained and tested resulting in a mean absolute error of 0.86% and 0.84%. The estimation results confirm the correlation analysis and further substantiate the need for an appropriate feature extraction method.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"105 ","pages":""},"PeriodicalIF":8.9000,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Correlation analysis and feature extraction using impedance spectroscopy over aging of lithium ion batteries\",\"authors\":\"Sebastian Pohlmann , Ali Mashayekh , Johannes Buberger , Julian Estaller , Andreas Wiedenmann , Manuel Kuder , Antje Neve , Thomas Weyh\",\"doi\":\"10.1016/j.est.2024.114715\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>To optimize the operation of batteries and to accelerate the transition to a renewable energy supply and sustainable transportation, it is crucial to determine the condition of Lithium-Ion Batteries at all time. A non-invasive tool for determining the State-of-Health of a battery cell is the electrochemical impedance spectroscopy, in which the impedance is calculated as a function of the excitation frequency. This paper presents a detailed correlation analysis between impedance and State-of-Health and, therefore, the dependency between capacity and impedance over the life cycle of a battery. After extensive testing series with 48 cells resulting in 25,344 impedance spectra, the highest correlations between the impedance and the State-of-Health could be obtained at a State-of-Charge of 10%. Further, features are extracted from the impedance based on their correlation to estimate the State-of-Health. To validate the results of the correlation analysis, a support vector regression and a multi-layer perceptron are trained and tested resulting in a mean absolute error of 0.86% and 0.84%. The estimation results confirm the correlation analysis and further substantiate the need for an appropriate feature extraction method.</div></div>\",\"PeriodicalId\":15942,\"journal\":{\"name\":\"Journal of energy storage\",\"volume\":\"105 \",\"pages\":\"\"},\"PeriodicalIF\":8.9000,\"publicationDate\":\"2024-11-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of energy storage\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352152X24043019\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of energy storage","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352152X24043019","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Correlation analysis and feature extraction using impedance spectroscopy over aging of lithium ion batteries
To optimize the operation of batteries and to accelerate the transition to a renewable energy supply and sustainable transportation, it is crucial to determine the condition of Lithium-Ion Batteries at all time. A non-invasive tool for determining the State-of-Health of a battery cell is the electrochemical impedance spectroscopy, in which the impedance is calculated as a function of the excitation frequency. This paper presents a detailed correlation analysis between impedance and State-of-Health and, therefore, the dependency between capacity and impedance over the life cycle of a battery. After extensive testing series with 48 cells resulting in 25,344 impedance spectra, the highest correlations between the impedance and the State-of-Health could be obtained at a State-of-Charge of 10%. Further, features are extracted from the impedance based on their correlation to estimate the State-of-Health. To validate the results of the correlation analysis, a support vector regression and a multi-layer perceptron are trained and tested resulting in a mean absolute error of 0.86% and 0.84%. The estimation results confirm the correlation analysis and further substantiate the need for an appropriate feature extraction method.
期刊介绍:
Journal of energy storage focusses on all aspects of energy storage, in particular systems integration, electric grid integration, modelling and analysis, novel energy storage technologies, sizing and management strategies, business models for operation of storage systems and energy storage developments worldwide.