MSFF-DM-based SOH Estimation for Lithium-ion Battery Packs Utilizing System and Cell FeaturesOA
Accurate estimation of the state of health(SOH)is crucial for safe operation and lifespan extension of lithium-ion battery packs.A multiscale feature fusion decision model(MSFF-DM)-based approach for SOH estimation of lithium-ion battery packs utilizing both system-level and cell-level features is proposed.First,the current,voltage,and temperature data of the lithium-ion battery pack are measured during the charging stage,and the interquartile range(IQR)is extracted through incremental energy(IE)analysis to characterize the pack-level system characteristics.Based on the individual cell temperature and voltage,a combined temperature-weighted voltage inconsistency is constructed to capture inter-cell differences.Subsequently,an MSFF-DM estimation model composed of a multiscale feature extraction layer,feature fusion layer,and decision-making layer is developed.After the two types of features are input into the model for training,the experimental data are used for testing to achieve accurate SOH estimation for the battery pack.The results showed that the average absolute error and root mean square error of SOH estimation using the proposed method are 0.36%and 0.44%,respectively,and the average coefficient of determination exceeded 99%.These results demonstrate that the proposed method outperforms the traditional approaches.
Chaolong Zhang;Yupeng Wu;Anxiang Wang;Min Xie;Panfen Yin;Guoqing Hua;Kaixin Cheng;Kui Shao
College of Intelligent Science and Control Engineering,Jinling Institute of Technology,Nanjing 211169,ChinaCollege of Intelligent Science and Control Engineering,Jinling Institute of Technology,Nanjing 211169,ChinaCollege of Intelligent Science and Control Engineering,Jinling Institute of Technology,Nanjing 211169,ChinaCollege of Intelligent Science and Control Engineering,Jinling Institute of Technology,Nanjing 211169,ChinaCollege of Intelligent Science and Control Engineering,Jinling Institute of Technology,Nanjing 211169,ChinaCollege of Intelligent Science and Control Engineering,Jinling Institute of Technology,Nanjing 211169,ChinaCollege of Intelligent Science and Control Engineering,Jinling Institute of Technology,Nanjing 211169,ChinaSchool of Future Technology,China University of Geosciences,Wuhan 430074,China
信息技术与安全科学
Lithium-ion battery packstate-of-health estimationincremental energy analysiscombined temperature-weighted voltage inconsistencymulti-scale feature fusion decision model
《Chinese Journal of Electrical Engineering》 2026 (1)
P.120-132,13
Supported by the Major Project of Basic Science(Natural Science)Research in Colleges and Universities of Jiangsu Province(23KJA480002)the“Qinglan Project”for Universities in Jiangsu Provincethe Scientific Research Foundation for High-Level Personnel of Jinling Institute of Technology(jit-rcyj-202202)the College Students’Innovative Entrepreneurial Training Plan Program(202513573053Y)。
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