Feature selection strategy optimization for lithium-ion battery state of health estimation under impedance uncertainties  

作　　者：Xinghao Du Jinhao Meng Yassine Amirat Fei Gao Mohamed Benbouzid 

机构地区：[1]UMR CNRS 6027 IRDL,University of Brest,Brest 29238,France [2]School of Electrical Engineering,xi'an Jiaotong University,Xi'an 710049,Shaanxi,China [3]L@bISEN,ISEN Yncrea Ouest,Brest 29200,France [4]Schoolof Energy and Computer Science,University of Technology of Belfort-Montbeliard,Belfort 90000,France

出　　处：《Journal of Energy Chemistry》2025年第2期87-98,I0003,共13页能源化学(英文版)

摘　　要：Battery health evaluation and management are vital for the long-term reliability and optimal performance of lithium-ion batteries in electric vehicles.Electrochemical impedance spectroscopy(EIS)offers valuable insights into battery degradation analysis and modeling.However,previous studies have not adequately addressed the impedance uncertainties,particularly during battery operating conditions,which can substantially impact the robustness and accuracy of state of health(SOH)estimation.Motivated by this,this paper proposes a comprehensive feature optimization scheme that integrates impedance validity assessment with correlation analysis.By utilizing metrics such as impedance residuals and correlation coefficients,the proposed method effectively filters out invalid and insignificant impedance data,thereby enhancing the reliability of the input features.Subsequently,the extreme gradient boosting(XGBoost)modeling framework is constructed for estimating the battery degradation trajectories.The XGBoost model incorporates a diverse range of hyperparameters,optimized by a genetic algorithm to improve its adaptability and generalization performance.Experimental validation confirms the effectiveness of the proposed feature optimization scheme,demonstrating the superior estimation performance of the proposed method in comparison with four baseline techniques.

关 键 词：Lithium-ion battery Stateof health Electrochemical impedance spectroscopy Extreme gradient boosting 

分 类 号：TM912[电气工程—电力电子与电力传动]