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Joint Estimation of SOH and RUL for Lithium-Ion Batteries Based on Improved Twin Support Vector Machineh

作     者:Liyao Yang Hongyan Ma Yingda Zhang Wei He 

作者机构:School of Electrical and Information EngineeringBeijing University of Civil Engineering and ArchitectureBeijing100044China Institute of Distributed Energy Storage Safety Big DataBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big DataBeijing100044China 

出 版 物:《Energy Engineering》 (能源工程(英文))

年 卷 期:2025年第122卷第1期

页      面:243-264页

核心收录:

学科分类:0808[工学-电气工程] 08[工学] 

基  金:funded by the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture under Grant GJZJ20220802 

主  题:State of health remaining useful life variational modal decomposition random forest twin support vector machine convolutional optimization algorithm 

摘      要:Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management ***,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is *** address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and ***,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance ***,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution *** Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction ***,the proposed models are validated using NASA and CALCE lithium-ion battery *** results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.

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