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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:
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.