Novel State of Health Estimation for Lithium-Ion Battery Based on Differential Evolution Algorithm-Extreme Learning Machine  

作　　者：LI Qingwei FU Can XUE Wenli WEI Yongqiang SHEN Zhiwen 李庆伟;付灿;薛雯莉;魏勇强;申志文

机构地区：[1]College of Energy and Mechanical Engineering,Shanghai University of Electric Power,Shanghai 200090,China

出　　处：《Journal of Shanghai Jiaotong university(Science)》2025年第2期252-261,共10页上海交通大学学报(英文版)

基　　金：the Shanghai Sailing Program(No.18YF1409000)。

摘　　要：To ensure a long-term safety and reliability of electric vehicle and energy storage system,an accurate estimation of the state of health(SOH)for lithium-ion battery is important.In this study,a method for estimating the lithium-ion battery SOH was proposed based on an improved extreme learning machine(ELM).Input weights and hidden layer biases were generated randomly in traditional ELM.To improve the estimation accuracy of ELM,the differential evolution algorithm was used to optimize these parameters in feasible solution spaces.First,incremental capacity curves were obtained by incremental capacity analysis and smoothed by Gaussian filter to extract health interests.Then,the ELM based on differential evolution algorithm(DE-ELM model)was used for a lithium-ion battery SOH estimation.At last,four battery historical aging data sets and one random walk data set were employed to validate the prediction performance of DE-ELM model.Results show that the DE-ELM has a better performance than other studied algorithms in terms of generalization ability.

关 键 词：lithium-ion battery state of health(SOH) extreme learning machine(ELM) differential evolution(DE)algorithm 

分 类 号：TK02[动力工程及工程热物理] TP391[自动化与计算机技术—计算机应用技术]