储能科学与技术2025，Vol.14Issue(6)：2476-2487,12.DOI:10.19799/j.cnki.2095-4239.2024.1253
基于容量增量分析与VMD-GWO-KELM的锂电池健康状态估计
State of health estimation for lithium battery based on incremental capacity analysis and VMD-GWO-KELM
摘要
Abstract
To overcome the limitations of traditional state of health(SOH)estimation methods—such as inadequate feature extraction,nonlinear complexity,and difficulty in model parameter optimization—this study proposes a novel SOH estimation approach based on incremental capacity analysis combined with variational mode decomposition(VMD),grey wolf optimization(GWO),and kernel extreme learning machine(KELM).First,an improved voltage-capacity model based on the Lorentz function is employed to fit voltage-capacity data during the constant-current charging process,enabling the extraction of health indicators such as peak voltage,peak value,and peak area.Model parameters are optimized using the GWO algorithm,thereby improving feature extraction accuracy and robustness.Next,VMD is applied to decompose SOH-related signals into multiple intrinsic mode functions.These components serve as inputs to individual sub-models,effectively capturing signal characteristics across distinct frequency domains while mitigating noise and mode mixing.Subsequently,the GWO algorithm is used to optimize the key parameters of the KELM model,significantly enhancing its nonlinear regression capability and estimation accuracy.The proposed method is evaluated through comparative analyses across different training data sizes,estimation models,and datasets from multiple batteries.Experimental results demonstrate that the proposed method achieves high-accuracy SOH estimation using only 100 cycles of data,with a mean absolute error of 0.9751%and a maximum error of 1.9340%.The model also exhibits strong robustness and generalization performance.关键词
锂离子电池/健康状态/容量增量分析/变分模态分解/灰狼优化/核极限学习机Key words
lithium-ion batteries/state of health/incremental capacity analysis/variational mode decomposition/grey wolf optimization/kernel extreme learning machine分类
信息技术与安全科学引用本文复制引用
陈峥,多功东,申江卫,沈世全,刘昱,魏福星..基于容量增量分析与VMD-GWO-KELM的锂电池健康状态估计[J].储能科学与技术,2025,14(6):2476-2487,12.基金项目
国家自然科学基金(52267022),云南省基础研究计划项目(202401AS070118). （52267022）
