综合智慧能源2025，Vol.47Issue(12)：25-33,9.DOI:10.3969/j.issn.2097-0706.2025.12.003
基于灰狼优化算法和组合核函数GPR模型的锂电池剩余使用寿命预测
Prediction of remaining useful life of lithium batteries based on grey wolf optimization and combined kernel function GPR model
摘要
Abstract
Accurately predicting the remaining useful life(RUL)of lithium-ion batteries can help users formulate reasonable maintenance strategies.As a data-driven method,Gaussian process regression(GPR)algorithm can effectively capture the nonlinear relationship between features and variables,and thus is widely used in the RUL estimation of lithium batteries.To address the deficiency of traditional single-kernel GPR in feature capture,a combined kernel GPR model based on the grey wolf optimization(GWO)algorithm was proposed.By combining kernel functions,the model's ability to capture nonlinear features was enhanced,and the GWO algorithm was utilized to overcome the difficulty in optimizing the hyperparameters of the combined kernel function.The NASA lithium battery cycle aging dataset was adopted to verify this model,and the single-kernel GPR model based on particle swarm(PSO)optimization was selected for comparison.The experimental results showed that the GWO-optimized combined kernel GPR model achieved a 37.89%reduction in root mean square error(RMSE)and a 70.42%reduction in mean absolute error(MAE)compared with the PSO-optimized single-kernel GPR model,demonstrating a stronger ability to capture capacity degradation.The results indicate that compared with the traditional GPR model,the GWO-optimized combined kernel GPR model has higher accuracy for the RUL prediction of lithium batteries.关键词
锂离子电池/剩余使用寿命/高斯过程回归/灰狼优化算法/电池老化/容量衰减/预测误差/粒子群优化算法Key words
lithium-ion battery/remaining useful life/Gaussian process regression/grey wolf optimization algorithm/battery aging/capacity degradation/prediction error/particle swarm optimization分类
信息技术与安全科学引用本文复制引用
胡林静,李在伟..基于灰狼优化算法和组合核函数GPR模型的锂电池剩余使用寿命预测[J].综合智慧能源,2025,47(12):25-33,9.基金项目
内蒙古自然科学基金项目(2025LHMS05031)Inner Mongolia Natural Science Foundation Project(2025LHMS05031) （2025LHMS05031）
