测试科学与仪器2024，Vol.15Issue(3)：397-407,11.DOI:10.62756/jmsi.1674-8042.2024041
基于集成高斯过程回归的锂离子电池健康状态预测
State of health prediction for lithium-ion batteries based on ensemble Gaussian process regression
摘要
Abstract
The performance of lithium-ion batteries(LIBs) gradually declines over time,making it critical to predict the battery's state of health(SOH) in real-time.This paper presents a model that incorporates health indicators and ensemble Gaussian process regression (EGPR) to predict the SOH of LIBs.Firstly,the degradation process of an LIB is analyzed through indirect health indicators(HIs) derived from voltage and temperature during discharge.Next,the parameters in the EGPR model are optimized using the gannet optimization algorithm(GOA),and the EGPR is employed to estimate the SOH of LIBs.Finally,the proposed model is tested under various experimental scenarios and compared with other machine learning models.The effectiveness of EGPR model is demonstrated using the National Aeronautics and Space Administration (NASA) LIB.The root mean square error(RMSE) is maintained within 0.20％,and the mean absolute error(MAE) is below 0.16％,illustrating the proposed approach's excellent predictive accuracy and wide applicability.关键词
锂离子电池/集成高斯过程回归/健康状态/健康因子/塘鹅优化算法Key words
lithium-ion batteryies (LIBs)/ensemble Gaussian process regression(EGPR)/state of health(SOH)/health indicators (HIs)/gannet optimization algorithm(GOA)引用本文复制引用
惠周利,王瑞洁,冯娜娜,杨明..基于集成高斯过程回归的锂离子电池健康状态预测[J].测试科学与仪器,2024,15(3):397-407,11.基金项目
This work was supported by Fundamental Research Program of Shanxi Province(No.202203021211088) （No.202203021211088）
and Shanxi Provincial Natural Science Foundation  （）
(No.202204021301049). （No.202204021301049）
