储能科学与技术2024，Vol.13Issue(5)：1643-1652,10.DOI:10.19799/j.cnki.2095-4239.2023.0865
基于蚁狮优化高斯过程回归的锂电池剩余使用寿命预测
Prediction of the remaining useful life of lithium batteries based on Antlion optimization Gaussian process regression
摘要
Abstract
Rapidly obtaining accurate information about the remaining useful life(RUL)and health status of a lithium battery is critical to maintaining its reliability.To solve the problems of low prediction accuracy regarding the RUL of lithium batteries,unsatisfactory hyperparameter optimization results,and poor prediction effect of the traditional Gaussian process regression(GPR)model,in this study,the Antlion optimization algorithm was used to optimize the hyperparameters of Gaussian process regression(hereinafter referred to as"ALO-GPR")to accurately predict the RUL of lithium batteries.First,according to the cycle curve of battery voltage during battery charging,six parameters were extracted as the health factors of the battery;subsequently,the correlation between these factors and the battery capacity was verified by using the Pearson correlation coefficient.Finally,the following four parameters were selected as the health factors:the average discharge voltage,the amount of charge amount stored by the battery in the constant current charging stage,the amount of charge stored by the battery in the whole charging stage,and the discharge temperature in the time integral.Finally,support vector regression,GPR,and ALO-GPR were used to predict the RUL of lithium batteries,and various indicators were compared and analyzed.The model proposed in this study is compared with models proposed in other literatures.The effectiveness of the proposed model is verified by using the NASA lithium battery dataset.The experimental results show that the RUL prediction model of ALO-GPR has a small error;the root mean square error is controlled within 1%;and,the average absolute error is controlled within 0.65%.Thus,ALO-GRP shows strong generalization and a good application prospect regarding the prediction of RUL of lithium batteries.关键词
锂电池/高斯过程回归/蚁狮优化算法/剩余使用寿命Key words
lithium-ion battery/Gaussian process regression/ant lion optimized algorithm/remaining useful life分类
信息技术与安全科学引用本文复制引用
冯娜娜,杨明,惠周利,王瑞洁,宁弘扬..基于蚁狮优化高斯过程回归的锂电池剩余使用寿命预测[J].储能科学与技术,2024,13(5):1643-1652,10.基金项目
山西省基础研究计划资助项目(202203021211088),国家自然科学基金项目(61971381,12272356). （202203021211088）
