| 改进灰狼优化最小二乘支持向量机的锂电池剩余寿命预测 |
| |
| 引用本文： | 郑青根,杨祥国,刘冬,李昕.改进灰狼优化最小二乘支持向量机的锂电池剩余寿命预测[J].重庆大学学报(自然科学版),2023,46(11):78-89. |
| |
| 作者姓名： | 郑青根 杨祥国 刘冬 李昕 |
| |
| 作者单位： | 武汉理工大学 船海与能源动力工程学院，武汉 430070 |
| |
| 基金项目： | 国家重点研发计划资助项目(2019YFE0104600)；国家自然科学基金资助项目(51909199，52271329)。 |
| |
| 摘 要： | 针对锂电池剩余寿命预测的直接健康因子难以测量以及预测精度不高等问题，提出一种改进灰狼优化最小二乘支持向量机（improved gray wolf optimization least-squares support vector machine, IGWO-LSSVM）的锂电池剩余寿命间接预测方法。该方法从电池放电特性曲线中获取3种表征电池性能退化的间接健康因子，通过引入tent混沌映射、收敛因子非线性递减与莱维飞行策略对灰狼算法加以改进，并结合LS-SVM模型，形成了具有全局优化的改进灰狼优化最小二乘支持向量机的锂电池寿命预测模型。最后利用NASA数据集对文中提出的方法进行了验证，并将实验结果与GWO-LSSVM、PSO-ELM和BP神经网络算法进行了对比分析，试验结果表明文中所提出的改进算法具有更好的预测性能。
|
| 关 键 词： | 锂电池 剩余寿命 灰狼优化 最小二乘支持向量机 莱维飞行 |
| 收稿时间： | 2022/6/22 0:00:00 |
Lithium battery remaining life prediction method based on improved grey wolf optimization least squares support vector machine |
| |
| ZHENG Qinggen,YANG Xiangguo,LIU Dong,LI Xin.Lithium battery remaining life prediction method based on improved grey wolf optimization least squares support vector machine[J].Journal of Chongqing University(Natural Science Edition),2023,46(11):78-89. |
| |
| Authors: | ZHENG Qinggen YANG Xiangguo LIU Dong LI Xin |
| |
| Affiliation: | School of Marine and Energy and Power Engineering, Wuhan University of Technology, Wuhan 430070, P. R. China |
| |
| Abstract: | To solve the problem of accurately predicting remaining life of lithium battery, this paper proposes an indirect prediction method based on improved grey wolf optimization least-squares support vector machine (IGWO-LSSVM). Three indirect health factors characterizing battery performance degradation are derived from discharge characteristic curves. To enhance prediction accuracy, the study incorporates a tent chaotic map, a nonlinear decreasing convergence factor, and a Levi flight strategy into the grey wolf algorithm. Combined with the LSSVM model, the lithium battery life prediction model with global optimization is formed. The proposed method is verified using the NASA data set and compared with GWO-LSSVM, PSO-ELM and BP algorithms. Experimental results show that the improved algorithm proposed in this paper outperforms other methods in terms of prediction accuracy. |
| |
| Keywords: | lithium battery remaining life gray wolf optimization least squares support vector machine Levy flight |
|
| 点击此处可从《重庆大学学报(自然科学版)》浏览原始摘要信息 |
|
点击此处可从《重庆大学学报(自然科学版)》下载全文 |
