基于改进GWO–SVR算法的锂电池剩余寿命预测  

Remaining useful life prediction for lithium-ion batteries based on an improved GWO–SVR algorithm

作　　者：金辉 胡寅逍 葛红娟[1] 郝志鹏 曾郑志远 唐泽鹏 JIN Hui;HU Yinxiao;GE Hongjuan;HAO Zhipeng;ZENG Zhengzhiyuan;TANG Zepeng(College of Civil Aviation,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China;China Academy of Civil Aviation Science and Technology,Beijing 100028,China)

机构地区：[1]南京航空航天大学民航学院,南京211106 [2]中国民航科学技术研究院,北京100028

出　　处：《工程科学学报》2024年第3期514-524,共11页Chinese Journal of Engineering

基　　金：国家自然科学基金民航联合基金重点资助项目(U2233205,U2133203)。

摘　　要：锂离子电池性能优越,已在B787等机型上得到应用.锂离子电池性能随着使用次数增加而衰退,准确预测锂电池剩余使用寿命从而及时维护/更换,对航班安全飞行具有重要意义.面向锂离子电池剩余寿命预测问题,本文采用容量增量分析等方法提取特征,基于灰色关联分析计算特征与电池容量的关联程度并筛选特征,提出一种基于改进灰狼优化算法(Improved grey wolf optimization, IGWO)和支持向量回归(Support vector regression, SVR)的锂离子电池剩余寿命预测方法.作为近年研究热点的灰狼优化(Grey wolf optimization, GWO)算法寻优性能出色,但是在应用中容易陷入局部最优.针对此问题,IGWO对GWO算法中的位置更新方程进行优化,对狼群中的个体添加了记忆与飞行功能,增强了算法全局搜索和收敛能力;同时基于Skew Tent映射产生混沌序列,优化狼群初始位置分布.基于标准测试函数对比GWO和IGWO算法的寻优能力,结果表明IGWO算法的收敛速度和寻优效果更好,能够避开GWO陷入的局部最优,在部分测试函数上将寻优精度提升了几十个数量级;基于NASA锂离子电池数据集开展IGWO–SVR、GWO–SVR和SVR的剩余寿命预测能力对比实验,结果证明IGWO–SVR能够有效提高预测精度,与GWO–SVR相比预测均方根误差值降低了10%以上.Lithium-ion batteries have been applied in civil aircraft such as the B787 with excellent performance.As the service time of lithium-ion batteries increases,their performance continues to decline.Therefore,accurately predicting the remaining useful life of lithium-ion batteries is helpful for timely maintenance or replacement,which is important for flight safety.This study extracts features from charge and discharge data of lithium-ion batteries with incremental capacity analysis to predict the remaining useful life of lithium-ion batteries.To this end,this study calculates the degree of correlation between the features and battery capacity based on grey correlation analysis,and then accordingly filters the features.Finally,a prediction method for the remaining useful life of lithium-ion batteries is proposed based on improved grey wolf optimization(IGWO)and support vector regression(SVR).The IGWO algorithm is proposed to solve the issue wherein grey wolf optimization(GWO)is prone to stagnation at local optima.As a research hotspot in the field of optimization algorithms in recent years,GWO has excellent optimization performance.However,it faces the problem of falling into local optimization and premature convergence in practical applications.To solve this problem,this study proposes IGWO to optimize and rewrite the position update equation and add memory and flight functions to each individual in the wolf pack so as to enhance the global search ability of the algorithm and improve its convergence speed.Furthermore,IGWO uses skew tent mapping to generate chaotic sequences to optimize the initial distribution of the grey wolf pack in the optimization space.Thus,it achieves a more uniform initial distribution effect than the traditional random generation method.This paper conducts an optimization comparison experiment based on commonly used benchmark functions to compare the optimization ability of GWO before and after improvement.The results show that the IGWO algorithm effectively avoids the stagnation at a local

关 键 词：锂离子电池 剩余寿命预测 容量增量分析 改进灰狼优化算法 支持向量回归 

分 类 号：TG142.71[一般工业技术—材料科学与工程]