基于优化支持向量回归算法的锂离子电池可用容量估计  

Available capacity estimation of lithium-ion batteriesbased on the optimized support vector regression algorithm

作　　者：陈峥[1] 陈洋 申江卫[1] 夏雪磊 沈世全 肖仁鑫[1] CHEN Zheng;CHEN Yang;SHEN Jiangwei;XIA Xueei;SHEN Shiquan;XIAO Renxin(Faculty of Transportation Engineering,Kunming University of Science and Technology,Kunming 650500,Yunnan,China)

机构地区：[1]昆明理工大学交通工程学院,云南昆明650500

出　　处：《储能科学与技术》2023年第10期3203-3213,共11页Energy Storage Science and Technology

基　　金：国家自然科学基金(52267022);云南省基础研究计划项目(202301AT070423);昆明理工大学自然科学研究基金(KK23202202021)。

摘　　要：为了解决当前基于数据驱动的锂离子电池可用容量估计算法存在的老化特征提取不准确、可用容量衰退趋势跟踪精度低以及模型参数寻优耗时长等问题,本工作探究了一种基于优化支持向量机回归算法,用来对锂离子电池的可用容量进行准确估算。首先,通过分析锂电池老化数据,提取了电池容量增量曲线峰值以及峰值对应电压作为表征电池老化状态的特征因子,通过皮尔逊相关系数分析了特征因子的合理性;随后,选用麻雀优化算法完成支持向量机回归算法的核函数参数寻优,并基于优化后的支持向量机回归模型实现了电池可用容量的准确估计;最后通过对比不同核参数寻优算法验证了麻雀优化算法在参数寻优方面的先进性,并通过与传统支持向量机、高斯过程回归、长短期记忆网络等算法估计可用容量对比,验证了模型的精确性。结果表明:本工作建立的优化支持向量回归模型,能够有效追踪锂离子电池的衰退轨迹,实现对电池可用容量的准确估计,并且在不同电池上取得了较好的估算结果,可用容量最大估计误差低于2%。The current data-driven available capacity estimation algorithms for lithium-ion batteries encounter various challenges,including an inaccurate aging feature extraction,a low tracking accuracy of the available capacity decline trend,and a long time required for model parameter optimization.This study addresses these issues by proposing an optimized support vector machine regression algorithm that accurately estimates the available capacity of lithium-ion batteries.First,through an analysis of the aging data of a lithium-ion battery,the peak value of the battery capacity increment curve and the corresponding voltage of this peak value are extracted as the characteristic factors for the battery's aging state.The rationality of these characteristic factors is then analyzed using the Pearson correlation coefficient.The sparrow optimization algorithm is used to optimize the kernel function parameters of the support vector machine(SVM) regression algorithm,and the available battery capacity is accurately estimated based on the optimized SVM regression model.Finally,the advanced nature of the sparrow optimization algorithm in parameter optimization is verifiedby comparing different kernel parameter optimization algorithms.The model accuracy is verified by comparing it with the traditional SVM,Gaussian process regression,long short-term memory network,and other algorithms for estimating the available capacity.In conclusion,the proposed optimized support vector regression model effectively tracks the decline trajectory of lithium-ion batteries and accurately estimatestheir available capacity.It obtains better estimation results on different batteries,with the maximum estimation error of available capacity being less than 2%.

关 键 词：锂离子电池 可用容量估计 支持向量回归 麻雀优化算法 

分 类 号：TM911[电气工程—电力电子与电力传动]