基于GWO-LSSVM的锂离子电池健康状态估算

State of Health Estimation of Li-ion Batteries Based on GWO-LSSVM

针对目前应用于电池健康状态(SOH)估算的算法需用大量数据样本来进行训练且估算效果不佳的问题,提出了一种基于灰狼优化(GWO)算法的最小二乘支持向量机(LSSVM)算法来估算电池SOH,依据灰色关联度分析法筛选出恒流充电时间作为适合估算电池SOH的输入特征参数。以18650钴酸锂电池充放电循环试验为例,采用所建立的算法模型在不同比例的训练集样本下对不同容量规格的电池进行SOH估算,并将估算结果与基于网格搜索法的LSSVM算法、基于粒子群优化算法的LSSVM算法的估算结果进行对比,结果表明,基于GWO算法的LSSVM算法模型适用于小样本数据且估算误差较小,用于电池SOH估算的效果更好。

The algorithms currently applied to state of health(SOH) estimation require numerous data samples for training and the estimation effect is not good. To address this issue, this study proposed a least-squares support vector machine(LSSVM)algorithm based on the grey wolf optimization(GWO) algorithm to estimate the SOH using the grey relational analysis method to choose constant current charging time as the input characteristic. Considering the 18650 lithium cobalt oxide battery charge/discharge cycle test as an example, the established algorithm model was used to estimate the SOH of batteries with different capacity specifications under different proportions of training set samples. The estimated results were compared with those obtained by the LSSVM algorithm based on the grid search method and the LSSVM algorithm based on the particle swarm optimization algorithm. The experimental results showed that the LSSVM algorithm model based on the GWO algorithm is suitable for small-sample data and is characterized by small estimation errors;therefore, it is more effective for battery SOH.