基于不同核函数SVR的锂离子电池SOH预测比较

Holistic comparison of different kernel functions for support vector regression based on state-of-health prediction of lithium-ion battery

电池健康状态(state of health,SOH)预测是电池故障诊断与健康管理的重要组成部分,准确地预测电池健康状态至关重要。支持向量回归方法因具有良好的回归能力而被作为预测电池剩余使用寿命的首选数据驱动方法之一。核函数将样本从低维空间映射到高维空间,以便进行精确分类,参数选择决定核函数的映射能力,训练集的大小和分布也会影响支持向量回归(SVR)的回归能力。将锂离子电池数据分为前期、中期和后期训练数据。采用遗传算法(GA)和粒子群算法(PSO)对四个核函数的参数进行了优化。实验结果对比表明,采用径向基函数(RBF)的GA-SVR和PSO-SVR可以较准确地预测锂离子电池的剩余使用寿命。

Battery health prediction is an important part of prognostic and health management.It is particularly important to accurately predict the state of health(SOH).Owing to its excellent regression ability,support vector regression(SVR)is the preferred data-driven method used to predict the remaining useful life(RUL)of batteries.Kernel functions are used to map samples from a lower to a higher dimensional space for accurate classification.Parameter estimation considerably affects the mapping ability of kernel functions.The size and distribution of the training set also affect the regression ability of SVR.Lithium-ion battery data are divided into prophase,metaphase,and anaphase training data.Particle swarm optimization(PSO)and a genetic algorithm(GA)are applied to optimize the parameters of the four kernel functions.The comparison of the experimental results show that GA-SVR with a radial basis function(RBF)and PSO-SVR with an RBF in the anaphase can accurately predict the RUL of lithium-ion batteries.