锂电池具有高能量密度、循环寿命长等优点而被广泛应用于电动汽车动力装置,但车辆运行状况复杂多变,且电池内部呈现高度非线性的性质,导致电池荷电状态(state of charge,SOC)难以准确计算.为优化锂电池SOC估计精度,构建结合Warburg元件的分数阶二阶RC模型,采用自适应遗传算法进行参数辨识;融合多新息理论和扩展卡尔曼滤波算法,提出基于多新息扩展卡尔曼滤波(multi innovation extended Kalman filter,MIEKF)的锂离子电池SOC估计算法,并利用试验数据验证该方法的有效性,为提高SOC估计精度和车载锂电池的循环使用寿命提供了新的方法途径和实践支撑.
SOC Estimation of Li-ion Battery Based on Multi Innovation Extended Kalman Filtering
Lithium batteries have the advantages of high energy density and long cycle life,and are widely used in electric vehicle power plants.However,the operating conditions of vehicles are complex and variable,and the battery exhibits highly nonlinear proper-ties,making it difficult to accurately calculate the state of charge(SOC)of the battery.In order to optimize the SOC estimation accu-racy of lithium batteries,a fractional second-order RC model combined with Warburg elements was constructed,and a adaptive genetic algorithm was used for parameter identification.Combining multi innovation theory and extended Kalman filter filter algorithm,an ion battery SOC estimation algorithm based on multi innovation extended Kalman filter(MIEKF)was proposed,and the effectiveness of this method was verified by experimental data,which provided a new approach and practical support for improving the SOC estimation accuracy and the cycle life of vehicle mounted lithium batteries.
lithium ion batteryfractional order modelmultiple innovation theoryextended Kalman filter(EKF)state of charge(SOC)
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