基于分数阶理论的车用锂离子电池建模及荷电状态估计

针对电动汽车动力锂离子电池的状态估计问题,提出一种基于分数阶等效电路建模方法,并采用分数阶卡尔曼滤波算法估计电池荷电状态(SOC)。首先建立基于二阶等效电路的分数阶电池模型,采用遗传算法辨识阶数,然后利用分数阶卡尔曼滤波算法估计电池SOC,并与扩展卡尔曼滤波算法进行比较。实验结果表明,在恒流放电下采用分数阶模型,其端电压最大绝对误差为0.014V,SOC最大估计误差不超过2%。本文提出的基于二阶等效电路的分数阶模型及分数阶卡尔曼滤波算法,不仅给出了一种准确、可靠的建模方法,而且为有效提高电池管理系统中SOC估计的准确性提供了途径。

Modeling and State of Charge Estimation of Lithium-Ion Battery Based on Theory of Fractional Order for Electric Vehicle

This paper presents a fractional order equivalent circuit model and uses fractional order Kalman filter (FOKF) method for state of charge (SOC) estimation of lithium-ion power batteries in electric vehicles. Firstly, a fractional order battery model was established based on second-order equivalent circuit and the fractional orders were identified by genetic algorithm. The SOC was estimated depending on the FOKF method. Compared with extend Kalman filter (EKF) method, it is shown that the maximum absolute error of the terminal voltage is 0.014V under constant current discharge test. The maximum SOC estimation error is under 2% by FOKF, which has higher accuracy and faster convergence speed. The fractional order model proposed in this paper not only presents an accurate and reliable battery model, but also provides an effective means for improving the accuracy of SOC estimation in battery management system.