锂离子电池荷电状态预测方法研究

针对电动汽车锂离子动力电池组能量管理中的荷电状态(SOC)预测问题,提出一种根据SOC及电流(SOC-I)计算库仑效率的方法,并建立电池SOC、充放电电流及充放电库仑效率的关系.以无迹卡尔曼滤波(UKF)算法为基础,采用自适应无迹卡尔曼滤波(AUKF)算法预测电池SOC,并将提出的库仑效率计算方法与UKF算法相结合构造了SOC-I-AUKF算法,该算法在预测过程中不断调整库仑效率、系统噪声协方差以及量测噪声协方差,以实现系统状态最优化预测.实验结果表明,SOC-I-AUKF算法有较好的SOC预测效果,与UKF算法相比,其SOC预测绝对误差、相对误差和平均误差水平都有显著提高.

State of Charge Evaluation of Lithium-ion Batteries

Evaluation of the state of charge (SOC) is a key technology for electric vehicle battery management. This work develops a method to establish the relationship among Coulomb efficiency, SOC and charge/discharge current (I). The curve of SOC to I (SOC-I) is provided that could supply a reasonable Coulomb efficiency during prediction. Moreover, the algorithm of adaptive unscented Kalman filtering (AUKF) is used for battery SOC evaluation. A new SOC-I-AUKF algorithm combined the AUKF algorithm with SOC-I curve is developed. During the process of SOC prediction, the new algorithm could adjust the Coulomb efficiency, process noise covariance and measurement noise covariance to reach the optimal evaluation. Experiment results indicate that the SOC-I-AUKF algorithm has better performance than UKF algorithm in prediction of absolute error, relative error and average error.