基于扩展卡尔曼滤波的电动汽车电池荷电状态估计

Chenguang Jiang, A. Taylor, Chen Duan, K. Bai
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引用次数: 37

摘要

提出了一种基于扩展卡尔曼滤波的电池荷电状态估计方法。首先,对锂离子电池SOC的扩展卡尔曼滤波器进行了数学设计。在MATLAB/Simulink中建立了电池荷电状态的仿真模型,结果表明,采用扩展卡尔曼滤波的电池荷电状态估计比采用库仑计数法的电池荷电状态估计更准确。这与数学分析一致。最后搭建了锂离子电池试验台,对理论分析和仿真结果进行了实验验证。实验结果表明,使用扩展卡尔曼滤波器估计SOC的平均误差小于1%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Extended Kalman Filter based battery state of charge(SOC) estimation for electric vehicles
This paper proposed a battery state of charge (SOC) estimation methodology utilizing the Extended Kalman Filter. First, Extended Kalman Filter for Li-ion battery SOC was mathematically designed. Next, simulation models were developed in MATLAB/Simulink, which indicated that the battery SOC estimation with Extended Kalman filter is much more accurate than that from Coulomb Counting method. This is coincident with the mathematical analysis. At the end, a test bench with Lithium-Ion batteries was set up to experimentally verify the theoretical analysis and simulation. Experimental results showed that the average SOC estimation error using Extended Kalman Filter is <;1%.
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