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本文提出一种由交互式多模型和扩展卡尔曼滤波两种算法相结合而形成的滤波器,并应用于锂离子电池非线性系统的状态估计.先采用两个不同参数的戴维宁电路模型描述锂离子电池的动态特征.再将交互式多模型扩展卡尔曼滤波器和传统扩展卡尔曼滤波器分别用于荷电状态估计,针对复合脉冲功率测试和城市道路循环工况进行了数值仿真实验,针对恒流放电进行了硬件仿真实验.最后,对实验结果分析表明交互式多模型卡尔曼滤波算法的有效性和相对以传统方法在估计误差方面的优势,计算量增加合理.
In this paper, we propose a filter which is formed by the combination of interactive multi-model and extended Kalman filter and applied to the state estimation of lithium-ion battery nonlinear system.We first use the two different parameters of the Thevenin circuit model to describe the lithium Ion battery dynamic characteristics.Another interactive multi-model extended Kalman filter and traditional extended Kalman filter were used for the state of charge estimation, for the composite pulse power test and urban road cycling conditions for numerical simulation experiments, for The constant current discharge is simulated by hardware.Finally, the experimental results show that the efficiency of the interactive multi-model Kalman filter is reasonable and the computational cost is increased reasonably compared with the traditional method in terms of estimation error.