不同温度的双卡尔曼滤波算法电池组SOC估计

动力锂电池组的荷电状态SOC(state of charge)是整个电池管理系统的重要参数,能直接反映电动汽车剩余可行驶里程,因此如何精确地估计电池组的SOC值是至关重要的。由于电池组各单体电池的不一致性,以及电动汽车在行驶过程中的复杂环境,所以在电池组内单体电池负载电压的最小值Vmin模型的基础上运用统计学的方法,对模型中的各参数进行有关温度因素的拟合,并通过模拟汽车的实际行驶环境,在不同温度下进行实验,从而得到改进的Vmin模型;结合双卡尔曼滤波算法,实现对整个电池组的SOC估计。仿真和实验结果表明该方法对电池组SOC的估计精度有优越性。

SOC Estimation of Battery Pack Based on Dual Kalman Filtering Algorithm at Different Temperatures

The state of charge(SOC) of a lithium-ion power battery pack is an important parameter for the entire batzt-ery management system, which can directly reflect the remaining mileage that electric vehicles can run. As a result, it is essential to accurately estimate the SOC of the battery pack. Due to the nonuniformity of each cell in the battery pack, as well as the complex driving environment in which electric vehicles run, statistical methods are used in this paper to fit the model parameters into temperature-related factors on the basis of the V_min model, which describes the minimum load voltage of a single cell in the battery pack. Through simulating the actual driving environment of electrical vehicles, an experiment is carried out at different temperatures, thus an improved V_min model can be obtained. With the combination of dual Kalman filtering algorithm, the SOC estimation of the entire battery pack is realized. Simulation and experimental results show that the proposed method has advantage in improving the SOC estimation accuracy of the battery pack.