Because it is necessary for electric vehicle (EV) driver to know the remaining power of the EV battery during driving, thus it is very valuable to research the precise state‐of‐charge estimation, which can update the residual capacity of battery and prevent the occurrence of overcharge and over discharge phenomenon for battery. As compared with most model‐based methods, neural network‐based methods can directly use battery surface temperature as the input parameters of the model. In here, the state‐of‐charge of LiNi_0.6Co_0.2Mn_0.2O₂/graphite batteries is estimated using the compound method with improved extended Kalman filter and long short‐term memory network. The improved extended Kalman filter algorithm is composed of recursive least square method with forgetting factor algorithm, generalized regression neural network and extended Kalman filter algorithm. For the compound state‐of‐charge estimation method, the state‐of‐charge values are first estimated using the improved extended Kalman filter, and then optimized using long short‐term memory network. The datasets of dynamic stress test at 25°C are applied to train the long short‐term memory network, while the datasets under different conditions are used as validation datasets. The results reveal that the mean absolute error, maximum absolute error, and root mean square error for their combination method are less than 1%, 3%, 1.1%, respectively. Among three kinds of state‐of‐charge estimation methods, the compound method has the highest estimation accuracy under different working conditions.

中文翻译：

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使用改进的扩展卡尔曼滤波器和长短期记忆网络的复合方法估算LiNi0.6Co0.2Mn0.2O2 /石墨电池的荷电状态

由于电动汽车（EV）驾驶员在行驶过程中必须知道EV电池的剩余电量，因此研究精确的荷电状态估计非常重要，它可以更新电池的剩余电量并防止电池剩余电量的产生。电池发生过充，过放现象。与大多数基于模型的方法相比，基于神经网络的方法可以直接使用电池表面温度作为模型的输入参数。在这里，LiNi _0.6 Co _0.2 Mn _0.2 O ₂的荷电状态/石墨电池是使用具有改进的扩展卡尔曼滤波器和长短期记忆网络的复合方法估算的。改进的扩展卡尔曼滤波算法由遗忘因子递推最小二乘法，广义回归神经网络和扩展卡尔曼滤波算法组成。对于复合荷电状态估计方法，首先使用改进的扩展卡尔曼滤波器估计荷电状态值，然后使用长短期存储网络对其进行优化。将25°C下的动态应力测试数据集用于训练长短期记忆网络，而将不同条件下的数据集用作验证数据集。结果表明，它们的组合方法的平均绝对误差，最大绝对误差和均方根误差均小于1％，分别为3％，1.1％。在三种荷电状态估计方法中，复合方法在不同的工作条件下具有最高的估计精度。