Estimation of the state of charge (SOC) of a lithium-ion battery is one of the key technologies in battery management systems. The accuracy of SOC estimation mainly depends on the accuracy of the battery model. The traditional Thevenin model has limited application due to its fixed parameters. In addition, its accuracy is not high. This paper proposes a variable parameter equivalent hysteresis model based on the Thevenin model. The parameters of this model are regarded as variables that vary with temperature and SOC. They can be identified by hybrid pulse power characteristic (HPPC) experiments. In addition, the model also considers the hysteresis characteristics of the open circuit voltage (OCV) and uses a mathematical recursive equation to describe it. Experimental and simulation results show that the proposed model has a higher accuracy and a wider application than the Thevenin model. On the basis of this model, SOC estimation is carried out based on modified covariance extended Kalman filter (MVEKF) at different temperatures. The results show that the SOC estimation accuracy of the MVEKF method is significantly higher than that of an extended Kalman filter (EKF).

估计锂离子电池的充电状态（SOC）是电池管理系统中的关键技术之一。SOC估计的准确性主要取决于电池模型的准确性。传统的戴维宁模型由于其固定参数而具有局限性。另外，其准确性不高。提出了一种基于戴维南模型的变参数等效滞后模型。该模型的参数被视为随温度和SOC变化的变量。可以通过混合脉冲功率特性（HPPC）实验来识别它们。此外，该模型还考虑了开路电压（OCV）的磁滞特性，并使用数学递归方程对其进行了描述。实验和仿真结果表明，所提出的模型比戴维南模型具有更高的精度和更广泛的应用。在此模型的基础上，基于修正的协方差扩展卡尔曼滤波器（MVEKF）在不同温度下进行SOC估计。结果表明，MVEKF方法的SOC估计精度明显高于扩展卡尔曼滤波器（EKF）。