College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China.
Ebara Great Pumps Co., Ltd., Wenzhou 325200, China.
Sensors (Basel). 2023 Jan 1;23(1):467. doi: 10.3390/s23010467.
The state of charge (SOC) for a lithium-ion battery is a key index closely related to battery performance and safety with respect to the power supply system of electric vehicles. The Kalman filter (KF) or extended KF (EKF) is normally employed to estimate SOC in association with the relatively simple and fast second-order resistor-capacitor (RC) equivalent circuit model for SOC estimations. To improve the stability of SOC estimation, a two-stage method is developed by combining the second-order RC equivalent circuit model and the eXogenous Kalman filter (XKF) to estimate the SOC of a lithium-ion battery. First, approximate SOC estimation values are observed with relatively poor accuracy by a stable observer without considering parameter uncertainty. Second, the poor accuracy SOC results are further fed into XKF to obtain relative stable and accurate SOC estimation values. Experiments demonstrate that the SOC estimation results of the present method are superior to those of the commonly used EKF method. It is expected that the present two-stage XKF method will be useful for the stable and accurate estimation of SOC in the power supply system of electric vehicles.
锂离子电池的荷电状态（SOC）是电动汽车供电系统中与电池性能和安全密切相关的关键指标。卡尔曼滤波器（KF）或扩展卡尔曼滤波器（EKF）通常用于结合相对简单和快速的二阶电阻-电容（RC）等效电路模型进行 SOC 估计。为了提高 SOC 估计的稳定性，通过将二阶 RC 等效电路模型与外部卡尔曼滤波器（XKF）相结合，开发了一种两阶段方法来估计锂离子电池的 SOC。首先，通过不考虑参数不确定性的稳定观测器，以相对较差的精度观察近似 SOC 估计值。其次，将精度较差的 SOC 结果进一步输入到 XKF 中，以获得相对稳定和准确的 SOC 估计值。实验表明，本方法的 SOC 估计结果优于常用的 EKF 方法。预计本两阶段 XKF 方法将有助于电动汽车供电系统中 SOC 的稳定和准确估计。
