基于变采样率UKF算法的动力锂电池SOC估算

电动汽车处于不同行驶工况时，固定采样率的无迹卡尔曼滤波（Fixed Sampling Rate Unscented Kalman Filter, FSR-UKF）算法不能精确估算荷电状态（State of Charge，SOC）。为克服这一缺点，以传统的UKF算法为基础，针对电动汽车不同行驶状态下电池电流的变化特点，将变采样率采集方法与UKF算法融合，根据受控电压源电池等效电路模型及其参数辨识结果，将变采样率UKF（Variable Sampling Rate UKF, VSR-UKF）算法应用于电池SOC的估算中，并设计估算流程图。最后，通过仿真实验，获得最大相对误差不超过1%的电池端电压仿真结果以及最大相对误差不超过5%的SOC估算结果，验证了本研究的正确性和准确性。

SOC Estimation of Power Lithium Battery Based on Variable Sampling Rate UKF Algorithm

When the electric vehicle is in different driving conditions, the SOC value can’t be accurately estimated by applying the Unscented Kalman Filter algorithm with fixed sampling rate (FSR-UKF). To overcome this shortcoming, the variable sampling rate acquisition method was combined with the traditional UKF algorithm according to the characteristics of the battery current in different driving states of electric vehicles. According to the equivalent circuit model of the controlled voltage source battery and its parameter identification results, this study applied the VSR-UKF algorithm to estimate SOC of battery and designed the estimation flow chart. Finally, the simulation verification experiment was designed. The simulation results show that the maximum relative error of the battery terminal voltage is less than 1% and the maximum SOC estimation relative error is less than 5%. So the correctness and accuracy of the study are verified.