【摘要】 锂电池的荷电状态估计(SOC)在动力电池管理系统中占有重要地位，准确的SOC预测是锂电池安全工作的关键保证。文章针对由电池模型的参数固定而导致模型参数辨识准确性不够以及传统无迹卡尔曼滤波精度较低、稳定性差等问题，采用可变遗忘因子最小二乘算法(VFFRLS)对电池模型进行在线参数辨识，再联合自适应无迹卡尔曼滤波算法(AUKF)来估计SOC。在UDDS工况下对联合估计算法进行验证，实验结果表明，联合估计算法可将SOC估计误差控制在2.07%以内，能够有效提高SOC估计的准确性和鲁棒性。更多还原

【Abstract】 The state of charge estimation(SOC) of lithium battery plays an important role in the power battery management system, and accurate SOC prediction is the key guarantee for the safe operation of lithium battery. Aiming at the problems such as insufficient accuracy of model parameter identification due to the fixed parameters of the battery model and low accuracy and poor stability of the traditional unscented Kalman filter, this paper uses the variable forgetting factor least square algorithm(VFFRLS) to identify the battery model online parameters, and then combines the adaptive unscented Kalman filter algorithm(AUKF) to estimate SOC. The experimental results show that the joint estimation algorithm can control the SOC estimation error within 2.07%, which can effectively improve the accuracy and robustness of SOC estimation.更多还原