【摘要】 精确的电池荷电状态(state of charge, SOC)估计对提高新能源汽车电池管理系统的性能、电池使用安全性以及整车能量管理策略的准确性具有至关重要的作用。综合考虑电池模型精度和复杂度,建立了锂离子电池二阶RC等效电路模型,运用自适应遗忘因子递推最小二乘法(adaptive forgetting factor-recursive least square, AFF-RLS)在线辨识模型参数。在此基础上,采用平方根容积卡尔曼滤波(square root cubature Kalman filter,SRCKF)估算电池SOC,使用动态应力测试工况(dynamic stress test, DST)对模型参数和SOC进行验证。研究结果表明,与无迹卡尔曼滤波(unscented Kalman filter,UKF)估算相比,SRCKF估算误差小、鲁棒性好。更多还原

【Abstract】 Accurate estimation of battery state of charge(SOC) plays a crucial role in improving the performance of battery management system, battery safety and the accuracy of vehicle energy management strategy. Considering the accuracy and complexity of the battery model, a second-order RC equivalent circuit model of lithium ion battery was established, and the model parameters were identified online by adaptive forgetting factor recursive least square method(AF-RLS). On this basis, square root cubature Kalman filter(SRCKF) was used to estimate the battery SOC,and dynamic stress test(DST) was used to verify the model parameters and SOC. The results show that compared with the unscented Kalman filter(UKF) estimation, SRCKF estimation has smaller error and better robustness.更多还原