蓄电池荷电状态闭环动态估算模型

Close-loop dynamic estimation model for battery state of charge

使用基于电化学理论的安时模型,实现蓄电池荷电状态的实时估算。为了改善电化学安时模型的估算性能,设计了闭环反馈模型。闭环模型采用基于最优估计的扩展卡尔曼滤波算法,将电化学安时模型作为滤波算法的状态方程,蓄电池的荷电状态作为过程状态量,滤波算法中的观测方程的参数通过特定实验数据分析确定。实验结果表明,对于可靠的测量数据,电化学模型具有较高的估算精度;在较大测量偏差存在的情况下,闭环模型可以有效地修正估算偏差,得到准确的估算值。

A simple electrochemical analytic model with few predetermined parameters was introduced to establish the dynamic battery model for the real time battery＇s state of charge （SOC） estimation, According to the error caused by measure bias, a close-loop Kalman filter system was designed for improving the estimation performance. In filter system, electrochemistry-based model was as the state equation, the process equation was established from special experiments data. The results show that electrochemical model can estimate SOC accurately in the condition without measure bias. And, for existence of bias, the close-loop model can adjust the estimation process to achieve accurate SOC.