基于组合模型的锂电池参数辨识和电池荷电状态在线联合估计

针对电池荷电状态(SOC)估算过程中开路电压与SOC之间的迟滞效应以及充放电电流和端电压中噪声的影响,提出了基于组合模型的Frisch方案双滤波(FSDF)法。先通过一阶RC等效电路模型结合Preisach离散模型建立新的模型,随后采用Frisch方案对模型的输入输出进行噪声方差估计,滤除部分输入输出噪声,最后使用扩展卡尔曼滤波结合无迹卡尔曼滤波进行参数实时更新和电池单体SOC估算。实验证明,FSDF方法对锂电池SOC估算结果与Frisch方案递推最小二乘-无迹卡尔曼滤波法等其他方法相比,具有精度高、鲁棒性好等特点。

Lithium Battery Parameter Identification and SOC Online Joint Estimation Based on Combined Model

Aiming at the hysteresis effects between open circuit voltage and SOC and the influences of noise in charge and discharge current and terminal voltage during SOC estimation, a Frisch scheme double filter (FSDF) method was proposed based on combined model. Firstly, a new model was established by using first-order RC equivalent circuit model combined with Preisach discrete model. Then the Frisch algorithm was used to estimate the noise variances of the input and output of models to filter out some of the input and output noises. Finally, the extended Kalman filter combined with the unscented Kalman filter was used to implement real-time parameter update and the battery cell SOC estimation. Experiments show that the FSDF method has higher accuracy and better robustness than that of the other methods such as Frisch scheme recursive least squares-unscented Kalman filter.