SOC Estimation Based on Hysteresis Characteristics of Lithium Iron Phosphate Battery

In order to improve the estimation accuracy of the state of charge (SOC) of lithium iron phosphate power batteries for vehicles, this paper studies the prominent hysteresis phenomenon in the relationship between the state of charge and the open circuit voltage (OCV) curve of the lithium iron phosphate battery. Through the hysteresis characteristic test of the battery, the corresponding SOC-OCV data when the battery is charged or discharged from different SOC states are analyzed. According to the approximation trend of the hysteresis main loop curve by the data points, a differential equation model for approximately solving the charge or discharge hysteresis small loop curve under any SOC state is established, and the adjustment parameters of the model are analyzed and debugged in sections. Then, based on the second-order Thevenin equivalent circuit model, the forgetting factor recursive least squares method is used to identify the model parameters online. When deriving the relationship between the OCV and SOC, according to the state of charge and discharge and the current SOC value, the approximate model of the real hysteresis small loop curve in the current state is solved in real time, and the extended Kalman recursion algorithm is substituted to correct the corresponding relationship between the OCV and SOC. Finally, the integrated forgetting factor recursive least squares online parameter identification and extended Kalman filter to correct the SOC-OCV hysteresis relationship in real time considering the hysteresis characteristics are used to complete the real-time estimation of the SOC of the lithium iron phosphate battery. The synthesis algorithm proposed in this paper and the Kalman filter algorithm without considering the hysteresis characteristics are compared and verified under the Dynamic Stress Test (DST) data. Based on the method proposed in this paper, the maximum error of terminal voltage is 0.86%, the average error of terminal voltage is 0.021%, the root mean square error (RMSE) of terminal voltage is 0.042%, the maximum error of SOC estimation is 1.22%, the average error of SOC estimation is 0.41%, the average error of SOC estimation is 0.41%, and the RMSE of SOC estimation is 0.57%. The results show that the comprehensive algorithm proposed in this paper has higher accuracy in both terminal voltage following and SOC estimation.