SOC Estimation of Lithium Battery Based on Improved Kalman Filtering Algorithm

The accurate prediction of battery state of charge (SOC) has a very important significance for the pure electric vehicle to run safely and reliably. The complex chemical reaction inside the cell determines the battery's nonlinear, at the same time, it is affected by the environment temperature, charge and discharge times and the aging of the battery, so it is difficult to obtain a more accurate SOC estimation results. Therefore, this paper establishes the PNGV model of the lithium iron phosphate battery, and proposes an improved Kalman filtering algorithm for the estimation of SOC. This method combines the method of the open circuit voltage and current time integral method, the experimental results show that the improved Kalman filter algorithm for the estimation of SOC is highly accurate.