Estimation of Lithium‐Ion Battery State of Charge for Electric Vehicles Using an Adaptive Joint Algorithm

Abstract As a new means of transportation, electric vehicles (EVs) have a lot of potential. On the other hand, EVs that employ lithium‐ion batteries face certain difficulties in forecasting the battery's health and remaining useful life. This paper uses the adaptive joint algorithm approach to calculate the battery's online parameters and accurate state of charge (SOC). To establish the battery online parameters, the forgetting factor recursive least square (FFRLS) technique is utilized, and the extended Kalman filter (EKF), unscented Kalman filter (UKF) are employed to estimate accurate SOC. Compared to the EKF/UKF method, the joint algorithm (FFRLS‐UKF) approach produces better results. The results are validated using the urban dynamometer driving schedule cycle and the ECE extra‐urban driving cycle (low powered vehicles) to determine the performance of the proposed algorithm. The error of the estimated SOC has fallen from 3.3% to 2%. The proposed adaptive joint algorithm has substantially improved the system's accuracy and provides better results than the EKF/UKF technique. Furthermore, the random variable noise is also supplied to the test data to ensure that the proposed method is robust.