A dual extended Kalman filter for the state of charge estimation of lithium-ion batteries

Document Type : Original Article


College of Electrical Engineering & New Energy, China Three Gorges University, Yichang, China


There are always various functions and features in battery management systems. Among these functions, state of charge estimation is considered a basic and fundamental function. Because the performance of many other functions is related to knowing the estimated SoC. Estimation of battery charge level using an adaptive dual extended Kalman filter (ADEKF) is the main goal of this paper. Conventional extended Kalman filters always have a small estimation error due to the presence of linearization in their process. On the other hand, if the number of variables in the battery model state increases, the volume of calculations will also increase. In order to solve these problems, this paper uses an ADEKF, in which the estimation process is performed by two parallel processes, and in addition, its measurement covariance matrix is adaptively selected during a separate path. Therefore, the volume of calculations is reduced, and on the other hand, the accuracy of charge level estimation using the desired method increases. In order to check the performance of this method, a series of simulation tests as well as practical tests have been performed and the proposed method’s performance has been compared with the conventional EKF methods. The results of practical tests and simulations confirm the good and successful performance of the desired method for estimating the battery charge level.