Li-ion battery SOC estimation method based on the reduced order extended Kalman filtering

The extended Kalman filter (EKF) method for SOC estimation has some problems such as the lack of an accurate model, and model errors due to the variation in the parameters of the model due to the nonlinear behavior of a battery. To solve the aforementioned issues, this paper proposes a reduced order EKF including the measurement noise model and data rejection. In order to do so, the model of a battery in the EKF is simplified into the type of reduced order to decrease the calculation time. Additionally, to compensate the model errors caused by the reduced order model and variation in parameters, a measurement noise model and data rejection are implemented because the model accuracy is critical in the EKF algorithm in order to obtain a good estimation. Finally, the proposed algorithm is verified by short and long term experiments.