Validation of EKF based SoC estimation using vehicle dynamic modelling for range prediction

Demand for Li-ion batteries is soaring daily in the global Electric Vehicle (EV) market. Therefore, developing an accurate, efficient and low-cost Battery Management System (BMS) plays a pivotal role in the battery’s performance. One of the significant roles of the BMS is the accurate State of Charge (SoC) estimation of batteries. This paper uses Thevenin’s model-based Extended Kalman Filter (EKF) algorithm for SoC estimation. In order to prove the validity of the estimation model, SoC estimated using the EKF model is verified with the SoC obtained from the vehicle dynamic model developed in the MATLAB Simulink environment considering various drive cycles like LA92, FTP-72, and NEDC. The Root Mean Square Error (RMSE) chosen as the performance index has achieved much smaller values as 0.0277%, 0.00278%, and 0.0145% for one cycle of LA92, FTP-72, and NEDC drive cycles, respectively, and hence this approves its real-time applications. The proposed work provides a reliable and trustable estimated SoC for range estimation of the EV since the estimated SoC value here takes into account not only the drive cycle load current demand but also it considers other external factors like the weight of the vehicle, wind, rolling resistance between road and tire etc. For that purpose, the SoC estimation algorithm in this proposed work incorporates a vehicle dynamic model developed in the Simulink environment. It delivers a reliable estimated SoC on which the EV driver can depend on calculating its remaining range. •An EKF based State of Charge method has been employed for a Turnigy Graphene 5000 mAh battery.•Thevenin’s battery model is used for representing the dynamic behaviour of the battery.•Robustness of the model is verified under different temperature like, −10°C, 10°C, 25°C, and 40°C and along with a 10% initial SoC offset.•Vehicle Glider model is developed in the MATLAB Simulink environment for a battery pack of 50 Ah.•The estimated SoC matches with vehicle dynamic model SoC for various drive cycles with low Root Mean Square Error (RMSE).