An Improved Thevenin Model-based State-of-Charge Estimation of a Commercial Lithium-Ion Battery Using Kalman Filter

Electric vehicles powered by Lithium-ion batteries are becoming increasingly popular due to global warming and fossil fuel depletion concerns. However, a major challenge lies in accurately estimating the state of charge (SoC) which affects the vehicle’s range capability. Accurately predicting the battery’s state of charge is crucial for battery management systems (BMS) in electric vehicles. However, simple algorithms like coulomb counting are insufficient due to the dynamic operational environment of electric vehicle batteries. A high-fidelity battery model with reliable parameter estimation strategy is required to accurately estimate the SoC. This paper presents a novel parameter identification strategy for the improved Thevenin equivalent circuit model (ECM), commonly found in the literature. The study involves experimental identification of the model parameters, building a simulation model in the MATLAB/Simulink environment, and verifying the performance of these models using actual data. The identified model is then used to employ a Kalman filtering method to estimate the SoC of a commercial lithium-ion battery. The presence of noise in the sensor measurements poses a challenge in the accurate estimation of SoC. This issue is also addressed in the current paper.