SOC and SOE Estimation in Lithium-ion Batteries for EV Application

Lithium-ion (Li-ion) batteries have indeed become the battery technology of choice for electric vehicle(EV) manufacturers for several compelling reasons such as high energy density, low weight, fast charging and many more. Accurate monitoring of battery states is crucial for optimizing the energy storage management system of electric vehicles. Battery state monitoring helps ensure efficient operation, prolonged battery life, and enhance safety. In this paper, we have proposed a simple method of combined state of charge (SOC) and state of energy (SOE) estimation. In this work, we begin with modeling of Li-ion cell using the 2RC model, then Adaptive Extended Kalman Filter (AEKF) for SOC estimation and Extended Kalman Filter (EKF) for SOE estimation which is implemented in MATLAB/Simulink. Lastly, the simulation results demonstrate the algorithm's precision and suitability.