Extended Kalman Filter Approach for State of Charge Estimation of Lithium-Ion Battery

In recent years, there has been a growing concern over the escalating severity of global environmental degradation and the energy problem. The lithium-ion battery is extensively used in automotive applications owing to its extended lifespan and superior energy density. The measurement of the state of charge (SOC) of a Li-ion battery has significant importance as an indication. Ensuring the safe functioning of Li-ion batteries relies on the precise calculation of State of Charge (SOC). Nevertheless, the conventional technique of estimation, known as the ampere-hour integration approach, is susceptible to cumulative error and lacks the ability to sustain accurate outcomes over prolonged periods within an operational setting. Subsequently, the extended Kalman filter (EKF) approach is used to address the estimate inaccuracy. The test system has been constructed in MATALAB/Simulink in order to examine the effectiveness of the suggested technique. The simulation results demonstrate that the suggested technique outperforms previous methods in terms of performance and estimate SOC accurately across various working situations.