Improved State-Of-Charge Estimation Algorithms for Lithium-ion Batteries in Electric Vehicles

As innovation burgeons and environmental consciousness rises, electric vehicles (EVs) are gaining traction over traditional fuel-powered counterparts, promising emissions-free transportation for a sustainable future. At the heart of an EV lies the lithium-ion battery (LIB), serving as the primary energy storage system. Integral to the LIB's reliable and secure operation is a micro-controller known as the Battery Management System (BMS). Among the critical parameters monitored by the BMS, real-time access to the state of charge (SoC) stands paramount. SoC denotes the remaining capacity of the battery, crucial for preventing overcharging, undercharging, enhancing capacity utilization, prolonging lifespan, improving reliability, cutting costs, and ensuring safety. However, directly measuring SoC is unfeasible due to its internal nature, prompting the need for accurate estimation methods. The highly nonlinear behavior of LIBs and diverse operating conditions further complicate this task. This paper presents the utilization of an Extended Kalman Filter (EKF) model for SoC estimation, showcasing superior performance compared to simplistic methods like coulomb counting. However, while EKF exhibits enhanced accuracy, potential drawbacks in real-time implementation merit consideration. Thus, there exists room for further methodological refinement to bolster performance.