An Introductory Study of State of Charge Estimation Methods for Lithium-Ion Batteries

Abstract

This work provides an introductory analysis of methods for estimating the State of Charge (SOC) in Lithium-ion Batteries (LIBs), summarizing key concepts, addressing challenges, and demonstrating the application of the Kalman Filter (KF) as one of these methods. It begins with a foundational overview of battery fundamentals to establish essential background knowledge for understanding SOC estimation techniques. The role of Battery Management Systems (BMS) is explored, highlighting their importance in monitoring, protecting, and optimizing battery performance, focusing on SOC estimation. A comprehensive review of SOC estimation methods is conducted, covering approaches such as lookup tables, Coulomb counting, model-based methods, filter-based algorithms, and data-driven techniques. The study also identifies key challenges in SOC estimation, including measurement inaccuracies, cell variability, temperature sensitivity, and limitations in battery modeling, emphasizing the nonlinear behavior of aging effects. To demonstrate the practical application of these methods, a simple state-space model representation of a battery system is introduced, illustrating the feasibility of using the KF for SOC estimation. A basic KF algorithm is implemented and validated, focusing on clarity and accessibility rather than full technical complexity. Finally, a Simulink-based simulation of the KF is performed, providing an analysis of the results to evaluate the accuracy, efficiency, and potential areas for further optimization. This work aims to establish a foundation for future research and practical advancements in SOC estimation methods for LIBs.