State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

TLDR

Accurate SOC estimation is critical for battery management systems in electric vehicles, and factors such as battery characteristics, models, algorithms, and cell unbalancing significantly influence its accuracy and robustness. The study reviews challenges in SOC estimation by examining various existing estimation methodologies. It analyzes five main SOC estimation approaches—conventional, adaptive filter, learning, nonlinear observer, and hybrid methods—providing an in‑depth comparison. The paper concludes by highlighting remaining SOC estimation challenges and proposing future research directions.

Abstract

Sate of charge (SOC) accurate estimation is one of the most important functions in a battery management system for battery packs used in electrical vehicles. This paper focuses on battery SOC estimation and its issues and challenges by exploring different existing estimation methodologies. The key technologies of lithium-ion battery state estimation methodologies of the electrical vehicles categorized under five groups, such as the conventional method, adaptive filter algorithm, learning algorithm, nonlinear observer, and the hybrid method, are explored in an in-depth analysis. Lithium-ion battery characteristic, battery model, estimation algorithm, and cell unbalancing are the most important factors that affect the accuracy and robustness of SOC estimation. Finally, this paper concludes with the challenges of SOC estimation and suggests other directions for possible research efforts.