An Overview and Comparison of Online Implementable SOC Estimation Methods for Lithium-Ion Battery

TLDR

The rapid growth of electric vehicles has spurred the lithium‑ion battery industry, creating a need for accurate, real‑time state‑of‑charge tracking to ensure safety and reduce lifecycle costs. This review classifies recent online SOC estimation methods into five categories. The authors compare these methods on accuracy, robustness, and computational load, and benchmark seven nonlinear filters on accuracy and execution time for online use.

Abstract

With the popularity of Electrical Vehicles (EVs), Lithium-ion battery industry is developing rapidly. To ensure the battery safe usage and to reduce its average lifecycle cost, an accurate State of Charge (SOC) tracking algorithms for real-time implementation are required for different applications. Many SOC estimation methods have been proposed in the literature. However, only a few of them consider the real-time applicability. This paper reviews recently proposed online SOC estimation methods and classifies them into five categories. Their principal features are illustrated, and the main pros and cons are provided. The SOC estimation methods are compared and discussed in terms of accuracy, robustness, and computation burden. Afterward, as the most popular type of model based SOC estimation algorithms, seven nonlinear filters existing in literature are compared in terms of their accuracy and execution time as a reference for online implementation.