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

• Published in: IEEE transactions on industry applications Vol. 54; no. 2; pp. 1583 - 1591
• Main Authors: Meng, Jinhao, Ricco, Mattia, Luo, Guangzhao, Swierczynski, Maciej, Stroe, Daniel-Ioan, Stroe, Ana-Irina, Teodorescu, Remus
• Format: Journal Article
• Language: English
• Published: IEEE 01-03-2018
• Subjects: Batteries; Battery charge measurement; Comparison; Current measurement; Estimation; lithium-ion battery; nonlinear filter; online implementation; State of charge; state of charge (SOC) estimation; Temperature measurement; Voltage measurement
• ISSN: 0093-9994; 1939-9367
• DOI: 10.1109/TIA.2017.2775179

With the popularity of electrical vehicles, the lithium-ion battery industry is developing rapidly. To ensure battery safe usage and to reduce its average lifecycle cost, 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 classifies the recently proposed online SOC estimation methods 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.