A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network. (16th December 2021)

• Record Type: Journal Article
• Authors: Cui, Zhenhua; Wang, Licheng; Li, Qiang; Wang, Kai
• Is Part Of: International journal of energy research. Volume 46:Number 5(2022)
• Page Start: 5423
• Page End: 5440
• Publication Date: 2021-12-16
• Subjects: deep learning -- lithium‐ion battery -- neural network -- state of charge estimation -- state of charge; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Power resources -- Research -- Periodicals; 621.042
• DOI: 10.1002/er.7545
• Languages: English
• ISSNs: 0363-907X

Abstract

Summary: Implementing carbon neutrality and emission peak policies requires a high‐level electric vehicle field. Lithium‐ion batteries have been considered an essential component of electric vehicle power batteries. Effective state of charge (SOC) estimation for lithium‐ion batteries is a critical problem that needs to be addressed at present. With the feature extraction and fitting capability, the neural network can achieve accurate SOC estimation without considering the internal electrochemical state of the battery. This article overviews the definition of SOC and the relationship with battery aging state. Then, by examining recent literature on estimating the SOC of Lithium‐ion batteries using neural network methods, the methods are classified into three categories: feed‐forward neural network method, deep learning method, and hybrid method. The progress of neural network methods in SOC estimation applications is systematically reviewed, including principles, advantages, disadvantages, current status, and estimation errors. Possible recommendations for next‐generation intelligent battery management systems and SOC estimation are also presented. This review's highlighted insights will inspire researchers in the battery field and point the way to developing electric vehicles. Abstract : SOC estimation is fundamental to the future development of the electric vehicles. Existing neural network‐based methods for SOC estimation are reviewed. The advantages and disadvantages of SOC estimation methods are summarized. Some selective advice and essential future directions are proposed for SOC estimation in this review.