A Review of SOH Prediction of Li-Ion Batteries Based on Data-Driven Algorithms

Author:

Zhang Ming1,Yang Dongfang2,Du Jiaxuan3,Sun Hanlei1,Li Liwei4,Wang Licheng5,Wang Kai1ORCID

Affiliation:

1. School of Electrical Engineering, Weihai Innovation Research Institute, Qingdao University, Qingdao 266000, China

2. Xi’an Traffic Engineering Institute, Xi’an 710300, China

3. Electrical Engineering and Automation, Northeast Electric Power University, Ji’lin 132012, China

4. School of Control Science and Engineering, Shandong University, Jinan 250100, China

5. School of Information Engineering, Zhejiang University of Technology, Hangzhou 310000, China

Abstract

As an important energy storage device, lithium-ion batteries (LIBs) have been widely used in various fields due to their remarkable advantages. The high level of precision in estimating the battery’s state of health greatly enhances the safety and dependability of the application process. In contrast to traditional model-based prediction methods that are complex and have limited accuracy, data-driven prediction methods, which are considered mainstream, rely on direct data analysis and offer higher accuracy. Therefore, this paper reviews how to use the latest data-driven algorithms to predict the SOH of LIBs, and proposes a general prediction process, including the acquisition of datasets for the charging and discharging process of LIBs, the processing of data and features, and the selection of algorithms. The advantages and limitations of various processing methods and cutting-edge data-driven algorithms are summarized and compared, and methods with potential applications are proposed. Effort was also made to point out their application methods and application scenarios, providing guidance for researchers in this area.

Funder

Youth Fund of Shandong Province Natural Science Foundation

Key Projects of Shandong Province Natural Science Foundation

Guangdong Provincial Key Lab of Green Chemical Product Technology

Zhejiang Province Natural Science Foundation

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

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