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

Ming Zhang, Dongfang Yang, Jiaxuan Du, Hanlei Sun (), Liwei Li, Licheng Wang and Kai Wang ()
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Ming Zhang: School of Electrical Engineering, Weihai Innovation Research Institute, Qingdao University, Qingdao 266000, China
Dongfang Yang: Xi’an Traffic Engineering Institute, Xi’an 710300, China
Jiaxuan Du: Electrical Engineering and Automation, Northeast Electric Power University, Ji’lin 132012, China
Hanlei Sun: School of Electrical Engineering, Weihai Innovation Research Institute, Qingdao University, Qingdao 266000, China
Liwei Li: School of Control Science and Engineering, Shandong University, Jinan 250100, China
Licheng Wang: School of Information Engineering, Zhejiang University of Technology, Hangzhou 310000, China
Kai Wang: School of Electrical Engineering, Weihai Innovation Research Institute, Qingdao University, Qingdao 266000, China

Energies, 2023, vol. 16, issue 7, 1-28

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.

Keywords: LIB; SOH; data-driven algorithms; data processing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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