A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries

被引：132

作者：

Luo, Kai ^{[1
]}

Chen, Xiang ^{[2
]}

Zheng, Huiru ^{[3
]}

Shi, Zhicong ^{[1
]}

机构：

[1] Guangdong Univ Technol, Sch Mat & Energy, Guangzhou 510006, Guangdong, Peoples R China

[2] Tsinghua Univ, Dept Chem Engn, Beijing 100084, Peoples R China

[3] Ulster Univ, Sch Comp, Belfast BT15 1ED, North Ireland

来源：

JOURNAL OF ENERGY CHEMISTRY | 2022年 / 74卷

基金：

中国国家自然科学基金;

关键词：

Lithium-ion battery; State of health; State of charge; Remaining useful life; Data-driven; SUPPORT VECTOR MACHINE; USEFUL LIFE PREDICTION; NEURAL-NETWORKS; HIGH-PERFORMANCE; MODEL; PROGNOSTICS; STRATEGIES; DIAGNOSIS; ELECTROCHEMISTRY; ELECTROLYTE;

D O I：

10.1016/j.jechem.2022.06.049

中图分类号：

O69 [应用化学];

学科分类号：

081704 ;

摘要：

In the field of energy storage, it is very important to predict the state of charge and the state of health of lithium-ion batteries. In this paper, we review the current widely used equivalent circuit and electro-chemical models for battery state predictions. The review demonstrates that machine learning and deep learning approaches can be used to construct fast and accurate data-driven models for the prediction of battery performance. The details, advantages, and limitations of these approaches are presented, com-pared, and summarized. Finally, future key challenges and opportunities are discussed.(c) 2022 Science Press and Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by ELSEVIER B.V. and Science Press. All rights reserved.

引用

页码：159 / 173

页数：15

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