A comprehensive review of state-of-charge and state-of-health estimation for lithium-ion battery energy storage systems

被引：12

作者：

Tao, Junjie ^{[1
]}

Wang, Shunli ^{[1
]}

Cao, Wen ^{[1
]}

Takyi-Aninakwa, Paul ^{[1
,2
]}

Fernandez, Carlos ^{[3
]}

Guerrero, Josep M. ^{[4
]}

机构：

[1] Southwest Univ Sci & Technol, Sch Informat Engn, Mianyang 621010, Sichuan, Peoples R China

[2] Inner Mongolia Univ Technol, Coll Elect Power, Hohhot 010080, Peoples R China

[3] Robert Gordon Univ, Sch Pharm & Life Sci, Aberdeen AB10 7GJ, Scotland

[4] Aalborg Univ, Dept Energy Technol, Pontoppidanstr 111, DK-9220 Aalborg, Denmark

来源：

IONICS | 2024年 / 30卷 / 10期

基金：

中国国家自然科学基金;

关键词：

Li-ion battery state estimation; Equivalent circuit model; Parameter identification; Kalman filtering; Deep learning; Based on data-driven algorithms; IMPEDANCE; VOLTAGE; MODEL;

D O I：

10.1007/s11581-024-05686-z

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

With the gradual transformation of energy industries around the world, the trend of industrial reform led by clean energy has become increasingly apparent. As a critical link in the new energy industry chain, lithium-ion (Li-ion) battery energy storage system plays an irreplaceable role. Accurate estimation of Li-ion battery states, especially state of charge (SOC) and state of health (SOH), is the core to realize the safe and efficient utilization of energy storage systems. This paper presents a systematic and comprehensive evaluation and summary of the most advanced Li-ion battery state estimation methods proposed in the past 3 years, focusing on analyzing data-driven state estimation algorithms. At the same time, the latest Li-ion battery data sets and data selection methods are analyzed, and future research trends and possible challenges are proposed. This review will provide a valuable reference for future academic research in Li-ion battery state estimation.

引用

页码：5903 / 5927

页数：25

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