Overview of Machine Learning-Enabled Battery State Estimation Methods

被引：4

作者：

Zhuge, Yingjian ^{[1
]}

Yang, Hengzhao ^{[1
]}

Wang, Haoyu ^{[1
,2
]}

机构：

[1] ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China

[2] Shanghai Engn Res Ctr Energy Efficient & Custom A, Shanghai, Peoples R China

来源：

2023 IEEE APPLIED POWER ELECTRONICS CONFERENCE AND EXPOSITION, APEC | 2023年

基金：

中国国家自然科学基金;

关键词：

Machine learning; deep learning; state of charge (SOC); state of health (SOH); LITHIUM-ION BATTERIES; OF-CHARGE ESTIMATION; OPEN-CIRCUIT VOLTAGE; NEURAL-NETWORK; HEALTH ESTIMATION; SOC ESTIMATION; ONLINE STATE; CAPACITY; MODEL; PREDICTION;

D O I：

10.1109/APEC43580.2023.10131605

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

To ensure safe usage and robust performance of energy storage batteries, accurate state-of-charge (SOC) and state-of-health (SOH) estimations are required. Due to recent breakthroughs in machine learning and artificial intelligence methods, data-driven methods have attracted increased attention. This paper reports state-of-the-art research progress in machine learning-enabled methods for SOC and SOH estimations. Comprehensive comparisons are made in terms of the dataset, estimation accuracy, and battery type to provide a clear picture for SOC and SOH estimation. Moreover, the challenges and research opportunities on future SOC and SOH estimation are disclosed.

引用

页码：3028 / 3035

页数：8

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