首页|Characterization and identification towards dynamic-based electrical modeling of lithium-ion batteries

Characterization and identification towards dynamic-based electrical modeling of lithium-ion batteries

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Lithium-ion batteries are widely recognized as a crucial enabling technology for the advancement of elec-tric vehicles and energy storage systems in the grid.The design of battery state estimation and control algorithms in battery management systems is usually based on battery models,which interpret crucial battery dynamics through the utilization of mathematical functions.Therefore,the investigation of bat-tery dynamics with the purpose of battery system identification has garnered considerable attention in the realm of battery research.Characterization methods in terms of linear and nonlinear response of lithium-ion batteries have emerged as a prominent area of study in this field.This review has undertaken an analysis and discussion of characterization methods,with a particular focus on the motivation of bat-tery system identification.Specifically,this work encompasses the incorporation of frequency domain nonlinear characterization methods and dynamics-based battery electrical models.The aim of this study is to establish a connection between the characterization and identification of battery systems for researchers and engineers specialized in the field of batteries,with the intention of promoting the advancement of efficient battery technology for real-world applications.

Lithium-ion batteryBattery dynamicsNonlinear characterizationNonlinear battery model

Chuanxin Fan、Kailong Liu、Yaxing Ren、Qiao Peng

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School of Automation,Nanjing Institute of Technology,Nanjing 211167,Jiangsu,China

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

School of Engineering,University of Lincoln,Lincoln LN6 7TS,UK

Information Technology,Analytics & Operations Group,Queen's University Belfast,Belfast BT9 5EE,UK

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National Natural Science Foundation of ChinaScientific Research Foundation of Nanjing Institute of TechnologyNanjing Overseas Educated Personnel Science and Technology Innovation ProjectOpen Research Fund of Jiangsu Collaborative Innovation Center for Smart Distribution Network,Nanjing Institute of Technology

62373224YKJ202212XTCX202307

2024

10.1016/j.jechem.2024.01.040

能源化学
中国科学院大连化学物理研究所 中国科学院成都有机化学研究所

能源化学

CSTPCDEI
影响因子：0.654
ISSN：2095-4956
年,卷(期)：2024.92(5)