首页|Physics-based battery SOC estimation methods:Recent advances and future perspectives

Physics-based battery SOC estimation methods:Recent advances and future perspectives

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The reliable prediction of state of charge(SOC)is one of the vital functions of advanced battery manage-ment system(BMS),which has great significance towards safe operation of electric vehicles.By far,the empirical model-based and data-driven-based SOC estimation methods of lithium-ion batteries have been comprehensively discussed and reviewed in various literatures.However,few reviews involving SOC estimation focused on electrochemical mechanism,which gives physical explanations to SOC and becomes most attractive candidate for advanced BMS.For this reason,this paper comprehensively sur-veys on physics-based SOC algorithms applied in advanced BMS.First,the research progresses of physical SOC estimation methods for lithium-ion batteries are thoroughly discussed and corresponding evaluation criteria are carefully elaborated.Second,future perspectives of the current researches on physics-based battery SOC estimation are presented.The insights stated in this paper are expected to catalyze the devel-opment and application of the physics-based advanced BMS algorithms.

Lithium-ion batteriesState of chargeElectrochemical modelBattery management system

Longxing Wu、Zhiqiang Lyu、Zebo Huang、Chao Zhang、Changyin Wei

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College of Mechanical Engineering,Anhui Science and Technology University,Chuzhou 233100,Anhui,China

Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle,Hubei University of Arts and Science,Xiangyang 441053,Hubei,China

School of Internet,Anhui University,Hefei 230039,Anhui,China

School of Mechanical and Electrical Engineering,Guilin University of Electronic Technology,Guilin 541004,Guangxi,China

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Open Project of Hubei Key Laboratory of Power System Design and Test for Electrical VehicleNational Natural Science Foundation of ChinaAnhui Provincial Natural Science Foundation

ZDSYS202304623030072308085ME142

2024

10.1016/j.jechem.2023.09.045

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)：2024.89(2)
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