Thermal state monitoring of lithium-ion batteries: Progress, challenges, and opportunities

被引:68
|
作者
Zheng, Yusheng [1 ]
Che, Yunhong [1 ]
Hu, Xiaosong [2 ]
Sui, Xin [1 ]
Stroe, Daniel-Ioan [1 ]
Teodorescu, Remus [1 ]
机构
[1] Aalborg Univ, Dept Energy, DK-9220 Aalborg, Denmark
[2] Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400044, Peoples R China
基金
中国国家自然科学基金;
关键词
Lithium-ion batteries; Electric mobility; Battery management; Thermal state monitoring; Temperature estimation; INTERNAL TEMPERATURE ESTIMATION; SPATIALLY-RESOLVED TEMPERATURES; ELECTROTHERMAL-COUPLED MODEL; ARTIFICIAL NEURAL-NETWORK; REAL-TIME PREDICTION; CELL TEMPERATURE; IMPEDANCE MEASUREMENTS; CHARGE ESTIMATION; MANAGEMENT-SYSTEMS; SENSOR PLACEMENT;
D O I
10.1016/j.pecs.2023.101120
中图分类号
O414.1 [热力学];
学科分类号
摘要
Transportation electrification is a promising solution to meet the ever-rising energy demand and realize sustainable development. Lithium-ion batteries, being the most predominant energy storage devices, directly affect the safety, comfort, driving range, and reliability of many electric mobilities. Nevertheless, thermal-related issues of batteries such as potential thermal runaway, performance degradation at low temperatures, and accelerated aging still hinder the wider adoption of electric mobilities. To ensure safe, efficient, and reliable operations of lithium-ion batteries, monitoring their thermal states is critical to safety protection, performance optimization, as well as prognostics, and health management. Given insufficient onboard temperature sensors and their inability to measure battery internal temperature, accurate and timely temperature estimation is of particular importance to thermal state monitoring. Toward this end, this paper provides a comprehensive review of temperature estimation techniques in battery systems regarding their mechanism, framework, and representative studies. The potential metrics used to characterize battery thermal states are discussed in detail at first considering the spatiotemporal attributes of battery temperature, and the strengths and weaknesses of applying such metrics in battery management are also analyzed. Afterward, various temperature estimation methods, including impedance/resistance-based, thermal model-based, and data-driven estimations, are elucidated, analyzed, and compared in terms of their strengths, limitations, and potential improvements. Finally, the key challenges to battery thermal state monitoring in real applications are identified, and future opportunities for removing these barriers are presented and discussed.
引用
收藏
页数:40
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