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A Comprehensive Review of Lithium-Ion Cell Temperature Estimation Techniques Applicable to Health-Conscious Fast Charging and Smart Battery Management Systems

Author

Listed:
  • Akash Samanta

    (Department of Applied Physics, Faculty of Electrical Engineering, University of Calcutta, Kolkata 700009, India)

  • Sheldon S. Williamson

    (Department of Electrical, Computer and Software Engineering, Faculty of Engineering and Applied Science, Ontario Tech University, Oshawa, ON L1G 0C5, Canada)

Abstract

Highly nonlinear characteristics of lithium-ion batteries (LIBs) are significantly influenced by the external and internal temperature of the LIB cell. Moreover, a cell temperature beyond the manufacturer’s specified safe operating limit could lead to thermal runaway and even fire hazards and safety concerns to operating personnel. Therefore, accurate information of cell internal and surface temperature of LIB is highly crucial for effective thermal management and proper operation of a battery management system (BMS). Accurate temperature information is also essential to BMS for the accurate estimation of various important states of LIB, such as state of charge, state of health and so on. High-capacity LIB packs, used in electric vehicles and grid-tied stationary energy storage system essentially consist of thousands of individual LIB cells. Therefore, installing a physical sensor at each cell, especially at the cell core, is not practically feasible from the solution cost, space and weight point of view. A solution is to develop a suitable estimation strategy which led scholars to propose different temperature estimation schemes aiming to establish a balance among accuracy, adaptability, modelling complexity and computational cost. This article presented an exhaustive review of these estimation strategies covering recent developments, current issues, major challenges, and future research recommendations. The prime intention is to provide a detailed guideline to researchers and industries towards developing a highly accurate, intelligent, adaptive, easy-to-implement and computationally efficient online temperature estimation strategy applicable to health-conscious fast charging and smart onboard BMS.

Suggested Citation

  • Akash Samanta & Sheldon S. Williamson, 2021. "A Comprehensive Review of Lithium-Ion Cell Temperature Estimation Techniques Applicable to Health-Conscious Fast Charging and Smart Battery Management Systems," Energies, MDPI, vol. 14(18), pages 1-25, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5960-:d:639299
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    References listed on IDEAS

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    1. Tanim, Tanvir R. & Rahn, Christopher D. & Wang, Chao-Yang, 2015. "State of charge estimation of a lithium ion cell based on a temperature dependent and electrolyte enhanced single particle model," Energy, Elsevier, vol. 80(C), pages 731-739.
    2. Liu, Binghe & Yin, Sha & Xu, Jun, 2016. "Integrated computation model of lithium-ion battery subject to nail penetration," Applied Energy, Elsevier, vol. 183(C), pages 278-289.
    3. Sumukh Surya & Vinicius Marcis & Sheldon Williamson, 2020. "Core Temperature Estimation for a Lithium ion 18650 Cell," Energies, MDPI, vol. 14(1), pages 1-21, December.
    4. Sun, Fengchun & Xiong, Rui & He, Hongwen & Li, Weiqing & Aussems, Johan Eric Emmanuel, 2012. "Model-based dynamic multi-parameter method for peak power estimation of lithium–ion batteries," Applied Energy, Elsevier, vol. 96(C), pages 378-386.
    5. Zhang, Jianan & Yang, Xiao-Guang & Sun, Fengchun & Wang, Zhenpo & Wang, Chao-Yang, 2020. "An online heat generation estimation method for lithium-ion batteries using dual-temperature measurements," Applied Energy, Elsevier, vol. 272(C).
    6. Sun, Li & Sun, Wen & You, Fengqi, 2020. "Core temperature modelling and monitoring of lithium-ion battery in the presence of sensor bias," Applied Energy, Elsevier, vol. 271(C).
    7. Jinlei Sun & Guo Wei & Lei Pei & Rengui Lu & Kai Song & Chao Wu & Chunbo Zhu, 2015. "Online Internal Temperature Estimation for Lithium-Ion Batteries Based on Kalman Filter," Energies, MDPI, vol. 8(5), pages 1-16, May.
    8. Yang, Fangfang & Wang, Dong & Zhao, Yang & Tsui, Kwok-Leung & Bae, Suk Joo, 2018. "A study of the relationship between coulombic efficiency and capacity degradation of commercial lithium-ion batteries," Energy, Elsevier, vol. 145(C), pages 486-495.
    9. Zheng, Fangdan & Jiang, Jiuchun & Sun, Bingxiang & Zhang, Weige & Pecht, Michael, 2016. "Temperature dependent power capability estimation of lithium-ion batteries for hybrid electric vehicles," Energy, Elsevier, vol. 113(C), pages 64-75.
    10. Saw, Lip Huat & Poon, Hiew Mun & Thiam, Hui San & Cai, Zuansi & Chong, Wen Tong & Pambudi, Nugroho Agung & King, Yeong Jin, 2018. "Novel thermal management system using mist cooling for lithium-ion battery packs," Applied Energy, Elsevier, vol. 223(C), pages 146-158.
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    2. Liu, Yongjie & Huang, Zhiwu & Wu, Yue & Yan, Lisen & Jiang, Fu & Peng, Jun, 2022. "An online hybrid estimation method for core temperature of Lithium-ion battery with model noise compensation," Applied Energy, Elsevier, vol. 327(C).

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