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Temperature rise prediction of lithium-ion battery suffering external short circuit for all-climate electric vehicles application

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  • Chen, Zeyu
  • Xiong, Rui
  • Lu, Jiahuan
  • Li, Xinggang

Abstract

External short circuit (ESC) is a severe fault that can cause the large current and high temperature of lithium-ion batteries (LiBs) immediately. Temperature rise prediction is crucial for LiB safety management in an all-climate electric vehicles application because many disastrous consequences are caused by high temperature. This study mainly investigates the ESC-caused temperature rise characteristics of LiB, and proposes an online prediction approach of the maximum temperature rise. Three original contributions are made: (1) Abusing tests of LiBs under ESC are conducted at varying ambient temperatures, and the influences of battery state of charge (SOC) and ambient temperature on the maximum temperature rise are revealed. (2) Characteristics of temperature rises are analysed, therein finding that the heat generation of LiBs caused by ESC presents two modes: Joule heat-dominant mode and reaction heat/Joule heat blended mode; leakage is an external manifestation of the latter. (3) Two heat generation modes are proved to be linearly separable at temperature rise discharge capacity plane, and then a two-step prediction approach of maximum temperature rise is proposed based on support vector machine. Finally, the presented approach is validated by the experimental data. The maximum temperature rise can be predicted up to 22.3 s ahead of time and very precise prediction results are obtained, where the mean prediction error for the eight test cells is 3.05%.

Suggested Citation

  • Chen, Zeyu & Xiong, Rui & Lu, Jiahuan & Li, Xinggang, 2018. "Temperature rise prediction of lithium-ion battery suffering external short circuit for all-climate electric vehicles application," Applied Energy, Elsevier, vol. 213(C), pages 375-383.
    • Handle: RePEc:eee:appene:v:213:y:2018:i:c:p:375-383
    DOI: 10.1016/j.apenergy.2018.01.068
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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. Xiong, Rui & Sun, Fengchun & Chen, Zheng & He, Hongwen, 2014. "A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 463-476.
    3. J.-M. Tarascon & M. Armand, 2001. "Issues and challenges facing rechargeable lithium batteries," Nature, Nature, vol. 414(6861), pages 359-367, November.
    4. Omar, Noshin & Monem, Mohamed Abdel & Firouz, Yousef & Salminen, Justin & Smekens, Jelle & Hegazy, Omar & Gaulous, Hamid & Mulder, Grietus & Van den Bossche, Peter & Coosemans, Thierry & Van Mierlo, J, 2014. "Lithium iron phosphate based battery – Assessment of the aging parameters and development of cycle life model," Applied Energy, Elsevier, vol. 113(C), pages 1575-1585.
    5. Xiong, Rui & Yu, Quanqing & Wang, Le Yi & Lin, Cheng, 2017. "A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter," Applied Energy, Elsevier, vol. 207(C), pages 346-353.
    6. Xiong, Rui & Cao, Jiayi & Yu, Quanqing, 2018. "Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 211(C), pages 538-548.
    7. Chao-Yang Wang & Guangsheng Zhang & Shanhai Ge & Terrence Xu & Yan Ji & Xiao-Guang Yang & Yongjun Leng, 2016. "Lithium-ion battery structure that self-heats at low temperatures," Nature, Nature, vol. 529(7587), pages 515-518, January.
    8. Chen, Zeyu & Xiong, Rui & Tian, Jinpeng & Shang, Xiong & Lu, Jiahuan, 2016. "Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles," Applied Energy, Elsevier, vol. 184(C), pages 365-374.
    9. Patil, Meru A. & Tagade, Piyush & Hariharan, Krishnan S. & Kolake, Subramanya M. & Song, Taewon & Yeo, Taejung & Doo, Seokgwang, 2015. "A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation," Applied Energy, Elsevier, vol. 159(C), pages 285-297.
    10. Xiong, Rui & Tian, Jinpeng & Mu, Hao & Wang, Chun, 2017. "A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries," Applied Energy, Elsevier, vol. 207(C), pages 372-383.
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