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Arrhenius Equation-Based Cell-Health Assessment: Application to Thermal Energy Management Design of a HEV NiMH Battery Pack

Author

Listed:
  • Yalian Yang

    (State Key Laboratory of Mechanical Transmission (SKLMT), Chongqing University, Chongqing 400030, China)

  • Xiaosong Hu

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Department of Signals and Systems, Chalmers University of Technology, 41296 Gothenburg, Sweden)

  • Datong Qing

    (State Key Laboratory of Mechanical Transmission (SKLMT), Chongqing University, Chongqing 400030, China)

  • Fangyuan Chen

    (State Key Laboratory of Mechanical Transmission (SKLMT), Chongqing University, Chongqing 400030, China)

Abstract

This paper presents a model-based cell-health-conscious thermal energy management method. An Arrhenius equation-based mathematical model is firstly identified to quantify the effect of temperature on the cell lifetime of a Nickel Metal Hydride (NiMH) battery pack. The cell aging datasets collected under multiple ambient temperatures are applied to extract the Arrhenius equation parameters. The model is then used as an assessment criterion and guidance for the thermal management design of battery packs. The feasibility and applicability of a pack structure with its cooling system, is then evaluated, and its design problems are studied by a computational fluid dynamics (CFD) analysis. The performance and eligibility of the design method is validated by both CFD simulations and experiments.

Suggested Citation

  • Yalian Yang & Xiaosong Hu & Datong Qing & Fangyuan Chen, 2013. "Arrhenius Equation-Based Cell-Health Assessment: Application to Thermal Energy Management Design of a HEV NiMH Battery Pack," Energies, MDPI, vol. 6(5), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:5:p:2709-2725:d:25925
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    References listed on IDEAS

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    Cited by:

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    3. Martin Henke & Getu Hailu, 2020. "Thermal Management of Stationary Battery Systems: A Literature Review," Energies, MDPI, vol. 13(16), pages 1-16, August.
    4. Mayank Jha & Frede Blaabjerg & Mohammed Ali Khan & Varaha Satya Bharath Kurukuru & Ahteshamul Haque, 2019. "Intelligent Control of Converter for Electric Vehicles Charging Station," Energies, MDPI, vol. 12(12), pages 1-25, June.
    5. Shasha Deng & Kuining Li & Yi Xie & Cunxue Wu & Pingzhong Wang & Miao Yu & Bo Li & Jintao Zheng, 2019. "Heat Pipe Thermal Management Based on High-Rate Discharge and Pulse Cycle Tests for Lithium-Ion Batteries," Energies, MDPI, vol. 12(16), pages 1-14, August.
    6. Xiaobin Hong & Nianzhi Li & Jinheng Feng & Qingzhao Kong & Guixiong Liu, 2015. "Multi-Electrode Resistivity Probe for Investigation of Local Temperature Inside Metal Shell Battery Cells via Resistivity: Experiments and Evaluation of Electrical Resistance Tomography," Energies, MDPI, vol. 8(2), pages 1-23, January.
    7. Yu Sui & Shiming Song, 2020. "A Multi-Agent Reinforcement Learning Framework for Lithium-ion Battery Scheduling Problems," Energies, MDPI, vol. 13(8), pages 1-13, April.

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