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Novel experimental approach for the characterisation of Lithium-Ion cells performance in isothermal conditions

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  • Landini, S.
  • O’Donovan, T.S.

Abstract

Lithium-Ion cells performance is sensitive to the cell temperature. Therefore, experimental evidence is needed to identify the optimal cell isothermal condition to be achieved by a Thermal Management System (TMS). Previous studies have experimentally investigated the cells electrical performance under adiabatic or controlled environmental temperature (i.e. isoperibolic) thermal boundary conditions. Notably, however, these conditions do not impose a uniform cell’s surface temperature, especially at high discharge rates (DR), or a controlled cooling rate, as a TMS would. This research study proposes a novel experimental test rig replicating an active TMS based on a thermal chamber, forced air convection, a micro wind tunnel, and a control system for testing cells in set isothermal conditions. The test rig is proposed in two cooling configurations (single-sided, double-sided). The double-sided configuration, characterised by a superior heat transfer coefficient of 128 Wm2K, guarantees a stable cell average surface temperature, equal to the set value, and a temperature disuniformity lower than 5 K for DRs of up to 2C and set temperatures in the range of 0Co–40Co. Moreover, the electrical, thermal, and electrochemical performance of a pouch cell is investigated at DR and at uniform and constant cell temperatures.

Suggested Citation

  • Landini, S. & O’Donovan, T.S., 2021. "Novel experimental approach for the characterisation of Lithium-Ion cells performance in isothermal conditions," Energy, Elsevier, vol. 214(C).
  • Handle: RePEc:eee:energy:v:214:y:2021:i:c:s0360544220320727
    DOI: 10.1016/j.energy.2020.118965
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    References listed on IDEAS

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    Cited by:

    1. Cheng, Gong & Wang, Xinzhi & He, Yurong, 2021. "Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network," Energy, Elsevier, vol. 232(C).
    2. Wang, Shuai & Ma, Hongyan & Zhang, Yingda & Li, Shengyan & He, Wei, 2023. "Remaining useful life prediction method of lithium-ion batteries is based on variational modal decomposition and deep learning integrated approach," Energy, Elsevier, vol. 282(C).
    3. Chen, Liping & Xie, Siqiang & Lopes, António M. & Li, Huafeng & Bao, Xinyuan & Zhang, Chaolong & Li, Penghua, 2024. "A new SOH estimation method for Lithium-ion batteries based on model-data-fusion," Energy, Elsevier, vol. 286(C).

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