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Fast self-heating battery with anti-aging awareness for freezing climates application

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  • Xiong, Rui
  • Li, Zhengyang
  • Yang, Ruixin
  • Shen, Weixiang
  • Ma, Suxiao
  • Sun, Fengchun

Abstract

Lithium-ion batteries (LIBs) need to be heated before use at low temperatures to avoid poor electric vehicle performance. In this study, a self-heating method for LIBs at low temperatures is proposed, where the influence of various heating parameters on heating performance is explored experimentally. To make the balance between heating speed and capacity degradation while achieving efficient preheating, a lumped parameter thermal model and an empirical capacity fade model are established to determine appropriate duty ratio and external resistance, which can predict the corresponding time required for LIBs to be heated to the target temperature and reveal the capacity loss of LIBs quantitatively after repeated heating. A multi-objective optimization method based on non-dominated sorting genetic algorithm II (NSGA-II) is employed to obtain the Pareto optimal front between heating speed and capacity degradation, which leads to the selection of the optimal electrical parameters with the help of K-means clustering algorithm and three newly defined heating performance indicators. Finally, the duty ratio and external resistance are preferably 80% and 203.98 mΩ through the NSGA-II method, respectively. The experimental results verify the optimal heating strategy which can heat the LIB quickly from – 20.56 °C to 0 °C within 70 s. This optimal heating strategy is applied to heat the LIB for 200 times, the battery capacity degradation is only about 7.72%.

Suggested Citation

  • Xiong, Rui & Li, Zhengyang & Yang, Ruixin & Shen, Weixiang & Ma, Suxiao & Sun, Fengchun, 2022. "Fast self-heating battery with anti-aging awareness for freezing climates application," Applied Energy, Elsevier, vol. 324(C).
    • Handle: RePEc:eee:appene:v:324:y:2022:i:c:s0306261922010443
    DOI: 10.1016/j.apenergy.2022.119762
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    References listed on IDEAS

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    1. Yang, Ruixin & Xiong, Rui & Ma, Suxiao & Lin, Xinfan, 2020. "Characterization of external short circuit faults in electric vehicle Li-ion battery packs and prediction using artificial neural networks," Applied Energy, Elsevier, vol. 260(C).
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    Cited by:

    1. Choon Kit Chan & Chi Hong Chung & Jeyagopi Raman, 2023. "Optimizing Thermal Management System in Electric Vehicle Battery Packs for Sustainable Transportation," Sustainability, MDPI, vol. 15(15), pages 1-14, August.
    2. Yang, Jufeng & Li, Xin & Sun, Xiaodong & Cai, Yingfeng & Mi, Chris, 2023. "An efficient and robust method for lithium-ion battery capacity estimation using constant-voltage charging time," Energy, Elsevier, vol. 263(PB).
    3. Wang, Qiao & Ye, Min & Wei, Meng & Lian, Gaoqi & Li, Yan, 2023. "Random health indicator and shallow neural network based robust capacity estimation for lithium-ion batteries with different fast charging protocols," Energy, Elsevier, vol. 271(C).
    4. Cai, Fengyang & Chang, Huawei & Yang, Zhengbo & Tu, Zhengkai, 2024. "Experimental study on self-heating strategy of lithium-ion battery at low temperatures based on bidirectional pulse current," Applied Energy, Elsevier, vol. 354(PB).

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