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Effect of the active material type and battery geometry on the thermal behavior of lithium-ion batteries

Author

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  • Miranda, D.
  • Almeida, A.M.
  • Lanceros-Méndez, S.
  • Costa, C.M.

Abstract

The effect of different thermal conditions on battery performance has been evaluated by computer simulation through a thermal model coupled to the electrochemical model. Three different active materials, lithium cobalt oxide, LiCoO2, lithium iron phosphate, LiFePO4 and lithium manganese oxide, LiMn2O4, were evaluated together with two battery geometries: conventional and interdigitated. The delivered capacity of the different active materials and both geometries were thus obtained as a function of the scan rate and correlated with the produced reversible, reaction, ohmic and total heat. For isothermal conditions, the highest capacity is obtained for LiCoO2, being 739,31 Ahm−2 at 1C for the conventional geometry. Further, battery performance as a function of the scan rate is independent of the geometry and similar for the different active materials. Under adiabatic conditions and independent geometry, LiFePO4 produces lower heat in the discharge process, the temperature ranging from 298 K to 308.9 K when the battery operates up to 500C for and interdigitated geometry with eight digits, which is critical for improving battery safety. This fact is also confirmed by the ohmic heat value along the cathode at the rate of 300C, which is 42700 W m−3, 118000 W m−3 and 69000 W m−3 for LiFePO4, LiMn2O4 and LiCoO2, respectively, for a conventional geometry as at a time of 50s of battery operation. Thus, it is demonstrated how battery geometry and the intrinsic parameters of the active materials affect the heat generated by the batteries and, considering the balance between cycle performance and thermal properties, the best active material for improved battery safety and performance is LiFePO4.

Suggested Citation

  • Miranda, D. & Almeida, A.M. & Lanceros-Méndez, S. & Costa, C.M., 2019. "Effect of the active material type and battery geometry on the thermal behavior of lithium-ion batteries," Energy, Elsevier, vol. 185(C), pages 1250-1262.
    • Handle: RePEc:eee:energy:v:185:y:2019:i:c:p:1250-1262
    DOI: 10.1016/j.energy.2019.07.099
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    2. Guo, Fei & Wu, Xiongwei & Liu, Lili & Ye, Jilei & Wang, Tao & Fu, Lijun & Wu, Yuping, 2023. "Prediction of remaining useful life and state of health of lithium batteries based on time series feature and Savitzky-Golay filter combined with gated recurrent unit neural network," Energy, Elsevier, vol. 270(C).
    3. Morali, Ugur, 2022. "A numerical and statistical implementation of a thermal model for a lithium-ion battery," Energy, Elsevier, vol. 240(C).
    4. Gao, Yizhao & Zhu, Chong & Zhang, Xi & Guo, Bangjun, 2021. "Implementation and evaluation of a practical electrochemical- thermal model of lithium-ion batteries for EV battery management system," Energy, Elsevier, vol. 221(C).
    5. Li, Changlong & Cui, Naxin & Wang, Chunyu & Zhang, Chenghui, 2021. "Reduced-order electrochemical model for lithium-ion battery with domain decomposition and polynomial approximation methods," Energy, Elsevier, vol. 221(C).
    6. Tian, Jiaqiang & Wang, Yujie & Liu, Chang & Chen, Zonghai, 2020. "Consistency evaluation and cluster analysis for lithium-ion battery pack in electric vehicles," Energy, Elsevier, vol. 194(C).
    7. Xu, Meng & Wang, Xia & Zhang, Liwen & Zhao, Peng, 2021. "Comparison of the effect of linear and two-step fast charging protocols on degradation of lithium ion batteries," Energy, Elsevier, vol. 227(C).

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