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Comparison of Lithium-Ion Anode Materials Using an Experimentally Verified Physics-Based Electrochemical Model

Author

Listed:
  • Rujian Fu

    (Independent Researcher, Novi, MI 48377, USA)

  • Xuan Zhou

    (Department of Electrical and Computer Engineering, Kettering University, Flint, MI 48504, USA)

  • Hengbin Fan

    (Department of Electrical and Computer Engineering, Kettering University, Flint, MI 48504, USA)

  • Douglas Blaisdell

    (Department of Electrical and Computer Engineering, Kettering University, Flint, MI 48504, USA)

  • Ajay Jagadale

    (Department of Electrical and Computer Engineering, Kettering University, Flint, MI 48504, USA)

  • Xi Zhang

    (School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai 200240, China)

  • Rui Xiong

    (National Engineering Laboratory for Electric Vehicles and Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

Abstract

Researchers are in search of parameters inside Li-ion batteries that can be utilized to control their external behavior. Physics-based electrochemical model could bridge the gap between Li+ transportation and distribution inside battery and battery performance outside. In this paper, two commercially available Li-ion anode materials: graphite and Lithium titanate (Li 4 Ti 5 O 12 or LTO) were selected and a physics-based electrochemical model was developed based on half-cell assembly and testing. It is found that LTO has a smaller diffusion coefficient ( D s ) than graphite, which causes a larger overpotential, leading to a smaller capacity utilization and, correspondingly, a shorter duration of constant current charge or discharge. However, in large current applications, LTO performs better than graphite because its effective particle radius decreases with increasing current, leading to enhanced diffusion. In addition, LTO has a higher activation overpotential in its side reactions; its degradation rate is expected to be much smaller than graphite, indicating a longer life span.

Suggested Citation

  • Rujian Fu & Xuan Zhou & Hengbin Fan & Douglas Blaisdell & Ajay Jagadale & Xi Zhang & Rui Xiong, 2017. "Comparison of Lithium-Ion Anode Materials Using an Experimentally Verified Physics-Based Electrochemical Model," Energies, MDPI, vol. 10(12), pages 1-20, December.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:2174-:d:123532
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    References listed on IDEAS

    as
    1. Xiong, Rui & Sun, Fengchun & Gong, Xianzhi & Gao, Chenchen, 2014. "A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 1421-1433.
    2. J.-M. Tarascon & M. Armand, 2001. "Issues and challenges facing rechargeable lithium batteries," Nature, Nature, vol. 414(6861), pages 359-367, November.
    3. 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.
    4. 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.
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    Cited by:

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    2. Carla Menale & Stefano Constà & Vincenzo Sglavo & Livia Della Seta & Roberto Bubbico, 2022. "Experimental Investigation of Overdischarge Effects on Commercial Li-Ion Cells," Energies, MDPI, vol. 15(22), pages 1-16, November.
    3. Bence Csomós & Dénes Fodor & István Vajda, 2020. "Estimation of Battery Separator Area, Cell Thickness and Diffusion Coefficient Based on Non-Ideal Liquid-Phase Diffusion Modeling," Energies, MDPI, vol. 13(23), pages 1-23, November.
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