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Electrochemical model-based aging-adaptive fast charging of automotive lithium-ion cells

Author

Listed:
  • Andersson, Malin
  • Streb, Moritz
  • Prathimala, Venu Gopal
  • Siddiqui, Aamer
  • Lodge, Andrew
  • Klass, Verena Löfqvist
  • Klett, Matilda
  • Johansson, Mikael
  • Lindbergh, Göran

Abstract

Fast charging of electric vehicles remains a compromise between charging time and degradation penalty. Conventional battery management systems use experience-based charging protocols that are expected to meet vehicle lifetime goals. Novel electrochemical model-based battery fast charging uses a model to observe internal battery states. This enables control of charging rates based on states such as the lithium-plating potential but relies on an accurate model as well as accurate model parameters. However, the impact of battery degradation on the model’s accuracy and therefore the fitness of the estimated optimal charging procedure is often not considered. In this work, we therefore investigate electrochemical model-based aging-adaptive fast charging of automotive lithium-ion cells. First, an electrochemical model is identified at the beginning of life for 6 automotive prototype cells and the electrochemically constrained fast-charge is designed. The model parameters are then periodically re-evaluated during a cycling study and the charging procedure is updated to account for cell degradation. The proposed method is compared with two reference protocols to investigate both the effectiveness of selected electrochemical constraints as well as the benefit of aging-adaptive usage. Finally, post-mortem characterization is presented to highlight the benefit of aging-adaptive battery utilization.

Suggested Citation

  • Andersson, Malin & Streb, Moritz & Prathimala, Venu Gopal & Siddiqui, Aamer & Lodge, Andrew & Klass, Verena Löfqvist & Klett, Matilda & Johansson, Mikael & Lindbergh, Göran, 2024. "Electrochemical model-based aging-adaptive fast charging of automotive lithium-ion cells," Applied Energy, Elsevier, vol. 372(C).
    • Handle: RePEc:eee:appene:v:372:y:2024:i:c:s0306261924010274
    DOI: 10.1016/j.apenergy.2024.123644
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    References listed on IDEAS

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    1. Yin, Yilin & Choe, Song-Yul, 2020. "Actively temperature controlled health-aware fast charging method for lithium-ion battery using nonlinear model predictive control," Applied Energy, Elsevier, vol. 271(C).
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    4. Dai, Haifeng & Jiang, Bo & Hu, Xiaosong & Lin, Xianke & Wei, Xuezhe & Pecht, Michael, 2021. "Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
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