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Calibration Optimization Methodology for Lithium-Ion Battery Pack Model for Electric Vehicles in Mining Applications

Author

Listed:
  • Majid Astaneh

    (Department of Mechanics and Maritime Sciences, Chalmers University of Technology, 412 96 Göteborg, Sweden)

  • Jelena Andric

    (Department of Mechanics and Maritime Sciences, Chalmers University of Technology, 412 96 Göteborg, Sweden)

  • Lennart Löfdahl

    (Department of Mechanics and Maritime Sciences, Chalmers University of Technology, 412 96 Göteborg, Sweden)

  • Dario Maggiolo

    (Department of Mechanics and Maritime Sciences, Chalmers University of Technology, 412 96 Göteborg, Sweden)

  • Peter Stopp

    (Gamma Technologies GmbH, Danneckerstrasse 37, D-70182 Stuttgart, Germany)

  • Mazyar Moghaddam

    (Northvolt, Gamla Brogatan 26, 111 20 Stockholm, Sweden)

  • Michel Chapuis

    (Northvolt, Gamla Brogatan 26, 111 20 Stockholm, Sweden)

  • Henrik Ström

    (Department of Mechanics and Maritime Sciences, Chalmers University of Technology, 412 96 Göteborg, Sweden)

Abstract

Large-scale introduction of electric vehicles (EVs) to the market sets outstanding requirements for battery performance to extend vehicle driving range, prolong battery service life, and reduce battery costs. There is a growing need to accurately and robustly model the performance of both individual cells and their aggregated behavior when integrated into battery packs. This paper presents a novel methodology for Lithium-ion (Li-ion) battery pack simulations under actual operating conditions of an electric mining vehicle. The validated electrochemical-thermal models of Li-ion battery cells are scaled up into battery modules to emulate cell-to-cell variations within the battery pack while considering the random variability of battery cells, as well as electrical topology and thermal management of the pack. The performance of the battery pack model is evaluated using transient experimental data for the pack operating conditions within the mining environment. The simulation results show that the relative root mean square error for the voltage prediction is 0.7–1.7% and for the battery pack temperature 2–12%. The proposed methodology is general and it can be applied to other battery chemistries and electric vehicle types to perform multi-objective optimization to predict the performance of large battery packs.

Suggested Citation

  • Majid Astaneh & Jelena Andric & Lennart Löfdahl & Dario Maggiolo & Peter Stopp & Mazyar Moghaddam & Michel Chapuis & Henrik Ström, 2020. "Calibration Optimization Methodology for Lithium-Ion Battery Pack Model for Electric Vehicles in Mining Applications," Energies, MDPI, vol. 13(14), pages 1-27, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3532-:d:382111
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    References listed on IDEAS

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    Cited by:

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    2. Anandh Ramesh Babu & Jelena Andric & Blago Minovski & Simone Sebben, 2021. "System-Level Modeling and Thermal Simulations of Large Battery Packs for Electric Trucks," Energies, MDPI, vol. 14(16), pages 1-15, August.
    3. Astaneh, Majid & Andric, Jelena & Löfdahl, Lennart & Stopp, Peter, 2022. "Multiphysics simulation optimization framework for lithium-ion battery pack design for electric vehicle applications," Energy, Elsevier, vol. 239(PB).
    4. Heewon Choi & Nam-gyu Lim & Seong Jun Lee & Jungsoo Park, 2020. "Feasibility Study for Sustainable Use of Lithium-Ion Batteries Considering Different Positive Electrode Active Materials under Various Driving Cycles by Using Cell to Electric Vehicle (EV) Simulation," Sustainability, MDPI, vol. 12(22), pages 1-29, November.
    5. Kim, Kyunghyun & Choi, Jung-Il, 2023. "Effect of cell-to-cell variation and module configuration on the performance of lithium-ion battery systems," Applied Energy, Elsevier, vol. 352(C).
    6. Dongcheul Lee & Seohee Kang & Chee Burm Shin, 2022. "Modeling the Effect of Cell Variation on the Performance of a Lithium-Ion Battery Module," Energies, MDPI, vol. 15(21), pages 1-15, October.
    7. García, Antonio & Monsalve-Serrano, Javier & Ponce-Mora, Alberto & Fogué-Robles, Álvaro, 2023. "Development of a calibration methodology for fitting the response of a lithium-ion cell P2D model using real driving cycles," Energy, Elsevier, vol. 271(C).
    8. Oliwia Pietrzak & Krystian Pietrzak, 2021. "The Economic Effects of Electromobility in Sustainable Urban Public Transport," Energies, MDPI, vol. 14(4), pages 1-28, February.
    9. Krystian Pietrzak & Oliwia Pietrzak, 2022. "Tram System as a Challenge for Smart and Sustainable Urban Public Transport: Effects of Applying Bi-Directional Trams," Energies, MDPI, vol. 15(15), pages 1-29, August.

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