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Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems

Author

Listed:
  • Manoj Mathew

    (Department of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada)

  • Stefan Janhunen

    (Nuvation Energy, 40 Bathurst Dr, Waterloo, ON N2V 1V6, Canada)

  • Mahir Rashid

    (Department of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada)

  • Frank Long

    (Department of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada)

  • Michael Fowler

    (Department of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada)

Abstract

Safe and efficient operation of a battery pack requires a battery management system (BMS) that can accurately predict the pack state-of-heath (SOH). Although there is no universal definition for battery SOH, it is often defined based on the increase in the battery’s internal resistance. Techniques such as extended Kalman filter (EKF) and recursive least squares (RLS) are two frequently used approaches for online estimation of this resistance. These two methods can, however, be computationally expensive, especially in the case of a battery pack composed of hundreds of cells. In addition, both methods require a battery model as well as chemistry specific parameters. Therefore, this paper investigates the performance of a direct resistance estimation (DRE) technique that requires minimal computational resources and can be implemented without any training data. This approach estimates the ohmic resistance only when the battery experiences sharp pulses in current. Comparison of results from the three algorithms shows that the DRE algorithm can accurately identify a degraded cell under various operating conditions while significantly reducing the required computational complexity. The findings will further advance diagnostic techniques for the identification of a weak cell in a large battery pack.

Suggested Citation

  • Manoj Mathew & Stefan Janhunen & Mahir Rashid & Frank Long & Michael Fowler, 2018. "Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems," Energies, MDPI, vol. 11(6), pages 1-15, June.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1490-:d:151259
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    References listed on IDEAS

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    Cited by:

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    2. Jiang, Yan & Jiang, Jiuchun & Zhang, Caiping & Zhang, Weige & Gao, Yang & Mi, Chris, 2019. "A Copula-based battery pack consistency modeling method and its application on the energy utilization efficiency estimation," Energy, Elsevier, vol. 189(C).
    3. Yongcun Fan & Haotian Shi & Shunli Wang & Carlos Fernandez & Wen Cao & Junhan Huang, 2021. "A Novel Adaptive Function—Dual Kalman Filtering Strategy for Online Battery Model Parameters and State of Charge Co-Estimation," Energies, MDPI, vol. 14(8), pages 1-18, April.
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    5. Massimo Ceraolo & Giovanni Lutzemberger & Davide Poli & Claudio Scarpelli, 2021. "Experimental Evaluation of Aging Indicators for Lithium–Iron–Phosphate Cells," Energies, MDPI, vol. 14(16), pages 1-15, August.
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    7. Anna I. Pózna & Katalin M. Hangos & Attila Magyar, 2019. "Temperature Dependent Parameter Estimation of Electrical Vehicle Batteries," Energies, MDPI, vol. 12(19), pages 1-18, September.

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