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Optimizing Current Profiles for Efficient Online Estimation of Battery Equivalent Circuit Model Parameters Based on Cramer–Rao Lower Bound

Author

Listed:
  • Prarthana Pillai

    (Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada)

  • Sneha Sundaresan

    (Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada)

  • Krishna R. Pattipati

    (Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT 06269, USA)

  • Balakumar Balasingam

    (Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada)

Abstract

Battery management systems (BMS) are important for ensuring the safety, efficiency and reliability of a battery pack. Estimating the internal equivalent circuit model (ECM) parameters of a battery, such as the internal open circuit voltage, battery resistance and relaxation parameters, is a crucial requirement in BMSs. Numerous approaches to estimating ECM parameters have been reported in the literature. However, existing approaches consider ECM identification as a joint estimation problem that estimates the state of charge together with the ECM parameters. In this paper, an approach is presented to decouple the problem into ECM identification alone. Using the proposed approach, the internal open circuit voltage and the ECM parameters can be estimated without requiring the knowledge of the state of charge of the battery. The proposed approach is applied to estimate the open circuit voltage and internal resistance of a battery.

Suggested Citation

  • Prarthana Pillai & Sneha Sundaresan & Krishna R. Pattipati & Balakumar Balasingam, 2022. "Optimizing Current Profiles for Efficient Online Estimation of Battery Equivalent Circuit Model Parameters Based on Cramer–Rao Lower Bound," Energies, MDPI, vol. 15(22), pages 1-21, November.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8441-:d:969875
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    References listed on IDEAS

    as
    1. Yasser Diab & François Auger & Emmanuel Schaeffer & Moutassem Wahbeh, 2017. "Estimating Lithium-Ion Battery State of Charge and Parameters Using a Continuous-Discrete Extended Kalman Filter," Energies, MDPI, vol. 10(8), pages 1-19, July.
    2. Balakumar Balasingam & Mostafa Ahmed & Krishna Pattipati, 2020. "Battery Management Systems—Challenges and Some Solutions," Energies, MDPI, vol. 13(11), pages 1-19, June.
    3. Marongiu, Andrea & Roscher, Marco & Sauer, Dirk Uwe, 2015. "Influence of the vehicle-to-grid strategy on the aging behavior of lithium battery electric vehicles," Applied Energy, Elsevier, vol. 137(C), pages 899-912.
    4. Waag, Wladislaw & Käbitz, Stefan & Sauer, Dirk Uwe, 2013. "Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application," Applied Energy, Elsevier, vol. 102(C), pages 885-897.
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