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Dual Extended Kalman Filter for State of Charge Estimation of Lithium–Sulfur Batteries

Author

Listed:
  • Lluís Trilla

    (Power Systems Research Group, Catalonia Institute for Energy Research (IREC), 08930 Barcelona, Spain)

  • Lluc Canals Casals

    (Energy Systems and Energy Harvesting Group, IREC, 08930 Barcelona, Spain)

  • Jordi Jacas

    (Department of Engineering Projects and Construction, Universitat Politècnica de Catalunya (UPC), 08034 Barcelona, Spain)

  • Pol Paradell

    (Power Systems Research Group, Catalonia Institute for Energy Research (IREC), 08930 Barcelona, Spain)

Abstract

Lithium-Sulfur is a promising technology for the next generation of batteries and research efforts for early-stage prototype implementation increased in recent years. For the development of a suitable Battery Management System, a state estimator is required; however, lithium-sulfur behavior presents a large non-observable region that may difficult the convergence of the state estimation algorithm leading to large errors or even instability. A dual Extended Kalman Filter is proposed to circumvent the non-observability region. This objective is achieved by combining a parameter estimation algorithm with a cell model that includes non-linear behavior such as self-discharge and cell degradation. The resulting dual Kalman filter is applied to lithium–sulfur batteries to estimate their State-of-Charge incorporating the effects of degradation, temperature, and self-discharge deviations.

Suggested Citation

  • Lluís Trilla & Lluc Canals Casals & Jordi Jacas & Pol Paradell, 2022. "Dual Extended Kalman Filter for State of Charge Estimation of Lithium–Sulfur Batteries," Energies, MDPI, vol. 15(19), pages 1-14, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:6989-:d:923302
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    References listed on IDEAS

    as
    1. Abbas Fotouhi & Daniel J. Auger & Laura O’Neill & Tom Cleaver & Sylwia Walus, 2017. "Lithium-Sulfur Battery Technology Readiness and Applications—A Review," Energies, MDPI, vol. 10(12), pages 1-15, November.
    2. Cynthia Thamires da Silva & Bruno Martin de Alcântara Dias & Rui Esteves Araújo & Eduardo Lorenzetti Pellini & Armando Antônio Maria Laganá, 2021. "Battery Model Identification Approach for Electric Forklift Application," Energies, MDPI, vol. 14(19), pages 1-26, September.
    3. Liu, Kailong & Ashwin, T.R. & Hu, Xiaosong & Lucu, Mattin & Widanage, W. Dhammika, 2020. "An evaluation study of different modelling techniques for calendar ageing prediction of lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    4. Deidre Wolff & Lluc Canals Casals & Gabriela Benveniste & Cristina Corchero & Lluís Trilla, 2019. "The Effects of Lithium Sulfur Battery Ageing on Second-Life Possibilities and Environmental Life Cycle Assessment Studies," Energies, MDPI, vol. 12(12), pages 1-19, June.
    5. Yash Kotak & Carlos Marchante Fernández & Lluc Canals Casals & Bhavya Satishbhai Kotak & Daniel Koch & Christian Geisbauer & Lluís Trilla & Alberto Gómez-Núñez & Hans-Georg Schweiger, 2021. "End of Electric Vehicle Batteries: Reuse vs. Recycle," Energies, MDPI, vol. 14(8), pages 1-15, April.
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