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Modelling and Estimation in Lithium-Ion Batteries: A Literature Review

Author

Listed:
  • Miquel Martí-Florences

    (ETSEIB, ESAII, Universitat Politécnica de Catalunya, Avinguda Diagonal, 647, 08028 Barcelona, Spain
    Insitut de Robòtica i Informàtica Industrial CSIC-UPC, C/Pau Gargallo, 14, 08028 Barcelona, Spain)

  • Andreu Cecilia

    (ETSEIB, ESAII, Universitat Politécnica de Catalunya, Avinguda Diagonal, 647, 08028 Barcelona, Spain)

  • Ramon Costa-Castelló

    (ETSEIB, ESAII, Universitat Politécnica de Catalunya, Avinguda Diagonal, 647, 08028 Barcelona, Spain)

Abstract

Lithium-ion batteries are widely recognised as the leading technology for electrochemical energy storage. Their applications in the automotive industry and integration with renewable energy grids highlight their current significance and anticipate their substantial future impact. However, battery management systems, which are in charge of the monitoring and control of batteries, need to consider several states, like the state of charge and the state of health, which cannot be directly measured. To estimate these indicators, algorithms utilising mathematical models of the battery and basic measurements like voltage, current or temperature are employed. This review focuses on a comprehensive examination of various models, from complex but close to the physicochemical phenomena to computationally simpler but ignorant of the physics; the estimation problem and a formal basis for the development of algorithms; and algorithms used in Li-ion battery monitoring. The objective is to provide a practical guide that elucidates the different models and helps to navigate the different existing estimation techniques, simplifying the process for the development of new Li-ion battery applications.

Suggested Citation

  • Miquel Martí-Florences & Andreu Cecilia & Ramon Costa-Castelló, 2023. "Modelling and Estimation in Lithium-Ion Batteries: A Literature Review," Energies, MDPI, vol. 16(19), pages 1-36, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6846-:d:1249201
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    References listed on IDEAS

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    Cited by:

    1. Yuhang Fan & Qiongbin Lin & Ruochen Huang, 2024. "Non-Invasive Method-Based Estimation of Battery State-of-Health with Dynamical Response Characteristics of Load Surges," Energies, MDPI, vol. 17(3), pages 1-17, January.
    2. Manuel R. Arahal & Alfredo Pérez Vega-Leal & Manuel G. Satué & Sergio Esteban, 2024. "Assessing SOC Estimations via Reverse-Time Kalman for Small Unmanned Aircraft," Energies, MDPI, vol. 17(20), pages 1-12, October.

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