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A Review on Battery Modelling Techniques

Author

Listed:
  • S. Tamilselvi

    (Department of Electrical & Electronics Engineering, SSN College of Engineering, Kalavakkam 603110, India)

  • S. Gunasundari

    (Department of Computer Science and Engineering, Velammal Engineering College, Chennai 600066, India)

  • N. Karuppiah

    (Department of Electrical and Electronics Engineering, Vardhaman College of Engineering, Hyderabad 501218, India)

  • Abdul Razak RK

    (Department of Mechanical Engineering, P. A. College of Engineering, (Affiliated to Visvesvaraya Technological University, Belagavi), Mangaluru 574153, India)

  • S. Madhusudan

    (Department of Electrical & Electronics Engineering, SSN College of Engineering, Kalavakkam 603110, India)

  • Vikas Madhav Nagarajan

    (Department of Chemical Engineering, SSN College of Engineering, Kalavakkam 603110, India)

  • T. Sathish

    (Department of Mechanical Engineering, Saveetha School of Engineering, SIMATS, Chennai 602105, India)

  • Mohammed Zubair M. Shamim

    (Department of Electrical Engineering, College of Engineering, King Khalid University, P.O. Box 394, Abha 61421, Saudi Arabia)

  • C. Ahamed Saleel

    (Department of Mechanical Engineering, College of Engineering, King Khalid University, P.O. Box 394, Abha 61421, Saudi Arabia)

  • Asif Afzal

    (Department of Mechanical Engineering, P. A. College of Engineering, (Affiliated to Visvesvaraya Technological University, Belagavi), Mangaluru 574153, India
    Department of Mechanical Engineering, School of Technology, Glocal University, Delhi-Yamunotri Marg, SH-57, Mirzapur Pole, Saharanpur District, Saharanpur 247121, India)

Abstract

The growing demand for electrical energy and the impact of global warming leads to a paradigm shift in the power sector. This has led to the increased usage of renewable energy sources. Due to the intermittent nature of the renewable sources of energy, devices capable of storing electrical energy are required to increase its reliability. The most common means of storing electrical energy is battery systems. Battery usage is increasing in the modern days, since all mobile systems such as electric vehicles, smart phones, laptops, etc., rely on the energy stored within the device to operate. The increased penetration rate of the battery system requires accurate modelling of charging profiles to optimise performance. This paper presents an extensive study of various battery models such as electrochemical models, mathematical models, circuit-oriented models and combined models for different types of batteries. It also discusses the advantages and drawbacks of these types of modelling. With AI emerging and accelerating all over the world, there is a scope for researchers to explore its application in multiple fields. Hence, this work discusses the application of several machine learning and meta heuristic algorithms for battery management systems. This work details the charging and discharging characteristics using the black box and grey box techniques for modelling the lithium-ion battery. The approaches, advantages and disadvantages of black box and grey box type battery modelling are analysed. In addition, analysis has been carried out for extracting parameters of a lithium-ion battery model using evolutionary algorithms.

Suggested Citation

  • S. Tamilselvi & S. Gunasundari & N. Karuppiah & Abdul Razak RK & S. Madhusudan & Vikas Madhav Nagarajan & T. Sathish & Mohammed Zubair M. Shamim & C. Ahamed Saleel & Asif Afzal, 2021. "A Review on Battery Modelling Techniques," Sustainability, MDPI, vol. 13(18), pages 1-26, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:18:p:10042-:d:631199
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    References listed on IDEAS

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    5. Wu, Chunxia & Sun, Yalong & Tang, Heng & Zhang, Shiwei & Yuan, Wei & Zhu, Likuan & Tang, Yong, 2024. "A review on the liquid cooling thermal management system of lithium-ion batteries," Applied Energy, Elsevier, vol. 375(C).
    6. Hegazy Rezk & A. G. Olabi & Tabbi Wilberforce & Enas Taha Sayed, 2023. "A Comprehensive Review and Application of Metaheuristics in Solving the Optimal Parameter Identification Problems," Sustainability, MDPI, vol. 15(7), pages 1-24, March.
    7. Cheng, Fangwei & Luo, Hongxi & Jenkins, Jesse D. & Larson, Eric D., 2023. "The value of low- and negative-carbon fuels in the transition to net-zero emission economies: Lifecycle greenhouse gas emissions and cost assessments across multiple fuel types," Applied Energy, Elsevier, vol. 331(C).
    8. Alessandro Falai & Tiziano Alberto Giuliacci & Daniela Misul & Giacomo Paolieri & Pier Giuseppe Anselma, 2022. "Modeling and On-Road Testing of an Electric Two-Wheeler towards Range Prediction and BMS Integration," Energies, MDPI, vol. 15(7), pages 1-27, March.
    9. Khan, F.M. NizamUddin & Rasul, Mohammad G. & Sayem, A.S.M. & Mandal, Nirmal K., 2024. "A computational analysis of effects of electrode thickness on the energy density of lithium-ion batteries," Energy, Elsevier, vol. 288(C).
    10. Aleksander Suti & Gianpietro Di Rito & Giuseppe Mattei, 2022. "Development and Experimental Validation of Novel Thevenin-Based Hysteretic Models for Li-Po Battery Packs Employed in Fixed-Wing UAVs," Energies, MDPI, vol. 15(23), pages 1-26, December.
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