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Review of the Li-Ion Battery, Thermal Management, and AI-Based Battery Management System for EV Application

Author

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  • Maryam Ghalkhani

    (Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada)

  • Saeid Habibi

    (Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada)

Abstract

With the large-scale commercialization and growing market share of electric vehicles (EVs), many studies have been dedicated to battery systems design and development. Their focus has been on higher energy efficiency, improved thermal performance and optimized multi-material battery enclosure designs. The integration of simulation-based design optimization of the battery pack and Battery Management System (BMS) is evolving and has expanded to include novelties such as artificial intelligence/machine learning (AI/ML) to improve efficiencies in design, manufacturing, and operations for their application in electric vehicles and energy storage systems. Specific to BMS, these advanced concepts enable a more accurate prediction of battery performance such as its State of Health (SOH), State of Charge (SOC), and State of Power (SOP). This study presents a comprehensive review of the latest developments and technologies in battery design, thermal management, and the application of AI in Battery Management Systems (BMS) for Electric Vehicles (EV).

Suggested Citation

  • Maryam Ghalkhani & Saeid Habibi, 2022. "Review of the Li-Ion Battery, Thermal Management, and AI-Based Battery Management System for EV Application," Energies, MDPI, vol. 16(1), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:185-:d:1013609
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    Cited by:

    1. Nasiri, Mahdieh & Hadim, Hamid, 2024. "Advances in battery thermal management: Current landscape and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).

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