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

Maryam Ghalkhani () and Saeid Habibi ()
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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

Energies, 2022, vol. 16, issue 1, 1-16

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).

Keywords: lithium-ion batteries; battery management systems; AI-based monitoring systems; electric vehicle (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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