Review of the Li-Ion Battery, Thermal Management, and AI-Based Battery Management System for EV Application
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 design...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/1/185 |
| _version_ | 1797625916342403072 |
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| author | Maryam Ghalkhani Saeid Habibi |
| author_facet | Maryam Ghalkhani Saeid Habibi |
| author_sort | Maryam Ghalkhani |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-03-11T10:03:15Z |
| format | Article |
| id | doaj.art-bb0d102c4c58443bb1e3cfbec9c27d7e |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T10:03:15Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-bb0d102c4c58443bb1e3cfbec9c27d7e2023-11-16T15:15:43ZengMDPI AGEnergies1996-10732022-12-0116118510.3390/en16010185Review of the Li-Ion Battery, Thermal Management, and AI-Based Battery Management System for EV ApplicationMaryam Ghalkhani0Saeid Habibi1Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L8, CanadaDepartment of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L8, CanadaWith 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).https://www.mdpi.com/1996-1073/16/1/185lithium-ion batteriesbattery management systemsAI-based monitoring systemselectric vehicle |
| spellingShingle | Maryam Ghalkhani Saeid Habibi Review of the Li-Ion Battery, Thermal Management, and AI-Based Battery Management System for EV Application Energies lithium-ion batteries battery management systems AI-based monitoring systems electric vehicle |
| title | Review of the Li-Ion Battery, Thermal Management, and AI-Based Battery Management System for EV Application |
| title_full | Review of the Li-Ion Battery, Thermal Management, and AI-Based Battery Management System for EV Application |
| title_fullStr | Review of the Li-Ion Battery, Thermal Management, and AI-Based Battery Management System for EV Application |
| title_full_unstemmed | Review of the Li-Ion Battery, Thermal Management, and AI-Based Battery Management System for EV Application |
| title_short | Review of the Li-Ion Battery, Thermal Management, and AI-Based Battery Management System for EV Application |
| title_sort | review of the li ion battery thermal management and ai based battery management system for ev application |
| topic | lithium-ion batteries battery management systems AI-based monitoring systems electric vehicle |
| url | https://www.mdpi.com/1996-1073/16/1/185 |
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