Machine learning driven digital twin model of Li-ion batteries in electric vehicles: a review
Electric Vehicles (EVs) have transformed the automotive industry and are becoming a more reliable and consistent mode of public transportation. The development of a pollutionfree environment and improved ecological surroundings is being significantly assisted by battery-powered vehicles. Lithium-ion...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
ELS Publishing (ELSP)
2023-05-01
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| Series: | Artificial Intelligence and Autonomous Systems |
| Subjects: | |
| Online Access: | https://elsp-homepage.oss-cn-hongkong.aliyuncs.compaper/journal/open/AIAS/2023/aias20230003.pdf |
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| Summary: | Electric Vehicles (EVs) have transformed the automotive industry and are becoming a more reliable and consistent mode of public transportation. The development of a pollutionfree environment and improved ecological surroundings is being significantly assisted by battery-powered vehicles. Lithium-ion (Li-ion) batteries are the most widely used type of batteries in EVs because of their superior performance as compared to their counterparts. The core of EVs is their battery management systems (BMS), which can unarguably improve a battery’s performance, operation, safety, and lifespan. Li-ion battery state estimation is one of the most important parts of the implementation of BMS, as it serves an important role in safe and reliable battery operation. Recently, researchers are working on the development of digital twin models to automate and optimize the BMS state estimation process by utilizing machine learning (ML) algorithms and cloud computing. The objective of this study is to review, characterize, and compare various ML-based approaches for the state estimation of different Li-ion battery states. Firstly, this study describes and characterizes several Li-ion battery state estimation approaches proposed in recent years. Secondly, the battery state estimation of electric vehicles is discussed. In addition, the challenges and prospects of Li-ion battery state estimation are put forward. |
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| ISSN: | 2959-0744 2959-0752 |