Driving sustainability - The role of digital twin in enhancing battery performance for electric vehicles

被引:7
作者
Rajesh, P. K. [1 ]
Soundarya, T. [1 ]
Jithin, K., V [1 ]
机构
[1] PSG Coll Technol, Dept Automobile Engn, Coimbatore, India
关键词
Battery digital twin; Electric vehicle; Machine learning; Artificial intelligence; Optimization; LITHIUM-ION BATTERIES; STATE; CHALLENGES; PREDICTION; MANAGEMENT; FRAMEWORK; MODELS;
D O I
10.1016/j.jpowsour.2024.234464
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
This review article explores the potential and significance of digital twins (DTs) in advancing battery technology, specifically in electric vehicles (EVs). Due to their ability to accurately predict, diagnose, and enhance energy systems, DTs offer a transformative solution for addressing environmental concerns and improving energy storage capabilities. Moreover, DTs hold promise in facilitating vehicle-to-grid (V2G) integration and testing autonomous driving systems, while robust cybersecurity measures will be essential to protect sensitive data. The applications of DTs in estimating state of charge (SOC) and state of health (SOH) parameters demonstrate their effectiveness in optimizing battery performance. In addition to exploring the application of artificial intelligence (AI) and machine learning models for improving battery performance while highlighting real-world case examples, the study also highlights how digital twin (DT) technology can enhance battery design, increase longevity, and ensure safety. Furthermore, this study discusses the challenges of implementing DTs for battery packs, and the potential applications of this technology in future directions are discussed. In conclusion, DTs are set to revolutionize the future of electric transportation, promoting sustainability and efficiency in the EV ecosystem.
引用
收藏
页数:20
相关论文
共 173 条
[1]  
Abbasi R, 2021, SSRN Electronic Journal, DOI [10.2139/ssrn.3860500, 10.2139/ssrn.3860500, DOI 10.2139/SSRN.3860500]
[2]   On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges [J].
Achouch, Mounia ;
Dimitrova, Mariya ;
Ziane, Khaled ;
Karganroudi, Sasan Sattarpanah ;
Dhouib, Rizck ;
Ibrahim, Hussein ;
Adda, Mehdi .
APPLIED SCIENCES-BASEL, 2022, 12 (16)
[3]   Digital Twin: Where do humans fit in? [J].
Agrawal, Ashwin ;
Thiel, Robert ;
Jain, Pooja ;
Singh, Vishal ;
Fischer, Martin .
AUTOMATION IN CONSTRUCTION, 2023, 148
[4]   The role of artificial intelligence in the mass adoption of electric vehicles [J].
Ahmed, Moin ;
Zheng, Yun ;
Amine, Anna ;
Fathiannasab, Hamed ;
Chen, Zhongwei .
JOULE, 2021, 5 (09) :2296-2322
[5]   The use of Digital Twin for predictive maintenance in manufacturing [J].
Aivaliotis, P. ;
Georgoulias, K. ;
Chryssolouris, G. .
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, 2019, 32 (11) :1067-1080
[6]   Digital Twin Conceptual Model within the Context of Internet of Things [J].
Al-Ali, A. R. ;
Gupta, Ragini ;
Zaman Batool, Tasneem ;
Landolsi, Taha ;
Aloul, Fadi ;
Al Nabulsi, Ahmad .
FUTURE INTERNET, 2020, 12 (10) :1-15
[7]   A Review of Digital Twin Technology for Electric and Autonomous Vehicles [J].
Ali, Wasim A. ;
Fanti, Maria Pia ;
Roccotelli, Michele ;
Ranieri, Luigi .
APPLIED SCIENCES-BASEL, 2023, 13 (10)
[8]   Digital-Twin-Inspired IoT-Assisted Intelligent Performance Analysis Framework for Electric Vehicles [J].
Alsubai, Shtwai ;
Alqahtani, Abdullah ;
Alanazi, Abed ;
Bhatia, Munish .
IEEE INTERNET OF THINGS JOURNAL, 2024, 11 (10) :18880-18887
[9]   Application of digital twins to the product lifecycle management of battery packs of electric vehicles [J].
Anandavel, Suriyan ;
Li, Wei ;
Garg, Akhil ;
Gao, Liang .
IET COLLABORATIVE INTELLIGENT MANUFACTURING, 2021, 3 (04) :356-366
[10]   Predictive Maintenance in the Automotive Sector: A Literature Review [J].
Arena, Fabio ;
Collotta, Mario ;
Luca, Liliana ;
Ruggieri, Marianna ;
Termine, Francesco Gaetano .
MATHEMATICAL AND COMPUTATIONAL APPLICATIONS, 2022, 27 (01)