Sang, V.T.D, Duong, Quang Huy ORCID: https://orcid.org/0000-0003-2108-2976, Zhou, Li ORCID: https://orcid.org/0000-0001-7132-5935 and Fernandez De Arroyabe Arranz, Carlos ORCID: https://orcid.org/0000-0002-6866-0684 (2024) Electric vehicle battery technologies and capacity prediction: a comprehensive literature review of trends and influencing factors. Batteries, 10 (12):451. ISSN 2313-0105 (Online) (doi:10.3390/batteries10120451)

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Abstract

Electric vehicle (EV) battery technology is at the forefront of the shift towards sustainable transportation. However, maximising the environmental and economic benefits of electric vehicles depends on advances in battery life cycle management. This comprehensive review analyses trends, techniques, and challenges across EV battery development, capacity prediction, and recycling, drawing on a dataset of over 22,000 articles from four major databases. Using Dynamic Topic Modelling (DTM), this study identifies key innovations and evolving research themes in battery-related technologies, capacity degradation factors, and recycling methods. The literature is structured into two primary themes: (1) “Electric Vehicle Battery Technologies, Development & Trends” and (2) “Capacity Prediction and Influencing Factors”. DTM revealed pivotal findings: advancements in lithium-ion and solid-state batteries for higher energy density, improvements in recycling technologies to reduce environmental impact, and the efficacy of machine learning-based models for real-time capacity prediction. Gaps persist in scaling sustainable recycling methods, developing cost-effective manufacturing processes, and creating standards for life cycle impact assessment. Future directions emphasise multidisciplinary research on new battery chemistries, efficient end-of-life management, and policy frameworks that support circular economy practices. This review serves as a resource for stakeholders to address the critical technological and regulatory challenges that will shape the sustainable future of electric vehicles.

Item Type:	Article
Uncontrolled Keywords:	electric vehicle battery, dynamic topic modelling, literature review
Subjects:	H Social Sciences > H Social Sciences (General) T Technology > T Technology (General) T Technology > TL Motor vehicles. Aeronautics. Astronautics
Faculty / School / Research Centre / Research Group:	Greenwich Business School Greenwich Business School > Networks and Urban Systems Centre (NUSC) Greenwich Business School > Networks and Urban Systems Centre (NUSC) > Connected Cities Research Group (CCRG) Greenwich Business School > School of Business, Operations and Strategy
Last Modified:	19 Dec 2024 17:53
URI:	http://gala.gre.ac.uk/id/eprint/48985

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