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Digital twins have played a vital role in the digitization of manufacturing processes by completely transforming the industry's approach to optimization, design, maintenance, safety, decision-making, and remote access and training methods. Despite growing interest, the literature that covers the evolution, classification, and integration of digital twins with Industry 5.0 is not comprehensive. Many studies either focused on specific technical aspects or did not provide a thorough overview of recent technological advancements, integrated frameworks, or other potential areas for future research. This review offers an in-depth examination of how digital twin technology is applied in the electric vehicle sector, particularly focusing on its role in managing electric vehicle batteries. This study introduced a structure resembling an inverted pyramid divided into five sections. Through a comprehensive bibliometric analysis of various databases, relevant literature on this topic was identified, resulting in the discovery and examination of 328 potential papers. This groundbreaking study offers a comprehensive overview of the development of digital twin technology since its inception in 2003. It explores general classifications and applications, followed by an in-depth examination of digital twin usage in the automotive industry, with a particular focus on electric vehicles and their battery management systems. A thorough critique of recent literature and an investigation into the reproducibility of these studies were presented, offering valuable insights for practitioners, researchers, and industry professionals to conceptualize the application of digital twins. • Identified recent research interest in DT in the Automotive sector, EV, and battery management of EVs. • This study introduces a new perspective on the evolution of DT, beginning with the emergence of simulation technologies and extending to the current rate of DT integration. • The applications of DT have been extensively studied in various areas, such as automotive, manufacturing, and energy, with particular emphasis on DT in EVs and battery systems. • A review-based analysis is provided on the prospects of DT, particularly in relation to Industry 5.0. • An in-depth examination of recent research, particularly concerning the implementation of DT platforms by various manufacturers, scholars, and industries, uncovers significant gaps, challenges, and opportunities within the DT field, emphasizing their impact on DT in the automotive and EV sectors. • The need for reproducibility in DT research was reviewed, including a study of data-driven insights to support implementation and optimization in future applications.
Jose et al. (Wed,) studied this question.