Digital design

The aim of this paper is to provide a comprehensive review of the literature from the last five years on the use of digital twin (DT) technology for Intelligent Transportation Systems (ITSs), focusing on electric and autonomous vehicles. In particular, with respect to the previous work, the focus has been expanded to include DT integration with other cutting-edge technologies, such as the Internet of Things (IoT), Big Data, artificial intelligence (AI), machine learning (ML), and 5G for ITS.

The study looks at how digital twin technology can be used in smart car systems by looking at its promise and the hurdles faced. Based on a comprehensive literature survey, this is the first in-depth look at how digital twin technology can be used in smart electric cars. The review has been organised into specific areas of the smart vehicle system, such as drive train system battery management system, driver assistance system, vehicle health monitoring system, vehicle power electronics.

The focus of this paper is to conduct a methodical review regarding the use of digital twins in the powertrain of electric vehicles (EVs). While reviewing the development of digital twin technology, its main application scenarios and its use in electric vehicle powertrains are analysed. Finally, the digital twins currently encounter several challenges that need to be addressed, and so the future development of their application to electric vehicles are summarized.

This study presents a smart EV charging infrastructure framework composed of a green power generation network, an energy storage network, and a charging network. The digital twin, as an enabling technology, is applied to realise essential smart features for the EV charging infrastructure, including cognisant, adaptive, taskable, and ethical. Based on the proposed smart charging station framework, we systematically review the existing digital twin implementations in the smart charging infrastructure.

Modern Smart Grids are complex systems incorporating physical components like distributed energy resources and storage, along with cyber components for advanced control, networking, and monitoring. This study proposes an integrated methodology for risk prioritization and failure mode classification into low, moderate and high-risk faults using Grey Relational Analysis (GRA) together with Failure Mode and Effects Analysis (FMEA) and Deep Learning algorithms. The results demonstrate that, especially in complex systems like Smart Microgrids, the proposed method more accurately captures the coupling relationships between failure modes compared to the conventional FMEA method.

This paper presents an overview of different DT platforms that can be used in EV applications. A deductive comparison between model-based and data-driven DT was performed. EV main systems have been discussed regarding the usable DT platform. DT platforms used in the EV industry were addressed. Finally, the review showed the superiority of data-driven DTs over model-based DTs due to their ability to handle systems with high complexity.

This work aims to bridge the gap between individual research to provide a comprehensive review from a technically informed and academically neutral standpoint. Conceptual groundwork of digital twin technology is built systematically for the reader, to allow insight into its inception and evolution. The study sifts the digital twin domain for contributions in smart vehicle systems, exploring its potential and contemporaneous challenges to realization.