Digital Twin (DT)-Enabled Next-Generation Safety Frameworks for Electric Vehicles (EVs): Cross-Domain Models and Applications

Ensuring the safety of electric vehicles (EVs) is a critical concern for next-generation mobility solutions, where the digital twin (DT) plays a vital role in addressing this challenge. This study provides a detailed analysis of the current state of knowledge in this domain. The findings highlight several effective applications of DT technology in enhancing EVs safety, particularly in battery maintenance and performance. However, our analysis reveals that most existing research is focused on battery-related applications, indicating the need to explore broader dimensions of EV safety. We identify key safety challenges, for example, high-voltage hazards, thermal runaway, system malfunction, and examine the role of DT in real-time monitoring and predictive maintenance to address them. We outline the DT-EVs enabling technologies, analyse the simulation platform, and present a unified DT-EVs architecture. This study classifies DT architectures and evaluates DT using artificial intelligence (AI) driven algorithms such as convolutional neural networks (CNNs), long short-term memory (LSTM), and generative adversarial networks (GANs), have achieved high accuracies in risk detection. By synthesising these insights, the objective of this study is to provide foundational conceptual support to researchers and, encourage the development of comprehensive, user-centric, and scalable DT-based safety solutions.