• Proposes a Digital Twin framework for EVs routing and charging optimization • Develops a Dual-Population Evolutionary Algorithm regarding to the dynamic environments • Demonstrates robustness under traffic disruptions, road closures, and charging station failures • Enhances adaptability and efficiency of EV operations in urban traffic networks The growing adoption of electric vehicles (EVs) presents new challenges for intelligent transportation systems (ITS), particularly in dynamic traffic environments where routing and charging decisions must adapt to fluctuating conditions. This paper proposes a Digital Twin-based Electric Vehicle Routing and Charging approach (DT-EVRC) that integrates real-time traffic data, predictive analytics, and a Dual-Population Evolutionary Algorithm (DPEA) to optimize EV travel and charging schedules. Unlike traditional static or simplified models, DT-EVRC continuously synchronizes with the physical transportation network, capturing variations in traffic density, charging station availability, and energy constraints. Experimental results on diverse grid-based urban scenarios demonstrate that DT-EVRC achieves robust and adaptive performance under traffic disruptions, road closures, and charging station failures. The proposed approach highlights the potential of digital twin technologies, combined with advanced optimization, to support next-generation ITS by enabling efficient, resilient, and sustainable urban mobility.
Xie et al. (Thu,) studied this question.