Integrating Real-Time Machine Learning and Dynamic Charging for Electric Vehicle Routing

As electric vehicles (EVs) gain popularity, the demand for efficient EV routing and innovative charging solutions is increasing. This study proposes a novel integration of Temporal Multimodal Multivariate Learning (TMML) and the Time-Dependent Shortest Path (TDSP) algorithm to dynamically optimize EV routing while incorporating stationary and wireless charging systems. TMML continuously updates travel time distributions based on real-time observations, reducing uncertainties and enabling more accurate predictions. These updated distributions are utilized by TDSP to determine the most efficient routes, considering time-dependent travel times, and the availability of charging infrastructure. The methodology integrates Mobile Energy Distributors (MEDs) and Dynamic Inductive Charging (DIC). MED dynamically dispatches portable charging units, while DIC enables on-the-move charging through road-embedded systems. Real-world data from the Washington, DC metropolitan area, sourced from Regional Integrated Transportation Information System (RITIS.org), provides granular insights into traffic patterns and charging infrastructure performance. This hybrid system operates by extracting feasible routes, evaluating them using TMML and TDSP, and selecting the optimal path to minimize travel time, waiting time, and charging costs.