A Graph-Based Optimization Approach for Resilient EV Rerouting in Disrupted Charging Networks

The growing adoption of electric vehicles (EVs) increases the demand for a reliable and efficient charging infrastructure. Unexpected disruptions at electric vehicle charging stations (EVCSs) can negatively impact traffic flow and energy distribution. Traditional EV rerouting strategies to EVCS are limited by their static nature and fail to consider the interdependence between transportation and power distribution networks. Thus, traditional rerouting strategies are unable to effectively reroute EVs during EVCS disruptions that might occur due to maintenance, lack of public EVCS, or power unavailability, leading to congestion, increased travel time, and energy inefficiencies. In this paper, we propose a graph-based rerouting framework that integrates transportation and power networks to dynamically redirect EVs from disrupted to functional EVCSs while accounting for EVCS-to-EVCS distance, station capacity, and route capacity. The proposed method incorporates three years of travel monitoring analysis system traffic data with a synthetic power distribution network to capture realistic operating conditions. We introduce the EV rerouting with disruptions and capacity constraints (EVR-DC) algorithm, which incorporates system-level constraints and enables decentralized decision-making under disruptions. Our results show that the proposed approach reduces disrupted EVCS congestion by 73% within an hour and maintains an annual EV user rerouting satisfaction rate of 86%, demonstrating the effectiveness of the proposed method in enhancing the resilience of EV charging infrastructure against disruptions.