Optimal Assignment of Mobile Charging Stations for On-the-Move Charging of Electric Vehicles

Electric vehicles (EVs) are gaining increasing interest due to their zero emissions and relatively reduced running costs. However, the availability of the charging energy is a main concern for many EV users along with the anticipated prolonged charging times. Mobile charging stations (MCSs) offer a non-intrusive, on-the-move charging solution that addresses the growing electricity demand of EVs while reducing the charging downtime in comparison with traditional fixed charging stations (FCSs). This work proposes an optimal EV-to-MCS assignment (EMA) algorithm to operate a fleet of MCSs to serve the charging demands of on-the-move EVs. A mixed integer non-linear programming (MINLP) model is formulated to determine the optimal assignment between EVs and MCSs along with the optimal meeting point(s) along the EV traveling routes. The proposed algorithm aims to maximize the total profit of the MCS operating agency while maximizing the percentage of covered demand. The performance of the proposed algorithm is tested using real-world traffic flow data from the UAE and is evaluated against variations in the different system parameters, to study its robustness. In addition, a benchmark scenario is defined and used to study the performance of the EMA algorithm in two test scenarios, namely road traffic congestion and multi-service-level EVs. A comparison with several other heuristic assignment algorithms is also presented. The proposed EMA algorithm demonstrates higher profitability in comparison with the other heuristics, while maintaining an acceptable level of covered demand. This accordingly confirms its validity and reliability in ensuring optimal operation of the MCS fleet.