Optimized Dynamic Electric Vehicles Charging in Smart Cities

Abstract

Recent technological advances have fueled interest in the development of smart cities where the convenience and health of inhabitants are core objectives. Increased automation and reduced green gas emission are prime means for achieving these objectives. No wonder that there is a major push for the adoption of electrical vehicles. Yet, the deployment of electric vehicles (EVs) is slow-paced. One of the primary obstacles hindering the widespread adoption of EVs is range anxiety, which refers to the fear that an EV will not have sufficient battery charge to reach its destination or a nearby charging station. Additionally, long charging times pose a significant barrier, as recharging an EV battery can take considerably longer compared to refueling a conventional gasoline vehicle, making it less convenient for users. Furthermore, the scarcity of charging infrastructure capable of handling a large number of EVs exacerbates these concerns, deterring potential buyers due to worries about accessibility and convenience. Most of the existing charging techniques require EVs to remain stationary while being charged. Wireless charging has emerged as a viable solution that mitigates such a shortcoming by enabling dynamic EV-to-EV charging. It can also expand the driving range of automobiles if the battery can be continuously charged while the vehicle is in motion, hence extending the traveled distance. EV-to-EV charging provides drivers with greater temporal and spatial flexibility, specifically in densely populated urban regions, while aiding in the reduction of energy consumption. It can also mitigate the burden of the grid during peak loads and optimize power usage during off-peak hours. EV-to-EV charging not only adds convenience to drivers, but it enables a new business model as well, where mobile suppliers may sell their energy to EVs on the road. This dissertation addresses the challenge of the dynamic charging and routing of EVs in smart cities. We first present RIMEC, a Routing for Increased Mobile Energy Charging algorithm that determines an optimized EV travel route for utilizing the Mobile Energy Disseminator (MEDs) in order to maximize the potential energy that can be harvested while minimizing the impact on travel time. Second, we introduce an EV-to-EV charging framework for energy suppliers. The framework opts to maximize the profit, while considering battery degradation and the overhead cost. The optimization is modeled as a time-space network and a dynamic programming-based solution strategy is pursued to optimally pair and route the energy supplier (ES) and requester (ER). Specifically, ES is incentivized to rendezvous ERs at encounter nodes to dispense the requested energy through platooning. The complexity of the problem arises from nonstationary consumers and service supply, which make monitoring and synchronizing the movement of ES and ER spatiotemporally challenging. Third, we tackle the problem of dynamic EV-to-EV charging that aims to maximize the supplier's profitability within a specific timeframe, taking into account overhead costs. Finally, we address the multi-supplier, multi-requester routing problem. We formulate the optimization problem mathematically as a mixed-integer program and develop a local search based-heuristic algorithm. Our objective is to optimize system-level metrics, including profitability and throughput. The simulation results validate the effectiveness of our proposed approach, demonstrating significant improvements in maximizing overall profit while minimizing energy consumption, travel time, and distance for requesters.