  The exponential growth of electric vehicles (EVs) presents novel challenges
in preserving battery health and in addressing the persistent problem of
vehicle range anxiety. To address these concerns, wireless charging,
particularly, Mobile Energy Disseminators (MEDs) have emerged as a promising
solution. The MED is mounted behind a large vehicle and charges all
participating EVs within a radius upstream of it. Unfortuantely, during such
V2V charging, the MED and EVs inadvertently form platoons, thereby occupying
multiple lanes and impairing overall corridor travel efficiency. In addition,
constrained budgets for MED deployment necessitate the development of an
effective dispatching strategy to determine optimal timing and locations for
introducing the MEDs into traffic. This paper proposes a deep reinforcement
learning (DRL) based methodology to develop a vehicle dispatching framework. In
the first component of the framework, we develop a realistic reinforcement
learning environment termed "ChargingEnv" which incorporates a reliable
charging simulation system that accounts for common practical issues in
wireless charging deployment, specifically, the charging panel misalignment.
The second component, the Proximal-Policy Optimization (PPO) agent, is trained
to control MED dispatching through continuous interactions with ChargingEnv.
Numerical experiments were carried out to demonstrate the demonstrate the
efficacy of the proposed MED deployment decision processor. The experiment
results suggest that the proposed model can significantly enhance EV travel
range while efficiently deploying a optimal number of MEDs. The proposed model
is found to be not only practical in its applicability but also has promises of
real-world effectiveness. The proposed model can help travelers to maximize EV
range and help road agencies or private-sector vendors to manage the deployment
of MEDs efficiently.
