Abstract
Since the rules and regulations strongly emphasize environmental preservation and greenhouse gas GHG reduction, researchers have progressively noticed a shift in the transportation means toward electromobility. Several challenges must be resolved to deploy EVs, beginning with improving network accessibility and bidirectional interoperability, reducing the uncertainty related to the availability of suitable charging stations on the trip path and reducing the total service time. Therefore, suggesting DQN supported by AIoT to pair EVs' requests and station invitations to reduce idle queueing time is crucial for long travel distances. The author has written a proposed methodology in MATLAB to address significant parameters such as the battery charge level, trip distance, nearby charging stations, and average service time. The effectiveness of the proposed methodology is derived from hybridizing the meta-heuristic techniques in searching DQN learning steps to obtain a solution quickly and improve the servicing time by 34%, after solving various EV charging scheduling difficulties and congestion control and enabling EV drivers to policy extended trips. The work results obtained from more than 2145 training hypothetical examples for EVs' requests were compared with the Bayesian Normalized Neural Network (BASNNC) algorithm, which hybridize the Beetle Antennae Search and Neural Network Classifier, and with other methods such as Grey Wolf Optimization (GWO) and Sine-cosine and Whale optimization, revealing that the mean overall comparison efficiencies in error reduction were 72.75%, 58.7%, and 18.2% respectively.