For an environmentally sustainable future, electric vehicle (EV) adoption rates have been growing exponentially around the world. There is a pressing need for constructing smart charging infrastructures that can successfully integrate the large influx of connected and autonomous EVs (CAEVs) into the smart grids. To fulfill the aspiration of massive deployment of autonomous mobility on demand (AMoD) services, the proposed fast and secure framework will need to address the long charging times and long waiting times of static charging. It will also need to consider dynamic wireless charging as a viable solution for the CAEVs on the move. In this thesis, a novel three-layer charging system design of static and dynamic wireless charging that can operate with the existing wired charging infrastructure and standards for Intelligent Transportation System (ITS) is presented. This internet of things (IoT) application is accompanied by a proposed handshake protocol with light-weight request message frames. It employs vehicle to infrastructure (V2I) and vehicle to grid (V2G) communications for fulfilling charging requests of CAEVs with the shortest possible route to the destination. The charging requests of the CAEV users are fulfilled by dynamically distributing the request over the three different types of charging equipment. Further, the requests are serviced and billed privately and securely using two different proposed payment schemes with the encrypted virtual currency. The hardware independent system can detect misalignment of the CAEVs on the wireless charging pads and the speed issue errors in dynamic wireless charging systems as well as avoid free-riders. Additionally, the proposed dynamic wireless charging network (DWCN) design specification tool is analyzed. The suggestions made by the tool for building a DWCN can enable implementers to achieve the desired charging delivery performance at the lowest cost possible. Finally, the presented system is simulated, and this verified and validated simulator is revealed to make reservations and plan trips with minimum waiting times, travel costs, and battery consumption per vehicle trip. The system results proved 90.25% charge delivery efficiency. This system is then compared with alternative system designs to help showcase its ability to aid implementers and analysts in making design choices with the simulation.