Recently, electric vehicles (EVs) have been introduced as an alternative method of transportation to help mitigate environmental issues, such as carbon emissions and fuel consumption, caused by conventional transportation systems. The implementation of effective EV charging systems is essential to motivate mass adoption of EVs. Accordingly, fast and reliable communications between the charging systems and the EVs are vital for efficient management of the charging process. Different radio access technologies (RATs) are discussed in the literature to enable communication between the highly mobile EVs and the charging subsystems, to collect and exchange information such as state of charge (SoC), users’ locations, and charging decisions between the different network entities. This information can be used to coordinate charging plans and select the optimal routes for moving vehicles. This paper presents a survey of existing literature on vehicular communications for EV charging coordination and management. The communication requirements and feasible communication technologies for vehicular communication are first discussed in details. A review of the physical layer security strategies is then presented and the role of the different RATs in EV charging coordination and management is described and studied.

The utilization of dynamic wireless charging (DWC) systems to charge on-the-move EVs is currently gaining an increasing popularity, as it addresses range and charging downtime issues of EV users. To ensure optimal utilization of this charging infrastructure, coordination of EV charging demand is essential to achieve grid load balancing and prevent grid overload. In contrast to offline, day-ahead charging scheduling, this work proposes an online, mobility-aware, spatial EV allocation algorithm within a DWC coordination strategy. This strategy allocates EVs requesting charge to the most optimal DWC lanes within an EV charging network (ECN) in an Internet of EVs (IoEVs). A detailed charging request scenario is presented to highlight the required communication for authentication between the EVs and the charging infrastructure, to achieve the desired coordination. Description of the proposed EV allocation algorithm is then presented and the performance of the algorithm is evaluated using a hypothetical case study of predicted EV traffic trips in the cities of Dubai and Sharjah, UAE. Upon parameter optimization, results of the conducted analysis reveal that the proposed EV allocation algorithm achieves an almost flattened load profile across the DWC lanes that reduces the PAER by more than 44% in comparison with a shortest distance allocation algorithm, for a maximum $2\times $ increase in trip length, and sufficient received energy to compensate for the energy consumed during the trip. This acknowledges grid supply limitations, EV traveling velocities and the maximum service capacity per DWC lane.

The growing penetration of electric vehicles (EVs) and the increasing EV energy demand pose several challenges to the power grid, the power distribution networks and the transportation networks. This growing demand drives the need for effective demand management and energy coordination strategies to maximize the demand covered by the EV charging stations, ensure EV users' satisfaction and prevent grid-side overload. As a result, several optimization problems are formulated and solved in the literature to provide optimal EV charging schedules (i.e. temporal coordination) as well as optimal EV-to-charging-station assignments and routing plans (i.e. spatial coordination). This paper presents a review of the state-of-the-art literature on the utilization of different deterministic optimization techniques to develop optimal EV charging coordination strategies. In particular, these works are reviewed according to their domains of operation (i.e. time-based scheduling, spatial coordination, and spatio-temporal charging coordination), their respective objectives (user-, grid- and operator-related objectives), and the solution algorithms adopted to provide the corresponding optimal coordination plans. This helps in identifying key research gaps and provide recommendations for future research directions to develop comprehensive and computationally efficient charging coordination models.

Dynamic wireless power transfer (DWPT) systems are becoming increasingly important for on-the-move electric vehicle (EV) charging solutions, to overcome range anxiety and compensate for the consumed energy while the EV is in motion. In this work, a DWPT EV charging system is proposed to be implemented on a straight road stretch such that it provides the moving EV with energy at a rate of 308 Wh/km. This rate is expected to compensate for the vehicle’s average energy consumption and allow for additional energy storage in the EV battery. The proposed charging system operates at an average power transfer efficiency that is higher than $90\%$ and provides good lateral misalignment tolerance up to $\pm 200$ mm. Details of the proposed system’s design are presented in this paper, including EV specifications, inductive link and compensation network design and power electronic circuitry.

In order to design a high efficiency Wireless Electric Vehicle Charging (WEVC) system, the design of the different system components needs to be optimized, particularly the design of a high-coupling, misalignment-tolerant inductive link (IL), comprising primary and secondary charging coils. Different coil geometries can be utilized for the primary and the secondary sides, each with a set of advantages and drawbacks in terms of weight, cost, coupling at perfect alignment and coupling at lateral misalignments. In this work, a Finite Element Method (FEM)-based systematic approach for the design of double-D (DD) charging coils is presented in detail. In particular, this paper studies the effect of different coil parameters, namely the number of turns and the turn-to-turn spacing, on the coupling performance of the IL at perfect alignment and at $\pm 200$ mm lateral misalignment, given a set of space constraints. The proposed design is verified by an experimental prototype to validate the accuracy of the FEM model and the simulation results. Accordingly, FEM simulations are utilized to compare the performance of rectangular, DD and DDQ coils. The FEM results prove the importance of utilizing an additional quadrature coil on the secondary side, despite the added weight and cost, to further improve the misalignment tolerance of the proposed inductive link design.

To address range anxiety concerns of current and potential EV drivers, on-the-move charging of electric vehicles (EV) is introduced through the development of roadway electrification solutions, i.e., wireless charging lanes (WCLs). These lanes allow EVs to recharge their batteries and compensate for the energy consumed during their motion. Nevertheless, the high deployment and operational costs of WCLs raise concerns about their expected utilization and returns on the investment. This calls for a detailed analysis of the potential locations for WCL implementation. This work proposes a two-staged multi-criteria decision making (MCDM) framework that integrates the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with binary goal programming (BGP), to evaluate potential locations for WCL implementation according to geospatial, environmental, technical and economic criteria. In the first stage, the TOPSIS method is utilized to rank several alternative locations according to their geospatial, environmental and technical specifications. The alternatives with the highest ranking are then shortlisted and evaluated using a mathematical BGP model, to provide recommendations on the most optimal locations and the corresponding WCL lengths that meet budget constraints according to the economic evaluation criteria. The proposed integrated TOPSIS-BGP model offers a comprehensive decision-making approach by acknowledging diverse criteria that are otherwise difficult to integrate into a single combinatorial mathematical program. A case study of major UAE cities, namely Dubai and Sharjah, is presented to illustrate the implementation of the proposed framework.