Eiman ElGhanam

Electric vehicles (EVs) are gaining increasing interest due to their zero emissions and relatively reduced running costs. However, the availability of the charging energy is a main concern for many EV users along with the anticipated prolonged charging times. Mobile charging stations (MCSs) offer a non-intrusive, on-the-move charging solution that addresses the growing electricity demand of EVs while reducing the charging downtime in comparison with traditional fixed charging stations (FCSs). This work proposes an optimal EV-to-MCS assignment (EMA) algorithm to operate a fleet of MCSs to serve the charging demands of on-the-move EVs. A mixed integer non-linear programming (MINLP) model is formulated to determine the optimal assignment between EVs and MCSs along with the optimal meeting point(s) along the EV traveling routes. The proposed algorithm aims to maximize the total profit of the MCS operating agency while maximizing the percentage of covered demand. The performance of the proposed algorithm is tested using real-world traffic flow data from the UAE and is evaluated against variations in the different system parameters, to study its robustness. In addition, a benchmark scenario is defined and used to study the performance of the EMA algorithm in two test scenarios, namely road traffic congestion and multi-service-level EVs. A comparison with several other heuristic assignment algorithms is also presented. The proposed EMA algorithm demonstrates higher profitability in comparison with the other heuristics, while maintaining an acceptable level of covered demand. This accordingly confirms its validity and reliability in ensuring optimal operation of the MCS fleet.

Dynamic charging of electric vehicles (EVs) is an attractive solution to the EV driving range limitations and the associated range anxiety problem. In dynamic wireless EV charging (DWC), charging lanes are deployed along city roads to wirelessly supply the needed charging power to EVs during their motion. However, due to the expected high infrastructure costs involved in the construction of electrified road structures (ERS) with wireless charging lanes, as well as the likely increase in the energy demand by EV owners, an optimal deployment plan is essential to maximize the net returns to the infrastructure owners and minimize the associated deployment costs while ensuring a maximal demand coverage. Therefore, to formulate a reliable optimization framework, accurate modeling of the charging lane operation is needed at each potential lane location. In this work, the traffic behavior at different locations is modeled analytically using queuing theory. This accurately represents the desired flow of vehicles on the charging lanes and provides a reliable estimate of the EV charging demand, particularly due to the lack of EV traffic flow datasets with the currently low but expanding penetration of EVs. A multi-objective optimization framework is then developed based on the established traffic model to determine the most optimal locations for the deployment of DWC lanes within a smart city infrastructure. The model is tested on 24 candidate roads selected from the United Arab Emirates map and the corresponding optimal locations are determined by solving the optimization problem on GAMS/CONOPT solver. Sensitivity analysis is also conducted to validate the results of the proposed model.

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.

The market share of electric vehicles (EVs) is increasing due to their low greenhouse gas (GHG) emissions and reduced running costs. However, range anxiety and charging downtime are some challenges slowing down the adoption of this new technology. Dynamic wireless charging (DWC) aims to minimize the problems above by allowing the EVs to charge while in motion. With DWC, optimizing the locations of the lanes is crucial to maximizing their utilization while minimizing the capital and operational expenditure of the system. Additionally, the extra load that DWC incurs on the power grid must be considered to avoid grid overload. In this work, a realistic traffic simulation is built. A new methodology is proposed to optimally deploy DWC lanes in the simulated road network and allocate distributed generation (DG) resources to support the power grid. The model is implemented for a large road network within Sharjah, UAE. Results reveal that, for a 30% EV penetration level, a DWC system is economically feasible and profitable to deploy to address the anticipated EV charging demand within the traffic network under consideration.

With the increasing investments in on-the-move electric vehicle (EV) charging solutions and wireless charging lanes (WCLs), coordination of the energy requirements of mobile EVs becomes essential to ensure load balancing while maximizing demand coverage. This necessitates the development of online and mobility-aware algorithms for assigning EV-to-charging lanes. In this work, a decentralized, blockchain-based EV assignment and energy allocation system is presented. The objective of system is to coordinate the charging requirements of mobile EVs among the available WCLs within a network of EV chargers in an Internet of Electric Vehicles (IoEVs). This blockchain-based system offers higher security, transparency and immutability over traditional rule-based coordination schemes. It also offers an integrated end-to-end framework that handles user registration, authentication, lane activation and energy reporting. This is in addition to its main functionality of establishing a real-time and load-balanced EV-to-WCL assignment process that addresses the EV energy requirements within constraints of traveling distance and remaining EV energy. The proposed system is tested on the Ethereum blockchain and its security and transparency are both validated accordingly.

In order to address range anxiety concerns and decrease the electric vehicle (EV) charging downtime, dynamic wireless EV charging (DWC) systems are developed. T

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.