Deployment Optimization of Dynamic Wireless Chargers for Electric Vehicles

Meligy, Ahmed O. El; ElGhanam, Eiman; Hassan, Mohamed S.; Osman, Ahmed

doi:10.1109/itec53557.2022.9813969

Cited by 6 publications

(2 citation statements)

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“…Furthermore, few works have simultaneously addressed grid-related and user-related objectives [50], [51]. Nevertheless, several deployment optimization strategies are currently being developed to optimize the deployment of DWC lanes within the city infrastructure, acknowledging the return-on-investments (RoI) of the infrastructure owner, the required coverage of customers' demand and the locations of power buses within PDNs [52]- [54]. This motivates the need to optimize EV charging behaviors to address both userand grid-related objectives to maximize the social welfare, by motivating mass EV adoption, improving EV users' QoE and ensuring grid stability against EV charging loads.…”

Section: A Related Workmentioning

confidence: 99%

“…EV charging networks are expected to be in high demand during these high congestion periods. Since there are currently no commercial deployments of DWC lanes within UAE, potential lane locations are used for the conducted analysis assuming an underlying deployment optimization model from earlier works in [54], [59]. Accordingly, the presumed-to-be optimal locations of the DWC lanes are pinned on the location map as shown in Figure 6.…”

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On the Coordination of Charging Demand of Electric Vehicles in a Network of Dynamic Wireless Charging Systems

et al. 2022

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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× 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.INDEX TERMS Electric vehicle charging network; dynamic wireless charging system; charging coordination; spatial allocation; peak-to-average-energy ratio. NOMENCLATURE Abbreviations CEMCentralized energy management.

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