Joint Delay and Cost Optimization of In-Route Charging for On-Demand Electric Vehicles

Ammous, Mustafa; Belakaria, Syrine; Sorour, Sameh; Abdel-Rahim, Ahmed

doi:10.1109/tiv.2019.2955374

Cited by 24 publications

(13 citation statements)

References 18 publications

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“…Zhang et al [19] investigated delay-optimal charge scheduling for a charging station under long-term cost constraint. Ammous et al [20] considered joint delay and cost optimization of on-demand EV charge scheduling.…”

Section: Notation Descriptionmentioning

confidence: 99%

Online EV Charge Scheduling Based on Time-of-Use Pricing and Peak Load Minimization: Properties and Efficient Algorithms

Lin

Liu

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Section: Notation Descriptionmentioning

confidence: 99%

Online EV Charge Scheduling Based on Time-of-Use Pricing and Peak Load Minimization: Properties and Efficient Algorithms

Lin

Liu

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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“…This work assumes that vehicles stop during ongoing trips at intermediate charging stations for recharging, whereas in this study we assumed that charging occurs only at the end of a trip without causing any discomfort to passengers, as detouring was not allowed. The same assumption of in-route charging was also adopted in [28,29].…”

Section: Charging Optimisation Of Shared Fleetsmentioning

confidence: 99%

An Integrated Optimisation-Simulation Framework for Scalable Smart Charging and Relocation of Shared Autonomous Electric Vehicles

2021

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Ride-hailing with autonomous electric vehicles and shared autonomous electric vehicle (SAEV) systems are expected to become widely used within this decade. These electrified vehicles can be key enablers of the shift to intermittent renewable energy by providing electricity storage to the grid and offering demand flexibility. In order to accomplish this goal, practical smart charging strategies for fleets of SAEVs must be developed. In this work, we present a scalable, flexible, and practical approach to optimise the operation of SAEVs including smart charging based on dynamic electricity prices. Our approach integrates independent optimisation modules with a simulation model to overcome the complexity and scalability limitations of previous works. We tested our solution on real transport and electricity data over four weeks using a publicly available dataset of taxi trips from New York City. Our approach can significantly lower charging costs and carbon emissions when compared to an uncoordinated charging strategy, and can lead to beneficial synergies for fleet operators, passengers, and the power grid.

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“…To name a few, Zhang et al proposed a charging guidance strategy for urgent charging EVs to fast charging stations based on the virtual service range [31]. Ammous et al proposed formulated a joint problem of minimizing the average trip time for an EV and the average cost of charging as a dual-objective convex optimization problem [32]. Moradipari and Alizadeh designed optimal pricing and routing schemes for the setting that users choose their priority level and energy request amount from the differentiated service offered by EV public charging stations owned by a charging network operator [33].…”

Section: Related Workmentioning

confidence: 99%

“…where Eqs. (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) are iterated until the traffic density is set for the road segment connecting l and m, and approval statuses are marked for the EVs in the prerouting and rerouting reservation queues.…”

Section: Real-time Congestion Control Of the Urban Traffic Control Centermentioning

confidence: 99%

A Distributed Urban Traffic Congestion Prevention Mechanism for Mixed Flow of Human-Driven and Autonomous Electric Vehicles

Huang¹,

Hu²,

Ho³

et al. 2021

IJCIS

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Traffic congestion in urban areas has become a critical problem that municipal governments cannot overlook. Meanwhile, mixed traffic systems containing both autonomous and human-driven electric vehicles ramp up the challenge for traffic management in urban areas. Although numerous researchers have proposed traffic control heuristics to alleviate traffic congestion problems in the recent literature, scant research has addressed the joint problems of route and charging strategies for electric vehicles along with urban traffic congestion prevention. Accordingly, this work tackles the complex task of traffic management in urban areas during peak periods by using practical congestion prevention strategies that consider the characteristics of mixed traffic flows and the charging demands of electric vehicle users. Notably, we apply support vector regressions to compute the charging time at each charging point and the traverse time of an electric vehicle at each road segment/intersection, based on historical traffic data. The simulation results reveal that the proposed algorithms are feasible because they can avoid possible occurrences of traffic congestion during rush hours and provide the routes and charging options that are chosen by electric vehicle users.

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Joint Delay and Cost Optimization of In-Route Charging for On-Demand Electric Vehicles

Cited by 24 publications

References 18 publications

Online EV Charge Scheduling Based on Time-of-Use Pricing and Peak Load Minimization: Properties and Efficient Algorithms

Online EV Charge Scheduling Based on Time-of-Use Pricing and Peak Load Minimization: Properties and Efficient Algorithms

An Integrated Optimisation-Simulation Framework for Scalable Smart Charging and Relocation of Shared Autonomous Electric Vehicles

A Distributed Urban Traffic Congestion Prevention Mechanism for Mixed Flow of Human-Driven and Autonomous Electric Vehicles

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