Optimal Cloud-Based Routing With In-Route Charging of Mobility-on-Demand Electric Vehicles

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

doi:10.1109/tits.2018.2867519

Cited by 43 publications

(22 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the future, we intend to consider the recharging station’s capability to attend a limited number of UAVs. In this case, the recharging stations could be modeled as a queuing system, as proposed in [ 40 ]. We also plan to adopt some strategies to avoid collisions in the presence of intersecting routes.…”

Section: Discussionmentioning

confidence: 99%

“…The charging planning for autonomous vehicle logistic system was combined with the scheduling problem [ 39 ]. Ammous et al [ 40 ] investigated the customers trip delay due to the charging time for an EVs allocation system, presenting a routing scheme that considers the charging station queues to minimize the expected overall trip time for all customers’ overall routes relative to their trip time, not considering the delay required for in-route charging.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Matheuristics for Multi-UAV Routing and Recharge Station Location for Complete Area Coverage

Santin

Assis

Andrade

et al. 2021

Sensors

View full text Add to dashboard Cite

This paper presents matheuristics for routing a heterogeneous group of capacitated unmanned air vehicles (UAVs) for complete coverage of ground areas, considering simultaneous minimization of the coverage time and locating the minimal number of refueling stations. Whereas coverage path planning (CPP) is widely studied in the literature, previous works did not combine heterogeneous vehicle performance and complete area coverage constraints to optimize UAV tours by considering both objectives. As this problem cannot be easily solved, we designed high-level path planning that combines the multiobjective variable neighborhood search (MOVNS) metaheuristic and the exact mathematical formulation to explore the set of nondominated solutions. Since the exact method can interact in different ways with MOVNS, we evaluated four different strategies using four metrics: execution time, coverage, cardinality, and hypervolume. The experimental results show that applying the exact method as an intraroute operator into the variable neighborhood descent (VND) can return solutions as good as those obtained by the closest to optimal strategy but with higher efficiency.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Matheuristics for Multi-UAV Routing and Recharge Station Location for Complete Area Coverage

Santin

Assis

Andrade

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Although above two issues are clear limitations of the proposed model, similar features are often assumed to model SAVs, automated vehicle operations, or ridesharing services in the literature [6], [18], [19], [43], [44]. Therefore, the proposed model could be still useful for designing SAV systems in the strategic planning phases; as discussed in Section I, this role is essential for urban transportation planning for example.…”

Section: F Limitationsmentioning

confidence: 99%

Multi-Objective Linear Optimization Problem for Strategic Planning of Shared Autonomous Vehicle Operation and Infrastructure Design

Seo

Asakura

2022

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

This study proposes a unified optimization framework for strategic planning of shared autonomous vehicle (SAV) systems that explicitly and endogenously considers their operational aspects based on macroscopic dynamic traffic assignment. Specifically, the proposed model optimizes fleet size, road network design, and parking space allocation of an SAV system with optimized SAVs' dynamic routing with passenger pickup/delivery and ridesharing. It is formulated as a multi-objective optimization problem that simultaneously minimizes total travel time of travelers, total distance traveled by SAVs, total number of SAVs, and infrastructure construction cost; thus, both the user-side cost and the system-side cost are taken into account, and their trade-off relations can be explicitly investigated. Furthermore, the problem is formulated as a linear programming problem, making it easy to solve. By leveraging the linearity, we mathematically derive a useful property of the problem: introduction of ridesharing can weakly monotonically and simultaneously decrease the user-side cost and system-side cost. The proposed model is evaluated by applying it to actual travel records obtained from New York City taxi data.

show abstract

“…It means that more and more EVs will be connected to the grid and interact with the utility in the future. On the one hand, EVs are a flexible power load [3], they will require charging from the grid at different times and at different locations [4,5]. As a consequence, the coordination of EVs' charging has been widely studied in the recent period [6,7].…”

Section: Motivation and Backgroundmentioning

confidence: 99%

“…Step 4. The travel path space and time between two consecutive trip destinations are determined by the path planning algorithm and Equations (3), (5), and (6).…”

Section: Travel Pattern Simulationmentioning

confidence: 99%

Spatio-Temporal Model for Evaluating Demand Response Potential of Electric Vehicles in Power-Traffic Network

2019

View full text Add to dashboard Cite

Electric vehicles (EVs) can be regarded as a kind of demand response (DR) resource. Nevertheless, the EVs travel behavior is flexible and random, in addition, their willingness to participate in the DR event is uncertain, they are expected to be managed and utilized by the EV aggregator (EVA). In this perspective, this paper presents a composite methodology that take into account the dynamic road network (DRN) information and fuzzy user participation (FUP) for obtaining spatio-temporal projections of demand response potential from electric vehicles and the electric vehicle aggregator. A dynamic traffic network model taking over the traffic time-varying information is developed by graph theory. The trip chain based on housing travel survey is set up, where Dijkstra algorithm is employed to plan the optimal route of EVs, in order to find the travel distance and travel time of each trip of EVs. To demonstrate the uncertainties of the EVs travel pattern, simulation analysis is conducted using Monte Carlo method. Subsequently, we suggest a fuzzy logic-based approach to uncertainty analysis that starts with investigating EV users’ subjective ability to participate in DR event, and we develop the FUP response mechanism which is constructed by three factors including the remaining dwell time, remaining SOC, and incentive electricity pricing. The FUP is used to calculate the real-time participation level of a single EV. Finally, we take advantage of a simulation example with a coupled 25-node road network and 54-node power distribution system to demonstrate the effectiveness of the proposed method.

show abstract

Optimal Cloud-Based Routing With In-Route Charging of Mobility-on-Demand Electric Vehicles

Cited by 43 publications

References 23 publications

Matheuristics for Multi-UAV Routing and Recharge Station Location for Complete Area Coverage

Matheuristics for Multi-UAV Routing and Recharge Station Location for Complete Area Coverage

Multi-Objective Linear Optimization Problem for Strategic Planning of Shared Autonomous Vehicle Operation and Infrastructure Design

Spatio-Temporal Model for Evaluating Demand Response Potential of Electric Vehicles in Power-Traffic Network

Contact Info

Product

Resources

About