Optimal Routing with In-Route Charging of Mobility-on-Demand Electric Vehicles

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

doi:10.1109/vtcfall.2017.8288310

Cited by 10 publications

(4 citation statements)

References 5 publications

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“…Reference [30] developed a routing solution for Mobility on Demand (MoD) EV systems. MoD is a one-way vehicle sharing system, a promising way to reduce greenhouse gas emissions, and a sustainable solution for private mobility over the current reliance on a personal vehicles.…”

Section: Related Workmentioning

confidence: 99%

“…Compared to earlier works, such as [28,29], where these constraints were not considered, this work allowed for better judgement on the true optimal route. Moreover, the mobile on-demand proposal includes a promising idea with the CS schedule, although this would, in turn, affect the privacy of drivers and their vehicles [30]. To further improve the above works, the inclusion of more constraints for CSs such as CS charging efficiency, price, and vehicles' waiting will ensure a greater accuracy in the computing of the optimal route.…”

Section: Comparative Studymentioning

confidence: 99%

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An Efficient Detour Computation Scheme for Electric Vehicles to Support Smart Cities’ Electrification

et al. 2022

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Achieving carbon-neutral transportation is the ultimate goal of the ongoing joint efforts of governments, policy-makers, and the transportation research community. Electrification of smart cities is a very important step towards the above objective; therefore, accelerating the adoption and widening the use of Electric Vehicles (EVs) are required. However, to achieve the full potential of EVs, ground-breaking detour computation and charging station selection schemes are needed. To this end, this paper developed a new scheme that finds the most suitable detour/route for an EV whenever an unexpected event occurs on the road. This scheme is based on A* and uses an original, Simple-Additive-Weighting (SAW)-based, charging station selection method. The performance evaluation carried out using the open-source traffic simulation platform SUMO under a grid map, as well as a real road network map highlighted that our scheme ensured more than 99% of EVs will reach their destination within a reasonable time even if a battery recharge is needed. This is a significant improvement compared to the baseline scheme that uses the A* only.

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Section: Related Workmentioning

confidence: 99%

Section: Comparative Studymentioning

confidence: 99%

An Efficient Detour Computation Scheme for Electric Vehicles to Support Smart Cities’ Electrification

et al. 2022

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“…degradation [16]. Recently, mobility-on-demand serviced by electric vehicles has triggered routing algorithms that minimize overall trip time for all customers considering charging needs [17].…”

Section: State-of-the-art Of Energy-efficient Routing For Electrimentioning

confidence: 99%

A Model for Range Estimation and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

Cauwer

Verbeke

Mierlo

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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This paper presents an integrated model for energy consumption and range estimation, capable of energy-efficient routing. This integrated model predicts the energy consumption on all road segments in the road network and applies shortest path algorithms to calculate energy-efficient routes. A temperature-dependent model of the battery internal resistance based on real-world data translates the energy consumption prediction into driving range. The graph representation of the road network is transformed from a node-based graph to an edge-based graph to allow cost allocation of one edge based on the characteristics of the preceding edge. The integrated model is used to perform an analysis of energy-efficient routes for a real-life scenario. The analysis showed that the energy-optimal route is different from the time-and distance-optimal route with energy-efficiency gains up to 37% for the chosen scenario. The energy-efficient route tends to lean toward a distance-optimal route in the case of low auxiliary consumption and to lean toward a time-optimal route in the case of high auxiliary consumption. The energy-efficient route generation is tested in real life for one case of the chosen scenario. The measured results showed a good match with the predicted values for the energy consumption with a 9% mean relative error and preserved the ranking of all routes in terms of the travel distance, travel time, and energy consumption. The proposed integrated model is a functional model for energy consumption in real-life that differentiates many energy consumption influencing factors and produces energy-efficient routes with good accuracy.

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“…The authors of [14] study scheduling algorithms for assigning MoD EVs to trips. To address the issue of EVs' need to perform in-route charging, [15] proposes a routing scheme that aims to reduce overall delays. As EVs could also be autonomous, the authors of [16] study an agent-based model to simulate the operations of an AMoD fleet of EVs under various vehicle and infrastructure scenarios.…”

Section: Introductionmentioning

confidence: 99%

Smart Charging Benefits in Autonomous Mobility on Demand Systems

Turan

Tucker

Alizadeh

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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In this paper, we study the potential benefits from smart charging for a fleet of electric vehicles (EVs) providing autonomous mobility-on-demand (AMoD) services. We first consider a profit-maximizing platform operator who makes decisions for routing, charging, rebalancing, and pricing for rides based on a network flow model. Clearly, each of these decisions directly influence the fleet's smart charging potential; however, it is not possible to directly characterize the effects of various system parameters on smart charging under a classical network flow model. As such, we propose a modeling variation that allows us to decouple the charging and routing problems faced by the operator. This variation allows us to provide closedform mathematical expressions relating the charging costs to the maximum battery capacity of the vehicles as well as the fleet operational costs. We show that investing in larger battery capacities and operating more vehicles for rebalancing reduces the charging costs, while increasing the fleet operational costs. Hence, we study the trade-off the operator faces, analyze the minimum cost fleet charging strategy, and provide numerical results illustrating the smart charging benefits to the operator.

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Optimal Routing with In-Route Charging of Mobility-on-Demand Electric Vehicles

Cited by 10 publications

References 5 publications

An Efficient Detour Computation Scheme for Electric Vehicles to Support Smart Cities’ Electrification

An Efficient Detour Computation Scheme for Electric Vehicles to Support Smart Cities’ Electrification

A Model for Range Estimation and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

Smart Charging Benefits in Autonomous Mobility on Demand Systems

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