Electric vehicle shortest path problem with replenishment constraint

Baouche, Fouad; Trigui, Rochdi; Billot, Romain; Faouzi, Nour‐Eddin El

doi:10.1109/iccve.2014.7297600

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…In another study, Wen et al [44] considered the problem of locating electronic replenishment stations for electric vehicles on a traffic network with flow-based demand to optimize the network performance. Baouche et al [45] presented a new approach for the EV routing problem with recharging stage(s) along the way on the available charging stations to solve the autonomy limitation. Yet, issues like compatibility of battery EVs with chargers in their Charging Station(CS)s, and the impact of recharging at public or private CSs are still overlooked in the literature.…”

Section: ) Replenishmentmentioning

confidence: 99%

Green Vehicle Routing Problem: State of the Art and Future Directions

Sabet

Farooq

2022

IEEE Access

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Green vehicle routing problem (GVRP) aims to consider greenhouse gas emissions reduction, while routing the vehicles. It can be either through adopting Alternative Fuel Vehicles (AFVs) or with existing conventional fossil fuel vehicles in fleets. GVRP also takes into account environmental sustainability in transportation and logistics. We critically review several variations and specializations of GVRP to address issues related to charging, pick-up, delivery, and energy consumption. Starting with the concepts and definitions of GVRP, we summarize the key elements and contributors to GVRP publications. Afterward, the issues regarding each category of green vehicle routing are reviewed, based on which key future research directions and challenges are suggested. It was observed that the main focus of previous publications is on the operational level routing decision and not the supply chain issues. The majority of publications used metaheuristic methods, while overlooking the emerging machine learning methods. We envision that in addition to machine learning, reinforcement learning, distributed systems, internet of vehicles (IoV), and new fuel technologies have a strong role in developing the GVRP research further.

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Section: ) Replenishmentmentioning

confidence: 99%

Green Vehicle Routing Problem: State of the Art and Future Directions

Sabet

Farooq

2022

IEEE Access

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“…Literature ranges from methods to generate E-routes for an individual driver [10], possibly with range restrictions and the option to charge [11], [12], to research that calculates routes to obtain network energy optima [13]. Other specific traditional cost-optimization problems such as the vehicle routing problem or travelling salesman problem, where a monetary cost-optimization of delivery problems is sought (i.e.…”

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

confidence: 99%

“…It has been proposed in several publications as the shortest path algorithm for EVs [22]. Other specific shortest path algorithms for electric vehicles have been proposed and evaluated: metaheuristic approach in [12], particle swarm optimization in [23], Dijkstras algorithm with shifting technique (Johnsons) [24]. Some authors extend the SPA to include state-of-charge requirements [25].…”

Section: A Shortest-path-algorithmmentioning

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 grids' sizes for the state of charge (SOC), the charged SOC and the vehicle speed are ∆E i = 0.5 % SOC, ∆r i = 0.5 % SOC and ∆v i = 1 km/h, respectively. The position of a value at index i within these multidimensional and discrete grids is described by indices k, l and w. They are defined in (4), (5) and (6). Overall, the cost function (1) minimizes the overall traveling time within the given boundaries according to this works objective.…”

Section: B Time-optimal Strategy Calculationmentioning

confidence: 99%

An All-Electric Alpine Crossing: Time-Optimal Strategy Calculation via Fleet-Based Vehicle Data

Cussigh

Straub

Frey

et al. 2020

IEEE Open J. Intell. Transp. Syst.

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Recently, individual electric mobility gains significance due to legislation and social discussion. Customers demand longer battery ranges. Advanced planning is a different and more sustainable approach. Potentially, they assist drivers in exploiting the installed range on long journeys. Earlier research of the authors showed that an optimal combination of speed, charging choice and amount potentially reduces overall traveling time on long trips. In this work, a dynamic programming algorithm controls this strategy set time-optimally on an all-electric route from Munich to Verona. For this, location-specific fleet-based data of over 600 000 km are used to improve the reliability of the strategy set in two ways. Firstly, the data provide more realistic location-and time-specific velocity bounds for speed control. Secondly, they provide fleet-sourced dynamics to a traceable analytical consumption model. These additional dynamics lead to 1.8-2.3 % more energy demand in the strategy planning compared to a less accurate consumption map-based approach. Here, the incorporation of dynamics increases the optimizations' reliability. Also, the time-dependent fleet-data allows finding an optimal departure time for the given route. In total, the incorporation of fleet information enhances the robustness of the optimization. This enables a more seamless experience of electric mobility on long trips.

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Electric vehicle shortest path problem with replenishment constraint

Cited by 7 publications

References 13 publications

Green Vehicle Routing Problem: State of the Art and Future Directions

Green Vehicle Routing Problem: State of the Art and Future Directions

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

An All-Electric Alpine Crossing: Time-Optimal Strategy Calculation via Fleet-Based Vehicle Data

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