Traffic aware electric vehicle routing

Basso, Rafael; Lindroth, Peter; Kulcsár, Balázs; Egardt, Bo

doi:10.1109/itsc.2016.7795588

Cited by 12 publications

(9 citation statements)

References 14 publications

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“…In addition, different factors that affect EV energy consumption were also discussed. We can cite the road gradient effect [23,24] which has a linear impact on fuel usage, and the effect of ambient temperature [25,26] affecting EV battery capacity linearly between 15C and 20C. An approach that considered both geographical and temperature factors was proposed in [27], estimating optimal routes and their duration.…”

Section: Literature Reviewmentioning

confidence: 99%

“…3) The energy cost computation This phase consists in determining the segments' energy costs. We pick up a realistic model of the energy computation for commercial EVs for transporting goods, proposed by [23]. An equation based on two main factors, the road network topology (road grade, distance, speed limit) and the vehicle's parameters (speed, weight, and the frontal surface) was proposed.…”

Section: ) the Slope Calculationmentioning

confidence: 99%

“…We use the Google Elevation API that provides elevations for each point on the earth. Based on vehicle parameters and (2) presented in section 4, we calculate the energy cost of each road segment using the vehicle parameters values shown in table Ⅳ as provided in [23].…”

Section: ) Road Energy Computationmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Electric Vehicles Route Planning with Traffic Flow Prediction and Real-Time Traffic Incidents

Sebai

Rejeb

Denden

et al. 2022

IJECER

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Electric Vehicles (EVs) are regarded to be among the most environmentally and economically efficient transportation solutions. However, barriers and range limitations hinder this technology’s progress and deployment. In this paper, we examine EV route planning to derive optimal routes considering energy consumption by analyzing historical trajectory data. More specifically, we propose a novel approach for EV route planning that considers real-time traffic incidents, road topology, charging station locations during battery failure, and finally, traffic flow prediction extracted from historical trajectory data to generate energy maps. Our approach consists of four phases: the off-line phase which aims to build the energy graph, the application of the A* algorithm to deliver the optimal EV path, the NEAT trajectory clustering which aims to produce dense trajectory clusters for a given period of the day, and finally, the on-line phase based on our algorithm to plan an optimal EV path based on real traffic incidents, dense trajectory clusters, road topology information, vehicle characteristics, and charging station locations. We set up experiments on real cases to establish the optimal route for electric cars, demonstrating the effectiveness and efficiency of our proposed algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Section: ) the Slope Calculationmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Electric Vehicles Route Planning with Traffic Flow Prediction and Real-Time Traffic Incidents

Sebai

Rejeb

Denden

et al. 2022

IJECER

View full text Add to dashboard Cite

show abstract

“…This mixed fleet routing problem was further examined by [14] considering different vehicle capacities, battery sizes, and acquisition costs. Reference [15] introduced additional constraints on the EV speed for different time intervals based on the corresponding traffic flow forecasts. Reference [16] first proposed to capture the nonlinear behavior of the charging process using a piecewise linear approximation in the EVRPs.…”

Section: B Electric Vehicle Routing Problemsmentioning

confidence: 99%

Operating Electric Vehicle Fleet for Ride-Hailing Services With Reinforcement Learning

Shi

Gao

Wang

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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Providing ride-hailing services with electric vehicles can help reduce greenhouse gas emissions and solve the last mile problem. This paper develops a reinforcement learning based algorithm to operate a community owned electric vehicle fleet, which provides ride-hailing services to local residents. The goals of operating the electric vehicle fleet are to minimize customer waiting time, electricity cost, and operational costs of the vehicles. A novel framework characterized by decentralized learning and centralized decision making is proposed to solve the electric vehicle fleet dispatch problem. The decentralized learning process allows the individual vehicles to share their operating experiences and deep neural network model for state-value function estimation, which mitigates the curse of dimensionality of state and action domains. The centralized decision making framework converts the vehicle fleet coordination problem into a linear assignment problem, which has polynomial time complexity. Numerical study results show that the proposed approach outperforms the benchmark algorithms in terms of societal cost reduction.

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“…Bruglieri et al [64] used VNS Branching (VNSB) as a math-heuristic method for solving the problem. Considering the dependence of energy consumption of EVs on various factors such as ground gradient, weight, and speed of the vehicle, Basso et al [65] developed a new E-VRP-TW model so that the speed of vehicles during different hours of the day was considered as a variable due to the volume of traffic. Barco et al [31] proposed a method for transporting passengers using E-VRP-TW, in which the vehicle charging schedule was considered to minimize costs and reduce battery degradation.…”

Section: Electric Vehicle Routing Problem With Time Windows (E-vrp-tw)mentioning

confidence: 99%

A Survey on Environmentally Friendly Vehicle Routing Problem and a Proposal of Its Classification

et al. 2020

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The growth of environmental awareness and more robust enforcement of numerous regulations to reduce greenhouse gas (GHG) emissions have directed efforts towards addressing current environmental challenges. Considering the Vehicle Routing Problem (VRP), one of the effective strategies to control greenhouse gas emissions is to convert the fossil fuel-powered fleet into Environmentally Friendly Vehicles (EFVs). Given the multitude of constraints and assumptions defined for different types of VRPs, as well as assumptions and operational constraints specific to each type of EFV, many variants of environmentally friendly VRPs (EF-VRP) have been introduced. In this paper, studies conducted on the subject of EF-VRP are reviewed, considering all the road transport EFV types and problem variants, and classifying and discussing with a single holistic vision. The aim of this paper is twofold. First, it determines a classification of EF-VRP studies based on different types of EFVs, i.e., Alternative-Fuel Vehicles (AFVs), Electric Vehicles (EVs) and Hybrid Vehicles (HVs). Second, it presents a comprehensive survey by considering each variant of the classification, technical constraints and solution methods arising in the literature. The results of this paper show that studies on EF-VRP are relatively novel and there is still room for large improvements in several areas. So, to determine future insights, for each classification of EF-VRP studies, the paper provides the literature gaps and future research needs.

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Traffic aware electric vehicle routing

Cited by 12 publications

References 14 publications

Optimal Electric Vehicles Route Planning with Traffic Flow Prediction and Real-Time Traffic Incidents

Optimal Electric Vehicles Route Planning with Traffic Flow Prediction and Real-Time Traffic Incidents

Operating Electric Vehicle Fleet for Ride-Hailing Services With Reinforcement Learning

A Survey on Environmentally Friendly Vehicle Routing Problem and a Proposal of Its Classification

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