Optimal Routing and Scheduling of Charge for Electric Vehicles: A Case Study

Barco, John; Guerra, Andres; Muñoz, Luis; Quijano, Nicanor

doi:10.1155/2017/8509783

Cited by 69 publications

(37 citation statements)

References 35 publications

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“…Objective functions can be complex with simultaneous minimization of vehicle number, total traveled distance [28], total travel times [29], total routing cost and planning horizon [11,27,30,31], GHG emission [32,33], energy consumption [34][35][36], etc. Total routing costs of BEVs usually consist of BEV acquisition cost, circulation tax, maintenance, costs related to the energy consumption (electric energy price), cost of battery pack renewal after its lifetime, labor costs, etc.…”

Section: Electric Vehicle Routing Problemmentioning

confidence: 99%

A Survey on the Electric Vehicle Routing Problem: Variants and Solution Approaches

Erdelić

Carić

2019

Journal of Advanced Transportation

165

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In order to ensure high-quality and on-time delivery in logistic distribution processes, it is necessary to efficiently manage the delivery fleet. Nowadays, due to the new policies and regulations related to greenhouse gas emission in the transport sector, logistic companies are paying higher penalties for each emission gram of CO2/km. With electric vehicle market penetration, many companies are evaluating the integration of electric vehicles in their fleet, as they do not have local greenhouse gas emissions, produce minimal noise, and are independent of the fluctuating oil price. The well-researched vehicle routing problem (VRP) is extended to the electric vehicle routing problem (E-VRP), which takes into account specific characteristics of electric vehicles. In this paper, a literature review on recent developments regarding the E-VRP is presented. The challenges that emerged with the integration of electric vehicles in the delivery processes are described, together with electric vehicle characteristics and recent energy consumption models. Several variants of the E-VRP and related problems are observed. To cope with the new routing challenges in E-VRP, efficient VRP heuristics and metaheuristics had to be adapted. An overview of the state-of-the-art procedures for solving the E-VRP and related problems is presented.

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Section: Electric Vehicle Routing Problemmentioning

confidence: 99%

A Survey on the Electric Vehicle Routing Problem: Variants and Solution Approaches

Erdelić

Carić

2019

Journal of Advanced Transportation

165

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“…Through simulations, authors demonstrated that the locally optimal scheduling scheme can achieve a performance close to the globally optimal scheduling scheme. Barco et al [12], a methodology to plan the charging and the routes assignment for a BEVs fleet has been presented. This methodology considers the search of optimal routes and the minimization of operation costs.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal recharging framework and simulation for electric vehicle fleet

Usman

Knapen

Yasar

et al. 2020

Future Generation Computer Systems

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Electrical vehicles are considered sustainable transport alternatives as compared to conventional combustion engine vehicles due to their lower energy consumption and less pollutants production. The increased penetration of electric vehicles in the market depends upon technology to overcome the driving range barrier. That can be achieved by significantly planning the use of available charging stations. In this paper, a planning simulation model is presented which evaluates the feasibility of electric vehicles driving range when recharging is considered at home, at work or at quick charging stations in Flanders, Belgium. The proposed procedure plans a charging strategy for each electric vehicle so that entire scheduled tours of the individual can be executed successfully. The simulation starts by activating an agent for each electric vehicle that takes the daily schedule of the driver, registered charging requests at each charging station, and devises a charging strategy that may or may not require a detour to a charging station. Detouring to a charging station causes the time loss that result in the utility drop due to decreased participation in the planned activities. The charging station that leads to the minimum detour travel time, waiting time and recharging time is selected to minimize the time loss. The simulation uses a realistic travel demand predicted by an activity-based model. The results show the percentage of the population that can drive electric vehicle with charging only at home and/or work location; it also shows those who need a stop for recharging at a charging station. The results indicate the use of all charging stations over the day and also the waiting time as function of charging points at each charging station.

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“…They used the Simulated Annealing (SA) and Genetic Algorithm (GA) approaches. Barco et al (2013) proposed an EVRP in which the total energy used is minimized. The authors used an approximate energy consumption model based on physical dependencies to compute energy consumption.…”

Section: Literature Reviewmentioning

confidence: 99%

The multi-depot electric vehicle location routing problem with time windows

Paz¹,

Echeverri²,

Escobar³

2018

10.5267/j.ijiec

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In this paper, the Multi-Depot Electric Vehicle Location Routing Problem with Time Windows (MDVLRP) is addressed. This problem is an extension of the MDVLRP, where electric vehicles are used instead of internal combustion engine vehicles. The recent development of this model is explained by the advantages of this technology, such as the diminution of carbon dioxide emissions, and the support that they can provide to the design of the logistic and energy-support structure of electric vehicle fleets. There are many models that extend the classical VRP model to take electric vehicles into consideration, but the multi-depot case for location-routing models has not been worked out yet. Moreover, we consider the availability of two energy supply technologies: the "Plug-in" Conventional Charge technology, and Battery Swapping Stations; options in which the recharging time is a function of the amount of energy to charge and a fixed time, respectively. Three models are proposed: one for each of the technologies mentioned above, and another in which both options are taken in consideration. The models were solved for small scale instances using C++ and Cplex 12.5. The results show that the models can be used to design logistic and energy-support structures, and compare the performance of the different options of energy supply, as well as measure the impact of these decisions on the overall distance traveled or other optimization objectives that could be worked on in the future.

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Optimal Routing and Scheduling of Charge for Electric Vehicles: A Case Study

Cited by 69 publications

References 35 publications

A Survey on the Electric Vehicle Routing Problem: Variants and Solution Approaches

A Survey on the Electric Vehicle Routing Problem: Variants and Solution Approaches

Optimal recharging framework and simulation for electric vehicle fleet

The multi-depot electric vehicle location routing problem with time windows

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