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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 8509783, 16 pages
Research Article

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

1Engineering Department, Electronics Engineering Program, Institución Universitaria CESMAG, Carrera 20A No. 14–54, San Juan de Pasto, Colombia
2Electrical and Electronics Engineering Department, Universidad de Los Andes, Carrera 1 Este No. 19 A 40, Bogotá, Colombia
3Mechanical Engineering Department, Universidad de Los Andes, Carrera 1 Este No. 19 A 40, Bogotá, Colombia

Correspondence should be addressed to L. Muñoz

Received 8 April 2017; Revised 4 September 2017; Accepted 3 October 2017; Published 14 November 2017

Academic Editor: Domenico Quagliarella

Copyright © 2017 J. Barco et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


There are increasing interests in improving public transportation systems. One of the proposed strategies for this improvement is the use of Battery Electric Vehicles (BEVs). This approach leads to a new challenge as the BEVs’ routing is exposed to the traditional routing problems of conventional vehicles, as well as the particular requirements of the electrical technologies of BEVs. Examples of BEVs’ routing problems include the autonomy, battery degradation, and charge process. This work presents a differential evolution algorithm for solving an electric vehicle routing problem (EVRP). The formulation of the EVRP to be solved is based on a scheme to coordinate the BEVs’ routing and recharge scheduling, considering operation and battery degradation costs. A model based on the longitudinal dynamics equation of motion estimates the energy consumption of each BEV. A case study, consisting of an airport shuttle service scenario, is used to illustrate the proposed methodology. For this transport service, the BEV energy consumption is estimated based on experimentally measured driving patterns.