Research Article Optimal Routing and Scheduling of Charge for Electric Vehicles: A Case Study J. Barco, 1,2 A. Guerra, 3 L. Muñoz, 3 and N. Quijano 2 1 Engineering Department, Electronics Engineering Program, Instituci´ on Universitaria CESMAG, Carrera 20A No. 14–54, San Juan de Pasto, Colombia 2 Electrical and Electronics Engineering Department, Universidad de Los Andes, Carrera 1 Este No. 19 A 40, Bogot´ a, Colombia 3 Mechanical Engineering Department, Universidad de Los Andes, Carrera 1 Este No. 19 A 40, Bogot´ a, Colombia Correspondence should be addressed to L. Mu˜ noz; lui-muno@uniandes.edu.co 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. Tis 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. Tere are increasing interests in improving public transportation systems. One of the proposed strategies for this improvement is the use of Battery Electric Vehicles (BEVs). Tis 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. Tis work presents a diferential evolution algorithm for solving an electric vehicle routing problem (EVRP). Te 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. 1. Introduction Te integration of Battery Electric Vehicles (BEVs) to the public transportation system has been encouraged by the favorable efciency in the use of energy and the reduction of CO 2 emissions [1]. From the energy consumption per- spective, BEVs are driven by high-efciency motors with the possibility of implementing a regenerative braking system. Additionally, charging a BEV is less expensive than refueling a conventional vehicle because the electric energy is cheaper than its equivalent in fossil fuel (e.g., gasoline and diesel). From the emissions point of view, when a BEV is used in combination with renewable sources for the electricity gen- eration, the outcome is a reduction in emissions associated with fossil fuel combustion; therefore, BEVs are one of the best alternatives to be integrated into cities as part of a public transportation system. Te use of BEVs in public transportation systems faces several challenges, mainly related to the combination of conventional-fuel-service characteristics with those of elec- tric vehicles. Examples of these challenges are the routing of electric vehicles used in public transportation, the recharge scheduling, and the battery state of health (SOH) [2]. Tese three challenges are treated in this work using a methodology developed for the optimal routing and scheduling of charge for BEVs. Te frst challenge is the routing of BEVs. In addition to the usual routing issues, the BEVs should be routed taking particular attention to minimizing energy consumption. For the routing of BEVs, two steps are considered. Te frst step consists in fnding the minimum consumption paths to travel between two points. In this step, it is necessary to consider the technical characteristics of BEVs. Te second step consists in determining optimal routes to satisfy the transportation demand in diferent places and at diferent schedules, while minimizing energy consumption. Similar to the frst step, the calculation of optimal routes is performed considering the BEVs’ characteristics. For this case, the vehicle’s traveling Hindawi Mathematical Problems in Engineering Volume 2017, Article ID 8509783, 16 pages https://doi.org/10.1155/2017/8509783