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

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  • J. Barco
  • A. Guerra
  • L. Muñoz
  • N. Quijano

Abstract

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.

Suggested Citation

  • J. Barco & A. Guerra & L. Muñoz & N. Quijano, 2017. "Optimal Routing and Scheduling of Charge for Electric Vehicles: A Case Study," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-16, November.
    • Handle: RePEc:hin:jnlmpe:8509783
    DOI: 10.1155/2017/8509783
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    Cited by:

    1. Yong Wang & Jingxin Zhou & Yaoyao Sun & Xiuwen Wang & Jiayi Zhe & Haizhong Wang, 2022. "Electric Vehicle Charging Station Location-Routing Problem with Time Windows and Resource Sharing," Sustainability, MDPI, vol. 14(18), pages 1-31, September.
    2. Lu, Chung-Cheng & Diabat, Ali & Li, Yi-Ting & Yang, Yu-Min, 2022. "Combined passenger and parcel transportation using a mixed fleet of electric and gasoline vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    3. Vanny Minanda & Yun-Chia Liang & Angela H. L. Chen & Aldy Gunawan, 2024. "Application of an Improved Harmony Search Algorithm on Electric Vehicle Routing Problems," Energies, MDPI, vol. 17(15), pages 1-22, July.
    4. Danny García Sánchez & Alejandra Tabares & Lucas Teles Faria & Juan Carlos Rivera & John Fredy Franco, 2022. "A Clustering Approach for the Optimal Siting of Recharging Stations in the Electric Vehicle Routing Problem with Time Windows," Energies, MDPI, vol. 15(7), pages 1-19, March.
    5. Nicholas D. Kullman & Justin C. Goodson & Jorge E. Mendoza, 2021. "Electric Vehicle Routing with Public Charging Stations," Transportation Science, INFORMS, vol. 55(3), pages 637-659, May.
    6. Hung, Ying-Chao & PakHai Lok, Horace & Michailidis, George, 2022. "Optimal routing for electric vehicle charging systems with stochastic demand: A heavy traffic approximation approach," European Journal of Operational Research, Elsevier, vol. 299(2), pages 526-541.
    7. Xiao, Yiyong & Zhang, Yue & Kaku, Ikou & Kang, Rui & Pan, Xing, 2021. "Electric vehicle routing problem: A systematic review and a new comprehensive model with nonlinear energy recharging and consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    8. Shehabeldeen, Ali & Foda, Ahmed & Mohamed, Moataz, 2024. "A multi-stage optimization of battery electric bus transit with battery degradation," Energy, Elsevier, vol. 299(C).
    9. Mariusz Izdebski & Marianna Jacyna & Jerzy Bogdański, 2024. "Minimisation of the Energy Expenditure of Electric Vehicles in Municipal Service Companies, Taking into Account the Uncertainty of Charging Point Operation," Energies, MDPI, vol. 17(9), pages 1-21, May.

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