Optimal Delivery Scheduling and Charging of EVs in the Navigation of a City Map

Fernando V. Cerna, Mahdi Pourakbari-Kasmaei, Ruben A. Romero, Marcos J. Rider

Research output: Contribution to journalArticleScientificpeer-review

21 Citations (Scopus)

Abstract

This paper presents a mixed integer linear programming (MILP) model to optimize the costs of maintenance and extra hours for scheduling a fleet of battery electric vehicles (BEVs) so that the products are delivered to prespecified delivery points along a route. On this route, each BEV must have an efficient charging strategy at the prespecified charging points. The proposed model considers the average speed of the BEVs, the battery states of charge (SOC), and a set of deliveries allocated to each BEV. The charging points are located on urban roads and differ according to their charging rate (fast or ultra-fast). Constraints that guarantee the performance of the fleet’s batteries are also taken into consideration. Uncertainties in the navigation of urban roads are modeled using the probability of delay due to the presence of traffic signals (PTS), schools (PS), and public works (PPW). The routes and the intersections of these routes are modeled as a predefined graph. The results and the evaluation of the model, with and without considering the extra hours, show the effectiveness of this type of transport technology. The models were implemented in AMPL and solved using the commercial solver CPLEX.
Original languageEnglish
Pages (from-to)4815-4827
Number of pages13
JournalIEEE Transactions on Smart Grid
Volume9
Issue number5
Early online date2017
DOIs
Publication statusPublished - 2018
MoE publication typeA1 Journal article-refereed

Keywords

  • Batteries
  • Delays
  • Mathematical model
  • Navigation
  • Roads
  • State of charge
  • Urban areas
  • Battery electric vehicles
  • charging points
  • charging rates
  • mixed integer linear programming

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