Optimization of the powertrain of electric vehicles for a given route

  1. Pablo Luque Rodríguez 1
  2. Daniel Álvarez Mántaras 1
  3. Jorge Roces García 1
  4. Luis Castejón Herrer 2
  5. Hugo Malón Litago 2
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

  2. 2 Universidad de Zaragoza
    info

    Universidad de Zaragoza

    Zaragoza, España

    ROR https://ror.org/012a91z28

Book:
R-evolucionando el transporte [Recurso electrónico]: XIV Congreso de Ingeniería del Transporte. Universidad de Burgos 6, 7 y 8 de julio 2021
  1. Hernán Gonzalo Orden (coord.)
  2. Marta Rojo Arce (coord.)

Publisher: Servicio de Publicaciones e Imagen Institucional ; Universidad de Burgos

ISBN: 978-84-18465-12-3

Year of publication: 2021

Pages: 2081-2093

Congress: Congreso de Ingeniería del Transporte (14. 2021. Burgos)

Type: Conference paper

Sustainable development goals

Abstract

The global challenge to reduce emissions of polluting gases and greenhouse gases has forced the development of alternatives to the traditional internal combustion engine vehicles, such as electric or hybrid vehicles. The electric powertrain are the most efficient for delivery trucks or urban buses, due to acceleration and deceleration patterns make them inefficient for using internal combustion engines. Despite this, its range and purchase cost are the main factors limiting the use of electric vehicles in these applications. Range and purchase cost of an electric vehicles are mainly related to the energy storage system. Therefore, the optimum size of the battery pack should be considered as the design goal of a vehicle when its application is known. This paper presents a methodology to optimize the battery pack of an electric vehicle according to a given route run in a target time. Therefore, it would be applicable to delivery vehicles, buses and any vehicle whose route and travel time are known in advance. The proposed methodology allows minimizing the energy consumption by determining the optimal powertrain ratio for a given track, setting the travel time as an objective. A full vehicle model and a multi-objective genetic algorithms are used for this matter.