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<dc:title>Optimization of the powertrain of electric vehicles for a given route</dc:title>
<dc:creator>Luque Rodríguez, Pablo</dc:creator>
<dc:creator>Álvarez Mántaras, Daniel</dc:creator>
<dc:creator>Roces García, Jorge</dc:creator>
<dc:creator>Castejón Herrer, Luis</dc:creator>
<dc:creator>Malón Litago, Hugo</dc:creator>
<dc:subject>Vehículos eléctricos</dc:subject>
<dc:subject>Formas de movilidad</dc:subject>
<dc:subject>Electric vehicles</dc:subject>
<dc:subject>Means of mobility</dc:subject>
<dc:description>Trabajo presentado en: R-Evolucionando el transporte, XIV Congreso de Ingeniería del Transporte (CIT 2021), realizado en modalidad online los días 6, 7 y 8 de julio de 2021, organizado por la Universidad de Burgos</dc:description>
<dc:description>The global challenge to reduce emissions of polluting gases and greenhouse gases has forced&#xd;
the development of alternatives to the traditional internal combustion engine vehicles, such&#xd;
as electric or hybrid vehicles.&#xd;
The electric powertrain are the most efficient for delivery trucks or urban buses, due to&#xd;
acceleration and deceleration patterns make them inefficient for using internal combustion&#xd;
engines. Despite this, its range and purchase cost are the main factors limiting the use of&#xd;
electric vehicles in these applications.&#xd;
Range and purchase cost of an electric vehicles are mainly related to the energy storage&#xd;
system. Therefore, the optimum size of the battery pack should be considered as the design&#xd;
goal of a vehicle when its application is known.&#xd;
This paper presents a methodology to optimize the battery pack of an electric vehicle&#xd;
according to a given route run in a target time. Therefore, it would be applicable to delivery&#xd;
vehicles, buses and any vehicle whose route and travel time are known in advance. The&#xd;
proposed methodology allows minimizing the energy consumption by determining the&#xd;
optimal powertrain ratio for a given track, setting the travel time as an objective. A full&#xd;
vehicle model and a multi-objective genetic algorithms are used for this matter.</dc:description>
<dc:date>2022-09-21T07:25:41Z</dc:date>
<dc:date>2022-09-21T07:25:41Z</dc:date>
<dc:date>2021-07</dc:date>
<dc:type>info:eu-repo/semantics/conferenceObject</dc:type>
<dc:identifier>978-84-18465-12-3</dc:identifier>
<dc:identifier>http://hdl.handle.net/10259/6964</dc:identifier>
<dc:identifier>10.36443/10259/6964</dc:identifier>
<dc:language>eng</dc:language>
<dc:relation>R-Evolucionando el transporte</dc:relation>
<dc:relation>http://hdl.handle.net/10259/6490</dc:relation>
<dc:relation>https://doi.org/10.36443/9788418465123</dc:relation>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:publisher>Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional</dc:publisher>
</ow:Publication>
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