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Efficient Design Methodology of an All-Electric Vehicle Powertrain using Multi-Objective Genetic Optimization Algorithm

Authors: Abdenour Abdelli; Fabrice Le Berr; Raouf Benlamine;

Efficient Design Methodology of an All-Electric Vehicle Powertrain using Multi-Objective Genetic Optimization Algorithm

Abstract

<div class="section abstract"><div class="htmlview paragraph">This paper presents a methodology to design the powertrain of an electrical vehicle (EV) in an optimal way. The electric vehicle optimal design is carried out using multiobjective genetic optimization algorithm. The developed methodology is based on the coupling of a genetic algorithm with powertrain component models. It allows determining the drive train components specifications for imposed vehicle performances, taking into account the dynamic model of the vehicle and all the components interactions. In this way, the components can be sized taking into account the whole system behavior in an optimal global design. The developed methodology is performed on the European driving cycle (NEDC) to estimate energy consumption gains but also powertrain mass reduction in comparison with a classical step-by-step methodology. This optimal procedure is notably important to increase electric vehicle range or reduce battery size and thus electric vehicle cost.</div></div>

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Average
Average
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