Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/347421
Type: Artigo
Title: Electric vehicle powertrain and fuzzy control multi-objective optimization, considering dual hybrid energy storage systems
Author: Eckert, Jony Javorski
Silva, Ludmila Corrêa de Alkmin
Dedini, Franco Giuseppe
Corrêa, Fernanda Cristina
Abstract: The hybrid energy storage system (HESS), which is composed of battery and ultracapacitor, is established to enhance the performance of an electric vehicle (EV). Moreover, several studies highlight the gains of split the traction power demand among electric motors (EMs) with different characteristics and drivetrain configurations, allowing these to operate at higher efficiency. In this paper, we unite the advantages of the multi EM drive train with the HESS power split and propose a novel two HESS system, in which each HESS is in charge of one of the drivetrain configurations (front and rear system). The EV is driven by two in-wheel EM at the front wheels and a single EM assembled with a differential transmission that moves the rear wheels. In this paper, a multiobjective optimization, based on a genetic algorithm (GA), is formulated to minimize the HESS sizing and maximize the driving range of the vehicle. Also employing the fuzzy control, this strategy is responsible to split the power between the front and rear wheels systems in a more reasonable way to satisfy the demands of better performance. The complete strategy has been developed under the FTP-75 (urban), HWFET (highway) and the US06 (high speed and required acceleration) driving cycles using the MATLAB/Simulink software environment. As compared to a similar EV with a single HESS system, the proposed dual-HESS configuration was able to improve the driving range in 145.15 km also decreasing 23.93% of the HESS mass
Subject: Otimização
Veículos elétricos
Country: Estados Unidos
Editor: Institute of Electrical and Electronics Engineers
Rights: Fechado
Identifier DOI: 10.1109/TVT.2020.2973601
Address: https://ieeexplore.ieee.org/document/8998144
Date Issue: 2020
Appears in Collections:FEM - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
2-s2.0-85083845342.pdf4.4 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.