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Improving regenerative braking strategy using genetic algorithm for electric vehicles / H. Taleghani ... [et al.]

The regenerative braking system is one of the most fundamental advantages of electric vehicles compared with internal combustion vehicles. With a proper regenerative braking strategy, a fraction of vehicle’s kinetic energy is harvested by the electric motor, which is configured as a generator during...

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Main Authors: Taleghani, H., Hassan, M.K, Abdul Rahman, R. Z., Che Soh, A.
Format: Article
Language:English
Published: Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM) 2018
Subjects:
Algorithms
TJ Mechanical engineering and machinery
Online Access:http://ir.uitm.edu.my/id/eprint/41026/1/41026.pdf
http://ir.uitm.edu.my/id/eprint/41026/
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id my.uitm.ir.41026
record_format eprints
spelling my.uitm.ir.410262021-01-26T01:29:42Z http://ir.uitm.edu.my/id/eprint/41026/ Improving regenerative braking strategy using genetic algorithm for electric vehicles / H. Taleghani ... [et al.] Taleghani, H. Hassan, M.K Abdul Rahman, R. Z. Che Soh, A. Algorithms TJ Mechanical engineering and machinery The regenerative braking system is one of the most fundamental advantages of electric vehicles compared with internal combustion vehicles. With a proper regenerative braking strategy, a fraction of vehicle’s kinetic energy is harvested by the electric motor, which is configured as a generator during braking. The strategy distributes the required braking force between friction brakes of both axles and regenerative breaks. This study presents a genetic algorithm brake force distribution strategy to increase energy recovery, considering the Economic Commission for Europe (ECE) regulations. The performance of the proposed regenerative braking control algorithm is evaluated by the ADVISOR which is based on MATLAB/Simulink environment. The results indicate that the driving range has maximum increased to 25 percent with regards to the drive cycle. Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM) 2018 Article PeerReviewed text en http://ir.uitm.edu.my/id/eprint/41026/1/41026.pdf Taleghani, H. and Hassan, M.K and Abdul Rahman, R. Z. and Che Soh, A. (2018) Improving regenerative braking strategy using genetic algorithm for electric vehicles / H. Taleghani ... [et al.]. Journal of Mechanical Engineering (JMechE), SI 6 (1). pp. 121-130. ISSN 18235514
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Algorithms
TJ Mechanical engineering and machinery
spellingShingle Algorithms
TJ Mechanical engineering and machinery
Taleghani, H.
Hassan, M.K
Abdul Rahman, R. Z.
Che Soh, A.
Improving regenerative braking strategy using genetic algorithm for electric vehicles / H. Taleghani ... [et al.]
description The regenerative braking system is one of the most fundamental advantages of electric vehicles compared with internal combustion vehicles. With a proper regenerative braking strategy, a fraction of vehicle’s kinetic energy is harvested by the electric motor, which is configured as a generator during braking. The strategy distributes the required braking force between friction brakes of both axles and regenerative breaks. This study presents a genetic algorithm brake force distribution strategy to increase energy recovery, considering the Economic Commission for Europe (ECE) regulations. The performance of the proposed regenerative braking control algorithm is evaluated by the ADVISOR which is based on MATLAB/Simulink environment. The results indicate that the driving range has maximum increased to 25 percent with regards to the drive cycle.
format Article
author Taleghani, H.
Hassan, M.K
Abdul Rahman, R. Z.
Che Soh, A.
author_facet Taleghani, H.
Hassan, M.K
Abdul Rahman, R. Z.
Che Soh, A.
author_sort Taleghani, H.
title Improving regenerative braking strategy using genetic algorithm for electric vehicles / H. Taleghani ... [et al.]
title_short Improving regenerative braking strategy using genetic algorithm for electric vehicles / H. Taleghani ... [et al.]
title_full Improving regenerative braking strategy using genetic algorithm for electric vehicles / H. Taleghani ... [et al.]
title_fullStr Improving regenerative braking strategy using genetic algorithm for electric vehicles / H. Taleghani ... [et al.]
title_full_unstemmed Improving regenerative braking strategy using genetic algorithm for electric vehicles / H. Taleghani ... [et al.]
title_sort improving regenerative braking strategy using genetic algorithm for electric vehicles / h. taleghani ... [et al.]
publisher Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM)
publishDate 2018
url http://ir.uitm.edu.my/id/eprint/41026/1/41026.pdf
http://ir.uitm.edu.my/id/eprint/41026/
_version_ 1690374304598851584
score 13.211869

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