Fuel efficient intelligent control of heavy trucks

Fuel efficient intelligent control of heavy trucks

This work answers the need for improvement in fuel Economy in heavy duty vehicles (HDV’s), in a manner simple enough to be used in open road missions. A lookahead anticipatory control (LA) method is designed to adjust longitudinal motion (signified by velocity of the vehicle system) using knowledge...

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Main Authors: Ahmed, H, Faris, Waleed Fekry, Akmeliawati, Rini
Format: Article
Language:English
Published: Asian Research Publishing Network (ARPN) 2016
Subjects:
TJ212 Control engineering
Online Access:http://irep.iium.edu.my/50582/
http://irep.iium.edu.my/50582/
http://irep.iium.edu.my/50582/2/50582_-_Fuel_efficient_intelligent_control_of_heavy_trucks1.pdf
id iium-50582
recordtype eprints
spelling iium-505822016-07-18T04:06:19Z http://irep.iium.edu.my/50582/ Fuel efficient intelligent control of heavy trucks Ahmed, H Faris, Waleed Fekry Akmeliawati, Rini TJ212 Control engineering This work answers the need for improvement in fuel Economy in heavy duty vehicles (HDV’s), in a manner simple enough to be used in open road missions. A lookahead anticipatory control (LA) method is designed to adjust longitudinal motion (signified by velocity of the vehicle system) using knowledge of fluctuations in road grade. The prediction of driving behaviour is done using a fuzzy logic function based on a predefined rule-base. Control action of the brake and throttle positions are implemented by taking the state-dependent riccati Equation approach. The results of the proposed controller are compared against those of a standard PI cruise controller. Moreover, results of simulations on a 40 ton vehicle show the proposed method capable of increasing fuel economy. Asian Research Publishing Network (ARPN) 2016-03-20 Article PeerReviewed application/pdf en http://irep.iium.edu.my/50582/2/50582_-_Fuel_efficient_intelligent_control_of_heavy_trucks1.pdf Ahmed, H and Faris, Waleed Fekry and Akmeliawati, Rini (2016) Fuel efficient intelligent control of heavy trucks. ARPN Journal of Engineering and Applied Sciences, 11 (6). pp. 4164-4171. ISSN 1819-6608 http://www.arpnjournals.com/jeas/
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
topic TJ212 Control engineering
spellingShingle TJ212 Control engineering
Ahmed, H
Faris, Waleed Fekry
Akmeliawati, Rini
Fuel efficient intelligent control of heavy trucks
description This work answers the need for improvement in fuel Economy in heavy duty vehicles (HDV’s), in a manner simple enough to be used in open road missions. A lookahead anticipatory control (LA) method is designed to adjust longitudinal motion (signified by velocity of the vehicle system) using knowledge of fluctuations in road grade. The prediction of driving behaviour is done using a fuzzy logic function based on a predefined rule-base. Control action of the brake and throttle positions are implemented by taking the state-dependent riccati Equation approach. The results of the proposed controller are compared against those of a standard PI cruise controller. Moreover, results of simulations on a 40 ton vehicle show the proposed method capable of increasing fuel economy.
format Article
author Ahmed, H
Faris, Waleed Fekry
Akmeliawati, Rini
author_facet Ahmed, H
Faris, Waleed Fekry
Akmeliawati, Rini
author_sort Ahmed, H
title Fuel efficient intelligent control of heavy trucks
title_short Fuel efficient intelligent control of heavy trucks
title_full Fuel efficient intelligent control of heavy trucks
title_fullStr Fuel efficient intelligent control of heavy trucks
title_full_unstemmed Fuel efficient intelligent control of heavy trucks
title_sort fuel efficient intelligent control of heavy trucks
publisher Asian Research Publishing Network (ARPN)
publishDate 2016
url http://irep.iium.edu.my/50582/
http://irep.iium.edu.my/50582/
http://irep.iium.edu.my/50582/2/50582_-_Fuel_efficient_intelligent_control_of_heavy_trucks1.pdf
first_indexed 2023-09-18T21:11:29Z
last_indexed 2023-09-18T21:11:29Z
_version_ 1777411282704531456

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