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Optimal selection for an air suspension system on buses through a unique high level parameter in genetic algorithms

Air suspension systems are being widely used on ground vehicles in general and buses in particular. The element that makes the superiority of this system is air spring. This paper introduces the application of genetic algorithms method to optimize the parameters of the air spring element. A full mod...

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Autor principal: Hung, Truong Manh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8917283/
https://www.ncbi.nlm.nih.gov/pubmed/35287329
http://dx.doi.org/10.1016/j.heliyon.2022.e09059
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author Hung, Truong Manh
author_facet Hung, Truong Manh
author_sort Hung, Truong Manh
collection PubMed
description Air suspension systems are being widely used on ground vehicles in general and buses in particular. The element that makes the superiority of this system is air spring. This paper introduces the application of genetic algorithms method to optimize the parameters of the air spring element. A full model of a bus using air suspension system with air spring element, which is modeled based on the Gensys model. From the real experiments, the bounds of seven optimized parameters of the air spring are determined. In which, by using a single α value, it is possible to determine the optimal parameters according to each desired criteria between the road safety and the ride comfort. Optimal results are verified in the time domain simulations with random road profile according to the ISO standard 8608. The results show that by using the optimal parameters of the air spring when α = 0.5, the ride comfort is improved about 15% while the road safety is still guaranteed.
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spelling pubmed-89172832022-03-13 Optimal selection for an air suspension system on buses through a unique high level parameter in genetic algorithms Hung, Truong Manh Heliyon Research Article Air suspension systems are being widely used on ground vehicles in general and buses in particular. The element that makes the superiority of this system is air spring. This paper introduces the application of genetic algorithms method to optimize the parameters of the air spring element. A full model of a bus using air suspension system with air spring element, which is modeled based on the Gensys model. From the real experiments, the bounds of seven optimized parameters of the air spring are determined. In which, by using a single α value, it is possible to determine the optimal parameters according to each desired criteria between the road safety and the ride comfort. Optimal results are verified in the time domain simulations with random road profile according to the ISO standard 8608. The results show that by using the optimal parameters of the air spring when α = 0.5, the ride comfort is improved about 15% while the road safety is still guaranteed. Elsevier 2022-03-04 /pmc/articles/PMC8917283/ /pubmed/35287329 http://dx.doi.org/10.1016/j.heliyon.2022.e09059 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Research Article
Hung, Truong Manh
Optimal selection for an air suspension system on buses through a unique high level parameter in genetic algorithms
title Optimal selection for an air suspension system on buses through a unique high level parameter in genetic algorithms
title_full Optimal selection for an air suspension system on buses through a unique high level parameter in genetic algorithms
title_fullStr Optimal selection for an air suspension system on buses through a unique high level parameter in genetic algorithms
title_full_unstemmed Optimal selection for an air suspension system on buses through a unique high level parameter in genetic algorithms
title_short Optimal selection for an air suspension system on buses through a unique high level parameter in genetic algorithms
title_sort optimal selection for an air suspension system on buses through a unique high level parameter in genetic algorithms
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8917283/
https://www.ncbi.nlm.nih.gov/pubmed/35287329
http://dx.doi.org/10.1016/j.heliyon.2022.e09059
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