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Fuel Control System on CNG Fueled Vehicles using Machine Learning: A Case Study on the Downhill

Automotive Experiences · Vol. 6 No. 1 (2023) · pp. 173–187

DOI
10.31603/ae.8107
ISSN
2615-6636, 2615-6202
Published
2023-04-28
Publisher
Universitas Muhammadiyah Magelang
Language
en

Abstract

Compressed Natural Gas (CNG) is an affordable fuel with a higher octane number. However, older CNG kits without electronic controls have the potential to supply more fuel when driving downhill due to the vacuum in the intake manifold. Therefore, this article presents a development of a CNG control system that accommodates road inclination angles to improve fuel efficiency. Machine learning is involved in this work to process engine speed, throttle valve position, and road slope angle. The control system is designed to ensure reduced fuel consumption when the vehicle is operating downhill. The results showed that the control system increases fuel consumption by 25.7% when driving downhill which an inclination of 5áµ’. The AFR increased from 17.5 to 22 and the CNG flow rate decreased from 17.7 liters/min to 13.8 liters/min which is promising for applying to CNG vehicles.

Keywords

Collaboration network of the authors

40 authors · 139 co-authorship links · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Walailak University
  • Badan Pengkajian dan Penerapan Teknologi
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Aditya KolakotiAffiliation unknown10
Muhammad Latifur RochmanAffiliation unknown7
Randi Purnama PutraAffiliation unknown6
Remon LapisaAffiliation unknown5
Thirunavukkarasu RajaAffiliation unknown4
Budi WaluyoWalailak University4
Tuessi Ari PurnomoAffiliation unknown4
Indra Chandra SetiawanAffiliation unknown3
Asep Bayu Dani NandiyantoAffiliation unknown3
Dani HarmantoAffiliation unknown3
Bagiyo Condro PurnomoAffiliation unknown3
Bambang SudarmantaAffiliation unknown3
Krismadinata KrismadinataAffiliation unknown3
Sri Rizki Putri PrimandariAffiliation unknown3
Olusegun David SamuelAffiliation unknown3
Dwi Sudarno PutraAffiliation unknown3
Djoko Wahyu KarmiadjiBadan Pengkajian dan Penerapan Teknologi2
Muchamad GozaliAffiliation unknown2
Retno RusdjijatiAffiliation unknown2
Margana MarganaAffiliation unknown2
Yusuf Dewantoro HerlambangAffiliation unknown2
Wahyu SulistiyoAffiliation unknown2
Nanang ApriandiAffiliation unknown2
Marliyati MarliyatiAffiliation unknown2
Jin Cherng ShyuAffiliation unknown2
Hendry Y. NanlohyAffiliation unknown2
Wanda AfnisonAffiliation unknown2
Milana MilanaAffiliation unknown2
Andre KurniawanAffiliation unknown2
Sunaryo SunaryoAffiliation unknown2
Deni Fajar FitriyanaAffiliation unknown2
Januar Parlaungan SiregarAffiliation unknown2
Rifky IsmailAffiliation unknown2
Roni Muhammad SusantoAffiliation unknown2

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://journal.unimma.ac.id/index.php/AutomotiveExperiences/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Fuel Control System on CNG Fueled Vehicles using Machine Learning: A Case Study on the Downhill</dc:title>
	<dc:creator xml:lang="en">Munahar, Suroto</dc:creator>
	<dc:creator xml:lang="en">Setiyo, Muji</dc:creator>
	<dc:creator xml:lang="en">Brieghtera, Ray Adhan</dc:creator>
	<dc:creator xml:lang="en">Saudi, Madihah Mohd</dc:creator>
	<dc:creator xml:lang="en">Ahmad, Azuan</dc:creator>
	<dc:creator xml:lang="en">Yuvenda, Dori</dc:creator>
	<dc:subject xml:lang="en">CNG</dc:subject>
	<dc:subject xml:lang="en">Control system</dc:subject>
	<dc:subject xml:lang="en">Road inclining angle</dc:subject>
	<dc:subject xml:lang="en">Fuel saving</dc:subject>
	<dc:subject xml:lang="en">AFR</dc:subject>
	<dc:subject xml:lang="en">Machine learning</dc:subject>
	<dc:description xml:lang="en">Compressed Natural Gas (CNG) is an affordable fuel with a higher octane number. However, older CNG kits without electronic controls have the potential to supply more fuel when driving downhill due to the vacuum in the intake manifold. Therefore, this article presents a development of a CNG control system that accommodates road inclination angles to improve fuel efficiency. Machine learning is involved in this work to process engine speed, throttle valve position, and road slope angle. The control system is designed to ensure reduced fuel consumption when the vehicle is operating downhill. The results showed that the control system increases fuel consumption by 25.7% when driving downhill which an inclination of 5áµ’. The AFR increased from 17.5 to 22 and the CNG flow rate decreased from 17.7 liters/min to 13.8 liters/min which is promising for applying to CNG vehicles.</dc:description>
	<dc:publisher xml:lang="en">Universitas Muhammadiyah Magelang</dc:publisher>
	<dc:date>2023-04-28</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unimma.ac.id/AutomotiveExperiences/article/view/8107</dc:identifier>
	<dc:identifier>10.31603/ae.8107</dc:identifier>
	<dc:source xml:lang="en">Automotive Experiences; Vol. 6 No. 1 (2023); 173-187</dc:source>
	<dc:source>2615-6636</dc:source>
	<dc:source>2615-6202</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unimma.ac.id/AutomotiveExperiences/article/view/8107/4182</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2023 Suroto Munahar, Muji Setiyo, Ray Adhan Brieghtera, Madihah Mohd Saudi, Azuan Ahmad, Dori Yuvenda</dc:rights>
</oai_dc:dc>