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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) · hlm. 173–187

DOI
10.31603/ae.8107
ISSN
2615-6636, 2615-6202
Terbit
2023-04-28
Penerbit
Universitas Muhammadiyah Magelang
Bahasa
en

Abstrak

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.

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Yusuf Dewantoro HerlambangAfiliasi tidak diketahui2
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Sumber metadata: OAI-PMH (metadata jurnal) http://journal.unimma.ac.id/index.php/AutomotiveExperiences/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<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>