Probabilistic Performance Prediction of a Hydrogen-Converted SI Engine Using a Markov-Chain-Wiebe Framework

Purnami; Nugroho, Willy Satrio; Yuliati, Lilis; Alamsyah, Fikrul Akbar; Wahid, Abdul Mudjib Sulaiman; Wardana, I Nyoman Gede

doi:10.31603/ae.15544

Open access Journal SINTA 1

Probabilistic Performance Prediction of a Hydrogen-Converted SI Engine Using a Markov-Chain-Wiebe Framework

Automotive Experiences · Vol. 9 No. 1 (2026) · pp. 204–222

DOI: 10.31603/ae.15544
ISSN: 2615-6636, 2615-6202
Published: 2026-05-14
Publisher: Universitas Muhammadiyah Magelang
Language: en
License: creativecommons.org/licenses/by-nc/4.0

Abstract

This study employs a novel Markov-chain modeling framework to analyze the combustion-performance interaction in a hydrogen-fueled spark-ignition engine. The methodology integrates a Wiebe heat-release model within a Markov-chain state-transition framework, where each discrete engine state defines combustion parameters and probabilistic transitions capture cycle-to-cycle variability. Results demonstrate that engine behavior is dominantly governed by combustion phasing, with spark timing exerting primary control over torque, efficiency, and brake-specific fuel consumption (BSFC). Sensitivity analysis confirms spark timing produces the steepest performance gradients, while ignition voltage offers secondary benefits and engine speed exhibits minimal influence. The model reveals a highly nonlinear torque response to spark advance, characterized by a rapid rise culminating in a narrow maximum brake torque (MBT) plateau at 8°–10° BTDC, corresponding to a distinct BSFC minimum. Significant data scatter underscores the stochastic nature of hydrogen combustion, arising from multidomain interactions between air-fuel ratio, ignition strength, and phasing. The Markov-chain approach successfully captures this coupled deterministic-probabilistic behavior, highlighting the critical need for precise spark-timing control to optimize performance in hydrogen applications.

Keywords

Collaboration network of the authors

13 authors · 29 co-authorship links

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Tadulako University
Affiliation unknown

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Co-author	Organisation	Joint articles
Siti Komariyah	Affiliation unknown	2
Ananda Fauziah Mukti	Affiliation unknown	1
Riniati	Affiliation unknown	1
Mustofa Mustofa	Tadulako University	1
Nely Supeni	Affiliation unknown	1
Moh Adenan	Affiliation unknown	1
Fajar Wahyu P	Affiliation unknown	1

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">Probabilistic Performance Prediction of a Hydrogen-Converted SI Engine Using a Markov-Chain-Wiebe Framework</dc:title>
	<dc:creator xml:lang="en">Purnami</dc:creator>
	<dc:creator xml:lang="en">Nugroho, Willy Satrio</dc:creator>
	<dc:creator xml:lang="en">Yuliati, Lilis</dc:creator>
	<dc:creator xml:lang="en">Alamsyah, Fikrul Akbar</dc:creator>
	<dc:creator xml:lang="en">Wahid, Abdul Mudjib Sulaiman</dc:creator>
	<dc:creator xml:lang="en">Wardana, I Nyoman Gede</dc:creator>
	<dc:subject xml:lang="en">Hydrogen internal combustion engine</dc:subject>
	<dc:subject xml:lang="en">Combustion phasing</dc:subject>
	<dc:subject xml:lang="en">Spark ignition</dc:subject>
	<dc:subject xml:lang="en">Markov-chain</dc:subject>
	<dc:subject xml:lang="en">Performance Prediction</dc:subject>
	<dc:description xml:lang="en">This study employs a novel Markov-chain modeling framework to analyze the combustion-performance interaction in a hydrogen-fueled spark-ignition engine. The methodology integrates a Wiebe heat-release model within a Markov-chain state-transition framework, where each discrete engine state defines combustion parameters and probabilistic transitions capture cycle-to-cycle variability. Results demonstrate that engine behavior is dominantly governed by combustion phasing, with spark timing exerting primary control over torque, efficiency, and brake-specific fuel consumption (BSFC). Sensitivity analysis confirms spark timing produces the steepest performance gradients, while ignition voltage offers secondary benefits and engine speed exhibits minimal influence. The model reveals a highly nonlinear torque response to spark advance, characterized by a rapid rise culminating in a narrow maximum brake torque (MBT) plateau at 8°–10° BTDC, corresponding to a distinct BSFC minimum. Significant data scatter underscores the stochastic nature of hydrogen combustion, arising from multidomain interactions between air-fuel ratio, ignition strength, and phasing. The Markov-chain approach successfully captures this coupled deterministic-probabilistic behavior, highlighting the critical need for precise spark-timing control to optimize performance in hydrogen applications.</dc:description>
	<dc:publisher xml:lang="en">Universitas Muhammadiyah Magelang</dc:publisher>
	<dc:date>2026-05-14</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/15544</dc:identifier>
	<dc:identifier>10.31603/ae.15544</dc:identifier>
	<dc:source xml:lang="en">Automotive Experiences; Vol. 9 No. 1 (2026); 204-222</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/15544/6715</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Purnami Purnami, Willy Satrio Nugroho, Lilis Yuliati, Fikrul Akbar Alamsyah, ING Wardana, Abdul Mudjib Sulaiman Wahid</dc:rights>
	<dc:rights xml:lang="en">http://creativecommons.org/licenses/by-nc/4.0</dc:rights>
</oai_dc:dc>