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All Journal Automotive Experiences
Wahid, Abdul Mudjib Sulaiman
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Mechanical Engineering

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Probabilistic Performance Prediction of a Hydrogen-Converted SI Engine Using a Markov-Chain-Wiebe Framework Purnami; Nugroho, Willy Satrio; Yuliati, Lilis; Alamsyah, Fikrul Akbar; Wardana, ING; Wahid, Abdul Mudjib Sulaiman
Automotive Experiences Vol. 9 No. 1 (2026): Issue in Progress
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.15544

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.
Co-Authors Fikrul Akbar Alamsyah ING Wardana Lilis Yuliati Purnami Willy Satrio Nugroho