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

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

Published
May 14, 2026

Vol. 9 No. 1 (2026)
Pages 204-222

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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

Hydrogen internal combustion engine; Combustion phasing; Spark ignition; Markov-chain; Performance Prediction

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Published May 14, 2026
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Copyright Copyright (c) 2026 Purnami Purnami, Willy Satrio Nugroho, Lilis Yuliati, Fikrul Akbar Alamsyah, ING Wardana, Abdul Mudjib Sulaiman Wahid
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Keywords
  • Hydrogen internal combustion engine
  • Combustion phasing
  • Spark ignition
  • Markov-chain
  • Performance Prediction