Machine Learning-Assisted Reconstruction of In-Cylinder Pressure in Internal Combustion Engines Under Unmeasured Operating Conditions

Qiao Huang, Tianfang Xie and Jinlong Liu ()
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Qiao Huang: College of Information Engineering, China Jiliang University, Hangzhou 310018, China
Tianfang Xie: School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN 47907, USA
Jinlong Liu: Power Machinery and Vehicular Engineering Institute, Zhejiang University, Hangzhou 310027, China

Energies, 2025, vol. 18, issue 19, 1-18

Abstract: In-cylinder pressure provides critical insights for analyzing and optimizing combustion in internal combustion engines, yet its acquisition across the full operating space requires extensive testing, while physics-based models are computationally demanding. Machine learning (ML) offers an alternative, but its application to direct reconstruction of full pressure traces remains limited. This study evaluates three strategies for reconstructing cylinder pressure under unmeasured operating conditions, establishing a machine learning-assisted framework that generates the complete pressure–crank angle (P–CA) trace. The framework treats crank angle and operating conditions as inputs and predicts either pressure directly or apparent heat release rate (HRR) as an intermediate variable, which is then integrated to reconstruct pressure. In all approaches, discrete pointwise predictions are combined to form the full P–CA curve. Direct pressure prediction achieves high accuracy for overall traces but underestimates HRR-related combustion features. Training on HRR improves combustion representation but introduces baseline shifts in reconstructed pressure. A hybrid approach, combining non-combustion pressure prediction with combustion-phase HRR-based reconstruction delivers the most robust and physically consistent results. These findings demonstrate that ML can efficiently reconstruct in-cylinder pressure at unmeasured conditions, reducing experimental requirements while supporting combustion diagnostics, calibration, and digital twin applications.

Keywords: internal combustion engine; pressure reconstruction; machine learning; combustion diagnostics; data-driven modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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