Machine Learning for Internal Combustion Engine Optimization with Hydrogen-Blended Fuels: A Literature Review

Mateusz Zbikowski and Andrzej Teodorczyk ()
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Mateusz Zbikowski: Faculty of Power and Aeronautical Engineering, Institute of Heat Engineering, Warsaw University of Technology, 00-665 Warsaw, Poland
Andrzej Teodorczyk: Faculty of Power and Aeronautical Engineering, Institute of Heat Engineering, Warsaw University of Technology, 00-665 Warsaw, Poland

Energies, 2025, vol. 18, issue 6, 1-20

Abstract: This study explores the potential of hydrogen-enriched internal combustion engines (H2ICEs) as a sustainable alternative to fossil fuels. Hydrogen offers advantages such as high combustion efficiency and zero carbon emissions, yet challenges related to NO x formation, storage, and specialized modifications persist. Machine learning (ML) techniques, including artificial neural networks (ANNs) and XGBoost, demonstrate strong predictive capabilities in optimizing engine performance and emissions. However, concerns regarding overfitting and data representativeness must be addressed. Integrating AI-driven strategies into electronic control units (ECUs) can facilitate real-time optimization. Future research should focus on infrastructure improvements, hybrid energy solutions, and policy support. The synergy between hydrogen fuel and ML optimization has the potential to revolutionize internal combustion engine technology for a cleaner and more efficient future.

Keywords: hydrogen; internal combustion engine; machine learning (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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