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

Author

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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)

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.

Suggested Citation

  • Mateusz Zbikowski & Andrzej Teodorczyk, 2025. "Machine Learning for Internal Combustion Engine Optimization with Hydrogen-Blended Fuels: A Literature Review," Energies, MDPI, vol. 18(6), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:6:p:1391-:d:1610220
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    References listed on IDEAS

    as
    1. Stefania Falfari & Giulio Cazzoli & Valerio Mariani & Gian Marco Bianchi, 2023. "Hydrogen Application as a Fuel in Internal Combustion Engines," Energies, MDPI, vol. 16(6), pages 1-13, March.
    2. Javed, Syed & Baig, Rahmath Ulla & Murthy, Y.V.V. Satyanarayana, 2018. "Study on noise in a hydrogen dual-fuelled zinc-oxide nanoparticle blended biodiesel engine and the development of an artificial neural network model," Energy, Elsevier, vol. 160(C), pages 774-782.
    3. Mehra, Roopesh Kumar & Duan, Hao & Luo, Sijie & Rao, Anas & Ma, Fanhua, 2018. "Experimental and artificial neural network (ANN) study of hydrogen enriched compressed natural gas (HCNG) engine under various ignition timings and excess air ratios," Applied Energy, Elsevier, vol. 228(C), pages 736-754.
    4. Tuttle, Jacob F. & Blackburn, Landen D. & Andersson, Klas & Powell, Kody M., 2021. "A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling," Applied Energy, Elsevier, vol. 292(C).
    5. Mallesh B. Sanjeevannavar & Nagaraj R. Banapurmath & V. Dananjaya Kumar & Ashok M. Sajjan & Irfan Anjum Badruddin & Chandramouli Vadlamudi & Sanjay Krishnappa & Sarfaraz Kamangar & Rahmath Ulla Baig &, 2023. "Machine Learning Prediction and Optimization of Performance and Emissions Characteristics of IC Engine," Sustainability, MDPI, vol. 15(18), pages 1-30, September.
    Full references (including those not matched with items on IDEAS)

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