Machine Learning for Internal Combustion Engine Optimization with Hydrogen-Blended Fuels: A Literature Review
Mateusz Żbikowski, A. Teodorczyk
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 NOx 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.