The ML based H2 blended fueled internal combustion engines: Optimization, challenges, and future directions

Authors: Gautam Panwar, Adarsh Kumar Sharma, Kuber Bassi, Piyushi Suyal, Bhumi Sehrawat, Praveen Kumar

Abstract: Engines running on hydrogen (H₂ICE) offer strong potential for reducing traditional fossil fuels while leveraging existing engine architectures. Hydrogen introduces key advantages, like high combustion efficiency, broad ignition ranges, and no carbon output. Yet problems such as the creation of NOₓ, combustion instability, and storage limitations halt its widespread rollout. In this context, machine learning (ML) techniques provide a practical way for handling the complex and nonlinear behavior of hydrogen combustion systems. Complex setups, covering artificial neural networks (ANN), support vector regression (SVR), random forest (RF), and gradient boosting algorithms such as XGBoost, demonstrate strong capability at forecasting how the engine runs, optimizing operating conditions, and controlling emissions. This paper presents a comprehensive assessment of ML-assisted optimization of hydrogen engine systems, aimed at boosting output and lowering pollution. By combining hydrogen fuel with data-driven strategies, real-world gains in engine efficiency, stable operation, and reduced emissions can be achieved, supporting the transition to sustainable energy systems.

DOI: http://doi.org/10.5281/zenodo.21768087