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Predicting and preventing combustion anomalies leads to safe and efficient operation of the hydrogen internal combustion engine. This research presents the application of three machine learning (ML) models – K-Nearest Neighbors (KNN), Random Forest (RF) and Logistic Regression (LR) – for the prediction of combustion anomalies in a hydrogen internal combustion engine. A small experimental dataset was used to train the models and posterior experiments were used to evaluate their performance and predicting capabilities (both in operating points -speed and load- within the training dataset and operating points in other areas of the engine map). KNN and RF exhibit superior accuracy in classifying combustion anomalies in the training and testing data, particularly in minimizing false negatives, which could have detrimental effects on the engine.

Mitigating human-made climate change means cutting greenhouse gas (GHG) emissions, especially carbon dioxide (CO2), which causes climate change. One approach to achieving this is to move to a carbon-free economy where carbon emissions are offset by carbon removal or sequestration. Transportation is a significant contributor to CO2 emissions, so finding renewable alternatives to fossil fuels is crucial. Green hydrogen-fueled engines can reduce the carbon footprint of transportation and help achieve a carbon-free economy. However, hydrogen combustion is challenging in an internal combustion engine due to flame instabilities, pre-ignition, and backfire. Numerical modeling of hydrogen combustion is necessary to optimize engine performance and reduce emissions. In this work, a numerical methodology is proposed to model lean hydrogen combustion in a turbocharged port fuel injection (PFI) spark-ignition (SI) engine for automotive applications.

Economy decarbonization will be one of the main goals for the following years. Research efforts are being focused on reducing carbon-based emissions, by increasing the efficiency of the transport power plants while developing new fuel production methods that reduce the environmental footprint of the refinement process. Consequently, the depletion of conventional fuels derived from petroleum with high carbon content, such as gasoline and diesel, motivated the development of propulsive alternatives for the transportation sector. In this paradigm, methane (CH4) fuel appears as a mid-term solution due to its low carbon content, if compared with traditional fuels, and the low CO2 emissions during its production from renewable sources. However, the intrinsic properties of methane compromise the combustion process, subsequently increasing the emission of CO2.