Hydrogen’s role in mitigating emissions variability: A chemical kinetic-thermodynamic digital framework for cleaner combustion technologies

• Chemical kinetics and 2-zone thermodynamic models are integrated in a digital diagnostic framework. • The diagnostic framework predicts cycle-to-cycle emissions using chemical kinetics. • Kinetic freezing during the engine expansion stroke drives the high NO variability. • Hydrogen reduces NO variability by stabilising combustion in dilute engine states. • N 2 O is driven by thermal quenching of its decomposition pathways during the expansion stroke. The combustion process in spark-ignition (SI) engines inherently presents cycle-to-cycle variations (CCV), leading to engine instability and variability in emissions formation. This work develops a digital framework that integrates chemical kinetics and a two-zone thermodynamic diagnostic model to understand the role of hydrogen in mitigating CCV and its impact on emissions formation. The framework predicts the crank angle degree resolved evolution of CCV of CO, H 2 , NO, and N 2 O in the engine combustion chamber’s burned gas zone. The framework is calibrated with experimental results from an SI engine working with gasoline-hydrogen fuel mixtures under stoichiometric and lean combustion conditions. This investigation has revealed that the formation of nitrogen-based emissions, particularly NO, exhibits higher variability than CO and exhaust unburnt H 2, with coefficients of variation ranging from 7% to 35%. The high NO variability is attributed to the rapid decrease in NO destruction rates (i.e., kinetic “freezing”) at different in-cylinder pressure and temperature conditions within each thermodynamic cycle. It is elucidated that N 2 O formation occurs predominantly during the expansion and exhaust strokes. New knowledge has been created to understand how the thermochemical properties of hydrogen reduce NO cycle-to-cycle variability. A synergistic effect is unveiled, hydrogen enrichment leads to an engine operational shift towards a more dilute state (i.e., increased residual gases), where hydrogen’s combustion-enhancing properties (e.g., high flame speed, low ignition energy) are crucial for stabilising combustion and thus reducing NO formation variability. Furthermore, the work proposes a new predictive statistical model capable of describing NO dispersion using only the residual–gas fraction and the mean NO level, offering a practical tool for engine calibration and emissions control. Research findings can guide the development of emissions abatement technologies for combustion-based powertrains operating with hydrogen under lean combustion conditions, where conventional catalysts are less effective and understanding gains are highly significant. The proposed digital framework offers an emissions variability predictive tool facilitating the stable operation of clean powertrain for future energy systems.