Design and development of an ignition system for heavy-duty transport applications through a novel AI-based approach

The Internal Combustion Engine (ICE) remains the dominant engine typology worldwide, converting chemical energy from fuel–air mixtures into mechanical work. As the automotive and industrial sectors face increasing pressure to reduce pollutant emissions and greenhouse gasses, significant efforts are being directed toward the development of cleaner combustion solutions, among which hydrogen represents a particularly compelling alternative. Hydrogen stands out because of its zero- carbon combustion products and its suitability for heavy-duty applications such as tractors, busses,and trucks, where battery-electric solutions often encounter limitations in terms of payload, range, and charging time. However, hydrogen combustion presents its own challenges: ignition is highly sensitive to in-cylinder conditions [15], particularly pressure and , which dictate whether the mixture is rich or lean. The operation of lean-burn hydrogen, in particular, requires a significantly higher ignition voltage [8], leading to physically larger ignition systems and imposing additional packaging restrictions. In addition, the ignition device must withstand the harsh environment of the engine, where limited space and elevated temperatures impose strict design constraints. This thesis addresses these challenges by focusing on the development of a novel high-power ignition coil specifically optimized for hydrogen engines. The design process is structured into multiple stages. First, a volumetric definition of the device is established in collaboration with Frenetic, providing optimized material selections and feasible geometries for physical realization. Subsequently, the electrical design is refined through the optimization of an equivalent circuit model using Python-based tools and an AI-driven methodology combining Deep Learning and Genetic Algorithms. This approach aims to achieve high output energy comparable to, or surpassing, that of traditional Capacitive Discharge Ignition (CDI) systems, while delivering the long-duration sparks required for stable hydrogen combustion. The results demonstrate the successful development of a robust AI-assisted design workflow capable not only of improving an existing prototype but also of providing comprehensive design guidelines for entirely new ignition devices. The methodology significantly reduces development time and enables the achievement of high-energy, long-duration spark characteristics that are essential for reliable hydrogen ignition. The study concludes with three different validation attempts: the first demonstrating the capability of the system to support the design of new devices, the second confirming, through prototype measurements, the accuracy of the model in reproducing real product behavior, and the third showcasing the ability of the method to guide corrective redesign of an existing device. The conclusions discuss the success of this innovative approach and outline the next steps for further refinement and development.