Muswathi Babulal, Aharishkumar (Eindhoven University of Technology), Heijne, Jelle (NPS Driven), Wezenbeek, Peter (Lumipol), Vlaswinkel, Maarten (Eindhoven University of Technology), Willems, Frank (Eindhoven University of Technology)

Data-Driven Misfire Detection in Hydrogen Gen-Sets Using a Production Exhaust Pressure Sensor

Scheduled for presentation during the Regular Session "Diagnostics, optimization and control for hydrogen combustion" (MoA1), Monday, June 16, 2025, 11:00−11:20, Kapel

AAC 2025 11th IFAC International Symposium on Advances in Automotive Control, June 15-18, 2025, Eindhoven, Netherlands

This information is tentative and subject to change. Compiled on May 31, 2025

Abstract

With the growing demand for climate-neutral powertrains, hydrogen combustion gen-sets are emerging as cleaner alternatives to diesel gen-sets. However, spark-ignited hydrogen engines are prone to misfires, impacting performance and engine lifespan. This study presents a novel approach for detecting misfires and identifying the misfiring cylinder using exhaust pressure signals from the production sensor, enabling a cost-effective, real-time diagnostic solution. Unlike complex feature extraction methods, the proposed approach is optimized for constant-speed gen-sets, ensuring computational efficiency and seamless integration within an Engine Management System. The technique utilizes exhaust pressure and crank angle signals to compute a tracking error feature—the squared deviation between the actual pressure signal and a reference signal. A common reference signal is modeled using normalized normal combustion exhaust pressure data from the training set and can be used for different loads. The method is validated at a 6° crank angle resolution in the hardware across multiple misfiring patterns, including single, continuous, and multiple cylinder misfire events, and the results demonstrated excellent performance under steady-state conditions. Finally, validation on the research engine demonstrated the method’s feasibility for real-time implementation.