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Misfire detection and monitoring on US passenger vehicles are required to comply with detailed and specific requirements contained in the OBD-II regulations. Numerous technical papers and patents discuss various methods and metrics for detecting misfire in conventional all-cylinder firing engines. However, the current methods are generally not suitable for detecting misfires in a dynamic skip fire engine. For example, a detection approach based on peak crankshaft angular acceleration may work well in conventional, all-cylinder firing engine operation, since it is expected that crankshaft acceleration will remain generally consistent for a given operating condition. In a skip fire engine, any cylinder or cycle may be skipped. As a result, the crankshaft acceleration peaks and profiles may change abruptly as the firing sequence changes. This paper presents two approaches for detecting misfires in a dynamic skip fire engine.

Dynamic skip fire (DSF) has shown significant fuel economy improvements via reduction of pumping losses that generally affect throttled spark-ignition engines. For production readiness, DSF engines must meet regulations for on-board diagnostics (OBD-II), which require detection and monitoring of misfire in all passenger vehicles powered by an internal combustion engine. Numerous misfire detection methods found in the literature, such as those using peak crankshaft angular acceleration, are generally not suitable for DSF engines due to added complexity of skipping cylinders. Specifically, crankshaft acceleration traces may change abruptly as the firing sequence changes. This article presents a novel method for misfire detection in a DSF engine using machine learning and artificial neural networks. Two machine learning approaches are presented.