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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.

Multi-level Miller-cycle Dynamic Skip Fire (mDSF) is a combustion engine technology that improves fuel efficiency by deciding on each cylinder-event whether to skip (deactivate) the cylinder, fire with low (Miller) charge, or fire with a high (Power) charge. In an engine with two intake and two exhaust valves per cylinder, skipping can be accomplished by deactivating all valves, while firing with a reduced charge is accomplished by deactivating one of the intake valves. This new ability to modulate the charge level introduces new failure modes. The first is a failure to reactivate the single, high-charge intake valve, which results in a desired High Fire having the air intake of a Low Fire. The second is a failure to deactivate the single intake valve, which results in a Low Fire having the air intake of a High Fire.