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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.

mDSF is a novel cylinder deactivation technology developed at Tula Technology, which combines the torque control of Dynamic Skip Fire (DSF) with Miller cycle engines to optimize fuel efficiency at minimal cost. mDSF employs a valvetrain with variable valve lift plus deactivation and novel control algorithms founded on Tula’s proven DSF technology. This allows cylinders to dynamically alternate among 3 potential states: high-charge fire, low-charge fire, and skip (deactivation). The low-charge fire state is achieved through an aggressive Miller cycle with Early Intake Valve Closing (EIVC). The three operating states in mDSF can be used to simultaneously optimize engine efficiency and driveline vibrations. Acceleration performance is retained using the all-cylinder, high-charge firing mode.