Search Results

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.

Further improvement of the trade-off between CO2 and pollutant emissions is the main motivating factor for the development of new diesel engine concepts, from light-duty car applications via medium-duty commercial vehicles up to large long-haul trucks. The deactivation of one or more cylinders of a light-duty diesel engine during low load operation can be a sophisticated method to improve fuel economy and reduce especially NOx emissions at the same time. Dynamic Skip Fire (DSF) is an advanced cylinder deactivation technology, where the decision to fire or skip singular units of a multi-cylinder engine architecture is taken just prior to each firing opportunity, based on a balanced rankling of multiple input parameters.

Dynamic skip fire (DSF) is an advanced cylinder deactivation technology where the decision to fire or skip a singular cylinder of a multi-cylinder engine is made immediately prior to each firing opportunity. A DSF-equipped engine features the ability to selectively deactivate cylinders on a cylinder event-by-event basis in order to match the requested torque demand at optimum fuel efficiency while maintaining acceptable noise, vibration and harshness (NVH). Dynamic Skip Fire (DSF) has already shown significant fuel economy improvements for throttled spark-ignition engines. This paper explores the potential benefits of DSF technology in improving fuel economy while maintaining ultra-low tailpipe emissions for light-duty (LD) Diesel powertrains.

Engines equipped with Dynamic Skip Fire (DSF) technology generate low frequency and high amplitude excitations that could reduce vehicles drive quality if not properly calibrated. The excitation frequency of each firing pattern depends on its length and on the rotational speed of the engine. Excitation amplitude mainly depends on the requested engine torque by the driver. During the calibration process, the torque characteristics that results in production level of noise, vibration, and harshness (NVH), must be identified, for each firing pattern and engine speed. This process is quite time consuming but necessary. To improve our process, a novel machine learning technique is utilized to accelerate the calibration effort. The idea is to automate the vibration rating procedure such that given the relevant power-train parameters, a vibration rating associated with that driving condition can be predicted. This process is divided into two (2) prediction models.

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.

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.