Transient Testing of Diesel Engines

Clark, Chuck; May, M.P.; Challen, Bernard J.

doi:10.4271/840348

Cited by 5 publications

(2 citation statements)

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“…Test bed control systems must track the pre-specified speed and torque schedule of the test cycle. Ideally, speed control is accomplished using the dynamometer's own controls and torque is controlled via the engine fueling (Clark et al, 1984). Conventionally, three-term, proportional-integral-derivative (PID) feedback controllers are used because of their low computational requirement and simplicity of tuning (Noble et al, 1988).…”

Section: List Of Tablesmentioning

confidence: 99%

“…Virtually no implementation of neural networks as controllers has been reported which is probably an implementation problem related to the computational effort required and the non-proven stability for the case of multi-layer perceptron networks. (Clark et al, 1984). Torque control is accomplished by actuating the speed demand lever (accelerator position signal in the case of a drive-by-wire control).…”

Section: Application Of Neural Network To Ic Enginesmentioning

confidence: 99%

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Mean value modeling and control of a diesel engine using neural networks

Yacoub¹

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Increasingly stringent emissions legislation and demands for improved fuel economy have mandated the need for advanced control algorithms and complicated the diesel engine calibration procedure. To this end, a neural networkbased mean value model of a modern turbocharged direct injection diesel engine has been developed and validated. For a pre-specified engine speed schedule and control vector trajectory, the engine model was shown to produce accurate predictions of the turbocharger manifold charging dynamics and combustion efficiency through engine brake torque predictions. The mean value model was coupled with several sub-models to predict exhaust gaseous and particulate emissions and satisfactory predictions were reported over highly transient engine test schedules. The mean value model was used to develop and validate through simulation a neural network-based engine torque controller for both non-governed as well as governed engine operation. Two types of proportional governors were considered where one governor employed a more aggressive fueling strategy than the other. The engine performance and exhaust gas emissions for both strategies were quantified through simulation, showing steeper rises in torque and larger excursions in transient emissions for the more aggressive fueling strategy. The controller was adapted online using the standard back-propagation algorithm. For a pre-specified engine speed schedule and desired engine torque trajectory, excellent torque tracking was predicted using the neural network (NN) controller over transient operation compared to a classical proportional plus integral (PI) controller, which was tuned heuristically. The mean value model was also used to develop and validate through simulation a neural network-based all-speed governor. For a pre-specified engine load schedule and accelerator position trajectory, accurate tracking was predicted for the desired engine speed for both classical and NN-based controllers under high load transients. For the test engine used, it was shown through simulation that tighter control of engine torque over the Federal Test Procedure (FTP) cycle resulted in higher brake specific emissions of carbon dioxide, oxides of nitrogen, and particulate matter. Also, the EPA validation criteria for the prescribed engine torque over the FTP cycle allow for significant variations in brake specific emissions, especially particulate matter, total hydrocarbons, and carbon monoxide emissions, while still meeting the legal requirements for a valid engine certification test. iii ACKNOWLEDGEMENTS The author wishes to express his gratitude to his thesis advisor, Dr. Chris Atkinson, for the invaluable opportunity he has given him to work on this very interesting subject. Thanks for many unlimited office hours of sincere discussions on engine controls, a subject greatly complicated by confidentiality agreements and propriety information. The author wishes to thank his committee members, specifically Dr. Napolitano for accepting him as a 'guest' in his advanced con...

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Section: List Of Tablesmentioning

confidence: 99%

Section: Application Of Neural Network To Ic Enginesmentioning

confidence: 99%