To meet the more stringent emissions and fuel economy regulations, engine control system has become significantly more complex than before. As a result of this, engine calibration on the dynamometer now occupies one of the longest time sections in the vehicle development process. One strategy automakers have adopted is to use the same engine in multiple applications to reduce the calibration effort. Even then, vehicle design constraints often require changes to be made to the engine’s external components such as the intake and exhaust manifolds. These changes can create variations in the engine combustion behavior so that the engine must be recalibrated on the dyno, resulting in additional cost and effort. This paper explores the potential of reusing existing engine dyno data for a modified engine in these scenarios through the use of the so-called eigenvariable to describe engine operating conditions. Traditionally, engine dyno data is referenced by engine load and speed along with actuator positions (such as camphaser positions). The proposed approach describes dyno data using eigenvariables or variables that describe the engine in-cylinder condition prior to combustion. Eigenvariables are invariant with respect to external engine hardware. This invariance enables the same dyno data to be applied to a modified engine with the same combustion system design.
- Dynamic Systems and Control Division
Engine Calibration Using “Eigenvariables”
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Hu, Y, Haskara, I, Chang, C, Khodadadi Sadabadi, K, Rezaeian, A, & Midlam-Mohler, S. "Engine Calibration Using “Eigenvariables”." Proceedings of the ASME 2017 Dynamic Systems and Control Conference. Volume 3: Vibration in Mechanical Systems; Modeling and Validation; Dynamic Systems and Control Education; Vibrations and Control of Systems; Modeling and Estimation for Vehicle Safety and Integrity; Modeling and Control of IC Engines and Aftertreatment Systems; Unmanned Aerial Vehicles (UAVs) and Their Applications; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Control of Smart Buildings and Microgrids; Energy Systems. Tysons, Virginia, USA. October 11–13, 2017. V003T34A001. ASME. https://doi.org/10.1115/DSCC2017-5093
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