Sangili Vadamalu, Raja (2018)
Estimation and Control Methods for Active Reduction of Engine-Induced Torsional Vibration in Hybrid Powertrains.
Technische Universität Darmstadt
Dissertation, Erstveröffentlichung
Kurzbeschreibung (Abstract)
Automotive powertrain development faces the formidable challenge of reducing fuel consumption, improving driving performance, meeting regulatory demands and satisfying customer demands. Besides powertrain hybridization, continuous optimization of the internal combustion engine (ICE) is actively pursued. Engine refinement using technologies like Miller/Atkinson cycle and Variable Compression Ratio leads to higher peak pressure values in the cylinder from the improved thermodynamic processes. This trend along with rightsizing and downspeeding of the ICE give rise to torque pulsations with higher amplitude and low frequencies. These resulting oscillations cause passenger discomfort and affect component durability.
Measures for passive torsional vibration isolation essentially modify the mechanical characteristics of the powertrain. Approaches employing isolation with the use of tuned mass dampers to reduce such torsional oscillations have also been applied. The increasing complexity and the tuning effort required for the passive mechanisms have increased the interest in adopting active vibration reduction measures. Active torsional vibration reduction uses the onboard electric machine for realizing compensation torque to reduce engine-induced powertrain oscillations.
The objective of this thesis is to develop methods for estimation and control to realize the active reduction of engine-induced torsional oscillations in automotive powertrains. The oscillatory dynamics of the powertrain is governed by the torsional dynamics of the powertrain system and external excitations, in the form of torque pulsations from the ICE. The developed estimation methods study both the torsional powertrain system and the excitations. The dynamic engine torque is estimated using Unknown Input Observer (UIO) techniques. The filtering of the mean value and harmonic components of the dynamic torque is performed using a Linear Parameter Variant (LPV) estimator. The characteristics of the torsional system are identified using Errors-In-Variables (EIV) setup in closed loop operation.
In this work, before initiating the development of control methods, a methodology has been defined to analyze the potential available for active vibration reduction. Besides, the developed vibration reduction controller shall be integrated with the hybrid vehicle energy management controller. A modular architecture has been presented to enable the integration of the controller irrespective of their realization. To characterize the losses associated with the active vibration reduction control, a harmonic characterization of the actuation unit efficiency has been formulated. Beyond the analysis of the losses involved, the harmonic efficiency characterization can be used to develop electric machines specific to vibration reduction of various harmonics. A generic approach has been employed for the development of vibration reduction controllers using measurements for feedback. This approach involves an abstract definition of the active vibration reduction problem. Such an abstract viewpoint offers an opportunity to apply different control methodologies such as adaptive, robust and time-delayed control. The developed controllers have been analyzed with simulations and validated using the experimental setup, demonstrating the desired torsional vibration reduction.
Typ des Eintrags: | Dissertation | ||||
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Erschienen: | 2018 | ||||
Autor(en): | Sangili Vadamalu, Raja | ||||
Art des Eintrags: | Erstveröffentlichung | ||||
Titel: | Estimation and Control Methods for Active Reduction of Engine-Induced Torsional Vibration in Hybrid Powertrains | ||||
Sprache: | Englisch | ||||
Referenten: | Beidl, Prof. Dr. Christian ; Rinderknecht, Prof. Dr. Stephan | ||||
Publikationsjahr: | 16 Oktober 2018 | ||||
Ort: | Darmstadt | ||||
Datum der mündlichen Prüfung: | 19 Dezember 2018 | ||||
URL / URN: | https://tuprints.ulb.tu-darmstadt.de/9007 | ||||
Kurzbeschreibung (Abstract): | Automotive powertrain development faces the formidable challenge of reducing fuel consumption, improving driving performance, meeting regulatory demands and satisfying customer demands. Besides powertrain hybridization, continuous optimization of the internal combustion engine (ICE) is actively pursued. Engine refinement using technologies like Miller/Atkinson cycle and Variable Compression Ratio leads to higher peak pressure values in the cylinder from the improved thermodynamic processes. This trend along with rightsizing and downspeeding of the ICE give rise to torque pulsations with higher amplitude and low frequencies. These resulting oscillations cause passenger discomfort and affect component durability. Measures for passive torsional vibration isolation essentially modify the mechanical characteristics of the powertrain. Approaches employing isolation with the use of tuned mass dampers to reduce such torsional oscillations have also been applied. The increasing complexity and the tuning effort required for the passive mechanisms have increased the interest in adopting active vibration reduction measures. Active torsional vibration reduction uses the onboard electric machine for realizing compensation torque to reduce engine-induced powertrain oscillations. The objective of this thesis is to develop methods for estimation and control to realize the active reduction of engine-induced torsional oscillations in automotive powertrains. The oscillatory dynamics of the powertrain is governed by the torsional dynamics of the powertrain system and external excitations, in the form of torque pulsations from the ICE. The developed estimation methods study both the torsional powertrain system and the excitations. The dynamic engine torque is estimated using Unknown Input Observer (UIO) techniques. The filtering of the mean value and harmonic components of the dynamic torque is performed using a Linear Parameter Variant (LPV) estimator. The characteristics of the torsional system are identified using Errors-In-Variables (EIV) setup in closed loop operation. In this work, before initiating the development of control methods, a methodology has been defined to analyze the potential available for active vibration reduction. Besides, the developed vibration reduction controller shall be integrated with the hybrid vehicle energy management controller. A modular architecture has been presented to enable the integration of the controller irrespective of their realization. To characterize the losses associated with the active vibration reduction control, a harmonic characterization of the actuation unit efficiency has been formulated. Beyond the analysis of the losses involved, the harmonic efficiency characterization can be used to develop electric machines specific to vibration reduction of various harmonics. A generic approach has been employed for the development of vibration reduction controllers using measurements for feedback. This approach involves an abstract definition of the active vibration reduction problem. Such an abstract viewpoint offers an opportunity to apply different control methodologies such as adaptive, robust and time-delayed control. The developed controllers have been analyzed with simulations and validated using the experimental setup, demonstrating the desired torsional vibration reduction. |
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URN: | urn:nbn:de:tuda-tuprints-90079 | ||||
Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau | ||||
Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Verbrennungskraftmaschinen und Fahrzeugantriebe (VKM) 16 Fachbereich Maschinenbau > Institut für Verbrennungskraftmaschinen und Fahrzeugantriebe (VKM) > Elektrifizierung 16 Fachbereich Maschinenbau > Institut für Verbrennungskraftmaschinen und Fahrzeugantriebe (VKM) > Methodik |
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Hinterlegungsdatum: | 25 Aug 2019 19:55 | ||||
Letzte Änderung: | 25 Aug 2019 19:55 | ||||
PPN: | |||||
Referenten: | Beidl, Prof. Dr. Christian ; Rinderknecht, Prof. Dr. Stephan | ||||
Datum der mündlichen Prüfung / Verteidigung / mdl. Prüfung: | 19 Dezember 2018 | ||||
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