Development of Advanced Conventional and Hybrid Powertrains by Mechanistic System Level Simulations

This paper presents a mechanistic system level simulation approach for modeling hybrid and conventional vehicles. It addresses the dynamic interaction between the different domains: internal combustion engine, exhaust after treatment devices, electric components, mechanical drive train, cooling circuit system and corresponding control units. Both vehicle topologies are powered by a spark ignition and compression ignition engine. Analyses concentrate on the transient phenomena caused by high interdependency of the sub–systems. Thereby the applicability of mechanistic system level models to adequately represent specific characteristics of the components is highlighted. To achieve high fidelity results of multi–domain simulations featuring high predictability and high computational speed it is necessary to develop adequate simulation tools considering all characteristic time scales of different domains and the nature of their interaction. Analyses are based on the verified models powertrain models. Simulation results of vehicles driven according to a legislative cycle provide the basis for comparative analyses of energy efficiency and exhaust gas emissions.

[1]  Peter Bartsch,et al.  Crank-Angle Resolved Real-Time Capable Engine and Vehicle Simulation - Fuel Consumption and Driving Performance , 2010 .

[2]  Johann C. Wurzenberger,et al.  Crank-Angle Resolved Real-Time Engine Simulation –Integrated Simulation Tool Chain from Office to Testbed , 2009 .

[3]  Zoran Filipi,et al.  Simulation Based Assessment of Plug-in Hybrid Electric Vehicle Behavior During Real-World 24-Hour Missions , 2010 .

[4]  Tomaž Katrašnik,et al.  Energy conversion efficiency of hybrid electric heavy-duty vehicles operating according to diverse drive cycles , 2009 .

[5]  Johann C. Wurzenberger,et al.  Real Time Capable Pollutant Formation and Exhaust Aftertreatment Modeling-HSDI Diesel Engine Simulation , 2011 .

[6]  M. Schüßler,et al.  Simulation of Exhaust Gas Aftertreatment Systems - Thermal Behavior During Different Operating Conditions , 2008 .

[7]  Tomaž Katrašnik,et al.  Analytical framework for analyzing the energy conversion efficiency of different hybrid electric vehicle topologies , 2009 .

[8]  Zhiming Gao,et al.  Comparisons of the simulated emissions and fuel efficiencies of diesel and gasoline hybrid electric vehicles , 2011 .

[9]  Federico Millo,et al.  A Comparison Between Different Hybrid Powertrain Solutions for an European Mid-Size Passenger Car , 2010 .

[10]  Thomas Schaden,et al.  Real-Time Simulation of Extended Vehicle Drivetrain Dynamics , 2011 .

[11]  Lukas Walter,et al.  Analysis of Transient Drive Cycles using CRUISE-BOOST Co-Simulation Techniques , 2002 .

[12]  Peter Eilts,et al.  Strategies for Reducing NO X - and Particulate Matter Emissions in Diesel Hybrid Electric Vehicles , 2009 .

[13]  Johann C. Wurzenberger,et al.  Multi-scale SCR modeling, 1D kinetic analysis and 3D system simulation , 2005 .

[14]  Marcello Canova,et al.  A real-time model of a small turbocharged Multijet Diesel engine: application and validation. , 2005 .

[15]  Henning Lohse-Busch,et al.  A Preliminary Investigation into the Mitigation of Plug-in Hybrid Electric Vehicle Tailpipe Emissions Through Supervisory Control Methods , 2010 .

[16]  Yongsheng He,et al.  Development and Validation of a Mean Value Engine Model for Integrated Engine and Control System Simulation , 2007 .

[17]  C. S. Daw,et al.  A proposed methodology for estimating transient engine-out temperature and emissions from steady-state maps , 2010 .

[18]  Johann C. Wurzenberger,et al.  Optimization of Hybrid Power Trains-Physical Based Modeling for Concept Design , 2012 .

[19]  Robert Fleck,et al.  Development of a Heavy Duty Hybrid Vehicle Model , 2009 .

[20]  Stefan Pischinger,et al.  HiL-Calibration of SI Engine Cold Start and Warm-Up Using Neural Real-Time Model , 2004 .