Integrated Engine, Vehicle, and Underhood Model of a Light Duty Truck for VTM Analysis

Transkript

1 Integrated Engine, Vehicle, and Underhood Model of a Light Duty Truck for VTM Analysis GT-SUITE Conference 2009 Dr. Philip Keller Dr. Wolfgang Wenzel Dr. Michael Becker December 7, 2009

2 Outline Introduction Goals & Motivation Cooling System Model Engine Model Detailed Mean Value Model Vehicle Model Front End Module Airflow Analysis Integration of sub models Conclusions 2

3 Introduction Goals & Motivation Goals Develop complete system model that enables feedback between all relevant engine, cooling system and vehicle parameters Quick run times so that vehicle cycle analysis is possible Motivation Simulation tool for new product development Control algorithm development 3

4 Cooling System Model Three coolant loops are calibrated EGR Loop High and Low pressure Oil Cooler Loop Radiator Loop All Heat Exchangers are taken into account Radiator CAC Cabin Heater Oil Cooler EGR Coolers Coolant Flow Diagram GT-Cool Representation 4

5 Cooling System Model Efficiency Coolant Pump is represented by performance maps Cooling System model was calibrated at 20 steady state points of varying speed and load Volume Flow Two representative plots of pressure and temperature taken at the EGR valve 5

6 Cooling System Model Heat rejection and fuel consumption behavior is captured by the model Coolant Heat Rejection Engine Fuel Consumption Oil Heat Rejection 6

7 Engine Model - Detailed 3.0L V6 Common rail Diesel engine VTG turbocharger High and low pressure EGR HP EGR LP EGR 7

8 Engine Model - Detailed Comparison of detailed engine model to test cell data 8

9 Engine Model Mean Value Model Turbocharger model is unchanged from detailed model HP and LP EGR have only minor changes from detailed model HP EGR LP EGR 9

10 Mean Value Model Neural Net Representation 6 Neural net models IMEP Vol. Efficiency Exhaust Temp. FMEP Heat to Coolant Heat to Oil Significantly more neurons were needed for the heat transfer to coolant neural net model 10

11 Mean Value Model Transient Conditions Neural net representation contains no thermal inertia Equivalent thermal inertia was added and calibrated to achieve the same transient behavior 11

12 Comparison of Detailed and Mean Value Models 20 steady state points are compared MVM compares well to detailed model Under prediction of heat rejection at high speeds and loads attributed to neural net fitting 12

13 Vehicle Model Light Duty Commercial Vehicle 2 wheel drive 5 speed automatic transmission Model run in dynamic mode with controllers for: Gear shift Lockup Clutch Accelerator and Brake Pedals Positions Idle speed control 13

14 Front End Module Airflow Analysis Besides the heat exchangers, the grill, fan, engine contour and openings in the engine compartment are modeled Aerodynamic behavior of fan is modeled based on test bench data Geometrical representation of front end package Discretization scheme used in Cool-3D Representation in GT-Cool 14

15 Integration of Sub Models Block Diagram of complete model which consists of: Engine Cooling System Controls Vehicle Underhood 15

16 Comparison of Detailed and Mean Value Models First 250 seconds of NEDC cycle 8.3 times real time for MVM vs. 600 times for detailed model 16

17 Effect of Shifting Strategy on Fuel Consumption First 500 seconds of NEDC cycle Effect of shifting strategy of torque converter as well as vehicle Cd Engine speed strategy was to keep speed within RPM range Model Initial model [gear shifting = f(vehicle speed), Cd = 0.4, Frontal Area = 4.7m^2] Shifting strategy model [gear shifting = f(engine speed), Cd = 0.4, Frontal Area = 4.7m^2] CD model [gear shifting = f(engine speed), Cd = 0.3, Frontal Area = 4.7m^2] Consumption [L/100km]

18 Conclusions The engine model is directly coupled to the cooling system which allows the feedback from the cooling system to the engine. The mean value engine model also included representations for the heat transfer to the coolant as well as the oil. The model presented also accounted for the flow of air through the front cooling system components which allows an accurate accounting of the energy flows from the cooling system to the underhood air flow. A vehicle model was also developed so that thermal system performance could be evaluated over a vehicle drive cycle. Complete system model is capable of calculation speeds of 8.3 times real time. 18

19 Acknowledgements Nick Tobin Gamma Technologies Benoît Despujols Gamma Technologies Brad Tillock - ENGSIM 19

20 thanks for Your Attention!