Printer Friendly

Simulation of vehicle's powertrain for performances evaluation.

1. INTRODUCTION

Nowadays the development of a new power train assembly for a motor vehicle, that means internal combustion engine--clutch--gearbox--transmission involve a great period of time and large teams of specialists. That's way the development using simulation technique is more and more present due to the advantages and reduced time that implies these.

In our case we develop studies for power train for a VW Caddy, 2.0 Diesel engine, 51kW/4200 rpm, that we use as a laboratory vehicle, testing different type of fuels and specially prepared for using biofules.

For the evaluation and for future testing we create a model, starting from know literature (Weeks & Moskwa, 1995), that simulate the engine, the clutch and the transmission, in order to predict the performances, the fuel consumption and the exhaust gases, to compare with the values that we measure for each test.

All these are required to improve and continue developing the models that we created for each subassembly.

On the market exist different type of models, developed by private companies, who offer a more or less adapted model, for different type of engines (Otto or Diesel) or fuels (petrol, diesel fuel, LPG, etc.), but not entirely adapted for alternative fuels such as biodiesel, alcohols, hydrogen. Our goal is to obtain a dedicated model for a power train consist of an engine fueled with biodiesel and a manual transmission and comparable with the existing models proposed by dSPACE (http://www.dspace.com), Tesis (www.tesis.de), AVL (www.avl.com) or Research Institute of Automotive Engineering and Vehicle Engines Stuttgart (www.fkfs.de).

2. MODELS

The most difficult phase is the development of the internal combustion engine due to the complex assembly and different phenomena starting from mechanical to thermodynamics and combustion aspects.

The model that we start to develop describes the simulation of a four cylinder diesel ignition internal combustion engine. The results shows how the simulation based on this model was validated against dynamometer test data. We key elements of the engine model consist in principal parts, such as gas exchange (throttle, intake manifold, mass flow rate), working conditions (compression stroke, torque generation and acceleration). Additional components will be added to the model to provide greater accuracy in simulation and to more closely replicate the behavior of the system.

The first element of the simulation is the throttle body. Here, the control input is the angle of the throttle plate. The rate at which the model introduces air into the intake manifold can be expressed as the product of two functions--one, an empirical function of the throttle plate angle only and the other, a function of the atmospheric and manifold pressures. In cases of lower manifold pressure (greater vacuum), the flow rate through the throttle body is sonic and is only a function of the throttle angle.

The simulation models for the intake manifold as a differential equation for the manifold pressure. The difference in the incoming and outgoing mass flow rates represents the net rate of change of air mass with respect to time. This quantity, according to the ideal gas law, is proportional to the time derivative of the manifold pressure. The model doesn't incorporate exhaust gas recirculation (EGR), although this will be added for the future development because the existing engine (normal aspirated, 2.0 liters diesel engine) hasn't such a device.

The mass flow rate of air that the model pumps into the cylinders from the manifold is described by an empirically derived equation. This mass rate is a function of the manifold pressure and the engine speed. To determine the total air charge pumped into the cylinders, the simulation integrates the mass flow rate from the intake manifold and samples it at the end of each intake stroke event. This determines the total air mass that is present in each cylinder after the intake stroke and before compression.

In an inline four-cylinder four-stroke engine, 180[degrees] of crankshaft revolution separate the ignition of each successive cylinder. This results in each cylinder firing on every other crank revolution. In this model, the intake, compression, combustion, and exhaust strokes occur simultaneously. To account for compression, the combustion of each intake charge is delayed by 180[degrees] of crank rotation from the end of the intake stroke.

The final element of the simulation describes the torque developed by the engine. An empirical relationship dependent upon the mass of the air charge, the air/fuel mixture ratio, the spark advance, and the engine speed is used for the torque computation. All the equations and values were based on the Romanian reference book (Burnete, et.al, 2008); (Ratiu & Mihon, 2008).

We incorporated the model elements described above into an engine model using Simulink (Kienke & Nielsen, 2000). Taking advantage of Simulink's hierarchical modeling capabilities, most of the blocks in Figure 1 are made also up of smaller blocks.

Simulink display windows show the engine speed, the throttle commands which drive the simulation, and the load torque which disturbs it.

The model presented in Figure 2 demonstrates the flexibility and extensibility of Simulink models. In the enhanced model, the objective of the controller is to regulate engine speed with a fast throttle actuator, such that changes in load torque have minimal effect, by adding a discrete-time PI controller to the engine model. The controller execution is synchronized with the engine's crankshaft rotation.

[FIGURE 1 OMITTED]

[FIGURE 2 OMITTED]

[FIGURE 3 OMITTED]

Typical simulation results are shown in Figure 3. The speed set point steps from 2000 to 3000 rpm at t = 5 sec.

Modeling the clutch system is difficult because of the topological changes in the system dynamics during lockup (models with strong discontinuities and constraints that may change dynamically), thus why the two plates that transmit torque between the engine and transmission support two distinct modes of operation: slipping, where the two plates have differing angular velocities and lockup, where the two plates rotate together. The magnitude of the torque drops from the maximum value supported by the friction capacity to a value that is necessary to keep the two halves of the system spinning at the same rate. The reverse transition, break-apart, is likewise challenging, as the torque transmitted by the clutch plates exceeds the friction capacity (Bosch, 2007). There are two methods for solving this type of problem:

* compute the clutch torque transmitted at all times, and employ this value directly in the model

* use two different dynamic models and switch between them at the appropriate times

We describe the simulation for the second method, where switching between two dynamic models must be performed with care to ensure that the initialized states of the new model match the state values immediately prior to the switch. Simulink facilitates accurate simulation due to its ability to recognize the precise moments at which transitions between lockup and slipping occur.

The simulation uses From blocks to communicate the state of the locked speed to the initial condition inputs of the two integrators. Each From block represents an invisible connection between itself and a Goto block somewhere else in the system, connected to the state ports of the integrators.

[FIGURE 4 OMITTED]

[FIGURE 5 OMITTED]

The various states remain constant while they are disabled and when transitions take place, the state handoff is both continuous and smooth.

3. CONCLUSIONS

The ability to model nonlinear, complex systems, such as the engine model and clutch described here are only the first steps for a much more complex simulation that a power train assembly implies. In particular, the Simulink modeling approaches allow rapid prototyping of any subsystem and controller that a part of a power train model needs.

4. ACKNOWLEDGMENT

This paper was possible through the CNCSIS Grant IDEI, code 1003/2008, director Liviu MIHON

5. REFERENCES

Weeks, R.W. & Moskwa, J.J., Automotive Engine Modeling for Real-Time Control Using Matlab/Simulink, SAE Intl. Congress 1995, paper 95041795

Burnete, N., et al., Diesel engines and biofuels for urban traffic, ISBN 978-973-713-217-8, Mediamira Publ., Cluj-Napoca, Romania, 2008

Ratiu., S. & Mihon, L., Internal Combustion Engines for Motor Vehicles, ISBN 978-973-52-0314-6, Mirton Publ., Timisoara, Romania, 2008

Kienke, U. & Nielsen, L., Automotive Control Systems, ISBN 978-3-540-23139-4, Springer Verlag, Berlin, 2000

Bosch, Automotive Handbook 7th Edition, ISBN 978-0-7680-1953-7, SAE Publ., 2007
COPYRIGHT 2009 DAAAM International Vienna
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2009 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Mihon, Liviu; Ostoia, Daniel; Negoitescu, Arina; Tokar, Adriana
Publication:Annals of DAAAM & Proceedings
Article Type:Report
Geographic Code:4EUAU
Date:Jan 1, 2009
Words:1371
Previous Article:Complex visual analysis of SMD devices.
Next Article:Aspects regarding a complex concept of multifunctional CNC machine-tool with large number of axis.
Topics:

Terms of use | Privacy policy | Copyright © 2020 Farlex, Inc. | Feedback | For webmasters