Efficient modeling of mechatronic systems regarding variety and complexity in the field of automotive

In the field of automotive engineering the development time is shrinking while the variety and complexity of vehicles is increasing steadily. Since the measuring process for evaluation of the component behavior is time-consuming, model based development is aspired. This paper describes different modeling techniques and presents an architecture to build up vehicular mechatronic systems considering the complexity and product variety. It presents how these methods can be applied to create a vehicular part: variable valve timing systems. According to the proposed architecture different layers of the real component are indicated and represented by the particular model. Subsequently the simulation results of the Valvetronic are evaluated by means of the measurement data. Concluding, the prerequisites for the acceptance of model based engineering are described.

[1]  Michael Muskulus,et al.  Experiences with object-oriented and equation based modeling of a floating support structure for wind turbines in Modelica , 2012, Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC).

[2]  Christiaan J. J. Paredis,et al.  Integrating Models and Simulations of Continuous Dynamics Into SysML , 2012, J. Comput. Inf. Sci. Eng..

[3]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[4]  Peter A. Fritzson,et al.  Principles of object-oriented modeling and simulation with Modelica 2.1 , 2004 .

[5]  W. Marsden.  I and J , 2012 .

[6]  Wha Wil Schilders,et al.  Introduction to model order reduction , 2008 .

[7]  F. Felgner,et al.  Object-oriented simulation model of thermoelectric devices for energy system design , 2012, 2012 16th IEEE Mediterranean Electrotechnical Conference.

[8]  A. Hepbasli,et al.  Electricity estimation using genetic algorithm approach: a case study of Turkey , 2005 .

[9]  H. V. D. Vorst,et al.  Model Order Reduction: Theory, Research Aspects and Applications , 2008 .

[10]  S. Sharif,et al.  Artificial Neural Network for Predicting Machining Performance of Uncoated Carbide (WC-Co) in Milling Machining Operation , 2009, 2009 International Conference on Computer Technology and Development.

[11]  Waguih ElMaraghy,et al.  Complexity of Multi-Disciplinary Design , 2007 .

[12]  Ehsan Mesbahi,et al.  Artificial neural networks: fundamentals , 2003 .

[13]  Christian Schwarz,et al.  Die Valvetronic: Erfahrungen aus sieben Jahren Großserie und Ausblick in die Zukunft , 2008 .

[14]  Andreas Zell,et al.  Simulation neuronaler Netze , 1994 .

[15]  Harald Unger,et al.  The Valvetronic: Experience from Seven Years of Mass Production and a Discussion of Future Prospects , 2008 .

[16]  A. Fay,et al.  Object-oriented engineering data exchange as a base for automatic generation of simulation models , 2009, 2009 35th Annual Conference of IEEE Industrial Electronics.

[17]  Marcello Bonfè,et al.  On the Use of UML for Modeling Physical Systems , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[18]  Takahiro Tomino,et al.  Monozukuri capability to address product variety: A comparison between Japanese and German automotive makers , 2014 .

[19]  Jing Ma,et al.  The comparison research of nonlinear curve fitting in Matlab and LabVIEW , 2012, 2012 IEEE Symposium on Electrical & Electronics Engineering (EEESYM).

[20]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.