Browse Publications Technical Papers 2004-01-0421

Physical Modelling and Use of Modern System Identification for Real-Time Simulation of Spark Ignition Engines in all Phases of Engine Development 2004-01-0421

The development of modern engine management systems makes ever-more stringent demands of the tools used. In future, the Hardware-in-the-Loop (HiL) simulation, used primarily for hardware and software tests to date, is also to be used for control function parameter adaptation tasks. This results in the need to provide highly precise, real-time-capable simulation models in all phases of the development process. This can be done by the use of modern methods for identification of non-linear, static and dynamic multi-variable systems, partly in conjunction with conventional physical model structures. In particular, artificial neural networks prove flexible in use in this case. This allows modelling dependent on the information available in the various phases of the engine development process. Thus, in the early phase, it is possible to develop engine models with computation results from complex engine simulation programs such as PROMO or GT Power. Methods of design of experiments (DOE) allow a high accuracy to be achieved with little modelling effort. Use of dynamic neural networks allows modelling for the non-stationary behaviour on the basis of measurements even where no confident statements are possible with complex simulation programs. This will be demonstrated by way of example of emissions.
This paper represents a supplement, comprising example applications of modern, non-linear identification methods, to a treatise [1] which was presented at the SAE World Congress and which predominantly deals with methods of real- time modelling in early development phases.


Subscribers can view annotate, and download all of SAE's content. Learn More »


Members save up to 17% off list price.
Login to see discount.
Special Offer: Download multiple Technical Papers each year? TechSelect is a cost-effective subscription option to select and download 12-100 full-text Technical Papers per year. Find more information here.
We also recommend:

SI Engine Modeling Using Neural Networks


View Details


Replacing Volumetric Efficiency Calibration Look-up Tables with Artificial Neural Network-based Algorithm for Variable Valve Actuation


View Details


Application of Model-Based Design Techniques for the Control Development and Optimization of a Hybrid-Electric Vehicle


View Details