Improving Real-Time SI Engine Models by Integration of Neural Approximators 1999-01-1164
Real-time models, which reflect dynamic behavior of the SI engine, are needed for building up ECU testing devices like HIL simulators. In this paper the thermodynamic processes are reduced to some basic assumptions and combined with neural approximators of testbench data. So the parameters of the approximators can be easily adapted to similar new engines, while the principle structure describing interaction of the time- and angle-based processes remains unchanged. The model has been implemented and tested in a HIL-simulator. The performance of the proposed modeling strategy could be proved by comparing measurement data from a test bench to real-time simulation results.
D. Lichtenthäler, M. Ayeb, H. J. Theuerkauf, T. Winsel
University of Kassel
International Congress & Exposition
Electronic Engine Control Technologies-PT-73, Electronic Engine Controls 1999: Neural Networks, Diagnostic and Electronic Hardware, and Controls-SP-1419, SAE 1999 Transactions - Journal of Engines-V108-3