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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.

Engine management systems in modern motor vehicles are becoming increasingly extensive and complex. The functionality of the control units which are the central components of such systems is determined by the hardware and software. They are the result of a lengthy development and production process. Road testing of control units, together with testing them on the engine test bench, is very time consuming and costly. An alternative is to test control units away from their actual environment, in a virtual context. This involves operating the control unit on a Hardware-in-the-Loop test bench. The control unit's large number of individual and interlinked functions necessitates a structured, reproducible test procedure. These tests can, however, only be conducted once an engine prototype has been completed, as the parameters for the existing conventional models are determined from the data measured on the test bench.