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Over the last decades, the use of computers has become an integral part of the engine development process. Computer-based tools are increasingly used in the design process, and especially the layout of the various subsystems is conducted by means of simulation models. Computer-aided engineering plays a central role e.g. in the design of the combustion process as well as with regards to work performed in the area of engine mechanics, where CFD, FEM, and MBS are applied. As a parallel trend, it can be observed that various engine performance characteristics such as e.g. the specific power output and the power-to-weight ratio have undergone an enormous increase, a trend which to some extent counteracts the increase in safety against malfunction and failure. As yet, due to the constant need for further optimization, mechanical testing and verification processes have not become redundant, and it is assumed that they will remain indispensable for the foreseeable future.

The modern engine design process is characterized by shorter development cycles and a reduced number of prototypes. However, simultaneously exhaust after-treatment and emission testing is becoming increasingly more sophisticated. The introduction of predictive real-time simulation tools that represent the entire powertrain can likely contribute to improving the efficiency of the calibration process. Engine models, which are purely based on physical first principles, are usually not capable of real-time applications, especially if the simulation is focused on cold start and warm-up behavior. However, the initial data definition for the ECU using a Hardware-in-the-Loop (HiL)-Simulator requires a model with both real-time capability and sufficient accuracy. The use of artificial intelligence systems becomes necessary, e.g. neural networks. Methods, structures and the realization of a hybrid real-time model are presented in this paper, which combines physical and neural network models.

Modern powertrain development is targeting to meet challenging, to some degrees contradictory development goals in a short timeframe. Looking to a development time schedule of 36 months from concept to SOP, it becomes a prerequisite that unnecessary design loops have to be avoided by all means. Now, in addition, the experimental development work has to be conducted more efficiently than in the past. In recent years methods for an efficient design process have been successfully applied. Testing and vehicle application work can take advantage of methods empowered by model based approaches. Today, models with different levels of detail are able to significantly improve nearly every development phase. Supported by standardized and automated test bench and vehicle procedures an efficient and comprehensive development process can be established and utilized, which is also necessary to tackle growing complexity.

Nowadays internal combustion engine design is characterized by a faster development time with increased levels of quality, NVH, specific power and lower weight all being demanded at a lower production cost. This requires a new and systemic design management from the outset of the concept to SOP (Start of Production). The design for Six Sigma (DFSS) process is the surest way to achieve the above mentioned development goals. Within a Six Sigma approach, manufacturing and serial production issues are considered from the beginning of the development phase. Based on examples, the methodology will be explained in single steps. The explanation will include QFD, FMEA (product and process), scorecards, DOE and kneading process with its tolerance analysis and process capability investigations. The use of these different tools for each phase of the design process will be described.