On Modeling and Nonlinear Model Reduction in Automotive Systems

The current control design development process in automotive industry and elsewhere involves many expensive experiments and hand-tuning of control parameters. Model based control design is a promising approach to reduce costs and development time. In this process low complexity models are essential and model reduction methods are very useful tools. This thesis combines the areas of modeling and model reduction with applications in automotive systems. A model reduction case study is performed on an engine air path. The heuristic method commonly used when modeling engine dynamics is compared with a more systematic approach based on the balanced truncation method.

The main contribution of this thesis is a method for model reduction of nonlinear systems. The procedure is focused on reducing the number of states using information obtained by linearization around trajectories. The methodology is closely tied to existing theory on error bounds and good results are shown in form of examples such as a controller used in real-world cars. Also, a model of the exhaust gas oxygen sensor, used for air-fuel ratio control in automotive spark-ignition engines, is developed and successfully validated.