A novel nonlinear model reduction method applied to automotive controller software
The automotive industry is experiencing tightening emission legislations together with high demands on performance and driveability. As a counteraction, controller software tends to become more and more complex. Intricate controller software has several downsides, the large number of controller parameters yields an exhaustive calibration task, often performed through costly experiments. In additio