Practical model-based control for next generation diesel engines

The performance of diesel engines in the automotive and maritime industries are intimately linked to the control algorithms used to determine the actuation levels and inputs.Legislative and increasing consumer requirements demand better control approaches than have been deployed in production vehicles to date, and motivate the use of model based techniques to meet performance and emissions specifications. However, the benefits of using advanced controllers are typically tempered by the need to spend more time and money on calibration of the algorithms as the tuning parameters are not related to time domain specifications.

With industry keen to adopt and integrate higher performing algorithms into their platforms, we have been working with researchers at Australia’s Defence Science and Technology Group (DSTG) and Japan’s Toyota Motor Corporation to develop robust yet practical optimisation-based engine controllers that are more easily calibrated.

These projects aim to develop  robust multivariable model predictive control algorithms for high performing diesel engines that reduce calibration effort. The algorithms will be implemented at state-of-the-art facilities both within Australia and internationally. The anticipated outcome is new model based control architectures that improve diesel engine operation yet reduce its calibration effort and cost.

Researchers involved: Chris Manzie, Iman Shames, Dragan Nesic, Rohan Shekhar, Gokul Sankar, Noam Olshina

Partners: Toyota Motor Corporation (TMC), Defence Science and Technology Group, Australian Research Council