Predictive approach underpins next-generation diesel engine powertrains

A commercial collaboration with Defence Science and Technology Group.

Better fuel efficiency and longer engine life are just two of the outcomes to be achieved from a new, dynamic approach to diesel engine control.

Engine performance in the automotive and maritime industries is intimately linked to the control algorithms used to determine actuation levels and inputs. However, the benefits of using advanced controllers are typically tempered by the need to spend more time and money on calibration of the algorithms.

With industry keen to integrate higher-performing algorithms into their platforms, Professor Chris Manzie has been working with researchers at Australia’s Defence Science and Technology Group and an international automotive manufacturer to develop robust yet practical optimisation-based engine controllers that are more easily calibrated.

Professor Manzie says the new system being developed for his industry partners involves versions of ‘model predictive control’ (MPC), using dynamic models of the engine within the controller.

This predicts the future response of the engine to the current inputs and environmental conditions in order to select the most appropriate control actions.

Existing technology relies on simple ‘proportional integral’ controllers that track errors (or deviation) from desired operating states as a form of feedback control.

In a submarine, MPC can be used to more effectively dampen engine speed and temperature fluctuations caused by waves. This can lead to lower signatures and increased longevity of the engine.

In the automotive industry, efficient calibration of advanced engine controllers can shorten vehicle development time and deliver better fuel economy and emissions performance, potentially leading to lower-cost and lower-emitting cars.

The new model-based algorithms are now in advanced trials within the development pipelines of Professor Manzie’s industry partner.

These industry partnerships are helping us better understand the gaps between theory and practice of model predictive control, Professor Manzie says. We can then structure fundamental research programs to target these gaps and deliver better applied results.

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