DoE in the Automotive Industry - Approaching the Limits of Current Methods?
Seminar Room 1, Newton Institute
The presentation outlines the main applications of DoE in the field of automotive engine development and calibration. DoE has been applied to engine calibration (optimising the settings of electronically-controlled engine parameters for low emissions and fuel consumption) for many years. The task has become significantly more complex in recent years due to the various new fuel injection technologies, and up to ten variables must be calibrated at each and every engine speed and load.
Many engine responses are non-linear and there are considerable interactions between control variables so the conservative approach of separate DoEs at multiple speed-load conditions still predominates. Polynomials are adequate for such "local" models with narrow variable ranges and six or fewer variables. But over wider ranges or when speed and load are included (so-called "global" models) responses are highly non-linear and polynomials are unsuitable.
Some practitioners use radial basis functions, neural networks or stochastic process models but such methods do not always yield the requisite accuracy for "global" models. Furthermore, the most reliable of these techniques, stochastic process models, are limited by computational considerations when datasets are large.
The overview of the current "state of the art" methods is presented with the aim of stimulating discussion on what mathematical methods could form the basis of future DoE tools for the automotive industry.