Adjustment range determination in engine development using support vector machines
Model-based methods, such as Design of Experiments (DoE), have become more and more established in recent years in optimizing control maps in engine ECUs from the aspects of ride comfort, fuel economy and emissions. As a result of the rising number of control parameters and ever shorter development times, the aim in this context is to develop automated intelligent setting strategies for test design. Doing so, it is imperative to find a range of settings where the engine works safely (adjustment range). In determining adjustment range limits, the aim is now to generate a sufficiently accurate hull model using as few starting measurement points as possible. The hull should capture all feasible measurement points and contains no superfluous spaces. SVMs represent a relatively new method in machine learning and are applied to learn a hull that models the unknown, actual test space on the basis of measurement points. They are characterized by good reproducibility and generalization ability and further by high learning speed.