Determining adjustment ranges for model-based approaches 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. In doing so, it is imperative to find a range of settings at which the engine works safely (adjustment range). This paper presents a method for determining adjustment range limits in engine measurement for high dimensional parameter spaces using the support vector machines (SVM). SVMs are 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.