Conventional methods of physicochemical models require various experts and a high measurement demand to achieve the required model accuracy. With an additional request for faster development time for diagnostic algorithms, this method has reached the limits of economic feasibility.
Machine learning algorithms are getting more popular in order to achieve a high model accuracy with an appropriate economical effort and allow to describe complex problems using statistical methods. An important point is the independence from other modelled variables and the exclusive use of sensor data and actuator settings.
The concept has already been successfully proven in the field of modelling for exhaust gas aftertreatment sensors. An engine-out nitrogen oxide (NOX) emission sensor model based on polynomial regression was developed, trained, and transferred onto a conventional automotive electronic control unit (ECU) and also proves real-time capability. Within this study several approaches are demonstrated to show the impact on model accuracy with varying numbers of inputs and polynomial structures. However, with increasing accuracy demands, these models require additional resources of storage and computational performance.
One solution is the use of a data flow processor (DFP, developed by DENSO), which can process parallelizable algorithms very efficiently and only requires a fraction of the computation time compared to a conventional ECU. It has been shown that the computation time of the proven polynomial regression model on the DFP can be reduced by a factor of 20.
This paper provides an overview of applications of machine learning algorithms and offers an outlook of the advantages of a DFP.