Multidimensional Data-Driven Modelling of Engine Test Cell Data

Abstract

In the journey towards a more sustainable vehicle fleet, requirements for lower emissions and improved energy efficiency in gasoline engines lead to more components being added to the internal combustion engines. This adds to the degrees of freedom when trying to model air flow in the engine using volumetric efficiency. This paper presents a way of modelling volumetric efficiency from engine test cell data provided by T-Engineering – a company that designs and develops control systems for vehicles. The model uses Gaussian process regression (GPR) for inter- and extrapolation, including noise reduction of the measurement data. Furthermore, a local interpretable model-agnostic explainer (LIME) is used to find regions of uncertainty by explaining what features contribute to increasing the variance of the GPR predictions. In addition, a neural network model is implemented in order to improve the prediction runtime, with the purpose of enabling real-time predictions in the control systems. The model(s) were found to give a more physically accurate description of volumetric efficiency than the one currently used at T-Engineering. The runtime for making predictions for 50 data points with the neural network was ~ 0.14 ms on an AMD Ryzen 7 PRO 4750U with Radeon Graphics 1.70 GHz and 32.0GB RAM. It remains to investigate what the runtime on a limited CPU in the control systems will be.