Application of RBF neural networks for real-time pressure prediction in a Diesel engine

Abstract

This study aims at building efficient and robust artificial neural networks (ANN) in order to reconstruct the in-cylinder pressure of a Diesel engine starting from the signal of a low-cost accelerometer placed on the engine block. The accelerometer is a perfect non-intrusive replacement for expensive probes and is prospectively suitable for production vehicles. In this view, the ANN is meant to be efficient in terms of response time. In addition, robustness is sought in order to provide flexibility in terms of operation parameters. The neural network considered here is based on radial basis functions (RBF). The network is trained using measurements from a single cylinder Diesel engine operating under varying conditions. Training data are composed of time series from the accelerometer and corresponding measured in-cylinder pressure signals. The network parameters, including the spread parameter of the radial basis functions and the number of neurons, are used to optimize the network quality. The RBF network is then validated. The results show good correspondence between the measured and the simulated pressure signal. The accuracy of the simulated pressure signals is analyzed in terms of mean square error and in terms of a number of parameters, such as pressure peak and mass burned fraction (MBF), and their angular locations. Robustness is sought with respect to changes in the engine parameters and in the nature of the fuel. The encouraging results indicate that the reconstruction model based on RBF neural networks can be incorporated in the design of fuel-independent real-time control of Diesel engines.