Reconstruction of cylinder pressure for SI engine using recurrent neural network

Cylinder pressure based engine control systems use variables deduced from cylinder pressure as a feedback input. Monitoring of cylinder pressure is possible through various intrusive and nonintrusive sensors but cost of these sensors limits their use in the engines of on-road vehicles. In the present work, a recurrent neural network (RNN) is proposed which can reconstruct cylinder pressure of spark ignition engine. The network uses instantaneous crankshaft speed and motored pressure as inputs. Initially, parameters of two-zone model are tuned at limited number of experimental points, so that cylinder pressure predicted by model matches to that of experimental results. Further, the tuned model is used to generate large number of training data. Validation has been carried out using experimental as well as simulated pressure trace. It has been found that RNN can reconstruct cylinder pressure with reasonably good accuracy.