Neural network modelling of the emissions and performance of a heavy-duty diesel engine

Internal combustion engines are being required to comply with increasingly stringent government exhaust emissions regulations. Compression ignition (C I) piston engines will continue to be used in cost-sensitive fuel applicati ons such as in heavy-duty buses and trucks, power generation, locomotives a nd off-highway applications, and will find application in hybrid electric v ehicles. Close control of combustion in these engines will be essential to achieve ever-increasing efficiency improvements while meeting increasingly stringent emissions standards. The engines of the future will require signi ficantly more complex control than existing map-based control strategies, h aving many more degrees of freedom than those of today. Neural network (NN)-based engine modelling offers the,potential for a multi dimensional, adaptive, learning control system that does not require knowle dge of the governing equations for engine performance or the combustion kin etics of emissions formation that a conventional map-based engine model req uires. The application of a neural network to model the output torque and e xhaust emissions from a modern heavy-duty diesel engine (Navistar T444E) is shown to be able to predict the continuous torque and exhaust emissions fr om a heavy;duty diesel engine for the Federal heavy-duty engine transient t est procedure (FTP) cycle and two random cycles to within 5 per cent of the ir measured values after only 100 min of transient dynamometer training. Ap plications of such a neural net model include emissions virtual sensing, on -board diagnostics (OBD) and engine control strategy optimization.