This paper describes the development of a mean value model for a turbocharged diesel engine. The objective is to develop a fast-running engine model with sufficient accuracy over a wide range of operating conditions for efficient evaluation of control algorithms and control strategies.
The mean value engine model was derived from a detailed 1D engine model, using the Design of Experiments (DOE) and hybrid Radial Basis Functions (RBF) to approximate the simulation results of the detailed model for cylinder quantities (e.g., the engine volumetric efficiency, the indicated efficiency, and the energy fraction of the exhaust gas). Furthermore, the intake and exhaust systems (especially intake and exhaust manifolds) were completely simplified by lumping flow components together. In addition, to compare with hybrid RBF, neural networks were also used to approximate the simulation results of the detailed engine model.
The developed mean value engine model with hybrid RBF was then integrated with a comprehensive controller model for non-real time simulation and analysis of the engine and control system. The mean value model was extensively validated against the detailed model over a step-change test condition, three step transient conditions, and a full FTP driving cycle. It is demonstrated that the dynamic responses of the engine and control system during all transient conditions have been captured reasonably well by the mean value model.
The trade-off of model accuracy and run speed was explored and evaluated for the developed mean value model, compared with the detailed model. It is shown that the developed mean value model achieved a good trade-off of model accuracy and run speed.