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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.

Over the last decade significant efforts have been made in the automotive industry to move into a math-based control development approach where much of the development could be done off-line using computer simulations. High-fidelity simulation of an engine and control system helps to shorten controller development time with reduced risk. This requires the integration of a detailed engine model with a representative controller model. This paper describes the development and validation of an integrated engine and controller model of a turbocharged diesel engine. The integrated model incorporates a detailed engine model in GT-Power and a comprehensive controller model in Simulink with functionalities like the production ECM. The focus of this study is a non-real time simulation and analysis of the control of EGR, turbocharger, and fueling with engine performance.