Development of an Extended Mean Value Model for Control-Oriented Modeling of Gasoline Engines Equipped with Continuously Variable Valve Timing

Model-based control system design is a well-established method for advanced engine control systems. These control systems maintain engine operation at levels that meet stringent environmental regulations on vehicular emissions. However, the models required for model-based design need to be accurate enough for design and pre-calibration and fast enough for optimization and implementation purposes. On the other hand, the variable valve timing (VVT) technology significantly affects the dynamic performance of internal combustion engines (ICEs). This study aims at developing a control-oriented extended mean-value model (EMVM) of a gasoline engine, taking into account the effects of VVT on the dynamic model. The developed model analyzes the engine performance characteristics in transient and steady-state regimes. The engine model incorporates four peripheral, nonlinear, dynamic subsystems: manifold, fuel injection, wall-film adhesion, and evaporation processes. Moreover, lying at the core of the developed model is a nonlinear, static, in-cylinder process (ICP) model which simulates gas exchange and combustion processes based on the cylinder boundary conditions. Based on the experimental data obtained from the engine test setup, an artificial neural network (ANN) has been trained to predict the ICPs as a single model. The ICP model was integrated into the dynamic peripheral models to form the final EMVM. The results of the developed model were compared to the engine experimental tests for two test scenarios: half-throttle and full-throttle cases. It was observed that the developed model could accurately simulate the engine speed, inlet air pressure, aspirated air mass, and exhaust temperature. Moreover, the EMVM could successfully predict the effects of VVT on the performance of ICEs.