In-cylinder air mass flow estimation of gasoline engines based on map self-learning

Abstract

Accurate in-cylinder air mass flow estimation is important for reducing emissions and improving the fuel efficiency of gasoline engines. Especially, the air-fuel ratio (AFR) control for gasoline engines is directly affected by the air mass flow estimation, which is used in feedforward control. At present, the transient air intake estimation for an engine is much more complex than the steady-state case. In practice, the working conditions of gasoline engines are always changeable to adapt to different traffic conditions, so it is important to improve the air intake estimation under the transient conditions. In order to improve the accuracy of intake air estimation, an appropriate filtering method is designed to eliminate the periodic fluctuations of the signals. And an estimation method based on the map self-learning algorithm is proposed, which considers the full working state of the engine, especially the transient conditions. The convergence of the proposed algorithm is guaranteed theoretically, and the method is designed to realize real-time identification and recursively correct of the volumetric efficiency value. The parameters of the observer are designed and adjusted through experiments to verify the effectiveness of the algorithm. With the continuous recursion of the algorithm, the accuracy of the air intake estimation will be improved and the error will be reduced. At last, the proposed strategy is analyzed and verified by a large number of experiments on the engine test bench to show its promising potentials.