LAUSR

Engine performance under full working conditions, especially dynamic ones, is indispensable in many vehicle-level research fields. To acquire the engine performance parameters, a novel whole-region engine model, considering both steady and dynamic conditions, was developed based on limited test data in this work. This model used throttle position, engine speed, and its acceleration as the input variables to predict torque and brake-specific fuel consumption under all practical conditions within its operating envelope. The engine bench test was first conducted under typical operating conditions to collect test data for model development and validation. Then, the backpropagation neural network with designed structure was employed to perform data fitting for test conditions. After the analysis of parameter distribution tendency, the two-step interpolation method was used to generalize performance parameters under conditions apart from those test ones. The cross-condition prediction accuracy of developed engine model was validated by test data under various operating conditions. Also, the parameter prediction error of proposed modeling method was lower compared to that of existing neural network methods, which further proved its applicability to dynamic engine modeling issues.