Multi-Sensor Data Fusion Prediction Model of Air Inflow Velocity Under Transitional Working Condition of Gasoline Engine

摘要

A neural network-based multi-sensor data fusion prediction model of air inflow velocity under working condition is proposed and a neural network topology of air inflow velocity prediction under transitional working condition is set in this paper to mitigate the air-fuel ratio control inaccuracy resulting from air flow sensor lag. Simulation is conducted on the basis of HQ495 engine experimental data, which shows that neural network-based multi-sensor data fusion prediction model of air inflow velocity, with better accuracy, excels engine average value model.