Modeling and Forecasting Engine Air Induction under Transient Condition Based on Neural Network
Precise measurement of the air induction flow is the basis of accurate control of air fuel ratio for gasoline engines. However during transient conditions,the serious fluctuation of air induction state and the lagging response of the airflow sensor seriously affect the accuracy of air fuel ratio control.In this paper,the characteristic of air induction flow under transient condition is analyzed and a method of airflow forecast under transient conditions based on transient parameter information and BP neural network is presented.Meanwhile,the topological structure of BP neural network is also established and the model is trained and simulated by using experiment data in the acceleration and deceleration condition of gasoline engine.The results show that this method can accurately forecast the engine induction airflow under transient condition and can eliminate the lagging characteristic of the airflow sensor.
Gasoline engine Transient condition Induction airflow Neural Network
Wu Yihu Gong Huanchun Ou Linli Wang Cui Zou Li
Department of Automobile and Mechanical Engineering University of ChangSha Science and Technology ChangSha, Hunan Province, 410076, China
国际会议
2007 IEEE International Conference on Automation and Lofistics
山东济南
英文
2007-08-18（万方平台首次上网日期，不代表论文的发表时间）