Fuel Injection Quantity Fluctuation Prediction and Compensation for Multiple Injections of Common Rail System

Abstract:

To solve the inaccurate injection quantity problem in multiple injections， in this paper， a simulation model of the electro injector is developed in the AMESim environment through dynamics analysis of injector， and the parameter matching and rationality verification of the model are also conducted. In addition， the influencing factors of fuel injection fluctuation are investigated. The results show that the change of injection pressure， pre-injection pulse width， the dwell time between the main and the pilot injection pulse can affect fuel fluctuation. Moreover， a fuel compensation control strategy based on self-learning system is constructed by using a certain sample of stimulation data to compensate for the deviation of multiple injection quantity. A self-learning system is realized by using the genetic algorithm （GA） and neural network， which can effectively improve injection control.