Method of generating rate-of-injection (ROI) profiles for a fuel injector and system implementing same

A system for generating a rate-of-injection (ROI) profile for a fuel injector using a machine learning model, the system comprising:

• a controller configured to:
• receive a first plurality of measurement sets for the fuel injector, each measurement set of the first plurality of measurement sets corresponding to an injection cycle during which the fuel injector was fluidly coupled to a fuel rail and driven for a first predetermined duration to output fuel while the fuel rail was set to a first predetermined pressure;
• identify at least two measurement sets of the first plurality of measurement sets with a first target standard deviation;
• generate a rate-of-injection (ROI) model for the fuel injector based on the at least two identified measurement sets;
• receive a first target pressure value for the fuel rail and a target operational set point for the fuel injector;
• and
• generate a predicted ROI profile for the fuel injector by providing the first target pressure value and the target operational set point into the ROI model.

A non-transitory computer-readable medium storing instructions that when executed by a controller of a computer device cause the controller to perform a method for determining a rate-of-injection (ROI) profile for a fuel injector, the method comprising:

• receiving, by the controller, a first plurality of measurement sets for the fuel injector, each measurement set of the first plurality of measurement sets corresponding to an injection cycle during which the fuel injector was fluidly coupled to a fuel rail and driven for a first predetermined duration to output fuel while the fuel rail was set to a first predetermined pressure;
• identifying, by the controller, at least two measurement sets of the first plurality of measurement sets with a first target standard deviation;
• instantiating, by the controller, a neural network in a memory based on the at least two identified measurement sets, the neural network having an input layer, a hidden layer, and an output layer, the hidden layer having a plurality of neurons and connections therebetween;
• performing, by the controller, a predetermined number of training iterations to induce overfitting of the neural network such that an overall convergence error for output of the neural network is equal to or less than 0.0001;
• receiving, by the controller, a first target pressure value for the fuel rail and a target operational set point for the fuel injector;
• and
• generating, by the controller, a predicted ROI profile for the fuel injector by providing the first target pressure value and the target operational set point into the neural network.