Vehicle Injector Control via Learning Closing Time Deviation
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Solution Overview
Problem
Existing fuel injection systems in vehicles face challenges in precisely controlling fuel injection, leading to misfires and increased harmful emissions due to variations in driving conditions, which affect fuel efficiency and engine performance.
Innovation Solution
A method for controlling vehicle injectors involves determining targeted opening and closing times based on current driving conditions, measuring actual closing times, and adjusting these times through a learning process to account for deviations, while also considering factors like fuel pressure and oil temperature, and synchronizing closing times across multiple injectors to ensure precise fuel injection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If fuel injection timing is not precisely controlled, then engine performance and fuel efficiency deteriorate, but increasing control precision requires complex learning and measurement systems
Solution Approach 1:
The system measures actual closing times of injectors and uses this feedback to calculate deviation from targeted closing times. The controller stores learning closing times that reflect these deviations and applies them to adjust future injection timing, creating a closed-loop feedback system that improves precision without requiring overly complex hardware modifications
Solution Approach 2:
The controller performs preliminary measurements of actual closing times during normal operation and calculates learning closing times in advance. These pre-computed learning values are stored and applied before subsequent injection events, allowing the system to proactively compensate for timing deviations rather than reacting after problems occur
2Object-generated harmful factors
If fuel amount is not properly controlled, then harmful emissions increase, but precise control requires continuous monitoring and adjustment mechanisms
Solution Approach 1:
The controller continuously monitors actual injector closing times and feeds this information back to adjust fuel injection parameters. By measuring deviation between targeted and actual closing times, the system automatically compensates to maintain proper fuel amounts, reducing harmful emissions through automated feedback control without requiring manual intervention
Solution Approach 2:
The system performs self-diagnosis and self-adjustment by measuring its own injector performance and automatically calculating correction values. The controller serves itself by using its own measurement capabilities to monitor injection timing and adjust fuel delivery parameters, eliminating the need for external monitoring devices or manual adjustment mechanisms
3Measurement precision
If learning closing time is stored for every driving condition, then injection precision improves, but memory requirements and processing load increase
Solution Approach 1:
Instead of storing uniform high-precision data for all possible driving conditions, the system applies learning closing time corrections locally to specific driving conditions where deviations are measured. The controller stores learning values only for conditions that have been encountered and measured, allowing high precision where needed while minimizing overall data storage requirements through localized rather than universal application
Data Source
AI summary
A method for controlling an injector of a vehicle may include: a step of determining, by a controller, a targeted opening time and a targeted closing time for at least one injector according to a current driving condition; a step of measuring, by the controller, an closing time of the injector controlled depending on the targeted closing time; and after the measuring step, a step of determining, by the controller, a deviation of the closing time with respect to the targeted closing time and determining a learning closing time obtained by reflecting the deviation to the targeted closing time to store a corresponding driving condition, in which in the determining step, when there is the learning closing time stored for the current driving condition, the targeted closing time is configured to be determined by reflecting the learning closing time.