Fuel Injector Learning Cycle for Injection Accuracy
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Solution Overview
Problem
Conventional internal combustion engines face challenges in achieving a deterministic and fast learning phase for small quantity adjustments in fuel injection, leading to inaccurate fuel injections and increased fuel consumption due to indefinite learning convergence times.
Innovation Solution
A method is introduced that involves a first learning cycle to determine a corrected energizing time for fuel injectors based on oxygen concentration measurements, allowing for more accurate and faster convergence, and a second learning cycle to refine the energizing time, reducing calibration efforts and fuel consumption by accounting for driveline disturbances and loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional learning phase is used for small quantity adjustments in fuel injection, then the system can compensate for fuel injection variations, but the learning convergence time is long and indefinite
Solution Approach 1:
The patent applies preliminary action by performing a first learning cycle before normal operation to pre-determine correction values for fuel injection quantities. This preliminary learning phase establishes initial correction factors that enable faster convergence during actual operation, avoiding the need for long indefinite learning periods during normal engine operation.
Solution Approach 2:
The learning phase is segmented into distinct cycles (first learning cycle, second learning cycle) with specific purposes. The first learning cycle focuses on determining correction values for small quantity adjustments, while the second learning cycle refines these values. This segmentation allows each cycle to be optimized for its specific function and enables deterministic timing.
2Measurement precision
If the learning phase duration is extended to achieve convergence, then fuel injection accuracy improves, but the system cannot meet regulatory requirements for deterministic monitoring
Solution Approach 1:
By performing the first learning cycle as a preliminary action before normal operation or regulatory monitoring begins, the system establishes accurate correction values in advance. This ensures that when regulatory monitoring starts, the learning phase is already complete or near-complete, meeting deterministic timing requirements while achieving the necessary accuracy.
Solution Approach 2:
The patent changes operational parameters during different learning cycles, using specific fuel injection quantities and measurement conditions optimized for each cycle's purpose. The first learning cycle uses small quantity adjustments with specific target values, while the second learning cycle uses different parameters for refinement, allowing each cycle to be completed efficiently within regulatory timeframes.
3Measurement precision
If more calibration efforts are made to improve learning convergence, then fuel injection precision increases, but the complexity and computational requirements increase
Solution Approach 1:
The calibration process is segmented into two distinct learning cycles with different objectives and computational requirements. The first learning cycle performs basic correction value determination with simpler computations, while the second learning cycle performs refinement with more complex calculations. This segmentation distributes computational load and makes the overall process more manageable and less complex than a single comprehensive calibration.
Solution Approach 2:
The first learning cycle performs partial calibration by determining correction values for small quantity adjustments without completing the full refinement process. This partial action provides sufficient accuracy for immediate use, while the second learning cycle performs the remaining refinement. This approach achieves necessary precision without requiring all calibration steps to be completed at once, reducing overall complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution enables faster and more accurate fuel injection learning phases, reducing fuel consumption and compliance with regulatory requirements by determining a corrected energizing time through oxygen concentration measurements and iterative adjustments, thus improving the precision and efficiency of fuel injection.
Implementation Method 1
a value of an oxygen concentration in an exhaust gas is measured
Data Source
AI summary
A method of operating a fuel injector of an internal combustion engine includes setting a value of a target fuel quantity to be injected by the fuel injector, initializing a value of a fuel quantity requested from the fuel injector to the value of the target fuel quantity, and correcting the value of the requested fuel quantity. A first learning cycle is performed to correct the value of the requested fuel quantity in which a difference between the target fuel quantity and the injected fuel quantity is calculated and added to the requested fuel quantity to provide a corrected value. The corrected value of the requested fuel quantity is used to determine a reference value of an energizing time that causes the fuel injector to inject a fuel quantity corresponding to the target fuel quantity. The fuel injector is operated based on the determined reference value of the energizing time.