Fuel Injection Controller Learning Method for Cylinder Variation
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Solution Overview
Problem
Existing fuel injection controllers face challenges in accurately learning and compensating for variations in injection characteristics among cylinders, particularly during pilot injections, due to individual differences in injectors, leading to potential noise and exhaust gas issues, and requiring an inefficiently long time for convergence.
Innovation Solution
A learning method that includes a convergence determination step to assess when the fluctuation correction value has converged, allowing for early completion of learning and adding a lead time to the threshold if the learning is finished before schedule, thereby avoiding unnecessary delays and ensuring sufficient learning time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feedback control is performed to obtain learning value for compensating injection quantity difference, then accurate learning of injection characteristics is achieved, but learning time is unnecessarily lengthened
Solution Approach 1:
The patent applies preliminary action by performing feedback control and obtaining learning values during the manufacturing process before the fuel injection controller is shipped. This preliminary learning allows the controller to have accurate injection characteristic data stored in advance, eliminating the need for lengthy learning periods after installation while maintaining high learning accuracy.
2Productivity
If learning is performed with a threshold for operation range, then learning completion is determined, but learning may be completed prematurely before sufficient time elapses
Solution Approach 1:
The patent implements feedback by continuously monitoring whether the learning value has converged within the operation range threshold and using this feedback to determine learning completion. The controller checks if the learning value change falls within a predetermined threshold range, and only then determines that learning is complete, ensuring both efficient completion and sufficient learning time.
3Adaptability or versatility
If learning is performed after product shipment, then deployment flexibility is maintained, but learning time is extended unnecessarily
Solution Approach 1:
The patent applies preliminary action by performing all necessary feedback control and learning value acquisition during the manufacturing phase before the product is shipped. The learned values are stored in the controller's memory, allowing the system to be deployed immediately without requiring lengthy learning periods after installation, thus maintaining both flexibility and time efficiency.
Data Source
AI summary
A learning time counter is started and updated if an operation state is stabilized after shifting to a certain operation range. Then, convergence of a FCCB correction value of a variation in an injection characteristic is determined. If the FCCB correction value is determined to have converged, a permit flag is turned on and the FCCB correction value is decided as a learning value. Even if the FCCB correction value does not converge, the learning value is compulsorily decided when the learning time counter reaches a threshold. If the FCCB correction value is decided early, a surplus time is added to a threshold of the next operation range. Thus, the variation of the injection characteristics among cylinders can be learned highly accurately while averting unnecessary lengthening of a learning time.