Wet Clutch Fill Parameter Cross-Learning for Shift Consistency
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Solution Overview
Problem
The existing methods for updating filling parameters for wet plate clutches are inefficient and require recalibration under specific conditions, which does not account for deviations in operating conditions, leading to suboptimal performance due to production tolerances, temperature effects, and wear, and require recalibration at fixed intervals.
Innovation Solution
A system and method using cross-learning to update the multidimensional matrix of fill parameters, allowing for adjustments based on learned information to improve the learning process and adapt to conditions deviating from the initial learning conditions, incorporating features like Gaussian distribution and surface gradient methods to distribute corrections effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a calibration process is performed at fixed intervals under specific operating conditions, then the filling parameters can be updated periodically, but the system cannot adapt to deviations in operating conditions such as temperature effects and wear, leading to suboptimal performance
Solution Approach 1:
The system continuously monitors actual clutch filling behavior during normal operation and uses this feedback to detect deviations from expected performance. When deviations are detected, the system automatically triggers a recalibration process to update the multidimensional matrix, creating a closed-loop feedback mechanism that ensures the system adapts to changing operating conditions such as temperature variations and component wear without requiring fixed-schedule interventions
Solution Approach 2:
The clutch control system performs self-diagnosis by monitoring its own filling characteristics and automatically initiates recalibration when performance degradation is detected. The system uses sensor data from normal operation to identify when recalibration is needed, eliminating the need for external scheduling or manual intervention, and performs the recalibration autonomously by adjusting its own control parameters
2Adaptability or versatility
If recalibration is performed frequently to account for operating condition deviations, then adaptability improves, but system complexity and operational interruptions increase
Solution Approach 1:
The system applies recalibration locally and selectively only when and where needed, rather than performing global recalibration at fixed intervals. The multidimensional matrix allows the system to update parameters for specific operating conditions independently, so recalibration is triggered only for the particular temperature, pressure, or wear conditions where deviations are detected, minimizing unnecessary system interruptions and complexity
Solution Approach 2:
The system performs partial recalibration by updating only the specific portions of the multidimensional matrix that are affected by detected deviations, rather than recalibrating the entire parameter set. This selective approach reduces the computational burden and operational complexity while maintaining adaptability for the specific conditions where performance degradation was observed
3Ease of manufacture
If traditional calibration methods are used with single operating condition testing, then the calibration process is simple, but the learned parameters do not generalize well to different operating conditions
Solution Approach 1:
The system transitions from single-point calibration to a multidimensional parameter space that incorporates multiple operating conditions (temperature, pressure, wear levels) as separate dimensions. This multidimensional matrix allows the system to store and retrieve optimized parameters for various condition combinations, maintaining calibration simplicity while dramatically improving parameter accuracy and generalizability across different operating scenarios through interpolation and cross-learning between dimensions
Data Source
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AI summary
A system and method for updating a set of filling parameters for a wet clutch system is provided. The clutch comprises a piston, a proportional valve, a controller, and a sensor. The method comprises the steps of providing the wet clutch system, providing the set of filling parameters, actuating the wet clutch system based on at least one of the set of filling parameters, sensing a response of the wet clutch system during actuation of the wet clutch system, comparing an observed filling parameter to at least one of the set of filling parameters, calculating a fill error between the observed filling parameter and the at least one of the set of filling parameters, and adjusting a plurality of the set of filling parameters based on the fill error.