Vehicle Clutch Torque Learning for Stable Micro Slip Control
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Solution Overview
Problem
The stability of micro slip control in dry clutch systems is compromised due to changes in clutch characteristics during learning, leading to unstable micro slip states and potential gear shifting impacts, which affects the accuracy and marketability of vehicles with automatic manual transmissions or dual clutch transmissions.
Innovation Solution
A control method that determines the possibility of learning clutch characteristics by maintaining a slip amount within a reference range, calculates the change in clutch torque before and after learning, and adjusts the clutch torque by incorporating this change to stabilize micro slip control, thereby preventing instability and improving learning accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If clutch characteristics learning is performed frequently to improve control accuracy, then manufacturing precision is improved, but reliability deteriorates due to unstable micro slip control
Solution Approach 1:
The system performs preliminary checks to determine whether learning conditions are satisfied before initiating clutch characteristics learning. This includes verifying that micro slip control is stable and that the operating state is appropriate for learning, thereby preventing unreliable learning operations from degrading control stability
Solution Approach 2:
The system continuously monitors clutch characteristics and micro slip control stability, using this feedback to determine when learning should be performed and when it should be avoided. The learning process itself provides feedback to update the T-S curve, which then improves subsequent control accuracy
2Manufacturing precision
If clutch characteristics learning is performed to improve control accuracy, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The clutch system performs self-learning of its own characteristics by automatically monitoring its operational state and updating its T-S curve based on observed behavior. This eliminates the need for external calibration equipment or complex manual adjustment mechanisms
Solution Approach 2:
The system changes the representation parameters from raw sensor data to a simplified T-S curve model that captures essential clutch characteristics. This model can be stored and reused, reducing the complexity of real-time control calculations while maintaining high precision
Data Source
AI summary
A control method of a clutch for a vehicle may include determining whether or not learning of clutch characteristics is possible; learning the clutch characteristics when the learning of the clutch characteristics is possible; determining a clutch torque for controlling the clutch in consideration of a change amount in the clutch torque before and after the learning and controlling the clutch by the determined clutch torque; and determining whether or not it is difficult to continue to learn the clutch characteristics.