Clutch meshing pressure self-learning method of double-clutch gearbox

Abstract

The invention discloses a clutch meshing pressure self-learning method of a double-clutch gearbox. The method includes: acquiring an expected pressure value of a clutch, outputting a control signal according to the expected pressure value to control an electromagnetic valve to apply pressure on the clutch, and testing an actual pressure value; when a difference of the actual pressure value and theexpected pressure value exceeds a preset range (like when the difference is increased suddenly), storing the actual pressure value; repeating the above steps, and storing multiple actual pressure values to form a sequence, wherein an average value of the actual pressure values in the sequence is the clutch meshing pressure self-learning value. By the method, the accurate clutch meshing pressure self-learning value can be acquired; the learning result is more stable and closer to actual application. In addition, the method can reduce calibration workload, thereby having obvious actual operation advantages.

Description

technical field [0001] The invention relates to the field of clutch testing, in particular to a clutch engagement pressure self-learning method of a dual-clutch gearbox. Background technique [0002] The intermediate medium from the engine to transmit torque to the transmission gear set is a two-plate friction clutch. The coupling and disengagement of the clutch is driven by hydraulic pressure. The pressure point at which torque transmission starts is the clutch KP. The oil filling and clutch pressure torque of the transmission control software The conversion control all depends on the KP point, so the KP point has a great influence on driving performance such as gearbox creep, starting, and shifting. The final KP is equal to the basic KP plus the self-learning KP value. [0003] The current self-learning principle used in mass-produced models is: before the clutch is engaged (i.e. before reaching the KP point), 1 / 2 of the input shaft generates a certain speed driven by the...

AI Technical Summary

Problems solved by technology

[0005] 1. At present, the results obtained by KP self-learning are unstable, and there will be problems of being too large or too small, and self-learning for many times in a row, the results are quite different, and the self-learning results are unstable

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