Research On Method Of Predicting The Oil Temperature At The Oil Outlet Of Wet Dual Clutch

Wet Dual-Clutch Transmission(DCT)becomes the key development in the field of automatic transmission with the advantages of shift without power interruption and high comfort gradually.In wet DCT vehicles,the temperature of the clutch surface is indirectly reflected by the oil temperature at oil outlet of wet dual clutch.Therefore,obtaining the oil temperature at oil outlet of wet dual clutch accurately is the precondition for preventing the thermal failure of clutch.In order to achieve the redundancy check and self diagnosis of the oil temperature sensor at the oil outlet of the wet dual clutch,a fuzzy prediction method based on granular computing reduction is proposed.The research object of this dissertation is a vehicle carried a domestic wet DCT,and the creating process of the prediction method is divided into the following aspects:(1)Firstly,the structure of wet DCT is introduced,then the working principle of main hydraulic system,shift control module,dual clutch hydraulic control module and cooling and lubricating module of the hydraulic system are analyzed emphatically.On this basis,the main influence factors of the oil temperature at oil outlet of wet dual clutch are analyzed,which provides a theoretical support for the proposed method.(2)Using fuzzy reasoning theory to design the prediction method of the oil temperature at oil outlet of wet dual clutch,and membership functions and fuzzy prediction rules of the prediction method are determined through real vehicle data.In order to further improve the prediction accuracy,the vehicle driving process is divided into the high friction power process and the low friction power process according to the real vehicle data,and then the corresponding membership functions and fuzzy prediction rules are determined respectively.(3)In order to improve the real-time performance of the prediction method of the oil temperature at oil outlet of wet dual clutch,this dissertation uses the granular computing theory to reduce the fuzzy prediction rules.By reducing the number of fuzzy input and the number of fuzzy rule,the redundant information of the fuzzy prediction rules is compressed effectively.(4)This dissertation compared the prediction accuracy and real-time performance between the prediction method of the oil temperature at oil outlet of wet dual clutch before and after reduction by building a test platform to verify the optimization of granular computing theory.The results show that the prediction method after reduction can effectively solve the defects of the prediction method before reduction which takes up more resource’s and lower practicality under the premise of ensuring prediction accuracy.