Wet dual clutch launching adaptive control considering service time

Effective performance prediction under different working conditions is crucial for the reliability evaluation and health management of wet multi-disc clutches throughout the service life. In light of the difficulty in data acquisition and dynamic assessment of degradation status within the clutch life cycle, a machine learning-based service period performance prediction method that does not rely on offline data is proposed. Firstly, the clutch lifecycle experiments are designed and conducted under different working conditions to verify the phased decline in friction performance, where changes in friction surface roughness lead to the continuous deterioration of tribological behavior. Then, based on the extracted average coefficient of friction (COF), engagement time, peak torque attenuation coefficient ( K torq), and chaotic feature parameters including correlation dimension ( D2) and standard deviation of distance matrix ( SD- DM), the attention-based deep learning model (Att-LSTM) is established to predict the performance degradation. Finally, the predictive performance of the proposed model is validated by using different historical data volumes. The results show that the proposed Att-LSTM model has an average MAPE of 7.70% and 6.54% on a limited historical dataset of 40% and 60%, respectively, demonstrating superior accuracy. This work is conducive to promoting the health management and conditional maintenance for clutch system during the on-duty phase.

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Adaptive Starting Control Strategy for Hybrid Electric Vehicles Equipped with a Wet Dual-Clutch Transmission

Ji-lian

Zhang

2023

Actuators

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To improve the starting performance of the P2.5 plug-in hybrid electric vehicles with a wet dual-clutch transmission, an adaptive starting control strategy is presented in this paper, which controls the two clutches simultaneously involved in the starting process. A fuzzy controller is designed to identify the starting intention and determine the target torque under different working conditions. The starting process is divided into five periods, and linear quadratic optimal control is adopted to obtain the reference torque trajectory for the third period, while the others are determined by adaptive control by changing the adjustment coefficients according to the starting conditions. The combined pressure feedback controller based on the PID algorithm is proposed to control the wet clutch torque to follow the reference torque trajectory. The MATLAB/Simulink software platform is used to simulate the control strategy. The results show that the proposed strategy can shorten the starting time and reduce the level of jerk. Moreover, with the first-gear starting, the friction work of the whole process and clutch C1 is, respectively, reduced by 1.68% and 4.62% for slowly starting and 23.37% and 23.6% for quickly starting, which can significantly prolong the service lifetime of the clutch compared with the traditional single-clutch starting strategy.

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Wet dual clutch launching adaptive control considering service time

Cited by 4 publications

References 32 publications

Adaptive Gearshift Control for Dual Clutch Transmissions Based on Hybrid Physical and Data Driven Modeling

Adaptive Gearshift Control for Dual Clutch Transmissions Based on Hybrid Physical and Data Driven Modeling

Experimental prediction model for full life friction performance of wet clutch via attention-based LSTM network

Adaptive Starting Control Strategy for Hybrid Electric Vehicles Equipped with a Wet Dual-Clutch Transmission

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