Wear Modelling for Remaining Lifetime Prediction of Dry Clutches

Abstract

Modern passenger cars are equipped with effective systems to increase vehicle dynamic performance, comfort and reduction of fuel consumption. Therefore it is crucial to sustain a robust powertrain performance for increasing requirements. The clutch is an important component of the powertrain system with wear-based behavior, which substantially influences comfort. Therefore knowledge of clutch wear is essential to estimate influences on the powertrain system behavior. The system robustness can be increased and comfort can be sustained by measures, which are based on estimation of abrasion. Furthermore the remaining lifetime can be predicted from material loss and friction behavior. That implies that future maintenance can be forecasted with high accuracy. On the assumption that the clutch wear is known, the remaining lifetime of the system can be increased by an optimization of the clutch control. The contribution summarizes and compares existing methods for modelling wear and remaining lifetime of clutches. Subsequently a data-based approach for dry clutch wear modelling will be presented. The approach is capable to estimate online the current abrasion of the clutch and predict future wear. Identification and verification of the wear model will be performed by experiments on a test bench.