Modeling, Observability, and Estimation of Thermal Effects and Aging on Transmitted Torque in a Heavy Duty Truck With a Dry Clutch

摘要

A transmission with both high comfort and high efficiency is the automated manual transmission (AMT). To be able to control and fully utilize this type of transmission it is of great importance to have knowledge about the torque transmissibility curve of the clutch. The transmitted torque in a slipping dry clutch is therefore studied in experiments with a heavy duty truck (HDT). It is shown that the torque characteristic has little or no dependence on the slip speed, but that there are two dynamic effects that make the torque vary up to 900 Nm for the same clutch actuator position. Material expansion with temperature can explain both phenomena, and a dynamic clutch temperature model that can describe the dynamic torque variations is used. The dynamic model is validated in experiments, and it is shown to reduce the error in a transmitted torque from 7% to 3% of the maximum engine torque compared to a static model. Clutch wear is also a dynamic phenomenon that is of interest to track and compensate for, and therefore the model is augmented with an extra state describing wear. An observability analysis is performed showing that the augmented model is fully or partially observable depending on the mode of operation. In particular, by measuring the actuator position the temperature states are observable, both during slipping of the clutch and when it is fully closed. An extended Kalman filter (EKF), which observes the temperature states, was developed since it is straight forward to incorporate different modes of operation. The EKF was evaluated on measurement data and the estimated states converged from poor initial values, enabling prediction of the translation of the torque transmissibility curve. The computational complexity of the EKF is low and thus it is suitable for real-time applications. Modeling, parameter estimation, observer design, and validation are all carried out using production sensors only and therefore it is straightforward to implem- nt the observer in a production HDT following the presented methodology.