Dry Clutch Modeling, Estimation, and Control
Increasing demands on comfort, performance, and fuel efficiency in vehicles lead to more complex transmission solutions. One such solution is the Automated Manual Transmission (AMT). It works just like an ordinary manual transmission but the clutch and the gear selection are computer controlled. In this way high efficiency can be accomplished with increased comfort and performance. To be able to control and fully utilize an AMT, it is of great importance to have knowledge about how torque is transmitted in the clutch. The transmitted torque in a slipping dry clutch is therefore studied in a series of experiments with Heavy Duty Trucks (HDT). It is shown that material expansion with temperature can explain torque variations up to 900 Nm for the same clutch actuator position. A dynamic clutch temperature model that can describe the torque variations is developed. The dynamic model is validated in experiments, and shown to reduce the error in transmitted torque from 7 % to 3 % of the maximum engine torque compared to a static model. Since all modeling, parameter estimation, and validation are performed with production HDTs, i.e. production sensors only, it is straightforward to implement the model in a production HDT following the presented methodology.
The clutch model is extended with lock-up/break-a-part dynamics and an extra state describing wear. The former is done using a state machine and the latter uses a slow random walk for a parameter corresponding to the thickness of the clutch disc. Two observability analyses are made: one with production sensors, and one with a torque sensor in addition to the production sensors. The analyses show that, in both cases, the temperature states and the wear state are observable both during slipping of the clutch and when it is fully closed. The latter is possible since a sensor measures the actuator position. The unknown offset in the torque sensor is possible to observe (at all times) if the model is further augmented with engine inertia dynamics. An Extended Kalman Filter (EKF) is developed and evaluated on measurement data for both cases. The estimated states converge from poor initial values, enabling prediction of the translation of the torque transmissibility curve and sensor offset. The computational complexity of the EKF is low and it is thus suitable for real-time applications.
The clutch model is also integrated into a driveline model capable of capturing vehicle shuffle (longitudinal speed oscillations) and engine torque fluctuations. Parameters are estimated to fit an HDT and the complete model shows good agreement with data. It is used to show that the effect of thermal expansion, even for moderate temperatures, is significant in clutch control applications. One such application is micro-slip control. A control structure has been made and the basic components are a reference-slip calculator, an LQ controller and an EKF that can compensate for the thermal dynamics of the clutch. The controller isolates the driveline from the engine oscillations without dissipating more heat than the clutch can handle. An analysis shows a noticeable fuel consumption increase. Nonetheless, the real benefits of micro-slip control will only be evident when combined with other cost-reducing changes in the powertrain. The feasibility of a micro-slip control system for a dry clutch HDT has been proven.
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Last updated: 2019-08-05