The automotive industry is facing a major challenge to reduce environmental impacts. As a consequence, the increasing diversity of powertrain configurations put a demand on testing and evaluation procedures. One of the key tools for this purpose is simulators. In this paper a powertrain model and a procedure for parameterizing it, using chassis dynamometers and a developed pedal robot are presented. The parameterizing procedure uses the on-board diagnostics of the car and does not require any additional invasive sensors.

Thus, the developed powertrain model and parameterization procedure provide a rapid non- invasive way of modelling powertrains of test cars. The parameterizing procedure has been used to model a front wheel drive Golf V with a 1.4L multi-fuel engine and a manual gearbox. The achieved results show a good match between simulation results and test data. The powertrain model has also been tested in real-time in a driving simulator.

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 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 developed. The dynamic model is validated in experiments, and it is shown to reduce the error in 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 computional 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 straight forward to implement the observer in a production HDT following the presented methodology.

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.

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 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 experiments with a heavy duty truck (HDT). It is shown that material expansion with temperature can explain torque variations up to 700 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.

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 using a slow random walk for a parameter corresponding to the clutch disc thickness. An observability analysis shows 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) was developed and evaluated on measurement data. 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 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). 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 launch control applications.

An alternative use of the driveline model is also investigated here. It is found that the amplitude discretization in production road-slope sensors can excite vehicle shuffle dynamics in the model, which is not present in the real vehicle. To overcome this problem road-slope information is analyzed and it is shown that a third-order butterworth low-pass filter can attenuate the vehicle shuffle, while the shape of the road profile is maintained. 

All experiments in the thesis are performed using production HDTs only, i.e. production sensors only. Since all modeling, parameter estimation, observer design and validation are performed with production sensors it is straight forward to implement the results in a production HDT following the presented methodology.

A dry clutch model with thermal dynamics is added to a driveline model of a heavy-duty truck equipped with an automated manual transmission. The model captures driveline oscillations and can be used to simulate how different clutch-control strategies affect vehicle performance, drivability and comfort. Parameters are estimated to fit a heavy-duty truck and the complete model is validated with respect to shuffle, speed trajectory, clutch torque and clutch lock-up/break-apart behavior. The model shows good agreement with data. Furthermore the model is used to study the effect of thermal expansion in the clutch on launch control. It is shown that the effect of thermal expansion, even for moderate temperatures, is significant in launch control applications.

In powertrain analysis, simulation of driveline models are standard tools, where efficient and accurate simulations are important features of the models. One input signal with high impact on the accuracy is the road slope. Here it is found that the amplitude discretization in production road-slope sensors can excite vehicle shuffle dynamics in the model, which is not present in the real vehicle. To overcome this problem road-slope information is analyzed with the aid of both measured and synthetic road profiles, where the latter are generated from regulatory road specifications. The analysis shows that it is possible to separate vehicle shuffle resonances and road-slope information, and designs are proposed for on- and off-line filtering of the road-slope-sensor signal in spatial coordinates. Applying the filter to measured data shows that vehicle shuffle is significantly attenuated, while the shape of the road slope profile is maintained. As a byproduct the use of smoothing the rolling resistance is shown.

The transmitted torque in a slipping dry clutch is studied in experiments with a heavy duty truck. It is shown that the torque characteristic has little or no dependence on 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 with two different time constants is developed. The dynamic model is validated in experiments, with an error of only 3% of the maximum engine torque, and is shown to improve the behavior significantly compared to a static model.

Torque sensors for automotive powertrains are becoming cheaper and a viable option for production vehicles. It is investigated how a torque sensor located on the input shaft of an Automated Manual Transmission (AMT) in a Heavy Duty Truck (HDT) affects the observability of a clutch model for torque transmission that includes thermal effects and aging. In order to handle an offset that is introduced with the new sensor the model is augmented with a random walk and an equation for the engine inertia dynamics. The sensor offset is observable regardless of mode of operation of the clutch and observability of the states in the original model is unchanged. Furthermore the design of an Extended Kalman Filter (EKF) built upon the augmented model requires a new mode of operation compared to an EKF built upon the original model. In addition a mode dependent process noise covariance matrix is used. The new EKF is evaluated using measurement data from an HDT equipped with a magnetoelastic torque sensor. The EKF is demonstrated to work and the estimates converge rapidly, even from poor initial values. It is also computationally simple and therefore suitable for real-time applications.