Zf Friedrichshafen Ag

Timon Busse

Timo Wehlen

Valerie Engel

Andreas Wendzel

Maik Dreher

Christian Baumann

Matthias Friedl

Pietro Pelizzari

A processor unit () is configured for executing an MPC algorithm () for model predictive control of a first component () of a motor vehicle () and of a second component () of the motor vehicle (). The MPC algorithm () includes a cost function () to be minimized and a dynamic model () of the motor vehicle (). The dynamic model () includes a loss model () of the motor vehicle (). The loss model () describes an overall loss of the motor vehicle (). The cost function () includes a first term, which represents the overall loss of the motor vehicle (). The overall loss depends on a combination of operating values, which includes a first value of a first operating parameter and a second value of a second operating parameter. The processor unit () is also configured for determining, by executing the MPC algorithm () as a function of the loss model (), that combination of operating values, by which the first term of the cost function () is minimized.

Model predictive control of multiple components of a motor vehicle

A processor unit () is configured for executing an MPC algorithm () for model predictive control of a motor vehicle (). The MPC algorithm () includes a longitudinal dynamic model () of the motor vehicle () and a cost function () to be minimized. The cost function () includes multiple terms, a first term of which represents an output of the cooling pump (). In addition, the processor unit () is configured for, by executing the MPC algorithm () as a function of the longitudinal dynamic model (), ascertaining a speed trajectory of the motor vehicle () situated within a prediction horizon and simultaneously ascertaining a pump operating value trajectory situated within the prediction horizon such that the first term of the cost function () is minimized.

Model predictive control of a motor vehicle

A device () for determining a discrete representation () of a road section ahead of a vehicle () includes an input interface () for receiving sensor data () of a sensor () with information about the road section ahead of the vehicle, a setting unit () for ascertaining a control distance at which a property of the road section ahead of the vehicle that is relevant for an open-loop control of the vehicle changes based on the sensor data and for setting a support point in a discrete representation of the road section corresponding to the control distance. The setting unit is configured for setting a lower predefined second number (n) of support points based on a predefined first number (n) of support points. The device also includes an output interface () for outputting the lower predefined second number of support points to an optimizer () in order to determine a profile of at least one control parameter for the open-loop control of an open-loop system, a vehicle function based on the second number (n) of support points.

Determining a discrete representation of a roadway section in front of a vehicle

A processor unit () is configured for accessing speed data of a second vehicle (), the speed data generated by a sensor of a first vehicle (). The processor unit is also configured for creating a driving behavior profile of the second vehicle () based on the speed data and making a prediction about the future driving behavior of the second vehicle () based on the driving behavior profile of the second vehicle (). Moreover, the processor unit is configured for determining a trajectory for the first vehicle () by executing an MPC algorithm, which includes a longitudinal dynamic model of the first vehicle and a cost function, such that the cost function is minimized. The prediction about the future driving behavior of the second vehicle () is taken into account in the determination of the trajectory.

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Munich, Germany

Timon Busse