Abstract: Model-based predictive control (MPC) of a motor vehicle involves an MPC algorithm, which comprises a high level solver module to calculate a high level longitudinal trajectory for an upcoming route segment, according to which the motor vehicle is to travel within a route-based high level prediction horizon. The high level longitudinal trajectory is sent to a tracker solver module in the MPC algorithm as an input value, which calculates a tracker longitudinal trajectory on the basis of the high level longitudinal trajectory, according to which the motor vehicle is to travel within the time-based tracker prediction horizon, wherein the tracker prediction horizon is shorter than the high level prediction horizon, such that the tracker prediction horizon only covers a portion of the high level prediction horizon.

Abstract: A processor unit (3) is configured for executing an MPC algorithm (13) for model predictive control of a first component (18) of a motor vehicle (1) and of a second component (19) of the motor vehicle (1). The MPC algorithm (13) includes a cost function (15) to be minimized and a dynamic model (14) of the motor vehicle (1). The dynamic model (14) includes a loss model (27) of the motor vehicle (1). The loss model (27) describes an overall loss of the motor vehicle (1). The cost function (15) includes a first term, which represents the overall loss of the motor vehicle (1). 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 (3) is also configured for determining, by executing the MPC algorithm (13) as a function of the loss model (14), that combination of operating values, by which the first term of the cost function (15) is minimized.

Abstract: A processor unit (3) is configured for executing an MPC algorithm (13) for model predictive control of a motor vehicle (1). The MPC algorithm (13) includes a longitudinal dynamic model (14) of the motor vehicle (1) and a cost function (15) to be minimized. The cost function (15) includes multiple terms, a first term of which represents an output of the cooling pump (28). In addition, the processor unit (3) is configured for, by executing the MPC algorithm (13) as a function of the longitudinal dynamic model (14), ascertaining a speed trajectory of the motor vehicle (1) 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 (15) is minimized.

Abstract: A method for model-based predictive control (MPC) of a motor vehicle includes executing an MPC algorithm having a high level solver module, a longitudinal dynamics model, and a cost function dedicated to the high level solver module. By executing the high level solver module for an upcoming route segment while taking the longitudinal dynamics model into account, a speed trajectory is calculated that minimizes the cost function, according to which the motor vehicle is to travel within a prediction horizon. The method includes sending the speed trajectory to a human-machine interface as an input value, processing the speed trajectory to obtain a control signal in the human-machine interface, and outputting the control signal to a driver of the motor vehicle with the human-machine interface, such that the driver can control the motor vehicle in accordance with the control signal.

Abstract: A device (16) for determining a discrete representation (30) of a road section ahead of a vehicle (12) includes an input interface (22) for receiving sensor data (20) of a sensor (14) with information about the road section ahead of the vehicle, a setting unit (24) 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 (n2) of support points based on a predefined first number (n1) of support points.

Abstract: A device for model predictive control of a vehicle component includes a first input receiving sensor data, a second input receiving data regarding a topology in a vehicle's environment, a control unit for executing a predictive algorithm for generating a control value for the component, an output for outputting the control value, wherein the predictive algorithm comprises a vehicle model including a battery model, wherein the sensor and topology data are processed in the predictive algorithm, the predictive algorithm also including an optimization function including energy consumption predicted by the battery model, travel time predicted by the vehicle model, information regarding a charging station along a route of the vehicle predicted by the vehicle model in a prediction horizon, and information regarding the predicted charging state of the battery at the charging station, and the predictive algorithm generates the control value through minimization in the optimization function.

Abstract: A processor unit (3) is configured for accessing speed data of a second vehicle (18), the speed data generated by a sensor of a first vehicle (1). The processor unit is also configured for creating a driving behavior profile of the second vehicle (18) based on the speed data and making a prediction about the future driving behavior of the second vehicle (18) based on the driving behavior profile of the second vehicle (18). Moreover, the processor unit is configured for determining a trajectory for the first vehicle (1) 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 (18) is taken into account in the determination of the trajectory.

Abstract: A device for model predictive control of a vehicle component includes a first input receiving sensor data, a second input receiving data regarding a topology in a vehicle's environment, a control unit for executing a predictive algorithm for generating a control value for the component, an output for outputting the control value, wherein the predictive algorithm comprises a vehicle model including a battery model, wherein the sensor and topology data are processed in the predictive algorithm, the predictive algorithm also including an optimization function including energy consumption predicted by the battery model, travel time predicted by the vehicle model, information regarding a charging station along a route of the vehicle predicted by the vehicle model in a prediction horizon, and information regarding the predicted charging state of the battery at the charging station, and the predictive algorithm generates the control value through minimization in the optimization function.

Abstract: A processor unit (3) is configured for executing an MPC algorithm (13) for model predictive control of a motor vehicle (1). The MPC algorithm (13) includes a longitudinal dynamic model (14) of the motor vehicle (1) and a cost function (15) to be minimized. The cost function (15) includes multiple terms, a first term of which represents an output of the cooling pump (28). In addition, the processor unit (3) is configured for, by executing the MPC algorithm (13) as a function of the longitudinal dynamic model (14), ascertaining a speed trajectory of the motor vehicle (1) 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 (15) is minimized.

Abstract: Determination of a trajectory for a first vehicle (1) by model predictive control (MPC) is provided. Trajectory information about a second vehicle (18) traveling in the area ahead of the first vehicle (1) is utilized. In particular, discretization points (P1, P2, P3) and arrival times of the vehicles (1, 18) at the discretization points (P1, P2, P3) are utilized to generate constraints for the model predictive control of the first vehicle (1).

Abstract: A processor unit (3) is configured for accessing speed data of a second vehicle (18), the speed data generated by a sensor of a first vehicle (1). The processor unit is also configured for creating a driving behavior profile of the second vehicle (18) based on the speed data and making a prediction about the future driving behavior of the second vehicle (18) based on the driving behavior profile of the second vehicle (18). Moreover, the processor unit is configured for determining a trajectory for the first vehicle (1) 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 (18) is taken into account in the determination of the trajectory.

Abstract: A device (16) for determining a discrete representation (30) of a road section ahead of a vehicle (12) includes an input interface (22) for receiving sensor data (20) of a sensor (14) with information about the road section ahead of the vehicle, a setting unit (24) 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 (n2) of support points based on a predefined first number (n1) of support points.

Abstract: A processor unit (3) is configured for executing an MPC algorithm (13) for model predictive control of a first component (18) of a motor vehicle (1) and of a second component (19) of the motor vehicle (1). The MPC algorithm (13) includes a cost function (15) to be minimized and a dynamic model (14) of the motor vehicle (1). The dynamic model (14) includes a loss model (27) of the motor vehicle (1). The loss model (27) describes an overall loss of the motor vehicle (1). The cost function (15) includes a first term, which represents the overall loss of the motor vehicle (1). 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 (3) is also configured for determining, by executing the MPC algorithm (13) as a function of the loss model (14), that combination of operating values, by which the first term of the cost function (15) is minimized.

Abstract: A processor unit (3) is configured for executing an MPC algorithm (13) for model predictive control of a prime mover (8) and of at least one vehicle component influencing energy efficiency of a motor vehicle. The MPC algorithm (13) includes a longitudinal dynamic model (14) of the drive train (7) and of the vehicle component influencing the energy efficiency of the motor vehicle (1) as well as a cost function (15) to be minimized. The cost function (15) includes at least one first term. The processor unit (3) is configured for determining a particular input variable for the prime mover (8) and for the at least one vehicle component influencing the energy efficiency of the motor vehicle (1) by executing the MPC algorithm (13) as a function of a particular term such that the cost function (15) is minimized.