Model-based predictive control of a vehicle taking into account a time of arrival factor
A processor unit ( 3 ) for model-based predictive control of a vehicle ( 1 ) taking into account an arrival time factor is configured to calculate a trajectory for the vehicle ( 1 ) based at least in part on at least one arrival time factor, with the trajectory including an entire route ( 20 ) to a specified destination ( 19 ) at which the vehicle ( 1 ) is to arrive, and with the at least one arrival time factor influencing an arrival time of the vehicle ( 1 ) at the specified destination ( 19 ). Additionally, the processor unit ( 3 ) is configured to optimize a section of the trajectory for the vehicle ( 1 ) for a sliding prediction horizon by executing a model-based predictive control (MPC) algorithm ( 13 ), where the MPC algorithm ( 13 ) includes a longitudinal dynamic model ( 14 ) of a drive train ( 7 ) of the vehicle ( 1 ) and a cost function ( 15 ) to be minimized.
1. A processor unit ( 3 ) for model-based predictive control of a vehicle ( 1 ) taking into account an arrival time factor, wherein the processor unit ( 3 ) is configured to:
calculate a trajectory for the vehicle ( 1 ) based at least in part on at least one arrival time factor, the trajectory including an entire route ( 20 ) to a specified destination ( 19 ) at which the vehicle ( 1 ) is to arrive, the at least one arrival time factor influencing an arrival time of the vehicle ( 1 ) at the specified destination ( 19 ); and
optimize a section of the trajectory for the vehicle ( 1 ) for a sliding prediction horizon by executing a model-based predictive control (MPC) algorithm ( 13 ), the MPC algorithm ( 13 ) includes a longitudinal dynamic model ( 14 ) of a drive train ( 7 ) of the vehicle ( 1 ) and a cost function ( 15 ), the cost function ( 15 ) including:
a first term, the first term being an electrical energy predicted according to the longitudinal dynamic model ( 14 ) and weighted with a first weighting factor, wherein the electrical energy is provided within the sliding prediction horizon by a battery ( 9 ) for driving an electric machine ( 8 ) of the drive train ( 7 ); and
a second term, the second term being a driving time predicted according to the longitudinal dynamic model ( 14 ) and weighted with a second weighting factor, the driving time being required by vehicle ( 1 ) to cover an entire distance predicted within the sliding prediction horizon,
wherein the processor unit ( 3 ) is configured to execute the MPC algorithm ( 13 ) as a function of the first term and as a function of the second term to minimize the cost function and determine an input variable for the electric machine ( 8 ).
2. The processor unit ( 3 ) of claim 1 , wherein the at least one arrival time factor comprises required break periods of a driver of the vehicle ( 1 ).
3. The processor unit ( 3 ) of claim 1 , wherein the at least one arrival time factor comprises one or both of a period for loading and a period for unloading the vehicle ( 1 ), with the vehicle ( 1 ) being a truck.
4. The processor unit ( 3 ) of claim 1 , wherein the at least one arrival time factor comprises one or both of a period of time for refueling the vehicle ( 1 ) and a period of time for charging a battery ( 9 ) of the vehicle ( 1 ).
5. The processor unit ( 3 ) of claim 1 , wherein the at least one arrival time factor comprises an availability of charging stations ( 22 ) for the vehicle ( 1 ) on the entire route ( 20 ).
6. The processor unit ( 3 ) of claim 1 , wherein the at least one arrival time factor comprises one or more of a traffic volume, traffic jam situations, and weather conditions on the entire route ( 20 ) to the specified destination ( 19 ).
7. The processor unit ( 3 ) of claim 1 , wherein the processor unit ( 3 ) is configured to optimize the section of the trajectory based at least in part on an arrival time at the specified destination ( 19 ) predefined by a driver of the vehicle ( 1 ).
8. The processor unit ( 3 ) of claim 1 , wherein the processor unit ( 3 ) is configured to optimize the section of the trajectory based at least in part on a range of the vehicle ( 1 ) specified by a driver of the vehicle ( 1 ).
9. The processor unit ( 3 ) of claim 1 , wherein the at least one arrival time factor comprises an availability of parking spaces ( 23 ) at rest areas ( 24 ).
10. The processor unit ( 3 ) of claim 1 , wherein the processor unit ( 3 ) is further configured for communicating with a processor unit ( 25 ) of a depot ( 19 ) to reserve one or both of a time for loading the vehicle and a time for unloading the vehicle based at least in part on the trajectory.
11. A driver assistance system ( 16 ) for a vehicle ( 1 ), the vehicle ( 1 ) being driven by an electric machine ( 8 ), the driver assistance system ( 16 ) being in communication with the processor unit ( 3 ) of claim 1 , the driver assistance system ( 16 ) being configured to:
access the input variable for the electric machine ( 8 ) by a communication interface, wherein the input variable has been determined by the processor unit ( 3 ); and
control, by way of an open-loop system, the electric machine ( 8 ) based on the input variable.
12. A vehicle ( 3 ), comprising:
the electric machine ( 8 );
the battery ( 9 ); and
the driver assistance system ( 16 ) of claim 11 .
13. A method for model-based predictive control of a vehicle ( 1 ) taking into account an arrival time factor, the method comprising:
calculating a trajectory for the vehicle ( 1 ) based at least in part on at least one arrival time factor, the trajectory including an entire route ( 20 ) to a specified destination ( 19 ) at which the vehicle ( 1 ) is to arrive, the at least one arrival time factor influencing an arrival time of the vehicle ( 1 ) at the specified destination ( 19 ); and
optimizing a section of the trajectory for the vehicle ( 1 ) for a sliding prediction horizon by executing a MPC algorithm ( 13 ), the MPC algorithm ( 13 ) includes a longitudinal dynamic model ( 14 ) of a drive train ( 7 ) of the vehicle ( 1 ) and a cost function ( 15 ), the cost function ( 15 ) including:
a first term, the first term being an electrical energy predicted according to the longitudinal dynamic model ( 14 ) and weighted with a first weighting factor wherein the electrical energy is provided within the sliding prediction horizon by a battery ( 9 ) for driving an electric machine ( 8 ) of the drive train ( 7 ); and
a second term, the second term being a driving time predicted according to the longitudinal dynamic model ( 14 ) and weighted with a second weighting factor, the driving time being required by vehicle ( 1 ) to cover an entire distance predicted within the sliding prediction horizon,
wherein executing the MPC algorithm ( 13 ) comprises executing the MPC algorithm ( 13 ) as a function of the first term and as a function of the second term to minimize the cost function and determine an input variable for the electric machine ( 8 ).
14. A computer program product ( 11 ) for model-based predictive control of a vehicle ( 1 ) taking into account an arrival time factor, the computer program product ( 11 ) comprising instructions stored on a non-transitory memory, wherein the computer program product ( 11 ), when run on a processor unit ( 3 ), instructs the processor unit ( 3 ) to:
calculate a trajectory for the vehicle ( 1 ) based at least in part on at least one arrival time factor, the trajectory including an entire route ( 20 ) to a specified destination ( 19 ) at which the vehicle ( 1 ) is to arrive, the at least one arrival time factor influencing an arrival time of the vehicle ( 1 ) at the specified destination ( 19 ); and
optimize a section of the trajectory for the vehicle ( 1 ) for a sliding prediction horizon by executing a MPC algorithm ( 13 ), the MPC algorithm ( 13 ) includes a longitudinal dynamic model ( 14 ) of a drive train ( 7 ) of the vehicle ( 1 ) and a cost function ( 15 ), the cost function ( 15 ) including:
a first term, the first term being an electrical energy predicted according to the longitudinal dynamic model ( 14 ) and weighted with a first weighting factor, wherein the electrical energy is provided within the sliding prediction horizon by a battery ( 9 ) for driving an electric machine ( 8 ) of the drive train ( 7 ); and
a second term, the second term being a driving time predicted according to the longitudinal dynamic model ( 14 ) and weighted with a second weighting factor, the driving time being required by vehicle ( 1 ) to cover an entire distance predicted within the sliding prediction horizon,
wherein the processor unit ( 3 ) is instructed to execute the MPC algorithm ( 13 ) as a function of the first term and as a function of the second term to minimize the cost function and determine an input variable for the electric machine ( 8 ).