Model-Based Predictive Control of a Drive Machine of the Powertrain of a Motor Vehicle and at Least One Vehicle Component Which Influences the Energy Efficiency of the Motor Vehicle

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.

Claims

1-11. (canceled)

12. A system for model predictive control of a prime mover (8) of a drive train (7) of a motor vehicle (1) and of at least one vehicle component influencing energy efficiency of the motor vehicle (1), comprising: a processor unit (3) 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 (1), the MPC algorithm (13) comprising a longitudinal dynamic model (14) of the drive train (7) and of the at least one vehicle component influencing the energy efficiency of the motor vehicle (1), the MPC algorithm (13) comprising a cost function (15) to be minimized, the cost function (15) comprising at least one first term that comprises a power loss weighted with a particular weighting factor and predicted according to the longitudinal dynamic model (14), which the motor vehicle (1) undergoes while covering a distance predicted within a prediction horizon, the at least one first term comprising an aerodynamic drag weighted with a first weighting factor and predicted according to the longitudinal dynamic model (14), to which the motor vehicle (1) is subjected while covering the distance predicted within the prediction horizon, wherein 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 by executing the MPC algorithm (13) as a function of the particular term such that the cost function (15) is minimized, and wherein the at least one vehicle component influencing the energy efficiency of the motor vehicle (1) comprises a height-adjustable chassis (18) of the motor vehicle (1), and the processor unit (3) is configured for adjusting a vehicle level.

13. The processor unit (3) of claim 12, wherein the height-adjustable chassis (18) comprises a plurality of actuators (19) for stepless adjustment of the vehicle level.

14. The processor unit (3) of claim 12, wherein: the cost function (15) comprises, as a second term, a residual friction torque weighted with a second weighting factor and predicted according to the longitudinal dynamic model (14), which results in losses at the at least one vehicle component influencing the energy efficiency of the motor vehicle (1) while covering the distance predicted within the prediction horizon; and the at least one vehicle component influencing the energy efficiency of the motor vehicle (1) comprises at least one disk brake (17) with a brake disk (20) and a brake shoe (21).

15. The processor unit (3) of claim 14, wherein the processor unit (3) is configured for determining the particular input variable for the prime mover (8) and for the at least one disk brake (17) by executing the MPC algorithm (13) as a function of the first term and as a function of the second term such that the cost function (15) is minimized.

16. The processor unit (3) of claim 15, wherein the processor unit (3) is configured for adjusting a gap between the brake disk (20) and the brake shoe (21) of the at least one disk brake (17).

17. The processor unit (3) of claim 14, wherein: the cost function (15) comprises, as a third term, an electrical energy weighted with a third weighting factor and predicted according to the longitudinal dynamic model (14), which is provided within a prediction horizon by a battery (9) of the drive train (7) to drive the prime mover (8); the cost function (15) comprises an energy consumption final value weighted with the third weighting factor, which the predicted electrical energy assumes at an end of the prediction horizon; the cost function (15) comprises, as a fourth term, a driving time weighted with a fourth weighting factor and predicted according to the longitudinal dynamic model (14), which the motor vehicle (1) requires to cover the entire distance predicted within the prediction horizon; the cost function (15) comprises a driving time end value weighted with the fourth weighting factor, which the predicted driving time assumes at the end of the prediction horizon; and the processor unit (3) is configured for determining the 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 the first term, as a function of the second term, as a function of the third term, and as a function of the fourth term such that the cost function (15) is minimized.

18. A motor vehicle (1), comprising: a driver assistance system (16); a drive train (7) with a prime mover (8); and at least one vehicle component influencing an energy efficiency of the motor vehicle (1), wherein the driver assistance system (16) is configured for accessing, via a communication interface, 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), wherein the particular input variable has been determined by the processor unit (3) of claim 12, and controlling, by way of an open-loop system, one or more of the prime mover (8) and the at least one vehicle component influencing the energy efficiency of the motor vehicle (1) based on the input variable.

19. A method for model predictive control of a prime mover (8) of a drive train (7) of a motor vehicle (1) and of at least one vehicle component influencing energy efficiency of the motor vehicle (1), the method comprising: executing, by a processor unit (3), an MPC algorithm (13) for model predictive control of a prime mover (8) of a drive train (7) and of at least one vehicle component of a motor vehicle (1) influencing energy efficiency of the motor vehicle (1), the MPC algorithm (13) comprising a longitudinal dynamic model (14) of the drive train (7) and of the at least one 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) comprising at least one first term that comprises a power loss weighted with a particular weighting factor and predicted according to the longitudinal dynamic model (14), which the motor vehicle (1) undergoes while covering a distance predicted within a prediction horizon, the at least one first term comprising an aerodynamic drag weighted with a first weighting factor and predicted according to the longitudinal dynamic model (14), to which the motor vehicle (1) is subjected while covering the distance predicted within the prediction horizon; and 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) as a function of the particular term by executing the MPC algorithm (13) by the processor unit (3) such that the cost function (15) is minimized, wherein the at least one vehicle component influencing the energy efficiency of the motor vehicle (1) comprises a height-adjustable chassis (18) of the motor vehicle (1), and the processor unit (3) is configured for adjusting a vehicle level.

20. A computer program product (11) for model predictive control of a prime mover (8) of a drive train (7) of a motor vehicle (1) and of at least one vehicle component influencing energy efficiency of the motor vehicle (1), wherein the computer program product (11), when run on a processor unit (3), instructs the processor unit (3) to execute an MPC algorithm (13) for model predictive control of a prime mover (8) of a drive train (7) and of at least one vehicle component of a motor vehicle (1) influencing energy efficiency of the motor vehicle (1), wherein the MPC algorithm (13) comprises 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) comprising at least one first term that comprises a power loss weighted with a particular weighting factor and predicted according to the longitudinal dynamic model (14), which the motor vehicle (1) undergoes while covering a distance predicted within a prediction horizon, the at least one first term comprising an aerodynamic drag weighted with a first weighting factor and predicted according to the longitudinal dynamic model (14), to which the motor vehicle (1) is subjected while covering the distance predicted within the prediction horizon; and determine 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 the particular term such that the cost function (15) is minimized, wherein the vehicle component influencing the energy efficiency of the motor vehicle (1) comprises a height-adjustable chassis (18) of the motor vehicle (1), and the processor unit (3) is configured for adjusting a vehicle level.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Exemplary embodiments of the invention are explained in greater detail in the following with reference to the sole diagrammatic drawing, wherein identical or similar elements are labeled with the same reference character, wherein

[0030] The FIGURE shows a highly simplified view of a vehicle including a drive train, which includes a prime mover and a battery, as well as a vehicle component influencing the energy efficiency of the motor vehicle according to a first example embodiment.

DETAILED DESCRIPTION

[0031] Reference will now be made to embodiments of the invention, one or more examples of which are shown in the drawings. Each embodiment is provided by way of explanation of the invention, and not as a limitation of the invention. For example, features illustrated or described as part of one embodiment can be combined with another embodiment to yield still another embodiment. It is intended that the present invention include these and other modifications and variations to the embodiments described herein.

[0032] The FIGURE shows a motor vehicle 1, for example, a passenger car. The motor vehicle 1 includes a system 2 for the model predictive control of a prime mover of a drive train of the motor vehicle 1 as well as multiple vehicle components influencing the energy efficiency of the motor vehicle 1. The first vehicle component influencing the energy efficiency of the motor vehicle 1 is a disk brake 17 represented by way of example, wherein the motor vehicle 1 can also include multiple disk brakes designed similarly thereto, for example, at each wheel of the motor vehicle 1. The disk brake 17 includes a brake disk 20 and a brake shoe 21, wherein, due to a frictional connection of the brake disk 20 with the brake shoe 21, a braking effect or a negative acceleration of the motor vehicle 1 is achievable. The second vehicle component influencing the energy efficiency of the motor vehicle 1 is a chassis 18, wherein the chassis 18 includes multiple actuators 19 in the present case, which are operatively connected to shock-absorbing struts (not shown here) in the area of the wheels at the present motor vehicle 1. A height adjustment of the vehicle level can be implemented by actuating one or all actuator(s) 19.

[0033] The system 2 includes a processor unit 3, a memory unit 4, a communication interface 5, and a detection unit 6 for detecting state data related to the motor vehicle 1. The motor vehicle 1 also includes a drive train 7, which can include, for example, a prime mover 8, which can be operated as a motor and as a generator, a battery 9, and a transmission 10. The prime mover 8, in the motor mode, can drive wheels of the motor vehicle 1 via the transmission 10, which can have, for example, a constant ratio. The electrical energy necessary therefor is provided by the battery 9 in this case. The battery 9 is chargeable by the prime mover 8 when the prime mover 8 is operated in the generator mode (recuperation). Optionally, the battery 9 can also be charged at an external charging station. Likewise the drive train 7 of the motor vehicle 1 can optionally include an internal combustion engine 12, which, alternatively or in addition to the prime mover 8, can drive the motor vehicle 1. The internal combustion engine 12 can also drive the prime mover 8 in order to charge the battery 9.

[0034] A computer program product 11 can be stored on the memory unit 4. The computer program product 11 can be run on the processor unit 3, for the purpose of which the processor unit 3 and the memory unit 4 are connected to each other by the communication interface 5. When the computer program product 11 is run on the processor unit 3, the computer program product 11 instructs the processor unit 3 to perform the functions described in the following and/or to carry out method steps.

[0035] The computer program product 11 includes an MPC algorithm 13. The MPC algorithm 13 includes a longitudinal dynamic model 14 of the drive train 7 of the motor vehicle 1 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 processor unit 3 executes the MPC algorithm 13 and thereby predicts a behavior of the motor vehicle 1 for an upcoming route section (for example, five kilometers (5 km)) based on the longitudinal dynamic model 14, wherein the cost function 15 is minimized. The optimization by the MPC algorithm 13 yields, as the output, an optimal gap between the brake disk 20 and the brake shoe 21 of the disk brake 17 and/or an optimal vehicle level for calculated points in the prediction horizon. For this purpose, the processor unit 3 can determine an input variable for the disk brake 17, and so, on the one hand, a gap between the brake disk 20 and the brake shoe 21 is adjusted. Depending on the route section, a gap can essentially switch between a first state of actuation, in which the brake disk 20 and the brake shoe 21 are in (sliding) contact, which negatively affects power losses, and a second state of actuation, in which the brake disk 20 and the brake shoe 21 are spaced apart from one another in order to temporarily avoid a residual friction torque. On the other hand, the processor unit 3 can also determine an input variable for the chassis 18 such that a vehicle level of the motor vehicle 1 is adjusted. The vehicle level can be adapted by the actuators 19 such that, depending on the route section, a frontal area of the motor vehicle 1 is enlarged or reduced, which, the larger the frontal area is or becomes, negatively affects the aerodynamic drag and, thereby, similarly affects the energy efficiency of the motor vehicle 1.

[0036] In addition, an optimal rotational speed and an optimal torque of the prime mover 8 for calculated points in the prediction horizon result as the output of the optimization by the MPC algorithm 13. For this purpose, the processor unit 3 can determine an input variable for the prime mover 8, enabling the optimal rotational speed and the optimal torque to set in. The processor unit 3 can control, by way of an open-loop system, the prime mover 8 as well as the particular vehicle component influencing the energy efficiency of the motor vehicle 1 based on the determined input variable. In addition, this can also be carried out by a driver assistance system 16, however.

[0037] The detection unit 6 can measure current state variables of the motor vehicle 1, record appropriate data, and supply the current state variables and data to the MPC algorithm 13. In this way, route data from an electronic map can be updated, in particular cyclically, for a prediction horizon (for example, 5 km) ahead of the motor vehicle 1. The route data can include, for example, uphill grade information, curve information, information about speed limits or the traffic arising on the route section, as well as information about upcoming traffic lights or traffic light cycles. Moreover, a curve curvature can be converted, via a maximum permissible lateral acceleration, into a speed limit for the motor vehicle 1. In addition, a position finding of the motor vehicle can be carried out by the detection unit 6, in particular via a GPS signal generated by a GNSS sensor 12 for the precise localization on the electronic map. The processor unit 3 can access this information, for example, via the communication interface 5.

[0038] The cost function 15 has exclusively linear and quadratic terms. As a result, the overall problem has the form of a quadratic optimization with linear constraints and a convex problem results, which can be solved well and quickly.

[0039] The cost function 15 includes, as a first term, an aerodynamic drag weighted with a first weighting factor and predicted according to the longitudinal dynamic model 14, to which the motor vehicle 1 is subjected while covering a distance predicted within the prediction horizon. The cost function 15 includes, as a second term, a residual friction torque weighted with a second weighting factor and predicted according to the longitudinal dynamic model 14, which results in losses at the vehicle component influencing the energy efficiency of the motor vehicle while covering the distance predicted within the prediction horizon. As a result, an energy-optimal speed trajectory for the motor vehicle is selected for the upcoming route section.

[0040] The cost function 15 includes, as a third term, an electrical energy weighted with a third weighting factor and predicted according to the longitudinal dynamic model, which is provided within a prediction horizon by the battery 9 of the drive train 7 for driving the prime mover 8. The cost function 15 includes, as a fourth term, a driving time weighted with a fourth weighting factor and predicted according to the longitudinal dynamic model 14, which the motor vehicle 1 needs in order to cover the predicted distance. As a result, depending on the selection of the weighting factors, a low speed cannot always be evaluated as optimal and, thus, the problem no longer exists that the resultant speed is always at the lower edge of the permitted speed.

[0041] The processor unit 3 is configured for determining the particular input variable for the prime mover 8 and for the at least one vehicle component influencing the energy efficiency of the motor vehicle by executing the MPC algorithm 13 as a function of the first term, as a function of the second term, as a function of the third term, and as a function of the fourth term such that the cost function is minimized and, as a result, an energy-efficient operation of the motor vehicle 1 is implemented.

[0042] Modifications and variations can be made to the embodiments illustrated or described herein without departing from the scope and spirit of the invention as set forth in the appended claims. In the claims, reference characters corresponding to elements recited in the detailed description and the drawings may be recited. Such reference characters are enclosed within parentheses and are provided as an aid for reference to example embodiments described in the detailed description and the drawings. Such reference characters are provided for convenience only and have no effect on the scope of the claims. In particular, such reference characters are not intended to limit the claims to the particular example embodiments described in the detailed description and the drawings.

REFERENCE CHARACTERS

[0043] 1 vehicle [0044] 2 system [0045] 3 processor unit [0046] 4 memory unit [0047] 5 communication interface [0048] 6 detection unit [0049] 7 drive train [0050] 8 prime mover [0051] 9 battery [0052] 10 transmission [0053] 11 computer program product [0054] 12 internal combustion engine [0055] 13 MPC algorithm [0056] 14 longitudinal dynamic model [0057] 15 cost function [0058] 16 driver assistance system [0059] 17 disk brake [0060] 18 chassis [0061] 19 actuator [0062] 20 brake disk [0063] 21 brake shoe