Model-Based Predictive Control of a Vehicle Taking into Account a Time of Arrival Factor

Abstract

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.

Claims

1-15: (canceled)

16. 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) to be minimized.

17. The processor unit (3) of claim 16, wherein the at least one arrival time factor comprises required break periods of a driver of the vehicle (1).

18. The processor unit (3) of claim 16, 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.

19. The processor unit (3) of claim 16, 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).

20. The processor unit (3) of claim 16, wherein the at least one arrival time factor comprises an availability of charging stations (22) for the vehicle (1) on the entire route (20).

21. The processor unit (3) of claim 16, 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).

22. The processor unit (3) of claim 16, 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).

23. The processor unit (3) of claim 16, 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).

24. The processor unit (3) of claim 16, wherein the at least one arrival time factor comprises an availability of parking spaces (23) at rest areas (24).

25. The processor unit (3) of claim 16, 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.

26. The processor unit (3) of claim 16, wherein the cost function (15) includes: 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 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 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).

27. 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 26, 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.

28. A vehicle (3), comprising: the electric machine (8); the battery (9); and the driver assistance system (16) of claim 27.

29. 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) to be minimized.

30. A computer program product (11) for model-based predictive control of a vehicle (1) taking into account an arrival time factor, 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) to be minimized.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Exemplary embodiments of the invention are explained in greater detail in the following with reference to the diagrammatic drawing, wherein identical or similar elements are labeled with the same reference character, wherein

[0048] FIG. 1 shows a schematic view of a vehicle including a drive train, which includes an electric machine and a battery, and

[0049] FIG. 2 shows a top-down view of a road on which the vehicle according to FIG. 1 autonomously travels in order to reach a destination point from a starting point.

DETAILED DESCRIPTION

[0050] 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.

[0051] FIG. 1 shows a schematic view of a vehicle in the form of a motor vehicle 1, for example, a passenger car or a truck. The motor vehicle 1 includes a system 2 for the model predictive control of an electric machine 8 of a drive train 7 of the motor vehicle 1 with regard to an arrival time factor. In the exemplary embodiment shown, 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 first motor vehicle 1. The motor vehicle 1 also includes a drive train 7, which includes, for example, an electric machine 8, which is operable as a motor and as a generator, a battery 9, and a transmission 10. The electric machine 8, in the motor mode, drives wheels of the motor vehicle 1 via the transmission 10, which has, for example, a constant ratio. The battery 9 provides the electrical energy necessary therefor. The battery 9 is chargeable by the electric machine 8 when the electric machine 8 is operated in the generator mode (recuperation). Optionally, the battery 9 is also chargeable at an external charging station if the motor vehicle 1 is a plug-in hybrid vehicle. Likewise, the drive train of the motor vehicle 1 optionally includes an internal combustion engine 21, which, alternatively or in addition to the electric machine 8, drives the motor vehicle 1. The internal combustion engine 21 is also configurable for driving the electric machine 8 to charge the battery 9.

[0052] A computer program product 11 is storable on the memory unit 4. The computer program product 11 is 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, it instructs the processor unit 3 to perform the functions described in conjunction with the drawing and/or to carry out method steps.

[0053] The computer program product 11 includes a model or model-based predictive control (MPC) algorithm 13. The MPC algorithm 13 includes a longitudinal dynamic model 14 of the drive train 7 of the motor vehicle 1 and a cost function 15 to be minimized. The processor unit 3 executes the MPC algorithm 13 and determines an optimized trajectory for the motor vehicle 1. A behavior of the motor vehicle 1 is predicted based on the longitudinal dynamic model 14, wherein the cost function 15 is minimized. For example, a rotational speed matched to the optimized trajectory and an optimal torque of the electric machine 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 ascertains an input variable for the electric machine 8, enabling the optimal rotational speed and the optimal torque to set in. The processor unit 3 controls the electric machine 8 based on the ascertained input variable. In addition, this can also be carried out by a driver assistance system 16, however.

[0054] The detection unit 6 measures current state variables of the motor vehicle 1, records appropriate data, and supplies these to the MPC algorithm 13. In this way, route data from an electronic map is updated, in particular cyclically, for a prediction horizon (for example, 500 m) ahead of the motor vehicle 1. The route data includes, for example, uphill grade information, curve information, and information about speed limits. Moreover, a curve curvature is 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 is carried out by the detection unit 6, in particular via a GNSS signal generated by a GNSS sensor 12 for the precise localization on the electronic map. The processor unit 3 accesses this information, for example, via the communication interface 5.

[0055] The vehicle 1 in the example shown in FIG. 2 is located in a parking space 17, which is adjacent to a road 18, which leads to a depot 19. The course of the road 18 is represented interrupted due to its length. A driver of the vehicle 1 specifies to the driver assistance system 16 that he/she wants to travel, for example, from the parking space 17 (start) to the depot 19 (destination). The driver assistance system 16 provides an autonomous driving function for the vehicle 1, enabling the vehicle 1 to travel autonomously from the parking space 17 to the depot 19.

[0056] For this purpose, the processor unit 3 or the driver assistance system 16 initially generates the entire route 20 from the parking space 17 to the depot 19 and assign a speed of the vehicle 1 to discrete waypoints of this route. In this way, the trajectory for the vehicle 1 is calculated. For this purpose, the processor unit 3 or the driver assistance system 16 utilizes the detection unit 6 and a planning module, which is implemented, for example, as software. The planning module has multiple levels, for example, a navigation level on a larger scale (for example, across multiple km; the trajectory for the vehicle 1 from start 17 to destination 19 being selectable within the navigation level on the larger scale), and a navigation level on a smaller scale (for example, for across the upcoming 50 m to 100 m, depending on the speed of the vehicle 1; the course and speed in the close vicinity of the vehicle 1 being selectable in the navigation level on the smaller scale in order to determine how the vehicle 1 is to move in traffic).

[0057] Arrival time factors, which influence an arrival time of the vehicle 1 at the specified destination 19, are taken into account in the determination or calculation of the trajectory for the vehicle 1. In particular, break periods of the driver (for example, a private individual, a truck driver, or a bus driver) of the vehicle 1 are taken into account. In addition, a period of time for loading the vehicle 1, in particular when it is a truck, and times for refueling and/or charging the battery 9 of the vehicle 1 are taken into account. Moreover, information regarding the availability of a charging station 22 for the battery 9 of the vehicle 1, and/or regarding available parking spaces 23 at rest areas 24 for trucks are taken into account. In addition, information regarding a traffic volume, traffic jams, as well as weather conditions on the route 20 are taken into account. The processor unit 3 of the vehicle 1 also communicates with a processor unit 25 of the depot 19 (for example, via a Car2I communication) in order to reserve a point in time for a loading and/or unloading, which are/is matched to the calculated trajectory for the vehicle 1. As a result, a reduced waiting time is enabled and the time is utilized instead for more energy-efficient travel.

[0058] If the vehicle 1 starts autonomously in order to reach the depot 19 via the road 18 starting from the parking space 17, the processor unit 3 executes the MPC algorithm 13 and optimizes a particular current section of the trajectory for the vehicle 1 for a sliding (i.e., shifting spatially or on the path) prediction horizon such that the cost function is minimized. For example, the processor unit 3 forms the above-described planning module (“top level” planning module), which plans the entire route 20 and trajectory for the vehicle 1 with regard to the aforementioned arrival time factors. This planning module then transmits sections or portions of the vehicle trajectory for the entire route to the MPC algorithm 13, by which an optimal trajectory of the vehicle 1 within the prediction horizon is ascertained. The processor unit 3 also optionally takes into account an arrival time at the specified destination 19 predefined by the driver of the vehicle 1 or a range specified by the driver of the vehicle 1 as a constraint in the optimization of the trajectory for the vehicle 1.

[0059] An exemplary longitudinal dynamic model 14 of the motor vehicle 1 is expressed mathematically as follows:

[00001]$\frac{\mathrm{dv}\left(t\right)}{\mathrm{dt}}=\left(F_{\mathrm{trac}}\left(t\right)-F_r\left(\alpha \left(t\right)\right)-F_{gr}\left(\alpha \left(t\right)\right)-F_d\left(v\left(t\right)\right)\right)/m_{\mathrm{eq}}$

Wherein:

[0060] v is the speed of the motor vehicle; [0061] F.sub.trac is the tractive force that is exerted by the prime mover or the brakes upon the wheels of the motor vehicle; [0062] F.sub.r is the rolling resistance, which is an effect of the deformation of the tires during rolling and depends on the load of the wheels (on the normal force between the wheel and the road) and, thus, on the inclination angle of the road; [0063] F.sub.gr is the gradient resistance, which describes the longitudinal component of gravity, which acts upon the motor vehicle during operation uphill or downhill, depending on the gradient of the roadway; [0064] F.sub.d is the drag force of the motor vehicle; and [0065] m.sub.eq is the equivalent mass of the motor vehicle; the equivalent mass includes, in particular, the inertia of the turned parts of the drive train, which are subjected to the acceleration of the motor vehicle (prime mover, transmission input shafts, wheels).

[0066] By converting time dependence into distance dependence

[00002]$\frac{d}{\mathrm{ds}}=\frac{d}{\mathrm{dt}}*\frac{\mathrm{dt}}{\mathrm{ds}}=\frac{d}{\mathrm{dt}}*\frac{1}{v}$

and coordinate transformation in order to eliminate the quadratic speed term in the aerodynamic drag with e.sub.kin=½*m.sub.eq*v(t).sup.2, the result is:

[00003]$\frac{{\mathrm{de}}_{\mathrm{kin}}}{\mathrm{ds}}=F_{\mathrm{trac}}\left(s\right)-F_r\left(\alpha \left(s\right)\right)-F_{gr}\left(\alpha \left(s\right)\right)-F_d\left(e_{\mathrm{kin}}\left(s\right)\right).$

[0067] In order to ensure that the problem is quickly and easily solvable by the MPC algorithm 13, the dynamic equation of the longitudinal dynamic model 14 is linearized, in that the speed is expressed, via coordinate transformation, by kinetic energy de.sub.kin. As a result, the quadratic term for calculating the aerodynamic drag F.sub.d is replaced by a linear term and, simultaneously, the longitudinal dynamic model 14 of the motor vehicle 1 is no longer described as a function of time, as usual, but rather as a function of distance. This fits well with the optimization problem, since the predictive information of the electrical horizon is based on distance.

[0068] In addition to the kinetic energy, there are two further state variables, which, within the scope of a simple optimization problem, must also be described in a linear and distance-dependent manner. On the one hand, the electrical energy consumption of the drive train 7 is usually described in the form of a characteristic map as a function of torque and prime mover speed. In the exemplary embodiment shown, the motor vehicle 1 has a fixed ratio between the electric machine 8 and the road on which the motor vehicle 1 moves. As a result, the rotational speed of the electric machine 8 is directly converted into a speed of the motor vehicle 1 or even into a kinetic energy of the motor vehicle 1. In addition, the electrical power of the electric machine 8 is converted into energy consumption per meter via division by the appropriate speed. In order to be able to utilize a corresponding characteristic map of the electric machine 8 for the optimization, it is linearly approximated: Energy.sub.perMeter≥α.sub.i*e.sub.kin b.sub.i*F.sub.trac for all i.

[0069] The cost function 15 to be minimized is expressed mathematically as follows:

[00004]$\min \left(-w_{\mathrm{Bat}}.Math.E_{\mathrm{Bat}}\left(s_E\right)+w_{\mathrm{Time}}.Math.T\left(s_E\right)+w_{\mathrm{Tem}}.Math.\underset{s=1}{\overset{s_E-1}{.Math.}}{\left(\frac{F_A\left(s\right)-F_A\left(s-1\right)}{\Delta s}\right)}^2+w_{\mathrm{TemStart}}.Math.{\left(F_A\left(s_1\right)-F_A\left(s_0\right)\right)}^2+\underset{s=1}{\overset{s_E-1}{.Math.}}{w}_{\mathrm{Slack}}.Math.{\mathrm{Var}}_{\mathrm{slack}}\right)$ [0070] Wherein: [0071] w.sub.Bat is the weighting factor for the energy consumption of the battery; [0072] E.sub.Bat is the energy consumption of the battery; [0073] S is the distance; [0074] S.sub.E-1 is the distance one time step before the end of the prediction horizon; [0075] F.sub.A is the drive force that is provided by the electric machine, transmitted by a transmission at a constant ratio, and applied at a wheel of the motor vehicle; [0076] W.sub.Tem is the weighting factor for torque gradients; [0077] W.sub.TemStart is the weighting factor for torque surges; [0078] T is the time that the vehicle needs in order to cover the entire distance predicted within the prediction horizon; [0079] W.sub.Time is the weighting factor for the time T; [0080] S.sub.E is the distance to the end of the horizon; [0081] w.sub.Slack is the weighting factor for the slack variable; and [0082] Var.sub.Slack is the slack variable.

[0083] 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 is solved well and quickly.

[0084] The cost function 15 includes, as a first term, an electrical energy E.sub.Bat weighted with a first weighting factor w.sub.Bat 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 electric machine 8.

[0085] The cost function 15 includes, as a second term, a driving time T weighted with a second weighting factor W.sub.Time 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 is not always evaluated as optimal and, thus, the problem that the resultant speed is always at the lower edge of the permitted speed no longer exists.

[0086] The energy consumption and the driving time are both evaluated and weighted at the end of the horizon. These terms are therefore active only for the last point of the horizon.

[0087] Excessively high torque gradients within the horizon are disadvantageous. Therefore, torque gradients are already penalized in the cost function 15, namely by the term

[00005]$w_{\mathrm{Tem}}.Math.{.Math.}_{s=1}^{s_E-1}{\left(\frac{F_A\left(s\right)-F_A\left(s-1\right)}{\Delta s}\right)}^2.$

The quadratic deviation of the drive force per meter is weighted with a weighting factor W.sub.Tem and minimized in the cost function. Alternatively to the drive force F.sub.A per meter, the torque M.sub.EM provided by the electric machine 8 is instead usable and weighted with the weighting factor W.sub.Tem, and so the alternative term

[00006]$w_{\mathrm{Tem}}.Math.{.Math.}_{s=1}^{s_E-1}{\left(\frac{M_{\mathrm{EM}}\left(s\right)-M_{\mathrm{EM}}\left(s-1\right)}{\Delta s}\right)}^2$

results. Due to the constant ratio of the transmission 10, the drive force and the torque are directly proportional to one another.

[0088] In order to ensure comfortable driving, one further term is introduced in the cost function 15 for penalizing torque surges, namely W.sub.TemStart.Math.(F.sub.A(s.sub.1)−F.sub.A(s.sub.0).sup.2. In alternative to the drive force F.sub.A, the torque M.sub.EM provided by the electric machine 8 is instead usable, and so the alternative term W.sub.TemStart.Math.(M.sub.EM(s.sub.1)−M.sub.EM(s.sub.0)).sup.2 results. For the first point in the prediction horizon, the deviation from the most recently set torque is evaluated as negative and weighted with a weighting factor W.sub.TemStart in order to ensure that there is a seamless and smooth transition during the change-over between an old trajectory and a new trajectory.

[0089] Speed limits are hard limits for the optimization that are not permitted to be exceeded. A slight exceedance of the speed limits is always permissible in reality and tends to be the normal case primarily during transitions from one speed zone into a second zone. In dynamic surroundings, where speed limits shift from one computing cycle to the next computing cycle, it can happen, in the case of very hard limits, that a valid solution for a speed profile is no longer found. In order to increase the stability of the computational algorithm, a soft constraint is introduced into the cost function 15. A slack variable Var.sub.Slack weighted with a weighting factor W.sub.Slack becomes active in a predefined narrow range before the hard speed limit is reached. Solutions that are situated very close to this speed limit are evaluated as poorer, i.e., solutions, the speed trajectory of which maintains a certain distance to the hard limit.

[0090] 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

[0091] 1 vehicle [0092] 2 system [0093] 3 processor unit [0094] 4 memory unit [0095] 5 communication interface [0096] 6 detection unit [0097] 7 drive train [0098] 8 electric machine [0099] 9 battery [0100] 10 transmission [0101] 11 computer program product [0102] 12 GNSS sensor [0103] 13 MPC algorithm [0104] 14 longitudinal dynamic model [0105] 15 cost function [0106] 16 driver assistance system [0107] 17 parking space [0108] 18 road [0109] 19 depot [0110] 20 entire route [0111] 21 internal combustion engine [0112] 22 charging station for the battery of the vehicle [0113] 23 truck parking space [0114] 24 rest area [0115] 25 processor unit of the depot