WO2023030647A1

WO2023030647A1 - Model-based predictive control of a motor vehicle

Info

Publication number: WO2023030647A1
Application number: PCT/EP2021/074384
Authority: WO
Inventors: Andreas Wendzel; Timon Busse; Valerie Engel; Lorenz Fischer; Matthias Zink; Timo Wehlen; Apurva Patel; Vasilis LEFKOPOLOUS; Joachim FERREAU
Original assignee: Zf Friedrichshafen Ag
Priority date: 2021-09-03
Filing date: 2021-09-03
Publication date: 2023-03-09

Abstract

The invention relates to the model-based predictive control of a motor vehicle. Here, an MPC algorithm (13) is executed, which comprises a high-level solver module (13.1), wherein a high-level longitudinal trajectory (31) is calculated by implementing the high-level solver module (13.1) for a section of road ahead, according to which high-level longitudinal trajectory the motor vehicle is intended to move along within a route-based high-level prediction horizon which is subdivided into route-based discretization points (38). The high-level longitudinal trajectory (31) calculated by the high-level solver module (13.1) is passed on to a tracker solver module (13.2) of the MPC algorithm (13) as an input value. The tracker solver module (13.2) is implemented, so that a tracker longitudinal trajectory (32) is calculated based on the high-level longitudinal trajectory (31) calculated by the high-level solver module (13.1), according to which tracker longitudinal trajectory the motor vehicle is intended to move along within a time-based tracker prediction horizon, wherein the tracker prediction horizon is defined to be shorter than the high-level prediction horizon, so that the tracker prediction horizon covers only a portion of the high-level prediction horizon. Here, a stop marker (39; 41) is set for at least one route-based discretization point (38.1; 40) at which the motor vehicle is intended to stand or is intended to be brought to a stop.

Description

Model-based predictive control of a motor vehicle

The invention relates to the model-based predictive control of a motor vehicle. In this context, a method for model-based predictive control of a motor vehicle is claimed in particular.

Today's intelligent cruise controls (so-called "Predictive Green ACCs") in motor vehicles can take the route topology into account in particular, but map the driving strategy and thus the longitudinal control based on rules. The rule-based implementation usually leads to suboptimal solutions in terms of energy consumption, comfort and travel time. With the increasing complexity of the drive system, such a set of rules also becomes complicated and requires a high application effort.

Optimal operation of a vehicle (e.g. in relation to the performance goals of energy consumption, comfort, driving time) is only possible with good knowledge of the route to be driven.

Furthermore, model-based predictive controls for optimized control of a longitudinal dynamics of the motor vehicle are known. In such methods, the “stationary vehicle” operating state and the transitions to and from a standstill can cause implementation problems. In particular, the "stationary vehicle" state cannot be mapped within the calculation of a high-level solver module of the model-based predictive control, since an infinite number of times would have to be assigned to a waypoint and no planning, e.g time t = infinity would lie. This could be circumvented by introducing a minimum speed. This would achieve a continuous course of the time information and waypoints after a standstill point could be planned. However, this minimum speed means that even standstills have to be considered as ferry operation and the controllers would try to regulate the standstill in a stationary manner. This is particularly difficult to do when disturbances occur (slope, wind), positional inaccuracies (torque accuracy, coefficients of friction) and measurement inaccuracies (position data, speeds at a virtual standstill, time delay in the signals mentioned) and is fundamentally not sensible in terms of energy. One object of the present invention can be seen as providing a regulation of a motor vehicle which takes into account the problems described above. The object is solved by the subject matter of the independent patent claims. Advantageous embodiments are the subject matter of the dependent claims, the following description and the figures.

The present invention uses what is known as the “Model Predictive Control (MPC)” approach. In such a model-based predictive control, three process steps in particular are used. In a first step, a virtual travel horizon (prediction horizon) is developed from available map data and sensor information. The prediction horizon is used by a trajectory planner and trajectory controller as a solution space for generating a longitudinal trajectory of the motor vehicle, e.g. a speed or torque trajectory. In a second step, an iterative online generation and control of a longitudinal trajectory takes place by optimizing the trajectory with regard to the existing performance goals according to the M PC approach. In a third step, the calculated trajectory is converted, in particular automatically, by its arbitration in the motor vehicle. The present invention includes, in particular, a modification of the second step of this process, so that specific computing time requirements in particular can be met for an application suitable for series production. For this purpose, the present invention provides an architecture that enables both the function of the second process step and series-relevant computing times, with a case-specific, advantageous parameterization of this architecture being proposed for implementation in an electric vehicle.

In particular, the present invention provides a functional architecture that includes two sequentially working model-based predictive controllers (=MPC solver). The two MPC solvers can be referred to as "high level solvers" (HLS) and trackers. The high-level solver module takes over the long-term planning of the optimal longitudinal trajectory and uses the MPC approach for this. The long-term rough planning of the trajectory is path-based. This allows in particular a correct Correct, optimal handling of non-dynamic horizon objects (slopes, speed limits and other traffic signs such as "Stop" or "Give way" signs, curves). In principle, dynamic horizon objects can also be taken into account, eg traffic lights. Due to the long computing times, however, this is only done to a large extent. The trajectory adapted to the dynamic objects must therefore be overwritten by a system that calculates faster. Accordingly, the high-level solver module does not pass on any direct trajectory requests to the vehicle. Instead, the desired trajectory is further processed in the fast-calculating tracker.

A method for model-based predictive control of a motor vehicle is proposed. The motor vehicle is, in particular, an electric motor vehicle which is driven by at least one electric machine without an internal combustion engine being provided for support. According to the method, an MPC algorithm is executed, which includes a high-level solver module, a high-level longitudinal trajectory being calculated for a route section ahead by executing the high-level solver module, according to which the motor vehicle is to move within a path-based high-level prediction horizon, divided into path-based discretization points. The high-level solver module solves a non-linear problem in particular and works with continuous substitute variables for discrete states (e.g. gears). This approach limits the solution space less than when considering discrete states. This results in advantages, in particular with regard to the optimum result.

The high level longitudinal trajectory calculated by the high level solver module is passed to a tracker solver module of the MPC algorithm as an input value, the tracker solver module being executed in such a way that a tracker longitudinal trajectory is calculated based on the high level longitudinal trajectory calculated by the high level solver module , according to which the motor vehicle is to move within a time-based tracker prediction horizon. The tracker solver module is characterized by a fast calculation time and a robust behavior. If current solutions of the high level solver module are not available, the tracker solver module still provide moments based on the last solutions of the high level solver module.

The tracker prediction horizon is set to be shorter than the high level prediction horizon, so the tracker prediction horizon only covers part of the high level prediction horizon. For example, the length of the high level prediction horizon can be set to 500 meters, with the tracker prediction horizon set to a length of 3 seconds. This combination of values has proven to be particularly advantageous in terms of good results and computing performance.

Since the high-level solver module carries out long-term rough planning, it can be expected that this process will require considerable computing effort and thus computing time, regardless of the hardware. Accordingly, it is advantageous that dynamic horizon objects (vehicles driving ahead/cutting in/cutting out or other road users) are processed with a higher calculation frequency in the tracker solver module. In order to make this possible, the Tracker solver module calculates with a significantly shorter (time-based) look-ahead than the high level solver module. A length of 500m for the high-level solver module is particularly advantageous in this context. Using the first solver module, for example, the speed trajectory of the vehicle can still be optimized online for the 500 m ahead. This is computationally intensive. On the other hand, it is advantageous to use the tracker solver module to calculate the corresponding trajectory for the relatively short look-ahead over time, in the present case for 3 seconds. This enables particularly short reaction times. Also, unlike the High Level solver module, the Tracker solver module is time-based. This ensures basic compatibility with arbitration systems.

The present invention provides a method by means of which, in addition to real ferry operations, the following standstill cases in particular are also covered: stopping in front of a red traffic light,

Stopping in front of a stationary object (e.g. vehicle driving ahead) and stopping at defined waypoints (e.g. intersections). For this purpose, according to the method according to the invention, a standstill marker is set for at least one path-based discretization point at which the motor vehicle is to stand or is to be brought to a standstill. In particular, a standstill marker is set for each path-based discretization point at which the motor vehicle is to stand still or is to be brought to a standstill.

The standstill marker can be set in preprocessing, e.g. to mark stopping positions based on map information (stops). In this sense it is provided according to one embodiment that the at least one standstill marker is set by means of a pre-processing unit before the high-level solver module is executed.

The standstill marking can still be done from dynamic considerations in the MPC solver, e.g. if it is determined that passing a green traffic light is not possible. In this sense, one embodiment provides that the at least one standstill marker is set by means of the high-level solver module while the high-level solver module is being executed. This occurs in particular when information about traffic lights or dynamic objects in the external environment of the motor vehicle is transferred to the high-level solver module, after which it is necessary or safer to stop the motor vehicle.

When changing from the distance range to the time range, the standstill markings can be transferred. In this sense, according to one embodiment, it is provided that the at least one standstill marker is transferred from the high-level solver module, which works path-based, to the tracker solver module, which works in the time domain.

In each calculation stage (preprocessing, high level solver module, tracker solver module) further flags or standstill markings can be added. In particular, for at least one time-based discretization point at which the motor vehicle should stand or be brought to a standstill is to be set using the Tracker solver module while the Tracker solver module is running.

At the output of the tracker solver module, there is a vector with standstill requirements, in particular above the tracker prediction horizon. In this sense, in one embodiment, based on the at least one set standstill marker, a vector is generated by means of the tracker solver module, which contains at least one standstill requirement for the tracker prediction horizon, the at least one standstill requirement being assigned to the at least one standstill marker.

The vector information can be used in a target generator module (interpolation within the optimized trajectory) to detect a standstill at the current position. In this sense, according to a further embodiment, the vector is transferred to a target generator module, by means of which setpoint values of the tracker longitudinal trajectory are interpolated, with a standstill at a current position being recognized by means of the target generator module based on the at least one standstill request of the vector. In particular, the target generator module converts the desired moments and desired forces worked out by the tracker solver module into arbitrable values. This is achieved in particular by interpolation.

The following formulation has proven to be particularly effective as a condition for the Target Generator Module to recognize a standstill request:

IF an actual speed is less than a threshold (e.g. 1 km/h), AND IF a standstill marker is set in a future time range, AND

IF a distance (determined in particular by integrating the planned speed over time) to the first point with a standstill marking is less than or equal to a threshold (e.g. 5 m),

THEN recognize a stall request. In this sense, a further embodiment is characterized in that a standstill at the current position is detected by means of the target generator module based on the at least one standstill request of the vector if

- It is detected that a speed of the motor vehicle is below a defined threshold value, and

- the at least one standstill marker or at least one of the standstill markers is set in a future time period, and

- a distance to the discretization point for which the standstill flag is set is less than a distance threshold.

The stop request can be processed as follows: The interpolation of the setpoint values from the calculated trajectories is terminated. Instead, the aggregates required for standstill (e.g. service brake) are set to a setpoint with a transition function (e.g. a ramp-shaped function). Any manipulated variables from the interpolation that may still be present can be masked out with a continuous transition (e.g. a ramp-shaped transition function). In this sense, according to a further embodiment, the interpolation between setpoint values of the tracker longitudinal trajectory is stopped by means of the target generator module if a standstill at the current position has been detected by means of the target generator module. Instead, at least one unit, by means of which the motor vehicle can be brought to a standstill, is set to a desired value by means of a transition function, which leads to the motor vehicle being stopped. If the standstill request is no longer present, you can suddenly switch to the interpolation of the MPC solution. The bumpless transition results from the fact that the MPC solution is initialized with measured variables.

Different actions can be carried out by adding a remaining downtime to the downtime information. For example, a relatively brief stop (eg, in the range of seconds) can take place via a service brake, and a relatively long standstill (eg, in the range of minutes or longer) via a parking brake of the motor vehicle. Furthermore, aggregates can be switched off depending on the downtime if these aggregates are switched off during the downtime. stands are not needed, which saves energy. Aggregates can also be switched on again (e.g. when a gear is engaged) depending on the duration of the standstill. In this sense, according to a further embodiment, it is provided that the standstill marker is supplemented with information about a standstill period for which the motor vehicle is to remain at a standstill, with at least one unit of the motor vehicle being controlled as a function of the standstill period.

In particular, the MPC algorithm includes a longitudinal dynamics model and a high-level cost function that is assigned to the high-level solver module, with the high-level longitudinal trajectory being calculated taking into account the longitudinal dynamics model and minimizing the high-level cost function. The high-level cost function can in particular have the following terms to be minimized, namely a first term for an energy consumption of the motor vehicle, a second term for a period of time that the motor vehicle requires to drive through the high-level prediction horizon, and a third term for a frequency , with which a brake system of the motor vehicle is actuated within the high-level prediction horizon.

In particular, the output signals of the high-level solver module describe a planned trajectory through the planned values for the vehicle speed, an SOC, and the mechanical braking force at each of the discretization points. In this sense, in one embodiment, the high-level longitudinal trajectory includes a speed trajectory according to which the motor vehicle is to move within the high-level prediction horizon.

Furthermore, the high-level longitudinal trajectory can alternatively or additionally include a state of charge trajectory that includes a course of a state of charge of a battery, which serves as an energy store for an electric machine of the motor vehicle, wherein the motor vehicle can be driven by the electric machine. The state of charge (in English: State of Charge or abbreviated SoC) is in particular the current energy content of the electric battery in relation to its maximum energy content. In addition, the high-level longitudinal trajectory can alternatively or additionally include a braking force trajectory for a brake system of the motor vehicle, according to which the brake system should provide braking forces (less than or equal to zero) within the high-level prediction horizon. Instead of the braking force mentioned above, a corresponding braking torque can also be provided by the longitudinal trajectory. The torque trajectory relates to torques on at least one wheel of the motor vehicle and includes, in particular, negative torques that are provided by the brake system of the motor vehicle.

A tracker cost function can be assigned to the tracker solver module, and the tracker longitudinal trajectory can be calculated taking into account the longitudinal dynamics model and minimizing the tracker cost function. The output of the tracker solver module can directly influence the driving comfort by minimizing the tracker-specific cost function. The tracker cost function can in particular have the following terms to be minimized, namely a first term for a deviation from the speed trajectory of the high level longitudinal trajectory, a second term for a deviation from the state of charge trajectory of the high level longitudinal trajectory and a third term for a frequency with which the braking system is actuated during the high-level prediction horizon.

In a further embodiment, the path-based high-level prediction horizon is subdivided into 100 path-based discretization points. This is done in particular by means of what is known as a constraint handler, which can be part of the preprocessing unit.

In particular, the tracker works with a time-based discretization of 0.1 seconds. In particular, the roughly planned, distance-based speed trajectory of the high-level solver module is converted into a time-based, detailed planned trajectory of target torques of the electric drive and target braking forces of the mechanical brake, taking into account the vehicle losses. In this sense, according to a further embodiment, it is provided that the time-based tracker prediction horizon is divided into time-based discretization points, each of which is 0.1 seconds apart. The tracker longitudinal trajectory includes a target torque trajectory according to which the electric machine is intended to provide a specific torque for driving the motor vehicle at each of the time-based discretization points. The tracker longitudinal trajectory also includes a target braking force trajectory, according to which the braking system is intended to provide a specific braking force for braking the motor vehicle at each of the time-based discretization points. The target torque trajectory and the target braking force trajectory of the tracker longitudinal trajectory are calculated based on the speed trajectory of the high-level longitudinal trajectory and taking into account the loss model of the motor vehicle.

In particular, the target generator module converts the desired moments and desired forces worked out by the tracker solver module into arbitrable values. These can be implemented in particular by a longitudinal controller on a brake control unit of the motor vehicle. In this sense, a further embodiment is advantageously characterized in that the target torque trajectory and the target braking force trajectory of the tracker longitudinal trajectory are converted into arbitrable values of the control signal by means of the target generator module, the control signal being converted by a longitudinal controller of a control unit of the brake system.

Exemplary embodiments of the invention are explained in more detail below with reference to the schematic drawing, with the same or similar elements being provided with the same reference symbols. Here shows

1 shows a schematic representation of a motor vehicle whose drive train comprises an internal combustion engine, an electric machine and a brake system,

Fig. 2 Details of an exemplary drive train for the motor vehicle according to Fig.

1 , 3 shows an exemplary embodiment of a method according to the invention for model-based predictive control of the motor vehicle according to FIG. 1 ,

4 two different prediction horizons for the method according to claim 4 and

5 shows an exemplary vector with standstill requests.

1 shows a motor vehicle 1, for example a passenger car. Motor vehicle 1 includes a system 2 for model-based predictive control of motor vehicle 1 . In the exemplary embodiment shown, the system 2 comprises a processor unit 3, a memory unit 4, a communication interface 5 and a detection unit 6, in particular for detecting status data relating to the motor vehicle 1.

Motor vehicle 1 also includes a drive train 7, which can include, for example, an electric machine 8 that can be operated as a motor and as a generator, a battery 9, a transmission 10, and a brake system 19 with a control unit 35 and a linear controller 36. The electric machine 8 can drive wheels of the motor vehicle 1 via the transmission 10 in motor operation. The battery 9 can provide the electrical energy required for this, in particular via power electronics 18. Conversely, the battery 9 can be charged by the electrical machine 8 via the power electronics 18 when the electrical machine 8 is operated in generator mode (recuperation). The battery 9 can optionally also be charged at an external charging station.

2 also shows that the drive train 7 can be a hybrid drive train, which can also have an internal combustion engine 17 purely as an option. In the exemplary parallel P2 architecture of hybrid drive train 7 shown in FIG. 2 , internal combustion engine 17 can drive motor vehicle 1 in addition to electric machine 8 when a clutch K0 arranged between internal combustion engine 17 and electric machine 8 is closed. The internal combustion engine 17 can optionally also be the electric machine 8 drive to charge the battery 9. In the exemplary embodiment shown, the electric machine 8 can (with the clutch KO engaged, supported by the internal combustion engine 17) drive two front wheels 22 and 23 of the motor vehicle 1 with a positive drive torque via the transmission 10 and via a front differential gear 21, which wheels are attached to a front axle 25 are. A first rear wheel 26 and a second rear wheel 28 on a rear axle 29 of the motor vehicle 1 are not driven in the exemplary embodiment shown (rear-wheel drive and all-wheel drive are, however, alternatively also possible). The front wheels 22, 23 and the rear wheels 26, 28 can be braked by the brake system 19 of the drive train 7, for which purpose the brake system 19 can provide a negative braking torque.

A computer program product 11 can be stored on the memory unit 4 . The computer program product 11 can be executed on the processor unit 3 , for which purpose the processor unit 3 and the memory unit 4 are connected to one another by means of the communication interface 5 . If the computer program product 11 is executed on the processor unit 3, it directs the processor unit 3 to fulfill the functions described in connection with the drawing or to carry out method steps.

The computer program product 11 contains an MPC algorithm 13, which includes or contains a high-level solver module 13.1. The MPC algorithm 13 also contains a longitudinal dynamics model 14 of the motor vehicle 1 . The high level solver module 13.1 can access the longitudinal dynamics model 14. Furthermore, the MPC algorithm 13 contains a high-level cost function 15.1 to be minimized, which is assigned to the high-level solver module 13.1. The task of the high-level solver module 13.1 is to propose an optimized guidance for the motor vehicle 1, for example with regard to speed limits and stopping points as well as traffic lights and gradients. The high-level cost function 15.1 can have the following terms to be minimized, for example, namely a first term for an energy consumption of the motor vehicle 1, a second term for a period of time that the motor vehicle 1 requires to reach a high-level prediction horizon 24 to drive through, as well as a third term for a frequency with which the brake system 19 of the motor vehicle 1 should be actuated during the high-level prediction horizon 24 .

The longitudinal dynamics model 14 includes a loss model 27 of the motor vehicle 1. The loss model 27 describes the operating behavior of efficiency-relevant components in terms of their efficiency or in terms of their loss, e.g. the electric machine 8, if necessary the optional internal combustion engine 17 and the brake system 19. This results in a Total Loss of Motor Vehicle 1 . The processor unit 3 executes the MPC algorithm 13 and predicts a behavior of the motor vehicle 1 for a sliding, path-based high-level prediction horizon 24 (Fig. 4) with a length of 500 m. The path-based high-level prediction horizon 24 is shown in the exemplary embodiment in path-based discretization points 38, e.g. divided into 100 path-based discretization points 38. This is done by means of a preprocessing unit 37. A first standstill marking is made for at least one path-based first discretization point 38.1, at which the motor vehicle 1 is to stand or is to be brought to a standstill 39 set. In particular, this takes place before the high level solver module 13.1 is executed. The first standstill marker 39 can thus be set in the pre-processing, e.g. to mark a position for stopping based on map information (stops).

The prediction is based on the longitudinal dynamics model 14. The processor unit 3 calculates an optimized high-level longitudinal trajectory 31 by executing the high-level solver module 13.1, according to which the motor vehicle 1 is to move within the high-level prediction horizon 24. The optimized high-level longitudinal trajectory 31 is calculated for a route section ahead, taking into account the longitudinal dynamics model 14, with the high-level cost function 15.1 being minimized. The high-level solver module 13.1 takes over the long-term rough planning of the longitudinal trajectory 31 and uses the MPC approach for this. The long-term rough planning of the high level longitudinal trajectory 31 is path-based. In particular, this allows correct, optimal handling of non-dyna Mix horizon objects (slopes, speed limits and other traffic signs such as "Stop" or "Give Way" signs, bends in curves, traffic lights).

In the exemplary embodiment shown, the high-level longitudinal trajectory 31 includes a speed trajectory 31.1, according to which the motor vehicle 1 is to move within the high-level prediction horizon 24. In this case, optimized speed values are assigned in particular to waypoints which the motor vehicle 1 is to travel. Furthermore, the high-level longitudinal trajectory 31 includes a state of charge trajectory 31.2, which describes an optimal course of a state of charge (SoC) of the battery 9. Furthermore, the high-level longitudinal trajectory 31 for the brake system 19 includes a brake force trajectory 31.3, which assigns a brake force to each of the discretization points, which the brake system 19 provides for braking the motor vehicle 1.

In addition to the high-level solver module 13.1, the M PC algorithm 13 includes a tracker solver module 13.2 with a tracker cost function 15.2 assigned to it. The tracker solver module 13.2 can access the longitudinal dynamics model 14. The high-level longitudinal trajectory 31 calculated by the high-level solver module 13.1 is also transferred to the tracker solver module 13.2 as an input value, e.g. by means of the processor unit 3 and the communication interface 5. The task of the tracker solver module 13.2 is to provide optimized guidance for the motor vehicle 1, in particular with regard to distances to be maintained and collision avoidance.

The processor unit 3 executes the tracker solver module 13.2. The tracker solver module 13.2 contains instructions or program code, which instructs the processor unit 3 to calculate a tracker longitudinal trajectory 32 for a future time segment based on the high level longitudinal trajectory 31 calculated by the high level solver module 13.1, according to which the motor vehicle moves 1 is to move within a time-based tracker prediction horizon 30 with a time length of 3 seconds. The Tracker solver module works with a time-based discretization of 0.1 seconds. In particular, the roughly planned, path-based speed trajectory 31.1 of the high-level solver module, taking into account the vehicle losses, into a time-based, detailed planned tracker trajectory 32 with target torques 32.1 of the electric drive 7 and target braking forces of the mechanical brake 19.

A tracker cost function 15.2 is assigned to the tracker solver module 13.2, the tracker longitudinal trajectory 32 being calculated taking into account the longitudinal dynamics model 14 and minimizing the tracker cost function 15.2. In the exemplary embodiment shown, the tracker cost function 15.2 has the following terms to be minimized, namely a first term for a deviation from the speed trajectory 31.1 of the high level longitudinal trajectory 31, a second term for a deviation from the state of charge trajectory 31.2 of the high level longitudinal trajectory 31 and a third term for a frequency with which brake system 19 is actuated during high-level prediction horizon 24 .

The tracker prediction horizon 30 is chosen to be significantly shorter than the high-level prediction horizon 24. The tracker prediction horizon 30 can thus cover those waypoints of the high-level speed trajectory 31.1 that the motor vehicle 1 is to travel over the next 3 seconds (with speeds according to the speed trajectory 31. 1 ). In this way, the tracker prediction horizon 30 only covers an initial section of the 500 meter long high-level prediction horizon 24, which is illustrated by way of example in FIG. In the exemplary embodiment shown, the tracker longitudinal trajectory 32 includes a braking force trajectory 32.1 for the brake system 19, according to which the brake system 19 is intended to provide braking forces within the tracker prediction horizon 30. Furthermore, the tracker longitudinal trajectory 32 for the electric machine 8 includes a torque trajectory 32.2, according to which the electric machine 8 should provide drive torques within the tracker prediction horizon 30. The target torque trajectory 32.2 and the target braking force trajectory 32.2 of the tracker longitudinal trajectory 32 are calculated based on the speed trajectory 31.1 of the high level longitudinal trajectory 31 and taking into account the loss model 27 of the motor vehicle 1. The detection unit 6 can measure current state variables of the motor vehicle 1, record corresponding data and feed them to the high-level solver module 13.1, the tracker solver module 13.2 and a target generator module 16 described further below. In the exemplary embodiment shown, the acquisition unit transfers the following information as an input value to the high-level solver module 13.1 for calculating the high-level longitudinal trajectory 31:

- a current speed of the electric machine 8,

- a current torque of the electric machine 8,

- a current speed of the motor vehicle 1,

- A current state of charge of the battery 9 of the motor vehicle 1,

- a current mass of the motor vehicle 1,

- a current temperature of the electrical machine 8,

- a current temperature of a cooling liquid which cools the electric machine 8,

- a maximum available torque of the electrical machine 8 over the high-level prediction horizon 31,

- a minimum available torque of the electrical machine 8 over the high-level prediction horizon 31,

- A current mechanical braking force provided by the braking system 19,

- a current distance to a vehicle in front that is driving in front of motor vehicle 1,

- a current speed of the vehicle ahead,

- A current status of a driving function of the motor vehicle 1 with regard to activation and deactivation and

- a current time.

Information about static objects and/or route data from an electronic map of a navigation system 20 of motor vehicle 1 for a preview horizon or prediction horizon (e.g. 500 m) in front of motor vehicle 1 can be updated in particular cyclically and transferred to modules 13.1, 13.2 and 16. The route data can, for example, incline information, curve information, and information about speed limits as well as traffic lights and include breakpoints. Furthermore, a curve curvature can be converted into a speed limit for the motor vehicle 1 via a maximum permissible lateral acceleration. In addition, the motor vehicle can be located by means of the detection unit 6, in particular via a signal generated by a GNSS sensor 12 for precise localization on the electronic map. Furthermore, the detection unit for detecting the external environment of motor vehicle 1 can include an environment sensor 33, for example a radar sensor, a camera system and/or a lidar sensor. In this way, in particular, dynamic objects in the area of the external surroundings of motor vehicle 1 can also be detected, for example moving objects such as other vehicles or pedestrians. In particular, the following additional information is transferred as an input value to the high-level solver module 13.1 for calculating the high-level longitudinal trajectory 31:

- a vector of discretization points,

- a vector of a slope angle at each of the discretization points,

- a vector of a maximum allowed velocity at each of the discretization points,

- a vector of a minimum velocity allowed by the MPC algorithm 13 at each of the discretization points,

- a vector of a maximum allowed arrival time at each of the discretization points, caused by green-red switching traffic lights,

- a vector of a minimum allowed arrival time at each of the discretization points, caused by red-green-switching traffic lights,

- a vector of a currently active traffic light phase at each of the discretization points,

- a binary vector of a stop request at each of the discretization points due to red lights, stationary objects or the like and

- a current time.

The processor unit 3 can access the above information from the elements mentioned, for example via the communication interface 5 . Based on this information, a further standstill marker 41 can be set from dynamic considerations in the MPC system 2, for example if it is determined that passing a green traffic light is not possible. For example, for at least a second path-based discretization point 40 within the high Level prediction horizon 24, a second standstill marker 41 can be set using the high-level solver module 13. 1, while the high-level solver module 13.

1 is running.

When changing from the distance range to the time range, the standstill markings 38, 39 are transmitted. In the exemplary embodiment shown, both the at least one first standstill marker 39 set by the preprocessing unit 37 and the at least one second standstill marker 41 set by the high-level solver module 13.1 (both standstill markers 39, 41 are path-based) passed to the Tracker solver module 13.2 working in the time domain. In each calculation stage (preprocessing 37, high level solver module 13.1, tracker solver module 13.2) further flags or standstill markings can be added. In particular, a third standstill marker 43 can be set using the tracker solver module 13.2 for at least one time-based third discretization point 42 at which the motor vehicle 1 should stand or be brought to a standstill while the tracker solver module 13.2 is being executed. The standstill markings 39, 41, 43 can be included in the longitudinal model 14 of the motor vehicle 1, in particular as restrictions or secondary conditions in the calculation of the high-level longitudinal trajectory 31, in the calculation of the tracker longitudinal trajectory 32 and in the calculations that the target Generator module 16 performs.

At the output of the tracker solver module 13.2 there is a vector 44 with standstill requirements above the prediction horizon. This vector 44 can be generated based on the standstill markings 39, 41, 43 described above using the tracker solver module 13.2. In the exemplary embodiment shown, the vector 44 can contain three standstill requests 45, 46, 47. A first standstill request 45 is assigned therein to the first standstill marker 39 set by the preprocessing unit 37 for the first discretization point 38.1. Furthermore, a second standstill request 46 is assigned to the second standstill marker 41 set by the high-level solver module 13.1 for the second discretization point 40. Furthermore, a third standstill request 47 is the third standstill marker 43 set by the tracker solver module 13.2 assigned for the third discretization point 42 . Vector 44 is illustrated by way of example in FIG.

Optimum speeds 31.1 of motor vehicle 1 and torques in particular result as the output of the optimization by MPC algorithm 13

32.1 of the electric machine 8 and braking forces 32.1 of the brake system 19. Furthermore, optimized states of charge 31.2 of the battery 9 are calculated for calculated time/way points within the high-level prediction horizon 24. One task of the target generator module 16 is, in particular, to calculate drive and braking forces as well as recuperation limits. Furthermore, a request for stopping the motor vehicle 1 is to be calculated and a so-called exit flag check is to be carried out. By means of the target generator module 16, in particular the moment trajectory proposed by the tracker solver module 13.2

32.2 and braking force trajectory 31.1 are processed to form a control signal 34, which includes arbitrable values by means of which motor vehicle 1 can be controlled, e.g. by controlling actuators of motor vehicle 1. Control signal 34 is calculated for a future time horizon, which is 3 seconds long, for example . The control signal 34 can contain, for example, drive forces (greater than or equal to zero), braking forces (less than or equal to zero), limit values for recuperation torques or a stop signal (“vehicle stop mark”) for the motor vehicle 1 . In the exemplary embodiment shown, the values that can be arbitrated are implemented by the longitudinal controller 36 on the control device 35 of the brake system 19 of the vehicle 1 . The data from the detection unit 6 and/or the high-level longitudinal trajectory 31 can also be used to calculate the control signal 34 . Furthermore, the tracker solver module 13.2 and the target generator module 16 can be called up at the same time, so that both modules 13.2, 16 start their above-described calculations at the same time.

The information of the vector 44 is used in the target generator module 16 (interpolation within the optimized trajectory) to detect a standstill at a current position. For this purpose, the vector 44 is transferred to the target generator module 16, which interpolates between target values of the tracker longitudinal trajectory 32. The target generator module 16 can be based on the three Standstill requests 45 to 46 of the vector 44 recognize a standstill at a current position. In detail, the target generator module 16 can recognize a standstill request if the following three conditions are cumulatively met, namely that

1 . an actual speed of motor vehicle 1 is less than a defined speed threshold value (e.g. 1 km/h) AND

2. at least one of the three standstill markers 45 to 47 is set in a future time range, AND

3. a distance (determined in particular by integrating the planned speed over time) to the nearest discretization point with standstill marking (e.g. to the first discretization point 38.1 with the first standstill marking 39) is less than or equal to a distance threshold value (e.g. 5 m).

If the target generator module 16 has recognized a standstill request, the interpolation of the target values from the calculated trajectories 31 and/or 32 by means of the target generator module 16 is terminated. Instead, the units required for the standstill (e.g. the brake system 19) are set to a setpoint using a transition function (e.g. a ramp-shaped function), which leads to the motor vehicle 1 coming to a standstill. Any manipulated variables from the interpolation that may still be present can be masked out with a continuous transition (e.g. a ramp-shaped transition function). If the standstill request is no longer present, you can suddenly switch to the interpolation of the MPC solution. The bumpless transition results from the fact that the MPC solution is initialized with measured variables.

The standstill markers 39, 41, 43 and/or the standstill requests 45 to 47 of the vector 44 can be linked to a remaining standstill duration 48 to 50, which is shown by FIG. In the exemplary embodiment shown, the first standstill marker 39 and the first standstill request 45 are linked to a first standstill period 48, the second standstill marker 41 and the second standstill request 46 to a second standstill period 49 and the third standstill marker 43 and the third standstill request 47 with a third standstill duration 50.

This allows different actions to be carried out. The duration of the standstill indicates how long the motor vehicle is to remain stationary, with at least one assembly of the motor vehicle 1 being controlled as a function of the duration of the standstill. For example, a relatively short stop (e.g. in the range of seconds) can take place via the brake system 19 (service brake), while a relatively long standstill (e.g. in the range of minutes or longer) can take place via a parking brake of the motor vehicle 1. Furthermore, units such as the internal combustion engine 17 can be switched off , depending on the duration of the downtime, if these aggregates are not used during the downtime, which can save energy. Aggregates can also be switched on again (e.g. when a gear is engaged) depending on the duration of the standstill.

reference sign

KO clutch

1 vehicle

2 systems

3 processor unit

4 storage unit

5 communication interface

6 registration unit

7 powertrain

8 electric machine

9 battery

10 gears

11 Computer program product

12 GNSS sensor

13 MPC algorithm

13.1 High Level Solver Module

13.2 Tracker solver module

14 longitudinal dynamics model

15.1 High Level Cost Function

15.2 Tracker Cost Function

16 target generator module

17 internal combustion engine

18 power electronics

19 brake system

20 navigation system

21 front differential gear

22 front wheel

23 front wheel

24 path based high level prediction horizon

25 front axle

26 rear wheel

27 loss model Rear wheel Rear axle Time-based tracker Prediction horizon High level Longitudinal trajectory Velocity trajectory (high level) State of charge trajectory (high level) Braking force trajectory (high level) Tracker Longitudinal trajectory Braking force trajectory (tracker) Moment trajectory (tracker) Surroundings sensor Control signal Brake system control unit Longitudinal controller Brake system control unit Preprocessing unit Path-based discretization point First path-based discretization point First standstill -Marking of the preprocessing unit second path-based discretization point second standstill mark of the high-level solver module time-based third discretization point third standstill mark of the tracker solver module vector first standstill request second standstill request third standstill request first standstill period second standstill period third standstill period

Claims

patent claims

1. A method for model-based predictive control of a motor vehicle (1), the method comprising the steps

- Execution of an MPC algorithm (13), which includes a high-level solver module (13.1), wherein a high-level longitudinal trajectory (31) is calculated for a route section ahead by executing the high-level solver module (13.1), according to which the Motor vehicle (1) is intended to move within a path-based high-level prediction horizon (24), which is divided into path-based discretization points (38),

- Transferring the high level longitudinal trajectory (31) calculated by the high level solver module (13.1) to a tracker solver module (13.2) of the MPC algorithm (13) as an input value,

- Running the tracker solver module (13.2), so that based on the high level solver module (13.1) calculated high level longitudinal trajectory (31) a tracker longitudinal trajectory (32) is calculated, according to which the motor vehicle (1) within a time-based tracker prediction horizon (30) is to move forward, with the tracker prediction horizon (30) being set shorter than the high level prediction horizon (24), so that the tracker prediction horizon (30) only covers part of the high level prediction horizon (24), where

- A standstill marker (39; 41; 43) is set for at least one path-based discretization point (38.1; 40) at which the motor vehicle (1) should stand or be brought to a standstill.

2. The method according to claim 1, wherein the at least one standstill marker (39) is set by means of a pre-processing unit (37) before the high-level solver module (13.1) is executed.

3. The method according to claim 1 or 2, wherein the at least one standstill marker (41) by means of the high level solver module (13.1) is set while the high level solver module (13.1) is running.

24

4. The method according to any one of the preceding claims, wherein the at least one standstill marker (39; 41) from the high level solver module (13.1), which works path-based, is transferred to the tracker solver module (13.2), which works in the time domain.

5. The method according to any one of claims 1 to 3, wherein a standstill marker (43 ) is set while the Tracker solver module (13.2) is running.

6. The method according to any one of the preceding claims, wherein

- Based on the at least one set standstill marker (39, 41, 43), a vector (44) is generated by means of the tracker solver module (13.2), which contains at least one standstill requirement (45; 46; 47) for the tracker prediction horizon (30) contains, and

- The at least one standstill request (45; 46; 47) is assigned to the at least one standstill marker (38.1; 40; 42).

7. The method of claim 6, wherein

- the vector (44) is transferred to a target generator module (16),

- the target generator module (16) is used to interpolate between target values of the tracker longitudinal trajectory (30), and

- A standstill at a current position is detected by means of the target generator module (16) based on the at least one standstill request (45; 46; 47) of the vector (44).

8. The method according to claim 7, wherein a standstill at the current position is detected by means of the target generator module (16) based on the at least one standstill request (45; 46; 47) of the vector (44) if

- It is detected that a speed of the motor vehicle (1) is below a defined threshold value, and - the at least one standstill marker (39; 41; 43) is set in a future time period, and

- a distance to the discretization point (38.1; 40; 42) for which the standstill marker is set is less than a distance threshold value.

9. The method of claim 8, wherein

- the interpolation between target values of the tracker longitudinal trajectory (30) is stopped by means of the target generator module (16) if a standstill at the current position has been detected by means of the target generator module (16), and

- At least one unit (19), by means of which the motor vehicle (1) can be brought to a standstill, is set to a desired value by means of a transition function, which leads to the standstill of the motor vehicle.

10. The method according to any one of the preceding claims, wherein

- information about a standstill period (48; 49; 50) is added to the standstill marker (39; 41; 43) for which the motor vehicle (1) should remain at a standstill, and

- At least one unit (17, 19) of the motor vehicle (1) depending on the standstill period (48; 49; 50) is controlled.