DE102021209704A1 - Model-based predictive control of a motor vehicle - Google Patents

Abstract

The invention relates to a method for model-based predictive control of a motor vehicle. The method comprises the steps of running an MPC algorithm (13) that includes a high-level solver module (13.1), a longitudinal dynamics model (14) and a cost function (15) that is associated with the high-level solver module (13.1), where by executing the high-level solver module (13.1) for a route section ahead, taking into account the longitudinal dynamics model (14), a longitudinal trajectory (31, 32, 36) minimizing the cost function (15) is calculated, according to which the motor vehicle (1) is within a prediction horizon is to move, - passing the longitudinal trajectory (31, 32, 36) calculated by the high level solver module (13.1) to a cruise control (16) as an input value, - processing the longitudinal trajectory (31, 32) calculated by the high level solver module (13.1), 36) to a control signal (34) by means of the cruise control (16), and controlling the motor vehicle (1) based on the control signal (34).

Description

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. A driver of the motor vehicle must therefore drive with foresight, but has only limited insight into the further course of the route and no insight into the vehicle-specific driving losses.

One object of the present invention can be seen as providing a regulation for 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.

To solve the problem, a functional architecture is proposed according to the present invention, comprising a model-based predictive controller (=MPC solver) and a cruise control, in particular a sequentially working, rule-based ACC controller. The MPC solver is called "High Level Solver" (HLS). The cruise control can largely behave like current, state-of-the-art ACC systems. However, the cruise control of the present invention differs in that it also receives a suggestion for the longitudinal trajectory (in particular a speed suggestion) as input, namely the longitudinal trajectory calculated by the high-level solver module, in addition to, for example, conventional sensor data and data on the vehicle status. Based in particular on the longitudinal trajectory, the cruise control calculates the control signal, according to which the cruise control can, for example, control an actuator of the motor vehicle, e.g. an actuator for regulating the speed of the motor vehicle. In other words, in particular a speed trajectory is calculated by a method of model-based predictive control and transferred to the cruise control for processing and for controlling the motor vehicle. The MPC solver can be described as a "high level solver module" (HLS), which takes over the long-term rough planning of the longitudinal trajectory of the vehicle and uses the MPC approach for this.

In this sense, a method for model-based predictive control of a motor vehicle is provided. According to the method, an MPC algorithm is executed, which includes a high-level solver module, a longitudinal dynamics model and a cost function, which is assigned to the high-level solver module, with the cost function being calculated by executing the high-level solver module for an upcoming route section, taking into account the longitudinal dynamics model minimizing longitudinal trajectory is calculated, according to which the motor vehicle is to move within a prediction horizon. In a preferred exemplary embodiment, the length of the prediction horizon is 500 m, for example, but can be varied. 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 longitudinal trajectory calculated by the high-level solver module is transferred to a cruise control as an input value. The cruise control works rule-based, in particular sequentially. The cruise control can in particular be a sequentially working, rule-based ACC controller. ACC stands for the English technical term Adaptive Cruise Control. The longitudinal trajectory calculated by the high-level solver module is processed into a control signal by means of the cruise control and the motor vehicle is controlled based on the control signal, for example by means of the cruise control or by means of an actuator which can be controlled by the cruise control, for example.

The present invention uses the so-called “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 serves as a solution for a trajectory planner and controller space for generating a longitudinal trajectory of the motor vehicle, eg 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 MPC approach. In a third step, the calculated trajectory is converted, in particular automatically, by its arbitration in the motor vehicle. The present invention includes 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.

As output values, the high-level solver module can supply, in particular, desired profiles of the speed, a state of charge of a vehicle battery, drive forces, drive torques, braking forces or braking torques. In this sense, in one embodiment, the longitudinal trajectory includes a speed trajectory according to which the motor vehicle is to move within the prediction horizon. Alternatively or additionally, the longitudinal trajectory for a drive unit of the motor vehicle can include a drive force trajectory according to which the drive unit should provide drive forces (greater than or equal to zero) within the prediction horizon. Furthermore, the longitudinal trajectory can alternatively or additionally include a course of a state of charge of a battery, which is used as an energy store for an electric machine of the motor vehicle, it being possible for the motor vehicle to be driven by means of 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. Furthermore, the longitudinal trajectory can alternatively or additionally include a braking force trajectory for a braking system of the motor vehicle, according to which the braking system should provide braking forces (less than or equal to zero) within the prediction horizon. Instead of the drive forces and braking forces mentioned above, corresponding drive torques or braking torques 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 both positive and negative torques that are provided by the electric machine, the internal combustion engine and the brake system of the motor vehicle.

The proposal made available by the high-level solver module, in particular the speed proposal, can be implemented by the ACC controller as a default value. In this sense, in one embodiment, the control signal is represented by the longitudinal trajectory calculated by the high level solver module.

The long-term rough planning of the trajectory is carried out by the high-level solver module using the MPC approach. In particular, this allows correct, optimal handling of non-dynamic (i.e. static) objects that are within the prediction horizon. Examples of such static objects are inclines, speed limits, other traffic signs (e.g. "stop" or "yield" signs), bends in curves or traffic lights. In this sense, in one embodiment, information about static objects is transferred to the high-level solver module as secondary conditions, which the high-level solver module takes into account when calculating the longitudinal trajectory. The information about static objects can originate, for example, from map data from a navigation system or from sensors.

Dynamic horizon objects can also be taken into account when calculating the longitudinal trajectory. This is done in the high-level solver module, due to the long computing times, preferably only within a rough framework. In this sense, in one embodiment, information about dynamic objects is passed to the high-level solver module as secondary conditions, which the high-level solver module takes into account when calculating the longitudinal trajectory. The information about dynamic objects can come from sensors, for example.

The longitudinal trajectory adapted to the dynamic objects may have to be overwritten by a faster computing system. Accordingly, the high-level solver module does not forward any direct trajectory request to the motor vehicle. Instead, the desired trajectory is further processed in the fast-calculating ACC controller. In this sense, in one embodiment, the cruise control overwrites a longitudinal trajectory MPC value calculated by the high level solver module with an alternative cruise control value of the control signal if the cruise control value deviates from the MPC value.

Such an overwriting can be necessary in particular if the cruise control recognizes through sensor data that the speed trajectory is specified too offensively by the high-level solver module, so that a There is a risk of accident, for example with a dynamic object. In this case, the cruise control will replace the higher speed or acceleration calculated by the high level solver module with a lower speed or acceleration. As such, in one embodiment, the cruise control overrides the MPC value with the cruise control value when the cruise control value provides for a lower speed or acceleration for the motor vehicle than the MPC value. The cruise control can access the information about static and dynamic objects described above in connection with the high-level solver module as input data.

• 1 eine schematische Darstellung eines Kraftfahrzeugs, dessen Antriebsstrang einen Verbrennungskraftmotor, eine elektrische Maschine und eine Bremsanlage umfasst,
• 2 Details eines beispielhaften Antriebsstrangs für das Kraftfahrzeug nach 1 und
• 3 ein Ausführungsbeispiel eines erfindungsgemäßen Verfahrens zur modellbasierten prädiktiven Regelung des Kraftfahrzeugs nach 1.

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 a schematic representation of a motor vehicle whose drive train comprises an internal combustion engine, an electric machine and a brake system,
• 2 Details of an exemplary powertrain for the motor vehicle 1 and
• 3 an exemplary embodiment of a method according to the invention for model-based predictive control of the motor vehicle 1 .

1 shows a motor vehicle 1, for example a passenger car. The motor vehicle 1 includes a system 2 for model-based predictive control of the motor vehicle 1. 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, in particular for detecting status data that the motor vehicle 1 concern.

The 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 . 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 FIG. 1 also shows that the drive train 7 is a hybrid drive train, which additionally has an internal combustion engine 17 . The internal combustion engine 17 can be carried out in the 2 shown exemplary parallel P2 architecture of the hybrid drive train 7 in addition to the electric machine 8 drive the motor vehicle 1 when a arranged between the internal combustion engine 17 and the electric machine 8 clutch K0 is closed. The internal combustion engine 17 can optionally also drive the electric machine 8 in order to charge the battery 9 . In the exemplary embodiment shown, the electric machine 8 can drive two front wheels 22 and 23 of the motor vehicle 1 with a positive drive torque (with the clutch K0 engaged, supported by the internal combustion engine 17) 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 cost function 15 to be minimized, which is assigned to the high-level solver module 13.1.

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, eg the electric machine 8, the internal combustion engine 17 and the brake system 19 in terms of their efficiency or in terms of their loss. This results in a total loss of the motor vehicle 1. The processor unit 3 executes the MPC algorithm 13 and pre specifies a behavior of the motor vehicle 1 for a sliding prediction horizon. This prediction is based on the longitudinal dynamics model 14. The processor unit 3 calculates an optimized speed trajectory 31 ( 3 ), according to which the motor vehicle 1 is to move within the prediction horizon. The optimized speed trajectory 31 is calculated for a route section ahead, taking into account the longitudinal dynamics model 14, with the speed cost function 15 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 trajectory 31 is path-based. In particular, this allows correct, optimal handling of non-dynamic horizon objects (slopes, speed limits and other traffic signs such as "Stop" or "Give way" signs, bends in curves, traffic lights). The length of the travel horizon is 500m in the present example. The processor unit 3 can also calculate an optimized course of a state of charge of the battery 9 (state of charge trajectory 36) by executing the high-level solver module 13.1 while minimizing the cost function 15 for the prediction horizon.

Alternatively or additionally, the processor unit 3 can calculate an optimized force trajectory 32 by executing the high-level solver module 13.1 while minimizing the cost function 15 for the prediction horizon for the electric machine 8, for the internal combustion engine 17 and for the brake system 19 of the motor vehicle 1, according to which the electric Machine 8 and/or internal combustion engine 17 and/or brake system 19 should provide forces within the prediction horizon (force values for electric machine 8 and internal combustion engine 17 are greater than or equal to zero; force values for brake system 19 are less than or equal to zero). Instead of calculating the trajectory for the forces mentioned above, corresponding moment trajectories can also be calculated.

The detection unit 6 can measure current state variables of the motor vehicle 1, record corresponding data and supply it to the high-level solver module 13.1 and to a cruise control 16 described further below. For this purpose, the detection unit 6 can include, for example, a speed sensor 24 and a force sensor 30 . A respective current speed of the motor vehicle 1 can be determined by means of the speed sensor 24 . A current driving force of motor vehicle 1 can be determined by means of force sensor 30, e.g. a current driving force provided by electric motor 8 or internal combustion engine 17, or a current braking force provided by brake system 19.

Furthermore, information about static objects and/or route data from an electronic map of a navigation system 20 of the motor vehicle 1 for a preview horizon or prediction horizon (e.g. 500 m) in front of the motor vehicle 1 can be updated in particular cyclically and transferred to the high-level solver module 13.1 and the cruise control. The route data can contain, for example, gradient information, curve information, and information about speed limits as well as traffic lights and stops. 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 6 for detecting the external environment of the motor vehicle 1 can include an environment sensor 33, e.g. a radar sensor, a camera system and/or a lidar sensor. Data from these sensors can be made available to the high-level solver module 13.1 and the cruise control 16 . In this way, in particular, dynamic objects in the area of the external environment of motor vehicle 1 can also be detected and taken into account in the calculation, e.g. moving objects such as other vehicles or pedestrians. The processor unit 3 can access information from the elements mentioned, for example via the communication interface 5 . This information can flow into the longitudinal model 14 of the motor vehicle 1, in particular as restrictions or secondary conditions in the calculation of the speed trajectory 31 and/or the force trajectory 32 and in particular also in the calculation of a control signal 34 by the cruise control 16.

The output of the optimization by the MPC algorithm 13 is optimal speeds of the motor vehicle 1, an optimal profile 36 of the state of charge of the battery 8 and forces or torques of the electric machine 8 and/or the internal combustion engine 17 and/or the brake system 19 for calculated Points within the prediction horizon. The speed trajectory 31, force trajectory 32 and state of charge trajectory 36 proposed by the MPC algorithm 13 are transferred to the cruise control 16 according to the exemplary embodiment shown, which is explained in more detail below in connection with 3 is described.

3 shows that the high-level solver module 13.1 and the cruise control 16, which is in particular an ACC controller, input data from the navigation system (in particular map data) are transferred. Furthermore, the high-level solver module 13.1 and the cruise control 16 are given input data from the acquisition unit 6, in particular status data of the motor vehicle 1 and other information described above about dynamic objects of the sensors 12, 24, 30, 33 of the motor vehicle 1. Taking the input data into account, the Processor unit 3 executing the high-level solver module 13.1 the speed trajectory 31, as has been described above. The speed trajectory 31 calculated by the high-level solver module 13.1 is then transferred to the cruise control 16 as an input value.

The high level solver module 13.1 transfers the calculated speed trajectory 31 to the cruise control 16. The high level solver module 13.1 can also transfer the force trajectory 32 and the state of charge trajectory 36 to the cruise control. Cruise control 16 includes a processor unit 37, a memory unit 38 and a communication interface 39. A computer program product 40 can be stored on memory unit 38. The computer program product 40 can be executed on the processor unit 37, for which purpose the processor unit 37 and the memory unit 38 are connected to one another by means of the communication interface 39. If the computer program product 40 is executed on the processor unit 37, it directs the processor unit 37 to fulfill the functions described in connection with the drawing or to carry out method steps. The computer program product 40 contains a rule-based ACC algorithm 41.

In the exemplary embodiment shown, the processor unit 37 of the cruise control 16 processes the speed trajectory 31 calculated by the high-level solver module 13.1, the force trajectory 32 and the state of charge trajectory 36 by executing the rule-based ACC algorithm 41. The processor unit 37 of the cruise control 16 optionally also processes the Input data that was transferred from the acquisition unit 6 and/or the navigation system 20. Based on the speed trajectory 31 calculated by the high-level solver module 13.1, the force trajectory 32, optionally combined with the input data of the acquisition unit 6 and the navigation system 20 as well as the course 36 of the state of charge of the battery 9, the processor unit 37 of the cruise control 16 generates the rule-based execution ACC algorithm' 41 a control signal 34. In one example, the control signal 34 may correspond to the velocity trajectory 31 calculated by the high level solver module 13.1. The cruise control 16 forwards the control signal 34 to an actuator 35 of the motor vehicle 1 so that the actuator 35 can control the motor vehicle 1 according to the control signal 34 .

As described above, dynamic horizon objects can also be taken into account in principle. However, this is only possible within a rough framework (due to long computing times). The speed trajectory 31 adapted to the dynamic objects can be corrected, for example, by the cruise control 16, which calculates faster than the high-level solver module 13.1. Against this background, the rough speed planning 31 of the high-level solver module 13.1 is purely a suggestion that the cruise control 16 will overwrite, especially if the cruise control's intrinsic rules provide for a lower speed or a lower acceleration than suggested by the high-level solver module 13.1. In this way, it is possible to deal with dynamic changes in the horizon, in particular caused by vehicles driving ahead. Due to the fast computing ability of the cruise control 16, in particular the ACC controller, the long-term optimal planning of the high-level solver module 13.1 can be overwritten by a safe trajectory during such dynamic horizon changes.

4 clarifies the possible overwriting in connection with the speed trajectory 31. In the example shown, the speed trajectory 31 calculated by the high-level solver module 13.1 includes n speed values v ₁ to v _n (“MPC values”) for n waypoints p ₁ to p _n , the lie in the area ahead of the motor vehicle 1 and within the prediction horizon.

As described above, the cruise control 16 calculates a control signal 34, in particular taking into account the speed trajectory 31, which in the exemplary embodiment shown comprises n speed values v _1T to v _nT (“cruise control values”) for n way points p ₁ to p _n , which are in the area ahead of the Motor vehicle 1 and lie within the prediction horizon.

Assuming that the detection unit 6 informs the cruise control 16 that there is a dynamic object in the immediate vicinity of the motor vehicle 1, so that the motor vehicle 1 must be braked promptly in order to ensure safe driving. In this case it may be that the first speed value v1 of the speed trajectory 31 is greater than that first speed value v1T of the control signal 34, since the control signal 34 takes the dynamic object into account, but the current speed trajectory 31 does not (yet). In this case, the processor unit 37 of the cruise control 16 will not accept the suggestion ("v ₁ ") of the speed trajectory 31 when executing the rule-based ACC algorithm 41, but will instead use the lower speed value v _1T of the control signal 34 to control the motor vehicle 1 to control as safely as possible. In particular, the speed trajectory 31 can represent the control signal 34 as a default. In the example described above, however, the processor unit 37 of the cruise control 16 will replace the default value (“v ₁ ”) of the speed trajectory 31 with the lower speed value v _1T of the control signal 34 while executing the rule-based ACC algorithm 41 .

Reference List

K0

coupling

p1

first position

p2

second position

PM

third position

v1

first speed according to speed trajectory

v2

second speed according to speed trajectory

vn

nth speed according to speed trajectory

v1T

first speed according to control signal

v2T

second speed according to control signal

vnT

nth speed according to the control signal

1

vehicle

2

system

3

processor unit

4

storage unit

5

communication interface

6

registration unit

7

powertrain

8th

electric machine

9

battery

10

transmission

11

computer program product

12

GNSS sensor

13

MPC algorithm

13.1

High level solver module

14

longitudinal dynamics model

15

cost function

16

cruise control

17

internal combustion engine

18

power electronics

19

braking system

20

navigation system

21

front differential gear

22

front wheel

23

front wheel

24

speed sensor

25

front axle

26

rear wheel

27

loss model

28

rear wheel

29

rear axle

30

force sensor

31

velocity trajectory

32

force trajectory

33

environment sensor

34

control signal

35

actuator

36

state of charge trajectory

37

processor unit

38

storage unit

39

communication interface

40

computer program product

41

rule-based ACC algorithm

Claims (10)

Method for model-based predictive control of a motor vehicle (1), the method comprising the steps of - executing an MPC algorithm (13) which comprises a high-level solver module (13.1), a longitudinal dynamics model (14) and a cost function (15) which is assigned to the high-level solver module (13.1), with a longitudinal trajectory (31, 32, 36) minimizing the cost function (15) being calculated by executing the high-level solver module (13.1) for an upcoming route section, taking into account the longitudinal dynamics model (14), according to which the motor vehicle (1) should move within a prediction horizon, - transfer of the longitudinal trajectory (31, 32, 36) calculated by the high-level solver module (13.1) to a cruise control (16) as an input value, - processing of the longitudinal trajectory (31 , 32, 36) to a control signal (34) by means of the cruise control (16), and - controlling the motor vehicle (1) based on the control signal (34). procedure after claim 1 , wherein the longitudinal trajectory comprises a speed trajectory (31), according to which the motor vehicle (1) is to move within the prediction horizon. Method according to one of the preceding claims, wherein the longitudinal trajectory for a drive unit (8, 17) of the motor vehicle (1) comprises a driving force trajectory (32), according to which the drive unit (8, 17) is to provide driving forces within the prediction horizon. The method according to any one of the preceding claims, wherein the longitudinal trajectory includes a course (36) of a state of charge of a battery (9), which serves as an energy store for an electric machine (8) of the motor vehicle (1), wherein the motor vehicle (1) by means of the electric Machine (8) can be driven. Method according to one of the preceding claims, wherein the longitudinal trajectory for a brake system (19) of the motor vehicle (1) comprises a braking force trajectory (32), according to which the brake system (19) should provide braking forces within the prediction horizon. Method according to one of the preceding claims, in which the control signal (34) represents the longitudinal trajectory (31, 32, 36) which has been calculated by the high-level solver module (13.1). Method according to one of the preceding claims, in which information about static objects is passed to the high-level solver module (13.1) as secondary conditions, which the high-level solver module (13.1) takes into account when calculating the longitudinal trajectory (31, 32, 36). Method according to one of the preceding claims, wherein information about dynamic objects is passed to the high-level solver module (13.1) as secondary conditions, which the high-level solver module (13.1) takes into account when calculating the longitudinal trajectory (31, 32, 36). procedure after claim 8 , wherein the cruise control (16) overwrites an MPC value (v ₁ ) of the longitudinal trajectory (31, 32, 36) calculated by the high-level solver module (13.1) with an alternative cruise control value (v _1T ) of the control signal (34), when the cruise control value (v _1T ) differs from the MPC value (v ₁ ). procedure after claim 9 , wherein the cruise control overwrites the MPC value (v ₁ ) with the cruise control value (v _1T ) if the cruise control value (v _1T ) provides a lower speed or a lower acceleration for the motor vehicle (1) than the MPC value (v ₁ ).

Images