Model-based predictive control of multiple components of a motor vehicle

Abstract

Processor unit (3) for model-based predictive control of several components (18, 19) of a motor vehicle (1), wherein - the processor unit (3) is configured to execute an MPC algorithm (13) for model-based predictive control of a first component (18) of a motor vehicle (1) and a second component (19) of the motor vehicle (1), - the first component (18) can be operated with different values (h1, h2, h3) of a first operating parameter (20), - the second component (19) can be operated with different values (y1, y2, y3) of a second operating parameter (24), - the MPC algorithm (13) contains a cost function (15) to be minimized, - the MPC algorithm (13) contains a dynamic model (14) of the motor vehicle (1), wherein the dynamic model (14) comprises a loss model (27) of the motor vehicle (1), - the loss model (27) describes a total loss of the motor vehicle (1), - the cost function (15) contains a first term which represents the total loss of the motor vehicle (1), - the total loss depends on an operating value combination which includes a first value (h1; h2; h3) of the first operating parameter (20) and a second value (y1; y2; y3) of the second operating parameter (24), and - the processor unit (3) is configured to determine, by executing the MPC algorithm (13) as a function of the loss model (14), that combination of operating values by which the first term of the cost function (15) is minimized, characterized in that - the first term contains an electrical energy weighted with a first weighting factor and predicted according to the dynamic model (14), which is provided within a prediction horizon by a battery (9) of the drive train (7) for driving the electric machine (8), - the cost function (15) contains as a second term a travel time weighted with a second weighting factor and predicted according to the dynamic model (14), which the motor vehicle (1) needs to cover the entire distance predicted within the prediction horizon, and - the processor unit (3) is configured to determine an input variable for the electrical machine (8) by executing the MPC algorithm (13) as a function of the first term and as a function of the second term, so that the cost function is minimized.

Description

[0001] The invention relates to the model-based predictive control of multiple components of a motor vehicle. In this context, it particularly relates to a processor unit, a motor vehicle, a method, and a computer program product.

[0002] Model-based predictive control (MPC) methods are used in the field of trajectory control, for example, in motor vehicle engine control. For example, EP 2 610 836 A1 describes the optimization of an energy management strategy based on a look-ahead horizon and other environmental information by minimizing a cost function. This involves creating a neural network for use in the vehicle, modeling the driver, and predicting the speed profile likely to be chosen. Furthermore, EP 1 256 476 B1 discloses a strategy for reducing energy consumption while driving and increasing range.This utilizes information from the navigation device, namely the current vehicle position, road patterns, geography including date and time, elevation changes, speed limits, intersection density, traffic monitoring, and the driver's driving pattern. Further processor units for model-based predictive control of motor vehicle components are known from DE 10 2018 114 336 A1 and DE 10 2018 217 845 A1.

[0003] An object of the present invention can be seen in providing an MPC control for several efficiency-relevant components of a motor vehicle, wherein the components are not necessarily found in a drive train of the motor vehicle.

[0004] The object is achieved by the subject matter of the independent patent claims. Advantageous embodiments are the subject matter of the subclaims, the following description, and the figures.

[0005] The present invention proposes the use of an MPC solver for planning various efficiency-relevant degrees of freedom at the overall vehicle level. More efficient (and more comfortable) actuator combinations can be identified by using an MPC solver to plan various efficiency-relevant degrees of freedom. The optimal degree of freedom planning can then be passed on to a target generator, which can be implemented, in particular, by a software module.

[0006] Modern motor vehicles are equipped with a wide variety of efficiency-relevant components, although these are not necessarily only found in the drive train. The present invention enables a reduction in the overall losses of the motor vehicle, for example through intelligent regulation of the vehicle's ride height or through intelligent activation of the vehicle's brakes. Depending on the type of motor vehicle, this results in a list of efficiency-relevant components (e.g., a height-adjustable vehicle) and degrees of freedom (e.g., the set height of the chassis), which extends across various parts of the overall vehicle system. The present invention enables the selection and implementation of an optimal actuator combination of the available degrees of freedom. The following cost function can optimally provide the best possible solution to the minimization problem min (f cost = c efficieny + c time + c comfort), where the individual terms of the cost function f cost describe the parameterizable costs in terms of efficiency, comfort and travel time.

[0007] The subject of the invention is therefore the general expansion of the degrees of freedom that can be planned by the MPC solver. In this context, the MPC solver is not only free to plan trajectories, but also to generally plan other efficiency-relevant degrees of freedom. The solver uses a comprehensive vehicle loss model, with the help of which any number of actuator combinations can be calculated with regard to the total costs f cost can be evaluated. The MPC solver identifies the solution that has the optimal value for the above-mentioned minimization problem and, in particular, the subsequent transfer of the optimal degree of freedom planning to the target generator.

[0008] In this sense, according to a first aspect of the invention, a processor unit for model-based predictive control of multiple components of a motor vehicle is provided. The processor unit is configured to execute an MPC algorithm for model-based predictive control of a first component of a motor vehicle and a second component of the motor vehicle. In order to find an optimal solution for a so-called "driving efficiency" driving function in every situation under given boundary conditions and restrictions, which is intended to provide an efficient driving style, the method of model-based predictive control (MPC) was chosen. The MPC method is based on a system model that describes the behavior of the system. Furthermore, the MPC method is based on an objective function or a cost function that describes an optimization problem and determines which state variables should be minimized.

[0009] The first component can be operated with different values of a first operating parameter, and the second component can be operated with different values of a second operating parameter. The present invention is not limited to the planning and control of the degrees of freedom for the first and second components. In addition to the first component and the second component, the motor vehicle can comprise further components whose degrees of freedom can be planned and controlled in the same way as explained in more detail for the first component and the second component.

[0010] The MPC algorithm contains a cost function to be minimized and a dynamic model of the motor vehicle, in particular a longitudinal dynamic model of the motor vehicle, wherein the dynamic model includes a loss model of the motor vehicle. The dynamic model, in particular the longitudinal dynamic model of the powertrain of the motor vehicle, can include a vehicle model with vehicle parameters and powertrain losses (sometimes approximated characteristic maps). In particular, the first component with the different values of the first operating parameter and the second component with the different values of the second operating parameter can be mapped and calculated or simulated by the dynamic model.

[0011] Knowledge about upcoming route topographies (e.g. curves and gradients) can also be incorporated into the dynamic model, in particular into the longitudinal dynamic model of the powertrain. Knowledge about speed limits on the upcoming route can also be incorporated into the dynamic model of the powertrain. The loss model can contain a list of efficiency-relevant components (e.g. a height-adjustable vehicle or a system set up for this purpose) and degrees of freedom (e.g. the set height of the chassis) that extends across various parts of the overall motor vehicle system. The loss model thus describes an overall loss of the motor vehicle. The degrees of freedom are the operating parameters of the components. “Components” according to the present invention include, for example, actuators that are used for steering and damping the motor vehicle.Furthermore, in particular the brake system and the drive train can form components according to the present invention.

[0012] The cost function contains a first term (“C efficiency “; see the cost function f above cost ), which represents the total loss of the vehicle. The total loss of the vehicle depends on a combination of operating values, which includes a first value of the first operating parameter and a second value of the second operating parameter. In addition, the cost function can contain further terms ("C time ", "C comfort “; see the cost function f above cost ), some of which are described in more detail below. The processor unit is designed to determine, by executing the MPC algorithm depending on the loss model, the operating value combination by which at least the first term (“C efficiency“) of the cost function is minimized. Furthermore, the entire cost function can be minimized by this determined combination of operating values (min (f cost = c efficieny + c time + C comfort )).

[0013] The MPC algorithm can comprise an MPC solver in the form of a software module. The MPC solver can contain instructions or program code, which instructs the processor unit to determine the operating value combination depending on the loss model in such a way that the cost function or at least its first term (“C efficiency "). In this way, the present invention makes it possible to select and implement an optimal combination of operating values or actuator combinations of the available degrees of freedom.

[0014] The present invention can be used in particular for an autonomous driving function of the motor vehicle. The autonomous driving function enables the motor vehicle to drive independently, i.e. without a vehicle occupant controlling the motor vehicle. The driver has transferred control of the motor vehicle to the driver assistance system. The autonomous driving function thus comprises the motor vehicle being configured - in particular by means of the central processor unit - to carry out, for example, steering, indicator, acceleration and braking maneuvers without human intervention and in particular to control the motor vehicle's exterior lights and signaling such as turn signals. Autonomous driving functions can also include driving functions that assist a driver of the motor vehicle in controlling the motor vehicle, in particular during steering, indicator, acceleration and braking maneuvers, whereby the driver continues to have control of the motor vehicle.

[0015] The processor unit can pass the optimized combination of operating values to a software module ("Target Generator"). Using this software module, the processor unit can convert the mathematically optimal planning of all available degrees of freedom into actually usable component signals. For example, the MPC control can optimally plan a vehicle's speed trajectory for the next 5,000 m. In this case, the Target Generator would "translate" the first (=currently required) speed value of this trajectory into, for example, a required torque of the electric motor (as an efficiency-relevant component) of the vehicle. The component software can then work with this value and adjust the desired speed.

[0016] In this sense, in one embodiment, the processor unit is configured to control a first actuator of the first component by executing a conversion software module such that the first actuator is operated with a first actuator value, whereby the first component is operated with the first value of the first operating parameter of the operating value combination by which at least the first term of the cost function is minimized. Furthermore, in this embodiment, the processor unit is configured to control a second actuator of the second component by executing the conversion software module such that the second actuator is operated with a second actuator value, whereby the second component is operated with the second value of the second operating parameter of the operating value combination by which at least the first term of the cost function is minimized.This embodiment allows the actuators to be controlled in such a way that the components operate as efficiently as possible. In other words, an optimal combination of actuator values can be determined and implemented so that the components, and thus the motor vehicle, operate as energy-efficiently as possible.

[0017] As already mentioned, the present invention is not limited to the first component and the second component, since modern vehicles incorporate a wide variety of efficiency-relevant components, although these are not necessarily found only in the drivetrain. All of these components, with their degrees of freedom, can be represented by the loss model. Thus, in one embodiment—particularly alongside other efficiency-relevant components—the first component can be a system for leveling the motor vehicle, and the second component can be a braking system. Through intelligent regulation of the ride height and / or intelligent activation of the brake (which is achieved by determining the optimal combination of operating values), a reduction in the overall losses of the motor vehicle can be achieved.

[0018] According to the invention, the first term contains electrical energy, weighted with a first weighting factor and predicted according to the dynamic model, which is provided within a prediction horizon by a battery of the drive train to drive the electric machine. Furthermore, the cost function contains, as a second term, a travel time, weighted with a second weighting factor and predicted according to the dynamic model, which the motor vehicle requires to cover the entire distance predicted within the prediction horizon. The processor unit is configured to determine an input variable for the electric machine by executing the MPC algorithm as a function of the first term and as a function of the second term, such that the cost function is minimized.

[0019] The state variables for the Driving Efficiency function can therefore be, for example, the vehicle speed or kinetic energy, the remaining energy in the battery, and the travel time. Energy consumption and travel time can be optimized, for example, based on the gradient of the road ahead and restrictions on speed and drive power, as well as on the current system state. Using the objective function or the cost function of the Driving Efficiency strategy, travel time can be minimized in addition to the total loss or energy consumption. This means that, depending on the choice of weighting factors, a low speed is not always considered optimal, thus eliminating the problem that the resulting speed is always at the lower end of the permitted speed.This makes it possible for driver influence to no longer be relevant for the vehicle's energy consumption and travel time, as the electric motor can be controlled by the processor unit based on the input variable determined by executing the MPC algorithm. In particular, the input variable can be used to set an optimal motor operating point for the electric motor. This allows for direct adjustment of the vehicle's optimal speed.

[0020] In particular, the cost function can contain exclusively linear and quadratic terms. This gives the overall problem the form of a quadratic optimization with linear constraints, resulting in a convex problem that can be solved quickly and easily. The objective function or cost function can be set up with a weighting (weighting factors), whereby, in particular, energy efficiency, travel time, and ride comfort are calculated and weighted. An energy-optimal speed trajectory can be calculated online for a horizon ahead on the processor unit, which can, in particular, form part of a central control unit of the motor vehicle. By using the MPC method, the target speed of the motor vehicle can also be cyclically recalculated based on the current driving state and the route information ahead.

[0021] Current state variables can be measured, corresponding data can be recorded and fed into the MPC algorithm. For example, route data from an electronic map can be updated, particularly cyclically, for a look-ahead or prediction horizon (e.g., 400 m) in front of the vehicle. The route data can include, for example, gradient information, curve information, and information about speed limits. Furthermore, a curve curvature can be converted into a speed limit for the vehicle using a maximum permissible lateral acceleration. Furthermore, the vehicle can be located, particularly via a GNSS signal for precise localization on the electronic map.

[0022] The cost function of the MPC algorithm minimizes travel time for the prediction horizon and minimizes consumed energy. In one embodiment, torque changes for the prediction horizon are also minimized. As far as the input for the model-based predictive control is concerned, speed limits, physical limits for torque, and speeds of the electric machine can be fed to the MPC algorithm as constraints. Control variables for optimization can also be fed to the MPC algorithm as input, in particular the speed of the vehicle (which can be proportional to the speed), the torque of the electric machine, and the battery charge level. As an output of the optimization, the MPC algorithm can provide an optimal speed and an optimal torque for calculated points in the look-ahead horizon.As far as the implementation of MPC control in the vehicle is concerned, a software module can be connected downstream of the MPC algorithm, which determines a currently relevant state and passes it on to power electronics.

[0023] Energy consumption and travel time can each be evaluated and weighted at the end of the horizon. This term is therefore only active for the last point of the horizon. In this sense, in one embodiment, the cost function contains a final energy consumption value weighted by the first weighting factor, which the predicted electrical energy assumes at the end of the prediction horizon, and the cost function contains a final travel time value weighted by the second weighting factor, which the predicted travel time assumes at the end of the prediction horizon.

[0024] To ensure comfortable driving, additional terms can be introduced to penalize torque jumps. In this sense, the cost function can have a third term with a third weighting factor, wherein the third term contains a torque value predicted according to the dynamic model, which the electric machine provides to drive the motor vehicle, and wherein the processor unit is configured to determine the input variable for the electric machine by executing the MPC algorithm as a function of the first term, as a function of the second term, and as a function of the third term, so that the cost function is minimized.

[0025] For the first point in the horizon, the deviation from the last set torque can be negatively evaluated to ensure a seamless and jerk-free transition when switching between the old and new trajectories. In this sense, the third term can contain a first value, weighted with the third weighting factor, of a torque predicted according to the dynamic model, which the electric machine provides to drive the motor vehicle to a first waypoint within the prediction horizon. The third term can contain a zeroth value, weighted with the third weighting factor, of a torque that the electric machine provides to drive the motor vehicle to a zeroth waypoint that lies immediately before the first waypoint. The zeroth torque can, in particular, be a real torque - not merely a predicted torque - provided by the electric machine.In the cost function, the zeroth value of the torque can be subtracted from the first value of the torque.

[0026] Alternatively, the third term can contain a first value, weighted with the third weighting factor, of a drive force predicted according to the dynamic model, which the electric machine provides to drive the motor vehicle to a first waypoint within the prediction horizon. The third term contains a zeroth value, weighted with the third weighting factor, of a drive force that the electric machine provides to drive the motor vehicle to a zeroth waypoint immediately before the first waypoint, wherein the zeroth value of the drive force is subtracted from the first value of the drive force in the cost function.

[0027] The waypoints considered by the MPC algorithm are, in particular, discrete waypoints that, for example, follow one another at a certain frequency. In this sense, the zeroth waypoint and the first waypoint represent discrete waypoints, with the first waypoint immediately following the zeroth waypoint. The zeroth waypoint can lie before the prediction horizon. The zeroth torque value can be measured or determined for the zeroth waypoint. The first waypoint, in particular, represents the first waypoint within the prediction horizon. The first torque value can be predicted for the first waypoint. Thus, the actually determined zeroth torque value can be compared with the predicted first torque value.

[0028] In addition, excessively high torque gradients within the horizon are disadvantageous, so that in one embodiment they are already penalized in the objective function. For this purpose, the quadratic deviation of the driving force per meter can be weighted and minimized in the objective function. In this sense, the cost function can have a fourth term with a fourth weighting factor, wherein the fourth term contains a torque gradient predicted according to the dynamic model or an indicator value for a torque gradient predicted according to the dynamic model. The processor unit is configured to determine the input variable for the electric machine by executing the MPC algorithm as a function of the first term, as a function of the second term, as a function of the third term, and as a function of the fourth term, such that the cost function is minimized.

[0029] In one embodiment, the fourth term contains a quadratic deviation of the torque gradient, multiplied and summed by the fourth weighting factor. Furthermore, the cost function can contain a quadratic deviation of a driving force, summed by the fourth weighting factor, which the electric machine provides to move the motor vehicle one meter in the longitudinal direction. In this sense, the fourth term can contain a quadratic deviation of a driving force, multiplied and summed by the fourth weighting factor, which the electric machine provides to move the motor vehicle one meter in the longitudinal direction.

[0030] Speed limits, which may be specified by road regulations, for example, are hard limits for optimization that should not be exceeded. In reality, slightly exceeding the speed limits is always permissible and is particularly common when transitioning from one speed zone to a second. In dynamic environments where speed limits shift from one computing cycle to the next, it may happen that a valid solution for a speed curve can no longer be found for very hard limits. To increase the stability of the calculation algorithm, a so-called "soft constraint" can be introduced into the objective function. In particular, a so-called "slack variable" can become active in a predefined narrow range before the hard speed limit is reached.Solutions that are very close to this speed limit, i.e., solutions whose speed trajectory is a certain distance from the hard limit, may be rated lower. In this sense, the cost function can contain a slack variable weighted with a fifth weighting factor as a fifth term. The processor unit is configured to determine the input variable for the electric machine by executing the MPC algorithm as a function of the first term, the second term, the third term, the fourth term, and the fifth term, so that the cost function is minimized.

[0031] According to a second aspect of the invention, a motor vehicle is provided which comprises a processor unit according to the first aspect of the invention. The motor vehicle further comprises a driver assistance system, a first component, and a second component. The driver assistance system is configured, in particular by means of a communication interface, to access an operating value combination which minimizes the first term of the cost function and which has been determined by the processor unit and which includes a first value of the first operating parameter and a second value of the second operating parameter. Furthermore, the driver assistance system is configured to control the first component based on the first value of the first operating parameter and to control the second component based on the second value of the second operating parameter.For this purpose, the driver assistance system can in particular make use of the target generator described above.

[0032] The motor vehicle can be, for example, an automobile (e.g., a passenger car weighing less than 3.5 t), a motorcycle, a scooter, a moped, a bicycle, an e-bike, a bus, or a truck (e.g., weighing more than 3.5 t), or a rail vehicle, a ship, or an aircraft such as a helicopter or airplane. The invention can also be used in small, lightweight electric micromobility vehicles, with these vehicles being used in particular in urban traffic and for the first and last mile in rural areas. The first and last mile can be understood to mean all routes and paths that are in the first and last link in a mobility chain. This is, for example, the route from home to the train station or the route from the train station to work. In other words, the invention can be used in all areas of transport, such as automotive, aviation, nautical science, astronautics, etc.The motor vehicle may, for example, belong to a vehicle fleet. The motor vehicle may be controlled by a driver, possibly assisted by a driver assistance system. However, the motor vehicle may also be controlled remotely and / or (semi-)autonomously.

[0033] According to a third aspect of the invention, a method for model-based predictive control of multiple components of a motor vehicle is provided, wherein a first component can be operated with different values of a first operating parameter, and wherein a second component can be operated with different values of a second operating parameter. The method comprises executing an MPC algorithm for model-based predictive control of the first component and the second component. The MPC algorithm contains a cost function to be minimized and a dynamic model of the motor vehicle, in particular a longitudinal dynamic model of the motor vehicle, wherein the dynamic model comprises a loss model of the motor vehicle, and wherein the loss model describes a total loss of the motor vehicle. Furthermore, the cost function contains a first term which represents the total loss of the motor vehicle.Furthermore, the total loss depends on a combination of operating values that includes a first value of the first operating parameter and a second value of the second operating parameter. According to a further method step, by executing the MPC algorithm as a function of the loss model, the combination of operating values that minimizes the first term of the cost function is determined, wherein the first term contains electrical energy weighted with a first weighting factor and predicted according to the dynamic model (14), which is provided within a prediction horizon by a battery (9) of the drive train (7) for driving the electric machine (8). - the cost function (15) contains as a second term a travel time weighted with a second weighting factor and predicted according to the dynamic model (14), which the motor vehicle (1) needs to cover the entire distance predicted within the prediction horizon, and - the processor unit (3) is configured to determine an input variable for the electrical machine (8) by executing the MPC algorithm (13) as a function of the first term and as a function of the second term, so that the cost function is minimized.

[0034] According to a fourth aspect of the invention, a computer program product for model-based predictive control of multiple components of a motor vehicle is provided, wherein a first component can be operated with different values of a first operating parameter, and wherein a second component can be operated with different values of a second operating parameter. When executed on a processor unit, the computer program product instructs the processor unit to execute an MPC algorithm for model-based predictive control of the first component and the second component. The MPC algorithm contains a cost function to be minimized and a dynamics model of the motor vehicle, in particular a longitudinal dynamics model of the motor vehicle, wherein the dynamics model comprises a loss model of the motor vehicle, and wherein the loss model describes a total loss of the motor vehicle.Furthermore, the cost function contains a first term which represents the total loss of the motor vehicle. Furthermore, the total loss depends on a combination of operating values which includes a first value of the first operating parameter and a second value of the second operating parameter. Furthermore, when executed on a processor unit, the computer program product instructs the processor unit to determine, by executing the MPC algorithm as a function of the loss model, the combination of operating values which minimizes the first term of the cost function, wherein the first term contains electrical energy weighted with a first weighting factor and predicted according to the dynamic model (14), which is provided within a prediction horizon by a battery (9) of the drive train (7) for driving the electric machine (8). - the cost function (15) contains as a second term a travel time weighted with a second weighting factor and predicted according to the dynamic model (14), which the motor vehicle (1) needs to cover the entire distance predicted within the prediction horizon, and - the processor unit (3) is configured to determine an input variable for the electrical machine (8) by executing the MPC algorithm (13) as a function of the first term and as a function of the second term, so that the cost function is minimized.

[0035] The statements in connection with the processor unit according to the first aspect of the invention also apply mutatis mutandis to the motor vehicle according to the second aspect of the invention, to the method according to the third aspect of the invention and to the computer program product according to the fourth aspect of the invention.

[0036] In the following, embodiments of the invention are explained in more detail with reference to the schematic drawing, wherein identical or similar elements are provided with the same reference numerals. Fig. 1 a schematic representation of a motor vehicle comprising several efficiency-relevant components, Fig. 2 degrees of freedom of a first efficiency-relevant component, Fig. 3 degrees of freedom of a second efficiency-relevant component and Fig. 4 a characteristic diagram of an electrical machine for the vehicle according to Fig. 1,

[0037] Fig. 1 shows a motor vehicle 1, e.g., a passenger car. The motor vehicle 1 comprises a system 2 for model-based predictive control of a plurality of components of the 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 recording unit 6, in particular for recording status data relating to the motor vehicle 1. The motor vehicle 1 further comprises a drive train 7, which may comprise, for example, an electric machine 8, which can be operated as a motor and as a generator, a battery 9, and a transmission 10. In engine mode, the electric machine 8 can drive wheels of the motor vehicle 1 via the transmission 10, which may, for example, have a constant gear ratio. The electrical energy required for this purpose can be provided by the battery 9.The battery 9 can be charged by the electric machine 8 when the electric machine 8 is operated in generator mode (regeneration). The battery 9 can optionally also be charged at an external charging station. Likewise, the drive train of the motor vehicle 1 can optionally include an internal combustion engine 17, which can drive the motor vehicle 1 alternatively or in addition to the electric machine 8. The internal combustion engine 17 can also drive the electric machine 8 to charge the battery 9.

[0038] The motor vehicle 1 further comprises several components that are relevant to the efficiency of the operation of the motor vehicle 1 ("efficiency-relevant components"), in particular when the motor vehicle 1 is operated in an autonomous driving mode. These components are not exclusively arranged in the drive train 7 of the motor vehicle 1. Fig. 1, a first component 18 and a second component 19 are shown purely as examples, although the motor vehicle 1 also includes a number of other efficiency-relevant components. In the embodiment according to Fig. 1 shows a first component in the form of a system 18 for level control of the motor vehicle 1 and a second component in the form of a braking system 19 of the motor vehicle 1. For example, the electric motor 8, the battery 9, the transmission 10, and the internal combustion engine 17 can also be considered efficiency-relevant components of the motor vehicle 1.

[0039] Fig. 2 shows that the system 18 for level control of the motor vehicle 1 can be operated with different values of a first operating parameter 20 (first degree of freedom). For example, the first operating parameter 20 can be the set height of a chassis 21 of the motor vehicle 1. Purely by way of example, the set height of the chassis 21 of the motor vehicle 1 can assume a first value h1, a second value h2, and a third value h3. The different heights h1, h2, and h3 of the chassis 21 can lead to different levels of aerodynamic drag of the motor vehicle 1. This can be represented by the longitudinal model 14 of the drive train 7 of the motor vehicle 1, described below.

[0040] The system 18 for leveling the motor vehicle 1 can be understood as an actuator of the motor vehicle 1. Furthermore, the system 18 for leveling the motor vehicle 1 itself can have at least one actuator 22 (e.g., a hydraulic cylinder, a pneumatic cylinder, or a hydropneumatic shock absorber), which actuates the system 18 for leveling the motor vehicle 1. The first actuator 22 can be operated with different actuator values 23, resulting in the different values h1, h2, and h3 for the system 18 for leveling the motor vehicle 1. For example, a first actuator value x1 (e.g., a first pressure value for a hydraulic cylinder) results in the first height h1 of the chassis 21, a second actuator value x2 results in the second height h2 of the chassis 21, and a third actuator value x3 results in the third height h3 of the chassis 21.

[0041] Fig. 3 shows that the braking system 19 can be operated with different values of a second operating parameter 24 (second degree of freedom). For example, the second operating parameter 20 can be the braking force of the braking system 19. Purely by way of example, the braking force can assume a first value y1, a second value y2, and a third value y3. The different braking forces y1, y2, and y3 of the braking system 19 can lead to different levels of traction force exerted by the braking system on the wheels of the motor vehicle 1. This can be represented by the longitudinal model 14 of the drive train 7 of the motor vehicle 1, described below.

[0042] The braking system 19 can be understood as an actuator of the motor vehicle 1. Furthermore, the braking system 19 itself can have an actuator 25 (e.g., a hydraulic cylinder) that actuates the braking system 19. The second actuator 25 can be operated with different actuator values 26, resulting in different values y1, y2, and y3 for the braking system 19. For example, a first actuator value z1 results in the first braking force y1, a second actuator value z2 results in the second braking force y2, and a third actuator value z3 results in the third braking force y3.

[0043] A computer program product 11 can be stored on the storage unit 4. The computer program product 11 can be executed on the processor unit 3, for which purpose the processor unit 3 and the storage unit 4 are connected to one another via the communication interface 5. When the computer program product 11 is executed on the processor unit 3, it instructs the processor unit 3 to perform the functions or method steps described in connection with the drawing.

[0044] The computer program product 11 contains an MPC algorithm 13. The MPC algorithm 13 in turn contains a dynamic model of the motor vehicle, in the embodiment shown a longitudinal dynamic model 14 of the drive train 7 of the motor vehicle 1. Furthermore, the MPC algorithm 13 contains a cost function 15 to be minimized, wherein a first term C efficiencythe cost function 15 represents the total loss of the motor vehicle 1. Thus, the cost function 15 to be minimized can be mathematically expressed as follows: min(cefficieny+ctime+comfort)

[0045] These include: c efficiency the parameterizable costs in terms of efficiency, c time the parameterizable costs in terms of travel time and c comfort the parameterizable costs in terms of comfort.

[0046] The longitudinal dynamics model 14 includes a loss model 27 of the motor vehicle 1. The loss model 27 describes the operating behavior of the efficiency-relevant components 18, 19 with regard to their efficiency and their loss, respectively. This results in the total loss of the motor vehicle 1. The processor unit 3 executes the MPC algorithm 13 and predicts a behavior of the motor vehicle 1 based on the longitudinal dynamics model 14, minimizing the cost function 15.

[0047] The total loss of motor vehicle 1 depends on an operating value combination. The operating value combination includes a first value of the first operating parameter and a second value of the second operating parameter. In the simplified exemplary embodiment shown, there are six possible operating value combinations. A first operating value combination includes the first height h1 of the chassis 21 and the first braking force y1 of the braking system 19. The first operating value combination results in a first total loss of motor vehicle 1. A second operating value combination includes the first height h1 of the chassis 21 and the second braking force y2 of the braking system 19. The second operating value combination results in a second total loss of motor vehicle 1. A third operating value combination includes the first height h1 of the chassis 21 and the third braking force y3 of the braking system 19.The third operating value combination results in a third total loss of motor vehicle 1. A fourth operating value combination includes the second height h2 of the chassis 21 and the first braking force y1 of the braking system 19. The fourth operating value combination results in a fourth total loss of motor vehicle 1. A fifth operating value combination includes the second height h2 of the chassis 21 and the third braking force y3 of the braking system 19. The fifth operating value combination results in a fifth total loss of motor vehicle 1. A sixth operating value combination includes the third height h3 of the chassis 21 and the third braking force y3 of the braking system 19. The sixth operating value combination results in a sixth total loss of motor vehicle 1.

[0048] The processor unit 3 can determine the six operating value combinations mentioned by executing the MPC algorithm 13 as a function of the loss model 14. The processor unit 3 can compare the total losses resulting from the six different operating value combinations. The processor unit 3 can determine, for example, that the third operating value combination (h1, y3) leads to the lowest total loss of the motor vehicle 1. The processor unit 3 can select the third operating value combination and output the corresponding values, e.g., to a target generator, which can be integrated into the MPC algorithm as a software module. Alternatively, the target generator can also be included, for example, as a software module in a driver assistance system 16.Based on the determined operating value combination (h1, y3), the first component 18 can be adjusted to the first value h1 of the first operating parameter 20, and the second component 19 can be adjusted to the third value y1 of the second operating parameter 24, in particular by means of the target generator. Furthermore, the processor unit 3 can also adjust the first actuator 22 to the first actuator value x1, so that the first value h1 of the first operating parameter 20 is set for the first component 18. In a similar way, the processor unit 3 can adjust the second actuator 25 to the third actuator value z3, so that the third y3 value of the second operating parameter 24 is set for the second component 19.

[0049] The output of the optimization by the MPC algorithm 13 can also be an optimal speed and an optimal torque of the electric machine 8 for calculated points in the look-ahead horizon. For this purpose, the processor unit 3 can determine an input variable for the electric machine 8, so that the optimal speed and the optimal torque are set. The processor unit 3 can control the electric machine 8 based on the determined input variable. However, this can also be done by the driver assistance system 16.

[0050] The detection unit 6 can measure current state variables of the motor vehicle 1, record corresponding data, and feed it to the MPC algorithm 13. Furthermore, route data from an electronic map for a look-ahead or prediction horizon (e.g., 400 m) in front of the motor vehicle 1 can be updated, in particular cyclically. The route data can contain, for example, gradient information, curve information, and information about speed limits. Furthermore, a curve curvature can be converted into a speed limit for the motor vehicle 1 using a maximum permissible lateral acceleration. In addition, the detection unit 6 can be used to locate the motor vehicle, 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 the motor vehicle 1 can comprise, for example, a radar sensor, a camera system, and / or a lidar sensor. The processor unit 3 can access information from these elements, for example, via the communication interface 5. This information can be incorporated into the longitudinal model 14 of the motor vehicle 1, in particular as constraints or constraints.

[0051] The longitudinal dynamics model 14 of the motor vehicle 1 can be mathematically expressed as follows: dv(t)dt=(Ftrac(t)−Fr(α(t))−Fgr(α(t))−Fd(v(t))) / meq

[0052] These include: v the speed of the motor vehicle; F trac Traction force exerted by the engine or the brakes on the wheels of the motor vehicle, e.g. influenced by the different braking forces y1, y2 and y3 of the braking system 19 described above; F r the rolling resistance force, which is an effect of the deformation of the tires when rolling and depends on the load on the wheels (the normal force between the wheel and the road) and thus on the angle of inclination of the road; F gr the gradient resistance force, which describes a longitudinal component of the force of gravity acting on the motor vehicle when driving uphill or downhill, depending on the gradient of the road; F d the aerodynamic drag of the motor vehicle, e.g. influenced by the different heights h1, h2 and h3 of the chassis 21 described above; and m eq the equivalent mass of the motor vehicle; the equivalent mass includes in particular the inertia of the rotating parts of the drive train which are subject to the acceleration of the motor vehicle (engine, transmission drive shafts, wheels).

[0053] By converting time dependence into path dependence dds=ddt∗dtds=ddt∗1v and coordinate transformation to eliminate the quadratic velocity term in air resistance with ekin=12∗meq∗v(t)2 follows dekinds=Ftrac(s)−Fr(α(s))−Fgr(α(s))−Fd(ekin(s)).

[0054] In order to solve the problem quickly and easily using the MPC algorithm 13, the dynamic equation of the longitudinal dynamic model 14 can be linearized by converting the velocity into kinetic energy by coordinate transformation. kin This makes the quadratic term for calculating the air resistance F d replaced by a linear term, and at the same time, the longitudinal dynamics model 14 of the motor vehicle 1 is no longer described as a function of time, as usual, but as a function of path. This fits well with the optimization problem because the predictive information of the electric horizon is path-based.

[0055] In addition to kinetic energy, there are two further state variables which, in the sense of a simple optimization problem, can also be described as linear and path-dependent. Firstly, 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 engine speed. In the exemplary embodiment shown, the motor vehicle 1 has a fixed transmission ratio between the electric machine 8 and the road on which the motor vehicle 1 is moving. This allows the speed of the electric machine 8 to be directly converted into a speed of the motor vehicle 1 or into a kinetic energy of the motor vehicle 1. Furthermore, the electrical power of the electric machine 8 can be converted into energy consumption per meter by dividing it by the corresponding speed. This gives the characteristic map of the electric machine 8 the form shown in Fig.4. In order to use this map for optimization, it is approximated linearly: Energy perMeter ≥ a i * e kin + b i * F trac for all i.

[0056] In detail, the cost function 15 to be minimized can be expressed mathematically as follows: min(cefficiency−wBat⋅EBat(sE)+wTime⋅T(sE)+wTem⋅∑s=1sE−1(FA(s)−FA(s−1)Δs)2+wTemStart⋅(FA(s1)−FA(s0))2+∑s=1sE−1wSlack⋅Varslack)

[0057] Here: C efficiency the parameterizable efficiency costs of the efficiency-relevant components, e.g. the components 19, 20 described above, w Bat Weighting factor for battery energy consumption E Bat Battery energy consumption S distance S E-1 Distance one time step before the end of the prediction horizon F ADriving force, which is provided by the electric machine, is constantly translated by a gearbox and is applied to a wheel of the motor vehicle W Tem Weighting factor for torque gradients W TemStart Weighting factor for torque jumps T Time required by the vehicle to cover the entire distance predicted within the prediction horizon w Time Weighting factor for time T S E Distance to the end of the horizon w Slack Weighting factor for the slack variable Var Slack Slack variable

[0058] The cost function 15 contains only linear and quadratic terms. Thus, the overall problem takes the form of a quadratic optimization with linear constraints, resulting in a convex problem that can be solved easily and quickly.

[0059] The cost function 15 contains, in addition to the parameterizable efficiency costs of the components 19, 20 described above, as a further term a first weighting factor w Bat weighted and predicted electrical energy E according to the longitudinal dynamics model Bat , which is provided within a prediction horizon by the battery 9 of the drive train 7 to drive the electric machine 8. The battery 9 and the electric machine 8 can - like the level control system 18 described above and the braking system 19 described above - be regarded as efficiency-relevant components of the motor vehicle 1. Accordingly, the efficiency calculated with the first weighting factor w Bat weighted and predicted electrical energy E according to the longitudinal dynamics model Bat also in term C efficiency flow in.

[0060] The cost function 15 contains as a further term a weighting factor W Time Weighted travel time T, predicted according to the longitudinal dynamics model 14, which motor vehicle 1 requires to cover the predicted distance. This means that, depending on the choice of weighting factors, a low speed is not always evaluated as optimal, thus eliminating the problem that the resulting speed is always at the lower limit of the permitted speed.

[0061] Energy consumption and travel time can be evaluated and weighted at the end of the horizon. These terms are then only active for the last point of the horizon.

[0062] Excessively high torque gradients within the horizon are disadvantageous. Therefore, torque gradients are already penalized in the cost function 15, namely by the term wTem⋅∑s=1sE−1(FA(s)−FA(s−1)Δs)2. The squared deviation of the driving force per meter is weighted with a weighting factor W Tem weighted and minimized in the cost function. Alternatively to the driving force F A per meter, the torque M provided by the electric machine 8 can also be EM used and with the weighting factor W Tem weighted so that the alternative term wTem⋅∑s=1sE−1(MEM(s)−MEM(s−1)Δs)2 Due to the constant gear ratio of the gear 10, the driving force and the torque are directly proportional to each other.

[0063] To ensure comfortable driving, an additional term is introduced in the cost function 15 to penalize torque jumps, namely W TemStart · (F A (s1) - F A (s0)) 2 . Alternatively to the driving force F A Here too, the torque M provided by the electric machine 8 can EMbe used, so that the alternative term w TemStart · (M EM (s1) - M EM (s0)) 2 For the first point in the prediction horizon, the deviation from the last set moment is negatively evaluated and assigned a weighting factor W TemStart weighted to ensure that there is a seamless and smooth transition when switching between the old and new trajectory.

[0064] Speed limits are hard limits for optimization that must not be exceeded. In reality, slightly exceeding the speed limits is always permissible and is particularly common when transitioning from one speed zone to a second. In dynamic environments, where speed limits shift from one computing cycle to the next, it may happen that a valid solution for a speed curve can no longer be found for very hard limits. To increase the stability of the calculation algorithm, a soft constraint is introduced into the cost function 15. This involves a weighting factor W Slack weighted slack variable Var Slackactive within a predefined narrow range before the hard speed limit is reached. Solutions that are very close to this speed limit, i.e. solutions whose speed trajectories remain a certain distance from the hard limit, are rated lower. Reference symbol h1 first value of first operating parameter h2 second value of first operating parameter h3 third value of first operating parameter x1 first actuator value of the first actuator x2 first actuator value of the first actuator x3 first actuator value of the first actuator y1 first value of second operating parameter y2 second value of second operating parameter y3 third value of second operating parameter z1 first actuator value of the second actuator z2 first actuator value of the second actuator z3 first actuator value of the second actuator 1 vehicle 2 systems 3 Processor unit 4 storage unit 5 Communication interface 6 Recording unit 7 Drivetrain 8 electric machine 9 Battery 10 gearboxes 11 Computer program product 12 GNSS sensors 13 MPC algorithm 14 Longitudinal dynamics model 15 Cost function 16 Driver assistance system 17 Internal combustion engine 18 Level control system 19 Brake system 20 first operating parameters 21 Chassis 22 first actuator 23 Actuator values of the first actuator 24 second operating parameter 25 second actuator 26 Actuator values of the second actuator 27 Loss model

Claims

[1] Processor unit (3) for model-based predictive control of several components (18, 19) of a motor vehicle (1), wherein - the processor unit (3) is configured to execute an MPC algorithm (13) for model-based predictive control of a first component (18) of a motor vehicle (1) and a second component (19) of the motor vehicle (1), - the first component (18) can be operated with different values (h1, h2, h3) of a first operating parameter (20), - the second component (19) can be operated with different values (y1, y2, y3) of a second operating parameter (24), - the MPC algorithm (13) contains a cost function (15) to be minimized, - the MPC algorithm (13) contains a dynamic model (14) of the motor vehicle (1), wherein the dynamic model (14) comprises a loss model (27) of the motor vehicle (1), - the loss model (27) describes a total loss of the motor vehicle (1), - the cost function (15) contains a first term which represents the total loss of the motor vehicle (1), - the total loss depends on an operating value combination which includes a first value (h1; h2; h3) of the first operating parameter (20) and a second value (y1; y2; y3) of the second operating parameter (24), and - the processor unit (3) is configured to determine, by executing the MPC algorithm (13) as a function of the loss model (14), the operating value combination by which the first term of the cost function (15) is minimized, characterized by , that - the first term contains an electrical energy weighted with a first weighting factor and predicted according to the dynamic model (14), which is provided within a prediction horizon by a battery (9) of the drive train (7) for driving the electric machine (8), - the cost function (15) contains as a second term a travel time weighted with a second weighting factor and predicted according to the dynamic model (14), which the motor vehicle (1) needs to cover the entire distance predicted within the prediction horizon, and - the processor unit (3) is configured to determine an input variable for the electrical machine (8) by executing the MPC algorithm (13) as a function of the first term and as a function of the second term, so that the cost function is minimized. [2] Processor unit (3) according to claim 1, wherein the processor unit (3) is configured to execute a conversion software module - to control a first actuator (22) of the first component (18) such that the first actuator (22) is operated with a first actuator value (x1; x2; x3), whereby the first component (18) is operated with the first value (h1; h2; h3) of the first operating parameter (20) of that operating value combination by which the first term of the cost function (15) is minimized, and - to control a second actuator (25) of the second component (19) such that the second actuator (25) is operated with a second actuator value (z1; z2; z3), whereby the second component (19) is operated with the second value (y1; y2; y3) of the second operating parameter (24) of that operating value combination by which the first term of the cost function (15) is minimized. [3] Processor unit (3) according to claim 1 or 2, wherein the first component is a system (18) for level control of the motor vehicle (1), and wherein the second component is a braking system (19) of the motor vehicle (1). [4] Processor unit (3) according to one of the preceding claims, wherein - the cost function (15) contains a final energy consumption value weighted by the first weighting factor, which the predicted electrical energy assumes at the end of the prediction horizon, and - the cost function (15) contains a final travel time value weighted with the second weighting factor, which the predicted travel time assumes at the end of the prediction horizon. [5] Processor unit (3) according to one of the preceding claims, wherein - the cost function (15) has a third term with a third weighting factor, - the third term contains a value of a torque predicted according to the dynamic model (14) which the electric machine (8) provides for driving the motor vehicle (1), and - the processor unit (3) is configured to determine the input variable for the electrical machine (8) by executing the MPC algorithm (13) as a function of the first term, as a function of the second term and as a function of the third term, so that the cost function (15) is minimized. [6] Processor unit (3) according to claim 5, wherein - the third term contains a first value, weighted by the third weighting factor, of a torque predicted according to the dynamic model (14), which the electric machine (8) provides for driving the motor vehicle (1) to a first path point within the prediction horizon, - the third term contains a zeroth value of a torque weighted with the third weighting factor, which the electric machine (8) provides for driving the motor vehicle (1) to a zeroth waypoint which lies immediately before the first waypoint, and - in the cost function (15) the zeroth value of the torque is subtracted from the first value of the torque. [7] Motor vehicle (3) comprising a processor unit (3) according to one of the preceding claims, a driver assistance system (16), a first component (18) and a second component (19), wherein the driver assistance system (16) is configured to - to access an operating value combination which minimizes the first term of the cost function (15), which combination has been determined by the processor unit (3) and which includes a first value (h1; h2; h3) of the first operating parameter (20) and a second value (y1; y2; y3) of the second operating parameter (24), - to control the first component (18) based on the first value (h1; h2; h3) of the first operating parameter (20), and - to control the second component (19) based on the second value (y1; y2; y3) of the second operating parameter (24). [8] Method for model-based predictive control of several components (18, 19) of a motor vehicle (1), wherein a first component (18) can be operated with different values (h1, h2, h3) of a first operating parameter (20), and wherein a second component (19) can be operated with different values (y1, y2, y3) of a second operating parameter (24), the procedure comprising the steps - Executing an MPC algorithm (13) for model-based predictive control of the first component (18) and the second component (19), wherein - the MPC algorithm (13) contains a cost function (15) to be minimized and a dynamic model (14) of the motor vehicle (1), - the dynamic model (14) comprises a loss model (27) of the motor vehicle (1), - the loss model (14) describes a total loss of the motor vehicle (1), - the cost function (15) contains a first term which represents the total loss of the motor vehicle (1), and - the total loss depends on an operating value combination which includes a first value (h1; h2; h3) of the first operating parameter (20) and a second value (y1; y2; y3) of the second operating parameter (24) and - Determining the operating value combination by executing the MPC algorithm (13) as a function of the loss model (27) by which the first term of the cost function (15) is minimized, where - the first term contains an electrical energy weighted with a first weighting factor and predicted according to the dynamic model (14), which is provided within a prediction horizon by a battery (9) of the drive train (7) for driving the electric machine (8), - the cost function (15) contains as a second term a travel time weighted with a second weighting factor and predicted according to the dynamic model (14), which the motor vehicle (1) needs to cover the entire distance predicted within the prediction horizon, and - the processor unit (3) is configured to determine an input variable for the electrical machine (8) by executing the MPC algorithm (13) as a function of the first term and as a function of the second term, so that the cost function is minimized. [9] Computer program product (11) for model-based predictive control of a plurality of components (18, 19) of a motor vehicle (1), wherein a first component (18) can be operated with different values (h1, h2, h3) of a first operating parameter (20), and wherein a second component (19) can be operated with different values (y1, y2, y3) of a second operating parameter (24), wherein the computer program product (11), when executed on a processor unit (3), instructs the processor unit (3), - to execute an MPC algorithm (13) for model-based predictive control of the first component (18) and the second component (19), wherein - the MPC algorithm (13) contains a cost function (15) to be minimized and a dynamic model (14) of the motor vehicle (1), - the dynamic model (14) comprises a loss model (27) of the motor vehicle (1), - the loss model (14) describes a total loss of the motor vehicle (1), - the cost function (15) contains a first term which represents the total loss of the motor vehicle (1), and - the total loss depends on a combination of operating values which includes a first value (h1; h2; h3) of the first operating parameter (20) and a second value (y1; y2; y3) of the second operating parameter (24), and - by executing the MPC algorithm (13) as a function of the loss model (27), to determine the operating value combination by which the first term of the cost function (15) is minimized, where - the first term contains an electrical energy weighted with a first weighting factor and predicted according to the dynamic model (14), which is provided within a prediction horizon by a battery (9) of the drive train (7) for driving the electric machine (8), - the cost function (15) contains as a second term a travel time weighted with a second weighting factor and predicted according to the dynamic model (14), which the motor vehicle (1) needs to cover the entire distance predicted within the prediction horizon, and - the processor unit (3) is configured to determine an input variable for the electrical machine (8) by executing the MPC algorithm (13) as a function of the first term and as a function of the second term, so that the cost function is minimized.

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