DE102020216250B4 - Model-based predictive control of a motor vehicle taking cross-traffic into account - Google Patents

Abstract

Processor unit (3; 3'; 3") for model-based predictive control of a motor vehicle (1) taking cross-traffic into account, the processor unit (3; 3'; 3") being set up to - access cross-traffic information, the cross-traffic - information describes cross-traffic on a section of road ahead of the motor vehicle (1) which the motor vehicle (1) is to travel on in the future, - executing an MPC algorithm (13) which uses a longitudinal dynamics model (14) of the motor vehicle (1) and a Cost function (15.1) includes - calculating a speed trajectory (50, 51) for the motor vehicle (1), according to which the motor vehicle (1) is to move within a prediction horizon on the route section in the area in front of the motor vehicle (1) by Processor unit (3; 3'; 3") - executes the MPC algorithm (13), - takes cross-traffic information into account as a restriction, - the longitudinal dynamics model (14) of the motor vehicle (1) is taken into account and the cost function (15.1) is minimized, with the MPC algorithm (13) having a first solver module (13.1), a second solver module (13.2), a third solver module (13.3) and three cost functions (15.1 to 15.3) comprises a first cost function (15.1) being assigned to the first solver module (13.1), a second cost function (15.2) being assigned to the second solver module (13.2) and a third cost function (15.3) being assigned to the third solver module (13.3), wherein the processor unit (3; 3'; 3") is set up - by executing the first solver module (13.1) - to calculate the speed trajectory (50, 51) in such a way that the first cost function (15.1) is minimized, - a progression of a state of charge that minimizes the first cost function (15.1). to calculate a battery (9), which serves as an energy store for an electric machine (8) of the motor vehicle (1), - by executing the second solver module (13.2) based on the speed trajectory (50, 51), based on the course of the state of charge the battery (9) and to calculate a second cost function (15.2) minimizing trajectory of integer control variables, taking into account constraints, and- by executing the third solver module (13.3) for an initial section of the prediction horizon for the electric machine (8), an internal combustion engine ( 17) and a brake system (19) of the motor vehicle (1) has a torque trajectory that minimizes the third cost function (15.3). e to calculate, according to which the electric machine (8), the internal combustion engine (17) and the brake system (19) should provide moments within the prediction horizon.

Description

The invention relates to the model-based predictive control of a motor vehicle, taking cross-traffic into account.

Energy-optimal operation of a vehicle is only possible with good knowledge of the route to be driven. A driver must therefore drive with foresight, but has only limited insight into the further course of the route. Advance planning is very difficult to take place at intersections or in similar traffic situations that require crossing vehicles or pedestrians to be taken into account. The optimal operation of a vehicle can be described using a cost function whose costs are increased, for example, by energy consumption resulting from unfavorable operating points. The costs mean that a desired optimum is not achieved. Such costs are, for example, unfavorable operating points of the aggregates (eg of the internal combustion engine and/or the electric motor) or unfavorable braking when the intention is actually to decelerate sufficiently through recuperation. Another advantageous operation is the avoidance of braking to a standstill. Today's intelligent cruise controls can take the route topology into account, but map the driving strategy based on rules. However, the rule-based implementation sometimes only leads to suboptimal solutions. Optimization-based strategies require a lot of computing power and therefore take a long time, they are often not suitable for online calculations. As far as the prior art is concerned, the EP 3 726 497 A1 , the DE 10 2019 216 457 A1 and the DE 10 2017 208 878 A1 a background to the present invention.

An object of the present invention can be seen as providing an alternative regulation of a motor vehicle which takes account of the problems described above. The object is solved by the subject matter of the independent patent claims. Advantageous embodiments are the subject matter of the dependent claims, the following description and the figures.

The present invention provides energy-efficient vehicle longitudinal guidance of a vehicle, in particular a hybrid vehicle. According to the present invention, an online calculation of an energy-optimal speed trajectory for a horizon ahead of a vehicle is proposed. The online calculation can take place in particular on a central control device in the vehicle (for which the online calculation is carried out). Furthermore, the online calculation can be carried out on distributed control units in this vehicle. Furthermore, the online calculation can take place on a processor unit of an infrastructure component, e.g. on a processor unit of an intelligent traffic sign. In addition, the online calculation can also be carried out on a master computer.

The online calculation is based on the knowledge or on information about cross traffic or about crossing traffic on a route section in the area in front of the vehicle, which the vehicle is to drive on in the future ("cross traffic information"). Cross traffic can occur, for example, at an intersection. For example, through Car2Car communication, the vehicle can obtain information about cross-traffic. Furthermore, the vehicle can also obtain information about cross-traffic via suitable sensors, such as an “intelligent traffic sign” that recognizes crossing vehicles and provides the vehicle with the relevant data via Car21 communication. The online calculation takes such "cross-traffic information" into account, so that a certain amount of time is used to operate the vehicle more energy-efficiently.

In this sense, according to a first aspect of the invention, a processor unit for model-based predictive control of a motor vehicle is provided, taking cross-traffic into account. The processor unit is set up to access cross-traffic information, the cross-traffic information describing cross-traffic on a section of road ahead of the motor vehicle, which the motor vehicle is to travel on in the future. Furthermore, the processor unit is set up to execute an MPC algorithm, which includes a longitudinal dynamics model of the motor vehicle and at least one cost function. Furthermore, the processor unit is set up to calculate a speed trajectory for the motor vehicle, according to which the motor vehicle is to move within a prediction horizon on the route section in the area in front of the motor vehicle, by the processor unit executing the MPC algorithm, which takes cross-traffic information into account as a restriction , the longitudinal dynamics model of the motor vehicle is taken into account and the cost function is minimized.

The cross-traffic information is therefore included as a restriction in the model-based predictive control of the motor vehicle. In this case, the prediction horizon comprises at least part of the route section in the area in front of the motor vehicle, which the motor vehicle is to travel on in the future.

“Cross traffic” or “crossing traffic” can be understood to mean at least one moving object. In particular, the at least one moving object moves in a lateral movement direction crossing a longitudinal movement direction of the vehicle in front of the vehicle. The longitudinal direction of the vehicle is in particular that direction in which the vehicle should move in order to reach a specified destination.

The "moving object" can be understood, for example, as another road user, e.g. a vehicle. The vehicle may be a motor vehicle powered by an engine, such as an automobile (e.g. a passenger car weighing less than 3.5 t), motorcycle, scooter, moped, e-bike or pedelec ( Acronym for Pedal Electric Cycle), bus or truck (e.g. with a weight of more than 3.5 t), but also a rail vehicle, a ship, an aircraft such as a helicopter or plane. However, the vehicle can also be equipped without a motor drive, e.g. a bicycle. The vehicle can be controlled by a driver, possibly supported by a driver assistance system. However, the vehicle can also be controlled remotely and/or (partially) autonomously, for example. The moving object can also be a pedestrian or an animal, for example.

In one embodiment, the cross-traffic information includes that there is a static cross-traffic element on the section of road ahead of the motor vehicle, the processor unit being set up to calculate the speed trajectory for the motor vehicle by using the static cross-traffic element as a restriction it is taken into account that the motor vehicle must potentially be stopped within the prediction horizon, in particular directly in front of the cross-traffic element or in the area in front of the cross-traffic element.

A "static cross-traffic element" can be understood to mean a traffic infrastructure that is stationary. The static cross-traffic element is distinguished in particular by the fact that it allows or provides for the movement of a moving object in a cross-direction. In this case, the transverse direction runs, in particular, transversely across a longitudinal direction of a road. Examples of a static transverse element in whose area crossing traffic can occur are, for example, a pedestrian crossing, an intersection, possibly with a stop sign or a give way sign, a speed bump, a rumble strip or a traffic light. Information about the corresponding traffic signs and route events can be recorded by sensors available in the vehicle (e.g. camera), by map data (provided e.g. via ADASIS V2, ADASIS V3) or by online communication between the vehicle and an infrastructure element or other road users (Car2I, Car2X). will. This static information can be displayed to the driver in the navigation system, for example. It is static information because it describes a stationary object, e.g. the stationary coordinates of the pedestrian crossing listed above.

The presence of a static cross-traffic element (e.g. a pedestrian crossing) can therefore be taken into account as a first step in the optimization to the effect that stopping could be possible there or that it may be necessary to stop the motor vehicle if it is in the area of the cross-traffic element or immediately in front of it. A detected static cross-traffic element can thus be taken into account as a restriction during the execution of the MPC algorithm.

In particular, the processor unit can be set up to prepare or initiate measures on the first stage in order to stop the motor vehicle or at least to reduce the speed of the motor vehicle. In the event that cross-traffic is detected in the area of the static cross-traffic element, this makes it possible to react particularly quickly, effectively and efficiently to the cross-traffic. This can be done, for example, by correspondingly maintaining a SoC level of a battery in the motor vehicle. The battery or accumulator serves as an energy store for an electric machine in the motor vehicle. This energy store can supply the electrical machine with electrical energy in order to drive the motor vehicle within the prediction horizon (possibly exclusively or in support of an internal combustion engine). The state of charge (in English: State of Charge or abbreviated SoC) is the current energy content of an electric battery in relation to its maximum energy content. Should it be necessary to decelerate strongly in a sporty driving mode, so an unfavorable deceleration behavior results. It may also be necessary to use the mechanical brake. This causes an efficiency disadvantage. However, if the charge level of the battery is kept at a certain level or even increased before the vehicle comes to a complete stop, purely electric restarting is possible after the obstacle. Furthermore, a possibly occurring waiting time can be taken into account in the further route optimization.

Depending on the selected weighting (e.g. sporty driving style or energy-efficient driving style), the vehicle speed can be increased or decreased at the cross-traffic element. The first stage described above describes the behavior of the algorithm as long as there is no detailed knowledge of the situation or of the occurrence of cross-traffic in the area of the static cross-traffic element. Only the static information is known. In this first stage, in sporty driving mode, it is planned to drive quite quickly, e.g. over the pedestrian crossing. As long as there is no information that cross-traffic is occurring in the area of the static cross-traffic element, the speed trajectory in the sporty driving mode is calculated such that it is not necessary to stop in the area of the cross-traffic element. On the other hand, in the energy-efficient driving mode, the speed trajectory is calculated to the effect that stopping is possible or could become necessary in the area of the cross-traffic element, even if there is still no information that cross-traffic occurs in the area of the static cross-traffic element.

In this sense, it is provided according to a further embodiment that the processor unit is set up to calculate a first speed trajectory for the motor vehicle and to calculate a second speed trajectory for the motor vehicle. According to the first speed trajectory, in the sense of a sporty driving mode, it is provided that the motor vehicle should pass the static cross-traffic element faster and at an earlier point in time than according to the second speed trajectory. Furthermore, according to the second speed trajectory, it is provided that the vehicle should reach and pass the static cross-traffic element with less energy expenditure or with better energy efficiency than according to the first speed trajectory in terms of an energy-efficient driving mode.

The static information described above about the presence of a static cross-traffic element does not yet describe whether there is actually a moving object, e.g. a pedestrian, in the local area, for example the pedestrian crossing. Accordingly, the static information about the presence of the static cross-traffic element also does not describe whether the potentially present cross-traffic intends to move transverse to the longitudinal direction of movement of the vehicle, e.g. by the pedestrian crossing the pedestrian crossing.

Such dynamic information about the specific traffic situation is provided by the dynamic cross-traffic information described in the following embodiment. In terms of a second level, one embodiment provides that the cross-traffic information contains whether cross-traffic has been detected or is occurring in the area of the static cross-traffic element.

According to a first alternative, if cross-traffic has been detected or occurs in the area of the static cross-traffic element, the processor unit in this embodiment is set up to calculate the speed trajectory for the motor vehicle by considering the cross-traffic as a limitation of the optimization in such a way that the Speed of the motor vehicle is reduced within the prediction horizon, so that the motor vehicle is stopped or at least braked before reaching the static cross-traffic element.

According to a second alternative, if no cross-traffic has been detected or occurs in the area of the static cross-traffic element, the processor unit in this embodiment is set up to calculate the speed trajectory for the motor vehicle, so that the motor vehicle crosses the static cross-traffic element without stopping or braking happened.

The information that cross traffic is occurring in the area of the static cross traffic element can be exchanged by Car2Car communication when approaching intersection areas between the motor vehicle and another road user, eg another motor vehicle, in order to identify crossing traffic at an early stage. In the case of traffic lights, these can be so-called "SPAT messages" (SPAT = Signal Phase and Timing). In addition, information from traffic control systems can be used (e.g. using signal transmitters in the vehicle floor). In addition, crossing vehicle or pedestrian traffic can also be determined by sensors fitted in the vehicle, for example via a radar sensor, a lidar sensor or a camera sensor. Thus, in the optimization, as soon as crossing traffic is determined at the static cross-traffic element, for example by sensors or Car2X, the preparation made in the first stage can be ended and instead, for example, braked. If, on the other hand, it is established that no cross-traffic occurs in the area of the cross-traffic element, then the measures prepared in the first stage can be dispensed with. In particular, the motor vehicle can be controlled or the speed trajectory of the vehicle can be determined or calculated in such a way that the motor vehicle passes the static cross-traffic element without stopping first. The characteristics of the second stage result from the traffic event, in the present case from cross traffic, and due to the typically short reaction time that occurs, leaves little room for optimization. The reaction time depends on the respective event. The earlier the detection takes place, the more reaction time there is.

1. a) Das Kraftfahrzeug muss anhalten, was insbesondere durch die Berechnung einer aktualisierten Geschwindigkeitstrajektorie mittels der Prozessoreinheit erfolgt. Hierzu ist nun eine starke Verzögerung mit Einbußen hinsichtlich des Komforts und der Energieeffizienz notwendig, wenn das Kraftfahrzeug der vorstehend beschriebenen ersten Geschwindigkeitstrajektorie folgt (sportlicher Fahrmodus). Wenn das Kraftfahrzeug hingegen der vorstehend beschriebenen zweiten Geschwindigkeitstrajektorie folgt (energieeffizienter Fahrmodus), kann das Kraftfahrzeug besonders effizient und komfortabel verzögert werden.
2. b) Das Fahrzeug muss nicht anhalten. Es ergibt sich ein Geschwindigkeitsvorteil, wenn das Kraftfahrzeug entsprechend der vorstehend beschriebenen ersten Geschwindigkeitstrajektorie (sportlicher Fahrmodus) betrieben wird.

As soon as the dynamic cross-traffic information is available, the following two options in particular arise:

1. a) The motor vehicle must stop, which is done in particular by calculating an updated speed trajectory using the processor unit. This now requires a strong deceleration with losses in terms of comfort and energy efficiency if the motor vehicle follows the first speed trajectory described above (sporty driving mode). If, on the other hand, the motor vehicle follows the second speed trajectory described above (energy-efficient driving mode), the motor vehicle can be decelerated particularly efficiently and comfortably.
2. b) The vehicle does not have to stop. There is a speed advantage if the motor vehicle is operated in accordance with the first speed trajectory described above (sporty driving mode).

• - auf Wahrscheinlichkeits-Informationen zuzugreifen, welche eine Wahrscheinlichkeit beschreiben, mit welcher Querverkehr berücksichtigt werden muss, und
• - die Geschwindigkeitstrajektorie für das Kraftfahrzeug zu berechnen, indem die Wahrscheinlichkeits-Informationen berücksichtigt werden.

The first stage can be varied relatively widely compared to the second stage. For this purpose, the probability with which a crossing object must be taken into account or with which probability cross-traffic will be detected in the area of the static cross-traffic element can also be considered. In this sense, it is provided in a further embodiment that the processor unit is set up to

• - access probability information describing a probability with which cross-traffic must be taken into account, and
• - calculate the speed trajectory for the motor vehicle, taking into account the probability information.

If, for example, a static cross-traffic element is on the route that the motor vehicle is to travel on in the following, the probability information can be used to decide whether one or more measures to reduce the speed of the motor vehicle are taken into account in the optimization and should be prepared. If the probability information describes, for example, a relatively high probability that cross-traffic will occur in the area of the static cross-traffic element, then at least one measure to reduce the speed of the motor vehicle can be taken into account in the optimization and prepared. If, on the other hand, the probability information describes a relatively low probability that cross-traffic will occur in the area of the static cross-traffic element, then a measure to reduce the speed of the motor vehicle in the optimization can be dispensed with and prepared, or fewer can be used appropriate measures are taken into account and prepared for optimization.

For example, the probability information can describe that at night the probability is relatively low that cross traffic will occur in the area of the static cross traffic element, because experience has shown that there is less traffic on the streets than during the day. The processor unit can then be set up to calculate the relatively low probability (cross-traffic occurring at night in the area of the static cross-traffic element) when calculating the speed trajectory for the motor vehicle, so that relatively few preparations for stopping or braking the motor vehicle in the first phase to be hit. On the other hand, in this context, the probability information can describe, for example, that during the day the probability is relatively high that cross-traffic will occur in the area of the static cross-traffic element. The processor unit can then do this be set up to calculate the relatively high probability (that during the day cross-traffic occurs in the area of the static cross-traffic element) when calculating the speed trajectory for the motor vehicle, so that a relatively large number of preparations for stopping or braking the motor vehicle are made in the first phase.

In this sense, according to a further embodiment, it is provided that the probability information describes that the probability of cross-traffic occurring in the area of the static cross-traffic element is lower at night than during the day, and that the processor unit is set up to make fewer preparations for the Stopping or braking the motor vehicle than during the day. This embodiment allows less preparations to be made in phase 1 at night (lower probability) than during the day. It is accepted that in the event of crossing traffic (contrary to the relatively low probability) occurring, it is less possible to react efficiently. On the other hand, e.g. during the day (higher probability) delays can already be made in the first phase. If there is no crossing traffic, the preparation was not necessary.

In addition, if the crossing traffic can also be observed during the crossing, the stopping time can be estimated. This contributes to optimally making discrete decisions in particular. In particular, the discrete decisions can be made based on the determined holding period. For example, if a cyclist crosses the street, the combustion engine can remain switched on (especially when using a start-stop system). However, if a slow pedestrian is crossing, it will make sense to switch off the combustion engine. In this sense, the processor unit can be set up in a further embodiment to access crossing information which describes the movement of the cross-traffic during a crossing of the static cross-traffic element, and based on the crossing information to calculate a holding period for which the motor vehicle must be stopped.

The processor unit according to the invention can in particular be an element of a central control unit of the motor vehicle or an element of several decentralized control units distributed in the motor vehicle or an element of a traffic infrastructure device or an element of a traffic control computer. Thus, the inventive online calculation of the energy-optimal speed trajectory for a horizon ahead can be carried out on a central control unit in the vehicle, on distributed control units in the vehicle, on infrastructure components such as an intelligent traffic sign or on a master computer.

The traffic infrastructure device can be a traffic sign, in particular an "intelligent" traffic sign with sensors (e.g. camera, lidar, radar), so that the traffic sign itself recognizes the road users, the motor vehicle and cross traffic. The traffic sign can communicate with the motor vehicle and cross-traffic via a Car2I ("Car-to-Infrastructure") interface. Car21 communication is about wireless communication between motor vehicles and infrastructure facilities such as radio beacons, traffic signs and traffic lights. The traffic sign can use the built-in sensors to determine the position and current speed of cross-traffic in particular, and receive the directional vector of cross-traffic in particular via the Car21 interface. The traffic sign can forward this information via the Car2I interface to the motor vehicle, which can then adjust its speed, as already described above. By reducing the speed, e.g. through recuperation, it can be avoided that the motor vehicle has to stop in the area of the cross-traffic element.

If the processor unit is installed in a traffic sign, the processor unit can calculate the speed trajectory for the motor vehicle and transmit it to the motor vehicle using the Car2I interface. The traffic sign requires in particular the positions, speeds and directional vectors of the motor vehicle and the cross traffic, which the named road users can transmit to the Car2I interface of the traffic sign using their communication interfaces. The motor vehicle can set or convert the speed trajectory determined by the processor unit of the traffic sign in order to be able to drive through an intersection with cross traffic, for example, without stopping.

As a further possibility, the speed trajectory for motor vehicles can also be determined on the processor unit of the master computer. The master computer can obtain the information required for this, for example from the motor vehicle and the cross traffic itself or from the intelligent Ver receive traffic signs. In the latter case, the "intelligent" traffic sign is only used for signal detection and communication with road users.

According to the present invention, it is proposed to solve a vehicle model-based optimization problem of a model-based predictive control of the motor vehicle while minimizing cost functions of a number of solver modules in order to enable a shorter reaction time. All solver modules are based on a model of the vehicle, the drive train, including the descriptive data and information about the route section ahead. This information can be topography information (e.g. curve and slope information), traffic information (e.g. vehicles driving ahead) or infrastructure information (e.g. traffic lights on the route).

In this case, a first solver module can be used for optimization, in particular on a central control unit of the motor vehicle, with an optimal speed profile and SoC profile being calculated for a complete prediction horizon. A mixed-integer problem can be solved by means of a second solver module, in particular an optimal gear selection and/or an optimal driving mode selection for a part of the prediction horizon. A distribution of torques to, for example, an internal combustion engine, an electric machine and a brake system of the motor vehicle can then be calculated using a third solver module, with this being able to be done for a short forecast in order to enable a short reaction time.

In this sense, it is proposed that the MPC algorithm comprises a first solver module, a second solver module, a third solver module, the longitudinal dynamics model and three cost functions, with a first cost function being associated with the first solver module, with a second cost function being associated with the second solver module and wherein a third cost function is associated with the third solver module. In particular, the first solver module, the second solver module and the third solver module are implemented by software.

The processing unit is configured to calculate the velocity trajectory by executing the first solver module such that the first cost function is minimized. The processor unit is also set up to calculate a course of a state of charge of a battery or accumulator that minimizes the first cost function by executing the first solver module for the route section ahead, taking into account the longitudinal dynamics model. The battery or accumulator serves as an energy store for an electric machine in the motor vehicle. This energy store can supply the electrical machine with electrical energy, so that the electrical machine can drive the motor vehicle within the prediction horizon (possibly exclusively or in support of an internal combustion engine).

The first solver module can be referred to as the "High Level Solver" (abbreviated: HLS). The HLS solves a non-linear problem 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 with regard to the optimum of the result and with regard to the computing time. In other words, the first solver module enables an online calculation of the particularly energy-optimal speed trajectory and the SoC trajectory for an upcoming prediction horizon, for example on a central control unit in the motor vehicle. In this case, the processor unit can be set up in particular to call up the first solver module twice. Initiated by a first call, initially by executing the first solver module for the route section ahead, taking into account the longitudinal dynamics model, a speed trajectory minimizing in particular the travel time term of the first cost function can be calculated, according to which the motor vehicle should move within a prediction horizon. Then, initiated by a second call by executing the first solver module for the route section ahead, taking into account the longitudinal dynamics model, a speed trajectory minimizing the loss term of the first cost function can be calculated, according to which the motor vehicle should move within a prediction horizon.

Furthermore, for example, traffic lights lying on the route section ahead can also be taken into account, in particular including their traffic light phases and remaining times. The traffic light information is converted into time limits at the waypoint of the traffic light ahead that is on the horizon. In addition, vehicles driving ahead can be taken into account in the first solver module. A probable trajectory can be used for the vehicle located in front of one's own vehicle to be determined. This results in a minimum time for the HLS trajectory over the path at the current position of the vehicle driving ahead.

Furthermore, the processor unit is set up to calculate a trajectory of integer control variables that minimizes the second cost function by executing the second solver module based on the speed trajectory, based on the course of the state of charge of the battery and taking into account secondary conditions. The trajectory of integer control variables can include a course of gears that can or should be engaged in a transmission of the motor vehicle within the prediction horizon. Furthermore, the trajectory of integer control variables can include a course of an engine state according to which an internal combustion engine of the motor vehicle is either switched on or switched off (in particular within the prediction horizon). Furthermore, the trajectory of integer control variables can include a course of a clutch state, according to which a clutch arranged between an internal combustion engine of the motor vehicle and the electric machine is either in an open position or in a closed position (in particular within the prediction horizon).

The second solver module can be called the "Mixed Integer Solver" (abbreviated: MIS). The second solver module is used to determine discrete states, e.g. the determination of the gears or the state of the clutch (open/closed) of the drive train of the motor vehicle. The second solver module thus enables an optimal operating strategy to be determined, which in particular includes the selection of the respectively optimal gear and driving mode. The calculations of the second solver module are based in particular on the underlying HLS trajectory and additional external boundary and secondary conditions. Secondary conditions that can be taken into account when calculating the optimal gait trajectory are, for example, a topography on the route section ahead (curves, inclines). Furthermore, knowledge of speed limits on the route section ahead can be taken into account as secondary conditions in the calculation of the gait trajectory. The trajectory of integer control variables is optimized over time and can be significantly shorter than that of the first solver module.

Furthermore, the processor unit is set up to, by executing the third solver module - in particular based on the speed trajectory and based on the SoC trajectory and / or based on the trajectory of integer control variables - for an initial section of the prediction horizon for the electric machine, an internal combustion engine and a brake system of the motor vehicle to calculate a torque trajectory that minimizes the third cost function, according to which the electric machine, the internal combustion engine and the brake system should provide torques within the prediction horizon. Forces can also be used instead of moments - both quantities can be converted linearly into one another. The third solver module can be called "tracker". The tracker is characterized by a fast computing time and a robust behavior. If current solutions from the other solvers (HLS and/or MIS) are not available, the tracker can still provide moments based on the last solutions from the other solvers. The output of the tracker directly influences the driving comfort by minimizing the tracker-specific third cost function. 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.

An optimal SoC course and an optimal speed trajectory are thus determined by means of the first solver module (HLS). Using the second solver module (MIS), based on the calculations of the first solver module (HLS), it can be decided which gears and which driving mode are optimal. The third solver module (tracker) calculates very frequently and determines the moments for a short period of time. The tracker can work both with the trajectory calculated in the MIS and without it. In the second case, the tracker only uses the gear currently engaged and the active driving mode, which is sufficient feedback for the tracker. This is possible because gait requirements typically do not change multiple times in a short space of time. Changing gears takes some time. Therefore, the tracker can also work if there is only one trajectory of the HLS. If this cannot be updated either, the tracker can continue to work with an older HLS trajectory. Then the "old" solution can be gradually run down. However, this only makes sense up to a certain point. If the HLS calculates but cannot find a solution, the tracker can be informed, e.g. via a so-called "error flag", i.e. a display of the error. Error handling can then be implemented in the tracker itself. If errors occur that the tracker cannot anticipate, this also leads to an error in the tracker. As a result, the automated driving function can then be deactivated.

It can thus be distributed to the different solver modules. This allows each solver module to run independently on the processor unit. The solver modules do not run strictly one after the other. Similar to a desktop PC, the processor unit can have several processor cores. The three solver modules can be distributed to the processor cores and thus calculate independently of one another and in parallel. This is a great advantage, because otherwise you might have to wait several seconds before the solver modules have calculated a solution that can be used to continue. When driving down a route, this may not be a problem. However, if a vehicle drives in front of your own vehicle or cuts in, the system cannot recognize this until the solvers have calculated with the new sensor data again. This could lead to a critical situation.

During the distribution, different runtimes can also be granted for the individual solver modules. The first solver module, for example, which is used to determine the velocity trajectory and the trajectory of integer control variables, can take much more time than the other solver modules. Since the solver modules calculate "in parallel", it is possible that one of the solver modules is called before the previous solver module has finished calculating, e.g. the tracker before the currently running HLS. In particular, all data that is used can always have a time stamp. Each solver solution can also have a timestamp. With this time stamp, it can always be traced which previous result was included in which calculation. Appropriate error handling can be implemented via this link, e.g. timeouts if information is too old. A certain degree of synchronization therefore takes place via the timestamps. Thus, the present invention provides real-time synchronization of inherently asynchronous solver calls.

Furthermore, the moments calculated by means of the third solver module can have a linear progression. This enables linear interpolation, thereby simplifying post-processing. Until now, the moments were determined for specific time steps. This resulted in a stair-step progression of the signals. According to the present invention, the third solver module results in a linear progression, which means that the signals are also linear between the time steps and do not jump from one time step to the next. This also allows correct interpolation between the time steps.

According to the present invention, the target speed (speed trajectory) is recalculated cyclically. The target torques (torque trajectory) and discrete states (gear trajectory) described in connection with the embodiment comprising three solver modules can also be cyclically recalculated on the basis of a current driving state and of route information ahead.

Furthermore, a targeted combination of SPP (Signal Post Processing) and tracker (same call time > synchronous) can take place so that the moments output by the tracker are matched to the gears calculated by the MIS (see the same input step). Signal Post Processing (SPP) is signal post-processing. Here, the partial results calculated in advance at different times are linked with one another in such a way that they are related to one another over time. This gives you a period of time in which all the information is available. In particular, the time base is that of the tracker. In addition, SPP and tracker are implemented in the same software module, which means that SPP and tracker have the same time base.

Using the first solver module, the speed trajectory of the vehicle can still be optimized online even with a relatively long or wide forecast over the route, e.g. with a forecast in the range of a few kilometers. This is computationally intensive. On the other hand, it is sufficient to use the other server modules to calculate the corresponding trajectories for a relatively short forecast over time, e.g. for the area immediately ahead of the vehicle. This enables particularly short reaction times. In this sense, the processor unit is set up in one embodiment to calculate the speed trajectory for a route section that is in the kilometer range and is in particular a few kilometers long by executing the first solver module, and by executing the other two solver modules for a few seconds immediately before the Motor vehicle lying initial section of the prediction horizon to calculate the target torque trajectory.

The MPC algorithm provides an operating strategy that, in addition to the optimal selection of the moments of the assemblies and the gear, also enables the optimal selection of a driving mode. In this way, an optimal driving strategy can provide that the powertrain within the prediction horizon in is operated in a specific driving mode. However, it may also be better to change the driving mode within the prediction horizon, for example because this increases efficiency.

1. 1. Fahrmodus: Verbrennungskraftmotor im Betrieb („an“) und Kupplung K0 (zwischen Verbrennungskraftmotor und elektrischer Maschine angeordnet) in geöffneter Stellung; die elektrische Maschine treibt im Motorbetrieb das Kraftfahrzeug an; der Verbrennungskraftmotor läuft, treibt jedoch nicht das Kraftfahrzeug an;
2. 2. Fahrmodus: Verbrennungskraftmotor nicht im Betrieb („aus“) und Kupplung K0 in geöffneter Stellung; die elektrische Maschine treibt im Motorbetrieb das Kraftfahrzeug an; der Verbrennungskraftmotor läuft nicht;
3. 3. Fahrmodus: Verbrennungskraftmotor im Betrieb („an“) und Kupplung K0 in geschlossener Stellung; Vorwärtsgang oder Rückwärtsgang im Getriebe eingelegt; die elektrische Maschine (im Motorbetrieb oder im Generatorbetrieb) und der Verbrennungskraftmotor (im Zugbetrieb oder im Schubbetrieb) treiben das Kraftfahrzeug gemeinsam an (Hybridantrieb); dabei ist insbesondere auch ein Leistungsfluss zwischen elektrischer Maschine und Verbrennungskraftmotor möglich (Lastpunktverschiebung)
4. 4. Fahrmodus: Verbrennungskraftmotor im Betrieb („an“) und Kupplung K0 in geschlossener Stellung; Neutral im Getriebe eingelegt; der Verbrennungskraftmotor treibt die elektrische Maschine an (Generatorbetrieb), sodass die Batterie geladen wird.

The motor vehicle, which can be predictively regulated using the present invention, is in particular driven by a motor. For example, the motor vehicle is an automobile (e.g. a passenger car weighing less than 3.5 t), motorcycle, scooter, moped, e-bike or pedelec (acronym for Pedal Electric Cycle), bus or truck (e.g. weighing over 3.5 t), or a rail vehicle, a ship, an aircraft such as a helicopter or plane. The motor vehicle can be controlled by a driver, possibly supported by a driver assistance system. However, the vehicle can also be controlled remotely and/or (partially) autonomously, for example. In particular, it is a hybrid vehicle. A “hybrid vehicle” can be understood to mean an electric vehicle that can be driven by at least one electric machine and at least one internal combustion engine. The hybrid vehicle can obtain energy both from a battery and from an additional fuel that is carried along, eg diesel, petrol or gas. The electric machine can be operated as a motor and as a generator. A hybrid drive train of the hybrid vehicle can also include a transmission and a clutch in addition to the electric machine, the internal combustion engine and the battery. Depending on the switching position (open and closed) of the clutch and the switching position (gear engaged, neutral) of the transmission, the drive train of the hybrid vehicle is operated in different driving modes. For example, the drive train can be built in P2 architecture, which can then result in the following drive modes:

1. 1. Driving mode: internal combustion engine in operation (“on”) and clutch K0 (arranged between the internal combustion engine and the electric machine) in the open position; the electric machine drives the motor vehicle in engine operation; the internal combustion engine is running but not propelling the motor vehicle;
2. 2. Driving mode: combustion engine not in operation ("off") and clutch K0 in open position; the electric machine drives the motor vehicle in engine operation; the combustion engine is not running;
3. 3. Driving mode: internal combustion engine in operation ("on") and clutch K0 in closed position; Forward gear or reverse gear engaged in the transmission; the electric machine (in motor mode or in generator mode) and the internal combustion engine (in traction mode or in overrun mode) drive the motor vehicle together (hybrid drive); in particular, a power flow between the electric machine and the combustion engine is also possible (load point shift)
4. 4. Driving mode: internal combustion engine in operation ("on") and clutch K0 in closed position; Neutral engaged in transmission; the internal combustion engine drives the electric machine (generator operation) so that the battery is charged.

If the motor vehicle - as described above - is a hybrid vehicle whose drive train can be operated at least in a first driving mode and in a second driving mode, then the processor unit can be set up in one embodiment to, by executing the second solver module based on the speed trajectory based on to calculate a driving mode trajectory that minimizes the cost function based on the course of the state of charge of the battery and taking into account secondary conditions. Particular attention can be paid to the fact that the switching of the driving modes cannot take place arbitrarily quickly (e.g. engine start, engine stop, clutch open/close) and this can also have an impact on continuous variables (e.g. interruptions in traction). A corresponding engine start and engine stop logic can also be implemented outside of the optimization in a downstream software module, as this simplifies the discrete problem formulation and reduces the computing time.

In a further embodiment, the energy consumption can be optimized while driving by knowing the losses (efficiency characteristics of the drive train components and driving resistances). For this purpose, the processor unit can access loss characteristics of components of the motor vehicle, in particular loss characteristics of the internal combustion engine, the electric machine and the transmission of the drive train of the motor vehicle. The loss characteristic diagrams can be stored, for example, on a storage unit inside the motor vehicle. The loss maps can be continuously differentiable “fitted”, ie formulas can be derived from the maps of the respective component, which formulas describe the loss map of the respective component. The losses of a pre-translation and of a differential of the drive train of the motor vehicle can only be stored as a characteristic. So-called “efficiency maps” can continue to be known when the calculations start. The characteristic diagrams can furthermore be adapted to the problem formulation of the first solver module, in that the characteristic diagrams provide, for example, zero losses below an idling characteristic. For example, the loss map of the internal combustion engine of the drive train of the motor vehicle can be changed in such a way that when the internal combustion engine is not used, its consumption is zero. This enables a transition from a discrete state (clutch open/closed) to a continuous state.

• - Bereitstellen einer Prozessoreinheit gemäß dem ersten Aspekt der Erfindung,
• - Bereitstellen eines Kraftfahrzeugs,
• - Bereitstellen der Querverkehr-Informationen an das Kraftfahrzeug durch
  • - ein weiteres Kraftfahrzeug mittels einer Car2Car-Kommunikation oder
  • - eine Verkehrsinfrastruktureinrichtung mittels einer Car21-Kommunikation, oder
  • - einen Verkehrsleitrechner, oder
  • - ein internetfähiges Gerät eines anderen Verkehrsteilnehmers, und
• - Berechnen einer Geschwindigkeitstrajektorie für das Kraftfahrzeug mittels der Prozessoreinheit, gemäß welcher sich das Kraftfahrzeug innerhalb eines Prädiktionshorizonts auf einem im Vorausbereich des Kraftfahrzeugs liegenden Streckenabschnitt fortbewegen soll, indem die Prozessoreinheit
• - einen MPC-Algorithmus ausführt, der ein Längsmodell des Kraftfahrzeugs, ein erstes Solvermodul, ein zweites Solvermodul, ein drittes Solvermodul und drei Kostenfunktionen umfasst, wobei eine erste Kostenfunktion dem ersten Solvermodul zugeordnet ist, wobei eine zweite Kostenfunktion dem zweiten Solvermodul zugeordnet ist und wobei eine dritte Kostenfunktion dem dritten Solvermodul zugeordnet ist,
  • - die Querverkehr-Informationen als Beschränkung berücksichtigt,
  • - das Längsdynamikmodell des Kraftfahrzeugs berücksichtigt und
  • - die Kostenfunktion minimiert.

According to a second aspect of the invention, a method for model-based predictive control of a motor vehicle is provided, taking cross-traffic into account. The procedure includes the steps:

• - providing a processor unit according to the first aspect of the invention,
• - Provision of a motor vehicle,
• - Providing the cross-traffic information to the motor vehicle
  • - another motor vehicle by means of Car2Car communication or
  • - a traffic infrastructure facility by means of a Car21 communication, or
  • - a traffic control computer, or
  • - an internet-enabled device belonging to another road user, and
• - Calculating a speed trajectory for the motor vehicle by means of the processor unit, according to which the motor vehicle is to move within a prediction horizon on a route section lying in the area in front of the motor vehicle, by the processor unit
• - runs an MPC algorithm comprising a longitudinal model of the motor vehicle, a first solver module, a second solver module, a third solver module and three cost functions, wherein a first cost function is assigned to the first solver module, wherein a second cost function is assigned to the second solver module and wherein a third cost function is assigned to the third solver module,
  • - the cross-traffic information is taken into account as a restriction,
  • - takes into account the longitudinal dynamics model of the motor vehicle and
  • - minimized the cost function.

By executing the first solver module, the speed trajectory is calculated in such a way that the first cost function is minimized, and a course of a state of charge of a battery that minimizes the first cost function is also calculated, which is used as an energy store for an electric machine in the motor vehicle. Furthermore, by executing the second solver module based on the speed trajectory, based on the course of the state of charge of the battery and taking into account secondary conditions, a trajectory of integer control variables that minimizes the second cost function is calculated. In addition, by executing the third solver module for an initial section of the prediction horizon for the electric machine, an internal combustion engine and a brake system of the motor vehicle, a torque trajectory minimizing the third cost function is calculated, according to which the electric machine, the internal combustion engine and the brake system should provide torques within the prediction horizon.

As far as the provision of the cross-traffic information to the motor vehicle by a further motor vehicle by means of Car2Car communication is concerned, the motor vehicle and the further motor vehicle can each have a Car2Car interface via which the Car2Car communication takes place. “Car2Car” communication can be understood as an exchange of information and data between motor vehicles, the purpose of the communication being to report critical and dangerous situations in particular to the vehicle or its drivers at an early stage. In particular, the motor vehicles can exchange their respective positions, their current speed and their direction vector with one another. In particular, the motor vehicle receives the position, current speed and direction vector of the other motor vehicle via the Car2Car interface. Based on this information, the processor unit recognizes that cross-traffic in the form of the other motor vehicle is actually occurring in the area of the intersection and, based on this, can calculate the speed trajectory for the motor vehicle using the MPC algorithm.

For example, an “intelligent” traffic sign with sensors (eg camera, lidar, radar) can be used as a traffic infrastructure device, so that it recognizes the motor vehicle and cross traffic itself. The traffic sign can communicate with the motor vehicle and cross-traffic via a Car21 (Car-to-Infrastructure) interface. The traffic sign can use the built-in sensors to determine the position and current speed of cross-traffic, and receive the directional vector of cross-traffic via the Car2I interface. The traffic sign can forward this information via the Car2I interface to the motor vehicle, whose speed trajectory can then be adjusted accordingly. By reducing the speed, for example by recuperation, it can be avoided that the motor vehicle has to stop.

In a further embodiment, the traffic sign can already provide the motor vehicle with a maximum speed, which the motor vehicle sets in order to pass a static cross-traffic element without stopping. For this purpose, the traffic sign can have a processor unit according to the invention. The traffic sign requires information about the motor vehicle and the cross traffic with regard to their positions, speeds and directional vectors.

A traffic control computer can also provide cross-traffic information. In addition, the traffic control computer can also have a processor unit according to the invention in order to calculate the speed trajectory for the motor vehicle and transmit it to the motor vehicle. In this case, the "intelligent" traffic sign described above can be used for signal detection and communication with road users.

If another road user, e.g. a pedestrian or cyclist, uses an internet-enabled device, in particular a smartphone, with a communication app, compare e.g. the ZF app "X2Smart" (https://www.heise.de/autos/artikel/Antikollisions- App-ZF-X2Smart-3523496.html), the cross-traffic information can be transmitted to the vehicle via the communication app. In this way, the processor unit of the motor vehicle receives information about the movement of pedestrians or cyclists, for example (cross-traffic information). The processor unit can take this information into account in particular for planning the speed and operating mode. This makes it possible to detect traffic situations that are outside the field of vision of the motor vehicle. Likewise, people who cross the street outside of pedestrian crossings are recognized and an early and intelligent response can be made.

• 1 eine schematische Darstellung eines Kraftfahrzeugs mit einer erfindungsgemäßen Prozessoreinheit,
• 2 Details eines beispielhaften Antriebsstrangs für das Kraftfahrzeug nach 1,
• 3 das Kraftfahrzeug nach 1 und 2 in einer Verkehrssituation, in welcher an einer Kreuzung Querverkehr auftritt, wobei die Vorfahrtsregel „rechts vor links“ gilt,
• 4 das Kraftfahrzeug nach 1 und 2 in einer Verkehrssituation, in welcher an einer Kreuzung Querverkehr auftritt, wobei „Vorfahrt gewähren“ gilt, was durch ein intelligentes Verkehrszeichen dargestellt wird,
• 5 das Kraftfahrzeug nach 1 und 2 in einer Verkehrssituation, in welcher an einer Kreuzung Querverkehr auftritt, wobei das Kraftfahrzeug geradeaus fahren oder abbiegen soll,
• 6 das Kraftfahrzeug nach 1 und 2 in einer Verkehrssituation, in welcher an einem Kreisverkehr Querverkehr auftritt, wobei das Kraftfahrzeug in den Kreisverkehr einfahren soll, auf dem sich bereits ein anderes Kraftfahrzeug befindet,
• 7 das Kraftfahrzeug nach 1 und 2 in einer Verkehrssituation, in welcher sich ein anderer Verkehrsteilnehmer vor dem Kraftfahrzeug hinter einem parkenden Auto befindet,
• 8 das Kraftfahrzeug nach 1 und 2 in einer Verkehrssituation, in welcher ein anderer Verkehrsteilnehmer die Straße überquert, auf welcher auch das Kraftfahrzeug fährt,
• 9 das Kraftfahrzeug nach 1 2 in einer Verkehrssituation, in welcher sich das Kraftfahrzeug auf eine übersichtliche Kreuzung zubewegt, im Bereich derer das Kraftfahrzeug eventuell einem querenden Fahrzeug Vorfahrt gewähren muss, und
• 10 zwei unterschiedliche Geschwindigkeitstrajektorien für das Kraftfahrzeug 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 with a processor unit according to the invention,
• 2 Details of an exemplary powertrain for the motor vehicle 1 ,
• 3 the motor vehicle 1 and 2 in a traffic situation in which cross traffic occurs at an intersection, where the priority rule "right before left" applies,
• 4 the motor vehicle 1 and 2 in a traffic situation in which cross traffic occurs at an intersection, where "give way" applies, which is represented by an intelligent traffic sign,
• 5 the motor vehicle 1 and 2 in a traffic situation in which cross traffic occurs at an intersection, where the motor vehicle is to drive straight ahead or turn,
• 6 the motor vehicle 1 and 2 in a traffic situation in which cross traffic occurs at a roundabout, where the motor vehicle is to enter the roundabout on which another motor vehicle is already located,
• 7 the motor vehicle 1 and 2 in a traffic situation in which another road user is in front of the motor vehicle behind a parked car,
• 8th the motor vehicle 1 and 2 in a traffic situation in which another road user crosses the road on which the motor vehicle is also driving,
• 9 the motor vehicle 1 2 in a traffic situation in which the motor vehicle is moving towards a clear intersection in which the motor vehicle may have to give way to a crossing vehicle, and
• 10 two different speed trajectories for 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 system 2 and in particular the processor unit 3 can form part of a central control unit of the motor vehicle 1 . Alternatively, however, the system 2 and the processor unit 3 can also form one of a plurality of control units of the motor vehicle 1 .

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 (see FIG 2 ). 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.

1 and 2 also show that the drive train 7 is a hybrid drive train, which also has an internal combustion engine 17 . The internal combustion engine 17 can be carried out in the 2 shown parallel P2 architecture of the hybrid drive train 7 drive the motor vehicle 1 in addition to the electric machine 8 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, electric machine 8 can drive two front wheels 22 and 23 of motor vehicle 1 (supported by internal combustion engine 17 when clutch K0 is engaged) via transmission 10 and via a front differential gear 21, which wheels are attached to a front axle 25. 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 torque (brake torque).

1. 1. Fahrmodus: Verbrennungskraftmotor 17 im Betrieb („an“) und Kupplung K0 in geöffneter Stellung; die elektrische Maschine 8 treibt im Motorbetrieb (Generatorbetrieb ebenfalls möglich) das Kraftfahrzeug 1 an; der Verbrennungskraftmotor 17 läuft, treibt jedoch nicht das Kraftfahrzeug 1 an;
2. 2. Fahrmodus: Verbrennungskraftmotor 17 nicht im Betrieb („aus“) und Kupplung K0 in geöffneter Stellung; die elektrische Maschine 8 treibt im Motorbetrieb (Generatorbetrieb ebenfalls möglich) das Kraftfahrzeug 1 an; der Verbrennungskraftmotor 17 läuft nicht;
3. 3. Fahrmodus: Verbrennungskraftmotor 17 im Betrieb („an“) und Kupplung K0 in geschlossener Stellung; Vorwärtsgang oder Rückwärtsgang im Getriebe 10 eingelegt; die elektrische Maschine 8 (im Motorbetrieb; Lastpunktverschiebung möglich) und der Verbrennungskraftmotor 17 treiben das Kraftfahrzeug 1 gemeinsam an (Hybridantrieb);
4. 4. Fahrmodus: Verbrennungskraftmotor 17 im Betrieb („an“) und Kupplung K0 in geschlossener Stellung; Neutral im Getriebe 10 eingelegt; der Verbrennungskraftmotor 17 treibt die elektrische Maschine an (Generatorbetrieb), sodass die Batterie geladen wird.

The hybrid powertrain 7 after 3 can be operated in particular in the following drive modes:

1. 1. Driving mode: internal combustion engine 17 in operation ("on") and clutch K0 in the open position; the electric machine 8 drives the motor vehicle 1 in motor mode (generator mode is also possible); the engine 17 is running but not driving the motor vehicle 1;
2. 2. Driving mode: internal combustion engine 17 not in operation ("off") and clutch K0 in the open position; the electric machine 8 drives the motor vehicle 1 in motor mode (generator mode is also possible); the engine 17 is not running;
3. 3. Driving mode: internal combustion engine 17 in operation ("on") and clutch K0 in the closed position; Forward gear or reverse gear engaged in transmission 10; the electric machine 8 (in motor mode; load point shifting possible) and the internal combustion engine 17 drive the motor vehicle 1 together (hybrid drive);
4. 4. Driving mode: internal combustion engine 17 in operation (“on”) and clutch K0 in the closed position; Neutral engaged in transmission 10; the internal combustion engine 17 drives the electric machine (generator operation), so that the battery is charged.

A computer program product 11 can be stored on the memory unit 4 . Computer program product 11 can be executed on processor unit 3, for which purpose processor unit 3 and memory unit 4 are connected to one another via communication interface 5, which can also be used to communicate with other processor units outside motor vehicle 1, as will be described in more detail below . 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, in the exemplary embodiment shown, includes or contains an optional first solver module 13.1, an optional second solver module 13.2 and an optional third solver module 13.3. The MPC algorithm 13 also contains a longitudinal dynamics model 14 of the motor vehicle 1. Each of the solver modules 13.1 to 13.3 can access the longitudinal dynamics model 14. Furthermore, the MPC algorithm 13 contains at least one cost function, in the exemplary embodiment shown three cost functions 15.1 to 15.3 to be minimized, a first cost function 15.1 being assigned to the first solver module 13.1, a second cost function 15.2 being assigned to the second solver module 13.2 and a third Cost function 15.3 is assigned to the third solver module 13.3.

In the exemplary embodiment shown, 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, e.g. the electric machine 8 and the brake system 19 in terms of their efficiency or in terms of their loss. 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 for a sliding prediction horizon. This prediction is based on the longitudinal dynamics model 14.

By executing the first solver module 13.1, the processor unit 3 calculates an optimized speed trajectory according to which the motor vehicle should move within the prediction horizon. The optimized speed trajectory is calculated for a route section ahead, taking into account the longitudinal dynamics model 14, with the first cost function 15.1 being minimized. Further details on the calculation of the velocity trajectory are given below in connection with the 3 until 10 shown traffic situations described.

Furthermore, by executing the first solver module 13.1 for the route section ahead, taking into account the longitudinal dynamics model, the processor unit 3 calculates a course of a state of charge of the battery 9 that minimizes the first cost function 15.1, by means of which the electrical machine 8 is supplied with electrical energy in order to move the motor vehicle 1 within the to drive the prediction horizon. Furthermore, traffic light information can be converted into time limits at the waypoint of the corresponding traffic light. As for preceding vehicles, a probable trajectory of the preceding vehicle can be determined. This results in a minimum time for the HLS trajectory over the path at the current position of the other vehicle.

By executing the second solver module 13.2, the processor unit 3 calculates a gear trajectory that minimizes the second cost function 15.2, based on the optimized speed trajectory and based on the optimized course of the state of charge of the battery 9, as well as taking into account secondary conditions, according to which at least one gear in the transmission 10 of the Motor vehicle 1 is to be inserted within the prediction horizon. Furthermore, the processor unit 3 calculates a driving mode trajectory that minimizes the second cost function 15.2 by executing the second solver module 13.2 based on the optimized speed trajectory, based on the optimized course of the state of charge of the battery 9 and taking into account secondary conditions, which determines in which of the driving modes described above the Hybrid powertrain 7 is to be operated within the prediction horizon.

By executing the third solver module 13.1, the processor unit 3 also calculates an optimized torque trajectory for the initial section of 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, while minimizing the third cost function 15.3, according to which the electric machine 8 , the internal combustion engine 17 and the brake system 19 should provide moments within the prediction horizon. While the route section ahead is a few kilometers long, the initial section immediately in front of motor vehicle 1 only lasts a few seconds.

The output of the optimization by the MPC algorithm 13 with its three solver modules 13.1, 13.2 and 13.3 is thus optimal torques of the electric machine 8, the internal combustion engine 17 and the brake system 19 for calculated points in the initial section of the prediction horizon. By distributing the solution of the MPC problem to the three solver modules, faster response times of the system 2 are made possible. For this purpose, the processor unit 3 can determine corresponding input variables for the electric machine 8, the internal combustion engine 17 and the brake system 19, so that the optimum torques are set. The processor unit 3 can control the electrical machine 8 control based on the determined input variable. However, this can also be done by the driver assistance system 16 .

The detection unit 6 can measure current state variables of the motor vehicle 1 , record corresponding data and feed them to the MPC algorithm 13 . Furthermore, route data from an electronic map for a preview horizon or prediction horizon (e.g. 5000 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 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. A detection unit 30 for detecting the external environment of motor vehicle 1 is also provided. This detection unit includes, for example, a radar sensor and/or a camera system and/or a lidar sensor. 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.

A cost function usually consists of a sum term and a final term. Depending on the signals present in the system, the terms can optionally be converted into one another. The physical variables described in the cost function can be converted into one another if they are dependent on one another. For example, moment terms could also be given as force terms and vice versa. Another example is velocity terms, which can be represented by kinetic energy terms.

The first cost function 15.1 can be defined, for example, as follows: $$\begin{array}{l}J={\displaystyle \sum _{i=1}^N(c_1{E}_{kin,minSlack}\left(s_i\right)+c_2{M}_{ICE}^2\left(s_i\right)+c_3{M}_{EM}^2\left(s_i\right)+c_4{f}_{B\mathrm{right}k}\left(s_i\right)}\\ +\lambda c_s\frac{\Delta s\left(s_i\right)}{v\left(s_i\right)}{\dot{m}}_{f\mathrm{and}el}\left(s_i\right)+c_{x}X\left(s_i\right))-\lambda c_6{E}_{Bat}\left(s_N\right)+\left(1-\lambda \right)c_7{t}_{TOtal}\left(s_N\right)\\ +c_{y}Y\left(s_N\right)\end{array}$$

s

Wegstrecke, unabhängige Variable

N → s1, ... ,sN

Wegpunkte, Horizontlänge

Δs(s) = Si - Si-1

Abstand zwischen den Wegpunkten s_i

λ ∈ [0,1]

Faktor zur Zeit- und Verbrauchsgewichtung der Kostenfunktion

c1, ... ,c7, cx, cy

Gewichtungsfaktoren der einzelnen Terme der Kostenfunktion

X, Y

Der Kostenfunktion können weitere Terme hinzugefügt werden, um weitere Zielvorgaben in der Optimierung zu berücksichtigen. Diese Terme können beispielsweise zusätzliche Komponenten (z.B. weitere elektrische Maschinen) oder deren Eigenschaften (z.B. Temperaturen) beschreiben.

Ekin,minSlack

Abweichung von der minimal gewünschten kinetischen Energie

MICE

Moment des Verbrennungskraftmotors

MEM

Moment der elektrischen Maschine

FBrk

Bremskraft

ṁFuel

Kraftstoffverbrauch - Massenstrom

v(si)

Geschwindigkeit des Kraftfahrzeugs

EBat

Energieinhalt der Batterie

tTotal

Gesamtzeit

Here is:

s

Distance traveled, independent variable

N → s1, ... ,sN

waypoints, horizon length

Δs(s) = Si - Si-1

Distance between waypoints s _i

λ ∈ [0,1]

Factor for time and consumption weighting of the cost function

c1, ... ,c7, cx, cy

Weighting factors of the individual terms of the cost function

X, Y

Additional terms can be added to the cost function to accommodate additional optimization objectives. These terms can, for example, describe additional components (eg further electrical machines) or their properties (eg temperatures).

Ekin,minSlack

Deviation from the minimum desired kinetic energy

MICE

Torque of the internal combustion engine

memes

moment of the electric machine

FBrk

braking power

ṁFuel

Fuel consumption - mass flow

v(si)

speed of the motor vehicle

EBat

energy content of the battery

tTotal

total time

The second cost function 15.1 can be defined, for example, as follows: $$\begin{array}{l}J={\displaystyle \sum _{i=1}^N(c_1{\left(v\left(t_i\right)-v*\left(t_i\right)\right)}^2+c_2{\left(E_{Bat}\left(t_i\right)-E_{Bat}^{*}\left(t_i\right)\right)}^2+c_3{\left(M_{ICE}\left(t_i\right)-M_{ICE}^{*}\left(t_i\right)\right)}^2}\\ +c_4{\left(M_{EM}\left(t_i\right)-M_{EM}^{*}\left(t_i\right)\right)}^2+c_5{\left(f_{B\mathrm{right}k}\left(t_i\right)-f_{B\mathrm{right}k}^{*}\left(t_i\right)\right)}^2\\ +{\mathrm{<figure-callout\; id="6"\; label="c"\; filenames="DE102020216250B4\_0008.png"\; state="\{\{state\}\}">c</figure-callout>}}_6\Delta t{\dot{m}}_f\left(t_i,b_{Cl\mathrm{and}tcH}\left(t_i\right),{\mathrm{e.g}}_{ICE}\left(t_i\right)\right)+c_7{G}_{Gea\mathrm{right}CHanGe}^2\left(t_i\right)\\ +c_{\mathrm{8th}}C{l}_{Cl\mathrm{and}tcHCHanGe}^2\left(t_i\right)+c_{x}X\left(t_i\right))-c_9{E}_{Bat}\left(t_N\right)+c_{y}Y\left(t_N\right)\end{array}$$

t

Wegzeit, unabhängige Variable

N → t1, ... , tN

Zeitpunkte, Horizontdauer

Δt = ti - ti-1

Abstand zwischen den Zeitpunkten t_i

c1, ... ,c9, cx, cy

Gewichtungsfaktoren der einzelnen Terme der Kostenfunktion

X, Y

Der Kostenfunktion können weitere Terme hinzugefügt werden, um weitere Zielvorgaben in der Optimierung zu berücksichtigen. Diese Terme können beispielsweise zusätzliche Komponenten (z.B. weitere elektrische Maschinen) oder deren Eigenschaften (z.B. Temperaturen) beschreiben.

v,v*

Geschwindigkeit, Referenzgeschwindigkeit

EBat,

Energieinhalt der Batterie, Referenzenergiegehalt der Batterie

MICE,

Moment des Verbrennungskraftmotors, Referenzmoment des Verbrennungskraftmotors

MEM,

Moment der elektrischen Maschine, Referenzmoment der elektrischen Maschine

FBrk,

Bremskraft, Referenzbremskraft

mƒ

Kraftstoffverbrauch

bClutch

Zustand der Kupplung

zICE

Zustand des Verbrenners

GGearChange

Aktivierung eines Gangwechsels

ClClutchChange

Aktivierung eines Kupplungszustandswechsels

Here is:

t

travel time, independent variable

N → t1, ... , tN

Points in time, horizon duration

Δt = ti - ti-1

Distance between the times t _i

c1, ... ,c9, cx, cy

Weighting factors of the individual terms of the cost function

X, Y

Additional terms can be added to the cost function to accommodate additional optimization objectives. These terms can, for example, describe additional components (eg further electrical machines) or their properties (eg temperatures).

v,v*

speed, reference speed

EBat,

Energy content of the battery, reference energy content of the battery

MIC,

Torque of the internal combustion engine, reference torque of the internal combustion engine

memes

Torque of the electrical machine, reference torque of the electrical machine

FBrk,

Braking force, reference braking force

mƒ

fuel consumption

bClutch

condition of the clutch

zICE

condition of the burner

GGearChange

Activation of a gear change

ClClutchChange

Activation of a clutch state change

The complexity of the problem increases as the number of mixed-integer decisions to be made increases. For reasons of performance, the decision about the state of the internal combustion engine 18 (on/off) can therefore be extracted from the mixed-integer problem and processed in a post-processing logic.

The third cost function 15.3 can be defined, for example, as follows: $$\begin{array}{l}J={\displaystyle \sum _{i=1}^N(\left(c_1{s}_v{\left(t_i\right)}^2+c_2{s}_{SOC}{\left(t_i\right)}^2\right)+{\mathrm{<figure-callout\; id="3"\; label="c"\; filenames="DE102020216250B4\_0010.png"\; state="\{\{state\}\}">c</figure-callout>}}_3\Delta t{\dot{m}}_f\left(t_i,b_{Cl\mathrm{and}tcH}\left(t_i\right),{\mathrm{e.g}}_{ICE}\left(t_i\right)\right)+c_4{f}_{B\mathrm{right}k}\left(t_i\right)}\\ +c_5{\left(\mathrm{i.e}{f}_{ICE,WHeel}\left(t_i\right)\right)}^2+c_6{\left(\mathrm{i.e}{f}_{EM,WHeel}\left(t_i\right)\right)}^2+c_7{\left(\mathrm{i.e}{f}_{B\mathrm{right}ake}\left(t_i\right)\right)}^2\\ +c_{x}X\left(t_i\right))+c_{y}Y\left(t_N\right)\end{array}$$

t

Wegzeit, unabhängige Variable

N → t1, ... , tN

Zeitpunkte, Horizont Dauer

Δt = ti - ti-1

Abstand zwischen den Zeitpunkten t_i

c1, ... ,c7, cx, cy

Gewichtungsfaktoren der einzelnen Terme der Kostenfunktion

X, Y

Der Kostenfunktion können weitere Terme hinzugefügt werden, um weitere Zielvorgaben in der Optimierung zu berücksichtigen. Diese Terme können beispielsweise zusätzliche Komponenten (weitere elektrische Maschinen) oder deren Eigenschaften (z.B Temperaturen) beschreiben.

sv

Slack-Variable der Geschwindigkeit: Die Abweichung der Geschwindigkeitstrajektorie von deren Referenztrajektorie wird innerhalb eines definierten Bereiches akzeptiert und außerhalb bestraft.

SSOC

Slack-Variable des SoC: Die Abweichung der SoC-Trajektorie von deren Referenztrajektorie wird innerhalb eines definierten Bereiches akzeptiert und außerhalb bestraft.

mƒ

Kraftstoffverbrauch

bClutch

Zustand der Kupplung

zICE

Zustand des Verbrennungskraftmotors

FBrk

Bremskraft

dFICE,Wheel

Änderung der Kraft am Rad, die durch den Verbrennungskraftmotor verursacht wird

dFEM,Wheel

Änderung der Kraft am Rad, die durch die elektrische Maschine verursacht wird

dFBrake

Änderung der Kraft am Rad, die durch die Bremskraft verursacht wird

Here is:

t

travel time, independent variable

N → t1, ... , tN

Points in time, horizon duration

Δt = ti - ti-1

Distance between the times t _i

c1, ... ,c7, cx, cy

Weighting factors of the individual terms of the cost function

X, Y

Additional terms can be added to the cost function to accommodate additional optimization objectives. These terms can, for example, describe additional components (further electrical machines) or their properties (eg temperatures).

sv

Velocity slack variable: The deviation of the velocity trajectory from its reference trajectory is accepted within a defined range and penalized outside.

SSOC

Slack variable of the SoC: The deviation of the SoC trajectory from its reference trajectory is accepted within a defined range and penalized outside.

mƒ

fuel consumption

bClutch

condition of the clutch

zICE

condition of the internal combustion engine

FBrk

braking power

dFICE,Wheel

Change in force at the wheel caused by the internal combustion engine

dFEM,Wheel

Change in the force on the wheel caused by the electric machine

dFBrake

Change in force at the wheel caused by braking force

in the through 3 shown traffic situation drives the motor vehicle 1 1 on a road 31 in a longitudinal movement direction L1 straight ahead towards an intersection 32. In order to reach a specified destination, the motor vehicle 1 is intended to cross the intersection 32 straight ahead, ie in its longitudinal direction of movement L1. Cross traffic can occur in the area of intersection 32. Intersection 32 is an example of a static cross-traffic element. How through 3 shown, another motor vehicle 33 is driving towards the intersection 32 . In the exemplary embodiment shown, the other motor vehicle 33 also wants to pass through the intersection 32 in its longitudinal movement direction L33 and therefore forms cross traffic for the motor vehicle 1 . In the area of the intersection 32, the traffic rule “right before left” applies in the exemplary embodiment shown.

The processor unit 3 of the motor vehicle 1 can access cross-traffic information which is carried out in the 3 The exemplary embodiment shown include that the intersection 32 is located as a static cross-traffic element on the route section lying in the area in front of the motor vehicle 1 . The intersection can be made available to the processor unit 3, for example via map data (eg ADASIS V2, ADASIS V3). Alternatively or additionally, the intersection 32 can also be determined, for example, by the detection unit 30 (eg a camera system with image recognition) for detecting the external environment of the motor vehicle 1 and made available to the processor unit 3 .

By executing the first solver module 13.1 of the MPC algorithm 13, the processor unit 3 calculates a speed trajectory for the motor vehicle 1, according to which the motor vehicle 1 is within a prediction horizon of the MPC control on the area in front of the motor vehicle 1 is intended to move along the section of road, with the first cost function 15.1 being minimized. In doing so, the processor unit 3 takes into account the longitudinal model 14 of the motor vehicle 14. Furthermore, the processor unit 3 takes into account the intersection 32 as a restriction, in such a way that it is taken into account that the motor vehicle 1 must be stopped within the prediction horizon, in particular immediately before the intersection 32 The processor unit 3 can already initiate preparatory measures to stop the motor vehicle 1, for example by preparing the SoC level of the battery 9. This can all happen when the processor unit 3 has not yet received any information about the fact that the area the intersection 32 is actually another road user 33 who could possibly cross the longitudinal movement direction L1 of the motor vehicle 1. In this context, probability information can describe the fact that the probability of cross traffic occurring in the area of the intersection 32 is lower at night than during the day. The processor unit 3 can take this probability information into account in such a way that it makes fewer preparations for stopping or braking the motor vehicle 1 at night than during the day.

The motor vehicle 1 and the other motor vehicle 33 in the exemplary embodiment shown 3 each have a Car2Car interface 34 . The two motor vehicles 1, 33 can communicate with one another via these Car2Car interfaces 34. In particular, the motor vehicles 1, 33 can exchange their respective current position and speed as well as their respective current direction vector L1, L33 with one another. Motor vehicle 1 in particular receives the position, speed and direction vector L 33 of the other motor vehicle 33 continuously ) are determined for detecting the external environment of the motor vehicle 1 and are made available to the processor unit 3 . Based on this information, the processor unit 3 recognizes whether cross traffic in the form of the other motor vehicle 33 is actually occurring in the area of the intersection 32 .

The processor unit 3 is therefore in the embodiment shown 3 calculate the speed trajectory for the vehicle 1 taking into account the cross traffic 33, so that the speed of the motor vehicle 1 is reduced within the prediction horizon. The reduction in speed can be selected in particular in such a way that the motor vehicle 1 is braked before reaching the intersection 32 but is not stopped. Using the information about the motor vehicle 33, the processor unit 3 of the motor vehicle 1 can thus adapt its speed in particular in such a way (eg reduce speed through recuperation) that stopping the motor vehicle 1 in an energetically less efficient manner can be avoided.

The processor unit 3 continuously re-executes the steps described above. The processor unit can access crossing information, which describes the movement of the other motor vehicle 33 while crossing the intersection 32, and based on the crossing information, calculate a stopping time for which the motor vehicle 1 must be stopped in order to avoid the other motor vehicle 33 to give way.

in the through 4 shown traffic situation drives the motor vehicle 1 1 on a road 31 in a longitudinal movement direction L1 straight ahead towards an intersection 32. In order to reach a specified destination, the motor vehicle 1 is intended to cross the intersection 32 straight ahead, ie in its longitudinal direction of movement L1. Cross traffic can occur in the area of intersection 32. Intersection 32 is an example of a static cross-traffic element. How through 4 shown, another motor vehicle 33 is driving towards the intersection 32 . In the exemplary embodiment shown, the other motor vehicle 33 also wants to pass through the intersection 32 in its longitudinal movement direction L33 and therefore forms cross traffic for the motor vehicle 1 . In the area of the intersection 32, in the exemplary embodiment shown, the motor vehicle 1 must give way to the other motor vehicle 33, for which purpose a corresponding traffic sign 35 “Give way” is set up in the area of the intersection 32 so that it is visible to the motor vehicle. The traffic sign 35 is an example of a traffic infrastructure facility.

The traffic sign 35 can be an “intelligent” traffic sign with sensors 36 (eg camera, lidar, radar), so that it recognizes the road users 1, 33 itself. The traffic sign 35 can communicate with the road users 1 , 33 via a Car21 (car-to-infrastructure) interface 37 . The traffic sign 35 can determine the position and the current speed of the other motor vehicle 33 via the built-in sensors 36 and can receive the direction vector of the other motor vehicle 33 via the Car2I interface 37 . The traffic sign 35 forwards this information via the Car2I interface 37 to the motor vehicle 1, whose processor unit 3 then calculates the speed trajectory of the motor vehicle 1 adjusts, for example, similar to this in connection with 3 is described. By reducing the speed, for example by recuperation, the motor vehicle 1 does not have to stop.

In a further embodiment, the traffic sign 35 can already provide the motor vehicle 1 with a maximum speed, which the motor vehicle 1 sets in order to drive through the intersection 32 without stopping. For this purpose, the traffic sign 35 can have a system 2' with a processor unit 3', which can be constructed and set up identically or similarly to the system 2 and in particular the processor unit 3 of the motor vehicle 1 according to FIG 1 . The traffic sign 35 requires all relevant information from both motor vehicles 1, 33, ie the positions, speeds and the directional vectors.

As a further possibility, the determination of the maximum speed for the motor vehicle 1 can also be carried out by a traffic control computer 38, which in 4 is shown. The traffic control computer 38 can have a system 2" and a processor unit 3", which can be constructed and set up identically or similarly to the system 2 and the processor unit 3 of the motor vehicle 1 according to FIG 1 . In this case, the "intelligent" traffic sign 35 is used only for signal detection and for communication with road users 1, 33. The traffic control computer 38 can in particular be set up at a considerable distance from the intersection 32.

in the through 5 shown traffic situation drives the motor vehicle 1 1 on a road 31 in a longitudinal movement direction L1 straight ahead towards an intersection 32. In order to reach a predetermined destination, the motor vehicle 1 can cross the intersection 32 straight ahead, ie in its longitudinal movement direction L1. Alternatively, however, motor vehicle 1 can also turn left or right at intersection 32 . Cross traffic can occur in the area of intersection 32. Intersection 32 is an example of a static cross-traffic element. How through 5 shown, another motor vehicle 33 is driving towards the intersection 32 . In the exemplary embodiment shown, the other motor vehicle 33 would like to turn right at the intersection 32 . The other motor vehicle 33 is not yet directly in the area of the intersection and does not yet indicate the change of direction to the right. Since motor vehicle 1 does not yet know that the other motor vehicle 33 wants to turn off, the other motor vehicle 3 (still) forms potential cross traffic for motor vehicle 1. In the area of intersection 32, in the exemplary embodiment shown, motor vehicle 1 follows the other motor vehicle 34 Must give way, including visible for the motor vehicle 1 in the area of the intersection 32 a corresponding traffic sign 35 "give way" is set up. This traffic sign 35 can also be an intelligent traffic sign (cf. 4 ), but this is not absolutely necessary. The traffic sign 35 is an example of a traffic infrastructure facility. Since the traffic sign 35 indicates “give way” and the motor vehicle 1 is not on the priority road, the motor vehicle 1 would have to stop if the other motor vehicle 33 were to continue straight ahead.

If the motor vehicle 1 knew in advance that the other motor vehicle 33 was turning, it would not have to stop. The road users 1, 33 therefore transmit their positions, speeds and direction vectors via a Car2Car interface 34. Based on this information, the processor unit 3 of the motor vehicle 1 can determine the speed trajectory for the first motor vehicle 1 in advance in such a way that it does not have to give way because the other motor vehicle 33 leaves the priority road. The motor vehicle 1 can continue driving unimpeded and does not have to stop or reduce its speed.

in the through 6 shown traffic situation drives the motor vehicle 1 1 on a road 31 towards a roundabout 39. Motor vehicle 1 is intended to drive through roundabout 39 in order to reach a specified destination. Cross traffic can occur in the area of roundabout 39. Roundabout 39 is an example of a static cross-traffic element. How through 6 shown, another motor vehicle 33 is driving in the roundabout 39. The other motor vehicle 33 would like to leave the roundabout in front of the motor vehicle 1 in the exemplary embodiment shown. The other motor vehicle 33 does not yet indicate that it wants to leave the roundabout 39 and therefore forms potential cross traffic for the motor vehicle 1. The motor vehicle 1 must give way to the other motor vehicle 33 driving in the roundabout 39 and possibly stop if the other motor vehicle 33 would drive past the motor vehicle 1 in the roundabout 39 .

If the motor vehicle 1 had advance knowledge that the other motor vehicle 33 is leaving the roundabout 39 as shown by the path 40, it would not have to stop. The road users 1, 33 therefore transmit their positions, speeds and directional vectors via a Car2Car interface 34. Base Based on this information, the processor unit 3 of the motor vehicle 1 can determine the speed trajectory for the first motor vehicle 1 in advance in such a way that it does not have to give way because the other motor vehicle 33 is leaving the roundabout 39 . The motor vehicle 1 can continue driving unhindered and does not have to stop and can enter the roundabout 39 and pass it unhindered.

If a pedestrian or other road user, e.g. a cyclist, uses an internet-enabled device with a communication app, compare e.g. the ZF app "X2Smart" (https://www.heise.de/autos/artikel/Antikollisions-App-ZF-X2Smart -3523496.html), the cross-traffic information can be transmitted to the motor vehicle 1 via the communication app. In this way, the processor unit 3 of the motor vehicle 1 receives information about the movement of the pedestrian or cyclist, for example. The processor unit 3 can take this information into account in particular for planning the speed trajectory and the operating mode. This makes it possible to detect traffic situations that are outside the field of vision of the motor vehicle. Likewise, people who cross the street outside of pedestrian crossings are recognized and an early and intelligent response can be made.

7 illustrates this by an embodiment, wherein the motor vehicle 1 after 1 going straight ahead on a road 31. A pedestrian 42 (or alternatively a cyclist, for example) is behind a car 41 parked at the side of the road and is moving towards the road 31 at high speed. The pedestrian 42 transmits corresponding movement data to the motor vehicle 1 via an app 44 running on his smartphone 43 , for which purpose the smartphone 43 has a communication interface 45 . Based on the information received from the smartphone 43, the processor unit 3 of the motor vehicle 1 calculates the speed trajectory for the first motor vehicle 1 by taking into account the pedestrian 42 or, for example, its position, speed and directional vector. In particular, the processor unit 3 can take into account the pedestrian 42 as an object standing on the road 31 in the MPC control, specifically until the pedestrian 42 is expected to have left the road 31 again. Correspondingly, the processor unit 3 of the motor vehicle 1 will determine the speed trajectory for the first motor vehicle 1 in such a way that it reduces its speed (and possibly brings the motor vehicle 1 to a standstill) so that a collision with the pedestrian 42 can be reliably avoided.

8th shows a similar embodiment as 7 , but without the car 41 parked at the side of the road. The motor vehicle 1 after 1 drives straight ahead again on a road 31. A pedestrian 42 (or alternatively, for example, a cyclist) crosses the road 31 in front of the motor vehicle 1. The pedestrian 42 transmits corresponding movement data via an app 44 running on his smartphone 43 to the motor vehicle 1, including the smartphone 43 has a communication interface 45 . Based on the information received from the smartphone 43, the processor unit 3 of the motor vehicle 1 calculates the speed trajectory for the first motor vehicle 1 by taking into account the pedestrian 42 or, for example, its position, speed and directional vector. In particular, the processor unit 3 can take into account the pedestrian 42 as an object standing on the road 31 in the MPC control, specifically until the pedestrian 42 is expected to have left the road 31 again. Correspondingly, the processor unit 3 of the motor vehicle 1 will determine the speed trajectory for the first motor vehicle 1 in such a way that it reduces its speed in such a way that a collision with the pedestrian 42 can be reliably avoided. The speed of the motor vehicle 1 can be adjusted at an early stage in such a way that the other road user 42 does not feel endangered and at the same time braking of the motor vehicle 1 to a standstill can be avoided. This is for the comfort of all road users, and at the same time energy is saved because restarting can be avoided.

9 shows another similar traffic situation, the motor vehicle 1 after 1 travels straight on a road 31 and a T-intersection 46 to be passed is ahead of the motor vehicle 1 . A cyclist 47 is approaching on another street 48 in the through 9 direction shown also the T-junction 46. The cyclist 47 moves on the priority road 48. The motor vehicle 1 must give the cyclist 47 the right of way. The intersection 46 is unclear due to an optical obstacle 49. The optical obstacle 49, for example a tree or a building, covers the cyclist 47 for the motor vehicle 1; in particular, its detection unit 30 for detecting the external environment of the motor vehicle 1 cannot detect the cyclist 47.

However, the cyclist 47 transmits his movement data to the motor vehicle 1 via an app 44 running on his smartphone 43 , for which purpose the smartphone 43 has a communication interface 45 points. Based on the information received from the smartphone 43, the processor unit 3 of the motor vehicle 1 calculates the speed trajectory for the first motor vehicle 1 by taking into account the cyclist 47 or, for example, his position, speed and directional vector. In particular, the processor unit 3 can adapt the speed trajectory of the motor vehicle 1 at an early stage based on the movement of the motor vehicle 1 that is known to it, so that the T-junction 46 can be driven over without having to stop.

10 shows two different speed trajectories in a period of time during which no detailed knowledge of cross-traffic is available and which extends to a traffic event, in the present case until a static cross-traffic element is reached. A first speed trajectory 50 is assigned to a sporty driving mode, whereas a second speed trajectory 51 is assigned to an energy-efficient driving mode. In the case of the speed trajectory 50, 51, a processor unit 3, 3' or 3" for the motor vehicle 1 1 calculated as above in connection with the 1 until 9 has been described.

1. a) Das Kraftfahrzeug 1 muss anhalten. Hierzu ist nun eine starke Verzögerung mit Einbußen hinsichtlich des Komforts und der Energieeffizienz notwendig.
2. b) Das Kraftfahrzeug 1 muss nicht anhalten. Es ergibt sich ein Geschwindigkeitsvorteil.

In the stated period, it is planned to pass a static cross-traffic element quite quickly in sporty driving mode. As soon as more detailed information about cross-traffic is available, there are two possibilities:

1. a) The motor vehicle 1 must stop. This now requires a strong delay with losses in terms of comfort and energy efficiency.
2. b) The motor vehicle 1 does not have to stop. There is a speed advantage.

In sporty driving mode - as long as no detailed information about crossing traffic is available - it is assumed that stopping is not necessary. In the energy-efficient driving mode, the speed trajectory is calculated more conservatively in the stated period. In the event of a stop, deceleration can therefore be carried out more efficiently and comfortably. In the energy-efficient driving mode - as long as no detailed information is available - it is expected that an energy-efficient and comfortable stopping process may be necessary. This is illustrated by the speed-distance diagram 10 clarified. The speed trajectory 50 of the sporty driving mode shows a higher level and a short lead (aggressive driving style). The speed trajectory 51 of the energy-efficient driving mode describes the limitation in the energy-efficient mode. Here is delayed early and more.

reference list

K0

coupling

L1

Longitudinal direction of movement motor vehicle (direction vector)

L33

Longitudinal direction of movement of another motor vehicle (direction vector)

1

motor vehicle

2

system of the motor vehicle

2'

Traffic sign processing unit

2"

Processor unit of the traffic control computer

3

Processor unit of the motor vehicle

3'

Traffic sign processing unit

3"

Processor unit of the traffic control computer

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

14

longitudinal dynamics model

15.1

first cost function

15.2

second cost function

15.3

third cost function

16

driver assistance system

17

internal combustion engine

18

power electronics

19

braking system

21

front differential gear

22

front wheel

23

front wheel

24

second operating parameter

25

front axle

26

rear wheel

27

loss model

28

rear wheel

29

rear axle

30

Detection unit for detecting the external environment of the motor vehicle

31

Street

32

Intersection (static cross-traffic element)

33

other motor vehicle (cross traffic)

34

Car2Car interface of motor vehicles

35

"Give way" road sign

36

Traffic sign sensors

37

Car2I interface of the road sign

38

traffic control computer

39

roundabout

40

Path of the other motor vehicle

41

Car parked at the roadside

42

pedestrians/cyclists

43

Smartphone (internet-enabled device)

44

App running on the smartphone (computer program product)

45

Communication interface of the smartphone

46

T junction

47

cyclist

48

further street

49

optical obstacle

50

Speed trajectory motor vehicle (sporty driving mode)

51

Vehicle speed trajectory (energy-efficient driving mode)

Claims (11)

Processor unit (3; 3'; 3") for model-based predictive control of a motor vehicle (1), taking cross traffic into account, the processor unit (3; 3'; 3") being set up to - access cross-traffic information, the cross-traffic information describing cross-traffic on a section of road ahead of the motor vehicle (1) which the motor vehicle (1) is to travel on in the future, - Execute an MPC algorithm (13) which includes a longitudinal dynamics model (14) of the motor vehicle (1) and a cost function (15.1), - to calculate a speed trajectory (50, 51) for the motor vehicle (1), according to which the motor vehicle (1) is to move within a prediction horizon on the route section in the area in front of the motor vehicle (1), in that the processor unit (3; 3' ; 3") - runs the MPC algorithm (13), - the cross-traffic information is taken into account as a restriction, - The longitudinal dynamics model (14) of the motor vehicle (1) is taken into account and - the cost function (15.1) minimized, the MPC algorithm (13) comprising a first solver module (13.1), a second solver module (13.2), a third solver module (13.3) and three cost functions (15.1 to 15.3), with a first cost function ( 15.1) is assigned to the first solver module (13.1), a second cost function (15.2) being assigned to the second solver module (13.2) and a third cost function (15.3) being assigned to the third solver module (13.3), the processor unit (3; 3 '; 3") is set up to - by running the first solver module (13.1) - calculate the speed trajectory (50, 51) in such a way that the first cost function (15.1) is minimized, - to calculate a course of a state of charge of a battery (9) which minimizes the first cost function (15.1), which is used as an energy store for an electric machine (8) of the motor vehicle (1), - by executing the second solver module (13.2) based on the speed trajectory (50, 51), based on the course of the state of charge of the battery (9) and taking into account constraints to calculate a second cost function (15.2) minimizing trajectory of integer control variables, and - by executing the third solver module (13.3) for an initial section of the prediction horizon for the electric machine (8), an internal combustion engine (17) and a brake system (19) of the motor vehicle (1) to calculate a third cost function (15.3) minimizing torque trajectory, according to which the electric machine (8), the internal combustion engine (17) and the brake system (19) should provide moments within the prediction horizon. processor unit (3; 3';3") claim 1 , wherein - the cross-traffic information contains that a static cross-traffic element (32, 39, 46) is located on the route section in the area ahead of the motor vehicle (1), and - the processor unit (3; 3';3") for this is set up to calculate the speed trajectory for the motor vehicle (1) by considering the static cross-traffic element (32, 39, 46) as a restriction such that the motor vehicle (1) must potentially be stopped within the prediction horizon. processor unit (3; 3';3") claim 2 , wherein the processor unit (3; 3';3") is set up to initiate preparatory measures in order to stop the motor vehicle (1) or at least to reduce the speed of the motor vehicle (1). processor unit (3; 3';3") claim 2 or 3 , wherein the processor unit (3; 3';3") is set up to - calculate a first speed trajectory (50) for the motor vehicle (1), and - to calculate a second speed trajectory (51) for the motor vehicle (1), - according to the first speed trajectory (50) in terms of a sporty driving mode, the motor vehicle (1) should pass the static cross-traffic element (32, 39, 46) faster and at an earlier point in time than according to the second speed trajectory (51), and - according to the second speed trajectory (51) in terms of an energy-efficient driving mode, the motor vehicle (1) uses the static cross-traffic element (32, 39, 46) with less energy or should reach and happen with a better energetic efficiency than according to the first speed trajectory (50). Processor unit (3; 3';3") according to one of claims 2 until 4 , wherein - the cross-traffic information contains that cross-traffic (33, 42, 47) occurs in the area of the static cross-traffic element (32, 39, 46), and - the processor unit (3; 3';3") is set up for this Calculate the speed trajectory (50, 51) for the motor vehicle (1) by taking the cross traffic (33, 42, 47) into account as a constraint in such a way that the speed of the motor vehicle (1) is reduced within the prediction horizon, so that the motor vehicle (1) is stopped or at least slowed down before reaching the static cross-traffic element (32, 39, 46). Processor unit (3; 3';3") according to one of claims 2 until 4 , wherein - the cross-traffic information includes that no cross-traffic (33, 42, 47) occurs in the area of the static cross-traffic element (32, 39, 46), and - the processor unit (3; 3';3") set up for this is to calculate the speed trajectory (50, 51) for the motor vehicle (1) so that the motor vehicle (1) passes the static cross-traffic element (32, 39, 46) without stopping or braking. Processor unit (3; 3';3") according to one of claims 2 until 6 , wherein the processor unit (3; 3';3") is set up to - access probability information which describes a probability with which cross traffic (33, 42, 47) must be taken into account, and - the speed trajectory for the motor vehicle (1) to be calculated by considering the probability information. processor unit (3; 3';3") claim 7 , where - the probability information describes that the probability of cross-traffic (33, 42, 47) occurring in the area of the static cross-traffic element (32, 39, 46) is lower at night than during the day, and - the processor unit (3 ; 3';3") is set up to make fewer preparations for stopping or braking the motor vehicle (1) at night than during the day. Processor unit (3; 3';3") according to one of Claims 5 until 8th , wherein the processor unit (3; 3';3") is set up to - access crossing information which shows the movement of the cross-traffic (33, 42, 47) during a crossing of the static cross-traffic element (32, 39, 46 ) describe - based on the crossing information to calculate a holding time for which the motor vehicle (1) must be stopped. Processor unit (3; 3'; 3") according to one of the preceding claims, wherein the processor unit (3; 3'; 3") - an element (3) of a central control unit of the motor vehicle (1), - is an element (3) of several decentralized control devices distributed in the motor vehicle (1), - an element (3') of a traffic infrastructure facility (35), - An element (3") of a traffic control computer (38). Method for model-based predictive control of a motor vehicle (1) taking cross traffic (33) into account, the method comprising the steps: - providing a processor unit (3, 3', 3") according to one of the preceding claims, - providing a motor vehicle (1) , - Providing the cross-traffic information to the motor vehicle (1) by - another motor vehicle (33) using Car2Car communication or - a traffic infrastructure device (35) using Car21 communication, or - a traffic control computer (38), or - a internet-enabled device (43) of another road user (42, 47); and - calculating a speed trajectory (50, 51) for the motor vehicle (1) by means of the processor unit (3, 3', 3"), the motor vehicle (1) according to the speed trajectory (50, 51) within a prediction horizon on a section of the route ahead of the motor vehicle (1) is to move by the process unit (3, 3', 3") - an MPC algorithm (13) executes a longitudinal model (14) of the motor vehicle (1), a first solver module (13.1), a second solver module (13.2), a third solver module (13.3) and three cost functions (15.1 to 15.3) comprises a first cost function (15.1) being assigned to the first solver module (13.1), a second cost function (15.2) being assigned to the second solver module (13.2) and a third cost function (15.3) being assigned to the third solver module (13.3), - the cross-traffic information is taken into account as a restriction, - the longitudinal dynamics model (14) of the motor vehicle (1) is taken into account and - the cost function (15) is minimized, with the speed trajectory (50, 51) being calculated in this way by executing the first solver module (13.1). that the first cost function (15.1) is minimized - a first cost function (15.1) minimizing course of a state of charge of a battery (9) is calculated, which is used as an energy store for an electrical he machine (8) of the motor vehicle (1) is used - by executing the second solver module (13.2) based on the speed trajectory (50, 51), based on the course of the state of charge of the battery (9) and taking into account secondary conditions one the second Cost function (15.2) minimizing trajectory of integer control variables is calculated, and - by executing the third solver module (13.3) for an initial section of the prediction horizon for the electric machine (8), an internal combustion engine (17) and a brake system (19) of the motor vehicle (1) a torque trajectory minimizing the third cost function (15.3) is calculated, according to which the electric machine (8), the internal combustion engine (17) and the brake system (19) should provide torques within the prediction horizon.

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