Model-based predictive control of vehicles taking arrival times into account

Abstract

The invention relates to model-based predictive control of a vehicle taking into account an arrival time factor. In particular, a processor unit is provided, which is configured to calculate a trajectory for a vehicle (1) taking into account an arrival time factor, wherein the trajectory comprises a general route (20) to a predetermined destination (19) to be reached by the vehicle (1), and wherein the arrival time factor influences the arrival time of the vehicle (1) at the predetermined destination (19). Furthermore, the processor unit is configured to optimize a section of the trajectory for the vehicle (1) for a slip prediction domain by implementing an MPC algorithm including a longitudinal dynamics model of the powertrain of the vehicle (1) and a cost function to be minimized, thereby minimizing the cost function.

Description

Technical Field

[0001] The invention relates to model-based predictive control of a vehicle taking into account at least one arrival time factor. In this respect, protection is claimed, inter alia, for a processor unit, a driver assistance system, a vehicle, a method and a computer program product. Background Art

[0002] Model-based predictive control methods (MPC for short) are used in the field of trajectory control, especially in the field of engine control in motor vehicles. Due to the online provision of route information and the required computing time, the driving strategy is usually calculated for a limited forward-looking domain in the model-based predictive control of vehicles. As a result, it is not possible to predict the arrival time of the vehicle at the destination and it is not possible to optimize the trajectory of the vehicle accordingly. In addition, refueling stops and charging stops of the vehicle are not taken into account. Summary of the invention

[0003] The object of the present invention may be to provide an improved MPC control of a powertrain of a motor vehicle. This object is achieved by the subject matter of the independent claims. Advantageous embodiments are the subject matter of the dependent claims, the following description and the drawings.

[0004] The invention provides energy-efficient and time-efficient route planning. In particular, it is suggested to optimize the driving speed of the vehicle for the optimal energy consumption and / or the optimal driving time to the desired destination. In addition to the route topology and the location of the refueling and charging possibilities, other information and influences on the vehicle reaching the desired final destination, such as necessary rest times, are also taken into account. The optimization is performed for pure electric vehicles or plug-in hybrid vehicles, so it is not necessary to use charging stations and further degrees of freedom are generated for the optimization problem.

[0005] According to the invention, the control of the model prediction of the vehicle is extended by calculating the complete route and trajectory taking into account the arrival time factor, which affects the travel time and the arrival time of the vehicle at the desired destination. Examples of arrival time factors are rest time (individual, truck driver, bus driver), time for loading the truck, time for refueling / charging the battery, information about available charging stations, traffic flow and congestion on the route, and weather conditions. The driving speed and energy consumption can also be calculated in advance taking into account the predetermined expected arrival time or expected driving range.

[0006] In this sense, according to a first aspect of the invention, a processor unit for predictive control of a vehicle based on a model taking into account an arrival time factor is provided. The processor unit is configured to calculate the trajectory of the vehicle taking into account at least one arrival time factor. The trajectory includes or takes into account the total route until the vehicle should arrive at the predetermined destination. The arrival time factor affects the arrival time of the vehicle at the predetermined destination. The trajectory of the vehicle calculated in this way may be relatively rough, wherein the vehicle, for example, has not yet reserved a gear. The predetermined destination is the location at which the vehicle should arrive. The route starts from the starting point and leads to the destination. The "total route" includes the total route between the starting point and the destination. The prediction domain of the model-based predictive control is sliding and detects a part of the total route respectively until the destination is reached. The destination can be predetermined by the driver of the vehicle, for example by input on the navigation device of the vehicle. The destination can also be predetermined, for example, by an external unit, for example, by a command center of the fleet arranged outside the vehicle.

[0007] The trajectory of the vehicle can be generated, for example, by a routing algorithm, which can be embedded in a system for autonomous driving (common term in English: Autonomous Driving System, automatic driving system, or AD system for short). AD systems typically include a module for sensing the surrounding environment with sensors and a planning module. The planning module can include multiple levels, such as a navigation level on a larger scale (e.g., several kilometers; here the trajectory of the vehicle from the starting point to the destination can be selected or calculated) and a navigation level on a smaller scale (e.g., 50m to 100m ahead, depending on the speed of the vehicle; here the route and speed can be selected in the closer surroundings of the vehicle in order to determine how the vehicle should move in traffic).

[0008] The processor unit is also configured to optimize the segments of the vehicle's trajectory for the sliding prediction boundary by implementing an MPC algorithm that includes a longitudinal dynamics model of the vehicle powertrain and a cost function to be minimized, thereby minimizing the cost function. The optimized trajectory for the sliding prediction boundary is more refined than the speed trajectory for the total route and can especially predetermine the gear for the vehicle. Therefore, the calculation of the vehicle trajectory for the total route is separated from the vehicle trajectory within the sliding prediction boundary based on MPC optimization. In particular, new or updated segments of the vehicle trajectory for the total route are optimized for each new prediction boundary. In addition, the processor unit can form the aforementioned planning module ("Top Level"-planning module), which plans the total vehicle trajectory while taking into account the arrival time factor. This planning module can then submit the segments of the total vehicle trajectory to the MPC algorithm, with the help of which the optimal trajectory of the vehicle within the prediction boundary can be obtained. In order to take into account the time-varying information, the planning module may set a suitable point at the end of the segment submitted to the MPC algorithm, which corresponds to the target time point at the end of the segment.

[0009] Furthermore, the processor unit can also be configured to control a vehicle, in particular a motor of the vehicle, based on the determined optimized trajectory. In this case, the processor unit can be configured to communicate the trajectory for the vehicle optimized by means of the MPC algorithm to a target generator, which can be implemented in particular by means of a software module. The processor unit can convert the mathematically optimal trajectory for the vehicle into a practically usable component signal by means of the software module. For example, the speed trajectory of the vehicle can be optimally planned for the next 500 m by means of the MPC control. In this case, the target generator can use adjustment variables or target variables corresponding to the current time point according to the trajectory.

[0010] The trajectory for the vehicle learned and optimized by the processor unit can be used in particular to provide the vehicle with an autonomous or at least semi-autonomous driving function. This driving function can be provided by the driver assistance system of the vehicle. The autonomous driving function enables the vehicle to drive automatically, that is to say, there is no need for the vehicle occupant to control the vehicle. The driver hands over the control of the vehicle to the driver assistance system. Therefore, the autonomous driving function includes: the vehicle (especially with the help of a central processor unit) is set up to perform, for example, steering actions, flashing actions, acceleration actions and braking operations without human intervention, and in particular control the external lights and signal transmission of the vehicle, such as turn signals. The semi-autonomous driving function can be understood as such a driving function: when controlling the vehicle, especially when steering actions, flashing actions, acceleration actions and braking operations, the driver of the vehicle is supported, wherein the driver still has control over the vehicle.

[0011] In order to find the optimal solution for the so-called "Driving Efficiency" driving function, which is supposed to provide an efficient driving style in every situation under given boundary conditions and restrictions, a model-based predictive control (MPC) method is selected. The MPC method is based on a system model that describes the system behavior. In addition, the MPC method is also based on an objective function or cost function, which describes the optimization problem and determines which state variables should be minimized. The state variables used for the driving efficiency driving function are in particular the vehicle speed or kinetic energy, the remaining energy in the battery, and the driving time. The optimization of energy consumption and driving time is carried out in particular based on the slope of the road section ahead and the restrictions and driving force on speed and driving force as well as on the current system conditions.

[0012] The objective function or cost function of the driving efficiency driving strategy can contain two terms in particular, by which the energy consumption and the driving time to the predetermined destination are minimized. This means that, depending on the selection of the weighting factors that can be assigned to the terms, a low speed is not always evaluated as optimal and it can therefore be avoided that the resulting speed is always at the lower limit of the permissible speed.

[0013] Furthermore, the present invention makes it possible to make the influence of the driver no longer necessarily important for the energy consumption and driving time of the motor vehicle, i.e., the electric motor for driving the vehicle can be controlled, in particular by the processor unit, based on the input variables for the electric motor known by executing the MPC algorithm. In particular, the optimal motor operating point of the electric motor can be set by means of the input variables. As a result, the optimal speed of the motor vehicle can be optimally adjusted.

[0014] The longitudinal dynamics model of the powertrain can include a vehicle model (e.g., an approximate performance map) with vehicle parameters and powertrain losses. In particular, knowledge of the topography of the road ahead (e.g., curves and slopes) can be incorporated into the longitudinal dynamics model of the powertrain. In addition, knowledge of speed limits on the road ahead can also be incorporated into the longitudinal dynamics model of the powertrain.

[0015] The cost function can include only linear and quadratic terms. The overall problem thus has a quadratically optimized form with linear auxiliary conditions and results in a convex problem that can be solved smoothly and quickly. The objective function or cost function can be expressed with weights (weighting factors), wherein in particular energy efficiency, driving time and driving comfort are calculated and weighted. The energy-optimal speed trajectory can be calculated online for the front boundary on a processor unit, which can in particular form part of the central controller of the motor vehicle. By using the MPC method, the target speed of the motor vehicle can also be recalculated based on the current driving state and the road section information ahead.

[0016] The current state variables can be measured, the corresponding data can be collected and fed to the MPC algorithm. Thus, the route data from the electronic map can be updated or renewed, especially cyclically, for the forward-looking or predicted range of the sliding in front of the vehicle (e.g. 500 m). The route data can, for example, contain information about slopes, curves and information about speed limits. In addition, the curve curvature can be converted into a speed limit for the motor vehicle via the maximum permissible lateral acceleration. In addition, the motor vehicle can be positioned, especially by means of a GNSS signal for accurate positioning on an electronic map.

[0017] The cost function of the MPC algorithm can in particular minimize the travel time for the prediction boundary and minimize the energy consumed. In one embodiment, the torque change for the prediction boundary is also minimized. As for the input for the model-based predictive control, physical boundaries such as speed limits, torque and speed of the motor can be supplied to the MPC algorithm as additional auxiliary conditions. In addition, control variables for optimization can also be supplied to the MPC algorithm as inputs, in particular the speed of the vehicle (which can be proportional to the speed), the torque of the motor and the battery charge state. As the output of the optimization, the MPC algorithm can then provide the optimal speed and the optimal torque for the calculated points within the look-ahead boundary. As for the use of MPC control in vehicles, a software module ("target generator") can be connected downstream of the MPC algorithm, which software module obtains the current important status and conveys it to the power electronics.

[0018] During the movement of the vehicle to the predetermined destination (especially supported by semi-autonomous or autonomous functions), the driver may or must take one or more breaks to recover energy. In this sense, the processor unit can be set up to take into account the rest period of the driver of the vehicle as an arrival time factor when calculating the trajectory for the vehicle. A break can be understood as a vehicle, for example, stopping at a rest area or parking space, and the vehicle remains parked for a certain period of time (rest period). The break can be partly predetermined by law, for example for drivers of trucks or buses. A break can be taken before starting to drive to the predetermined final destination and / or during driving to the predetermined destination and / or after driving to the predetermined destination.

[0019] When and where the driver takes a break depends on the overall route to the intended final destination. If the route includes, for example, a highway, then the location where the break can be taken depends on the availability of parking spaces or rest areas along the highway. The time point at which the break can be taken depends in this example on how far the expected parking space or rest area is from the current location of the vehicle and at what speed the vehicle can or should continue to move to the expected parking space or rest area.

[0020] Loading and unloading of vehicles is also important when planning the speed of the vehicle in order to achieve the most energy-efficient and time-optimized travel of the vehicle on the overall route. In this sense, the processor unit is configured in another embodiment to take into account the time period for loading and / or unloading the vehicle, in particular a truck, as an arrival time factor when calculating the trajectory for the vehicle.

[0021] If the vehicle is a hybrid vehicle which, in addition to the electric motor, also includes an internal combustion engine running on chemical fuel, the time period required for refueling the vehicle is also important. This includes the refueling process at the filling station, but also includes, for example, the time required to pay for the added fuel and the driving time from the original route to the filling station and back to the original route. In addition, the time period for charging the battery that provides electrical energy to drive the electric motor is important. In this sense, the processor unit is set up in another embodiment to take into account the time period for refueling the vehicle and / or charging the battery of the vehicle as an arrival time factor when calculating the trajectory for the vehicle. With regard to charging the battery, information about the availability of corresponding charging stations for the battery can also be taken into account by the processor unit.

[0022] Furthermore, the processor unit can also take into account the traffic flow and / or congestion conditions and / or weather conditions on the general route to the predetermined destination as auxiliary conditions when determining the trajectory of the vehicle as an arrival time factor.

[0023] Furthermore, it can be advantageously provided that the processor unit performs a precalculation of the driving speed and the energy consumption taking into account a pre-determination of the expected arrival time. This can alternatively or additionally also be done taking into account a pre-determination of the expected mileage.

[0024] In addition, information about available parking spaces at rest areas, especially for heavy vehicles, can be used for speed planning and route planning. In this sense, the processor unit can be set up to take into account the availability of parking spaces at rest areas as an arrival time factor when calculating the trajectory for the vehicle.

[0025] Furthermore, the processor unit can communicate with the warehouse (e.g. via Car2I, “car-to-infrastructure”) to reserve a time for loading and / or unloading the vehicle, thereby reducing waiting times and using the freed-up time for more energy-efficient driving of the vehicle.

[0026] In another embodiment, the powertrain includes an electric motor and a battery. The cost function includes as a first item the electric energy provided by the battery of the powertrain to drive the electric motor within the prediction range, which is weighted with a first weighting factor and predicted according to the longitudinal dynamics model. The cost function includes as a second item the driving time required for the motor vehicle to travel the entire road section predicted within the prediction range, which is weighted with a second weighting factor and predicted according to the longitudinal dynamics model. The processor unit can be configured to minimize the cost function by: determining the input parameters for the electric motor by implementing the MPC algorithm in dependence on the first item and in dependence on the second item. This embodiment enables the objective function or cost function of the driving efficiency driving strategy to include other items in addition to energy consumption, thereby also minimizing the driving time. This results in that, depending on the selection of the weighting factors, low speed is not always evaluated as optimal and therefore there is no problem that the generated speed is always at the lower limit of the allowed speed.

[0027] Energy consumption and driving time can be evaluated and weighted at the end of the boundary respectively. This term is therefore only valid for the last point of the boundary. In this sense, the cost function in one embodiment contains the energy consumption final value of the electric energy occupied at the end of the prediction boundary weighted by the first weighting factor, and the cost function also contains the driving time final value of the driving time occupied at the end of the prediction boundary weighted by the second weighting factor.

[0028] In order to ensure comfortable driving, a term for penalizing torque jumps can be additionally introduced. In this sense, the cost function can include a third term with a third weighting factor, wherein the third term includes a value predicted according to the longitudinal dynamics model for the torque provided for driving the motor vehicle electric machine, and wherein the processor unit is further configured to: by executing the MPC algorithm, determine the input variables for the electric machine in dependence on the first term, in dependence on the second term and in dependence on the third term, so as to minimize the cost function.

[0029] For the first point in the domain, the deviation from the last set torque can be evaluated as negative in order to ensure a seamless and smooth transition when switching between the old and new trajectories. In this sense, the third term can contain a first value of the torque provided by the electric motor for driving the motor vehicle at the first travel point within the prediction domain, which is predicted according to the longitudinal dynamics model, weighted by a third weighting factor. Here, the third term can contain the zeroth value of the torque provided by the electric motor for driving the motor vehicle at the zeroth travel point immediately before the first travel point, weighted by the third weighting factor. The zeroth torque can in particular be the torque that is actually (and not purely predicted) provided by the electric motor. In the cost function, the zeroth value of the torque is subtracted from the first value of the torque.

[0030] Alternatively, the third term may include a first value of the driving force provided by the electric motor for driving the vehicle at a first travel point within the prediction range predicted according to the longitudinal dynamics model, weighted by a third weighting factor. The third term here includes the zeroth value of the driving force provided by the electric motor for driving the vehicle at a zeroth travel point immediately before the first travel point, weighted by the third weighting factor, wherein in the cost function, the zeroth value of the driving force is subtracted from the first value of the driving force.

[0031] The travel points taken into account by the MPC algorithm are in particular discrete travel points, which follow one another, for example, at a certain frequency. In this sense, the zeroth travel point and the first travel point are discrete travel points, wherein the first travel point directly follows the zeroth travel point. The zeroth travel point can be located before the prediction boundary in time. The zeroth torque value can be measured or known for the zeroth travel point. The first travel point is in particular the first travel point within the prediction boundary. The first torque value can be predicted for the first travel point. Therefore, the actually known zeroth torque value can be compared with the predicted first torque value.

[0032] Furthermore, excessively high torque gradients within the bounds are disadvantageous, so that these torque gradients are already penalized in the objective function in one embodiment. For this purpose, the squared deviation of the driving force per meter can be weighted and minimized in the objective function. In this sense, the cost function can have a fourth term with a fourth weighting factor, wherein the fourth term contains the gradient of the torque predicted by the longitudinal dynamics model or an indication of the gradient of the torque predicted by the longitudinal dynamics model. The processor unit is hereby configured to determine the input variables for the electric machine by executing the MPC algorithm in dependence on the first term, in dependence on the second term, in dependence on the third term and in dependence on the fourth term, so as to minimize the cost function.

[0033] In one embodiment, the fourth term includes the square deviation of the torque gradient multiplied by a fourth weighting factor and accumulated. In addition, the cost function may include the square deviation of the driving force provided by the motor to move the motor vehicle forward one meter in the longitudinal direction added by the fourth weighting factor. In this sense, the fourth term may include the square deviation of the driving force provided by the motor to move the motor vehicle forward one meter in the longitudinal direction multiplied by the fourth weighting factor and accumulated.

[0034] The speed limit that may be clearly defined by traffic road regulations, for example, is a hard boundary for optimization, which should not be exceeded. Slightly exceeding the speed limit is always allowed in reality and is especially common when transitioning from one speed zone to the second zone. In a dynamic environment where the speed limit moves from one calculation cycle to the next calculation cycle, it may happen that an effective solution for the speed change curve can no longer be found under a completely hard boundary. In order to improve the stability of the calculation algorithm, the so-called "soft constraint" can be introduced into the objective function. Before reaching the hard speed limit, the so-called "slip variable" or "slack variable" can become effective in a predetermined narrow range in particular. The solution that is very close to this speed limit may be evaluated as worse than the solution that the speed trajectory keeps a certain distance from the hard boundary. In this sense, the cost function can include a slack variable weighted by the fifth weighting factor as the fifth item, wherein the processor unit is set up to be used for: by implementing the MPC algorithm, the input variables for the motor are learned by relying on the first item, relying on the second item, relying on the third item, relying on the fourth item and relying on the fifth item, so as to minimize the cost function.

[0035] In order to respect the physical limits of the powertrain components, the traction can be limited by limiting the overall characteristic curve of the electric machine. For example, for maximum recuperation, the battery is the limiting element. In order not to damage such a battery, a certain negative power value should not be fallen below.

[0036] According to a second aspect of the present invention, a driver assistance system for a vehicle is provided, and the vehicle is driven by an electric motor. The driver assistance system is configured to retrieve input variables of the electric motor by means of a communication interface, wherein the input variables are known by a processor unit according to the first aspect of the present invention. The processor unit thereby learns the input variables of the electric motor by implementing an MPC algorithm, depending on a first term of a cost function of the MPC algorithm and depending on a second term. The first term represents the electric energy provided by a battery to drive the electric motor within a prediction domain, which is weighted with a first weighting factor and predicted according to a longitudinal dynamics model of the vehicle. The second term represents the driving time required for the vehicle to travel the entire road section predicted within the prediction domain, which is weighted with a second weighting factor and predicted according to the longitudinal dynamics model. In addition, the driver assistance system is further configured to control the electric motor based on the input variables.

[0037] According to a third aspect of the present invention, there is provided a vehicle, the vehicle comprising a motor, a battery and the driver assistance system according to the second aspect of the present invention.

[0038] The vehicle is, in particular, a motor vehicle driven by an engine, such as a car (e.g., a passenger vehicle weighing less than 3.5 tons), a motorcycle, a scooter, a moped, a bicycle, an electric bicycle or a pedal electric vehicle (Pedelec, an abbreviation of PedalElectric Cycle), a bus or a truck (e.g., a vehicle weighing more than 3.5 tons), but also a rail vehicle, a ship, an air vehicle such as a helicopter or an airplane. The present invention can also be used in small, light electric vehicles of micromobility, wherein these vehicles are particularly used for the first and last mile of urban traffic and rural areas. The first mile and the last mile can be understood as the sections and trips in the first and last links of the motor chain. This is, for example, a trip from home to the train station or a section from the train station to the workplace. In other words, the present invention can be used in all areas of the transport industry, such as automobiles, aviation, navigation, aerospace, etc. For example, the vehicle can belong to a fleet. The vehicle can be controlled by the driver, possibly with the support of a driver assistance system. But the vehicle can also be controlled, for example, remotely and / or (semi-) autonomously.

[0039] The vehicle can be an electric vehicle, a hybrid vehicle or a plug-in hybrid vehicle. An "electric vehicle" can be understood as a vehicle that is driven by electrical energy. Driving energy in the form of electrical energy can be supplied to the electric vehicle. The electrical energy can be stored in the battery of the electric vehicle (battery electric vehicle). Alternatively, the electrical energy can be permanently supplied from the outside, for example via conductor rails, overhead lines or by induction. A "hybrid vehicle" can be understood as an electric vehicle that can be driven by at least one electric motor and at least one internal combustion engine. A hybrid vehicle can obtain energy both from the battery and from an additionally carried fuel, such as diesel, gasoline or natural gas. A "plug-in hybrid vehicle" can be understood as a hybrid vehicle whose battery can be charged both by the internal combustion engine and by a power grid that is located outside the plug-in hybrid vehicle and connected to the electrical interface of the plug-in hybrid vehicle. A plug-in hybrid vehicle can be regarded as a hybrid between a hybrid vehicle and an electric vehicle.

[0040] According to a fourth aspect of the present invention, there is provided a method for model-based predictive control of a vehicle taking into account arrival time factors. The method comprises the following steps:

[0041] - calculating a trajectory for the vehicle taking into account at least one arrival time factor, wherein the trajectory comprises a general route to a predetermined destination at which the vehicle is to arrive, and wherein the arrival time factor influences the arrival time of the vehicle at the predetermined destination, and

[0042] - Optimizing the segment of the trajectory for the vehicle for the predicted bounds of slip by implementing an MPC algorithm incorporating a longitudinal dynamics model of the vehicle's powertrain and the cost function to be minimized, thereby minimizing the cost function.

[0043] According to a fifth aspect of the present invention, there is provided a computer program product for predictively controlling a vehicle based on a model taking into account an arrival time factor. When the computer program product is executed on a processor unit, the computer program product instructs the processor unit to

[0044] - calculating a trajectory for the vehicle taking into account at least one arrival time factor, wherein the trajectory comprises a general route to a predetermined destination that the vehicle is to reach, and wherein the arrival time factor influences the arrival time of the vehicle at the predetermined destination, and

[0045] - Optimizing the segment of the trajectory for the vehicle for the predicted bounds of slip by implementing an MPC algorithm incorporating a longitudinal dynamics model of the vehicle's powertrain and the cost function to be minimized, thereby minimizing the cost function.

[0046] The explanations made in conjunction with the processor unit according to the first aspect of the invention also apply in a meaningful manner to the driver assistance system according to the second aspect of the invention, the vehicle according to the third aspect of the invention, the method according to the fourth aspect of the invention and the computer program product according to the fifth aspect of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Exemplary embodiments of the invention are explained in more detail below with reference to schematic drawings, wherein identical or similar elements are provided with the same reference numerals.

[0048] Figure 1 showing a side view of a vehicle having a powertrain including an electric motor and a battery; and

[0049] Figure 2 shows a top view of the road, according to Figure 1 The vehicle 1 can automatically travel on the road to reach the target point from the starting point. DETAILED DESCRIPTION

[0050] Figure 1 A vehicle in the form of a motor vehicle 1 is shown, for example a passenger vehicle (PKW) or a truck (LKW). The motor vehicle 1 comprises a system 2 for model-predictive control of an electric machine 8 of a powertrain 7 of the motor vehicle 1 taking into account arrival time factors. In the illustrated embodiment, the system 2 comprises a processor unit 3, a memory unit 4, a communication interface 5 and a detection unit 6 for detecting state data relating to the motor vehicle 1. The motor vehicle 1 also comprises a powertrain 7, which can comprise, for example, an electric machine 8 that can be operated as a motor and as a generator, a battery 9 and a transmission 10. The electric machine 8 can drive the wheels of the motor vehicle 1 via the transmission 10 in motor operation, which can have, for example, a constant transmission ratio. The battery 9 can provide the electrical energy required for this. When the electric machine 8 is operated in generator operation (recovery), the battery 9 can be charged by the electric machine 8. If the motor vehicle 1 is a plug-in hybrid vehicle, the battery 9 can optionally also be charged at an external charging station. The powertrain of the motor vehicle 1 can also optionally have an internal combustion engine 21, which can drive the motor vehicle 1 as an alternative to the electric machine 8 or in addition to the electric machine. The internal combustion engine 21 can also be configured to drive the electric motor 8 in order to charge the battery 9 .

[0051] A computer program product 11 can be stored in the memory unit 4. The computer program product 11 can be executed on the processor unit 3, for which purpose the processor unit 3 and the memory unit 4 are connected to each other via a communication interface 5. When the computer program product 11 is executed on the processor unit 3, it instructs the processor unit 3 to perform the functions or implement the method steps described in conjunction with the figures.

[0052] The computer program product 11 includes an MPC algorithm 13. The MPC algorithm 13 in turn includes a longitudinal dynamics model 14 of the powertrain 7 of the motor vehicle 1 and a cost function 15 to be minimized. The processor unit 3 implements the MPC algorithm 13 and obtains an optimized trajectory of the motor vehicle 1. Here, the behavior of the motor vehicle 1 is predicted based on the longitudinal dynamics model 14, wherein the cost function 15 is minimized. As an optimization output by the MPC algorithm 13, for example, the speed and optimal torque of the motor 8 for the calculated points within the prediction range that are coordinated with the optimized trajectory are obtained. For this purpose, the processor unit 3 can obtain the input variables for the motor 8, so that the optimal speed and the optimal torque are generated. The processor unit 3 can control the motor 8 based on the obtained input variables. However, this can also be done by the driver assistance system 16.

[0053] The detection unit 6 can measure the current state parameters of the motor vehicle 1, receive the corresponding data and transmit them to the MPC algorithm 13. Therefore, the route data from the electronic map can be upgraded or updated for the forward-looking boundary or prediction boundary (e.g. 500m) in front of the motor vehicle 1, especially cyclically. The route data can, for example, contain slope information, curve information and information about speed limits. In addition, the curve curvature can be converted into the speed limit of the motor vehicle 1 via the maximum permissible lateral acceleration. In addition, the motor vehicle 1 can be positioned with the help of the detection unit 6, especially via the GNSS signal generated by the GNSS sensor 12 for accurate positioning on the electronic map. The processor unit 3 can retrieve this information, for example, via the communication interface 5.

[0054] Transport 1 is passing Figure 2 In the example shown, the vehicle 1 is located at a parking space 17 adjacent to a road 18 leading to a warehouse 19. The course of the road 18 is shown interrupted due to its length. The driver of the vehicle 1 can specify to the driver assistance system 16 that the vehicle 1 wants to drive, for example, from the parking space 17 (starting point) to the warehouse 19 (destination). The driver assistance system 16 can provide an autonomous driving function for the vehicle 1 so that the vehicle 1 drives autonomously from the parking space 17 to the warehouse 19.

[0055] For this purpose, the processor unit 3 or the driver assistance system 16 first generates a general route 20 from the parking space 17 to the warehouse 19 and assigns the speed of the vehicle 1 to the individual travel points on the route. In this way, the trajectory for the vehicle 1 is calculated. The processor unit 3 or the driver assistance system 16 can use the detection unit 6 and the planning module for this purpose, which can be implemented as software, for example. The planning module can include multiple levels, such as a navigation level on a larger scale (for example, several kilometers; here the trajectory of the vehicle 1 from the starting point 17 to the destination 19 can be selected) and a navigation level on a smaller scale (for example, 50m to 100m ahead, depending on the speed of the vehicle 1; here the route and speed can be selected in the closer surroundings of the vehicle 1 in order to clarify how the vehicle 1 should move in traffic).

[0056] When the trajectory for the vehicle 1 is known or calculated, arrival time factors that influence the arrival time of the vehicle 1 at the predetermined destination 19 can be taken into account. In particular, rest periods of the driver of the vehicle 1 (e.g., an individual, a truck driver, or a bus driver) can be taken into account. In addition, in particular when the vehicle is a truck, the time period for loading the vehicle 1 can also be taken into account, as well as the time for refueling and / or charging the battery 9 of the vehicle 1. In addition, information about available charging stations 22 for the battery 9 of the vehicle 1 and information about available parking spaces 23 at rest areas 24 for trucks can also be taken into account. In addition, information about traffic flow and congestion on the road section 20 and weather conditions can also be taken into account. The processor unit 3 of the vehicle 1 can also communicate with the processor unit 25 of the warehouse 19 (e.g., via Car2I communication, "car-to-base" communication) in order to reserve a time point for loading and / or unloading, which can be coordinated with the calculated trajectory of the vehicle 1. This can reduce waiting times and instead use time for more energy-efficient driving.

[0057] When the vehicle 1 starts autonomously in order to reach the warehouse 19 from the parking space 19 via the road 18, the processor unit 3 implements the MPC algorithm 13 and optimizes the current section of the trajectory of the vehicle 1 for the sliding (that is, spatially or moving on the road) prediction boundary respectively, so as to minimize the cost function. The processor unit 3 can, for example, form the aforementioned planning module ("top-level" planning module), which plans the overall route 20 and the trajectory of the vehicle taking into account the arrival time factors mentioned. This planning module can communicate the sections or segments of the vehicle trajectory of the overall route to the MPC algorithm 13, with the help of which the optimal trajectory of the vehicle 1 within the prediction boundary can be determined. The processor unit 3 can also take into account the arrival time predetermined by the driver of the vehicle 1 to the predetermined destination 19 or the mileage predetermined by the driver of the vehicle 1 as auxiliary conditions when optimizing the trajectory of the vehicle 1.

[0058] An exemplary longitudinal dynamics model 14 of the motor vehicle 1 can be expressed mathematically as follows:

[0059]

[0060] Here:

[0061] v is the speed of the vehicle;

[0062] F trac is the traction force applied to the wheels of a motor vehicle by means of a motor or brakes;

[0063] F r is the rolling resistance, which has the effect of deforming the tire when rolling and depends on the load on the wheel (normal force between wheel and road) and therefore on the inclination angle of the road;

[0064] F gr is the slope resistance which describes the longitudinal component of the weight which acts on the motor vehicle during uphill or downhill driving, depending on the slope of the roadway; and

[0065] F d is the air resistance of the vehicle; and

[0066] m eq is the equivalent mass of the motor vehicle; this equivalent mass includes in particular the inertia of the rotating parts of the drive train (motor, transmission drive shaft, wheels) that are subject to the acceleration of the motor vehicle.

[0067] By converting time correlation into travel correlation and for use The coordinate transformation that eliminates the squared velocity term in air resistance gives

[0068] In order to be able to solve it quickly and easily by the MPC algorithm 13, the dynamic equations of the longitudinal dynamics model 14 are linearized in such a way that the velocity is linearized by means of a coordinate transformation through the kinetic energy de kin Thus, the linear term is used to replace the term used to calculate the air resistance F d The square term of and the longitudinal dynamics model 14 of the motor vehicle 1 is no longer described in a time-dependent manner as usual, but in a distance-dependent manner. In this respect, this is well suited for optimization problems, since the forward-looking information of the electrical domain is present in a distance-based manner.

[0069] In addition to the kinetic energy, there are two further state variables which, in the sense of a simple optimization problem, must also be described linearly and in relation to the distance travelled. On the one hand, the electrical energy consumption of the powertrain 7 is usually described in the form of a comprehensive characteristic curve which is a function of the torque and the engine speed. In the illustrated embodiment, the motor vehicle 1 has a fixed transmission ratio between the motor vehicle 8 and the road on which the motor vehicle 1 moves. As a result, the rotational speed of the motor vehicle 8 can be converted directly into the speed of the motor vehicle 1 or precisely into the kinetic energy of the motor vehicle 1. Furthermore, the electrical power of the motor vehicle 8 can be converted into energy consumption per meter by dividing by the corresponding speed. In order to be able to use the corresponding comprehensive characteristic curve of the motor vehicle 8 for optimization, a linear approximation is made to it: for all i, Energy perMeter ≥a i *e kin +b i *F trac ,(Energy perMeter : energy per meter).

[0070] The cost function 15 to be minimized can be expressed mathematically as follows:

[0071]

[0072] Here,

[0073] w Bat is a weighting factor for the energy consumption of the battery

[0074] E Bat Is the energy consumption of the battery

[0075] S is the distance

[0076] S E-1 is the distance in time steps before the end of the prediction horizon,

[0077] F A The driving force is provided by the motor, which is converted into a constant transmission ratio through the transmission mechanism and applied to the wheels of the motor vehicle.

[0078] W Tem is the weighting factor of the torque gradient

[0079] W TemStart is the weighting factor of the torque jump

[0080] T is the time required for the vehicle to travel the predicted total distance within the prediction domain

[0081] w Time is the weighting factor of time T

[0082] S E It is the journey at the end of the realm

[0083] w Slack is the weighting factor of the slack variable

[0084] Var Slack is the slack variable

[0085] The cost function 15 has only linear and quadratic terms. The overall problem thus has a quadratically optimized form with linear auxiliary conditions and results in a convex problem that can be solved smoothly and quickly.

[0086] The cost function 15 includes a first weighting factor W Bat The weighted electric energy E provided by the battery 9 of the powertrain 7 to drive the electric motor 8 within the prediction range and predicted based on the longitudinal dynamics model Bat .

[0087] The cost function 15 also includes a second weighting factor W Time The second term is the weighted travel time T required for the motor vehicle 1 to travel the predicted distance, predicted according to the longitudinal dynamics model 14. This means that, depending on the selection of the weighting factors, low speeds will not always be evaluated as optimal and therefore there is no longer the problem of the resulting speed always being at the lower limit of the permissible speed.

[0088] Energy consumption and travel time can be evaluated and weighted respectively at the end of the domain. These terms are therefore only valid for the last point of the domain.

[0089] Too high a torque gradient within the bounds is unfavorable. Therefore, the torque gradient is already penalized in the cost function 15, i.e., by the term The squared deviation of the driving force per meter is penalized with a weighting factor W Tem Weighted and minimized in the cost function. As the driving force per meter F A Alternatively, the torque M provided by the motor 8 can also be used EM And use the weighting factor W Tem Weighted to get the alternative items Due to the constant transmission ratio of the transmission 10 , the drive force and the torque are directly proportional to one another.

[0090] In order to ensure comfortable driving, an additional term for penalizing torque jumps is introduced into the cost function 15, namely w TemStart ·(F A (s1)-F A (s0)) 2 As the driving force F A Alternatively, the torque M provided by the motor 8 can also be used. EM , thus generating the alternative term w TemStart ·(M EM (s1)-M EM (s0)) 2 For the first point within the prediction domain, the deviation from the last set torque is evaluated as negative and weighted by the factor W. TemStart Weighted to ensure there is a seamless and smooth transition when switching between old and new tracks.

[0091] The speed limit is a hard boundary for the optimization, which should not be exceeded. Slightly exceeding the speed limit is always allowed in reality and is a normal situation in particular when transitioning from one speed zone to a second zone. In a dynamic environment, in which the speed limit is shifted from one calculation cycle to the next, it can happen that a valid solution for the speed profile can no longer be found under completely hard boundaries. In order to increase the stability of the calculation algorithm, so-called "soft constraints" can be introduced into the cost function 15. In this case, before the hard speed limit is reached, a weighting factor W is used. slack Weighted slack variable Var Slack It becomes valid within a predetermined narrow range. Solutions that are very close to this speed limit are evaluated as worse than solutions whose speed trajectory remains at a certain distance from the hard limit.

[0092] Reference numerals list

[0093] 1. Transportation

[0094] 2 System

[0095] 3 Processor Units

[0096] 4 Memory cells

[0097] 5 Communication Interface

[0098] 6 Detection unit

[0099] 7 Powertrain

[0100] 8 Motor

[0101] 9. Battery

[0102] 10 Transmission mechanism

[0103] 11 Computer program products

[0104] 12 GNSS Sensor

[0105] 13 MPC algorithm

[0106] 14 Longitudinal dynamics model

[0107] 15 Cost Function

[0108] 16 Driver Assistance Systems

[0109] 17 parking spaces

[0110] 18 Road

[0111] 19 Warehouse

[0112] 20 General Route

[0113] 21 Internal combustion engine

[0114] 22 Charging stations for vehicle batteries

[0115] 23 Parking spaces for heavy vehicles

[0116] 24 Rest Area

[0117] 25 Warehouse Processor Unit

Claims

1. A processor unit (3) for predictive model-based control of a vehicle (1) taking into account arrival time factors, wherein: The processor unit (3) is configured to: - calculating a trajectory for the vehicle (1) taking into account at least one arrival time factor, wherein the trajectory comprises a general route (20) to a predetermined destination (19) at which the vehicle (1) is to arrive, and wherein the arrival time factor influences the arrival time of the vehicle (1) at the predetermined destination (19), and - optimizing the segment of the trajectory of the vehicle (1) for the predicted bounds of slip by implementing an MPC algorithm (13) comprising a longitudinal dynamics model (14) of the powertrain (7) of the vehicle (1) and a cost function (15) to be minimized, so as to minimize the cost function (15), in, - the powertrain (7) comprises a motor (8) and a battery (9), the cost function (15) comprises as a first term the electrical energy provided by the battery (9) to drive the electric motor (8) within the prediction domain, weighted by a first weighting factor and predicted according to the longitudinal dynamics model (14), the cost function (15) contains as a second term the travel time required for the vehicle (1) to travel the total distance predicted within the prediction domain, which is weighted by a second weighting factor and predicted according to the longitudinal dynamics model (14), and The processor unit (3) is designed to determine the input variables for the electric machine (8) by executing the MPC algorithm (13) as a function of the first term and as a function of the second term, in order to minimize a cost function.

2. The processor unit (3) according to claim 1, wherein: The processor unit (3) is configured to take into account a rest period of the driver of the vehicle (1) as an arrival time factor when calculating a trajectory for the vehicle (1).

3. The processor unit (3) according to claim 1, wherein: The processor unit (3) is configured to take into account the time period for loading and / or unloading the vehicle (1) as an arrival time factor when calculating a trajectory for the vehicle (1).

4. The processor unit (3) according to any one of claims 1 to 3, wherein: The processor unit (3) is configured to take into account a time period for refueling the vehicle (1) and / or for charging a battery (9) of the vehicle (1) as an arrival time factor when calculating a trajectory for the vehicle (1).

5. The processor unit (3) according to any one of claims 1 to 3, wherein: The processor unit (3) is designed to take into account charging stations (22) available for the vehicle (1) on the overall route (20) as an arrival time factor when calculating a trajectory for the vehicle (1).

6. The processor unit (3) according to any one of claims 1 to 3, wherein: The processor unit (3) is configured to take into account the traffic flow and / or the vehicle speed on the overall route (20) to the predetermined destination (19) when calculating the trajectory for the vehicle (1). - congestion and / or - Weather conditions as an arrival time factor.

7. The processor unit (3) according to any one of claims 1 to 3, wherein: The processor unit (3) is designed to take into account, when optimizing a trajectory for the vehicle (1), an arrival time at a predetermined destination (19) predetermined by the driver of the vehicle (1) as an auxiliary condition.

8. The processor unit (3) according to any one of claims 1 to 3, wherein: The processor unit (3) is designed to take into account a range of the vehicle (1) predetermined by a driver of the vehicle (1) as an auxiliary condition when optimizing a trajectory for the vehicle (1).

9. The processor unit (3) according to any one of claims 1 to 3, wherein: The processor unit (3) is configured to take into account the availability of parking spaces (23) at rest areas (24) as an arrival time factor when calculating a trajectory for the vehicle (1).

10. The processor unit (3) according to any one of claims 1 to 3, wherein: The processor unit (3) is configured to communicate with a processor unit (25) of a warehouse (19) in order to reserve a time point for loading and / or unloading the vehicle (1).

11. The processor unit (3) according to any one of claims 1 to 3, wherein: The vehicle (1) is a truck and the processor unit (3) is configured to take into account the time period for loading and / or unloading the truck as an arrival time factor when calculating a trajectory for the vehicle (1).

12. A driver assistance system (16) for a vehicle (1) driven by means of an electric motor (8), wherein: The driver assistance system (16) is configured to: - calling up input variables for the electric machine (8) by means of a communication interface, wherein the input variables are known by the processor unit (3) according to claim 1, and - controlling the electric motor (8) based on the input variable.

13. Vehicle (3) comprising an electric machine (8), a battery (9) and a driver assistance system (16) according to claim 12.

14. A method for model-based predictive control of a vehicle (1) taking into account arrival time factors, the method comprising the following steps: - calculating a trajectory for the vehicle (1) taking into account at least one arrival time factor, wherein: The trajectory comprises a general route (20) to a predetermined destination (19) at which the vehicle (1) should arrive, and wherein the arrival time factor affects the arrival time of the vehicle (1) at the predetermined destination (19), and - optimizing the segment of the trajectory of the vehicle (1) for the predicted bounds of slip by implementing an MPC algorithm (13) comprising a longitudinal dynamics model (14) of the powertrain (7) of the vehicle (1) and a cost function (15) to be minimized, so as to minimize the cost function (15), in, - the powertrain (7) comprises a motor (8) and a battery (9), the cost function (15) comprises as a first term the electrical energy provided by the battery (9) to drive the electric motor (8) within the prediction domain, weighted by a first weighting factor and predicted according to the longitudinal dynamics model (14), the cost function (15) contains as a second term the travel time required for the vehicle (1) to travel the total distance predicted within the prediction domain, which is weighted by a second weighting factor and predicted according to the longitudinal dynamics model (14), and By executing the MPC algorithm (13), the input variables for the electric machine (8) are determined as a function of the first term and as a function of the second term, so that the cost function is minimized.

15. A computer program product (11) for model-based predictive control of a vehicle (1) taking into account arrival time factors, wherein: When the computer program product (11) is executed on the processor unit (3), the computer program product instructs the processor unit (3) - calculating a trajectory for the vehicle (1) taking into account at least one arrival time factor, wherein the trajectory comprises a general route (20) to a predetermined destination (19) at which the vehicle (1) is to arrive, and wherein the arrival time factor influences the arrival time of the vehicle (1) at the predetermined destination (19), and - optimizing the segment of the trajectory of the vehicle (1) for the predicted bounds of slip by implementing an MPC algorithm (13) comprising a longitudinal dynamics model (14) of the powertrain (1) of the vehicle (1) and a cost function (15) to be minimized, so as to minimize the cost function (15), in, - the powertrain (7) comprises a motor (8) and a battery (9), the cost function (15) comprises as a first term the electrical energy provided by the battery (9) to drive the electric motor (8) within the prediction domain, weighted by a first weighting factor and predicted according to the longitudinal dynamics model (14), the cost function (15) contains as a second term the travel time required for the vehicle (1) to travel the total distance predicted within the prediction domain, which is weighted by a second weighting factor and predicted according to the longitudinal dynamics model (14), and By executing the MPC algorithm (13), the input variables for the electric machine (8) are determined as a function of the first term and as a function of the second term, so that the cost function is minimized.

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