Apparatus and method for model predictive based regulation of vehicle components

Abstract

The present invention relates to an apparatus for model-based predictive regulation of components of a vehicle having a battery and an electric motor, the apparatus comprising: a first input interface (20) for receiving sensor data from sensors (14) of the vehicle (34); a second input interface (22) for receiving topological data about the external environment of the vehicle (34); a control unit (25) for implementing a prediction algorithm (26) to generate control values ​​for components (18) of the vehicle (34); and an output interface (24) for outputting the control values ​​for components (18) of the vehicle (34) obtained in the control unit (25); wherein the prediction algorithm (26) includes a vehicle model (2 8) and optimization function (30); the vehicle model (28) includes a battery model (32) and processes sensor data and data about topology in the prediction algorithm (26); the optimization function (30) includes electrical energy and driving time, wherein the energy is predicted by the battery model (32) and the driving time is predicted by the vehicle model (28); the optimization function (30) includes information on charging points (48) on the vehicle (34) along the predicted distance (44) by the vehicle model (28) within the prediction range and information on the predicted energy of the battery (33) at the location of the charging point (48); and the prediction algorithm (26) generates control values ​​by minimizing the optimization function (30). The present invention also relates to systems and methods for model-based predictive regulation of vehicle components.

Description

Technical Field

[0001] The present invention relates to an apparatus, system, and method for model-based predictive adjustment of vehicle components, the vehicle having a battery and an electric motor. Background Technology

[0002] Modern vehicles (cars, trucks, cargo trucks, motorcycles, etc.) include numerous systems that provide information to the driver and partially or fully automate the control of various vehicle functions. Sensors detect the vehicle's external environment and generate an external environment model, which is then integrated into the existing vehicle model. Due to the gradual development of autonomous and semi-autonomous vehicles, the impact and scope of such driver assistance systems (ADAS) are expanding. Especially in vehicles with electric motors as drives, model-based predictive control (MPC) is applied, for example in trajectory control, particularly in motor control of motors. For instance, predictive control of the drive motor can optimize energy consumption.

[0003] The optimization of a known energy management strategy, as described in EP2610836 A1, is implemented by minimizing a cost function and based on prediction range and other external environmental information. A neural network is built for use within the vehicle. Furthermore, driver modeling and prediction of possible speed curves chosen by the driver are also implemented. EP1256476 B1 discloses a strategy for reducing driving energy consumption and increasing achievable range. Here, information from navigation devices is used, namely, the current vehicle location, road patterns, geographic information including date and time, altitude changes, speed limits, intersection density, traffic monitoring, and the driver's driving patterns.

[0004] In motor vehicle operation, the driver and their driving style have the greatest impact on energy consumption. Even when using known cruise control systems to control vehicle speed, energy consumption is not taken into account. Known predictive driving strategies are usually based on regularity and cannot provide optimal results in all situations. Furthermore, optimization-based strategies are computationally complex and have so far only been implemented as offline solutions or using dynamic programming.

[0005] In battery-powered electric vehicles, other components that receive electrical energy also play a decisive role in total energy consumption and, consequently, the vehicle's achievable range. However, these components are often not considered or are not adequately considered in energy management systems. Summary of the Invention

[0006] Based on this, the objective of this invention is to propose a system with improved MPC regulation, wherein improved energy management is applied to enable energy consumption to flexibly match given boundary conditions.

[0007] To address this task, the present invention, in a first aspect, relates to an apparatus for model-based predictive adjustment of components of a vehicle, the vehicle having a battery and an electric motor, the apparatus comprising:

[0008] The first input interface for receiving sensor data from the vehicle's sensors;

[0009] A second input interface for receiving topology data from the vehicle's external environment;

[0010] A control unit used to implement a predictive algorithm, which generates control values ​​for vehicle components;

[0011] An output interface used to output component control values ​​for the vehicle obtained in the control unit;

[0012] in,

[0013] The prediction algorithm includes a vehicle model and an optimization function;

[0014] The vehicle model includes a battery model, and the prediction algorithm processes sensor data and data about topology.

[0015] The optimization function includes electrical energy and driving time, where energy is predicted by the battery model and driving time is predicted by the vehicle model.

[0016] The optimization function includes information on charging points along the vehicle's predicted route within the prediction range, as well as information on the predicted energy content of the battery at the location of the charging point; and

[0017] The prediction algorithm generates control values ​​by minimizing the optimization function.

[0018] In another aspect, the present invention relates to a system for model-based predictive adjustment of components of a vehicle, the vehicle having a battery and an electric motor, the system comprising:

[0019] The aforementioned apparatus includes a sensor for acquiring sensor data including information about the vehicle's external environment and a topological unit for providing data about topology.

[0020] Other aspects of the invention relate to corresponding methods, vehicles, and computer program products having program code that, when run on a computer, performs the steps of the method, and to storage media having a computer program stored thereon that, when run on a computer, causes the implementation of the method.

[0021] Preferred embodiments of the invention are described in the dependent claims. It should be understood that the features mentioned above and further elaborated below can be applied not only in the given combinations, but also in other combinations or individually, without departing from the scope of the invention. In particular, methods and computer program products can be implemented according to the embodiments described in the dependent claims for the apparatus and system.

[0022] The apparatus according to the invention particularly utilizes a control unit for implementing a predictive algorithm to generate control values ​​for vehicle components. Here, the predictive algorithm is based on a vehicle model and an optimization function. The vehicle model, for example, may include, in addition to a dynamic model of the vehicle's longitudinal movement, other models for the various components of the vehicle. For example, the vehicle model includes a battery model, on which battery management and battery modeling are performed. Information and parameters for modeling other components (e.g., air conditioning, brakes, or different cameras) also belong to the vehicle model. The navigation system, along with electronic maps stored therein and information on the topology of the vehicle's external environment (e.g., road patterns and other information), also belong to this model. The battery model includes, in addition to the battery itself, models such as battery temperature management and models that describe battery charging cycles and energy output based on battery temperature. The battery model may include a battery cooling pump or temperature control system.

[0023] The predictive algorithm implemented by the control unit is based on an MPC solver or MPC algorithm (Model Predictive Control). This predictive algorithm can effectively plan various degrees of freedom related to overall vehicle energy efficiency at the vehicle level. Here, by applying the predictive algorithm to plan different degrees of freedom, energy-efficient and, in some cases, comfortable combinations of actuators can be used. The predicted and planned degrees of freedom can be optimized and transmitted to a so-called target generator, which can then control individual actuators, such as actuators in the drivetrain or other components and actuators in the vehicle.

[0024] Modern vehicles incorporate various energy-efficiency-related components (not limited to the drivetrain). Energy consumption optimization and energy management optimization, including planning battery charging phases considering driving time and energy consumption, can be achieved through predictive algorithms implemented by the control unit. Thus, given boundary conditions and constraints, an optimized solution for achieving "Driving Efficiency" driving functionality, a fuel-efficient driving mode, can be determined under any circumstances. A system model or vehicle model based on this describes the overall behavior of the vehicle. The predictive algorithm applies an objective function or optimization function (also known as a cost function). This clarifies and defines the optimization problem, specifying which state parameters should be optimized.

[0025] Here, optimization involves minimizing an optimization function. The optimization function specifies the state parameters that should be minimized. Specifically, the vehicle's energy consumption and travel time are relevant parameters. Optimizing energy consumption and travel time can be designed, for example, based on the road segment ahead of the vehicle predicted by the vehicle model and the predicted range. Speed ​​and / or traction limitations, as well as general vehicle conditions, can be considered. In addition to optimizing energy consumption and travel time, the optimization function also includes information on charging points along the vehicle's predicted route within the predicted range. Therefore, the location of the next possible charging point along the predicted travel route is considered. Furthermore, the optimization function includes information on the predicted energy content of the battery at the location of the charging point along the predicted route. Based on these values, specific control values ​​for vehicle components can be determined for the application.

[0026] If, for example, the vehicle is a bus, then the device according to the invention can also consider points classified as stops along the predicted route of the bus. The optimization function can be adjusted accordingly, wherein, if necessary, the arrival time to the stop can be incorporated into the optimization function. Additional charging points can also be provided at the stops, which can represent the optimization function.

[0027] Vehicle components can be, for example, components of the vehicle's drive system. Here, the optimization function can utilize a longitudinal dynamics model of the vehicle, which may also include, for example, a vehicle loss model. Different vehicle parameters and drive system losses can be considered, for example, stored in the model through operating parameters or characteristic curves. The corresponding values ​​can be calculated or simulated, for example. The application of such loss models is known.

[0028] To obtain the input values ​​required for the prediction algorithm and optimization function, the device according to the invention utilizes sensor data from one or more sensors of the vehicle, as well as topological data about the vehicle's external environment. This data is transmitted to the device via a first or second input interface. These sensors can be, for example, various optical sensors. Possible sensors include radar sensors, lidar sensors, or camera sensors. This allows the vehicle's surrounding environment to be known. To determine the vehicle's position, GNSS or GPS sensors can be used. They are applied, for example, in combination with topological data from topological units to estimate the vehicle's journey within the prediction range. The topological information and the vehicle's current position information can be used to consider, for example, road patterns such as uphill sections.

[0029] It is known in the prior art that the energy supplied by the battery within the predicted range and the travel time predicted by the vehicle model up to the waypoint or the predicted range should be considered. Energy-saving driving strategies can be further improved by extending the optimization function and considering information about charging points along the predicted route and the predicted energy content upon reaching the charging point. This can be adjusted and improved through the selection and weighting of boundary conditions, such as optimization based on driver expectations. Particularly feasible is to consider energy consumption based on the distance to the charging point or the predicted energy content of the battery at the charging point. For example, a specially optimized motor operating point can be set using input parameters. In this way, an optimized vehicle speed can be directly adjusted.

[0030] The components of a vehicle controlled by the device can also be components outside the drive system. To achieve energy-efficient vehicle regulation, it is necessary to adjust the individual electrical components based on the vehicle model. For example, it is feasible to reduce the energy consumption of each electrical component if relevant topology or external environmental information about the vehicle exists. For instance, it is conceivable to reduce the power consumption of each electrical component in the event of an expected uphill slope within the predicted range, thereby optimizing the use of existing energy. If, for example, a weather model is considered in the vehicle model, the vehicle's energy consumption and speed trajectory can be matched to weather conditions.

[0031] For example, it is also feasible to close the electric windows to reduce vehicle air resistance if the predicted energy content of the battery at the charging point is below a predetermined limit, or if, for example, information from the charging point indicates that the battery cannot be charged or can only be charged in an uncomfortable manner. In this case, the vehicle can be driven to another charging point, requiring efficient and energy-saving driving techniques to reach the alternative charging point.

[0032] The advantage of the device is that it not only plans the charging process but also performs consumption planning, thus adjusting and coordinating energy consumption and battery charging.

[0033] According to a preferred embodiment, energy, driving time, charging point information, and / or the energy content of the battery at the charging point are considered as weighting terms within the optimization function. Each feature in the optimization function has its own weighting coefficient, which can be independent of each other. Thus, the optimization function includes multiple weighting terms. An advantage is that individual parameters can be weighted differently depending on predetermined boundary conditions or other constraints. For example, it is also possible to degrade individual parameters given specific boundary conditions.

[0034] The optimization function preferably includes information about waypoints classified as parking areas. These waypoints lie on the routes predicted by the vehicle model within the prediction range. A waypoint classified as a parking area is a point the vehicle will inevitably pass through, where it stops with a given probability. If the vehicle is, for example, a bus or coach, a parking area is a point where the coach is scheduled to stop whenever a passenger wishes to disembark or a new passenger stops there. However, it is also feasible to classify other points of interest or waypoints of interest as parking areas. These might be, for example, special attractions that are worth stopping at under certain conditions. It is also conceivable to classify intersections with traffic lights as parking areas.

[0035] Preferably, information about waypoints classified as parking areas is included as a weighting term in the optimization function. Thus, the optimization function includes other terms with a different weighting coefficient to achieve personalized weighting.

[0036] In another preferred embodiment, the optimization function includes not only waypoints categorized as stops but also the estimated arrival time of the vehicle at the stop. In this way, arrival times and timetables can be implemented, for example, in the case of buses in short-distance public transport, taking this into account in model-based predictive regulation. Depending on the expected or required arrival time, the optimization function may produce different results over short periods or over specific distances compared to not taking this into account.

[0037] The optimization function of the prediction algorithm includes not only information about the location of the charging point but also a term with a charging point occupancy parameter that takes into account the point's availability. For example, it can be considered that the charging point is temporarily used by other vehicles, and therefore the current vehicle cannot use it at least at a certain point in time. The occupancy parameter may also include the expected duration of charging point occupancy. The driving algorithm or speed trajectory can be matched to this to arrive at the charging point when it is not occupied. In this way, the charging process can begin immediately upon arrival at the charging point.

[0038] In a preferred embodiment, the control value obtained in the control unit using a predictive algorithm is the motor control value for the electric motor of the vehicle. Therefore, the electric drive motor can be driven as part of the drive system of the device according to the invention. In this case, the vehicle model may include a longitudinal dynamic model of the drive system and, for example, take into account the speed trajectory leading to the direct drive motor.

[0039] In another preferred embodiment, the control value generated in the control unit using a predictive algorithm is a pump control value. This pump control value is used to drive the battery cooling pump to perform temperature control on the battery according to the battery model. Thus, the battery cooling pump is also part of the battery model and is considered in the vehicle model. For example, it might be meaningful to control the battery cooling pump to save energy. If, for example, the battery temperature is very low and should be heated to operate at its optimal operating point, it would be meaningful under certain conditions to cancel the battery temperature control and heating. This is, for example, in a situation where, after driving a short distance of a few kilometers, the predicted driving distance includes a long and steep uphill section requiring higher battery energy consumption. Due to the high energy consumption, the battery is heated, and if it has been preheated, it must be cooled again. Although by canceling preheating, the vehicle may initially operate in a poor energy-efficient state, the overall result is a significant energy-saving characteristic of the vehicle by canceling the previously performed heating and necessary cooling. This or similar scenarios can be advantageously considered in preferred embodiments of the invention.

[0040] In another preferred embodiment, the control value generated by the control unit using a predictive algorithm is the interior air conditioning control value. The interior air conditioning control value is used to drive the vehicle's interior air conditioning system. Advantageously, the interior air conditioning system can be shut off, for example, if a large amount of battery energy needs to be consumed and / or only a significantly reduced amount of battery energy is available. This might occur, for example, on an incline, especially when using the vehicle to transport or tow additional loads.

[0041] A system for model-based predictive regulation of components in a vehicle equipped with a battery and electric motor includes the aforementioned devices and at least one sensor for acquiring sensor data including information about the vehicle's external environment. Furthermore, the system includes a topology unit for providing data about the topology. Sensors can be, for example, optical sensors, such as radar sensors, lidar sensors, or camera sensors typically installed in the vehicle. Other sensors can be location sensors for determining the vehicle's position. This information, combined with data from an electronic map, allows the determination of the vehicle's position within a predetermined external environment. Topology data from the topology unit is also required to obtain route-related information, such as uphill, downhill, curves, distance or road type, and speed limits or other boundary condition data. In addition to general data about the vehicle's external environment, information about the surrounding environment, such as other vehicles, people, or objects in the external environment, is acquired via the optical sensors.

[0042] According to another aspect, the present invention relates to a vehicle having a battery and an electric motor. The vehicle includes the aforementioned system and components, by which the system generates control values ​​for these components and by which the system predictively adjusts these components based on a model.

[0043] In a preferred embodiment, the vehicle is a passenger vehicle, most preferably a bus. The optimization function includes information on waypoints classified as stops along the vehicle's predicted route by the vehicle model within the prediction range. The optimization function may also include a term that considers the arrival time to the waypoints classified as stops.

[0044] In addition to the boundary conditions mentioned above, boundary conditions and parameters used in the prior art can also be considered. This includes, for example, speed limits predetermined by road type or vehicle location (e.g., urban areas). It also includes speed limits that change, for example, when a vehicle leaves the city and enters a suburban road. Other known limitations may be torque limits, whereby the vehicle cannot accelerate significantly and results in an uncomfortable driving experience for passengers.

[0045] In addition to these boundary conditions, other boundary conditions, such as those concerning parking locations along the predicted vehicle path, are also satisfied in this device. Importantly, the vehicle is a passenger car or bus. Here, the arrival time at the parking location can be considered as another parameter of the optimization function or as a boundary condition (so-called constraint) and incorporated into the optimization function.

[0046] The vehicle model includes not only the battery model but also the vehicle's driving dynamics model or longitudinal dynamics model. The vehicle's driving dynamics model can include, for example, the traction force applied to the vehicle's wheels, rolling resistance considering tire deformation and wheel load during rolling, uphill resistance describing the longitudinal component of gravity and dependent on the lane slope, and the vehicle's air resistance. Mathematically, the vehicle model can be understood as the time derivative of velocity, where the sum of forces relates to the vehicle's equivalent mass. The vehicle's equivalent mass can include the inertia of the rotating components of the drivetrain. The optimization function is a cost function, which includes, for example, a weighting factor for battery energy consumption, battery energy consumption, distance, driving force, time, charging point information, information on the energy content of the battery at the charging point location, information on the start of the prediction range, and different weighting factors for each item (which may, for example, be summed). Current state parameters can be measured, the corresponding data recorded, and provided to the prediction algorithm. This allows for updates, for example, to the estimated or predicted range ahead of the vehicle, such as cyclically updating route data from electronic maps, topology cells, or navigation systems. The prediction range preferably includes at least 100m, very preferably at least 500m, and particularly preferably at least 1km. Route data or topology data may include uphill information, curve information, speed limits, etc. The predicted route is an estimated distance within the prediction range created by the vehicle model. A charging point is a location with facilities suitable for charging the vehicle's battery. This could be, for example, a charging station. Inductive charging options, such as charging via an electrical receiver, can also be specified. This is especially true for buses, which transport passengers on pre-planned or determined routes that can be marked on an electronic map or stored in the vehicle model. Particularly in the case of buses in line traffic, the route is known in principle. However, the vehicle may deviate from the planned and known route. Route information can be considered and processed in the vehicle model and / or optimization functions. Attached Figure Description

[0047] The following description, in conjunction with the selected embodiments and accompanying drawings, Figure 1 This further illustrates and explains the present invention. (See figures:)

[0048] Figure 1 This is a schematic diagram of a system according to one aspect of the present invention;

[0049] Figure 2 It is a schematic diagram of a vehicle with a system;

[0050] Figure 3 It is a diagram illustrating the driving conditions; and

[0051] Figure 4 This is a schematic diagram of the method of the present invention. Detailed Implementation Plan

[0052] Figure 1The system 10 is shown, which has a device 12, a sensor 14 and a topology unit 16 according to the invention. Figure 1 Component 18 is shown attached, which is connected to and controlled by system 10.

[0053] Device 12 includes a first input interface 20, which is connected to sensor 14 and receives sensor data from sensor 14. Device 12 has a second input interface 22 for receiving topology data about the external environment of the vehicle. The second input interface 22 is connected to topology unit 16 and receives data from topology unit 16. Control unit 25 of device 12 has a predictive algorithm 26 that generates control values ​​for a component, such as component 18. Output interface 24 outputs the control values ​​for component 18, which are learned in device 12. These control values ​​are transmitted to component 18 via output interface 24.

[0054] The prediction algorithm 26 of device 12 processes sensor data from the first input interface 20 and data from the second input interface 22. The prediction algorithm 26 includes a vehicle model 28 and an optimization function 30, which consider different parameters of the vehicle and parameters provided by the vehicle model 28. The optimization function 30 is preferably a cost function. The optimization function 30 is minimized, wherein a quadratic minimization function or another minimization function may be applied. Such an optimization function or cost function, and the minimization optimization function, are known in principle in the prior art.

[0055] Vehicle model 28 includes battery model 32, which can be used to model the vehicle battery, including battery energy management and battery cooling pumps or battery temperature control units. Furthermore, vehicle model 28 includes driving dynamics model 31, which, for example, considers the drive system and its components.

[0056] The optimization function 30 may include, for example, electrical energy and vehicle travel time, wherein preferably, battery model 32 predicts energy, and vehicle model 28 or driving dynamics model 31 predicts travel time. The optimization function 30 may also include information on charging points along the vehicle's journey predicted by vehicle model 28 within the prediction range, as well as, for example, information on the predicted energy of the battery at the location of a charging point along the journey. The prediction algorithm 26 processes the data provided to it and, for example, the prediction values ​​from vehicle model 28, and obtains control values ​​for the vehicle's components 18 by minimizing the optimization function 30.

[0057] Figure 2A schematic diagram of a vehicle 34 with device 12 and topology unit 16 is shown. This topology unit is the vehicle 34's navigation system 36 and transmits data about the topology. This data may, for example, come from an electronic map of the navigation system 36. The vehicle 34 includes multiple sensors 14, which are, for example, radar sensors 38 and lidar sensors 40. These two sensors transmit data about the vehicle's surrounding environment, such as sensor data about other vehicles located nearby.

[0058] Device 12 controls component 18 of vehicle 34, which in this case is electric motor 42 of the drive system. Electric motor 42 drives the wheels of vehicle 34. Battery 33 provides the required energy. It is modeled using battery model 32.

[0059] Figure 3 A schematic diagram illustrating traffic conditions includes a vehicle 34 designed as a bus 42. The bus 42 travels along a route 44 that includes parking spots. These parking spots are, for example, short-distance public transport route stops 46. There is a possibility of charging the vehicle 34 at these parking spots 46, thus the parking spots 46 include charging points 48. To optimize the energy management of the bus 42, for example, the predicted energy content of the vehicle's battery 33 at the parking spots 46 can be considered.

[0060] When optimizing vehicle energy consumption and / or vehicle mileage, the arrival times at parking locations 46, as specified in the timetable, can also be considered. The predetermined arrival times can be compared to and aligned with the predicted arrival times of the vehicle model. This can be achieved, for example, by appropriately weighting the predicted arrival times in the optimization function.

[0061] For example, individual components of the vehicle can be shut down to conserve energy. The saved energy is provided to the driving dynamics model and can be consumed, for example, by the drive motor to generate at least temporarily higher torque and consequently higher vehicle speed, thus arriving at the planned parking location 46. The known speed trajectory is then adjusted based on the optimization function.

[0062] It goes without saying that charging points unrelated to parking spot 46 can also be set up along the route. These charging points might be occupied by other vehicles, making charging impossible. In this case, one can either wait for or pass by charging point 48 and drive to another charging point. The occupancy of charging point 48 can be considered in the optimization function. This is preferably implemented using a weighting factor that degrades occupied charging point 48. It is also conceivable to disregard occupied charging point 48 in the optimization function.

[0063] Similar scenarios and other boundary conditions can be implemented as soft or hard boundary conditions and stored in the vehicle model. Arrival time, for example, can be a hard boundary condition to ensure adherence to a predetermined timetable. This boundary condition has priority and is always satisfied. It is feasible to increase the vehicle's energy consumption to arrive at the parking location 46 at the predetermined time. It is also feasible to shut down individual electrical appliances or components of the vehicle to provide sufficient energy. In this way, advantageous and further developed energy management can be achieved.

[0064] Figure 4 The schematic flow diagram illustrates the method of the present invention for predictively adjusting components 18 of a vehicle 34 having a battery and an electric motor based on a model. In a first receiving step S10, sensor data from sensors 14 of the vehicle 34 is received. In another receiving step S12, topological data about the external environment of the vehicle 34 is received. Implementation step S14 implements a predictive algorithm to generate control values ​​for components 18 of the vehicle 34. Output step S16 sends the obtained control values ​​for components 18 to components 18. The control values ​​are obtained in the control unit in step S14.

[0065] In a preferred embodiment of the method according to the invention, a plurality of other sub-steps are performed in step S14 of implementing the prediction algorithm. These sub-steps are optional, and thus in Figure 4 The values ​​are shown in dashed lines. Step S20 may include predicting electrical energy. Prediction step S22 may include predicting travel time based on the vehicle model. Step S24 may include implementing an optimization function, which includes the predicted electrical energy and travel time, as well as charging point information along the predicted route of vehicle 34 and the predicted energy content of battery 33 at the location of charging point 48. Preferably, the optimization function is minimized at this step so that control values ​​for component 18 are preferably obtained.

[0066] This invention has been fully described and set forth in conjunction with the accompanying drawings and specification. These descriptions and descriptions should be understood as exemplary and non-limiting. The invention is not limited to the disclosed embodiments. Other embodiments or variations will be apparent to those skilled in the art upon application of the invention and upon accurate analysis of the drawings, disclosure, and the following claims.

[0067] In the claims, "comprising" and "having" do not exclude the presence of other elements or steps. The indefinite article "a" does not exclude the presence of a plurality. A single element or unit can implement the function of multiple units described in the claims. Elements, units, interfaces, devices, and systems can be implemented, partially or wholly, in hardware and / or software. Merely mentioning certain measures in several different dependent claims should not be construed as implying that a combination of these measures is not advantageously applicable. Computer programs can be stored / distributed on non-volatile storage media, such as optical storage or solid-state drives (SSDs). Computer programs can be distributed, for example, via the Internet or via wired or wireless communication systems, together with and / or as part of the hardware. The reference numerals in the claims of this patent are not to be interpreted restrictively.

[0068] List of reference numerals

[0069] 10 System

[0070] 12 devices

[0071] 14 Sensors

[0072] 16 topology units

[0073] 18 parts

[0074] 20 First Input Interface

[0075] 22 Second Input Interface

[0076] 24 Output Interfaces

[0077] 25 Control Unit

[0078] 26 Prediction Algorithms

[0079] 28 vehicle models

[0080] 30 Optimization Function

[0081] 31 Driving Dynamics Model

[0082] 32 Battery Model

[0083] 33 batteries

[0084] 34 vehicles

[0085] 35 Electric Motor

[0086] 36 Navigation Systems

[0087] 38 Radar Sensors

[0088] 40 LiDAR Sensors

[0089] 42 passenger buses

[0090] 44 Distance

[0091] 46 Parking area

[0092] 48 charging points

Claims

1. A device (12) for predictively adjusting the electrically powered components (18) of a vehicle (34) having a battery (33) and an electric motor (35), the device comprising: - A first input interface (20) for receiving sensor data of the vehicle's (34) external environment information from the sensor (14). - A second input interface (22) for receiving topology data from the external environment of the vehicle (34); - Control unit (25), which is used to generate control values ​​for the components (18) supplied with electrical energy for the vehicle (34) by means of implementing a prediction algorithm (26), the prediction algorithm including a vehicle model (28) with a battery model (32) and an optimization function (30) and the prediction algorithm processing sensor data of the sensor (14) and data about topology; - Output interface (24) of the component (18) that provides electrical energy to the vehicle (34) for outputting control values ​​obtained in the control unit (25). in, The optimization function (30) includes electrical energy and driving time, wherein the energy is predicted by the battery model (32) and the driving time is predicted by the vehicle model (28); The optimization function (30) includes information on charging points (48) on the vehicle (34) along the route (44) predicted by the vehicle model (28) within the prediction range, and information on the predicted energy content of the battery (33) at the location of the charging point (48). The optimization function (30) includes information on waypoints classified as parking locations (46) on the route (44) of the vehicle (34) predicted by the vehicle model (28) within the prediction range, wherein the optimization function considers the arrival time of the waypoints classified as parking locations (46) as a term, and wherein the optimization function (30) includes information on waypoints classified as parking locations (46) as a weighting term; and The prediction algorithm (26) generates the control value by minimizing the optimization function (30).

2. The apparatus (12) according to claim 1, wherein, The optimization function (30) includes information on energy, driving time, charging point and / or energy-related information as weighting terms.

3. The apparatus (12) according to claim 1, wherein, The arrival time is considered as a hard boundary condition in the optimization function (30).

4. The apparatus (12) according to claim 1, wherein, The optimization function (30) takes into account the arrival time at the parking spot (46) as specified in the timetable and makes it consistent with the predicted arrival time of the vehicle model.

5. The apparatus (12) according to any one of claims 1 to 4, wherein, The optimization function (30) includes a term with an occupancy parameter that takes into account the occupancy of the charging point (48).

6. The apparatus (12) according to any one of claims 1 to 4, wherein, The control value generated in the control unit (25) by means of the prediction algorithm (26) is the motor control value for the electric motor (35) of the vehicle (34).

7. The apparatus (12) according to any one of claims 1 to 4, wherein, The control value generated by the prediction algorithm (26) is the pump control value used to drive the battery cooling pump that controls the temperature of the battery (33).

8. The apparatus (12) according to any one of claims 1 to 4, wherein, The control value generated by the prediction algorithm (26) is the interior space air conditioning control value used to drive the interior space air conditioning equipment of the vehicle (34).

9. A system (10) for model-based predictive regulation of electrically powered components (18) of a vehicle (34), the vehicle having a battery (33) and an electric motor (35), the system comprising: The apparatus (12) according to any one of claims 1 to 8; Sensor (14) for obtaining sensor data containing information about the external environment of the vehicle (34); and Topological units (16) used to provide data about topology.

10. The system (10) according to claim 9, wherein, The sensor (14) used to obtain sensor data of the external environment information of the vehicle (34) is an optical sensor.

11. The system (10) according to claim 9, wherein, The sensor (14) used to obtain sensor data of the external environment of the vehicle (34) is a radar sensor (38), a lidar sensor (40), a camera, a GNSS sensor or a GPS sensor.

12. A vehicle (34) comprising a battery (33), an electric motor (35), a system (10) according to claim 9, 10 or 11, and a component (18) supplied with electrical energy, wherein the system (10) generates control values ​​for the component supplied with electrical energy.

13. The vehicle (34) according to claim 12, wherein, The vehicle (34) is a bus (42), and the optimization function (30) includes information on waypoints classified as parking spots (46) on the route (44) of the vehicle (34) predicted by the vehicle model (28) within the prediction range, and the optimization function (30) includes a term that takes into account the arrival time to reach the waypoints classified as parking spots (46).

14. A method for model-based predictive regulation of a component (18) of a vehicle (34) supplied with electrical energy, the vehicle having a battery (33) and an electric motor (35), the method comprising the following steps: Receive sensor data from the sensor (14) of the vehicle (34) containing information about the external environment of the vehicle; Receive topology data about the external environment of the vehicle (34); By means of implementing a prediction algorithm (26), control values ​​for the component (18) that is supplied with electrical energy are generated, the prediction algorithm having: a vehicle model (28) including a battery model (32) and an optimization function (30) including electrical energy and driving time. The sensor data and topology data of the sensor (14) are processed in the prediction algorithm (26). The obtained control value is output to the component (18) that is supplied with electrical energy; in, The energy is predicted by the battery model (32), and the driving time is predicted by the vehicle model (28); The optimization function (30) includes information on charging points (48) on the vehicle (34) along the route (44) predicted by the vehicle model (28) within the prediction range, and information on the predicted energy content of the battery (33) at the location of the charging point (48). The optimization function (30) includes information on waypoints classified as parking spots (46) on the route (44) of the vehicle (34) predicted by the vehicle model (28) within the prediction range. The optimization function considers the arrival time of the waypoints classified as parking locations (46) as a term, and the optimization function (30) includes information about the waypoints classified as parking locations (46) as a weighting term; and The prediction algorithm (26) generates the control value by minimizing the optimization function (30).

15. The method according to claim 14, wherein, The optimization function (30) includes information on waypoints classified as parking spots (46) on the route (44) of the vehicle (34) predicted by the vehicle model (28) within the prediction range.

16. A computer program product having program code, wherein when the program code is run on a computer, the program code is used to perform the steps of the method according to claim 14.

Images