DEVICE AND METHOD FOR THE MODEL-BASED PREDICTED CONTROL OF A COMPONENT OF A VEHICLE

Abstract

A device for model predictive control of a vehicle component includes a first input receiving sensor data, a second input receiving data regarding a topology in a vehicle's environment, a control unit for executing a predictive algorithm for generating a control value for the component, an output for outputting the control value, wherein the predictive algorithm comprises a vehicle model including a battery model, wherein the sensor and topology data are processed in the predictive algorithm, the predictive algorithm also including an optimization function including energy consumption predicted by the battery model, travel time predicted by the vehicle model, information regarding a charging station along a route of the vehicle predicted by the vehicle model in a prediction horizon, and information regarding the predicted charging state of the battery at the charging station, and the predictive algorithm generates the control value through minimization in the optimization function.

Claims

1-15. (canceled)

16. A device for model predictive control of a component in a vehicle that has a battery and an electric motor, the device comprising: a first input interface configured to receive sensor data from a sensor in the vehicle; a second input interface configured to receive data regarding a topology of the vehicle's environment; a control unit configured to execute a predictive algorithm to generate a control value for the component in the vehicle; an output interface configured to output the control value for the component in the vehicle generated in the control unit, wherein the predictive algorithm comprises a vehicle model and an optimization function, wherein the vehicle model comprises a battery model and, and wherein the sensor data and data regarding the topology are processed in the predictive algorithm; wherein the optimization function comprises energy consumption and travel time, wherein the energy consumption is predicted by the battery model and the travel time is predicted by the vehicle model; the optimization function comprises information regarding a charging station along a route for the vehicle predicted by the vehicle model in a prediction horizon, and information regarding the predicted charging state of the battery at the charging station; and the predictive algorithm generates the control value through minimization in the optimization function.

17. The device according to claim 16, wherein the optimization function comprises the energy consumption, travel time, information regarding the charging station, and/or the charging state as weighted variables.

18. The device according to claim 16, wherein the optimization function comprises information regarding a point categorized as a stop on the route of the vehicle in the prediction horizon predicted by the vehicle model, wherein the information regarding the stop is included in the optimization function as a weighted variable.

19. The device according to claim 18, wherein the optimization function includes a variable that takes an arrival time at the stop into account.

20. The device according to claim 16, wherein the optimization function contains an occupancy parameter that accounts for an occupancy of the charging station.

21. The device according to claim 16, wherein the control value generated by the predictive algorithm in the control unit is a control value for the electric motor in the vehicle.

22. The device according to claim 16, wherein the control value generated by the predictive algorithm is a pump control value for controlling a cooling pump configured to control a temperature of the battery.

23. The device according to claim 16, wherein the control value generated by the predictive algorithm is an air conditioning control value for controlling an air conditioner in the vehicle.

24. A system, comprising the device according to claim 16; the sensor configured to acquire the sensor data with information regarding the vehicle's environment; and a topology device configured to acquire the data regarding the topology.

25. The system according to claim 24, wherein the sensor is an optical sensor, a radar sensor, a lidar sensor, a camera, a Global Navigation Satellite System (GNSS) sensor, or a Global Positioning System (GPS) sensor.

26. A vehicle comprising: a battery; an electric motor; and the system according to claim 24; and a component for which the control value is generated by the system.

27. The vehicle according to claim 26, wherein the vehicle is a bus, wherein the optimization function comprises information regarding a point categorized as a stop on the route of the vehicle in a prediction horizon predicted by the vehicle model, and wherein the optimization function includes a variable that takes the arrival time at the stop into account.

28. A method for model predictive control of a component in a vehicle that has a battery and an electric motor, the comprising: receiving sensor data form a sensor in the vehicle; receiving data regarding a topology in the vehicle's environment; executing a predictive algorithm to generate a control value for the component, wherein the predictive algorithm comprises a vehicle model and an optimization function, the vehicle model comprises a battery model and the sensor data and data regarding topology are processed in the predictive algorithm, the optimization function comprises energy consumption and travel time, wherein the energy consumption is predicted by the battery model and the travel time is predicted by the vehicle model, the optimization function comprises information regarding a charging station along the route of the vehicle in a prediction horizon predicted by the vehicle model, and information regarding the predicted charging state of the battery at the charging station, and wherein executing the predictive algorithm comprises generating, with the predictive model, the control value through minimization in the optimization function; and outputting the control value for the component.

29. The method according to claim 28, wherein the optimization function comprises information regarding a point categorized as a stop on the route of the vehicle predicted by the vehicle model in a prediction horizon.

30. The method according to claim 28, wherein the optimization function comprises the energy consumption, travel time, information regarding the charging station, and/or the charging state as weighted variables.

31. The method according to claim 28, wherein the optimization function includes a variable that takes an arrival time at the stop into account.

32. The method according to claim 28, wherein the optimization function contains an occupancy parameter that accounts for an occupancy of the charging station.

33. The method according to claim 28, wherein the control value generated by the predictive algorithm in the control unit is a control value for the electric motor in the vehicle.

34. The method according to claim 28, wherein the control value generated by the predictive algorithm is a pump control value for controlling a cooling pump configured to control a temperature of the battery.

35. A non-transitory computer readable medium having stored thereon programming code that, when executed by a computing device, cause the computing device to perform a method comprising: receiving sensor data form a sensor in a vehicle; receiving data regarding a topology in the vehicle's environment; executing a predictive algorithm to generate a control value for a component of the vehicle, wherein the predictive algorithm comprises a vehicle model and an optimization function, the vehicle model comprises a battery model, wherein the sensor data and data regarding the topology are processed in the predictive algorithm, the optimization function comprises energy consumption and travel time, wherein the energy consumption is predicted by the battery model and the travel time is predicted by the vehicle model, the optimization function comprises information regarding a charging station along the route of the vehicle in a prediction horizon predicted by the vehicle model, and information regarding the predicted charging state of the battery at the charging station, and wherein executing the predictive algorithm comprises generating, with the predictive model, the control value through minimization in the optimization function; and outputting the control value for the component.

Description

[0047] The invention shall be described and explained in greater detail below on the basis of selected exemplary embodiments, in reference to the drawings. Therein:

[0048] FIG. 1 shows a schematic illustration of the system according to one aspect of the present invention;

[0049] FIG. 2 shows a schematic illustration of a vehicle that has the system;

[0050] FIG. 3 shows a schematic illustration of a driving situation; and

[0051] FIG. 4 shows a schematic illustration of the method according to the invention.

[0052] FIG. 1 shows a system 10 with a device 12 according to the invention, a sensor 14, and a topology unit 16. FIG. 1 also shows a component 18 that is connected to the system 10 and controlled therewith.

[0053] The device 12 comprises a first input interface 20, which is connected to the sensor 14 and receives sensor data from the sensor 14. The device 12 has a second input interface 22 for receiving data regarding the topology of the vehicle's environment. The second input interface 22 is connected to the topology unit 16 and receives data from the topology unit 16. A control unit 25 in the device 12 contains a predictive algorithm 26 that generates a control value for a component, e.g. the component 18 connected to the system. An output interface 24 outputs the control value for the component 18 generated in the device 12. This control value is sent from the output interface 24 to the component 18.

[0054] The predictive algorithm 26 in the device 12 processes the sensor data from the first input interface 20 and the data from the second input interface 22. The predictive algorithm 26 comprises a vehicle model 28, and an optimization function 30, which takes the various parameters of a vehicle and the parameters provided by the vehicle model 28 into account. The optimization function 30 is preferably a cost function. The minimization in the optimization function 30 is obtained with a quadratic function or some other minimizing function. These optimization functions, or cost functions, are basically known from the prior art, as is minimization in an optimization function.

[0055] The vehicle model 28 contains a battery model 32 with which a battery in a vehicle, including the energy management of the battery and a model of the cooling pump or a temperature control unit for the battery, can be depicted. The vehicle model 28 can also contain a driving dynamics model 31, which takes the drive train and its components into account, for example.

[0056] By way of example, the optimization function 30 can include power consumption and vehicle travel time, in which the power consumption is preferably predicted by the battery model 32 and the travel time is predicted by the vehicle model 28 or driving dynamics model 31. The optimization function 30 can also contain information regarding a charging station on the route predicted with the vehicle model 28 in a prediction horizon, as well as information regarding the predicted charging state of the battery at the charging station along the route. The predictive algorithm 26 processes the data and predicted values that it acquires from the vehicle model 28, and generates a control value for a component 18 in a vehicle through minimization in the optimization function 30.

[0057] FIG. 2 shows a schematic illustration of a vehicle 34, which contains the device 12, and a topology unit 16 that functions as a navigation system 36 for the vehicle 34 and provides data regarding topology. This data can come from the electronic map in the navigation system 36. The vehicle 34 has numerous sensors 14, comprising a radar sensor 38 and a lidar sensor 40 in this case. The two sensors provide data regarding the immediate environment of the vehicle, e.g. sensor data regarding other vehicles in the vicinity of the vehicle.

[0058] The device 12 controls a component 18 in the vehicle 34, which is an electric motor 42 in the drive train in this case. The electric motor 42 drives one of the wheels on the vehicle 34. A battery 33 supplies the necessary energy. It is depicted by the battery model 32.

[0059] FIG. 3 shows a schematic illustration of a traffic situation in which the vehicle 34 is a bus 42. The bus 42 travels along a route 44 that contains a stop. The stop is a bus stop 46 in a public transportation system. The vehicle 34 can be recharged at the stop 46, and thus contains a charging station 48. For purposes of energy management for the bus 42, the charging state of the battery 33 in the vehicle at the stop 46 can be predicted.

[0060] Planned arrival times at the stop 46 can also be taken into account when optimizing the energy consumption by the vehicle and/or optimizing the range of the vehicle. These predefined arrival times can be compared and coordinated to arrival times predicted with the vehicle model. This can be achieved by weighting the predicted arrival time accordingly in the optimization function.

[0061] It is also possible to shut off individual components in the vehicle in order to conserve energy. The conserved energy is then available for the driving dynamics model and can be used by the motor to temporarily obtain a higher torque therefrom, and therefore accelerate the vehicle, in order to arrive at the stop 46 according to the schedule. The speed trajectory that is determined is then adjusted on the basis of the optimization function.

[0062] It is also possible for there to be a charging station along the route that is not a scheduled bus stop 46. The charging station may be occupied by other vehicles, such that it is not possible to recharge the battery upon arrival. In this case, the vehicle can either wait, or it can pass by the charging station 48 and drive to another charging station. The occupancy of the charging station can be taken into account in the optimization function. This is preferably obtained with a weighting factor, which can sanction an occupied charging station 48. It is also possible to ignore an occupied charging station in the optimization function.

[0063] Similar scenarios and other constraints can be defined as soft constraints or a hard constraints, and stored in the vehicle model. By way of example, the arrival time can be a hard constraint for maintaining a predefined schedule. This constraint is then given priority and must be satisfied. This may require an increase energy consumption in order to arrive at the stop 46 on time. It is also possible to shut off certain components in the vehicle in order to conserve energy. This results in an advantageous and advanced energy management.

[0064] FIG. 4 shows a schematic flow chart for the method according to the invention for model predictive control of a component 18 in a vehicle 34 that contains a battery and an electric motor. In a first step S10, sensor data is received from a sensor 14 in the vehicle 34. In a second step S12, data is received regarding the topology of the environment of the vehicle 34. The predictive algorithm is executed in step S14, to generate a control value for a component 18 in the vehicle 34. In an output step S16, the control value for the component 18 is output to the component 18. The control value is determined in the control unit in step S14.

[0065] In a preferred embodiment of the method according to the invention, numerous sub-steps are carried out in step S14, in which the predictive algorithm is executed. These sub-steps are optional and therefore indicated by a broken line in FIG. 4. Step S20 can thus comprise predicting energy consumption. Step S22 can comprise predicting a travel time on the basis of the vehicle model. Step S24 can comprise executing an optimization function in which the predicted energy consumption and travel time, as well as information regarding a charging state along the predicted route for the vehicle 34 and information regarding a predicted charging state of the battery 33 at the charging state 48, are taken into account. A minimization is preferably carried out in the optimization function in this step, to thus obtain the control value for the component 18.

[0066] The invention has been comprehensively described and explained in reference to the drawings. The descriptions and explanations are to be regarded as exemplary, and not limiting. The invention is not limited to the disclosed embodiments. Other embodiments or variations can be derived by the person skilled in the art through the use of the present invention and through a precise analysis of the drawings, the disclosure, and the following claims.

[0067] The words, comprising and with in the claims do not exclude the presence of other elements or steps. Indefinite articles, a or an do not exclude pluralities. A single element or single unit can execute the functions of numerous units specified in the claims. An element, unit, interface, device, or system can be partially or entirely formed by hardware and/or software. Simply specifying certain measures in numerous dependent claims is not to be understood to mean that a combination of these measures cannot also be used advantageously. A computer program can be stored/distributed on non-volatile data carrier, e.g. an optical storage medium or a solid state drive (SSD). A computer program can be distributed with hardware and/or as a part of a hardware, e.g. through the internet or hardwired or wireless communication systems. Reference symbols in the claims are not to be regarded as limiting.

REFERENCE SYMBOLS

[0068] 10 system [0069] 12 device [0070] 14 sensor [0071] 16 topology unit [0072] 18 component [0073] 20 first input interface [0074] 22 second input interface [0075] 24 output interface [0076] 25 control unit [0077] 26 predictive algorithm [0078] 28 vehicle model [0079] 30 optimization function [0080] 31 vehicle dynamics model [0081] 32 battery model [0082] 33 battery [0083] 34 vehicle [0084] 35 electric motor [0085] 36 navigation system [0086] 38 radar sensor [0087] 40 lidar sensor [0088] 42 bus [0089] 44 route [0090] 46 stop [0091] 48 charging station