DE102019125220A1 - Method for determining an energy-optimized driving profile for a vehicle - Google Patents

Abstract

A method comprising determining an optimized driving profile for a section from a set of stored driving profiles for the section, taking into account current environmental conditions and / or current vehicle operating conditions.

Description

The invention relates to a method, a system and a computer program for determining an energy-optimized driving profile for a vehicle. This method and the system can be used in particular to support the driver of a vehicle with regard to an economical driving style.

The fuel consumption of vehicles in road traffic is largely determined by the individual driving style of the driver, in particular by how anticipatory the motor vehicle is operated, i.e. how early possible external factors are recognized in order to react appropriately. Anticipatory driving is characterized, among other things, by maintaining an appropriate safety distance. This means that it is possible to react earlier to incorrect behavior by other road users and, for example, to avoid emergency braking. Avoided brake actuation reduces the fuel consumption of the own vehicle, since there is no need to re-accelerate the vehicle. The driving style of the driver can be expressed, for example, in a high or low engine speed, a gentle or strong acceleration, a high or low freeway speed or the like.

A model calculation for a battery-operated bus shows that there are typically 12.42% energy losses, of which 7.55% are based on regeneration losses, 4.84% are based on inverter losses, and 0.03% are based on battery losses. Assuming, for example, 144 kWh of regenerated braking energy over the course of a day, the total loss amounts to 32.4 kWh, which roughly corresponds to the energy requirement for a distance of 27 km.

In addition to the driver's driving style, factors such as the car's concept and technology have a significant influence on fuel consumption. Other influencing variables are also: weather conditions (outside temperature, moisture, snow, wind), road surface condition (curve shape, incline), additional energy requirements (e.g. air conditioning, seat heating, blower), load and air resistance (e.g. roof rack), tires (rolling resistance, tire air pressure), maintenance status (Engine oil, air filter, air mass meter, lambda probe, etc.).

In particular, the fuel consumption is particularly high at temperatures in the minus range. This is due, among other things, to the fact that the cooling water takes longer to reach operating temperature. Fuel consumption is highest during this warm-up phase. In addition, starting the engine at low temperatures is a major strain on the material. At minus 20 degrees, for example, common engine oil of the type 10W40 is very viscous. Accordingly, it takes longer for the oil to reach all important points in the engine. The viscous oil also means that the frictional resistance in the engine is higher, so that consumption increases.

From the German Offenlegungsschrift DE 10 2010 011 088 A1 A method and a device are known by means of which the driver of a motor vehicle is supported in optimizing his driving style with regard to a parameter dependent on the operation of the motor vehicle, in particular with regard to the fuel consumption and / or emissions of the motor vehicle. In the method according to the invention, the actual value of a parameter dependent on the operation of the motor vehicle is recorded in succession and compared with a stored base value. In the method, a graphic symbol is displayed in the motor vehicle when the actual value of the parameter falls below or exceeds the base value, the graphic symbol increasing as a function of the detected deviations of the actual value of the parameter from the base value.

From the German Offenlegungsschrift DE 10 2010 041 539 A1 A driver assistance system is known to support the driver in consumption-controlled driving, which combines haptic and optical feedback functions, in particular in the form of a drive configuration, a display concept and / or a deceleration assistant, with a modified accelerator pedal characteristic on the one hand and ECO tips on the interactive output of efficient driving instructions in the foreground .

The German Offenlegungsschrift DE 10 2012 221 827 A1 describes a fuel economy training method by activating an on-board training mode. According to the method, the driver is instructed to carry out a vehicle maneuver. A calculation module calculates the fuel economy for this maneuver and then feeds this information to a calculation module, which analyzes the speed / acceleration profile and determines an optimized speed / acceleration profile. Corrective feedback is generated by comparing subsequent vehicle maneuvers with the optimized speed / acceleration profile.

The German Offenlegungsschrift DE 10 2017 119 453 A1 discloses a navigation system and a method for determining an optimally energy-efficient route in a vehicle on the basis of cloud information and vehicle-specific energy consumption features.

Proceeding from this prior art, the present invention is based on the object of providing a method and a system for determining an energy-optimized driving profile for a vehicle, which allows the energy consumption to be further optimized.

This object is achieved by the method of claim 1 and the driver assistance system of claim 10 for determining an energy-optimized driving profile for a vehicle. Further advantageous embodiments of the invention emerge from the subclaims and the following description of preferred exemplary embodiments of the present invention.

The exemplary embodiments show a method comprising the determination of an optimized driving profile for a partial route from a set of stored driving profiles for the partial route, taking into account current environmental conditions and / or current vehicle operating conditions. In this way, based on driving data, in particular speed / acceleration profiles, which are generated by the driver of the vehicle or by drivers of other vehicles, optimal courses in terms of duration and energy consumption can be learned or generated on a route.

A driving profile stored in the set of stored driving profiles preferably includes recorded environmental conditions and / or recorded vehicle operating conditions that correspond to the driving profile, as well as recorded manipulated variable information for the implementation of the driving profile.

The recorded manipulated variable information can include, for example, a driving speed, a drive power, a drive torque, an engaged gear, and / or a status of electrical consumers such as the air conditioning system or a cooling circuit controller.

According to one embodiment, determining an optimized driving profile includes filtering the set of stored driving profiles according to the current environmental conditions and / or current vehicle operating conditions, as well as selecting, from the filtered driving profiles, that driving profile with the lowest total energy consumption than the optimized driving profile.

A driving profile stored in the set of stored driving profiles includes, for example, information corresponding to the driving profile about total energy consumption and / or information corresponding to the driving profile about a driving time required for the partial route.

According to one embodiment variant, the method further comprises a recording of a driving profile carried out, for example, on a given route under given traffic conditions in the set of stored driving profiles. In this way, new profiles are added to the set of stored driving profiles. This has the advantage that routes and traffic patterns are learned. In this way, the vehicle can collect information for optimal energy consumption.

It is particularly advantageous if, in the method, artificial variations are made in the manipulated variables in order to obtain as many different driving profiles as possible in the set of stored driving profiles for a partial route. In this way, for example, changes in the driving dynamics can be brought about artificially.

The method further includes realizing (converting) the optimized driving profile on the basis of manipulated variable information recorded in the optimized driving profile.

This implementation of the optimized driving profile includes, for example, an autonomous setting of the manipulated variable information. By realizing the optimized driving profile, for example, an optimized use of energy can be achieved, for example by avoiding unnecessary braking and acceleration processes or by minimizing braking times. This also leads to a longer service life of components (e.g. brakes and batteries). Furthermore, this also leads to more comfortable driving and lower operating costs.

Additionally or alternatively, the implementation of the optimized driving profile can include setting the optimal acceleration / deceleration when the driver accelerates or brakes. The driving profile can be implemented, for example, by “playing” learned courses (or recorded manipulated variable information) while driving. However, this is preferably not done completely autonomously, that is, independently of the driver's actions. For example, the optimal acceleration / deceleration is preferably set directly when the driver accelerates or brakes.

Additionally or alternatively, the implementation of the optimized driving profile can include outputting the manipulated variable information to the driver by means of a user interface such as a display board or a loudspeaker.

Additionally or alternatively, the implementation of the optimized driving profile can include influencing a driver's request by means of an active accelerator pedal, which is designed to exert a counterforce opposing a force exerted by the driver, which depends on a determined actual speed and a target speed specified by the driving profile.

An extension to this would be that traffic is taken into account and, in the event of a certain deviation from the normal state due to the traffic, takes over the automatic control.

The invention also includes a driver assistance system which is designed to carry out the described method.

The invention also comprises a computer program which contains program instructions which are designed to carry out the described methods.

The described method and the driver assistance system can be implemented based on existing technology for, for example, partially autonomous driving and computer vision.

• 1 ein Blockdiagramm zeigt, das schematisch die Konfiguration eines Fahrzeugs gemäß einem Ausführungsbeispiel der vorliegenden Erfindung darstellt,
• 2 ein Blockdiagramm zeigt, das eine beispielhafte Konfiguration einer Fahrerassistenzeinheit darstellt,
• 3 schematisiert die Erzeugung eines optimierten Fahrprofils durch die erfindungsgemäße Fahrerassistenzeinheit zeigt,
• 4 schematisiert den Prozess der Gewinnung von Informationen über die Fahrstrecke (im Folgenden „Streckenmerkmale) durch die Fahrerassistenzeinheit zeigt,
• 5 ein Ausführungsbeispiel für den Prozess der Gewinnung von Informationen über die Fahrstrecke (Streckenmerkmale) der 4 näher beschreibt,
• 6a-d Beispiele für Fahrprofile zeigt, die bei wiederholtem Abfahren einer Teilstrecke im manuellen Fahrzeug-Betriebsmodus mit dem in 5 dargestellten Prozess erhalten wurden,
• 7 ein Flussdiagramm darstellt, das die Verfahrensschritte zur Bestimmung eines optimalen Fahrprofils für eine gegebene Teilstrecke durch die Fahrerassistenzeinheit anzeigt,
• 8 den Zusammenhang zwischen Umgebungsbedingungen und Kraftstoffverbrauch veranschaulicht,
• 9 die Selektion eines optimierten Fahrprofils aus einer Vielzahl gespeicherter Fahrprofile anhand eines Beispiels veranschaulicht,
• 10 in einem Diagramm ein Bremsprofil eines Busses im manuellen Fahrmodus ohne Eingriff des erfindungsgemäßen Fahrerassistenzsystems zeigt, und
• 11 im Vergleich ein Beispiel für ein nicht-optimiertes Geschwindigkeitsprofil zeigt, das im manuellen Fahrmodus über eine Fahrstrecke erzeugt wurde und ein optimiertes Geschwindigkeitsprofil über dieselbe Fahrstrecke, das durch das erfindungsgemäße Fahrerassistenzsystem optimiert und bei der teilautonomen Steuerung des Fahrzeugs umgesetzt wurde.
• 1 zeigt ein Blockdiagramm, das schematisch die Konfiguration eines Fahrzeugs gemäß einem Ausführungsbeispiel der vorliegenden Erfindung darstellt.

Embodiments will now be described by way of example and with reference to the accompanying drawings, in which:

• 1 Fig. 10 is a block diagram schematically showing the configuration of a vehicle according to an embodiment of the present invention;
• 2 shows a block diagram illustrating an exemplary configuration of a driver assistance unit,
• 3rd shows schematically the generation of an optimized driving profile by the driver assistance unit according to the invention,
• 4th shows schematically the process of obtaining information about the route (hereinafter "route features") by the driver assistance unit,
• 5 an embodiment for the process of obtaining information about the route (route features) of the 4th describes in more detail
• 6a-d Shows examples of driving profiles that occur when repeatedly driving a section in the manual vehicle operating mode with the in 5 process shown have been obtained,
• 7th represents a flowchart that shows the method steps for determining an optimal driving profile for a given section of the route by the driver assistance unit,
• 8th illustrates the relationship between ambient conditions and fuel consumption,
• 9 illustrates the selection of an optimized driving profile from a large number of stored driving profiles using an example,
• 10 shows in a diagram a braking profile of a bus in manual driving mode without intervention of the driver assistance system according to the invention, and
• 11 in comparison shows an example of a non-optimized speed profile that was generated in manual driving mode over a route and an optimized speed profile over the same route that was optimized by the driver assistance system according to the invention and implemented in the semi-autonomous control of the vehicle.
• 1 FIG. 13 is a block diagram schematically showing the configuration of a vehicle according to an embodiment of the present invention.

In the in 1 The example shown includes the vehicle 1 a driver assistance unit 10 . The driver assistance unit 10 is designed to drive the vehicle 1 to be controlled in such a way that it can act completely or partially without the influence of a human driver. The driver assistance unit 10 , in the 2 and the associated description is described in more detail, controls one or more vehicle systems while the vehicle is operated in the autonomous or semi-autonomous mode, in particular the braking system, the drive train and the steering of the vehicle. The driver assistance unit 10 for example via a data bus 20th with a brake system controller 12th , a drive train control 14th and a steering controller 16 communicate.

The brake system control 12th controls the braking system. The braking system refers to the entirety of transmission and actuation devices for decelerating the vehicle. The brake system can include, for example, a service brake system that is designed to decelerate the vehicle during normal operation and bring it to a standstill. The driver can actuate the service brake using a brake pedal which is set up so that the driver applies a braking force to him to decelerate the vehicle. The degree of actuation of the brake pedal can be recorded by a brake pedal module.

The drive train control 14th controls the drive train. The drive train relates on the drive components of the vehicle via which the torque provided by the drive motor is transmitted to the wheels of the vehicle. The drive train includes, for example, the engine, clutch, transmission, drive shaft and axle differential. The drive train can be influenced by the driver through instructions via an accelerator pedal and a pedal value transmitter. Thus, with increasing deflection of the accelerator pedal, the driver can increase the torque of the drive motor and thus the driving speed of the vehicle, or he can reduce the engine output and thus the driving speed by reducing the deflection of the accelerator pedal. In addition, in the case of autonomous or semi-autonomous driving, the drive train can also be operated by the driver assistance unit 10 can be controlled by the driver assistance unit 10 corresponding control commands to the drive train control 14th sends.

In the drive train, there may be a need to temporarily store kinetic energy during a braking process of the vehicle in order to be able to provide this energy as drive energy at a later point in time. For example, the electrical power gained from kinetic braking energy can be used to charge batteries. This type of kinetic energy recovery is also known as recuperation.

The steering control 16 controls the steering. The steering refers to the components that enable directional control of the vehicle. The steering 16 has the task of turning the wheels of the steering axle into a desired position in order to steer the vehicle in a corresponding direction. Movements of the steering wheel are transmitted via the steering gear, the steering linkage and the steering levers to the steering knuckle of the steering axle and thus to the front wheels.

The vehicle systems mentioned above, such as the brake system control 12th , the powertrain control 14th and the steering control 16 , can also detect vehicle operating parameters by means of one or more vehicle sensors and transmit them to the driver assistance unit 10 hand off. Vehicle sensors include, for example, those sensors that detect a state of the vehicle or a state of vehicle parts, in particular their state of motion. The sensors may include a throttle position sensor, a vehicle speed sensor, an acceleration sensor, a vehicle load sensor, a tire pressure sensor, and the like. The throttle valve position sensor detects a throttle valve opening angle in order to generate a throttle valve position signal and to the driver assistance unit 10 to pass on. The vehicle speed sensor detects a vehicle speed in order to generate a vehicle speed signal and to the driver assistance unit 10 to pass on. The acceleration sensor detects a vehicle acceleration in order to generate a vehicle acceleration signal and to the driver assistance unit 10 to pass on. The vehicle load sensor detects a passenger weight, a baggage weight and the like as an engine load to generate a vehicle load signal and to the driver assistance unit 10 to pass on. The tire pressure sensor detects a tire pressure in order to generate a tire pressure signal and to the driver assistance unit 10 to pass on.

The vehicle 1 further includes multiple environmental sensors 24-1 ... 24-N for collecting information regarding the vehicle environment. The environmental sensors 24-1 ... 24-N include, for example, an image acquisition unit and one or more environment sensors for detecting objects and / or states in the vehicle environment. The image acquisition unit can be selected, for example, from a time-of-flight (ToF) camera, a stereo camera, a monocular camera, an infrared camera and the like. In particular, within the scope of the present invention, an image generated by the image acquisition unit of an area in front of the vehicle can be evaluated for the presence of a traffic sign. Through pattern recognition in an input image, for example, signs such as stop signs, right-of-way signs or other signage can be recognized. Additionally or alternatively, the image evaluation can include the recognition of stop lines at traffic lights, at stop signs and / or at right of way signs, which are often arranged in connection with signage.

The environmental sensors can include, for example, a sensor for detecting current atmospheric or weather conditions, in particular a temperature sensor for detecting the outside temperature, a raindrop sensor for detecting rain, a fog sensor for detecting fog, a sun sensor for detecting a degree of sunshine and a snow sensor for detecting from snowfall. Furthermore, the environmental sensors can include a sensor for detecting another vehicle, an obstacle, a pedestrian or the like, in particular an ultrasonic sensor, a radar device and / or a LIDAR device (Light Detection and Ranging Device or Laser Imaging Detection and Range Device). Each of the image capturing unit and the environmental sensors can be provided as an independent sensor or device or as a device in which a plurality of sensors or devices are integrated.

The vehicle 1 further comprises a position detection unit 18th to detect the position of the vehicle. The position of the vehicle can be recorded by GPS (Global Positioning System), Galileo, Compass, GLONASS, IRNSS or other positioning systems that are satellite-supported, for example. The position determination can also be used to determine the position of the vehicle in relation to another moving body and to issue a collision prognosis.

The vehicle 1 further includes a plurality of user interfaces 22nd that enable a driver to interact with one or more vehicle systems. These user interfaces 22nd include means for entering instructions or control commands such as a steering device, a brake pedal, an accelerator pedal, gear lever and / or indicator lever, switches, buttons, touch-sensitive screens (touchscreen) and the like. Furthermore, inputs can also be made via an input interface for receiving a voice input or input by means of gestures, head or eye movements. The user interfaces also include output interfaces, such as, for example, displays for speed, range, driving or transmission settings, and the like.

When the vehicle is operated in autonomous driving mode, the driver assistance unit determines 10 , on the basis of available data over a specified route, environmental information captured by environmental sensors, and vehicle operating parameters that the driver assistance unit 10 are supplied by the vehicle systems, parameters for the autonomous operation of the vehicle (for example target speed, target torque, steering process and the like).

2 FIG. 10 shows a block diagram showing an exemplary configuration of a driver assistance unit 10 represents. The control or regulation function of the driver assistance unit 10 is preferably implemented by a computer program running on a processor 101 the driver assistance unit 10 expires. At the processor 101 For example, it can be a computing unit such as a central processing unit (CPU) that executes program instructions. The driver assistance unit also includes 10 an external storage drive 102 (e.g. an external hard disk drive (HDD) or an SSD data storage device (solid state drive: SSD), and a RAM memory 103 . These are used to store program instructions and to store data. The driver assistance unit 10 further comprises a communication interface 104 to the data bus 20th of the vehicle. For example, the communication interface includes 104 a CAN (controller area network), a LIN (local interconnect network), a LAN (local area network) or a MOST interface, via which a connection to a standard CAN, LIN, LAN installed in the vehicle or MOST data bus can be established. The interface could also be an Ethernet interface, a FlexRay interface to a FlexRay data bus or any other suitable interface to an internal vehicle data bus. Several different bus types can also be used in the vehicle. In this case the communication interface includes 104 a suitable physical interface for each of the bus types. Furthermore, the driver assistance unit 10 also via a vehicle-to-infrastructure interface 105 for communication with other facilities, such as an infrastructure facility (traffic lights, traffic control system, cloud services, etc.) that the driver assistance unit has 10 can receive further information. The information can be received wirelessly, for example, via Bluetooth, WLAN, ZigBee or WiMax or cellular radio systems such as GPRS, UMTS or LTE.

The data storage area of the driver assistance unit 10 furthermore comprises a database which, according to the description below, contains data that are used to determine an optimized driving profile. The database contains, for example, information that relates to a subdivision of the route into different sections, and information about where on the route planned stops, such as bus stops, are located. The database can also contain digital map information relating to the factors associated with each leg, such as the course of the road, inclines and the like. The database can be, for example, a central database which provides the above-mentioned vehicle-independent information and updates it regularly.

The driver assistance unit 10 can also be in communication with and receive cloud information from a remote cloud. Cloud information can include, for example, weather data, traffic conditions, accident reports and the like. This cloud information can be used when determining the optimized driving profile by the driver assistance unit 10 must be taken into account. Furthermore, parts of the driving profile database, in particular for sections of the route to be driven ahead, can also be provided by cloud services.

The driver assistance unit 10 is designed to determine a driving profile that is optimized in terms of energy consumption and according to which the vehicle 1 from its current position to the next stop must be made, as described in more detail below. This is what the processor is for 101 the driver assistance unit 10 designed to carry out information evaluation processes, for example the evaluation of the data from the environmental sensors 24-1 to 24-N , for example from image data of an image recorded by a camera in the direction of travel of the area in front of the vehicle.

3rd shows schematically the generation of an optimized driving profile by the driver assistance unit according to the invention. According to the present invention, the driver assistance unit calls ( 10 in 1 ) Continuous driving dynamics (vehicle operating conditions) and environmental information 31 (Environmental conditions) that are determined by the vehicle sensors of the vehicle systems mentioned above (braking system, drive train, steering and the like) and by the environmental sensors ( 24-1 to 24-N in 1 ) of the vehicle 1 can be detected in order to determine different states of vehicle components and the vehicle environment. The retrieved driving dynamics and environment information 31 are from the driver assistance unit 10 using an optimization algorithm 32 evaluated. In particular, is the driver assistance unit 10 designed based on the retrieved vehicle dynamics and environment information 31 and using the information stored in the database to determine a driving profile that is optimized with regard to energy consumption and according to which the vehicle is 1 is to drive from its current position to the next stop, as described in more detail below. This is what the processor is for 101 the driver assistance unit 10 designed to carry out information evaluation processes using an optimization algorithm, for example the evaluation of the vehicle sensors and environmental sensors 24-1 to 24-N captured data, such as image data of an image of the area in front of the vehicle recorded by a camera in the direction of travel 1 . For example, the following information can be used to determine a driving profile optimized with regard to energy consumption: throttle valve opening angle, vehicle speed, vehicle acceleration, vehicle load signal, tire pressure, location of a stop line (or traffic light, stop sign), atmospheric conditions, outside temperature, engine temperature, weather conditions, image data, vehicle position and like that. The driver assistance unit also pulls 10 also access information that is in the database of the driver assistance unit 10 such as digital map information.

Based on that from the driver assistance unit 10 The optimized driving profile determined is the driver assistance unit 10 designed to output control signals 33 (Manipulated variables) to the brake system control 12th and / or powertrain control 14th output so that the brake system control 12th and the powertrain control 14th control the braking system and / or the drive train in such a way that the vehicle 1 is accelerated or decelerated in order to move according to the optimized driving profile. Alternatively, the driver assistance unit 10 via the user interface ( 22nd in 1 ) Display instructions given to the driver of the vehicle 1 request to control the vehicle in such a way that it moves according to the optimized driving profile.

4th shows schematically the process of obtaining information about the route (hereinafter “route features”) by the driver assistance unit 10 . According to the present invention is the driver assistance unit 10 designed for this through continuous retrieval and evaluation of vehicle dynamics and environmental information 41 (see also 31 in 3rd ) in a learning process 42 To learn route features and store them in a memory as learned route features 43 to save. The driving dynamics and environment information 41 (see also 31 in 3rd ) are from the vehicle sensors of the vehicle systems mentioned above (brake system, drive train, steering and the like) and from the environmental sensors ( 24-1 to 24-N in 1 ) of the vehicle 1 detected when driving the route one or more times in order to determine the states of vehicle components and the vehicle environment, which allow conclusions to be drawn about route features. For example, route points at which the vehicle repeatedly stops, such as bus stops, traffic lights and stop lines, can be recognized and saved as stopping points through the learning process. More from the driver assistance unit 10 learned route features are for example: route guidance, traffic patterns, topography, road conditions, temperatures, weather conditions, traffic situations and the like.

Those through the learning process 42 learned route features 43 flow when determining the optimized driving profile using the optimization algorithm (they 3rd ) with a.

The driver assistance unit 10 uses a machine learning process via which the driver assistance unit 10 learns from the necessary information to determine a driving profile that is optimized in terms of energy consumption for the route.

Extraction of information about the route (route features)

The following is now based on the 5 an embodiment for the process of obtaining information about the route (route features) of the 4th described in more detail.

This in 5 The flowchart shown shows an exemplary process of creating driving profiles for sections of a route to be covered. The in 5 The embodiment described relates to an application in a bus which, according to a special timetable, follows a fixed route with several stations. However, the method is not limited to buses and the treatment of bus stops, but could be used for any type of vehicle, for example for delivery services with fixed routes or the like. The vehicle can be a bus, but also small vans, cars, trucks for transporting goods, as well as vehicles from parcel deliverers can be used in connection with the method. The vehicle is preferably designed in such a way that it enables partially autonomous or purely manual driving.

In a step S500, the bus driver actuates the accelerator pedal when starting the journey in order to start the bus and then controls the bus in the manual or semi-autonomous vehicle operating mode along a route to be covered.

In a step S502, the driver assistance unit 10 (please refer 1 ) continuously retrieved manipulated variable information from the vehicle sensors of the vehicle systems mentioned above (braking system, drive train, steering, etc.) of the vehicle 1 can be recorded in order to determine different states of vehicle components (see 3rd ) by the driver or the driver assistance unit 10 are directly or indirectly adjustable. This can be, for example, information about the current driving speed obtained by wheel speed sensors (and information about the acceleration of the vehicle is implied therein) and information about the gear currently engaged. In order to create driving profiles while driving along the route, in addition to driving dynamics parameters such as driving speed or acceleration, drive power, drive torque, changes in pressure and the position of the throttle valve, brake and clutch, as well as gear changes or the like, can be recorded as manipulated variable information and stored in the driving profile will. This can be done by detection units, such as pressure or proximity sensors. In particular, it is also helpful to record other manipulated variables, such as the state of the air conditioning system (switched off / on or intensity level) or the manipulated variables of a cooling circuit control. This is how the driver assistance unit learns 10 whether switching off the air conditioning system under certain circumstances or on certain route sections is particularly helpful in terms of energy-saving operation. The condition of the air suspension system can also be recorded.

In a step S504, the driver assistance unit 10 continuously retrieve environmental conditions and vehicle operating conditions, for example from the environmental sensors ( 24-1 to 24-N in 1 ) of the vehicle 1 or from vehicle sensors of the vehicle systems mentioned above (braking system, drive train, steering, etc.) of the vehicle 1 are recorded. The environmental information can be, for example, information such as the current weather conditions, information received via navigation services about the current traffic volume on the route section being traveled, the current time (from which empirical values can be used to draw conclusions about time-of-day traffic conditions) and the like. In addition, information from, for example, GPS or traffic control systems about the current position of the bus can be recorded. The vehicle operating conditions can be information about the current engine oil temperature or the like, for example. The driver or the driver assistance unit can access this environmental information or vehicle operating conditions 10 have essentially no direct influence.

In a step S506, the current energy consumption of the vehicle is determined. The term “energy” as used herein refers to both fuel and electricity, such as from a vehicle battery or fuel cell. Examples of fuels are gasoline, diesel, ethanol, biofuel, or other alternative fuels. The energy mix depends on the type of drive train of the vehicle, i.e. on whether the vehicle is a conventional vehicle, a hybrid electric vehicle or a battery electric vehicle. The energy consumption is determined by calling up an energy consumption value, for example in liters of gasoline per 100 km covered distance (l / 100 km), from a central on-board computer, in which the information about the current energy consumption is usually available.

In a step S508, the driver assistance unit 10 determines whether a stop (stop, traffic light, stop sign or the like) is present. This is done, for example, by image recognition based on images generated by the image acquisition unit, by evaluating a “stop” button that is pressed by passengers to signal the bus driver to stop at the next stop, or by evaluating information from the in 4th described learning process ( e.g. repeated stops at the same point on the route). Alternatively or additionally, for example, mobile devices of people waiting at a stop can be detected in order to recognize whether a stop is present and whether a stop may be necessary. If the determination in step S508 shows that there is no breakpoint, then steps S502 to S508 are repeated. If the determination in step S508 shows that there is a breakpoint, the process continues with a step S510.

In a step S510, based on the manipulated variable information retrieved in step S502 and the ambient conditions or vehicle operating condition determined in step S504, a driving profile is created for the section covered up to the stopping point identified in step S508. Examples of such driving profiles are given below in connection with the 6a to 6d described in more detail.

In a step S512, the travel time required to complete the partial route is determined. This is done by determining the difference between the time recorded at the stopping point and the time recorded at the starting point.

In a step S514, the total energy consumption for the section covered is determined by integrating the individual energy consumption values determined in step S506. This total energy consumption is stored in a memory together with the manipulated variable information determined in step S502 (manipulated variable trajectory for the section), the ambient conditions and vehicle operating conditions for the section determined in step S504 and the travel time determined in step S512 as a driving profile for the section.

For the in 4th or. 5 described learning process, the vehicle drives under the control of the driver (in manual or semi-autonomous operating mode) a predetermined route, which consists of several partial routes, repeatedly and with different speed or acceleration curves (manipulated variable trajectories) and continuously performs the above-described process of obtaining driving profiles out. The driving profiles obtained are collected in a memory in order to carry out an optimization process based on them, such as this one in the 7th is described in more detail below.

The driving profiles obtained should preferably cover the space of possible driving profiles as far as possible. For example, the speed maintained by the driver depends on the traffic situation and the ambient conditions. It is possible that the vehicle, in this case a bus, covers the sections of the planned route at higher speeds at night than during rush hour. Conversely, it can be assumed, for example, that the bus covers the sections of the planned route at a lower speed when visibility is poor than when visibility is good. Furthermore, in the learning phase, the driver can consciously test different speed profiles as long as the speeds are within the applicable speed limits. In this way, a large number of driving profiles are obtained, which can be related to the respective determined environmental information and the total energy consumption of the vehicle.

In order to avoid that there is only a slight variation in the driving profiles even after repeated driving on a route, the driver assistance unit can 10 also be designed to artificially (for example, based on a model) provide for variations in the driving dynamics in a partially autonomous operating mode. For example, a combined vehicle drive train model can be used for this, with which a driving profile is optimized in advance in a simulation. For example, the driver assistance unit 10 Realize speeds and / or accelerations that deviate from the driver's request in order to “explore” the space of possible driving profiles as far as possible. Drivers could also be trained with regard to energy-optimized driving and, in this context, it could be pointed out that it is advantageous to implement variations in the driving style in order to find driving profiles that are as energy-optimized as possible.

Optimal efficiency can be expected when driving at constant speed, with minimum throttle and with the transmission in top gear. In general, energy efficiency is also maximized when acceleration and braking are minimized. As a rule, the driver will at least partially implement such principles independently, so that the driver assistance unit 10 will win corresponding driving profiles with the above method. The driver assistance unit 10 but can also be designed to artificially provide for variations or deviations from the driver's request in a partially autonomous operating mode in order to capture the space of the energy-optimized driving profiles as efficiently as possible. The driver assistance unit can 10 apply such energy-optimized driving strategies. For example, the driver assistance unit 10 be designed to test driving strategies such as Pulse and Glide (PnG) (also called “Burn and Coasting”), which consist of a rapid acceleration to a certain speed (the “Pulse” or “Burn”), followed by a period from coasting down or sliding down to a slower speed, and a repetition of this burn coast sequence.

In the embodiment of 5 Recognized bus stops are used to delimit the sections of the route, thus dividing the route into sections according to the bus timetable. In alternative embodiments, partial routes can also be defined by other stopping points such as stop signs, intersections with traffic lights, delivery addresses of a delivery service, or the like, which are provided by the driver assistance unit 10 can be recognized automatically or given to it. The stop points (e.g. traffic light stops) can be found using pattern recognition. Alternatively, breakpoints can be specified by an expert in the sense of “supervised learning”. In this case, the expert specifies the stop points, the energy consumption and travel times are compared and the optimum speed (drive power, torque) is determined as in 7th selected in more detail below.

6a-d shows examples of driving profiles for a section of 1000 m length, which are generated when you repeatedly drive the section in the manual vehicle operating mode with the in 5 process illustrated.

In 6a A driving profile A is shown in which the vehicle is accelerated constantly over about 300 m by implementing a corresponding manipulated variable trajectory until it reaches a maximum speed of about 45 km / h, then driving at constant speed over about 400 m is continued before the vehicle leaves about 300 m before the stopping point is braked with constant deceleration to a standstill.

In 6b a driving profile B is shown in which the vehicle is constantly accelerated over approx. 450 m until it reaches a maximum speed of approx. 45 km / h and is then immediately braked with constant deceleration to a standstill at the stopping point.

In 6c A driving profile C is shown in which the vehicle is accelerated over about 700 m with initially decreasing and then constant acceleration until it reaches a maximum speed of 45 km / h and is then immediately braked with constant deceleration to a standstill at the stopping point.

In 6d A driving profile D is shown in which the vehicle is accelerated over about 200 m with constant acceleration until it reaches a maximum speed of 45 km / h, then the journey is continued with alternating, short deceleration and acceleration phases before the vehicle is from about 300 m before the stopping point is braked with constant deceleration to a standstill.

In this way, a large number of driving profiles are obtained, which, as shown in FIG 9 shown table, with the respective determined vehicle operating and environmental information, the respective total energy consumption of the vehicle over the given section and the travel time required for completing the section are stored in a memory.

To simplify the representation, the 6a to 6d only the driving speed is plotted against the distance traveled. As described above, for example, by means of the process of FIG 5 The driving profile obtained also prefers further manipulated variable information (e.g. engaged gear, position of the throttle valve, operating state of the air conditioning), which were determined according to step S502 above, as well as vehicle operating information (e.g. information about the temperature of the engine oil), as well as further information such as environmental information (from environmental sensors, Position sensors, navigation services, etc.), which were determined in accordance with step S504 above. These are like those in the 6a to 6d Shown driving speeds, stored in the driving profile, but in the 6a to 6d Not shown.

In 7th a flowchart is shown which shows the method steps for determining an optimal driving profile for a given partial route by the driver assistance unit.

In a step S700 the driver assistance unit determines ( 10 in 1 ) the currently traveled section. For this purpose, the driver assistance unit uses, for example, information from GPS or traffic control systems about the current position and information stored in the database relating to the subdivision of the route into different sections.

In a step S702, the driver assistance unit determines current vehicle operating and environmental conditions by reading the information from the vehicle sensors of the above-mentioned vehicle systems (brake system, drive train, steering and the like) and from the environmental sensors ( 24-1 to 24-N in 1 ) of the vehicle 1 retrieves and evaluates recorded driving dynamics and environmental information. The captured information about the surroundings can be, for example, the following information: the current weather conditions or atmospheric conditions, outside temperature, information received via navigation services Information about the volume of traffic on the section currently being traveled, image data of an image of the area in front of the vehicle recorded by a camera in the direction of travel and the like. The detected vehicle operating conditions can include, for example, a vehicle load signal, a tire pressure signal, an engine temperature, or the like. The driver assistance unit also pulls 10 also access information that is in the database of the driver assistance unit 10 such as digital map information.

In a step S704, the driver assistance unit determines any requirements for completing the partial route currently being traveled. This can in particular be about boundary conditions such as a travel time specified by the timetable, applicable speed limits and the like. The driver assistance unit receives this information, for example, by calling up route-relevant information stored in the database, recognizing traffic signs by means of image recognition or the like.

In a step S706, the driver assistance unit determines from a set of stored driving profiles (such as these using the method of 5 recorded) an optimized driving profile for the current partial route taking into account the current vehicle operating and environmental conditions determined in step S702 and, if applicable, the requirements for completing the current partial route determined in step S704 as boundary conditions. For this purpose, the driver assistance unit, based on the determined current vehicle operating and ambient conditions and taking the boundary conditions into account, selects a driving profile that is optimized with regard to energy consumption from a large number of stored driving profiles. For this purpose, the driver assistance unit is designed to carry out information evaluation processes using an optimization algorithm. The selection of an optimized driving profile from a large number of stored driving profiles is shown below in 9 explained in more detail using a specific example.

For this determination of the optimized driving profile for the current section, for example clustering methods can be used to filter the measurements according to certain similar environmental conditions or vehicle operating conditions. An optimum of energy consumption could also be searched for on the filtered data in the sense of the Pareto optimum for a specified travel time (e.g. less than or equal to specification from the bus schedule). The variables according to which clustering is carried out are, for example, the vehicle operating and environmental conditions. It is assumed here that the ambient conditions and vehicle operating conditions tend to form clusters. For example, it is conceivable that a traffic jam occurs on a section of the route every weekday during rush hour. Consequently, a particularly large number of the determined driving profiles will have ambient conditions and vehicle operating conditions which represent the traffic jam. However, these driving profiles will not have identical environmental conditions and vehicle operating conditions, but only similar ones. By applying a cluster analysis such as the k-means algorithm to the stored profiles, that profile cluster is found that corresponds to the currently determined ambient conditions and vehicle operating conditions. By limiting the optimization to the driving profiles of the cluster found, the optimization is thus limited to driving profiles that were obtained under conditions similar to those currently prevailing.

The clustering results in a point cloud (e.g. x-axis: time, y-axis: energy consumption). As described above, a manipulated variable trajectory, e.g. the speed, is coupled at each point of the point cloud. The Pareto-optimal points can then be calculated for this cloud, from which the point with the minimum energy consumption is selected, with appropriate compliance with the secondary condition of reaching (or possibly falling below) the desired travel time. The manipulated variable trajectory that is linked to the selected point is then implemented. If the search for the optimal driving profile takes place, as described above, in a cluster of driving profiles that reflect the current environmental and vehicle operating conditions, the optimization process has sufficient "leeway" to find a "good" driving profile from the multitude of possible driving profiles in the cluster to find.

Above, the utilization of the cluster formation in the ambient conditions and vehicle operating conditions was described using the example of a cyclically occurring traffic jam. However, other environmental conditions, such as snowfall, slippery ice or the like, also tend to form clusters in the environmental conditions.

The partial route, for which an optimized driving profile is determined by means of clustering methods, as described above, can for example be specified by a fixed route that is specified for the vehicle, such as a bus, by the timetable. Alternatively, the specified section can also be specified by the route selected in the navigation system (for example section from the starting position to the first traffic light intersection, junction or the like, on the selected route). The data for the route segments ahead can then be called up from the cloud. So not all data of all possible segments have to be available locally in the vehicle. Furthermore, it is also possible that the sections (driving specifications) are put together by parameterized curve segments, as is the case in the international patent application WO 2012/055457 A1 is described using the example of target curves for valve control.

In addition, one embodiment provides for the data to be discretized in a more coarse manner in terms of time (e.g. a data tuple every 1s instead of every 10 ms) and / or to be compressed.

In a step S708, the driver assistance unit implements the determined optimized driving profile in the context of semi-autonomous driving on the basis of the manipulated variable information contained in the selected driving profile. The driver assistance unit can, for example, be designed to use the brake system control ( 12th in 1 ) and / or drive train control ( 14th in 1 ) to control which signals from the driver assistance unit convert into control commands to the vehicle subsystems. The driver assistance unit can, for example, continuously read out a speed setpoint from the determined optimized driving profile and compare this with the actual speed of the vehicle. According to the determined difference, the drive train or the brake system are then activated in such a way that the vehicle is accelerated or braked to the target speed. If the actual speed is below the target speed, the driver assistance unit controls 10 the drive train control 14th to increase the speed in the same way as a cruise control which controls the acceleration in accordance with a speed prescribed by the driver. If, on the other hand, the actual speed is above the setpoint speed, the brake system controller controls 12th the braking system to reduce the vehicle speed. The driver assistance unit can also autonomously set manipulated variables such as throttle valves or operating parameters of the air conditioning system or a cooling circuit controller so that this corresponds to the specifications from the optimized driving profile.

However, the implementation (implementation) of the optimal manipulated variables from the selected driving profile does not necessarily have to be fully automatic. For example, an active accelerator pedal can be provided which is designed so that the driver applies a force to it in order to increase the speed of the vehicle. Associated with this accelerator pedal is an actuator which is set up to apply a counterforce to the accelerator pedal in a targeted manner, which counteracts the force applied by the driver. Depending on the determined actual speed and the setpoint speed specified by the driving profile, a counterforce can be impressed on the accelerator pedal, for example, in order to signal to the driver that he should release the accelerator pedal in order to initiate a reduction in the vehicle speed. Likewise, if the determined actual speed deviates from the target speed specified by the driving profile, the accelerator pedal can also give haptic feedback in order to signal to the driver that he should either relieve the accelerator pedal or depress it more strongly depending on the direction of the deviation. For example, vibrations can warn the driver not to accelerate any more if the specified target speed is exceeded. This allows the driver to adjust his driving behavior intuitively.

The driver assistance unit can also be used via a user interface ( 22nd in 1 ) Transmit instructions that request the driver of the vehicle to control the vehicle so that it moves according to the optimized driving profile. For this purpose, the driver assistance unit can show the driver information about optimized vehicle manipulated variables, for example via a display panel, such as the optimal gear to be engaged, the current optimal driving speed, or a message to switch off the air conditioning system. By displaying this information, the driver is motivated and prompted to set the optimal control variables. This display of the optimized manipulated variables via the user interface also trains the driver, as he constantly receives feedback on the energy efficiency of his driving style. The total energy consumption of a section of the route can also be displayed to the driver via the user interface, so that the driver learns what effect his driving style has on energy consumption. This also motivates the driver to implement an energy-optimized driving style. In an alternative exemplary embodiment, the instructions can also be transmitted to the driver via a loudspeaker.

The following is now based on the 8th illustrates the relationship between ambient conditions and fuel consumption in more detail. In particular shows 8th an exemplary relative fuel consumption RFC ("relative fuel consumption") as a function of the given outside temperature. How out 8th As can be seen, the fuel consumption in the exemplary case is minimal at an outside temperature of 15 ° C. At an outside temperature of -20 ° C, the relative fuel consumption is 1.25, ie the fuel consumption is 25% higher than at the optimal outside temperature. At an outside temperature of 30 ° C, the relative fuel consumption is 1.11, ie the fuel consumption is 11% higher than at the optimal outside temperature. To simplify the representation, the 8th only shows the relative fuel consumption as a function of the outside temperature. The dependence of the relative fuel consumption on the ambient conditions and on the vehicle operating conditions can, however, also be more complex and depend on a large number of different factors, as has already been explained above. It is therefore helpful to take into account as many of the available vehicle operating and environmental conditions as possible when determining the optimal driving profile.

9 illustrates the step of selecting an optimized driving profile from a large number of stored driving profiles (step S706 in FIG 7th ) based on an example. As from the in 9 shown in the table were obtained by the in 4th or. 5 The learning process described contains a multitude of driving profiles a to I, which with the respective determined control variable information, vehicle operating and ambient conditions (in this example the given outside temperature), the driving time required to complete the section and the respective total energy consumption of the vehicle over the given section in stored in a memory. In this exemplary embodiment, the memory contains the driving profile a, which was recorded in connection with a determined outside temperature of 19 ° C., a determined driving time of 2 min 55 sec and a determined energy consumption of 6.2 l / 100 km. Driving profile b was recorded in connection with a determined outside temperature of 19 ° C, a determined driving time of 3 min 15 sec and a determined energy consumption of 6.5 1/100 km. The driving profile c was recorded in connection with a determined outside temperature of 19 ° C, a determined driving time of 3 min 2 sec and a determined energy consumption of 6.4 l / 100 km. The driving profile d was recorded in connection with a determined outside temperature of 19 ° C, a determined driving time of 3 min 18 sec and a determined energy consumption of 6.8 l / 100 km. Furthermore, the driving profile e was recorded in connection with a determined outside temperature of 20 ° C, a determined driving time of 2 min 40 sec and a determined energy consumption of 6.3 l / 100 km. The driving profile f was recorded in connection with a determined outside temperature of 20 ° C, a determined driving time of 3 min 5 sec and a determined energy consumption of 6.9 l / 100 km. The driving profile g was recorded in connection with a determined outside temperature of 20 ° C, a determined driving time of 3 min 8 sec and a determined energy consumption of 6.6 1/100 km. The driving profile h was recorded in connection with a determined outside temperature of 20 ° C, a determined driving time of 2 min 52 sec and a determined energy consumption of 6.5 l / 100 km. Furthermore, the driving profile i was recorded in connection with a determined outside temperature of 21 ° C, a determined driving time of 2 min 58 sec and a determined energy consumption of 6.7 1/100 km. The driving profile j was recorded in connection with a determined outside temperature of 21 ° C, a determined driving time of 3 min 12 sec and a determined energy consumption of 6.2 l / 100 km. The driving profile k was recorded in connection with a determined outside temperature of 21 ° C, a determined driving time of 2 min 45 sec and a determined energy consumption of 6.8 l / 100 km; and the driving profile I was recorded in connection with a determined outside temperature of 21 ° C, a determined driving time of 3 min 10 sec and a determined energy consumption of 6.4 l / 100 km. The in step S706 of the 7th The selection of the optimized travel profile that has been carried out takes place in the following manner in this context. The driver assistance unit filters the driving profiles collected during the learning phase, taking into account the current vehicle operating and environmental conditions. If, for example, an outside temperature of 19 ° C is assumed here as the current ambient condition, the driver assistance unit selects those driving profiles that were recorded under corresponding ambient conditions, namely an outside temperature of 19 ° C, in the illustrated embodiment, driving profiles a, b, c and d. The other travel profiles are discarded. Furthermore, the driver assistance unit filters the selected driving profiles ad taking into account any given boundary conditions, here the maximum driving time required for the driving route. In the exemplary embodiment of the bus that is supposed to adhere to a schedule, this is done by comparing the respective required travel times of the travel profiles with a travel time specified by the schedule for the current section of the route (e.g. less than or equal to specification from the bus schedule). Those driving profiles for which the determined driving time does not match the driving time specified by the timetable or is outside a specified tolerance are discarded. The specified, available travel time results, for example, from the difference between the planned arrival time and the current actual time. Delays or early departure due to slow / fast loading and unloading are taken into account. If, for example, a driving time of 3 minutes and a tolerance of a maximum of plus 10 seconds is assumed as the specified driving time, the driver assistance unit selects those driving profiles for which the determined driving time is less than 3 min 10 sec and discards those driving profiles for which the determined Driving time is more than 3 min 10 sec. in the The illustrated embodiment, the driving profiles b and d, which are above the specified tolerance with a driving time of 3 min 15 sec and 3 min 18 sec, respectively, are discarded. From the remaining driving profiles a and c, the driving profile under which the lowest energy consumption was recorded is selected, in the illustrated embodiment driving profile a, which was recorded with an energy consumption of 6.2 l / 100 km. This driving profile a corresponds to the optimized driving profile.

In the exemplary embodiment mentioned, the driver assistance unit filters the selected driving profiles a-d, taking into account any given boundary conditions, here the maximum driving time required for the driving route. This is not essential for the invention. In alternative exemplary embodiments and in many applications (such as delivery services that do not have to adhere to a strict timetable), longer travel times can be accepted for a section, so that travel profiles can be selected for which the determined travel time exceeds a specified travel time. Even a bus that is supposed to adhere to a timetable has certain leeway in this regard. For example, with a view to optimizing energy for a certain section of the route, a slightly excessive travel time in relation to the timetable specification can be accepted, which can possibly be compensated for in one of the following sections. Model calculations have shown that the extension of the driving time due to the selection of an energy-optimized driving profile depends on the situation and the route. Calculations show between 0.7% and 6% longer driving times. In this model case, a total travel time of 38 minutes between the terminal stops and 40 stops on the entire route was assumed. 6% longer travel times means 139 seconds of additional travel time for the entire route and 4 seconds for each section. Such an increase in travel time would be tolerable for buses.

10 shows in a diagram a braking profile of a bus in manual driving mode without intervention of the driver assistance system according to the invention. The diagram shows the angle of the brake pedal along the distance traveled for a test drive of a bus. The distance traveled is plotted in meters on the right axis. The angle α of the brake pedal is plotted in degrees (deg) on the high value axis. Two bus stops are exemplarily marked by circles. Due to the bus stops, the entire route is divided into sections, during which strong variations in the brake pedal angle can be seen, which indicates that the driver's driving style is not optimized in terms of energy consumption.

11 shows in comparison an example of a non-optimized speed profile that was generated in manual driving mode and an optimized speed profile that was optimized by the driver assistance system according to the invention and implemented in the semi-autonomous control of the vehicle. In 11 represents a curve 1101 represents a non-optimized speed profile in which on the high value axis a speed v in km / h, which was set when completing a route in manual driving mode, is plotted against a distance in meters on the right value axis. A curve 1102 In comparison to the non-optimized speed profile, this represents an optimized speed profile in which a speed v in km / h, which was optimized by the driver assistance system according to the invention when completing the same route in semi-autonomous driving mode, is shown over the route. It can be seen that the curve 1101 of the non-optimized speed profile shows stronger variations than the curve 1102 of the optimized speed profile. It can also be seen that the driver assistance system has the effect that lower top speeds are achieved than with manual driving, which has a positive effect on energy consumption.

The methods described above can save energy in a short time. According to model calculations, there is the potential to save more than 10% energy. In the case of a battery electric bus (BEV), estimates showed an energy saving potential of 12.4%. The battery life is extended by the energy saved. In this exemplary case mentioned, 32 kWh of energy are saved in one day, which means with a 110 kWh battery pack that 0.14 fewer battery cycles per day or 52 fewer cycles per year are required. This corresponds to a 5% longer battery life, which means 54 days more in a period of 3 years. Assuming € 0.29 / KWh and the above-mentioned savings potential of 32 kWh / day, this results in total savings of € 9.30 per day, or € 3,368 per year and bus.

List of reference symbols

1

vehicle

10

Driver assistance unit

12th

Brake system control

14th

Powertrain control

16

Steering control

18th

Position detection unit

20th

Data bus

22nd

User interfaces

24-1 - 24-N

Environmental sensors

31, 41

Driving dynamics and environment information

32

Optimization algorithm

33

Output control signals

42

Learning process

43

Route features

100

internal bus driver assistance unit

101

processor

102

external storage drive

103

RAM

104

Communication interface

105

Vehicle-to-infrastructure interface

1101

non-optimized speed profile

1102

optimized speed profile

QUOTES INCLUDED IN THE DESCRIPTION

This list of the documents listed by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Patent literature cited

• DE 102010011088 A1 [0006]
• DE 102010041539 A1 [0007]
• DE 102012221827 A1 [0008]
• DE 102017119453 A1 [0009]
• WO 2012/055457 A1 [0079]

Claims (11)

A method comprising determining an optimized driving profile for a section from a set of stored driving profiles for the section, taking into account current environmental conditions and / or current vehicle operating conditions. Procedure according to Claim 1 , wherein a driving profile stored in the set of stored driving profiles comprises recorded environmental conditions and / or recorded vehicle operating conditions corresponding to the driving profile, as well as recorded manipulated variable information for the implementation of the driving profile. Procedure according to Claim 2 , wherein the recorded manipulated variable information includes a driving speed, a drive power, a drive torque, an engaged gear, and / or a status of electrical consumers such as an air conditioning system or a cooling circuit controller. Method according to one of the preceding claims, wherein determining an optimized driving profile comprises: Filtering the set of stored driving profiles according to the current environmental conditions and / or current vehicle operating conditions; Select, from the filtered driving profiles, that driving profile with the lowest total energy consumption as the optimized driving profile. Method according to one of the preceding claims, wherein a driving profile stored in the set of stored driving profiles comprises information corresponding to the driving profile about a total energy consumption and / or information corresponding to the driving profile about a driving time required for the partial route. Method according to one of the preceding claims, which further comprises recording a driving profile in the set of stored driving profiles. Procedure according to Claim 6 , in which variations in the manipulated variables are created artificially in order to obtain as many different travel profiles as possible in the set of stored travel profiles for a section. Method according to one of the preceding claims, which further comprises realizing the optimized driving profile on the basis of manipulated variable information recorded in the optimized driving profile. Method according to one of the preceding claims, wherein the implementation of the optimized driving profile comprises: an autonomous setting of the manipulated variable information; and or setting the optimal acceleration / deceleration when the driver accelerates or brakes; outputting the manipulated variable information to the driver by means of a user interface such as a display board or a loudspeaker; and or influencing a driver's request by means of an active accelerator pedal which is designed to exert a counterforce which is directed against a force applied by the driver and which depends on a determined actual speed and a setpoint speed specified by the driving profile. Driver assistance system that is designed to perform the method according to one of the Claims 1 to 9 to execute. Driver assistance system according to Claim 10 , wherein the driver assistance system is connected to a cloud service which provides stored driving profiles for determining the optimized driving profile for a section of the route.

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