AT510101B1 - Method for the computer-based generation of a driving cycle data record and a longitudinal profile data record and method for testing a motor vehicle - Google Patents

Abstract

A driving cycle data set (1), which represents a driving cycle for a driving simulation of a vehicle, is generated computer-based. Here, the drive cycle data set (1) is generated automatically with a driving model (2) from a road map (3) by using a route in the road map (3) as at least one input variable for the driving model (2).

Description

Austrian Patent Office AT 510 101 B1 2014-01-15

The invention relates to a method for the computer-based generation of a driving cycle data set, which represents a driving cycle for a driving simulation of a motor vehicle, in particular of a vehicle with hybrid drive, wherein the driving cycle data record is generated automatically with a driving model from a road map by a route in the road map is used as at least one input for the driving model.

In the development of motor vehicles, in particular motor vehicle drives or components for this purpose, driving cycles are needed, which are used in purely virtual driving simulations or driving simulations on a test bench.

There are legally prescribed driving cycles, for example, to determine the fuel consumption of a passenger car (PKW) with internal combustion engine. However, to simulate different driving situations, such as city traffic or cross-country driving, different driving cycles are required. Conventionally, such driving cycles are generated by (real) driving off of a specific route with specially equipped vehicles which metrologically record the information required for the driving cycle while driving.

JP 2011/010 617 A1 discloses a vehicle test system that simulates dynamic loads on a real vehicle using route-dependent data from a GPS or generated from road maps. Also height information, air pressure and temperature are considered. Curve information about the route is output on the main screen.

It is the object of the invention to simplify the development of vehicles.

According to a first aspect of the invention it is provided that the route is automatically determined for a given starting point and destination point by means of the road map.

A second aspect relates to a method for generating a longitudinal profile record. This includes at least two one-dimensional quantities derived from at least one two- or three-dimensional size of the drive cycle data set generated by the above method and related to the longitudinal direction along the route. One of these quantities is location, speed or acceleration of the vehicle in the longitudinal direction, and another of the quantities is the vehicle braking or accelerating force in the longitudinal direction.

Finally, a third aspect relates to a method for testing a motor vehicle, wherein the motor vehicle in a driving simulation on a test bench a longitudinal profile record that was generated with the above, is driven: Here, the vehicle is on the test on the one hand causes to drive its drive wheels in accordance with the size, location, speed or acceleration given in the longitudinal profile; On the other hand, its drive wheels are braked or accelerated by the test bench according to the force prescribed in the longitudinal profile.

The "drive cycle record " and the derived "longitudinal profile record " In this case, each denote a sequence of instructions for the driving simulation (ie not the driving simulation itself).

[0010] The "driving simulation" For example, a drive following the instructions of the drive cycle data set or the longitudinal profile data record with a real vehicle or vehicle drive (or the component for this purpose) to be considered is under scrutiny. This is therefore a partial simulation (the journey is simulated, but not the vehicle or the vehicle drive or the component for this purpose).

In other examples, the driving simulation is a full simulation in which a drive according to the driving cycle or longitudinal profile record is simulated with a virtual vehicle or vehicle drive (or a component thereof). 1/12 Austrian Patent Office AT 510 101 B1 2014-01-15 [0012] In the following, optional refinements, some of which are also the subject matter of the dependent claims, are described in more detail.

The road map comprises a road network with at least one road, wherein a road in particular comprises two ends. A road may be connected to at least one other road via at least one node. If the node is located between the ends of the road, it divides the road into two road sections.

The route is to be understood as a path in the road network with a starting point, a different destination point and at least one intervening road. Overall, start and end points are selected in the road map such that a certain number of roads (connected by knots) are arranged between them. Thus, the route includes at least length information about a distance between the starting point and the destination point along the road (s) covered by the route. In addition, further information can be saved in the road map (see below). Here and below, regarding the road map and information, the term "store " to understand that the information is represented in the road map. The road map can thus have such information directly as date or dataset. Alternatively, such information may also be contained indirectly in other data so that it can only be derived from it by an intermediate step.

By means of the driving model, the route is virtually traced to gain information for generating the driving cycle data set. During virtual descendants, time-dependent location information and / or time-dependent speed information and / or time-dependent acceleration information, such as longitudinal and / or lateral acceleration information (in each case along the route) are generated (at least) from a location information (route) and stored in the data set represents the driving cycle, stored. The drive cycle data record thus includes, for example, a route-time profile, a speed-time profile, a speed-location profile, an acceleration-time profile and / or a height-location profile. In such a profile, the location, speed and / or acceleration information in some embodiments is a one, two or three dimensional size.

In some embodiments, the drive cycle data set for a driving simulation of an electric vehicle, a hybrid vehicle and / or an electric vehicle with range extender is provided.

"Electric vehicle" in the present case denotes a vehicle with pure electric drive and "hybrid vehicle". a vehicle whose drive (when ready for operation) always comprises two drive units of different technology.

Hybrid vehicles are therefore equipped with at least two (different) energy sources for drive energy, for example with an electric energy storage and a tank for conventional fuel (eg gasoline or diesel), as well as with corresponding different units (eg electric motor or internal combustion engine with generator) , In this case, it is necessary to match the different energy sources and / or the drive components designed for each other, for example with regard to performance and / or ranges.

Range Extender " refers to a range enlargement device with which the electric vehicle can be additionally equipped (if required) in order to increase its range. The range extender is equipped, for example, with an internal combustion engine with an electric generator to provide additional electric drive power for the electric vehicle. Incidentally, in functional terms, the pure (and also operable as such) electric vehicle can be operated in a hybrid mode by means of the range extender (as required, namely in the mounted state of the range extender).

With regard to the road map, various embodiments are described below: In some embodiments, the road map includes altitude information that is used as a further input variable for the driving model as an alternative. Here, in some embodiments, the road network is stored two-dimensionally in the road map and the third dimension represented by the altitude information (separately), for example, as a separate terrain elevation model. In other embodiments, altitude information is directly associated with the roads, the road sections, the road ends and / or the node.

In some embodiments, height information from a separate data source is additionally or alternatively used as a further input variable for the driving model. The separate data source is provided in addition to the road map and is for example a separate altitude map or a height model. The altitude information from the separate data source can be overlaid (the road information) of the road map.

For a higher accuracy of the simulation, a higher spatial density of the height information is provided, for example by a road several altitude information along its course are assigned and / or in that the height information does not fall below a certain spatial density. For example, height information is provided at least every 0.5, 1.2, 5, 10 or 50 vertical meters and / or every 1, 5, 10, 50 or 100 meters along a road. Overall, the height information is stored as an absolute variable, for example in relation to the sea level, and / or as a relative variable, for example as a gradient.

From the altitude information, in some embodiments, a change in the potential energy during (virtual) driving along the route is taken into account by the driving model. For example, the driving model is used to derive tractive and thrust forces on uphill or downhill driving. Accordingly, a driving cycle data set generated in this way is designed for the simulation of recuperation processes while driving along the route.

In some embodiments, the road map includes road describing information that is used as another input to the driving model. The road-describing information includes, for example, locations of traffic lights, speed limits, curve radii, expansion state, and / or road type. Here, the state of expansion indicates, for example, information about the width of the road, the nature and / or the wear of the road surface. As road types, for example, highways, highways, main roads, side roads, inner city roads, non-local roads and / or agricultural and forest roads are distinguished. By means of such road information, the driving model selects suitable speeds for (virtual) driving along the route.

As a further input for the driving model traffic information is used in some embodiments, which are stored in the road map. The traffic information includes, for example, information about traffic, traffic obstructions, traffic jams and / or construction sites. Incidentally, in some of these embodiments, the traffic information is stored in terms of time and / or date specific. Thus, for example, different traffic volumes on Sundays and public holidays as well as on working days, holiday travel and / or commuter traffic in the driving cycle data record can be taken into account.

The driving model is designed in some embodiments for at least two driving behaviors, for example, for sporty and / or energy-saving driving behavior. Accordingly, different driving cycle data sets are generated for the simulation of different driving behavior. Incidentally, this makes it possible to take into account different driver groups with the driving model, for example different age groups. In some of these embodiments, different directional speeds, acceleration, braking and / or shifting behavior are provided in the driving model for different driving behaviors. As a directional speed, a preferred according to the driving behavior preferred driving speed on the respective road is called, but which is influenced by other factors, such as a speed limit, traffic and / or environmental factors (see below). In some embodiments, environmental influences are taken into account as a further input variable of the driving model, for example wind strength, wind direction, precipitation, snow / ice smoothness, brightness, temperature, date and / or time , Environmental influences affect both the driver and his driving behavior, such as slower driving in the dark or rainfall, as well as the vehicle and the driving dynamics, such as the wind load. Also, additional aggregates of the vehicle, for example windscreen wipers, lighting and / or air conditioning, which are operated depending on the environment and which compete with the drive power, can be considered. From the date and / or time information, for example, a position of the sun and the resulting lighting conditions can be determined and their influence on the driving behavior taken into account.

In some embodiments, information about a vehicle class is taken into account by the driving model. As vehicle classes, for example, passenger cars and trucks are distinguished or even sports cars, small cars, SUVs, vans o.ä .. Using the information about the vehicle class can be considered by the driving model, for example, with what speeds a road or route is virtually retraced, or which roads are allowed or forbidden for the respective vehicle class.

In some embodiments, at least one property of a particular vehicle model is considered by the driving model, for example, mass, center of gravity, air resistance, cornering stability, braking and / or acceleration performance of the particular vehicle model.

Incidentally, on the one hand determines the driving cycle data set in the simulation (within certain limits), the driving dynamics, however, on the other hand specific driving maneuvers or driving behaviors are possible or impossible only with certain vehicle models and / or vehicle classes. In order to reflect this in the driving cycle data set, in some embodiments at least one driving-dynamically relevant variable (of the vehicle) is taken into account by the driving model. As a result, the driving cycle data record can be adapted to different vehicle models and / or vehicle classes, for example. As driving dynamics relevant variables, for example, mass, center of gravity, air resistance, cornering stability, braking and / or acceleration performance of the vehicle and / or the operation of additional equipment such as air conditioning, seat and / or window heating are taken into account.

For a simplified generation of the driving cycle data set, the route is automatically determined in certain embodiments for predetermined start and end points by means of the road map. For this purpose, start and end point are determined in the road map and calculated as a route automatically connecting these points along roads connected by nodes. In addition, in some of these embodiments, at least one intermediate destination is considered, which is finally detected by the route.

In some of these embodiments, when determining the route, certain types of roads (see above) are preferred or avoided over other types of roads. Although it is tolerated that the route thus determined is longer than an alternative route or requires a longer journey time, a more favorable ratio of preferred or avoidable roads to non-preferred or unavoidable roads is achieved.

In some of these embodiments, vehicle-specific criteria are taken into account in the determination of the route, in particular restrictions of headroom and / or passage restrictions for cars, trucks and / or two-wheelers. In this way, unrealistic routes can be automatically filtered out.

The invention simplifies the creation of driving cycle data sets, which map the real conditions in detail. It also facilitates the creation of a large number of different variants of driving cycle data sets, which can be determined, for example, by geographical, climatic, traffic-related, vehicle-specific and / or by different driving regulations

Differentiate behavioral influences. This is particularly useful for the development of hybrid vehicles since they are complex in terms of hybrid control (e.g., tuning and dimensioning of the different drive components, storage strategy) and therefore have a relatively large "simulation width". is beneficial in development. The same applies to pure electric vehicles or for the development of appropriate range extenders.

According to the second aspect of the invention, in some embodiments of the drive cycle data set, the longitudinal profile data set is generated which also (but in another form) reflects the driving cycle. The "longitudinal profile" denotes a data set with one-dimensional quantities, namely at least one location, speed or acceleration information and a force information representing a (total) load and the simulation of load moments that would act on a real vehicle during a real ride. For this purpose, (if appropriate) multidimensional variables such as location, speed and / or acceleration data of the drive cycle data set are each referenced to a one-dimensional location or speed indication, e.g. as a function of time (namely along the longitudinal direction of the route underlying the driving cycle) as well as the total force acting on the vehicle, also e.g. as a function of time or path. The longitudinal profile data set thus formed is an input variable for a test bench, with which a partial simulation of the vehicle is carried out: the drive of the (real) vehicle operates such that the wheel speed of the vehicle the (speeded down to the forward direction) local or speed specification of the driving cycle corresponds without the vehicle actually moving. Thus, in this type of sub-simulation, the forces caused by the vehicle's travel are absent (e.g., inertial forces in vehicle acceleration, weight force when riding on an inclined path, aerodynamic drag, rolling resistance, and possibly braking forces when cornering). These were "missing" in the test bench test due to the vehicle standstill ". Forces are generated with the roller test bench by a suitable braking and possibly also drive torque as a function of time or the distance traveled. This braking and possibly also drive torque is applied by the one or more rollers of the test stand, act on the drive wheels of the vehicle to be tested. The mapping of the drive cycle to the longitudinal profile is such that this (one-dimensional) braking and driving torque corresponds to the time or the braking and driving torque that would be applied to the drive of the vehicle in real driving according to the driving cycle. In the test bench test according to the longitudinal profile of the vehicle is therefore driven with that (one-dimensional) speed (relative to the test stand rollers) as a function of time corresponding to the driving cycle, and it experiences at the drive wheels that braking or driving torque that it at real Ride according to the driving cycle would learn. The mapping of the drive cycle to the longitudinal profile thus takes into account those variables of the driving dynamics which do not actually occur due to the partial simulation (i.e., the vehicle standstill) and provides the specification of the moments to be applied with the test stand rollers in order to pretend to the vehicle drive these driving dynamics variables.

Incidentally, in some embodiments between a location, speed, acceleration or force information is converted, such as by means of differentiation, integration or a physical law such as the law of inertia. For example, from (time-dependent) location information of the drive cycle data set, a (time-dependent) speed variable of the longitudinal profile data set is calculated.

In some embodiments, the at least two one-dimensional sizes of the longitudinal profile dataset are time-dependent quantities. For example, the longitudinal profile record (and / or the drive cycle record) is stored as a series of values associated with approximately consecutive constant time intervals to represent the time dependency. Thus, each follower can be assigned a specific time information. Accordingly, the instructions corresponding to the individual follower elements in the test stand in the test simulation can be converted one after the other according to the duration of the time intervals. In some embodiments, the two one-dimensional quantities are associated with time information also stored in the data set, for example, a triple of a speed, a force, and a time stamp.

The force information reflects a load acting on a real vehicle or occurring during real driving. The force information, in some embodiments, accounts for one or more of the following aspects of different loads (optionally adding the contributions of different loads, particularly longitudinal force, ie longitudinal force): By mass of the (to be tested ) Motor vehicle, the load (force) is determined in acceleration in the direction of travel, with the second Newton's law "force = mass * acceleration". In addition, the mass of the vehicle flows into the determination of the vehicle accelerating component of the weight force on downhill slopes (according to the height information) along the route, corresponding to "force = sin (angle of the longitudinal direction to the horizontal) * mass * acceleration of gravity" ;.

From the amount of three-dimensional velocity, the load is modeled due to the air resistance of the vehicle (which generally depends on the speed approximately quadratically). Similarly, from the magnitude of the three-dimensional velocity, the rolling resistance is modeled (which is generally less than quadratic in speed). In some embodiments of the longitudinal profiling it is also considered that the rolling resistance increases when cornering due to the transverse deformation associated with tire deformation.

According to the third aspect of the invention, in the method for testing the motor vehicle, the motor vehicle (or the vehicle drive or a component thereof) is driven in a driving simulation, in accordance with the determined from the driving cycle longitudinal profile. In some embodiments, the conversion of the drive cycle 'in the longitudinal profile is carried out in advance in a separate step. In other embodiments, however, the control computer of the test rig is equipped and programmed so that it carries out the computational conversion of the driving cycle to the longitudinal profile itself, e.g. time before actually test bench ride or even during their course. In the case of the advance implementation, therefore, the longitudinal profile data record forms the input variable for the test bench, while in the case of implementation in the control computer of the test bench, it appears that it formed the drive cycle data record. In fact, however, in both cases, the longitudinal profile dataset controls the actuators of the test bench (ie, for example, the accelerator pedal actuation and the load to be opposed to the vehicle drive), regardless of whether the longitudinal profile is in a separate step in advance or only in the test state. Control unit is calculated from the drive cycle record.

Regardless of the fact that vehicle-class or vehicle model-specific properties have already been taken into account in the generation of the drive cycle data set, at least the mass and the air resistance of the vehicle to be tested are taken into account in the generation of the longitudinal profile from the drive cycle data set.

An embodiment of the method will be described with reference to the following drawings. 1 shows a schematic illustration of the method for the computer-based generation of a drive cycle data record, which represents a drive cycle for a driving simulation.

The following embodiment shows the method for generating the drive cycle data set, which represents a drive cycle for the driving simulation of a hybrid vehicle. Other embodiments differ in that the drive cycle data set for a pure electric vehicle or an electric vehicle with a range extender is generated.

The driving cycle data set 1 is automatically generated according to FIG. 1 with the driving model 2 from the road map 3. For this purpose, a route in the road network 6 of the road map 3 is generated in FIG. 4 and used as an input variable for the driving model 2. In the following, the road network 6, the generation of the route, the road map 3 and finally the generation of the driving cycle data set 1 will be described in the order. These and the operations described below are determined by a computer program stored in memory and, unless explicitly stated otherwise, are executed by a computer programmed to execute the computer program. The information mentioned below is stored at least in the generation of the driving cycle data set 1 in the computer.

In order to determine the route, the starting point and the destination point as well as, if necessary, intermediate destinations are manually set in FIG. 4. In addition, the vehicle class to be simulated, for example a passenger car, as well as further route options, for example an optimization according to the shortest route or alternatively after the shortest travel time and / or the preferred use of highways, are likewise set manually. In compliance with these requirements, the route within the road network 6 is automatically determined. Incidentally, vehicle-specific criteria are also taken into account in this case, for example passage restrictions for the selected vehicle.

The road network 6 of the road map 3 comprises in this embodiment, a two-dimensional map of a road network of a particular geographical area, with roads in the road map 3 are stored as (fine-line) polygons, so that also (approximately) information about curve radii indirectly in the road map. 3 are included. Inter-street links are represented by nodes. The spatial (two-dimensional) position of the roads, their individual course and the location of the nodes are coded by geo-coordinates.

In addition to the road network 6, a terrain elevation model 9 of the area covered by the road network 6 is stored in the road map 3. From terrain elevation model 9, the altitude of the earth's surface above sea level can be determined depending on the geographic coordinates (at least within the area covered by the road network 6). In other examples, the terrain elevation model is implemented as a separate record from the road map 3.

Also, the road map 3 includes road information 10, namely locations of traffic lights, speed limits of the roads and a typing of roads, for example, in highways, single or multi-lane roads, domestic and non-local roads. These road information 10 are assigned as records to the respective roads of the road network 6.

The driving cycle data set 1 is generated, as mentioned above, with the driving model 2 from the road map 3. For this purpose, the driving model 2 travels virtually along the route from the starting point to the destination point, wherein the route represents an input variable of the driving model 2. This virtual driving, which is used to generate the driving cycle data set 1, is to be distinguished from the abovementioned driving simulation of the vehicle on the test bench, for which the driving cycle data set 1 is ultimately provided.

For the virtual driving along the route 2 different driving behaviors 12 are implemented in the driving model, whereby the braking and acceleration behavior, as well as the switching behavior is considered (for vehicles with manual transmission) for different types of drivers. For example, driving behaviors 12 of a sporty driver who is relatively fast, slows down, shifts late and drives at maximum permissible speed, and uses an energy-efficient driver who slows down a little, uses longer coast-downs, gently accelerates, shifts early, and about 0% -90 % of the maximum permissible speed, but maximum about 130 km / h, provided in the driving model 2.

The (delicate) polygonal representation of the road network 6 allows the driving model 2 on the one hand to determine actual distances exactly. On the other hand, for the selected driving behavior 12 from the curve of the road guidance a realistic 7/12 Austrian Patent Office AT 510 101 B1 2014-01-15

Velocity profiles along the route determined, in addition, the road information 10 and traffic information 11 are considered as further input variables. In an alternative embodiment, the roads are implemented as (straight) routes between the nodes in the road network 6. Information about the respective curve of the roads are stored as separate data in the road map 3.

From the elevation model 9 of the road map 3 as a further input determines the driving model 2 by means of geopositions along the route height changes in virtual driving and stores this as altitude profile in addition to the speed profile in the driving cycle data set 1. Furthermore, taking into account the driving dynamics described below relevant variables derived from the height profile shear and traction forces on inclines and slopes along the route.

As a further input variables driving dynamics relevant variables 13 are used by the driving model 2 to implement the virtual driving along the route for the selected vehicle, so for a specific vehicle model, as realistic as possible. Here are, for example, the mass, the air resistance, maximum allowable lateral acceleration forces and maximum braking and acceleration performance of the vehicle available. These variables influence, for example, the maximum acceleration and / or speed of the vehicle and are accordingly taken into account in the generation of the driving cycle data set 1, in particular the speed profile.

As a further input traffic information 11 are taken into account by the driving model 2. The traffic information 11 contains, for example, empirical values about the respective traffic volumes of the respective roads as a function of the time of day as well as taking into account working days and Sundays and public holidays. The traffic information 11 are assigned as records to the respective roads of the road network 6. However, such traffic information 11 is not available for all roads, so that possibly existing traffic information 11 for streets of the same road type in spatial proximity is used.

For a more realistic driving simulation environmental influences 14 along the route in the generation of the driving cycle data set 1 are considered by the driving model 2 as a further input variable. These include, for example, wind strength and wind direction, precipitation and / or snow and ice slipperiness. Such environmental influences 14 on the one hand affect the vehicle itself, for example by additional drive power having to be applied to overcome the wind resistance. On the other hand, they also have an effect on the driver who, for example, reduces the driving speed during heavy precipitation. Accordingly, the environmental influences 14 are used as correction terms regarding the driving behaviors 12. Finally, it is also possible to generate different driving cycle data records 1, for example for day and night driving or for different weather conditions, for example for summer and winter rides.

The driving cycle data set 1 (taking into account the aforementioned input variables) generated by the driving model 2 is stored as a data set that includes a speed-time profile. In this profile, the speed data is stored as a three-dimensional size. In some embodiments, additionally or alternatively, a location-time profile and / or an acceleration-time profile is created. In some embodiments, the location, speed, or acceleration data of the respective drive cycle data sets are stored as one, two, or three-dimensional information.

Incidentally, the longitudinal profile record is generated in a further embodiment of the drive cycle data set. This in turn is done by a suitably equipped computer, namely a computer with a computer program stored in a memory of a computer and, when executed, causing the computer to perform the described method. For this purpose, multidimensional data of the driving cycle data set, such as location, speed or acceleration data, both time-dependent, one-dimensional longitudinal velocities and time-dependent, one-dimensional longitudinal forces are calculated (load), their values relate respectively to the longitudinal directions along the route. The longitudinal profile data set consists in this embodiment of a sequence of value pairs of a respective longitudinal velocity and longitudinal force. Here, the sequence represents a time sequence in uniform time steps.

In further embodiments, the motor vehicle, the vehicle drive (or the component thereof) is tested on the test bench by being driven in a driving simulation according to the calculated from the driving cycle longitudinal profile. This is represented by a longitudinal profile record in various variants of embodiments. This represents a sequence of instructions for the driving simulation, so that the (respective) data set is an input variable for the test bench.

In this case, the vehicle is caused on the test stand on the one hand to its drive wheels according to the in the longitudinal profile record, e.g. driving location, speed or acceleration as a function of time or distance; On the other hand, its driving wheels are measured from the test bench according to the longitudinal profile, e.g. also braked or accelerated as a function of time or distance given force. The drive of the (real) vehicle then operates so that the wheel speed of the vehicle corresponds to the (or forward direction reduced) location or speed setting of the drive cycle without the vehicle actually moving. Thus, in this type of sub-simulation, the forces caused by the vehicle's travel are absent (e.g., inertial forces in vehicle acceleration, weight force when riding on an incline, drag, rolling resistance, and possibly cornering braking forces). These were "missing" in the test bench test due to the vehicle standstill ". Forces are generated with the roller test bench by a suitable braking and possibly also drive torque as a function of time or the distance traveled. This braking and possibly also drive torque is applied by the one or more rollers of the test stand, act on the drive wheels of the vehicle to be tested. In the test bench test according to the longitudinal profile of the vehicle is thus driven with that (one-dimensional) speed (relative to the test stand rollers) as a function of time, which corresponds to the driving cycle, and it learns at the drive wheels that or brake. Driving torque that it would experience in real driving according to the driving cycle. The mapping of the drive cycle to the longitudinal profile thus takes into account those variables of the driving dynamics which do not actually occur due to the partial simulation (i.e., the vehicle standstill) and provides the specification of the moments to be applied with the test stand rollers in order to pretend to the vehicle drive these driving dynamics variables to a certain extent. 12.9

Claims (13)

Austrian Patent Office AT 510 101 B1 2014-01-15 Claims 1. A method for the computer-based generation of a driving cycle data set (1), which represents a driving cycle for a driving simulation of a motor vehicle, in particular of a hybrid drive vehicle, the driving cycle data set (1 ) is generated automatically with a driving model (2) from a road map (3) by using a route in the road map (3) as at least one input for the driving model (2), characterized in that the route automatically for a given starting point and destination point by means of the road map (3) is determined. 2. The method according to claim 1, wherein the road map (3) altitude information (9), which are used as a further input to the driving model (2). 3. The method according to claim 1 or 2, wherein height information from a separate data source is used as a further input variable for the driving model (2). 4. Method according to one of the preceding claims, wherein the road map (3) comprises road describing information (10) which is used as further input variable for the driving model (2), in particular road descriptive information (10) about traffic lights, speed limits, curve radii, state of disassembly and / or street type. 5. The method according to any one of the preceding claims, wherein the road map (3), in particular time-and / or date-specific, traffic information (11), which are used as a further input variable for the driving model (2). 6. The method according to one of the preceding claims, wherein the driving model (2) is designed for at least two, in particular sporty and / or energy-saving, driving behavior (12), in particular by the driving model (2) directing speeds and / or an acceleration, braking, and / or switching behavior taken into account. 7. Method according to one of the preceding claims, wherein at least one environmental influence (14) is taken into account by the driving model (2), in particular wind strength, wind direction, precipitation, snow / ice smoothness, brightness, temperature, date and / or time information. 8. The method according to any one of the preceding claims, wherein of the driving model (2) at least one vehicle dynamics relevant variable (13) of the vehicle is taken into account, in particular mass, center of gravity, air resistance, cornering stability, braking and / or acceleration performance of the vehicle and / or the Operation of additional units such as air conditioning, seat and / or window heating. 9. The method according to any one of the preceding claims, wherein in the determination of the route an intermediate goal is taken into account. 10. The method according to claim 8, wherein in the determination of the route certain types of roads, especially highways and / or highways, are preferred or avoided. 11. The method according to claim 8 or 9, wherein in the determination of the route vehicle-specific criteria are taken into account, in particular restrictions on headroom and / or passage restrictions for cars, trucks and / or two-wheelers. 12. A method of producing a longitudinal profile data set comprising at least two one-dimensional quantities, wherein the sizes are derived from at least a two- or three-dimensional size of the driving cycle data set (1) generated according to one of claims 1 to 11 and along the longitudinal direction the route, and wherein one of the sizes location, speed or acceleration of the vehicle in the longitudinal direction and a further of the variables are the vehicle braking or accelerating force in the longitudinal direction. 10/12 Austrian Patent Office AT 510 101 B1 2014-01-15 13. A method for testing a motor vehicle, wherein the vehicle in a driving simulation on a test bench a longitudinal profile record is traversed according to claim 12, wherein the vehicle is caused on the test stand on the one hand, its drive wheels according to the predetermined size in the longitudinal profile size, speed or to drive acceleration, and on the other hand, its drive wheels are braked or accelerated by the test stand according to the predetermined force in the longitudinal profile. For this 1 sheet drawings 11/12