Method for specifying driving speed with optimized energy consumption

Abstract

The invention relates to a method for specifying a current travel speed with optimized energy consumption, taking into account a wind distribution (P) along a travel path and selecting a required average speed (vv) for the planned travel path. The method according to the invention is characterized in that the wind distribution is provided in the form of wind values for a plurality of consecutive locations along the planned travel route or in a local region thereof by means of a vehicle-external server (15), the local wind power values are based on measured values acquired by a plurality of vehicles of the fleet (11) by means of wind power sensors (12) and shared with a vehicle external server (15).

Description

Technical Field

[0001] The invention relates to a method for defining the current driving speed in an energy-optimized manner at a selected desired average speed and taking into account the wind distribution along the driving route. Background Art

[0002] Static or adaptive cruise control (cruise control in English) has long been known in current general technology. This technology has been used in the United States since the 1950s and in Europe since the early 1960s. In this case, a defined speed is maintained constantly with the cruise control (static) and / or, in the case of adaptive cruise control (ACC), it is automatically adjusted to the traffic situation (vehicle ahead / speed limit).

[0003] For physical reasons, it is often assumed that driving with cruise control at a constant average speed will reduce fuel consumption compared to driving without it, because even for a well-trained driver, the speed fluctuates around the average speed when driving manually. The energy consumed to overcome wind resistance is proportional to the square of the speed. Therefore, segments of the journey with speeds above the average speed will result in increased energy consumption compared to the energy saved in segments with speeds below the average. However, this only applies if there are no additional forces, especially wind forces, acting on the vehicle.

[0004] There are novel theoretical approaches, for example in Jia et al., 2019, “Energy-Optimal Adaptive Cruise Control for Electric Vehicles in Both Time and Space Domain based on Model Predictive Control”, IFAC-Papers On Line, 2019, Vol. 52, No. 5, pp. 13-20, which specifically optimize the current adaptive speed control according to the current traffic conditions, especially according to the distance to the vehicle in front, while taking into account the specific characteristics of the battery electric vehicle drive unit, for example.

[0005] The relationship between the change in the required force and the wind force acting on the vehicle, e.g. (1985-1986) The influence of aerodynamic measures on the fuel consumption and driving performance of modern passenger cars - Parts 1-3 (ATZ Automotive Technology Magazine) is described.

[0006] Patent US2019 / 0283602 A1 describes a method for regulating driving speed in an energy-optimized manner. Here, an ideal target speed is determined for each subsequent road section based on the desired average speed, basic physical laws (e.g. rolling friction) and empirical vehicle characteristics.

[0007] Here, weather data including wind is also taken into account in patent US2019 / 0283602 A1. Here, the vehicle speed is ideally planned based on the wind forecast for the next section of the road. However, accurate wind forecasts are very difficult to create. Specifically, for the wind distribution along the road, not only the wind direction must be taken into account, but also the location of buildings, landscape heights, lowlands, vegetation areas, etc. Therefore, wind forecasts often have significant errors, especially in areas with complex terrain.

[0008] A driving speed control device is known from DE 10 2019 004 883 A1. The wind speed at the vehicle's location or in front of the vehicle is recorded so that the speed can be adjusted accordingly depending on whether it is a "headwind" or a "tailwind". Furthermore, in an extended application of the device, it is also described that the internal measurement can be combined with external measurements, which are collected by the vehicle fleet and transmitted to a central server, for example.

[0009] For more prior art, reference can also be made to DE 10 2018 221 264 A1, which describes that the wind speed can be derived based on driving data, in particular the acquired driving power and driving direction changes, without using a wind sensor.

[0010] DE 10 2015 000 394 A1 describes a method for measuring environmental data based on a vehicle fleet, which environmental data are transmitted to a server device. In this case, measurement deviations caused by noise are compensated, for example, by probability values ​​or probability density values. Summary of the invention

[0011] The object of the invention is to provide an improved method according to the preamble of claim 1 which in particular does not require the carrying out of such a wind forecast.

[0012] According to the invention, this object is achieved by a method having the features of claim 1, here in particular the characterizing part of claim 1. Advantageous embodiments and developments of the method result from the associated dependent claims.

[0013] In this method, instead of wind forecasts, which are often erroneous and particularly difficult to create, statistical wind distributions are created on the basis of local wind values, which are based on measurements collected by numerous vehicles in the fleet equipped with suitable wind sensors and shared with a server outside the vehicles. This server aggregates the measurements and makes available the wind values ​​created in this way for a number of consecutive locations along the planned travel route or along the area in which it is located.

[0014] In other words, the method utilizes the communication connection between a plurality of vehicles in the fleet and a vehicle-external server and shares the data of the wind sensors with this vehicle-external server. This allows the creation of an up-to-date data base on wind conditions in the form of a table, a characteristic curve or preferably a wind map, in particular for the purpose of recording crosswind conditions. This makes it possible to calculate or derive a wind distribution along the planned driving route using these wind values ​​better and more reliably than forecasts based on weather models, thereby providing such a wind distribution. This makes it possible to utilize the wind conditions and set the vehicle speed with the aid of cruise control so that the desired specified average speed can be achieved in a manner that optimizes energy consumption.

[0015] Based on the numerous values ​​collected for each location by a plurality of vehicles of the fleet using wind sensors, the method according to the invention can create for each available location, i.e. for each location for which there is a corresponding wind value or measurement, a location-dependent wind probability density function as a function of wind volume and wind direction. Said function can now be used for speed control that optimizes energy consumption.

[0016] Preferably, according to a particularly advantageous refinement of the method according to the invention, a probability density function of the wind along the route, in particular the probability density function of the crosswind, is calculated with the aid of the planned route using the location-dependent wind probability density function. This makes it possible to obtain statistically expected wind values ​​along the entire route. If the wind conditions change significantly, this can be detected by means of the wind values ​​or measured values ​​that are continuously transmitted by the vehicles in the convoy, after which this wind probability density function along the route can also be updated or recalibrated if necessary.

[0017] According to a particularly advantageous improvement of the method according to the invention, the measured values ​​here comprise wind speed values, ie the magnitude of the wind force and the wind direction. In a particularly advantageous design, the measured values ​​can be calculated as a time average and / or a location average, thereby obtaining the wind force value.

[0018] According to another particularly advantageous embodiment, the measured values ​​can be weighted as a function of the time span between their use and their measurement. Thus, for example, when calculating time and / or location averages, older measured values ​​are taken into account less than the corresponding most recent measured values ​​in the calculation of the average. In particular, starting from a certain time span since the measurement, the weighting factor can also be set to zero, so that older measured values ​​are no longer taken into account after a specified "validity period".

[0019] In addition, other information collected on site can be used in the aggregation. For example, the traffic situation can be taken into account. For this purpose, for example, the number of vehicles in the lane or the oncoming lane, the vehicle speed, the type and number of vehicles parked on the roadside, or other situations that may affect the wind conditions can be used. If this data is collected during the wind measurement and taken into account accordingly, for example, the dispersion of the measured values ​​can be reduced, for example, if a vehicle passes by during the measurement, the weight of the relevant measured value can be reduced or, if necessary, completely discarded.

[0020] In a particularly advantageous embodiment of the method according to the invention, a threshold function is defined based on the probability density of the wind force along the driving path by means of an optimization method, which divides the area formed by the wind speed value and the wind direction angle into two intervals, so that in one interval the vehicle speed can be increased without having to exert more net force, and in another interval the vehicle speed must be reduced so that no more net force needs to be exerted. In other words, the threshold function provides corresponding intervals so that it is possible to drive faster than the specified average speed without expending additional force, so as to save time accordingly. In another interval, the wind conditions lead to the need for a slower driving method in order not to require more force than desired. With the help of the driving path and the required average speed, these intervals can be used so that the vehicle can drive faster in certain intervals in order to correspondingly slow down in other intervals, thus saving energy over the entire driving path.

[0021] According to a particularly advantageous improvement of the method according to the invention, the efficiency can be optimized particularly conveniently if the vehicle collects current measured values ​​in order to determine the current wind value and, based on this, determines whether the vehicle is in one or another interval or, in the extreme case, exactly in the threshold function itself. If it is in one interval, the speed is increased accordingly dynamically or with a fixed speed value, and if it is in another interval, the speed is reduced accordingly. In this way, a particularly convenient and efficient control can be achieved, especially when the wind probability density along the driving path and the threshold function are determined in advance, for example by a server external to the vehicle. With a minimum amount of calculation and optimal use of existing resources, the speed can be conveniently adjusted in an energy-optimized manner during driving by comparing the currently collected measured values ​​with the threshold function. It should also be added here that if the collected measured values ​​are directly on the threshold function, the speed can be kept constant accordingly.

[0022] As already mentioned above, according to a particularly advantageous refinement of the method according to the invention, the wind probability threshold value can be updated for changing measured values ​​from the fleet.

[0023] In addition, in a particularly advantageous embodiment, the drag coefficient or c of the respective vehicle is taken into account. W The value of , which also depends on the wind force and in particular on the wind direction angle. In this way, possible crosswinds can be taken into account and incorporated into the optimization of the threshold function. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Furthermore, further advantageous embodiments of the method according to the invention are obtained from the exemplary embodiments, which are described in more detail below with reference to the drawings.

[0025] in:

[0026] Figure 1 An overview diagram for illustrating the present method is shown;

[0027] Figure 2 An embodiment is shown to illustrate aggregating measurements from a fleet of vehicles in a location and time resolved manner;

[0028] Figure 3 The wind probability density according to the present invention based on the corresponding data is shown;

[0029] Figure 4 shows how to create wind probability density along a planned travel path;

[0030] Figure 5 shows how to provide the vehicle with Figure 4 Wind probability density created along the planned travel path;

[0031] Figure 6 shows how to create a globally optimized threshold function;

[0032] Figure 7 A diagram and a schematic diagram are shown for illustrating the relationship between air friction and wind direction angle;

[0033] Figure 8 Shown with Figure 7 Similar content is used to illustrate the relationship between the difference in air friction and the wind direction angle when the speed changes;

[0034] Fig. 9 A diagram is shown for schematically illustrating various optimization relationships to achieve a threshold function;

[0035] Fig.10 A threshold function with two intervals within the wind probability density is shown;

[0036] Fig.11 Shows something like Fig.10 , and each interval has two sub-intervals;

[0037] Fig.12 This is shown in the form of a probability / force diagram Fig.11 and

[0038] FIG. 13 shows a flow chart for illustrating a possible flow of the method according to the present invention. DETAILED DESCRIPTION

[0039] Figure 1 A convoy 11 is shown, whose vehicles collect measured values ​​by means of (transverse) wind sensors 12. These measured values ​​include the current wind speed value and wind direction. That is, the convoy 11 measures the wind force 13 in a location- and time-resolved manner using the wind sensor 12. The convoy sends this information to a vehicle-external server, such as a cloud / computing center 15, by means of a data communication unit 14. This information is aggregated here and a location- and time-averaged (transverse) wind force distribution is created. The wind force values ​​can be stored in different ways, for example in a table, a characteristic curve, or particularly preferably in a digital wind force distribution map. Such a crosswind distribution map can average the currently measured wind speed values ​​and wind direction by location and further mark them with a probability distribution P of the values. In this case, full use is made of the fact that many vehicles in the convoy 11 can pass the same location with a relatively short time difference, and for the values ​​determined in this process, an average value for a time period can be calculated. Advantageously, past crosswind distribution maps can be saved (depending on time) and used as initial values ​​for the currently determined crosswind distribution map. For example, the crosswind distribution map may be affected by seasonal fluctuations.

[0040] For this reason, Figure 2 As shown, for each location O, wind information 26-29 is measured by different vehicles 21-24 of the fleet 11 at different times 30-33 (where 33 is the current time and 30-32 are past times), and forwarded to the cloud / computing center 15 by means of the data communication unit 14, where the location- and time-resolved wind information is aggregated. Advantageously, the information is aggregated to create a wind map for each location O as shown in FIG. Figure 3 shown

[0041]

[0042] Wind force value (|v s |) and wind direction or wind angle (α s ) is a location-dependent wind probability density function. To this end, the wind information 26-29 is weighted according to their time component, for example, the weight of older information is reduced compared to newer information. Figure 3 The stars are shown in different sizes, which represent Figure 3 Wind probability density P O Here, each area surrounded by a ring or an ellipse represents a different value range of the probability density.

[0043] The aggregated information can then be transmitted to the vehicles in the fleet 11. Figure 1 In the embodiment, the wind information after being summarized and analyzed by location and time is transmitted to the vehicle 16 through the data communication unit 14. Now, if the vehicle 16 starts to drive, a driving path R from the starting point A to the destination B is calculated by the navigation system 19, and Figure 4 As shown, the probability distribution P of the crosswind condition on the driving path R is obtained in combination with the information of the crosswind distribution map.

[0044] For the planned driving path R of the vehicle 16 from A to B, each location O on the path i Determine the site-resolved wind probability density function information, and if Figure 5 As shown schematically in the figure, the wind probability distribution P of the entire path is summarized.

[0045] The information can be processed locally in the vehicle 16 , or in the computing center 5 , in the first case the location-resolved wind probability density has to be sent to the vehicle 16 , in the second case the path information has to be sent to the computing center.

[0046] For the planned path of the vehicle 16 from A to B, based on the wind probability distribution P of the entire path and other requirements that will be further described below, a decision function or threshold function SW is defined by means of an optimization method, which is as follows: Figure 6 As shown, the area of ​​wind speed and wind direction angle is divided into two intervals.

[0047] This is to utilize Figure 7 Based on the considerations explained in the following figures:

[0048] At a constant speed |v v |(Bold indicates vector here) The air friction force F required when driving air (solid line curve) at wind speed |v v |Constant and drag coefficient c W The wind direction angle α is constant (i.e., it is assumed to be unaffected by the wind direction angle). s As an element between 0° and 180°, with the wind direction angle α s The increase of air friction F air will become smaller because the relative velocity of the inflowing air |v rel |As v v and v s The vector according to the formula:

[0049]

[0050] Become smaller.

[0051] In α s = 0° (maximum headwind) or α s =180° (maximum tailwind), |v rel | value is maximum or minimum. Usually, the drag coefficient c W As the relative angle α of the incoming air rel and increases with the wind direction angle α when the speed is constant. s increases with the increase of (e.g., in a quadratic relationship), and as Figure 7 As shown by the dashed curve, the air friction F air At wind direction angle α s It will increase when it is larger.

[0052] Figure 8 The constant speed |v v |+Δv and at a constant speed |v v |The air friction force ΔF required when driving air The difference in wind speed value |v s |Constant depending on wind direction angle α s As an element between 0° and 180°, with the wind direction angle α s The increase in the required additional air friction ΔF air decreases because the relative velocity of the inflowing air |v rel|As v v and v s The vector sum of the above formula will decrease and be reflected in the force in a square relationship, while the drag coefficient c is usually related to the wind direction angle. W For each α s As far as the value is concerned, it is considered to be constant or approximately constant.

[0053] like Figure 7 and Figure 8 As shown, at a constant speed |v v |Air friction force F to be generated during driving air and the additional force ΔF to increase the travel speed by Δv air The value of wind speed |v s |Constant depending on wind direction angle α s With the wind direction angle α s The increase in the required air friction force ΔF air Smaller (see Figure 8 ). If the vehicle speed now increases by Δv, or decreases by Δv', the air friction is

[0054] or

[0055] and correspondingly |ΔF air |For

[0056] or

[0057] From the threshold angle α s,th The threshold angle usually depends on the value of the vehicle speed, the wind speed vector Right now and α s

[0058] and Δv' and Δv (in Fig. 9 The additional air friction required when the speed is increased by Δv is less than that saved when the speed is reduced by Δv' and the angle is reduced. In other words, if you slow down when the wind direction angle decreases, you can speed up when the wind direction angle increases without applying more force, provided that the wind speed value |v s |The average remains at least constant. This gives us two intervals (1,2) in which the velocity can be decreased (1) or increased (2) without applying more net force.

[0059] If you choose

[0060]

[0061] Then the average speed is exactly |v v |, the prerequisite is to use

[0062] or

[0063] Travel the same segment length and with |v v |Drive the remaining distance.

[0064] That is Fig.10 As shown, usually for the probability density function P(|v s |,α s ) There is a threshold function SW, which depends on the vehicle speed |v v | and current wind conditions (|v s | and α s ) divides all possible / potential wind conditions (i.e., intervals 1 and 2 together) into two intervals 1 and 2, where the vehicle speed can be reduced in interval 1 and increased in interval 2 without applying more net force. As long as the total length of the road segment traveled in intervals 1 and 2 is the same, then Fig. 9 As shown, the average speed is exactly |v v Advantageously, the threshold function SW is determined by a force F which is given in the form of a mean force and which must be applied to the wind force probability distribution P.

[0065] In this way, the probability density function P(|v s |,α s ), it is possible to determine for each current wind condition measured by the vehicle whether it is in interval 1 or interval 2, and at the same time determine the probability of this wind condition occurring on the path (the area in the circle or oval represents the different value ranges of the probability density).

[0066] If the air friction is given, the result of the probability density function P|v s |、α s and |v v |Integrate so that |v s | and α s To generate the wind, you will get the total required mean force, which is the force averaged over the path for these wind conditions.

[0067] Of course, the probability density is standardized here, that is, the integral of the probability quarter over the entire interval is:

[0068]

[0069] If the air friction is given, the result of the probability density function P|v s |、α s and the speed increased by Δv |v v |+Δv is integrated in interval 2, then the total required average force F can be obtained loss , so that the speed can be increased by Δv while driving.

[0070]

[0071] If the air friction is given, the result of the probability density function P|v s |、α s and reduced the speed of Δv'|v v |-Δv' is integrated in interval 1, and the total required average force F can be obtained gain , so that the speed can be reduced by Δv' while driving.

[0072]

[0073] At each time t, the vector wind force value applicable at that time can be used to select the speed v to be driven by knowing the force v , in order to minimize the total energy consumption along the entire path.

[0074] For example, you can easily control each group element Calculates whether the value is greater than or less than if The speed can be increased. The speed should be reduced, wherein the ratio of the two situations must be calculated and taken into account in order to achieve a constant speed over the entire distance.

[0075] In general, we can identify two regions 1* and 2* in P that are subsets of interval 1 or 2, provided that the total probability of the two subsets is the same. To do this, we identify two regions 1* and 2* in P, or by |v s |、α s The area formed is premised on

[0076] And in 1*, the speed is reduced by Δv', and in 2*, the speed is increased by Δv, so that the average speed is |v v |. In addition, the two regions are given by F gain and F loss The force gain is obtained by taking the difference between

[0077]

[0078] and

[0079]

[0080] The ideal intervals / regions 1* and 2* can be found by standard optimization methods:

[0081] argmax 1*,2* (F gain -F loss ),in:

[0082] In this way, the current control in the vehicle can be derived from the ideal area and the current wind conditions, ie the current wind conditions can be used to determine whether the vehicle is located specifically within or outside of area 1*, 2*.

[0083] In addition, the vehicle-specific drag coefficient c W (English drag coefficient) usually depends on the relative angle α of the incoming air rel , which in turn passes through the angle α of the crosswind s and v v and v s is given and can be taken into account during the optimization process. The vehicle-specific drag coefficient c W In particular, it can be used with α rel The relevant details are widely known and can be found at the beginning of this article. and (1985 and 1986).

[0084] In another embodiment, the average speed can be changed automatically and / or manually by the driver's choice on the basis of the relevant speed limit on the different road sections i. Subsequently, each road section i will be regarded as a separate path, and the ideal control strategy of the adaptive cruise control system GRA can be calculated for each road section i.

[0085] During driving, the vehicle 16 measures the current local wind direction and wind speed (in Figure 1 and Figure 5 In the figure, the number 17 is used. Based on the 0 The value currently measured at the time t is checked to see whether it is higher or lower than the decision function or threshold function SW. If the value is higher than the threshold function SW, the speed can be reduced by a fixed value Δv', thereby saving energy. Before starting the journey, the decision function or threshold function SW is optimized so that at another time t x and increase the speed by a fixed value Δv for other measured wind conditions in order to achieve a prescribed average speed |v on the path v |, and at the same time at tx The energy required to accelerate the vehicle at any time is lower than that required at t 0 Energy saved at any time. Figure 6 Reference numeral 40 .

[0086] Furthermore, such regulation is particularly energy-efficient, since the ideal threshold function SW only needs to be calculated once, and the wind value measured at any moment in conjunction with the threshold function SW is sufficient to decide whether to travel at an above-average speed, a below-average speed or at an average speed.

[0087] In the two parts of Figure 13, namely Fig.13a and Fig.13b , a flow chart is shown to illustrate the method according to the invention. The flow chart starts at the top of FIG. 13A with the start of the method. The driver of the vehicle 16 selects a driving route R and a driving speed in the form of an average speed. The following decision diamond determines whether the information processing is carried out in the vehicle 16 or in a server 15 outside the vehicle. In one case, the route information is transmitted to a computing center, in the other case, the location-resolved fleet data and the resulting wind map are transmitted to the vehicle 16. Next, the probability distribution P of the wind conditions is calculated for the selected driving route R in the vehicle 16 or on a server outside the vehicle and the above-mentioned optimization purpose, i.e., the determination of the threshold function SW, is achieved.

[0088] Next, the method flow continues to jump to Fig.13b The second part is shown in FIG. Here, a basic check is now performed to determine whether the vehicle 16 has reached its destination, in the above exemplary embodiment and the figures, destination B. If so, the method stops. Otherwise, the current wind conditions around the vehicle 16 are determined with the aid of the wind sensor 12. Next, a check is performed to determine whether the acquired measured values ​​are located in area 1* or in area 2*. In the first case, the vehicle speed |v v |reduce Δv', and if it is in area 2*, the driving speed |v v | correspondingly increases Δv. If the current measured value is neither in area 1* nor in area 2*, that is, is located between these areas, in particular within the interval of the threshold value function SW, then the driving speed |v v |The average speed will be maintained next|v v |, or if the driving speed has been changed before, it is reset to the average driving speed. This process will be repeated until the vehicle 16 arrives at the destination B.

[0089] The first theoretical embodiment illustrates the principle of the method. A vehicle 16 travels along a road section s having two different directions α s = 0° (headwind) and α s =45° (right front against the wind) and wind speed vs Constant wind. The different wind directions are evenly distributed, that is, over the entire section s s =0° and α s =45°. For example, in the first segment s / 2, the angle α s = 0°, and on the second segment α s =45°. If the vehicle 16 now travels at a speed reduced by Δv' in the first segment and at a speed increased by Δv in the second segment, the total energy consumption is W=W1+W2=F1*s / 2+F2*s / 2, where F1=γ*(|v v |-Δv'+v s ) 2 And F2=γ*(v rel ) 2 , where v rel 2 Yes|v v |+Δv function, α s =45° has passed (|v v |+Δv) 2 +v s 2 +2(|v v |+Δv)v s / √2, and γ contains all factors that are constant or assumed to be constant (ie, air density, drag coefficient, area).

[0090] A practical embodiment of the method according to the present invention is as follows. A vehicle selects a route from Manchester State Park to San Francisco along California State Route 1 (CA 1 or Highway 1 for short). The wind probability density associated with the route segment gives a high probability of continuous headwinds from different directions with different wind values. That is, the wind probability density associated with the route segment is within a small angle interval α. s(For example, between 0° (headwind) and 45° crosswind) with a higher probability value. For example, the speed distribution is also normally distributed around 30 km / h and has a variance of 10 km / h. Based on the wind probability density associated with the road section, two intervals 1* and 2* are now found for which the difference between the resultant force F+ that may be caused by reducing the speed and the resultant force F- that may be caused by increasing the speed is the largest. Now if the vehicle 16 is moving, its wind sensor 12 will measure the current / present wind speed and wind direction. If the value pair is within the interval 1*, the vehicle speed is reduced by Δv'. In this way, when the wind direction angle is small and the speed is high, the vehicle speed slows down. But if the value pair is within the interval 2*, the vehicle speed is increased by Δv. In this way, if the wind direction angle is relatively large (but the wind always comes from the front) and the wind speed is small, the vehicle 16 will travel faster. In this case, the required net force is reduced because the gain, that is, the difference between F+ and F- is positive, and the average speed continues to be constant at v v .

Claims

1. A method for selecting a desired average speed (|v v |), a method for determining the current driving speed in an energy-optimized manner, wherein: The wind force distribution (P) is provided in the form of wind force values ​​for a plurality of consecutive locations along the planned driving route or in a local area thereof by a vehicle-external server (15), wherein the local wind force values ​​are based on measured values ​​collected by a plurality of vehicles of the fleet (11) by means of wind force sensors (12) and shared with the vehicle-external server (15), characterized in that for each location (O i ) will use at least one available measurement value according to the wind speed value (|v s |) and wind direction (α s ) Create a wind probability density function (P Oi ).

2. The method according to claim 1, characterized in that With the help of the planned driving path, the wind probability density (P O ) calculates the probability distribution (P) of wind force on the driving path.

3. The method according to claim 1 or 2, characterized in that: The measured value includes the wind speed value (|v s |) and the wind direction (α s ).

4. The method according to claim 1, 2 or 3, characterized in that: The vehicle external server (15) aggregates wind force values ​​using measured values ​​calculated by time and / or location averages.

5. The method according to any one of claims 1 to 4, characterized in that: Depending on the time span between measured value use and measured value acquisition, the measured values ​​are weighted by means of a weighting factor which decreases as the time span increases.

6. The method according to any one of claims 3 to 5, characterized in that: Further parameters are used in the aggregation, which are determined at the time of the measurement value acquisition at the location (O) where the measurement value was acquired and which include, in particular, the traffic situation.

7. The method according to any one of claims 1 to 6, characterized in that: On the basis of the wind probability distribution (P) along the driving path, a threshold function (SW) is defined by means of an optimization method, wherein the threshold function (SW) is composed of the wind speed value (|v s |) along the wind direction (α s ) is divided into two intervals (1, 2), so that in one of the intervals (2) the vehicle speed can be increased without having to apply more net force, and in the other interval (1) the vehicle speed must be reduced so that no more net force needs to be applied.

8. The method according to claim 7, characterized in that The current wind force value is determined using the current measurement value collected by the vehicle (16), thereby determining whether the current wind force value is in the one or the other interval (1, 2) or on the threshold function (SW), wherein if it is in the interval (2), the speed is increased, if it is in the other interval (1), the speed is reduced, and if it is on the threshold function, the speed is kept constant.

9. The method according to any one of claims 1 to 8, characterized in that: In case of changes in the measured values, the vehicles in the fleet (11) will update the values ​​of the wind force distribution.

10. The method according to claim 8 or 9, characterized in that: When determining the threshold function (SW), the drag coefficient (c w ) and wind force value.

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