ADAPTING ELECTROMECAR CHARGING PROCESSES

Description

[0001] The invention relates to a method for adapting charging processes of electric vehicles, in which a fleet operator of a fleet of electric vehicles receives energy request information from an operator of a first energy supply network. The invention also relates to a charging control system configured to implement the method. The invention further relates to a computer program product comprising code that, when executed on a data processing device of the charging control system, executes the method.

[0002] Energy storage devices, especially batteries, of electric vehicles (e.g., plug-in hybrid vehicles or fully electric vehicles) are charged at charging stations, each of which is assigned to the energy supply grid of a specific operator or energy supplier. The charging processes can be influenced by a variety of factors, e.g., the amount of energy provided by the energy supplier, the utilization of the energy supply grid, charging conditions agreed between the electric vehicle user and the electric vehicle manufacturer, etc. If it is known which electric vehicles with which charging characteristics are connected to which charging stations, their charging behavior can be adapted to the current requirements of the energy supply grid, thus better balancing the energy supply grid. Such energy supply management is generally known.

[0003] For example, if the amount of energy available in a particular power grid is currently limited, the operator of the power grid can issue energy demand information to the electric vehicle fleet operators cooperating with it, requesting that the power consumption of electric vehicles currently charging on this power grid be reduced. The fleet operators can then remotely adjust the charging processes of these charging electric vehicles (e.g., by sending appropriate SMS messages) to the energy demand information, e.g., reducing the charging power, terminating charging processes early, etc.

[0004] Knowing which electric vehicle is connected to a charging station in the energy supply network under consideration can be achieved through data exchange between the charging partners, i.e., the charging station and the electric vehicle. For example, this can be achieved through the transmission of data that uniquely identifies the electric vehicle, such as its VIN, e.g., according to ISO 15118, or a "fingerprint" of the electric vehicle's charging characteristics. This data, as well as the fact that a specific electric vehicle is currently charging, can be transmitted from the electric vehicle to an associated fleet operator, e.g., via radio. The fleet operator then clearly knows whether this electric vehicle is drawing its charging current from the energy supply network from which it received energy demand information.

[0005] DE 10 2015 210 726 A1 discloses a method for determining a charging pair, each comprising a charging partner from two different sets of possible charging partners. The two different sets comprise a set of possible charging stations and a set of possible vehicles. The charging pair comprises a charging station and a vehicle that is carrying out a charging process at the charging station. The method comprises initiating a stimulus relating to the charging process by a stimulating charging partner from a first set of the two sets of possible charging partners. The method also comprises detecting a reaction to the stimulus by a reacting charging partner from a second set of the two sets of possible charging partners. Furthermore, the method comprises forming a charging pair comprising the stimulating charging partner and the reacting charging partner.

[0006] DE 10 2016 212 245 A1 is directed to a method for operating a charging station, which allows an electrically powered vehicle to be supplied with the maximum charging power only after positive identification. It is directed to a method for scheduling an electrical charging process of a vehicle that is electrically coupled to a charging station by means of a charging cable.

[0007] DE 10 2016 212 244 A1 is directed to a method for planning an electrical charging process of a vehicle which is electrically coupled to a charging station by means of a charging cable.

[0008] DE 10 2018 212 283 A1 discloses a method for determining a charging pair, each with a charging partner from two different sets of possible charging partners, wherein the two different sets comprise a set of possible charging stations and a set of possible hybrid or electric vehicles, wherein the charging pair comprises a charging station and a hybrid or electric vehicle that carries out a charging process at the charging station.The method comprises determining at least one temporal response property of at least one possible charging partner from the possible charging partners; initiating a stimulus related to the charging process by a stimulating charging partner from a first set of the two sets of possible charging partners, wherein the stimulus is initiated based on the at least one temporal response property; detecting a response to the stimulus by a responding charging partner from a second set of the two sets of possible charging partners; and forming a charging pair comprising the stimulating charging partner and the responding charging partner based on the detected response.

[0009] US 2017 / 043671 A1 discloses a control system for monitoring an electric vehicle service network comprising a plurality of service stations that charge a fleet of electric vehicles with electrical energy. The control system is configured and operable to communicate with the electric vehicles via a communication network and comprises: a processing unit configured to aggregate vehicle route planning information associated with at least some of the electric vehicles to generate forecast data of the flow of electric vehicles to the service stations over time; and a service time estimation unit configured and operable to utilize the forecast data and estimate the service duration for servicing a particular electric vehicle at a particular service station and at a particular service time based on the forecast.

[0010] EP 2 760 696 B1 discloses a method for charging electric vehicles by charging stations, comprising the steps of a) assigning electric vehicles to different electric vehicle supply devices of the charging stations and b) charging the electric vehicles according to electric vehicle charging information and provided charging power by the electric vehicle supply devices of the charging stations, wherein a match between electric vehicle charging information, preferably load profiles, of electric vehicles and charging power information, preferably provided charging power, of different charging stations is predicted based on electric vehicle information and a charging station parameter, and wherein steps a) and b) are carried out based on the predicted match. The present invention relates to.

[0011] US 2017 / 036560 A1 discloses a method for load balancing charging stations for mobile loads within a charging station network, comprising performing a distribution of an energy-power range limit for each of the charging stations p, taking into account a definable optimization parameter based on a prediction of a charging demand of the mobile loads, where p = 1, ... , n, and n and p are integers. Taking the distribution into account, an adaptation and / or selection of at least one transport parameter of a mobile load is performed in order to at least partially fulfill the energy-power range limit for each or for a definable number of the charging stations p.

[0012] EP 3 726 455 A2 discloses a method for predicting the availability of at least one charging station for an electric vehicle, wherein a specific charging requirement is determined for a plurality of electric vehicles, wherein each of the electric vehicles issues a request regarding the availability of at least one charging station depending on the specific charging requirement, and wherein a real demand for a charging process is estimated for the at least one charging station based on the requests from the electric vehicles, and from this a first probability for the availability of the at least one charging station at a future time is determined.

[0013] An electric vehicle can also be uniquely identified by a clear spatial correlation ("1-to-1 mapping") between its position and the position of a charging station. This is the case, for example, if the position of the electric vehicle matches the position of the charging station, which is also known with high accuracy, with the positioning device having a high accuracy (e.g., ± 1 m), possibly with the additional knowledge that no other electric vehicle is in the vicinity of the charging station.

[0014] However, a problem arises when the fleet operator knows that an electric vehicle in its fleet is charging at a charging station near the energy supply network in question, but the charging station cannot be clearly identified. This can occur, for example, if the charging partners do not exchange clearly identifying data and the position of the electric vehicle is only known imprecisely (e.g., within a radius of 10 m, for example, due to inaccurate positioning technology used or the presence of buildings, mountains, etc.) and / or charging stations, especially those of different energy suppliers, are located close to one another and / or their positions are not precisely known.

[0015] It is the TaskThe present invention aims to at least partially overcome the disadvantages of the prior art and, in particular, to provide an improved possibility for a fleet operator to adapt charging processes of electric vehicles charging at charging stations of this energy supply network to the request of an operator of an energy supply network.

[0016] This object is achieved according to the features of the independent claims. Preferred embodiments can be found in particular in the dependent claims.

[0017] The task is solved by a method for adapting charging processes of electric vehicles, in which a fleet operator of a fleet of electric vehicles receives energy demand information from an operator of an energy supply network (hereinafter referred to as the "first" energy supply network without restriction of generality); assigns a probability to currently charging electric vehicles in the fleet that are located in the vicinity of the first energy supply network or at least one charging station thereof and are not clearly assigned or assignable to a charging station that they are currently charging from the first energy supply network or are connected to at least one charging station thereof; based on these probabilities, classifies the electric vehicles as charging from the first energy supply network or not charging from the first energy supply network; and adapts the charging processes of the electric vehicles classified as charging from the first energy supply network or connected to the first energy supply network based on the received energy demand information.

[0018] This provides the advantage that even if a clear 1-to-1 mapping cannot be established between a charging electric vehicle and a charging station in the primary energy supply network, the fleet operator can still include this electric vehicle in its charging strategy for the primary energy supply network. This, in turn, improves the balancing of the primary energy supply network.

[0019] This assumes that the electric vehicles located near the primary energy supply network can be individually identified remotely, e.g., by radio transmission or radio query of their VIN. If an electric vehicle is individually identifiable, its charging characteristics are typically also known, for example, vehicle-inherent charging features such as energy storage capacity, charging speed, energy storage age, etc., and possibly also contractually negotiated charging conditions such as the right to a full or partial charge. Furthermore, the remotely identifiable electric vehicle can transmit its geoposition and charging status (e.g., charging / not charging) to the fleet operator. This vehicle information can be transmitted automatically by the electric vehicle to the fleet operator ("push"), e.g., at regular intervals or event-driven, e.g., at the start of a charging process.Alternatively or additionally, the vehicle information can be queried remotely by the fleet operator ("pull").

[0020] The electric vehicle can be a car, truck, bus, motorcycle, etc.

[0021] The fleet operator may be an electric vehicle manufacturer or may operate electric vehicles from different manufacturers. The fleet operator may, in particular, maintain a charging control system to implement the method. The charging control system is, in particular, configured to adapt the charging processes based on a charging strategy based on energy supply management.

[0022] Energy demand information can include a current or forecast status of the first energy supply network that could influence charging processes for electric vehicles, e.g., the available amount of electrical energy or power, or changes thereto. For example, a failure of energy generation units (power plants, solar systems, etc.), the failure of power lines, increased energy consumption by other consumers of the energy supply network, etc., can lead to a short-term reduction in the amount of energy available in the first energy supply network. The energy demand information can then, for example, include a request to temporarily reduce the charging power used by charging electric vehicles.The fleet operator will then attempt to meet this request by remotely adjusting the charging processes of currently charging electric vehicles in such a way that the users of the charging electric vehicles are affected as little as possible.

[0023] In particular, the following currently charging electric vehicles can be clearly assigned to the first energy supply network by the fleet operator: All electric vehicles that have a one-to-one relationship with a charging station of the primary energy supply network; all electric vehicles that are clearly located within the geographical extent or area of ​​the primary energy supply network, even if they do not have a one-to-one relationship with a charging station.

[0024] On the other hand, an electric vehicle "near" the first energy supply network can be understood as an electric vehicle that, from the fleet operator's perspective, could be connected to the first energy supply network or to a neighboring ("second") energy supply network, taking into account the positioning inaccuracies of the electric vehicle and / or the charging station. The fact that a currently charging electric vehicle cannot be clearly assigned to a charging station includes, in particular, that the fleet operator is aware that the electric vehicle is charging but cannot clearly determine whether it is charging from a charging station in the first energy supply network or from a charging station in the neighboring second energy supply network.

[0025] The operator of the first energy supply network and the operator of the second energy supply network may be different operators or the same operator. In particular, if the operator is the same, the first energy supply network and the second energy supply network may be subnetworks of a higher-level energy supply network.

[0026] Uncertainties regarding the position of the electric vehicle and / or a charging station may be caused by one or more of the following reasons: The positioning device or tracking technology used by electric vehicles only provides relatively inaccurate positions; the positioning is subject to geographical uncertainties, e.g., due to tall buildings on a street, mountains, etc.; the position of the charging station has not been accurately recorded. For example, a scenario is conceivable that certain buildings on a street are equipped with charging stations (e.g., with corresponding Schuko plugs), but the energy supplier does not know the position of the Schuko plugs on the buildings or even which buildings on the street have a charging station. A further complication may be that one side of the street is supplied from the first energy supply network (or "grid"), while the other side is supplied from a second energy supply network.

[0027] The charging stations can be dedicated charging stations. They can also be Schuko plugs or other household connection plugs, such as wall boxes. In a further development, an energy supply network can have multiple charging stations that cannot be distinguished electrically or in terms of charging technology by the energy supply network, e.g., because they are connected to the same power distribution board or electricity meter.

[0028] In a further development, the position and, if applicable, the associated positioning accuracy of a specific electric vehicle is determined using a positioning device of that electric vehicle, e.g., based on GPS, GLONASS, Wi-Fi, etc. The positioning accuracy may depend on the positioning device installed in the electric vehicle (e.g., more or less accurate GPS) and / or the positioning technology used by the electric vehicle (e.g., GPS, Wi-Fi, etc.). In a further development, the positioning device of the electric vehicle and / or its positioning technology can also be queried via remote identification.

[0029] It is also possible that typical positioning accuracies for certain areas of a power grid are known or predetermined. Such positioning accuracies may, for example, have been determined representatively through tests or measurements.

[0030] For example, from the position (in)accuracies of the electric vehicles and the at least one charging station in the vicinity of the respective electric vehicles, as well as possibly further information such as the length of a charging cable, etc., probabilities can be determined for the electric vehicles that they are connected to a charging station of the first energy supply network for charging. With knowledge of at least these probabilities, the fleet operator can determine or establish which electric vehicles are assumed to be connected to the first energy supply network, and the fleet operator can adapt the charging processes of these electric vehicles to the received energy requirement information and thus to its charging strategy, if possible and / or sensible. The fleet operator can tend to be more likely to assign electric vehicles to a charging station in the first energy supply network, for example, the higher the probabilities are.

[0031] In one embodiment, an electric vehicle is assigned probability values ​​that it is connected to a specific charging station of the first energy supply network, wherein the probability values ​​are dependent at least on a determined distance between the electric vehicle and this charging station. In other words, a specific electric vehicle is assigned a link between a probability of connection to a charging station of the energy supply network near the electric vehicle and the distance to the charging station. The probability values ​​can be determined, for example, experimentally, based on empirical values, and / or through simulations.

[0032] The probability values ​​can be plotted as a distance-dependent curve. The probability values ​​can be stored, for example, as a table, characteristic curve, etc.

[0033] It is a design that the probability value is greater the smaller the distance between the electric vehicle and the charging station.

[0034] One embodiment allows the probability values ​​to depend on a positioning device and / or tracking technology used by the electric vehicle in question, or its accuracy(ies). This allows the probabilities to be determined more realistically. The probability values ​​are then typically vehicle-dependent or inherent and may depend, for example, on the type and / or equipment of the remotely identifiable, electrically powered electric vehicle. In one variant, electric vehicles with the same positioning device and / or tracking technology can be assigned the same probability values.

[0035] One embodiment allows the probability values ​​to be dependent on the accuracy of the knowledge of the position of at least one charging station. This also improves the knowledge of the probability of a specific electric vehicle connecting to a charging station. As already indicated above, this position can be known relatively accurately or only very imprecisely, in the latter case, for example, only within the width of a housing or the length of a street, in a high housing canyon, etc.

[0036] In a further training, only those charging stations are considered for which a certain minimum probability exists, e.g., at least 1% or 5%. On the other hand, a 1-to-1 assignment can be assumed in a further training if the probability reaches or exceeds an upper probability threshold, e.g., 98% or 99%.

[0037] As a further development, only charging stations within a specified maximum distance are considered. This maximum distance can be approximately 10 m, for example, and thus includes typical charging cable lengths.

[0038] One embodiment is such that the probability that an electric vehicle is connected to a charging station in an energy supply network depends on the number of nearby charging stations in the first energy supply network. This embodiment is particularly advantageous in the event that the operator or electricity supplier cannot distinguish which charging station in a group of charging stations in the energy supply network the electric vehicle is connected to, e.g. because several charging stations are supplied with power via the same power distributor. Such a scenario can arise, for example, if several charging stations on a street, e.g. Schuko plugs, are connected to a common power distributor via a common power supply line and the operator of the energy supply network has no other way of assigning the connection of an electric vehicle to a specific one of these charging stations.The more such charging stations an electric vehicle could be connected to, the higher the probability tends to be that the electric vehicle could be connected to one of these possible charging stations. The charging stations to which the electric vehicle can or could be connected can exclude charging stations to which another electric vehicle is securely connected. The probability that an electric vehicle is connected to any charging station in the primary energy supply network can be calculated, for example, by linking or "cumulating" the individual probabilities for a possible connection to the respective charging station under consideration.

[0039] As a further development, charging stations from a different, second energy supply network in the vicinity of the electric vehicle are not taken into account when calculating the probability of being charged from the first energy supply network. This provides the advantage of a particularly simple calculation.

[0040] It is an embodiment that the (total) probability P (F) that an electric vehicle F is connected to (any) charging station L i from a group of n = 1, 2, 3, 4, ... possible charging stations L i of the first energy supply network in the vicinity of the electric vehicle F is determined according to P F = 1 − ∏ i = 1 n 1 − P L i d i with P (L i , di ) being the probability of the electric vehicle connecting to the charging station L i , which is located at a distance di from the electric vehicle F. This calculation variant has the advantage that the probability P (F) increases with each possible charging station Li in the vicinity, but the value P (F) = 1 or 100 % is not reached or exceeded. If there is only a single charging station L nearby, and therefore n = 1, the direct result is P (F) = P (L, d), i.e. the resistance value valid for this distance d.

[0041] This probability P (F) can be used to classify whether the electric vehicle F should be assigned to the first energy supply network or not.

[0042] It is therefore a further development that only electric vehicles for which P (F) > 0.5 or P (F) ≥ 0.5 applies are or can be assigned to the first energy supply network.

[0043] If several electric vehicles can be connected to a particular charging station, it is a further development that the fleet operator decides that the electric vehicle with the highest probability is classified as being connected to this charging station.

[0044] If a electric vehicles can be connected to b charging stations of the same energy supply network with a > b, it is a further development that the fleet operator decides that the b electric vehicles with the highest probabilities are classified as being connected to these charging stations or to this energy supply network.

[0045] One embodiment is that the probability P (F) that an electric vehicle F is connected to (any) charging station L i from a group of possible charging stations L i of the first energy supply network in the vicinity of the electric vehicle F is calculated with additional consideration of m = 1, 2, 3, 4, .... charging stations N j of a neighboring second energy supply network in the vicinity of the electric vehicle F. This achieves the advantage that the probability P (F) can be determined even more precisely.

[0046] The calculation can be designed in such a way that the probability P (F) of a connection to a charging station L i of the first energy supply network decreases if there is a probability that the electric vehicle F could be connected to at least one charging station N j of the second energy supply network.

[0047] It is an embodiment that the probability P (F) that a particular electric vehicle F is connected to a charging station L i from a group of charging stations L i of the first energy supply network in the vicinity of the electric vehicle F and is also located in the vicinity of a group of charging stations N j of a second energy supply network, according to P F = 1 − ∏ i = 1 n 1 − P L i d i ⋅ ∏ j = 1 m 1 − P N j d j with P (L i , di ) the probability of a connection of the electric vehicle F to a respective charging station L i , which is located at a distance di from the electric vehicle F, and P (N j , dj ) the probability of a connection of the electric vehicle F to a respective charging station N j , which is located at a distance dj from the electric vehicle F.

[0048] It is an embodiment that, in order to classify by the fleet operator whether a particular electric vehicle F is charging from the first energy supply network or is connected to a charging station L i of the first energy supply network in the vicinity of the electric vehicle F or not, the probability P 1 (F) for a connection to nearby charging stations L i is first calculated, without considering charging stations N j of the second energy supply network in the vicinity of the electric vehicle F, i.e. P 1 F = 1 − ∏ i = 1 n 1 − P L i d i

[0049] In addition, the probability P 2 (F) for a connection to nearby charging stations N j of the second energy supply network is calculated without considering charging stations L i of the first energy supply network in the vicinity of the electric vehicle F, i.e. P 2 F = 1 − ∏ j = 1 m 1 − P N j d j

[0050] The electric vehicle F can, for example, be classified as charging on the first energy supply network if P 1 > P 2 applies.

[0051] In a further development, probabilities of belonging to different energy supply networks can be associated with a specific charging station L i . This can be helpful, for example, for charging stations for which no network affiliation data is available. In this case, the probability of an electric vehicle F connecting to a charging station L i can be calculated, for example, according to P F = 1 − ∏ i = 1 n 1 − P ′ L i d i be calculated, where P' (L i , di ) = P (L i , di ) · Φ* (L i ) · (1 - Φ° (Li) is set, where Φ* (L i ) indicates a probability that L i belongs to the first energy supply network and Φ* (L i ) indicates a probability that the charging station L i belongs to the second energy supply network. This can be applied analogously to other calculations of the probability values ​​above.

[0052] In a further development, the fleet operator can simulate a network behavior for charging a group of electric vehicles, for which the boundary conditions resulting from the received energy demand information are met, in particular a specified maximum amount of energy that can be called up at the charging stations of the energy supply network ("energy buffer"). Within the scope of this simulation, the energy quantities allocated to the charging stations or electric vehicles can be varied in order to meet the boundary conditions with a high degree of probability. This variation can take into account the charging characteristics of individual electric vehicles.The boundary conditions can, for example, include the occurrence of certain processes or "events" that influence the energy buffer. This allows the simulation to estimate how the grid behavior changes when such processes occur and how it can be improved by changing the amount of energy allocated to the charging stations. Such events can include, for example, the failure or connection of energy generation units (power plants, solar systems, etc.), the failure of power lines, increased or decreased energy consumption of other consumers in the energy supply grid, etc. The simulations can be carried out for different groups of electric vehicles, which differ, for example, in the number and / or type of electric vehicles considered. The simulations can be compared with real grid behavior, including, if applicable,the occurrence of certain events, with knowledge of the amount of energy drawn from the charging stations.

[0053] The better the charging characteristics of the electric vehicles connected to the power grid are known, the better the power grid can react to an event based on simulation results. This is particularly the case when electric vehicles connected to charging stations with only a certain probability are given priority for charging their energy storage devices over those electric vehicles whose connection to charging stations is virtually certain, e.g., due to easier compliance with contractually agreed charging conditions, faster charging, a longer lifetime of the energy storage device, etc. Such electric vehicles can, for example, be considered with a weighting factor dependent on the probability of their connection to the charging station.

[0054] The object is also achieved by a charging control system configured to implement the method described above. The charging system can be designed analogously to the method, and vice versa, and has the same advantages.

[0055] In one embodiment, the charging control system comprises a data processing device configured to carry out the method described above. The charging control system can be operated by a fleet operator.

[0056] The charging control system can, for example, be set up to to receive energy demand information from an operator of a first energy supply network; to identify currently charging electric vehicles in the vicinity of the first energy supply network or in the vicinity of the charging stations of the first energy supply network based on their positions; to assign a probability to electric vehicles that cannot be clearly assigned to the first energy supply network that they are connected to the first energy supply network or a charging station thereof; to determine, based on the probabilities, which of these electric vehicles are classified as being connected to a charging station of the first energy supply network, e.g.to select a charging strategy derived from the simulations described above for the electric vehicles charging at the first energy supply network, which strategy accommodates the energy requirement information, in particular fulfills it, to remotely instruct the charging electric vehicles to adapt or vary their charging processes according to the selected charging strategy and thus also based on the received energy requirement information.

[0057] The object is also achieved by a computer program product comprising code which, when executed on a data processing device, in particular the charging control system, carries out the method described above.

[0058] The above-described properties, features and advantages of this invention, as well as the manner in which they are achieved, will become clearer and more readily understood in connection with the following schematic description of an embodiment, which is explained in more detail in connection with the drawing. Fig. 1 shows a possible process for charging electric vehicles connected to charging stations of a power grid; and Fig. 2 shows a plot of probability values ​​for connecting two different electric vehicles to a specific charging station versus their respective distances to the charging station.

[0059] Fig.1 shows a possible process for adapting charging processes of electric vehicles by a fleet operator of these electric vehicles.

[0060] In a first step S1, the fleet operator receives energy demand information from an operator of a first energy supply network, e.g., that a certain event has temporarily reduced or will temporarily reduce the previously available charging power for the fleet operator's electric vehicles. Alternatively, the fleet operator could be notified, for example, that more charging power is now available.

[0061] The fleet operator now attempts to determine which electric vehicles in its fleet are currently charging from the primary energy supply network. This includes all electric vehicles that have a one-to-one relationship with a charging station in the primary energy supply network and all electric vehicles that are clearly located within the geographical extent or area of ​​the primary energy supply network. One or both conditions can apply to a vehicle.

[0062] The fleet operator also attempts to consider electric vehicles that may or only with a certain probability charge from the first energy supply network. To do so, in step S2, the fleet operator identifies currently charging electric vehicles in the fleet that are located near the first energy supply network and are not clearly assigned to a charging station, meaning that they could be connected to the first energy supply network without the fleet operator being able to clearly determine this.

[0063] In a step S3, the fleet operator assigns to the electric vehicles identified in step S2 respective probabilities that they charge from the first energy supply network, e.g. by being connected to a charging station of the first energy supply network.

[0064] In a step S4, the fleet operator classifies each of the electric vehicles considered in steps S2 and S3 as charging from the first energy supply network or not charging from the first energy supply network based on the associated probabilities.

[0065] In a step S5, the charging processes of the electric vehicles clearly belonging to the first energy supply network as well as the charging processes of the electric vehicles classified in step S4 as charging from the first energy supply network are adapted based on the received energy demand information, e.g. according to a charging strategy that was obtained from simulations within the framework of an energy supply management and is best adapted to the boundary conditions of the first energy supply network determined by the energy demand information.

[0066] Fig.2shows a plot of purely exemplary curves of probabilities or probability values ​​P (F1, d) and P (F2, d) for connecting remotely identifiable electric vehicles F1 and F2 with different positioning devices or tracking technologies to a charging station against a determined distance d from the charging station. These probability curves can be used, for example, to assign respective probabilities P (F1), P (F2) to the electric vehicles F1, F2 according to step S3.

[0067] The probability values ​​P (F1, d) and P (F2, d) can be retrieved from the electric vehicles F1, F2 in a further development, or alternatively, they can be retrieved from a database after determining the VINs of the electric vehicles. The probability values ​​P (F1, d) and P (F2, d) can be different for the same electric vehicles F1, F2 in a further development for different charging stations, e.g., for charging stations on different streets or in different parts of town, since the positions of the electric vehicles F1, F2 and / or the positions of the charging stations can be determined more or less accurately there.

[0068] For example, if the electric vehicle F1 is measured to be within 1 m of the charging station (corresponding to d < 1 m or d ≤ 1 m), the electric vehicle F1 is assigned the probability value P (F1, d) = P (F1, 1 m) = 0.9. Similarly, the electric vehicle F1 is assigned the probability values ​​0.85, 0.8, 0.7, 0.5, 0.4, etc. for distances d < 2 m, 3 m, 4 m, 5 m, etc. The electric vehicle F2 is assigned corresponding probability values ​​P (F2, d).

[0069] In one scenario, it is assumed that the two electric vehicles F1 and F2 are located near a single charging station, for example with the same measured or determined distance of d < 3 m. In step S3, the electric vehicle F1 is therefore assigned a probability P (F1) corresponding to the probability value P (F1, 3 m) = 0.7, and the electric vehicle F2 is assigned a probability P (F2) corresponding to the probability value P (F2, 3 m) = 0.5.

[0070] Since the probability P (F1) for electric vehicle F1 is higher than the probability P (F2) for electric vehicle F2, electric vehicle F1 is preferred and consequently in step S4 the classification is made that electric vehicle F1 is connected to the charging station, but electric vehicle F2 is not.

[0071] Therefore, in step S5, the fleet operator only adjusts the charging process of electric vehicle F1.

[0072] In one variant, the method or a corresponding charging system can be designed in such a way that additional charging stations in the vicinity of the electric vehicles F1 and F2 (here, for example, within a radius of 10 m) are taken into account, which also belong to the first energy supply network under consideration.

[0073] The cumulative probability P that an electric vehicle is connected to a charging station from a group of, for example, two charging stations (here referred to as L 1 and L 2) can then be calculated as P = 1 - (1 - P(L 1 , d 1 )) · (1 - P(L 2 , d 2 )), where d 1 denotes the distance to the charging station L 1 and d 2 denotes the distance to the charging station L 2. For example, if electric vehicle F1 is located at a distance d 1 = 3 m from the charging station L1 and at a distance d 2 = 4 m from the charging station L 2 , the probability P (F1) that the electric vehicle F1 is connected to (any) of the two charging stations L 1 , L 2 can be calculated as P F 1 = 1 − 1 − P L 1 , d 1 ⋅ 1 − P L 2 , d 2 = 1 − 1 − 0 , 7 ⋅ 1 − 0 , 5 = 1 − 0 , 3 ⋅ 0 , 5 = 0 , 85 This is also higher than the probability P(F2) of 0.5 if the electric vehicle F2 continues to be located only near a single charging station. Thus, it would still be assumed that the electric vehicle F1 is connected to one of the charging stations L1, L2, and not the electric vehicle F2. In this calculation, the probability for a particular electric vehicle increases with the number of possible connected charging stations.

[0074] This scheme for calculating the (total) probability P (F) of a connection to the first energy supply network can be applied analogously for a specific electric vehicle F to any number of charging stations L i with i = 1, ..., n and their individual probabilities P (L i , di ) depending on the distances di according to P F = 1 − ∏ i = 1 n 1 − P L i d i be expanded.

[0075] The above probability calculations do not take into account charging stations from a second, neighboring energy supply network. This makes the calculations particularly simple and fast and also enables consistent calculations even if the fleet operator does not know the charging stations in the second energy supply network.

[0076] However, the probabilities can also be calculated taking into account charging stations N j with j = 1, ..., m of the second energy supply network. This is particularly advantageous if the second energy supply network should not be influenced by charging processes of electric vehicles incorrectly classified as charging on the first energy supply network. In the following development of the above example, charging stations of the second energy supply network are referred to as N 3 , N 4 , .... It is now assumed that electric vehicle F1 is additionally located at a distance d 3 < 2 m from charging station N 3 and at a distance d 4 < 3 m from charging station N 4. Furthermore, it is assumed that electric vehicle F2 has a distance d 3 < 10 m from charging station N 3. Then, for each of the two electric vehicles F1 and F2, an (overall) probability or indication can be calculated according to and P F 2 = 0 , 5 ⋅ 1 − P N 3 , d 3 = 0 , 5 ⋅ 1 − 0 = 0 , 5 which, in the present example, helps to decide in favor of electric vehicle F2, since P(F2) > P(F1). The probability P(F1) that electric vehicle F1 is connected to the first energy supply network is thus reduced because it could also be connected to charging stations in the second energy supply network.

[0077] Such a probability P (F) can be generally calculated for charging stations N j with j = 1, ..., m as: P F = 1 − ∏ i = 1 n 1 − P L i d i ⋅ ∏ j = 1 m 1 − P N j d j

[0078] In general, this calculation reduces the probabilities of a particular electric vehicle F being connected to a charging station of an energy supply network if this electric vehicle could also be connected to charging stations of another energy supply network.

[0079] Furthermore, probability values ​​can be associated with a specific charging station, which provide information about the probability that it is connected to different energy supply networks. This can be helpful, for example, for charging stations for which no network affiliation data is available. In this case, the probability of an electric vehicle F connecting to a charging station L i can be calculated, for example, according to P F = 1 − ∏ i = 1 n 1 − P ′ L i d i be calculated, where P' (L i , di ) = P (L i , di ) · Φ* (L i ) · (1 - Φ° (Li), where Φ* (L i ) indicates a probability that L i belongs to a first energy supply network or charging network, while Φ° (L i ) indicates a probability that the charging station L i belongs to a second energy supply network or charging network.

[0080] Of course, the present invention is not limited to the embodiment shown.

[0081] In general, "a", "an", etc., can be understood as a singular or a plural, in particular in the sense of "at least one" or "one or more", etc., as long as this is not explicitly excluded, e.g. by the expression "exactly one", etc. List of reference symbols

[0082] dDistance S1-S5Procedure steps PProbability P (F1)Probability curve for electric vehicle F1 P (F2)Probability curve for electric vehicle F2 P (F1, d)Probability value for electric vehicle F1 at a distance d from a charging station P (F2, d)Probability value for electric vehicle F2 at a distance d from a charging station

Claims

1. Computer-implemented method (S1-S5) for adapting charging operations of electric vehicles (F1, F2), wherein a fleet operator of a fleet of electric vehicles - receives power request information from an operator of a first power supply network (S1); - assigns to electric vehicles (F1, F2) of the fleet which are currently being charged and which are located in the vicinity of the first power supply network and are not uniquely assigned to a charging station a probability (P(F1), P(F2)) that they are being charged from the first power supply network (S2, S3); - on the basis of these probabilities, classifies the electric vehicles (F1, F2) as being charged from the first power supply network or not being charged from the first power supply network (S4) and - adapts the charging operations of the electric vehicles classified as being charged from the first power supply network on the basis of the received power request information (S5).

2. Method (S1-S5) according to Claim 1, wherein an electric vehicle (F1, F2) is assigned probability values (P(F1, d), P (F2, d)) that it is connected to a specific charging station of the first power supply network (S3), these probability values (P(F1, d), P (F2, d)) being dependent at least on an ascertained distance (d) between the electric vehicle (F1, F2) and this charging station.

3. Method (S1-S5) according to Claim 2, wherein a probability value (P(F1, d), P (F2, d)) is all the greater, the smaller the distance (d) between the electric vehicle (F1, F2) and the charging station.

4. Method (S1-S5) according to any of Claims 2 to 3, wherein the probability values (P(F1, d), P (F2, d)) are dependent on a position determining device and / or locating technology used by the relevant electric vehicle (F1, F2).

5. Method (S1-S5) according to any of Claims 2 to 4, wherein the probability values (P(F1, d), P (F2, d)) are dependent on an accuracy of the knowledge of the position of the at least one charging station.

6. Method (S1-S5) according to any of Claims 2 to 5, wherein the probability that an electric vehicle (F1, F2) is connected to a charging station of the first power supply network is dependent on the number of charging stations of the first power supply network which are located in the vicinity of the electric vehicle (F1, F2).

7. Method (S1-S5) according to Claim 6, wherein the probability P (F) that an electric vehicle is connected to a charging station Li from a group of charging stations Li of the first power supply network in the vicinity of the electric vehicle is calculated in accordance with P F = 1 − ∏ i = 1 n 1 − P L i d i where P (Li, di) is the probability of the electric vehicle being connected to the respective charging station Li which is located at a distance di from the electric vehicle (S3).

8. Method (S1-S5) according to Claim 6, wherein the probability P (F) that a specific electric vehicle is connected to a charging station Li from a group of charging stations Li of the first power supply network in the vicinity of the electric vehicle and is additionally located in the vicinity of a group of charging stations Nj of a second power supply network is calculated in accordance with P F = 1 − ∏ i = 1 n 1 − P L i d i ⋅ ∏ j = 1 m 1 − P N j d j where P (Li, di) is the probability of the electric vehicle being connected to a respective charging station Li which is located at a distance di from the electric vehicle, and P (Nj, dj) is the probability of the electric vehicle being connected to a respective charging station Nj which is located at a distance dj from the electric vehicle (S3).

9. Charging control system configured to carry out the method (S1-S5) according to any of the preceding claims.

10. Computer program product comprising code which, when it is executed on a data processing device, carries out the method (S1-S5) according to any of the preceding claims.