Adjusting the charging process for electric vehicles

Abstract

The present invention relates to a method (S1 to S5) for adjusting the charging process of electric vehicles (F1, F2), wherein a fleet operator of an electric vehicle fleet receives energy request information from an operator of a first energy supply network (S1); assigns a probability (P(F1), P(F2)) to the electric vehicles (F1, F2) in the fleet that are located near the first energy supply network and are not explicitly assigned to a charging station, indicating whether they are being charged from the first energy supply network (S2, S3); classifies the electric vehicles (F1, F2) as being charged from the first energy supply network or not based on the probability (S4); and adjusts the charging process of the electric vehicles classified as being charged from the first energy supply network based on the received energy request information (S5).

Description

Technical Field

[0001] This invention relates to a method for regulating the charging process of electric vehicles, in which a fleet operator of an electric vehicle fleet receives energy request information from an operator of a first energy supply network. The invention also relates to a charging control system configured to perform the method. Furthermore, the invention relates to a computer program product having code that, when executed on a data processing device of the charging control system, enables the method to run. Background Technology

[0002] The energy storage, particularly the battery, of electric vehicles (such as plug-in hybrid vehicles or fully electric vehicles) is charged at charging stations, which are allocated to specific operators or energy providers' power supply networks. This charging process can be influenced by various factors, such as the amount of energy supplied by the energy provider, the load level of the power supply network, and the charging conditions agreed upon between the electric vehicle user and the electric vehicle manufacturer. If it is known which electric vehicles with which charging characteristics are connected to which charging stations, the charging behavior of electric vehicles can be adapted to the current demands of the power supply network, thereby better balancing the power supply network. This principle of power supply management is generally known.

[0003] For example, if the amount of energy available in a power supply network is currently limited, the network operator can send an energy request to the fleet operators of the electric vehicle fleets it partners with, requesting a reduction in the power consumption of the electric vehicles currently charging on that network. The fleet operators can then remotely control the charging process of these electric vehicles based on the energy request (e.g., by sending corresponding text messages), such as reducing charging power or stopping charging prematurely.

[0004] Data exchange between charging partners, i.e., charging stations and electric vehicles, such as by transmitting data that uniquely identifies an electric vehicle, like its Vehicle Identification Number (VIN) (e.g., according to ISO 15118) or a “fingerprint” of its charging characteristics, reveals which electric vehicle is connected to a charging station on the observed power supply network. This data, along with the fact that a particular electric vehicle is currently charging, can be transmitted from the electric vehicle, for example, via radio, to its fleet operator. Thus, the fleet operator knows definitively whether the electric vehicle is receiving charging current from a power supply network from which the fleet operator has already received energy request information.

[0005] DE102015210726A1 discloses a method for determining a charging pair comprising one charging partner from each of two different sets of possible charging partners. These two different sets include a set of possible charging stations and a set of possible vehicles. The charging pair includes a charging station and a vehicle performing a charging process at said charging station. The method includes initiating an excitation related to the charging process by an excitation-initiating charging partner from a first set of the two sets of possible charging partners. Furthermore, the method includes detecting a response to the excitation by a responsive charging partner from a second set of the two sets of possible charging partners. Additionally, the method includes forming a charging pair comprising the excitation-initiating charging partner and the responsive charging partner.

[0006] DE102016212245A1 relates to a method for operating a charging station, which ensures that maximum charging power is provided to an electrically operated vehicle only after a positive identification is obtained. It also relates to a method for planning the charging process of a vehicle electrically connected to a charging station via a charging cable.

[0007] DE102016212244A1 relates to a method for planning the charging process of a vehicle, the vehicle being electrically connected to a charging station via a charging cable.

[0008] DE102018212283A1 discloses a method for determining a charging pair, the charging pair comprising one charging partner from each of two different sets of possible charging partners, the two different sets comprising a set of possible charging stations and a set of possible hybrid or electric vehicles, the charging pair comprising a charging station and a hybrid or electric vehicle performing a charging process at the charging station. The method includes determining at least one time-response characteristic of at least one possible charging partner from the possible charging partners; initiating an excitation related to the charging process by an excitation from a charging partner in a first set of the two sets of possible charging partners, the excitation being initiated based on the at least one time-response characteristic; detecting a response of a responding charging partner in a second set of the two sets of possible charging partners to the excitation; and forming a charging pair comprising the excitation charging partner and the responding charging partner based on the detected response.

[0009] Electric vehicles can also be uniquely identified through a unique location correlation (“one-to-one correspondence”) between their vehicle location and the location of a charging station. This is the case, for example, if the location of an electric vehicle, with high positioning accuracy (e.g., ±1m), coincides with the location of a charging station, which is also known with high accuracy, and where possible, it is also known that there are no other electric vehicles near the charging station.

[0010] However, problems arise if a fleet operator knows that one of its electric vehicles is charging at a station near the observed power supply network, but cannot uniquely identify that station. This can occur, for example, when charging partners have not exchanged data that uniquely identifies them and the electric vehicle's location is only known inaccurately (e.g., within a 10m radius, due to inaccurate positioning technology or the presence of buildings, mountains, etc.) and / or especially when charging stations from different power suppliers are close to each other and / or their locations are not known inaccurately. Summary of the Invention

[0011] The objective of this invention is to at least partially overcome the shortcomings of the prior art and, in particular, to provide fleet operators with better possibilities for adjusting the charging process of electric vehicles charging at charging stations on a power supply network, according to the request of the power supply network operator.

[0012] This task is solved according to the features of the independent claim. Preferred embodiments can be learned, in particular, from the dependent claims.

[0013] This task is addressed through a method for adjusting the charging process of electric vehicles, in which fleet operators of electric vehicle convoys...

[0014] - Receive energy request information from the operator of an energy supply network (hereinafter referred to as the "first" energy supply network, but not in a general sense);

[0015] - Assign the probability that the electric vehicles in the fleet that are currently charging and located near the first energy network or near at least one charging station of the first energy network and are not explicitly assigned or available to be assigned to a charging station are currently charging from the first energy network or connected to at least one charging station of the first energy network.

[0016] - Based on the stated probability, electric vehicles are classified as either charged from the first power supply network or not charged from the first power supply network; and

[0017] - Adjust the charging process of electric vehicles classified as charging from or connected to the first energy supply network based on the received energy request information.

[0018] This achieves the following advantages: even if a clear one-to-one correspondence cannot be established between an electric vehicle being charged and a charging station in the first energy supply network, the fleet operator can still include the electric vehicle in the fleet operator's charging strategy for the first energy supply network. This further improves the balance of the first energy supply network.

[0019] This is based on the premise that electric vehicles located near the primary power supply network can be remotely identified individually, for example, by radio transmission or radio querying of their VINs. If an electric vehicle can be identified individually, its charging characteristics are generally also known, such as inherent charging features of the vehicle, such as the capacity of its energy storage device, charging speed, energy storage life, etc., and, where necessary, charging conditions negotiated according to treaty, such as the right to full charging or partial charging only. Furthermore, remotely identifiable electric vehicles can transmit their geographical location and charging status (e.g., charging / not charging) to the fleet operator. This vehicle information can be automatically transmitted by the electric vehicle to the fleet operator (“push”), for example, at regular time intervals or by event-controlled transmission, such as with the start of a charging process. Alternatively or additionally, vehicle information can be remotely queried by the fleet operator (“pull”).

[0020] Electric vehicles can be, for example, cars, trucks, buses, motorcycles, etc.

[0021] Fleet operators can be manufacturers of electric vehicles or can operate electric vehicles from different manufacturers. Fleet operators, in particular, can maintain the charging control system to implement this method. The charging control system is specifically configured to adjust the charging process according to a power supply management-based charging strategy.

[0022] Energy request information may include the current or predicted state of the first energy supply network, which may affect the charging process of electric vehicles, such as the amount or power of available electrical energy or changes thereof. For example, failures of energy production units (power plants, solar equipment, etc.), power line faults, or increased energy consumption of other electrical appliances in the energy supply network can also temporarily reduce the amount of available energy in the first energy supply network. The energy request information may then include, for example, a request to temporarily reduce the charging power used by the charging electric vehicles. The fleet operator will then attempt to meet this demand by remotely adjusting the charging process of the currently charging electric vehicles, more specifically, to minimize the impact on the users of the charging electric vehicles.

[0023] In particular, the following electric vehicles currently charging can be explicitly assigned to the primary energy supply network through fleet operators:

[0024] - All electric vehicles that have a one-to-one correspondence with charging stations in the primary power supply network;

[0025] - Identify all electric vehicles that are within the geographical extension or area of ​​the first power supply network, even if they do not have a one-to-one correspondence with charging stations.

[0026] On the other hand, electric vehicles “near” the first energy supply network can be understood as electric vehicles that, from the perspective of the fleet operator, may be connected to the first energy supply network or an adjacent (“second”) energy supply network, taking into account the inaccuracy of the location of electric vehicles and / or charging stations. “Electric vehicles currently charging cannot be clearly assigned to charging stations” specifically includes situations where the fleet operator knows that an electric vehicle is charging, but cannot definitively determine whether it is charging from a charging station of the first energy supply network or from a charging station of the adjacent second energy supply network.

[0027] The operators of the primary energy supply network and the secondary energy supply network can be different operators or the same operator. In particular, when the operators are the same, the primary and secondary energy supply networks can be sub-networks or parts of the superior energy supply network.

[0028] Uncertainty regarding the location of electric vehicles and / or charging stations may be caused by one or more of the following reasons:

[0029] - The positioning device or positioning technology used in electric vehicles only outputs a relatively inaccurate position;

[0030] -Location is affected by geographical uncertainties, such as tall buildings on the street, mountains, etc.;

[0031] - The locations of charging stations are not accurately recorded. For example, one could imagine a scenario where some buildings on a street are equipped with charging stations (such as those with corresponding Schuko plugs), but the energy supplier does not know the location of the Schuko plugs on the buildings or even which buildings on the street have charging stations. This situation may be exacerbated by the fact that one side of the street is supplied by a first energy network (or the power grid), while the other side is supplied by a second energy network.

[0032] Charging stations can be dedicated charging stations. They can also be Schuko plugs or other residential connection plugs, such as wall boxes. In one extended configuration, the power supply network can have multiple charging stations that cannot be distinguished electrically or technically by the network, for example, because they are connected to the same power distributor or meter.

[0033] One extended approach is to determine the location of a specific electric vehicle, and if necessary, the accuracy of that location, using the vehicle's positioning device, such as GPS, GLONASS, or WLAN. Positioning accuracy can depend on the positioning device installed in the electric vehicle (e.g., high- or low-accuracy GPS) and / or the location technology used by the electric vehicle (e.g., GPS, WLAN). In another extended approach, the electric vehicle's positioning device and / or location technology can also be queried via remote identification.

[0034] The typical location accuracy for certain areas of the power supply network may also be known or predetermined. This location accuracy may, for example, have been determined representatively through testing or measurement.

[0035] For example, the probability that an electric vehicle will connect to a charging station of the first energy supply network for charging can be determined from the (not) accurate location of the electric vehicle and the corresponding at least one charging station near the electric vehicle, as well as other possible information such as the length of the charging cable. With at least this probability known, the fleet operator can determine or set which electric vehicles are assumed to be connected to the first energy supply network, and, if possible and / or meaningful, the fleet operator can adjust the charging process for these electric vehicles based on received energy request information and therefore its charging strategy. Trend-wise, the fleet operator may, for example, assign electric vehicles more likely to be assigned to charging stations of the first energy supply network if the probability of these electric vehicles connecting to charging stations of the first energy supply network is higher.

[0036] One implementation involves assigning an electric vehicle a probability value to a specific charging station within a first power supply network, the probability value depending at least on a determined distance between the electric vehicle and that charging station. In other words, it's the correlation between the probability of a particular electric vehicle being assigned to a charging station within a power supply network connected to that vehicle and the distance to the charging station. This probability value may be determined experimentally, based on empirical values, and / or through simulation, or has already been determined.

[0037] Probability values ​​can be plotted as curves related to distance. For example, probability values ​​can exist as tables, feature curves, etc., and can be stored.

[0038] One approach is to increase the probability of electric vehicles being closer to charging stations.

[0039] One implementation involves determining the probability value based on the positioning device and / or location technology used by the relevant electric vehicle, or its accuracy. This allows for a more realistic determination of the probability. The probability value is then typically vehicle-related or vehicle-inherent and depends, for example, on the type and / or equipment of the remotely identifiable electric vehicle. In a variation, the same probability value can be assigned to electric vehicles with the same positioning device and / or location technology.

[0040] One implementation involves the probability value depending on the accuracy of the knowledge of the location of the at least one charging station. This also improves the understanding of the probability of a particular electric vehicle connecting to a charging station. As mentioned above, the location can be known relatively precisely or only very imprecisely, in the latter case, for example, only within the width of a house or the length of a street, in a canyon of higher houses. medium.

[0041] In one extended scheme, only charging stations are observed for which a definite minimum probability, for example, at least 1% or 5%, exists. On the other hand, in another extended scheme, when the probability reaches or exceeds an upper probability threshold of, for example, 98% or 99%, a one-to-one correspondence can be assumed.

[0042] One extended approach is to only observe charging stations whose distance is within a predetermined maximum distance. This maximum distance could be, for example, approximately 10 meters and thus include the typical length of the charging cable.

[0043] One implementation is that the probability of an electric vehicle connecting to a charging station in the power supply network depends on the number of nearby charging stations in the first power supply network. This implementation is particularly advantageous when the operator or power supplier cannot distinguish which charging station in the power supply network the electric vehicle is connecting to, for example, because multiple charging stations are powered by the same power distribution unit. This scenario arises, for example, if multiple charging stations on a street, such as Schuko plugs, are connected to a common power distribution unit via a common power line, and the power supply network operator has no other feasible solution to assign the electric vehicle's connection to a specific charging station among these charging stations. In an extended implementation, the more charging stations the electric vehicle can potentially connect to, the higher the trend in probability that the electric vehicle is likely to connect to one of these possible charging stations. The charging stations that the electric vehicle can or may connect to may not include charging stations on which another electric vehicle is definitely connected. For example, the probability of the electric vehicle connecting to any charging station in the first power supply network can be calculated by associating or "accumulating" the individual probabilities of potentially connecting to the respective observed charging stations.

[0044] One extension is to disregard charging stations of a second power supply network near the electric vehicle when calculating the probability of charging from the first power supply network. This results in the advantage of particularly simple calculations.

[0045] One implementation involves calculating a set of n = 1, 2, 3, 4, ... possible charging stations L that the electric vehicle F can connect to in a first power supply network near the electric vehicle F, according to the following formula. i Any one of the charging stations L i The (total) probability P(F),

[0046]

[0047] And P(L) i ,d i ) is the distance d between the electric vehicle and the electric vehicle F. i charging station L i The probability of this calculation variant is that the probability P(F) increases with each possible nearby charging station L. iThe value increases, but does not reach or exceed the value P(F) = 1 or 100%. If there is only one charging station L nearby and therefore n = 1 applies, then P(F) = P(L,d) is directly derived, which is the resistance / probability value applicable to that distance d.

[0048] The probability P(F) can be used to classify whether the electric vehicle F should be assigned to the first power supply network.

[0049] Therefore, one extension is that electric vehicles with P(F) > 0.5 or P(F) ≥ 0.5 are either assigned to the first power supply network.

[0050] If multiple electric vehicles can connect to a particular charging station, one extended approach is for the fleet operator to decide which electric vehicle has the highest probability of being connected to that charging station.

[0051] If a electric vehicle may connect to b charging stations on the same power supply network and a > b, then one extension is for the fleet operator to decide which of the b electric vehicles with the highest probability are connected to either of those charging stations or the power supply network.

[0052] One implementation involves additionally considering m = 1, 2, 3, 4, ... charging stations N of a second power supply network adjacent to the first power supply network near the electric vehicle F. j Calculate a set of possible charging stations L of the first power supply network near electric vehicle F, in the case of electric vehicle F connecting to electric vehicle F. i Any one of the charging stations L i The probability P(F) is thus determined. Therefore, it achieves the advantage of being able to determine the probability P(F) more precisely.

[0053] The calculation can be designed such that when there is an electric vehicle F that can be connected to at least one charging station N of a second power supply network. j The possibility of reducing the number of charging stations L connected to the first power supply network is reduced. i The probability P(F).

[0054] One implementation involves calculating a set of charging stations L of a first power supply network near a specific electric vehicle F, according to the following formula. i One of the charging stations L i It is also located in a group of charging stations N of the second power supply network. j The probability P(F) of being nearby:

[0055]

[0056] And P(L) i ,d i) is the distance d between electric vehicle F and electric vehicle F. i The corresponding charging station L i The probability, and P(N) j ,d j ) is the distance d between electric vehicle F and electric vehicle F. j The corresponding charging station N j The probability of.

[0057] One implementation involves classifying by the fleet operator whether a specific electric vehicle F is being charged from a first power supply network or connected to a charging station L of the first power supply network near the electric vehicle F. i First, calculate the connection to the nearby charging station L. i The probability P1(F) is given, without considering the charging station N of the second energy network near the electric vehicle F. j ,Right now

[0058]

[0059] Additionally, calculate the charging station N located nearby that is connected to the second power supply network. j The probability P2(F) is given, without considering the charging station L of the first power supply network near the electric vehicle F. i ,Right now

[0060]

[0061] If P1 > P2, then, for example, the electric vehicle F can be classified as being charged on the first power supply network.

[0062] In one expansion scheme, a specific charging station L can be... i This is related to the probability that (it) belongs to a different power supply network. This is helpful, for example, for charging stations without power supply network affiliation data. In this case, the connection of electric vehicle F to charging station L can be calculated, for example, according to the following formula. i probability

[0063]

[0064] It is specified that P′(L) i d i )=P(L i d i )·φ ＊ (L i )·(1-φ°(L i ), Φ ＊ (L i ) represents L i The probability of belonging to the first power supply network and Φ°(L) i ) indicates charging station L iThe probability of belonging to the second power supply network. This can be similarly applied to the other calculations of the probability value described above.

[0065] In one extended approach, the fleet operator can simulate network behavior for charging a group of electric vehicles, maintaining boundary conditions derived from received energy request information, particularly a predetermined maximum amount of energy available at charging stations in the power supply network (“energy buffer”). The amount of energy allocated to charging stations or electric vehicles can be varied within the simulation to satisfy the boundary conditions with a high probability. This variation can take into account the charging characteristics of individual electric vehicles. Boundary conditions may include, for example, the occurrence of certain processes or “events” affecting the energy buffer, allowing the simulation to estimate how network behavior changes when these processes occur and how to improve network behavior by varying the amount of energy allocated to charging stations. Such events may include, for example, the failure or activation of energy production units (power stations, solar equipment, etc.), power line failures, increases or decreases in energy consumption of other electrical appliances in the power supply network, etc. The simulation can be conducted for different groups of electric vehicles, distinguished, for example, by the number and / or type of electric vehicles observed. The simulation can be further improved by comparing it with actual network behavior (including specific events that may occur), with an understanding of the amount of energy available at charging stations.

[0066] The better the charging characteristics of electric vehicles connected to a power supply network are known, the better the network can respond to events based on simulation results. This is especially true when electric vehicles that connect to a charging station with only a certain probability to charge their energy storage are prioritized over those that will almost certainly connect, for example, based on factors such as easier adherence to contractual charging conditions, faster charging, and longer energy storage lifespan.

[0067] Such electric vehicles can be considered, for example, by using a weighted factor that depends on the probability of them being connected to a charging station.

[0068] This task is also addressed by a charging control system configured to perform the methods described above. The charging control system can be constructed similarly to the methods described above, and vice versa, and possesses the same advantages.

[0069] In one implementation, the charging control system includes a data processing unit configured to execute the method described above. The charging control system can be operated by a fleet operator.

[0070] The charging control system can be configured, for example, to,

[0071] - Receive energy request information from the operator of the primary energy supply network;

[0072] - Identify currently charging electric vehicles near the first power supply network or near a charging station of the first power supply network based on the location of the electric vehicle;

[0073] - Assigning the probability of electric vehicles that cannot be explicitly assigned to the first energy supply network to connect to the first energy supply network or a charging station of the first energy supply network.

[0074] - Determine which of these electric vehicles are classified as charging stations connected to the first power supply network based on the probability.

[0075] - Select a charging strategy, for example derived from the above simulation, for electric vehicles charging on the first power supply network, that conforms to the energy request information, and in particular satisfies the energy request information.

[0076] - Remotely instruct electric vehicles that are charging to adjust or change their charging process according to the selected charging strategy and therefore also according to the energy request information received.

[0077] This task is also accomplished by a computer program product with code that performs the above-described method when executed on a data processing device, particularly a charging control system. Attached Figure Description

[0078] The above-described features, characteristics, and advantages of the present invention, as well as the ways in which these features, characteristics, and advantages are realized, become clearer and easier to understand in conjunction with the following illustrative description of embodiments, which are further illustrated in conjunction with the accompanying drawings. The drawings are as follows:

[0079] Figure 1 This illustrates a possible process for charging electric vehicles at charging stations connected to a power supply network; and

[0080] Figure 2 This plot shows the probability values ​​of two different electric vehicles connecting to a particular charging station as a function of their respective distances from the charging station. Detailed Implementation

[0081] Figure 1 This illustrates a possible process for fleet operators of electric vehicles to adjust the charging process for their electric vehicles.

[0082] In the first step S1, the fleet operator receives energy request information from the operator of the first energy supply network, such as a situation where the currently available charging power for the fleet operator's electric vehicles has decreased or will decrease in the short term due to some event. Alternatively, it may be stated, for example, that more charging power is now available.

[0083] Fleet operators are now attempting to determine which electric vehicles in their fleet are currently being charged from the primary energy network. This explicitly includes all electric vehicles with a one-to-one correspondence to charging stations on the primary energy network, as well as all electric vehicles explicitly located within the geographical extension or area of ​​the primary energy network. One or both conditions may apply to a vehicle.

[0084] The fleet operator also considers electric vehicles that may, or only have a certain probability, be charged from the first power supply network. To this end, in step S2, the fleet operator identifies the electric vehicles in the fleet that are currently being charged. These electric vehicles are located near the first power supply network and are not explicitly assigned to charging stations, meaning they may be connected to the first power supply network, but the fleet operator cannot definitively determine this.

[0085] In step S3, the fleet operator assigns a corresponding probability to the electric vehicles identified in step S2, for example, that they can be charged from the first power supply network via charging stations connected to the first power supply network.

[0086] In step S4, the fleet operator classifies each of the electric vehicles observed in steps S2 and S3 according to its probability as either charging from the first power supply network or not charging from the first power supply network.

[0087] In step S5, based on the received energy request information—for example, a charging strategy that is obtained by simulation within the energy management scope and is optimally coordinated with the boundary conditions of the first energy supply network determined by the energy request information—both the charging process of electric vehicles explicitly allocated to the first energy supply network and the charging process of electric vehicles classified as being charged from the first energy supply network in step S4 are adjusted.

[0088] Figure 2 The diagram shows a purely exemplary graph illustrating the probability or probability value P(F1,d) or P(F2,d) of remotely identifiable electric vehicles F1 and F2 with different positioning devices or location technologies connecting to a charging station at a predetermined distance d. These probability curves can be used, for example, to assign corresponding probabilities P(F1) and P(F2) to electric vehicles F1 and F2 according to step S3.

[0089] In one extended scheme, the probability values ​​P(F1,d) or P(F2,d) can be retrieved from electric vehicles F1 and F2; alternatively, the probability values ​​are retrieved from a database after the VIN of the electric vehicles is determined. In another extended scheme, the probability values ​​P(F1,d) and P(F2,d) for the same electric vehicles F1 and F2 can be different for different charging stations, for example, for charging stations located on other streets or in other areas, because the locations of electric vehicles F1 and F2 and / or the charging stations there may be determined more or less precisely.

[0090] For example, if electric vehicle F1 is measured to be within 1m of the charging station (corresponding to d < 1m or d ≤ 1m), then the probability value assigned to electric vehicle F1 is P(F1,d) = P(F1,1m) = 0.9. Similarly, for distances d < 2m, 3m, 4m, 5m, etc., the probability values ​​assigned to electric vehicle F1 are 0.85, 0.8, 0.7, 0.5, or 0.4, etc. The corresponding probability value P(F2,d) is assigned to electric vehicle F2.

[0091] In one scenario, two electric vehicles F1 and F2 are assumed to be near a single charging station, for example, having the same measured or determined distance d < 3m. Therefore, in step S3, electric vehicle F1 is assigned a probability P(F1) corresponding to the probability value P(F1, 3m) = 0.7, and electric vehicle F2 is assigned a probability P(F2) corresponding to the probability value P(F2, 3m) = 0.5.

[0092] 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 is therefore classified in step S4 as electric vehicle F1 connected to the charging station, while electric vehicle F2 is not connected to the charging station.

[0093] Therefore, in step S5, the fleet operator only adjusts the charging process of electric vehicle F1.

[0094] In one variant, the method or corresponding charging system can be constructed such that other charging stations near electric vehicles F1 and F2 (e.g., within a 10m radius) are also considered, and these charging stations also belong to the observed first power supply network.

[0095] The cumulative probability P of an electric vehicle connecting to one of two charging stations (hereinafter referred to as L1 and L2) in a set can be calculated as P = 1 - (1 - P(L1,d1))·(1 - P(L2,d2)), where d1 represents the distance from charging station L1 and d2 represents the distance from charging station L2. For example, if electric vehicle F1 is 3m away from charging station L1 and 4m away from charging station L2, then the probability P(F1) of electric vehicle F1 connecting to any one of the two charging stations L1 and L2 can be calculated as:

[0096] P(F1)=1-(1-P(L1,d1))·(1-P(L2,d2))=1-(1-0.7)·(1-0.5)=1-0.3·0.5=0.85.

[0097] This is also higher than the probability of P(F2) of 0.5 if electric vehicle F2 is still located near only one charging station. Therefore, it will still be assumed that electric vehicle F1 is connected to one of charging stations L1 or L2, while electric vehicle F2 is not connected. In this calculation, the probability for a particular electric vehicle increases with the number of possible charging stations.

[0098] This scheme for calculating the (total) probability P(F) of connecting to the first power supply network can be similarly extended to any number of charging stations L for a specific electric vehicle F. i And the number of charging stations i = 1, ..., n, and multiple charging stations depends on the distance d. i The probabilities P(L) i d i That is, according to the following formula:

[0099]

[0100] In the above probability calculations, charging stations in adjacent second power supply networks are not considered. This makes the calculations particularly simple and fast, and ensures consistent calculations even when fleet operators are unaware of the charging stations in the second power supply network.

[0101] However, charging stations N could also be considered for a second power supply network. j The probability is calculated when j = 1, ..., m. This is particularly advantageous if the second power supply network should be as unaffected as possible by the charging process of electric vehicles incorrectly classified as charging on the first power supply network. In the following extended scheme of the above example, the charging stations of the second power supply network are denoted as N3, N4, ... Now assume that electric vehicle F1 is also located at a distance d3 < 2m from charging station N3 and at a distance d4 < 3m from charging station N4. It should also be assumed that the distance between electric vehicle F2 and charging station N3 is d3 < 10m. Thus, the (total) probability or index can be calculated for each of the two electric vehicles F1 and F2 according to the following formula.

[0102] P(F1)=[1-(1-P(L1,d1))*(1-P(L2,d2))]*(1-P(N3,d3))*(1- P(N4,d4))=0,85*(1-0,85)*(1-0,7)=0,85*0,15*0,3=0,03825

[0103] and

[0104] P(F2)=0,5·(1-P(N3,d3))=0,5·(1-0)=0,5,

[0105] In this example, this helps in making a decision for electric vehicle F2, since P(F2) > P(F1). Therefore, the probability P(F1) of electric vehicle F1 connecting to the first power supply network decreases, because it may also connect to a charging station of the second power supply network.

[0106] In summary, for charging station N j And j = 1, ..., m, this probability P(F) can be calculated as:

[0107]

[0108] Therefore, in summary, this calculation reduces the probability that a particular electric vehicle F will connect to a charging station of one power supply network if the electric vehicle may also connect to a charging station of another power supply network.

[0109] Furthermore, a specific charging station can be associated with a probability value representing the probability that the charging station is connected to different power supply networks. This can, for example, help charging stations without network affiliation data. In this case, the connection of electric vehicle F to charging station L can be calculated using the following formula. i The probability of:

[0110]

[0111] It is specified that P′(L) i d i )=P(L i d i )·Φ*(L i )·(1-Φ°(Li),φ*(L) i ) represents L i The probability of belonging to the first power supply network or charging network, and Φ°(L i ) indicates charging station L i The probability of belonging to a secondary energy supply network or charging network.

[0112] Of course, the present invention is not limited to the embodiments shown.

[0113] In general, "one" can be understood as singular or plural, especially in the sense of "at least one" or "one or more", as long as this is not explicitly excluded, for example, by expressing "exactly one".

[0114] List of reference numerals

[0115] d Distance

[0116] S1-S5 Method Steps

[0117] P probability

[0118] P(F1) represents the probability curve for electric vehicle F1.

[0119] P(F2) represents the probability curve for electric vehicle F2.

[0120] P(F1,d) represents the probability value of electric vehicle F1 located at a distance d from the charging station.

[0121] P(F2,d) represents the probability value of electric vehicle F2 located at a distance d from the charging station.

Claims

1. A method (S1-S5) for adjusting the charging process of electric vehicles (F1, F2), wherein, electric vehicle fleet operators Receive energy request information from the operator of the first energy supply network (S1); Assign the probabilities (P(F1), P(F2)) (S2, S3) of the electric vehicles (F1, F2) in the fleet that are located near the first power supply network and have not been explicitly assigned to a charging station and are currently charging from the first power supply network. Based on the stated probability, electric vehicles (F1, F2) are classified as either being charged from the first power supply network or not being charged from the first power supply network (S4); and The charging process (S5) of electric vehicles classified as being charged from the first energy supply network is adjusted based on the received energy request information.

2. The method according to claim 1 (S1-S5), wherein, Assign a probability value (P(F1,d), P(F2,d)) to an electric vehicle (F1, F2) for it to connect to a specific charging station of the first power supply network (S3), wherein the probability value (P(F1,d), P(F2,d)) depends at least on a determined distance (d) between the electric vehicle (F1, F2) and the charging station.

3. The method according to claim 2 (S1-S5), wherein, The smaller the distance (d) between the electric vehicles (F1, F2) and the charging station, the greater the probability values ​​(P(F1,d), P(F2,d)).

4. The method (S1-S5) according to any one of claims 2 to 3, wherein, The probability values ​​(P(F1,d), P(F2,d)) depend on the positioning device and / or location technology used by the relevant electric vehicles (F1, F2).

5. The method (S1-S5) according to any one of claims 2 to 4, wherein, The probability values ​​(P(F1,d), P(F2,d)) depend on the accuracy of the knowledge of the location of the at least one charging station.

6. The method (S1-S5) according to any one of claims 2 to 5, wherein, The probability that an electric vehicle (F1, F2) connects to a charging station of the first power supply network depends on the number of charging stations of the first power supply network located near the electric vehicle (F1, F2).

7. The method according to claim 6 (S1-S5), wherein, The following formula is used to calculate a set of charging stations L that the electric vehicle connects to in the first power supply network near the electric vehicle. i One of the charging stations L i The probability P(F)(S3) is given. And P(L) i ,d i ) is the distance d between the electric vehicle and the electric vehicle. i The corresponding charging station L i The probability of.

8. The method according to claim 6 (S1-S5), wherein, The following formula is used to calculate a set of charging stations L that a specific electric vehicle connects to in a first power supply network near the electric vehicle. i One of the charging stations L i It is also located in a group of charging stations N of the second power supply network. j The probability of being nearby is P(F)(S3): And P(L) i ,d i ) is the distance d between the electric vehicle and the electric vehicle. i The corresponding charging station L i The probability, and P(N) j ,d j ) is the distance d between the electric vehicle and the electric vehicle. j The corresponding charging station N j The probability of.

9. A charging control system configured to perform the method (S1-S5) according to any one of the preceding claims.

10. A computer program product having code that, when executed on a data processing device, performs the method (S1-S5) according to any one of the preceding claims.

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