Method for determining defective charging stations

Abstract

The invention relates to a method for determining faulty charging stations (1) for battery-powered vehicles, whereby the charging stations (1) collect usage data and transmit the usage data to a central computing unit, which analyzes the usage data, and which subsequently determines a fault of at least one charging station (1) if at least one usage parameter of the charging station (1), contained in the usage data, is within or outside a defined target range. The method according to the invention is characterized in that at least two charging stations (1) of one or more charging station networks, which are arranged adjacent to one another in a defined geographical area (2), are associated with a common charging station cluster (C1, C2, C3, C4), and the target ranges of the individual usage parameters are respectively derived from the usage data of at least one charging station (1) classified as a reference charging station in the charging station cluster (C1, C2, C3, C4).

Description

[Technical field]

[0001] The present invention relates to a method for determining defective charging stations for battery-powered vehicles as specified in the preamble of claim 1. [Background technology]

[0002] Charging stations, often also called charging points, provide for the recharging of the electric vehicle battery in the vehicle. Charging stations exist in various forms, i.e., they are differentiated for example by the charging interface, i.e. the available plug or socket type, as well as the current used for charging. Thus, there exist a number of charging stations, each with a charging interface for charging with direct current, alternating current and / or three-phase alternating current, so that each can provide a different voltage and / or current strength and thus a different charging output. The higher the charging output, typically the shorter the time required to charge the electric vehicle battery.

[0003] The charging process at a charging station takes longer than filling a tank with liquid fuel, which makes it necessary to plan in advance multiple charging processes for a vehicle that can be driven by battery electricity, especially when traveling long distances. For this purpose, the vehicle driver typically obtains information about where the charging stations are located along his or her travel route and what the surrounding conditions for charging are, such as the number of charging stations in each location, the load factor, i.e. the availability of each charging station, the electricity price, etc. For this purpose, the vehicle driver can also use a charging stop planning assistant. For various reasons, a charging station may not be available at the time of the vehicle's arrival, for example due to a defect. This is particularly frustrating in places where the number of charging stations is limited but is frequently used, such as service areas on highways, because the vehicles must wait until a charging station is free or it may not be possible to charge all the vehicles at the same time.

[0004] A charging station typically detects and transmits a status to a third party, e.g., an operator of the charging station. The status includes, for example, one of the states "available", "in use / occupied", "stopped", and "unavailable". However, it may happen that the charging station erroneously detects its actual state, i.e., transmits the status "available" despite having a malfunction. This may limit the user comfort for the driver of a vehicle that can be driven by battery electricity, since the driver may possibly believe that he or she can charge his or her vehicle at the defective charging station. Of course, the erroneous detection may also result in a different state, e.g., "occupied", being transmitted.

[0005] Therefore, there is a need to provide an improved method for determining which charging stations are actually faulty.

[0006] From the patent DE 10 2004 1 033 599 A1 a method and a device for assessing the reliability of a charging station for electric vehicles are known. According to the method disclosed in this publication, a calculation unit analyzes usage data collected over time for a charging station and checks for the presence of a minimum number of charging processes performed at the charging station with charging times below a defined limit value. A reliability factor for the charging station is then determined depending on the number of such charging processes. Further characteristic values, such as the load of the charging station, the number of charging processes in which an energy amount below a defined limit value was transferred, the number of unsuccessful charging processes, etc. can also be included in the calculation. A similar method for monitoring charging stations is known from US Pat. No. 5,399,633. In this method, a machine learning model is used to analyze charging station usage data received from a charging station network. With the method disclosed in this publication, faulty charging stations can be identified and future faulty charging stations can be predicted using a probability value. For this purpose, usage data of not only one charging station but also several charging stations of a charging station network can be evaluated. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] WO2021 / 089914A1 [Patent Document 2] WO2021 / 028615A1 Summary of the Invention [Problem to be solved by the invention]

[0008] The problem on which the present invention is based is to provide an improved method for determining a faulty charging station for a battery-electrically powered vehicle, which provides highly reliable information as to whether the charging station is actually at fault or is available for carrying out a charging process. [Means for solving the problem]

[0009] According to the invention, this problem is solved by an improved method for determining a faulty charging station for a battery-powered vehicle having the features of claim 1. Advantageous embodiments and developments emerge from the dependent claims.

[0010] In the method for determining a defective charging station as described at the beginning, the charging station transmits usage data to a central computing unit for analysis, which then determines a fault of at least one charging station if at least one usage parameter contained in the usage data is outside a specified target range, wherein, according to the present invention, at least two charging stations of one or more charging station networks, which are arranged adjacent to each other in a specified geographical area, are associated with a common charging station cluster, and the target ranges of the individual usage parameters are each derived from the usage data of at least one charging station classified as a reference charging station in the charging station cluster.

[0011] Using the method according to the invention, the certainty as to whether a charging station is actually defective can be improved compared to known methods. This improvement is based on a direct comparison of charging stations that are located close to each other, because such charging stations that are located close to each other exhibit similar usage characteristics and therefore can be directly compared to ascertain "typical" usage patterns and corresponding deviations from the typical usage patterns. By determining the "non-typical" usage of at least one charging station in this manner, for example, a defective charging station does not first need to be manually reported by a human being in order to identify it as such.

[0012] For example, a defined geographical area is a parking lot of a shopping mall or a hardware store, a service area of ​​a highway, a part of a road, etc. "Adjacent to each other" in this context means that the charging stations are located directly or indirectly close to each other. The distance between two charging stations that are directly adjacent to each other is typically one or several vehicle widths or lengths. In indirectly spaced charging stations, there may be obstacles between the individual charging stations belonging to the charging station cluster, such as trees, walls, street lights, green areas, buildings, parts of buildings, or parking spaces that do not include charging stations. The charging stations may be arranged in a row or in multiple rows, for example in a grid pattern with respect to each other. The individual rows may be offset from each other and / or inclined at a predefined angle with respect to each other. The charging station may also have multiple, for example two, charging interfaces for simultaneously charging multiple vehicles.

[0013] For example, a charging station cluster is formed by four charging stations (and corresponding parking spaces) that are arranged directly adjacent to each other in a row between the sidewalk and the roadway in front of a restaurant. In another charging station cluster, for example, two charging stations are located in front of a first building face of a high-rise building and two other charging stations are located on the opposite building face of the high-rise building. Here, despite the presence of a high-rise building between the individual charging point pairs, the four charging stations can be associated with a common charging station cluster because the usage characteristics of the four charging points are the same and therefore the same typical usage pattern can be identified from the usage parameters.

[0014] For example, the central computing unit determines at least one reference charging station.

[0015] The central computing unit may, for example, determine only one charging station as the reference charging station. For this purpose, the central computing unit may, for example, select the newest charging station, i.e. the charging station that was installed last, since due to new components the probability of failure is lower than for older charging stations. The utilization characteristics of the reference charging station are then considered. Various utilization parameters will be described below, but here, as the utilization parameter, the charging time of the charging process is mentioned by way of example. For the reference charging station, the charging time of more than 78% of the charging processes performed at the reference charging station within a defined period, for example, a day, a week or several months, is in a value range of 30 to 40 minutes. This value range is used as a target range for the other charging stations of the charging station cluster. In this case, if such a charging station has a charging time of only 5 minutes or, for example, a full 60 minutes for one or at least 50% of the charging processes performed at the charging station within a defined period. An "too short" charging time may exist if the vehicle driver notices early on that the charging process did not occur as planned, e.g. due to an excessively low charging output, and terminates the charging process accordingly. An "too long" charging time may exist if the vehicle driver does not notice the fault and the vehicle's electric vehicle battery is not fully charged after 60 minutes, e.g. due to a low charging output.

[0016] The central computing unit can for example set that only "successful" charging processes should be taken into account to determine the valid value range, e.g. a charging process is only successful if a defined minimum amount of energy, e.g. 20 kWh, has been charged.

[0017] Here, the various charging stations of the shared charging station cluster can be associated with one or more charging station networks. For example, a charging station network refers to the totality of all charging stations operated by a certain vendor. That is, the method according to the invention makes it possible to simultaneously compare charging stations of various vendors, for example Allego, Ionity, EnBW, etc. For this purpose, the central computing unit communicates with the corresponding infrastructure of the charging station network operator.

[0018] The at least one reference charging station may also be determined as a function of usage patterns identified in the usage data.

[0019] A usage pattern can be identified in various ways in the usage data received from the charging stations over time by the central computing unit. For example, a usage pattern is considered to exist if the respective usage parameter is within a usage parameter-specific value range for a defined percentage of charging processes performed at the charging stations of the charging station cluster. The defined percentage can be, for example, a frequency of at least 50%, 60%, 70%, 80% or 90% of the charging processes. That is, the corresponding usage parameter (e.g. charging time) is within a usage parameter-specific value range (e.g. 30-45 min) in more than half of the charging processes performed at the charging stations of the charging station cluster within a defined span. To discover a usage pattern, multiple usage parameters can also be considered simultaneously (e.g. charging power of X-Y kW during a charging process of M-N minutes), whereby multiple usage parameters may be interdependent.

[0020] Thus, in an advantageous development of the method, a group of reference charging stations, i.e. at least two reference charging stations, is taken into account to derive a target range specific to the respective utilization parameter, whereby a charging station having respective utilization parameters that match within a defined tolerance threshold is used as the reference charging station. This eliminates the need for a central computing unit to arbitrarily determine the reference charging station(s). In other words, a number of charging stations in a charging station cluster are automatically determined as reference charging stations, from which typical utilization characteristics of the charging stations are also derived. This can, for example, reduce the risk that such a charging station does not in fact exhibit a typical utilization characteristic, or at least a charging station exhibiting a deviating utilization characteristic, is arbitrarily selected as the reference charging station.

[0021] Returning to the example of charging time, the charging times of three charging stations of a charging station cluster including four charging stations are all within the range of 8 to 125 minutes over a defined observation time, for example, one day, two weeks or one month. However, for the faulty fourth charging station of the charging station cluster, the charging times of all charging processes are in the range of 30 seconds to two and a half minutes, thus deviating significantly from the charging times of the functional charging stations. Therefore, the functional charging station is determined as the reference charging station, and the target range for the charging time is set to 20 to 25 minutes, since 75% of the charging processes are within this time window. If a larger or smaller value is selected instead of 75%, the charging time is shifted accordingly, for example to 18 to 35 minutes or 21 to 22 minutes.

[0022] According to a further advantageous configuration of the method according to the invention, at least half of the charging stations of the charging station cluster are used as reference charging stations. By using at least half of the charging stations of the charging station cluster, the usage parameters of the charging stations in a functional state, which is typical for the charging station cluster, can be determined with very high reliability. If only one of the charging stations in a charging station cluster with four charging stations is used as the reference charging station, there is a risk that the usage characteristics of this charging station do not fully reflect the actual typical usage characteristics. That is, when performing an automatic selection, it is not clear which charging station should be determined as the reference charging station. On the other hand, if at least half of the charging stations of the charging station cluster are used as the reference charging stations, this risk can be reduced, since more usage data is taken into account for discovering the usage pattern.

[0023] Here, in the above process, first, the utilization parameters of all charging stations are evaluated for a defined period of time. For each utilization parameter, a tolerance threshold is determined, which defines how much the utilization parameters of the individual charging points may differ in order to characterize the individual charging points as a reference charging station. A fixed value may be defined as the tolerance threshold, which may result in the reference charging station not being found in some cases (e.g., because the charging stations differ from each other too much). Alternatively, such a value may be defined depending on the utilization parameters received from the central computing unit. Then, the reference charging station is determined and target ranges for the utilization parameters are defined. Then, it is checked whether the utilization parameters of the remaining charging stations are outside the respective target ranges, for example because at least one charging process has been performed that is outside the corresponding target range.

[0024] For example, the charging time for more than 80% of the charging processes performed at the charging stations is 23-31 minutes for the first charging station, 18-22 minutes for the second charging station, 1-4 minutes for the third charging station, and 24-29 minutes for the fourth charging station. Therefore, the first, second and fourth charging stations are selected as reference charging stations, and a target range of the duration of the charging process is determined, for example, 16-35 minutes, for at least 65% of the charging processes performed at the charging stations. In this case, due to the significant difference, the third charging station is determined as a defective charging station.

[0025] In a further advantageous configuration of the method, at least two charging station clusters are determined for one region. By further subdividing the charging stations installed in the region into a plurality of charging station clusters, the usage data of the individual charging stations can be more precisely subdivided and thus more fine differences in the usage patterns derived from or identified in the usage parameters can be identified. This allows the charging stations to be divided into individual charging station clusters according to their characteristics. This allows a more reliable distinction between functional and non-functional charging stations.

[0026] According to a further advantageous configuration of the method according to the invention, in order to determine that at least two charging stations are arranged adjacent to one another in a common area, at least one of the following charging station characteristics is checked: -geographical location; -Identification number; -Identifier; - an identifying address; and / or -Area tokens.

[0027] In general, the charging station can transmit certain charging station characteristics or metadata to the central computing unit or the central computing unit can call up the corresponding charging station characteristics from the charging station. The charging station characteristics include, for example, a geographical location, a unique identification number, an identification name, an identification address, and / or an area token. At least a part of the identification numbers of charging stations arranged in a charging station cluster, i.e., charging stations installed in a certain road section of a road that runs through a location, may be identical. The identification number may include, for example, 15 digits, in which case, for charging stations of the same charging station cluster, five consecutive digits are the same. The same applies for identification names in which letters are used instead of numbers. Letters and numbers and / or special characters may be mixed. That is, in general, the identification address of a charging station may include any combination of letters, numbers, and / or special symbols such as spaces or hyphens. The meta information may also include an area token that represents a defined geographical area or at least a part of a defined geographical area. Such an area token may contain, for example, the following information: parking lot in front of restaurant XY, address Hauptstrasse 17, or parking lot in a service area of ​​the motorway A5, kilometer 217, parking lot rows 4 and 5. The geographical location may contain, for example, geographical coordinates, in particular GPS coordinates. Using the above-mentioned charging station characteristics, an unambiguous location determination of the installation location of each charging station is realized, which allows an unambiguous association of a charging station with a charging station cluster.

[0028] In a further advantageous configuration of the method according to the invention, at least one of the following utilization parameters is furthermore used to form the utilization data: - charging voltage and / or current strength during the charging process; -Charging output during the charging process; - Duration of the charging process; - the amount of energy transferred during the charging process; - the time ratio of the duration of a charging process carried out at the charging station to the periods of non-use of the charging station; - the number of charging processes over a stipulated period; -Diagnostic condition; and / or -Warning messages.

[0029] In order to classify a charging station as functional or non-functional, individual utilization parameters of the charging station can generally be compared or any combination of them can also be taken into account. That is, for example, the charging voltage and / or the current strength during the charging process can be combined with the number of charging processes performed within a defined period of time. That is, for example, a target range for a functional charging station can be that no more than five charging processes with a charging voltage below 500V must be performed within a day, and at the same time, at least 90% of the charging processes performed at the charging station within a day must be performed with a charging voltage above 500V. In general, two or more utilization parameters can be combined, i.e., three utilization parameters, for example. That is, for example, a target range can be defined such that a charging process must last at least 20 minutes in order for the corresponding charging station to be classified as functional.

[0030] According to a further advantageous configuration of the method according to the invention, only charging stations with matching charging interface types and / or matching available charging powers within a defined tolerance threshold are included in a common charging station cluster. This allows similar charging stations to be integrated into a charging station cluster. This prevents a particular charging station from being classified as a defective charging station due to a structural mismatch with other nearby charging stations. Furthermore, it is assumed that charging stations of different construction types have different usage characteristics. That is, for example, a charging station with a relatively high charging power is typically used for a shorter charging process than a charging station with a relatively low charging power. Thereby, target ranges for the individual usage parameters can be defined that are more closely matched with the usage parameters of a functional charging station of the same type.

[0031] That is, for example, only charging stations having a combined charging system (CCS) as a charging interface are included in the charging station cluster, and these charging stations can be divided into individual sub-clusters according to their charging output, for example, charging output less than 50 kW, charging output between 50 kW and 149 kW, and charging output of 150 kW or more.

[0032] In a further advantageous embodiment of the method according to the invention, the central computing unit further uses artificial intelligence for the analysis of the usage data. With the aid of artificial intelligence, usage patterns can be more reliably identified in the usage data, even when no logical relationship is expected between the different individual usage parameters. This allows for a clearer definition of the target area for the functional charging stations. That is, the corresponding AI model can also define, for example, a tolerance threshold value that indicates to what extent the individual usage parameters of the charging stations can differ in order for the charging stations to be characterized as reference charging stations.

[0033] Preferably, information on the functional or non-functional operating state of at least one charging station is transmitted from the central computing unit for output to at least one vehicle. Correspondingly, information on charging stations that are currently or will not be functional in the future can be used by the vehicle driver to plan his travel route. The vehicle driver can be informed that a planned charging stop cannot be performed at a motorway service area located on the route to the holiday destination, since a charging station that is functional and marked by the charging station operator as free, i.e. not occupied, at which a charging stop is planned is not actually functional. The vehicle driver can thus be informed of information on a defective charging station at an early stage and can adapt his travel plan accordingly. This can improve the user comfort for the vehicle driver, since the vehicle driver is more reliably prevented from performing a charging process at a non-functional charging station using the method according to the invention. Corresponding information can also be called up via a web client, for example from outside the vehicle via a mobile terminal device or a PC using an existing Internet connection.

[0034] The transmission of information between the charging station, the charging station cluster, the charging station network, the central computing unit and / or the vehicle can be performed in any manner. The information transmission can be performed, for example, by wire or wirelessly. In particular, at least a part of the information is transmitted via the Internet. Preferably, the vehicle is communicatively connected to the central computing unit via mobile radio.

[0035] Even in the method according to the invention, there is a risk that the charging station is erroneously classified. Therefore, a confidence value can be determined as to how reliably the correct classification of the charging station was performed, and the confidence value can be indicated in the vehicle. The vehicle driver can constructively decide on the basis of the confidence value whether he wants to trust the assessment of the central computing unit or not. For example, the confidence value can be determined depending on the extent to which the utilization characteristics of the individual charging stations of the charging station cluster, in particular the reference charging station, differ. If the utilization parameters of the charging stations are close to each other (i.e., for example, the tolerance threshold within which the respective utilization parameters of the reference charging stations may differ has a relatively small value), a relatively high confidence value, for example 95%, can be specified.

[0036] Further advantageous configurations of the method according to the invention for determining defective charging stations emerge from the exemplary embodiment which is described in detail below with reference to the only drawing. [Brief description of the drawings]

[0037] [Figure 1] 1 shows a schematic plan view of an area including multiple charging stations. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0038] FIG. 1 shows a defined geographical area 2, here a service area of ​​a motorway. In the area 2 there is a gas station 3, a restaurant 4 and an adjacent large car park 5. Next to the gas station 3 and in front of the restaurant 4 there are individual parking spaces. The parking spaces next to the gas station 3, the parking spaces in front of the restaurant 4 and some of the parking spaces in the car park 5 each have a charging station 1 for carrying out a charging process of a vehicle that can be driven by battery electricity. For the sake of clarity, not all charging stations 1 are provided with reference symbols. The vehicle that can be driven by battery electricity may be a vehicle that can be driven exclusively by battery electricity or may be a plug-in hybrid vehicle.

[0039] During the service life of a charging station 1, operators of the charging station often report that the charging station 1 is in a functional state, when in fact the charging station 1 is defective. Such truly defective charging stations 1 can be determined using the method according to the invention. For this purpose, the utilization of the charging station 1 is compared with neighboring charging stations 1. Thereby, deviations with respect to the typical utilization pattern can be identified, which deviations indicate a defect in the charging station 1.

[0040] For this purpose, the charging stations 1 adjacent to each other in the area 2 are divided into at least one charging station cluster C1, C2, C3, C4. In the example of FIG. 1, the charging station 1 next to the gas station 3 is divided into a first charging station cluster C1, the charging station 1 in front of the restaurant 4 is divided into a second charging station cluster C2, some of the parking spaces in the parking lot 5 are divided into a third charging station cluster C3, and some of the parking spaces in the parking lot 5 are divided into a fourth charging station cluster C4. Based on the positional proximity of the charging stations 1 divided into each charging station cluster C1 to C4, it can be assumed that the usage characteristics of each charging station 1 are similar to each other. This makes it possible to classify a charging station 1 as defective if the usage characteristics of a certain charging station 1 deviate from the usage characteristics of the adjacent charging stations 1 in each charging station cluster.

[0041] To identify deviations from the typical usage characteristics, the usage data transmitted from the individual charging stations 1 to the central computing unit are analyzed, and a fault of at least one charging station 1 is determined if at least one usage parameter of the usage data of the charging stations 1 is outside a defined target range. According to the invention, this target range is defined individually for each charging station cluster C1-C4 depending on the usage data transmitted from the individual charging stations 1. This allows a very precise definition of the target range typical for a functional charging station 1, whereby also defective charging stations 1 can be found with correspondingly very high reliability.

[0042] For example, the load factor of the charging points over a period of one hour can be evaluated. For example, the load factors of the charging points 1 of the second sub-cluster C2.2, which will be further described below, are 91%, 93%, 8%, 6% and 94%. Here, the target range of the load factor is determined depending on the current load factor. For example, it is provided that in order to be considered functional, the individual charging stations may show at least half of the maximum load factor deviation from 35% of the charging stations 1 present in the second sub-cluster C2.2. This applies to charging stations 1 with load factors of 91%, 93% and 94%. The central computing unit therefore determines a fault for two charging stations 1 with load factors of 8% and 6%. In this example, the maximum deviation between the charging station 1 with a load factor of 91% and the charging station 1 with a load factor of 94% is only 3%, so that a lower value, for example 5%, can be selected as the tolerance threshold instead of 35%. The corresponding tolerance threshold can be hard-coded or flexibly determined depending on the actual characteristics of the utilization parameters. In particular, artificial intelligence determines this tolerance threshold.

[0043] In order for multiple charging stations 1 to be accommodated in a common charging station cluster C1 to C4, the charging stations 1 need to be installed adjacent to one another. In this context, "adjacent to one another" means directly adjacent or indirectly adjacent. This generally means that any of the charging stations 1 associated with the charging station clusters C1 to C4 in FIG. 1 can also be associated with a single charging station cluster (not shown).

[0044] In Fig. 1, some charging stations 1 aligned with the charging station cluster C4 are not associated with the charging station cluster C4. There may be various reasons for this. For example, the charging stations 1 are excluded from the fourth charging station cluster C4 because they are structurally different. For example, the charging stations 1 that are not added to the fourth charging station cluster C4 have a different charging interface and therefore should be excluded from determining the functional state.

[0045] The charging stations 1 may be differentiated by the maximum charging power provided by those charging stations 1. That is, for example, the second charging station cluster C2 may be divided into two sub-clusters C2.1 and C2.2. In this case, the charging stations 1 of the first sub-cluster C2.1 have a maximum charging power of, for example, 50-149 kW, and the charging stations 1 of the second sub-cluster C2.2 have a maximum charging power of 150 kW or more. Correspondingly, fewer sub-clusters may be provided, or more sub-clusters may be provided if a finer subdivision according to charging power is to be made.

Claims

1. A method for determining a faulty charging station (1) for a battery-powered vehicle, comprising: The charging station (1) collects usage data and transmits the usage data to a central computing unit, the central computing unit analyzes the usage data, and the central computing unit subsequently determines a fault of at least one charging station (1) if at least one usage parameter of the charging station (1), contained in the usage data, is outside a defined target range, The method, characterized in that at least two charging stations (1) of one or more charging station networks, which are located adjacent to each other in a specified geographical area (2), are associated with a common charging station cluster (C1, C2, C3, C4), and the target ranges of individual usage parameters are each derived from usage data of at least one charging station (1) classified as a reference charging station in the charging station cluster (C1, C2, C3, C4).

2. The method according to claim 1, characterized in that a group of reference charging stations is taken into account when deriving the respective target ranges, and a charging station (1) having respective utilization parameters that match within defined tolerance thresholds is used as the reference charging station.

3. 3. The method according to claim 2, characterized in that at least half of the charging stations (1) of the charging station cluster (C1, C2, C3, C4) are used as reference charging stations.

4. Method according to any one of claims 1 to 3, characterised in that at least two charging station clusters (C1, C2, C3, C4) are determined for one area (2).

5. In order to determine that at least two charging stations (1) are located adjacent to each other in a common area (2), the following charging station characteristics are considered: -Geographical location; - identification number; - identification name; - an identification address; and / or - Area Token 4. The method according to claim 1, wherein at least one of the following is checked:

6. To form the utilization data, the following utilization parameters are provided: - charging voltage and / or current strength during the charging process; - charging output during the charging process; - duration of the charging process; - the amount of energy transferred during the charging process; the time ratio of the duration of a charging process carried out at a charging station (1) to the duration of non-use of said charging station (1); - the number of charging processes over a given period of time; - a diagnostic condition; and / or - Warning message 4. The method according to claim 1, wherein at least one of the following is used:

7. The method according to any one of claims 1 to 3, characterized in that only charging stations (1) whose charging interface type matches and / or whose available charging power matches within a specified tolerance threshold are included in a common charging station cluster (C1, C2, C3, C4).

8. 4. The method according to claim 1, characterized in that artificial intelligence is used by the central computing unit for the analysis of the usage data.

9. 4. The method according to claim 1, further comprising transmitting information about a functional or non-functional operating state of at least one charging station (1) from the central computing unit for output to at least one vehicle.

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