Method for determining a charging willingness probability of an electric vehicle and charging point utilization prediction method

Abstract

The invention relates to a method for determining a charging willingness probability of an approaching electric vehicle (50) for at least one charging point (100-n), comprising the steps: acquiring a geographic position of the at least one charging point (100-n); determining a near area (130-n) around the geographic position of the at least one charging point (100-n); acquiring a number of electric vehicles (50) driving into the near area (130-n) of the at least one charging point (100-n) per time interval; acquiring a number of charging processes per time interval, determining a charging willingness probability based on a quotient of the number of charging processes per time interval divided by the number of electric vehicles (50) driving into the near area (130-n) in the same time interval. The invention further relates to a method for predicting a utilization of a plurality of charging points (100-n) based on determined charging willingness probabilities of electric vehicles (50) approaching the charging points.

Description

Technical Field

[0001] This invention relates to a method for determining the probability of charging intentions of nearby electric vehicles for at least one charging point. Furthermore, this invention relates to a method for predicting the utilization rate of multiple charging points. Background Technology

[0002] For owners of electric vehicles who must charge their electric vehicle's electrical storage before driving begins and / or during driving interruptions for propulsion, it is advantageous to be able to determine the utilization rate of publicly accessible charging points as accurately as possible.

[0003] Here, a charging point is considered a geographically located charging infrastructure, where a single charging point can have multiple charging spots for electric vehicles, allowing electrical energy to be simultaneously charged into the electric vehicles' storage. The infrastructure units supplying these charging spots are also referred to as charging piles. Therefore, a charging point can include multiple charging piles.

[0004] CN 114954129 discloses a method and apparatus for recommending charging station information. The method includes the following steps: responding to a recommendation request input by a user, and determining a first time and a first remaining battery level of the current vehicle traveling to each charging station consistent with the travel route; obtaining charging queue information for each charging station at the first time from a cloud server; and sorting the charging stations according to the first remaining battery level and the charging queue information, and recommending charging station information based on the sorting result. The technical solution according to the described embodiment can provide reasonable travel charging suggestions by integrating various factors, and can improve the user's charging experience.

[0005] US20170276503A1 relates to a method or system for recommending electric vehicle charging stations with the shortest charging wait times based on big data. The system includes: a driving information receiving unit for receiving a charging station search request from an electric vehicle, along with information about estimated discharge time and current location; a charging station information receiving unit for receiving real-time power consumption data of the charging stations; a charging wait time calculation unit for receiving power consumption data from the charging station information receiving unit and, in response to the charging station search request, calculating the charging wait time of the charging stations based on their power consumption; and a best charging station providing unit for receiving information about estimated discharge time and current location from the driving information receiving unit and receiving the charging wait time from the charging wait time calculation unit, and providing information about at least one best charging station that can be reached within the estimated discharge time from the current location and can recharge the vehicle in the shortest possible time.

[0006] KR 20190102589 A discloses a mobile terminal and a method for guiding the mobile terminal to a charging station. According to one embodiment, the method for guiding the mobile terminal to a charging station includes the following steps: in response to entry into a vehicle, identifying a connection to the vehicle; identifying a charging station guidance request; in response to the charging station guidance request, receiving information from a server about available charging stations; and displaying at least one filtered and searched charging station information on a navigation screen based on learned user behavior patterns and vehicle-related information.

[0007] DE 10 2022 115 122 A1 describes a method for determining the number of electric vehicles waiting for an available charging point at a charging infrastructure having at least one charging point, wherein the charging infrastructure is located within a geofenced area defined by a geofence, wherein, in this method, an electric vehicle with geofencing capability is automatically classified as waiting for a charging point when all charging points are occupied, at least one state of charge of the electric vehicle's drive battery meets the corresponding state of charge criterion, and at least one motion parameter of the electric vehicle meets the corresponding motion criterion. A charging infrastructure having at least one charging point is also described for charging electric vehicles located within a geofenced area defined by a geofence, wherein the charging infrastructure is configured to perform this method.

[0008] To implement the methods described in the prior art, it is typically necessary to exchange very large amounts of data and highly specialized information about a large number of electric vehicles. However, in some cases where the utilization rate of charging points should be predicted, or for some organizations that should make such predictions, the aforementioned preconditions are not met. Instead, only limited information is available, such as information about electric vehicles from manufacturers. Even in this case, it is desirable to be able to make statements about the utilization rate of charging points. Furthermore, charging points are often not installed far from other infrastructure. For example, charging points may be located in parking spaces at commercial stores or restaurants. In order to make statements about the potential utilization rate of charging points, it is desirable to know how likely it is that nearby electric vehicles will want to charge at that charging point. Summary of the Invention

[0009] The technical problem to be solved by the present invention is to make a statement about the charging intention probability of an electric vehicle approaching a charging point based on as little information as possible for at least one, preferably multiple, charging points, and to implement a method for predicting the utilization rate of multiple charging points even when only a limited amount of data for each individual charging point is available.

[0010] The present invention is achieved by a method having the features of claim 1 and a method having the features of claim 10. Advantageous designs are derived from the dependent claims.

[0011] The concept upon which this invention is based is to evaluate data from a vehicle fleet, such as data from a manufacturer's electric vehicles, in order to derive the probability that these electric vehicles, when approaching a charging point, actually intend to charge their memory at that charging point. According to one aspect of the invention, this determines the probability of a vehicle's charging intention when approaching a particular charging point.

[0012] According to a second aspect of the invention, charging points can be classified based on their environment. For this purpose, characteristics such as proximity to highways or expressways, proximity to specific commercial establishments, the type of commercial establishment, the type, number, and size of catering establishments, and charging capacity relative to the number of available charging spots where charging can be performed simultaneously can be utilized. Based on these characteristics, charging points can be classified. Using this classification, the probability of charging intentions of nearby electric vehicles determined for a particular charging point can be transferred to another charging point of the same classification. Combined with a currently determined number of nearby electric vehicles within a time interval, the utilization rate of the charging point can be inferred.

[0013] In particular, a method is provided for determining the probability of charging intention of an electric vehicle near at least one charging point, comprising the following steps:

[0014] Obtain the geographical location of the at least one charging point;

[0015] Determine the near area (also referred to as the capture area) surrounding the geographical location of the at least one charging point.

[0016] The number of electric vehicles that enter the near area of ​​the at least one charging point in each time interval is obtained.

[0017] Obtain the number of charging processes in each time interval.

[0018] The probability of charging intention is determined by dividing the number of charging processes in each time interval by the number of electric vehicles entering in the same time interval.

[0019] The locations of different public charging points can be obtained from different sources. Typically, the charging point operators provide this information in a publicly available manner, such as on the internet.

[0020] Such a geographical location surrounding a charging point defines a proximity area, also known as a capture area. Electric vehicles entering this proximity area or capture area should be captured whenever possible. Automakers can achieve this, for example, by iteratively checking the current position of the electric vehicle in the vehicle's computer or controller, associated with the vehicle's navigation and location determination devices, to determine whether the electric vehicle has entered a defined proximity area surrounding one of the charging points. If so, this information is transmitted to the automaker.

[0021] Because modern cars have mobile radio units, data packets can be sent to the vehicle manufacturer via a mobile radio connection, indicating that the electric vehicle has entered the capture area of ​​the charging point. Preferably, the corresponding charging point is identified by an identifier transmitted along with the data packet. Additional data about the electric vehicle and its status can also be transmitted. Furthermore, it is preferable to transmit time information indicating the time or time range within which the electric vehicle entered the vicinity of the charging point.

[0022] The entry of electric vehicles into the vicinity can also be detected using sensors, such as cameras, induction coils, and gratings.

[0023] Furthermore, electric vehicles are preferably equipped such that they also transmit data packets to the vehicle manufacturer when they begin the charging process. Here, it is also preferable to transmit the identifier of the charging point where the electric vehicle begins charging its energy storage. Additionally, and again preferably, the time or time range at which the electric vehicle begins the charging process at the charging point is also transmitted.

[0024] Vehicle manufacturers receive information transmitted to them regarding entry into the capture area and actual charging. For both types of information, it is preferable to record the time of transmission, preferably the time of day and also preferably the day of the week, and assign this information to the data. Alternatively or additionally, the time may be included in the data itself.

[0025] For data protection reasons, it can be stipulated that additional land price time information for electric vehicles is inserted into data packets regarding entry into the capture area and / or data packets containing information about actual charging. This time information includes the time of day and / or the corresponding day of the week, but does not indicate the exact time; rather, it only assigns a time range. This ensures a certain degree of anonymity for the transmitted data.

[0026] In both cases, vehicle manufacturers can determine the probability of charging willingness for electric vehicles approaching the charging point based on the acquired data by calculating the ratio of the number of electric vehicles actually charging in a time interval to the number of electric vehicles entering the nearby area.

[0027] As already noted, the probability of an electric vehicle's willingness to charge near the charging point varies depending on the time of day and / or the day of the week. Therefore, in a preferred embodiment, it is specified that the probability of charging willingness is determined for multiple time periods, which fall at different times of day and / or on different days of the week, such that the probability of an approaching electric vehicle charging is determined in relation to the time of day and / or the day of the week. In many cases, what is important is not the exact day of the week, but rather the type of weekday, such as weekdays or Sundays and public holidays.

[0028] The statement can be improved by additionally obtaining information on whether the charging point was fully loaded or had ever been fully loaded during that time interval. If all charging slots at a charging point are occupied, the possibility that some electric vehicles entering the vicinity may have the intention to charge but are unable to do so because no charging slots are available cannot be ruled out.

[0029] To take this into account, an uncertainty metric is assigned to the charging intention probability, which makes a statement about how reliably the charging intention probability can be determined. If information about charging point utilization exists and the charging point is not fully utilized, the determined charging intention probability has high reliability. If it is known that the charging point was once fully utilized, the charging intention probability has low reliability. If the charging point utilization is unknown, a moderate uncertainty value can be assumed. Uncertainty can also be determined by incorporating a share factor, based on the relationship between the number of charging spots and the actual charging activity that occurred during that time interval, where the share factor indicates the share of the fleet or manufacturer's electric vehicles relative to the total number of electric vehicles. For example, the market share of electric vehicles sold by a manufacturer relative to the total number of electric vehicles sold could be used for this purpose.

[0030] To improve the reliability of the statements, the separately acquired charging intention probabilities are fused together. In the simplest variant, a geometric mean is formed. Here, it is preferable to fuse only the charging intention probabilities for the same time of day and the same day of week. Similarly, the charging intention probabilities related to the time of day for weekdays, Saturdays, and Sundays can be determined separately. Here, it is additionally preferable to perform a time-related aggregation. The grading can be selected and / or determined based on the quantity and quality of available data. If only a small amount of data exists, the charging intention probabilities can be determined for a larger time range, such as the time range from 6:00 to 9:00, 9:00 to 12:00, 12:00 to 16:00, 16:00 to 21:00, and 21:00 to 6:00. It will be self-evident to those skilled in the art that this is an arbitrarily chosen division.

[0031] If reliability information exists, it should be considered during fusion.

[0032] It has been shown to be particularly advantageous to determine the environmental characteristics used for charging points. Environmental characteristics are understood to describe and characterize all features of the surrounding environment and the charging point itself. These environmental characteristics include, for example, proximity to highways or other expressways, proximity to commercial establishments, the type and size of the commercial establishments, the type, number, and size of catering establishments, the type and number of leisure facilities such as sports facilities, cinemas, or theaters, the number of parking spaces, and the charging capacity for available charging spots. This list should not be construed as exhaustive.

[0033] These environmental characteristics can be used to classify charging points. Therefore, in one embodiment, it is specified that environmental characteristics of the charging point are obtained, and the charging point is classified based on these environmental characteristics.

[0034] The quality of the statement, especially when only a small amount of data is available, can be improved by fusing the charging intention probabilities of charging points of the same category when fusing the charging intention probabilities.

[0035] Therefore, it is particularly desirable to obtain a set of charging intention probabilities that are determined in relation to the time of day and the day of the week and are, for example, retrievable from memory and available for use by different categories of charging points.

[0036] The information thus obtained can now be used to predict the utilization rate of charging points. For this, it is only necessary to determine the number of electric vehicles that have entered the corresponding nearby area of ​​the charging point. Combining this with the corresponding charging intention probabilities of electric vehicles approaching the charging point for the corresponding category of charging points, and taking into account the charging capacity of the charging point, such as the number of available charging slots, the current utilization rate of the charging point can be determined. These charging intention probabilities preferably exist in relation to the time of day and the day of the week, or are retrieved from a set, such as a database.

[0037] A method for predicting the utilization rate of multiple charging points specifies that, for each of the charging points...

[0038] Obtain environmental characteristics;

[0039] Get the number of charging positions at this charging point;

[0040] Classify based on the environmental characteristics acquired at the charging point.

[0041] Retrieve charging willingness probability data for this charging point or similar categories from the storage device;

[0042] Obtain the geographical location of the charging point and determine its nearby area;

[0043] Acquire and / or measure the number of electric vehicles entering the vicinity of the charging point in each time interval, and multiply this number by the probability of being called for charging and divide by the number of charging positions at the charging point to determine the utilization value.

[0044] And it provides such a specific utilization rate value,

[0045] Wherein, for at least one of the charging points, the probability of invoking the charging intention is not determined for the corresponding charging point itself.

[0046] Therefore, this method also enables reliable predictions for charging points where there is no probability of obtaining a charging intention.

[0047] Furthermore, the overall behavior of all electric vehicles can be inferred from a sample of electric vehicles, i.e., those manufactured by a single manufacturer. It is assumed here that the charging behavior of electric vehicle users is approximately identical regardless of the manufacturer. This applies at least when the charging intention probability is determined based on a fleet of electric vehicles, the fleet being well-representative of various available electric vehicles in terms of range, storage capacity, and comfort. Therefore, the charging intention probability only needs to be determined for a subset of electric vehicles near a charging point. This information can then be used to predict charging point utilization once the number of electric vehicles in the vicinity has been determined.

[0048] This determination can be made, for example, using sensors such as cameras to identify electric vehicles approaching the charging point. However, the data can also be collected by the electric vehicles themselves, which then transmit this information to a fleet management system, such as the automaker, as in determining the probability of charging intention. Based on knowledge of the automaker's market share of electric vehicles and, if necessary, other knowledge of driving behavior compared to electric vehicles from other manufacturers, the total number of electric vehicles approaching one of the charging points in the vicinity can be inferred; this other knowledge is determined through traffic monitoring measurements. Utilization rates can be inferred by combining the determined probabilities of charging intention for the corresponding charging point or charging points of the same category. This utilization information can be provided, for example, by the automaker and used by the automaker's electric vehicles or, if necessary, by all electric vehicles. Thus, the selection of charging points already reached or awaiting destination can be improved with minimal waiting time. Attached Figure Description

[0049] The invention will now be described in more detail with reference to the accompanying drawings, in which:

[0050] Figure 1 A schematic diagram of multiple charging points is shown, for which the probability of charging intention and / or the predicted utilization rate are determined. Detailed Implementation

[0051] exist Figure 1 The diagram schematically illustrates multiple charging points 100-n and their respective environments 150-n. The additional term "-n" represents an index, which is selected differently for similar characteristics to enable explicit identification of these characteristics. The index takes an integer value, such as 1, 2, 3, ... Each of the charging points 100-n includes one or more charging piles 110, each corresponding to a charging position 120.

[0052] Environment 150-n is described and characterized by so-called environmental features. These environmental features include information about private and public facilities and infrastructure, etc. An important environmental feature is proximity to highway 310 or expressway / country road 320. Other important features may be the number, type and size of catering establishments 220, the number, type and size of commercial shops 210, the number, type and size of leisure facilities 230, the number of parking spaces 240, the number, type and size of service businesses 250, etc., to list a few. The charging capacity of charging points with respect to their available charging spaces is also an environmental feature. It should be clearly stated that this list is merely exemplary and not exhaustive.

[0053] Electric vehicles 50 approach charging point 100-n. Here, these electric vehicles enter the corresponding proximity area 130-n. This can be determined using, for example, a sensor 140 designed as a camera, or it can be determined using an inductive sensor or other different sensors. The proximity area 130-n is also referred to as the capture area, and the proximity area is determined around the geographical location of charging point 100-n. The geographical location of charging point 100-n is, for example, published and announced by charging point operator 700.

[0054] The fleet management system 500 is, for example, an automobile manufacturer. In one embodiment, the fleet management system uses a capture area determination device 530 to determine near areas 130-n, and transmits these near areas to the vehicle communication devices 58 of the electric vehicles 50 in its fleet via a communication device 510. Here, for example, each electric vehicle manufactured by the manufacturer is understood as an electric vehicle in the fleet.

[0055] Each electric vehicle 50 is additionally equipped with a navigation device 52, which enables it to determine its own geographical location. This can be accomplished, for example, by a so-called satellite navigation unit (not shown), which evaluates signals from a satellite navigation system, such as the Global Positioning System (GPS). In the electric vehicle 50, a controller 54 is coupled to the navigation device 52, which monitors whether the electric vehicle 50 has entered a pre-determined capture area or near-area 130-n transmitted by the fleet management system 500.

[0056] Information about the proximity area 130-n can also be stored in memory during the manufacturing of the electric vehicle 50. Once the electric vehicle 50 determines that it has entered the proximity area 130-n of one of the charging points 100-n, the electric vehicle records this information. The controller 54 then prompts the transmission of a data packet to the fleet management system 500. This transmission can be timely or delayed. In addition to the geographical location or other identifier of the corresponding charging point approached by the electric vehicle 50, time information can also be transmitted, which preferably includes the date or day of the week in addition to the time of day. For data protection reasons, the electric vehicle 50 can allocate time (i.e., the moment of entry into the proximity area 130-n) to a time range and transmit only the corresponding time range at a time, including the arrival time or the entry time into the proximity area 130-n.

[0057] When the electric vehicle 50 begins the charging process at one of the charging positions 120 of the charging points 100-n, the electric vehicle behaves similarly. The controller 54 transmits a data packet instructing the electric vehicle to charge. In addition to the geographical location and / or identification of the charging point 100-n, the data packet preferably also includes time information, which includes the hour or time range and preferably includes the day of the week or the full date.

[0058] The fleet management system 500 uses an evaluation device 520 to determine the probability of charging intention of electric vehicles approaching specific charging points 100-n. To do this, the number of electric vehicles 50 that begin charging at the corresponding charging point 100-n in each time interval is divided by the number of electric vehicles 50 that enter the proximity area 130-n of the corresponding charging point 100-n in the corresponding time interval. This acquisition is preferably performed in relation to the time of day and the day of the week. The corresponding results are stored in a data storage device 550.

[0059] If the charging point operator 700 provides utilization information, this information is considered by the evaluation device 520 when determining the probability of charging intention. If the charging points 100-n are fully loaded in terms of their charging capacity in the sense that all charging positions 120 are occupied during a time interval, the determined number of charging processes during that time interval is uncertain because there is no guarantee that every electric vehicle 50 with a charging intention will actually be able to start a charging process. Conversely, if one of the charging positions 120 is available at any time during that time interval, the determined number of charging processes has no uncertainty or only a very small uncertainty. This uncertainty is particularly considered when fusing the determined probability of charging intention.

[0060] Additionally, the fleet management system 500 acquires environmental characteristics of the environment 150-n of charging points 100-n. Based on these environmental characteristics, each charging point 100-n is classified. In the schematic, highly simplified example shown, charging points 100-1 and 100-2 have similar environments. Both are accessed via highway 310 and have catering establishments 220, commercial enterprises 210, and recreational facilities 230 in environment 150-n, respectively. Again, a highly simplified representation has been chosen here. Conversely, another charging point 100-3 shown only has catering establishments 210 and is accessed only via rural road 320.

[0061] If the charging intention probability of electric vehicle 50 is determined for charging point 100-1, then that charging intention probability can be transferred to charging point 100-2 of the same category.

[0062] Therefore, it is sufficient to classify charging points in terms of environmental characteristics and, additionally, determine the probability of charging willingness of nearby electric vehicles for a limited number of charging points.

[0063] If the number of electric vehicles near or in the vicinity of a charging point is determined, the utilization rate of the charging point can be predicted in a simple way, given the charging point capacity of the existing charging sites and the probability of charging intention.

[0064] Therefore, to determine charging point utilization, it is only necessary to create a set of probabilities of charging intentions of electric vehicles near the charging points, categorized by environmental characteristics. Then, for charging points whose utilization data is to be predicted, the surrounding environmental characteristics and charging capacity of the charging points must be obtained at least in terms of available charging spots, and the charging points must be categorized. To determine the current utilization rate, it is now only necessary to determine the number of electric vehicles entering the nearby area, and then the utilization rate of the corresponding charging point can be calculated. For this purpose, the number of electric vehicles in the nearby area is measured, or the number of electric vehicles entering the nearby area per time interval is determined, or this number is determined based on data packets transmitted by electric vehicles. If only a subset of electric vehicles transmit data packets when they enter the nearby area of ​​one of the charging points, then the total number of electric vehicles entering needs to be predicted. This can be done, for example, based on the ratio of electric vehicles transmitting data packets to the total number of electric vehicles. If only one-tenth of the electric vehicles transmit data packets, then the total number of electric vehicles entering is ten times the number determined based on the received data packets.

[0065] If the number of electric vehicles is multiplied by the corresponding probability of charging intention retrieved from data storage 550 and divided by the number of charging positions at charging points, a statement about utilization is obtained. Utilization is determined in the prediction device 560 of the fleet management system 500.

[0066] The utilization rate thus determined is provided through output device 570. This output device can be designed, for example, as a database server from which the utilization rate statement can be retrieved. Electric vehicles can use these utilization rate statements for route planning.

[0067] Charging point operators, in particular, can utilize the probability of charging intentions to predict the utilization rate of existing charging points and the utilization rate of new charging points.

[0068] List of reference numerals

[0069] 50 electric vehicles

[0070] 52 navigation device

[0071] 54 controller

[0072] 56 Data Storage

[0073] 58 communication device

[0074] 100 charging points

[0075] 110 charging pile

[0076] 120 charging slots

[0077] 130 near area

[0078] 140 sensors

[0079] 150 Environment

[0080] 210 Commercial Store

[0081] 220 Catering establishments

[0082] 230 recreational facilities

[0083] 240 parking spaces

[0084] 250 service companies

[0085] 310 Expressway

[0086] 320 rural road

[0087] 500 fleet management system

[0088] 510 communication device

[0089] 520 Evaluation Device

[0090] 530 Capture Area Determination Device

[0091] 540 sorting device

[0092] 550 memory

[0093] 560 Prediction Device

[0094] 570 output device

[0095] 700 charging point operators

Claims

1. A method for determining the charging willingness probability of an approaching electric vehicle (50) for at least one charging point (100-n), comprising the steps of: Obtain the geographical location of the at least one charging point (100-n); Determine the near area (130-n) of the geographical location surrounding the at least one charging point (100-n). Obtain the number of electric vehicles (50) in the near area (130-n) of the at least one charging point (100-n) in each time interval; Obtain the number of charging processes in each time interval. The charging intention probability is determined by dividing the number of charging processes in each time interval by the number of electric vehicles (50) entering the near area (130-n) in the same time interval.

2. The method according to claim 1, characterized in that, The charging intention probability is determined for different times of day and / or multiple time periods of different days of week, such that the charging probability of close electric vehicles (50) is determined in relation to the time of day and / or the day of week.

3. The method according to claim 1 or 2, characterized in that, The utilization rate of charging points (100-n) is obtained, and an uncertainty metric is assigned to the determined probability of charging intention, which is related to the utilization rate of charging position (120) of charging point (100-n) in the time interval.

4. The method according to any one of the preceding claims, characterized in that, The probability of charging willingness for a period of time when the at least one charging point (100-n) is fully loaded is marked as uncertain.

5. The method according to claim 4, characterized in that, The probability of charging intention is fused in relation to the time of day and / or the day of the week, taking into account the uncertainty of the probability of charging intention.

6. The method according to any one of the preceding claims, characterized in that, Obtain environmental characteristics of the at least one charging point (100-n), and classify the charging points (100-n) based on the environmental characteristics of the charging points.

7. The method according to claim 6, characterized in that, The environmental characteristics of the charging point (100-n) include proximity to the highway (300), the number of parking spaces (240) in the environment (150), the number, type and / or size of commercial shops (210), the number, type and / or size of catering establishments (220), and the number, type and / or size of leisure facilities (230).

8. The method according to claim 6 or 7, characterized in that, The probability of charging intention is determined for multiple charging points (100-n), which are classified based on the environmental characteristics.

9. The method according to claim 8, characterized in that, The charging willingness probability is averaged for charging points (100-n) of the same category.

10. A method for predicting the utilization rate of multiple charging points (100-n), wherein, For each of the charging points (100-n) Obtain environmental characteristics; Get the number of charging positions (120) at this charging point; Classify based on the environmental characteristics acquired at the charging point. Retrieve charging intention probability data for this charging point or similar categories of charging points (100-n) from the storage device; Obtain the geographical location of the charging point and determine the nearby area of ​​the charging point (130). Acquire and / or measure the number of electric vehicles (50) entering the near area (130) of the charging point in each time interval, and multiply this number by the probability of being invoked for charging and divide by the number of charging positions (120) at the charging point. And it provides such a specific utilization rate value, Wherein, for at least one of the charging points (100-2), the probability of the charging intention to be invoked is not determined for the corresponding charging point (100-2) itself.

Images