Optimization of the network of electric vehicle charging stations

Abstract

Various example embodiments relate to optimization of a network of electric vehicle charging stations. A computer-implemented method applicable to a network including a plurality of electric vehicle charging stations operated by different parties may be provided. In an embodiment, common data including general information associated with the charging stations is used to estimate utilization of the network at different locations. Advantageously, good new locations for charging stations may be determined based on the estimates without requiring detailed data from each of the various parties. A computing device, method, and computer program are disclosed.

Description

Technical Field

[0001] The present application generally relates to electric vehicle charging stations. In particular, some example embodiments of the present application relate to methods for optimizing a network of electric vehicle charging stations. Background Art

[0002] As the popularity of electric vehicles rises, the network of charging stations for electric vehicles is expanding. New charging stations can be placed where there are no charging stations yet, based on guesses about good locations, in order to provide users with a broad charging network. However, placing charging stations in places that do not meet the needs of users of electric vehicles is inefficient. Summary of the invention

[0003] This Summary is provided to introduce some concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

[0004] Example embodiments may enable more accurate and extensive information on the utilization of a network of electric vehicle charging stations to be provided in situations where accurate charging data from each charging station of the network is not available. The network may include multiple electric vehicle charging stations with different characteristics and operators, who may not share detailed data on the use of their charging stations with others. Thus, since decisions about new locations for charging stations can be made based on utilization estimates for the entire network, the optimization of the network is improved. This and other benefits may be achieved by the features of the independent claims. Further implementation forms are provided in the dependent claims, the description and the drawings.

[0005] According to a first aspect, a computer-implemented method is provided. The method includes: obtaining attributes of a plurality of electric vehicle charging stations, the attributes including location information and state changes associated with charging of electric vehicles at each of the plurality of charging stations; obtaining reference data including accurate values ​​of a plurality of utilization metrics, the utilization metrics including charging power of a collection of the plurality of electric vehicle charging stations; determining an estimated charging detail record including a plurality of estimated utilization metrics for each of the plurality of electric vehicle charging stations based on the attributes and the reference data, the estimated utilization metrics including charging duration based on consecutive state changes and timestamps of the state changes and used energy based on the duration and the reference data; obtaining criteria for installing new electric vehicle charging stations; and outputting data indicating utilization of electric vehicle charging stations by location based on the estimated charging detail record and the criteria.

[0006] In an embodiment, the attribute includes a maximum power, and the plurality of electric vehicle charging stations are classified based on their maximum power; and the reference data includes an average charging power for each category; and wherein the energy used is determined based on the average power of the category of the corresponding electric vehicle charging station.

[0007] In one embodiment, the estimated utilization metric additionally or alternatively includes an income indicative of a user's needs, the income being determined based on the energy used, duration, session fee, price per minute, and price per kilowatt-hour.

[0008] In an embodiment, additionally or alternatively, the multiple utilization metrics of the reference data include at least one of: the energy used, the duration, or the charging income of a set of electric vehicle charging stations, and the method further includes: determining a combined value of at least one of the energy used, the duration, or the income of the set of multiple electric vehicle charging stations based on the reference data and based on the estimated charging detail records; determining an adjustment factor for at least one estimated utilization metric based on the difference between the combined value determined based on the estimated charging detail record and the combined value determined based on the reference data; and updating each of the estimated charging detail records based on the at least one adjustment factor.

[0009] In one embodiment, additionally or alternatively, the plurality of estimated utilization metrics includes a number of charge times, the number of charge times being based on the number of consecutive state changes.

[0010] In one embodiment, additionally or alternatively, the plurality of estimated utilization metrics includes a utilization rate based on a total charging duration of a day divided by minutes in a day.

[0011] In an embodiment, additionally or alternatively, the method comprises: determining one or more location types for each of the estimated charging detail records based on location information of the corresponding charging station; and wherein the criteria include at least one location type for which a new electric vehicle charging station is to be considered for installation.

[0012] In an embodiment, additionally or alternatively, each estimated charging detail record comprises a timestamp; and wherein the criterion comprises at least one time interval, and the output data comprises a growth factor for at least one of the estimated utilization metrics based on the criterion and the timestamp of the estimated charging detail record.

[0013] In an embodiment, additionally or alternatively, the attributes include model information of the electric vehicle charging station, and the outputted data includes a preferred type of charging station based on the estimated utilization metric.

[0014] In an embodiment, additionally or alternatively, the criteria include one or more thresholds for at least one of the estimated utilization metric or the growth factor, and the outputted data includes recommendations for locations to install new charging stations based on the one or more thresholds.

[0015] In one embodiment, the criteria additionally or alternatively includes at least one of: a geographic location, a set of geographic locations, one or more location types, one or more thresholds for optimal location, or one or more estimated utilization metrics.

[0016] In one embodiment, the method additionally or alternatively comprises: determining a location score for a particular location based on the criteria and the estimated charging detail records associated with the location; and wherein the outputted data comprises the location score.

[0017] According to a second aspect, a computing device is provided, the computing device comprising at least one processor; and at least one memory comprising computer program code; the at least one memory and the computer code are configured to, together with the at least one processor, enable the computing device to perform the method of the first aspect.

[0018] According to a third aspect, there is provided a computer program product comprising instructions which, when executed by a computer, cause the computer to perform the method of the first aspect.

[0019] According to a fourth aspect, there is provided a computer readable medium comprising instructions which, when executed by a computer, cause the computer to perform the method of the first aspect.

[0020] Many of the accompanying features will be more readily appreciated as the same become better understood by reference to the following detailed description considered in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which are included to provide a further understanding of example embodiments and constitute a part of this specification, illustrate example embodiments and together with the description help explain the principles of example embodiments. In the drawings:

[0022] Figure 1 An example flow chart illustrating a method for generating an improved estimate of utilization of electric vehicle charging stations to optimize a network of electric vehicle charging stations according to an example embodiment;

[0023] Figure 2 An example of a method for optimizing a network of electric vehicle charging stations to locate new electric vehicle charging stations based on data indicative of utilization of a current network according to an example embodiment is shown;

[0024] Figure 3 shows an example of obtained attributes and created estimated charging detail records for an electric vehicle charging station according to an example embodiment;

[0025] Figure 4 shows an example of an estimated charging detail record updated based on a reference charging detail record according to an example embodiment;

[0026] Figure 5 An example of a method for updating an estimated charging detail record based on an adjustment factor according to an example embodiment is shown;

[0027] Figure 6 shows an example of outputted data indicating utilization of a plurality of electric vehicle charging stations according to an example embodiment;

[0028] Figure 7 shows an example of a user interface configured to output data indicative of utilization of a plurality of electric vehicle charging stations based on set criteria according to an example embodiment;

[0029] Figure 8 Another example of a user interface configured to output data indicative of utilization of a plurality of electric vehicle charging stations based on set criteria according to an example embodiment is shown;

[0030] Fig. 9 shows an example of a utilization growth rate of location-based electric vehicle charging stations according to an example embodiment;

[0031] Fig.10 shows an example calculation of a growth rate for a collection of electric vehicle charging stations according to an example embodiment,

[0032] Fig.11 shows an example of outputted data according to an example embodiment, the data including a growth rate of utilization of electric vehicle charging stations based on set criteria;

[0033] Fig.12 shows an example of a user interface and outputted data for optimizing a network of electric vehicle charging stations based on obtained attributes, generated data, and set criteria according to an example embodiment;

[0034] Fig.13 shows an example of data provided for estimating a new charging station location based on a location score according to an example embodiment;

[0035] Fig.14 An example of a computing device configured to implement one or more example embodiments according to an example embodiment is shown.

[0036] Fig.15 An example of a computing device configured to obtain data from multiple data sources to optimize a network of electric vehicle charging stations is shown according to an example embodiment. DETAILED DESCRIPTION

[0037] Reference will now be made in detail to example embodiments, examples of which are illustrated in the accompanying drawings. The detailed description provided below in conjunction with the accompanying drawings is intended as a description of examples of the present invention and is not intended to represent the only form in which examples of the present invention may be constructed or utilized. The description sets forth the functions of the example and possible sequences of operations for constructing and operating the example. However, the same or equivalent functions and sequences may be implemented by different examples.

[0038] Charging stations (CS) are used to charge electric vehicles (EVs). There are a large number of different charging station operators (CPO) companies that own and operate different charging stations in different locations.

[0039] Charging stations may have various publicly available point of interest (POI) data. Generally, a POI may refer to a specific geographic point location that someone may find useful or interesting. POI data may include location information and additional data, such as a name, time information, or other data that may be found useful to interested parties.

[0040] POI data may have both static and dynamic attributes. Static POI attributes may include, for example, location (coordinates), connector (model 2, CCS, CHaDeMo, etc.), maximum charging power (kW), address (city, street name, number, etc.), country, etc. In general, static POI data includes different attributes of a charging station that may not change frequently.

[0041] The dynamic POI data may describe the current status of the charging station. The dynamic status may include, for example, available, faulty, charging, unavailable, offline, and / or reserved. However, there may not be one standard for dynamic POI data or status, and they may differ depending on the CPO and POI data. In addition, the price for charging may be included in the dynamic POI data.

[0042] Both static and dynamic POI data can be obtained from various different public POI data sources. The POI data can be obtained, for example, from different operators of charging stations. Many companies working on EV charging need different data to optimize their charging network structure. The data of interest may include, for example, how many charges there are at certain locations, what the utilization rate is, and how much energy is charged at different stations. However, the required data may not be public information. On the contrary, many companies want to keep this information confidential and do not share their data publicly.

[0043] Example embodiments enable optimal decisions to be made regarding the placement of electric vehicle charging stations. Advantageously, a simple method is provided to obtain improved data for deciding where to install new charging stations, which enables efficient utilization of the new charging stations. The optimization can be performed using publicly available data, so that detailed data from the owners of the charging stations may not be required. This means that utilization metrics of a CS network can be accurately estimated without having access to actual measurements regarding the utilization metrics. Furthermore, estimates can be performed for the entire network based on public data, even when the network includes charging stations of different manufacturers and different operators and at any location worldwide.

[0044] Since the data is not based on charging stations of a single operator or geographic area, but data from multiple sources can be aggregated for better decision making, optimization can be improved. For example, positioning decisions can be based on the generated information about the worst locations where there may already be many charging stations with low utilization and the best locations with high utilization. In addition, the data can provide insights into how different attributes associated with charging stations, such as connection type, maximum power, or pricing type, affect the utilization of charging stations.

[0045] In an example embodiment, at least one dynamic operating state is used to calculate the number of charging sessions. In addition, POI data can be used to create estimates of, for example, the duration of charging at a particular charging station, the energy used for charging, and the utilization of the charging station. The estimates can indicate user demand for charging stations at different kinds of locations. The attractiveness of different charging station types at different locations can be estimated based on the POI data.

[0046] Figure 1 An example of obtained properties and created estimated charging detail records for an electric vehicle charging station is shown according to an example embodiment.

[0047] exist Figure 1 In the example of a first obtained POI dataset 100 and a second obtained POI dataset 101, there are examples. The POI datasets 100, 101 may include various types of information associated with one or more charging stations. The POI data may include multiple attributes of the charging station, such as the identification, address, status, model, maximum power, etc. of the charging station.

[0048] In the second POI data set 101, for one of the charging stations, there may be a change from available (at 100) to occupied state. This state change may be indicated by a continuous dynamic operating state with changed data. Therefore, charging at the corresponding station (VRT_0001) may have started.

[0049] In response to the state change, a new CDR record 103_1 may be created. The CDR record 103_1 may include, for example, an identification of the station and the start time of the charging event, i.e., a timestamp of the data update associated with the state change. Later, another POI data set 102 may again indicate a change in state of the charging station. Now, the state may have changed from occupied to available, indicating that charging has stopped. In response, the CDR record 103_2 may be updated with a timestamp of the new state change indicating the stop time of charging. In addition, or alternatively, the total duration of charging (from start to end) may be updated to the CDR based on the timestamp.

[0050] Figure 2 An example of an estimated charging detail record 203 updated based on reference data according to an example embodiment is shown. In general, a charging detail record may include multiple utilization metrics of a CS, such as a state change, a timestamp of the state change, a number of charges, a duration of each charge, an energy used for each charge, and / or a utilization rate of a charging station. The charging detail record may further include attributes associated with the CS, such as its ID, model information, connector type, geographic location, maximum power, and / or operator.

[0051] Based on attributes from POI data 200, it is known that the maximum power of the charging station is 10kW. Estimated CDR 201 may include the identification of the charging station and the start time, end time, and duration of charging at the charging station. Reference data 202 may include average charging power values ​​for categories selected based on the maximum power of the charging station.

[0052] Because an electric vehicle may not charge at full power from start to finish, an average power associated with the maximum power may be determined based on reference data 202. The reference data set may include detailed data with known values ​​of at least one utilization metric. Reference data 202 may be obtained from a collection of electric vehicle charging stations.

[0053] By simply calculating E = Pmax*t, an incorrect result will be produced regarding the energy used. The average power over the reference data 202 can be obtained by listing all the charges in the reference data and the power of each charge ( Where E represents the energy used and t represents the time, i.e., the duration of charging). Charging can be grouped into multiple categories based on the maximum power of the charging station that performs individual charging. For example, the maximum power of the charging station can be divided into multiple categories, such as 1kW-3.6kW, 3.7kW-11kW, 11.1kW-22kW, 22.1kW-50kW, 50.1kW-150kW, >150kW. These categories can be based on typical charging station models. The average power of each category can be calculated by dividing the total power of the charge by the total number of charges in the category, as follows: In which ChargeCount category Indicates the total number of charging times at charging stations in this category.

[0054] In an exemplary embodiment, the duration and maximum power of charging at the charging station used for charging may be obtained from public data, such as Figure 1 Then, instead of using E=Pmax*t, which may provide erroneous results, the energy used for charging can be calculated as follows: E=P category_avg * t. Therefore, a more accurate value for the used energy can be obtained for the estimated CDR.

[0055] Figure 3 An example flow diagram of a method 300 for generating improved estimates of utilization of electric vehicle charging stations to optimize a network of electric vehicle charging stations is shown according to an example embodiment.

[0056] At 301, a set of charging detail records (CDRs) is selected from a reference data set. The reference data set may include detailed data obtained from a set of charging stations with known values ​​of parameters of interest.

[0057] At 302, an estimated CDR dataset may be created based on public data such as POI data. The estimated CDR dataset may include CDRs created for the same charging station for which a set of CDRs from the reference dataset was selected.

[0058] At 303, a plurality of estimated utilization metrics may be determined based on the estimated CDR. The utilization metrics may include, for example, estimates of at least one of charging duration(s), energy usage, and utilization. Each of the plurality of utilization metrics determined for the same set of charging stations for which the reference set is selected may be aggregated into a combined value of the estimated utilization metric.

[0059] In addition to energy usage, duration, and utilization, other metrics that help optimize the charging network can also be calculated. One of the metrics can be the revenue of a charging session (one or more). This revenue can be used as an indication of user demand and the attractiveness of the charging location. Therefore, the revenue can be used to estimate the development needs of the charging network. The charging price of the charging station can be known from the POI dataset. For example, charging can be priced using a fixed session fee, a time-based fee, or an energy-based fee. The total revenue for a single charge can be calculated using the following formula: Revenue = (1*session fee) + (time*time-based fee) + (energy*ebergy-based fee). In this equation, time is the charging duration, and energy is the energy used calculated based on the duration of charging and the average power from the reference set.

[0060] At 304, a plurality of utilization metrics may be calculated based on the reference data set. Each of the reference utilization metrics associated with the set of charging stations may be aggregated for a combined value of the respective reference utilization metrics.

[0061] An adjustment factor may be calculated for each of the plurality of utilization metrics at 305. The adjustment factor may include a percentage value indicating how much the estimated utilization metric needs to be adjusted.

[0062] The difference between the estimated utilization metric and the reference-based utilization metric can be calculated to determine an adjustment factor. For example, the result may be that the duration is actually 3.2% lower than estimated, the energy used is 4.1% higher, and the revenue is 2.5% higher. Using the adjustment factor, the estimated utilization metric for any charging station can be updated using detailed, accurate data obtained from a reference database with actual values.

[0063] At 306, a final estimated utilization metric may be determined based on the adjustment factor and the estimated CDR. For example, if a normal estimation method would tell us that the duration of charging is 100 minutes, then 100 x 1.032 = 103.2 minutes may be used as the figure for duration. At 307, the correction of the estimate may be repeated at regular intervals, and then the latest figure for the estimate in the CDR is used.

[0064] Figure 4 An example of using an adjustment factor to determine an improved CDR according to an exemplary embodiment is shown.

[0065] At 400, a CDR dataset may be created based on the public POI data and the reference data. The CDR dataset may include, for example, a list of detected charging stations with identifiers of charging stations and estimated and referenced utilization metrics for a single charge, such as duration, energy usage, and revenue. Figures 1 to 3 The algorithm described in the description is used to calculate the estimated utilization metric regarding the CDR.

[0066] At 401, the difference between the estimated utilization metric and the reference utilization metric may be calculated based on the total value of the single charge. At 402, the CDR of the data set may be updated using the adjustment factor by multiplying the estimated utilization metric of each single charge by the corresponding adjustment factor. This allows the accuracy of the estimated value to be improved.

[0067] Improved data on the utilization of charging stations can be used to develop the charging network. In an embodiment, the best location for new charging stations can be determined based on the updated CDR. In addition, the preferred type of charging station can be determined. For example, if there are multiple charging stations with CPO at the same location, but one of them has a significantly higher utilization rate than the others, it can be determined whether more charging stations with some specific technical or user experience related design features are needed.

[0068] Figure 5 An example of a flow chart 500 for determining optimal locations of electric vehicle charging stations based on generated CDRs according to an example embodiment is shown. The criteria for the optimal location may include, for example, a small number of charging stations with high utilization and / or a high demand for charging stations. For example, the demand may be estimated based on high energy used or high revenue from charging stations. The locations of charging stations may be categorized based on location type to gain broader insight into the development needs of a charging station network.

[0069] At 501, one or more target location types may be selected to install an electric vehicle charging station. The location types may include, for example, coordinates, cities, streets, street types, gas stations, shopping centers, office buildings, proximity to specific locations or buildings, etc. The location types may be dynamic attributes such that new location types may be added as needed. A single charging station may be associated with multiple location types in the CDR.

[0070] At 502 , each CDR for an existing charging station associated with a selected location type may be retrieved.

[0071] At 503, a sum of key attributes may be calculated based on the CRD of the charging stations associated with each location type. The key attributes may be determined by a user, or they may be pre-set. The key attributes may include, for example, the number of charging sessions, utilization, total energy used, or any of the plurality of estimated utilization metrics.

[0072] At 504, the key attributes may be sorted based on location type.

[0073] At 505, a report of the data sorted by the location type may be displayed. In one embodiment, the data may be provided based on any criteria set by the user. The criteria may include, for example, location type or geographic location group, charging station type, thresholds for estimated utilization metrics, or combinations thereof.

[0074] The best locations for charging stations can be displayed on a map based on aggregated location type data. The best locations may have already existing stations, or they may be completely new locations.

[0075] In one embodiment, data collected from charging stations located in shopping malls may reveal good estimated utilization metrics, such as an average of 40% utilization, 239 kWh daily energy, and 235 euros daily revenue. Based on this data, shopping malls that do not yet have any charging stations can be searched for, and new stations can be recommended to be installed there. Using the same logic, poor location types can be discovered, such as public streets with low utilization. Therefore, installing charging stations on public streets that do not yet have any charging stations can be avoided, but as an example, shopping malls close to public streets can be discovered to install charging stations there.

[0076] When a charging station needs to be set up in a completely new location, such as a small city that does not have any previous charging stations, geographic planning may not be used because there is no reference data from the city. However, the location type and the generated CDR can be used to estimate the usage, such as Figure 6 and 7 shown.

[0077] The output data 600 may include a list of charging stations, associated location types, and a plurality of estimated utilization metrics for each charging station. Data 600 may, for example, show that a total of 3,290 charging stations have a location type of "small city", and these charging stations have an average of 38 charging sessions per month. The user may use user interface 700 to change the set criteria of the output data to examine a combination of location types of charging stations, for example, a "small city" location type. Data 701 may show that the combination of "small city, parking house, office" has 20 sessions per month, while "small city, gas station" has 39 sessions per month. Based on the data provided, the user may make better decisions when developing a network of charging stations. Based on the above, the user may conclude that in this case, a good location for a new charging station in a small city would be a gas station in the city.

[0078] In an example embodiment, it may be considered whether to install a new charging station into a gas station or into a shopping mall. Again, based on the location type, it may be calculated that a charging station with a location type of "gas station" has an average of 32 sessions per month, while a charging station with a location type of "shopping mall" has an average of 42 charging sessions per month. In this case, the decision would be to first search for shopping malls that do not already have charging stations, and install the new station there. This decision may be based on an estimated utilization metric of a wide area network of charging stations that is based on publicly available data rather than simple guesswork, or a small sample of the network based on data from only one operator's charging stations.

[0079] In an exemplary embodiment, the geographic attributes on the POI data may be hierarchically organized, for example, in the following manner:

[0080] Continent (Europe, Asia, etc.)

[0081] Country (Germany, France, etc.)

[0082] Region (Bavaria, Hesse, etc.)

[0083] City (Munich, Nuremberg, etc.)

[0084] Region (Altstadt, Lehel, etc.)

[0085] Street (Ettstrasse, wait)

[0086] When POI data includes these attributes and they form a hierarchy, the POI data can be used for analysis to create reports and categorize data, such as Figure 8As shown. For example, a European company may be interested in installing a new charging station somewhere in Europe. Using the method presented above, the company can optimize the location of the charging station. The method can enable the creation and output of summary reports 801, 802 to compare all countries in Europe. The user can set different criteria for the report from the interface at 800, and, for example, select a country to view statistics from different cities in the country. The criteria may include, for example, one or more cities to view statistics from different regions of the country. In addition, the criteria may include (for example) one or more regions to view statistics from different streets in the region. Thus, the user can search for the best new location by narrowing the scope and trying different options based on the estimated CDR created for the current network of charging stations.

[0087] The provided method may allow to easily find good locations to install new charging stations and create early estimates of the usage of the charging stations at that location. In an example embodiment, the geographic data may be combined with location type analysis. For example, a user may first search based on geographic data for a city, then switch to location type data and view statistics from different locations in the city (gas stations, shopping centers, etc.).

[0088] The previous examples show snapshots of the current situation based on different criteria set by the user. However, when planning locations for new charging stations, it may be important to understand trends rather than just the current status.

[0089] Looking at just a snapshot of today's situation, we can see that Berlin has a total of 5,500 charging trips per month, while Munich has only 4,300. Therefore, we can conclude that Berlin is a better location for a new charging station. However, time series data might look like Fig. 9 As shown, from the time series data 900, we can see that even though Berlin has more charging sessions today, the number of charging sessions in Munich is growing faster, which makes it more attractive in terms of location.

[0090] Each CDR may have a timestamp to tell when the CDR was created. Based on these timestamps, a growth factor percentage (%) may be calculated for a certain time span (e.g., 2019-2021). Additionally or alternatively, the growth factor may be calculated for a specific time interval (e.g., monthly level) grouped by geography or location type, such as Fig.10 As shown in the example using the generated data 1000.

[0091] In an example embodiment, a user may select a first interval of a time span (e.g., January 2021). Key measurements (one or more utilization metrics, such as charging session count, utilization %, energy) may be calculated for that time span. Next, the user may select a second interval (e.g., February 2021). The same measurements may be calculated for that next interval. The measurements for the second interval may be compared to the previous interval to calculate a percentage value for growth for each interval: Growth = Measurement current / Measurement previous .

[0092] The location of a new charging station may be determined based on the estimated utilization metric and the growth factor. Criteria for a good location may include, for example, a large growth percentage of charging sessions and a fairly high utilization rate, which may mean that there may be too few charging stations at that location. One or more thresholds may be determined for the growth percentage and the estimated utilization metric that indicate a good or bad location.

[0093] Fig.11 An example of a user interface 1100 and data 1101 provided based on selections made by a user from the user interface is shown. Based on user-set criteria, data may be configured to be displayed, for example, based on growth percentages and selected utilization metrics. Fig.11 In the example data, Frankfurt has the most charging sessions, but the utilization of the stations is quite low and growing downwards. Meanwhile, Berlin has the fewest charging sessions, but the utilization of the stations is quite high and growing rapidly, which means that more charging stations may be needed there soon. This makes Berlin an attractive location for new stations. The user can determine where the best location is (based on a set geography or location type).

[0094] Fig.12 Another example of a user interface and data provided based on a user's selection is shown, where data is displayed based on location type. Based on the analysis of the location type selected by the user at 1200, the data at 1201 shows that gas stations have fairly low utilization, but a lot of energy is being charged there and the amount of charged energy is growing. At the same time, the energy charged in office buildings is fairly low, even though the utilization is high. Based on this data, the user can conclude that gas stations are good locations for new charging stations, even though their utilization is fairly low, because a lot of energy is being charged there.

[0095] However, reading this data may be somewhat difficult and may require some data analysis expertise to understand it. In one embodiment, the data may include recommendations 1202 for new locations that are easily understood by non-experts. Importantly, more accurate and faster decisions can be made for the construction of the network. The recommendations may be based on a location score. The location score may be calculated by combining several statistics from estimated charging detail records and set criteria. The location score may also be based on the growth factor.

[0096] The location score may be limited to a certain region (e.g. Europe, Germany, etc.) and / or to a certain location type. The determined data 1301 may be used to test how good a certain location is evaluated, e.g. Fig.13 The location may not need to have any existing charging stations, but the recommendation or location score may provide information as to whether the location would be best to install a new charging station based on the estimated CDR and location type.

[0097] In an example embodiment, a user plans to install a new charging station to a shopping mall in Munich's city center. Based on the set criteria, the location score may be calculated using a number of estimated utilization metrics such as current charging session count, current utilization, current energy, and a 12-month growth factor for each of the current metrics.

[0098] The location score can be calculated by determining an average of the estimated utilization metric and growth factor based on the estimated CDRs associated with the selected geographic location and / or location type (e.g., a city in Germany and a gas station). The best value within the estimated utilization metric and growth factor can be determined for reference based on all CDRs from the region (e.g., Germany and all location types). The average of the selected location can be divided by the best value for the region and multiplied by a set maximum number of points for the estimated utilization metric and growth factor.

[0099] For example, in Berlin, at an office location, the average station sees 18 charges per month, while the best charging station in Germany sees 54 charges per month. The maximum number of points for charging session counts can be chosen to be 15. Therefore, the location score for charging session counts can be (18 / 54)×15=5 points. Similarly, the location score can be calculated for each estimate using the metric and growth factor. The location scores can be aggregated to obtain an overall location score for a given location / location type. Fig.13 In the example of , the total location score is 73 out of the maximum total score of 100 for the selected location criteria. The user can try to change the score using different criteria to determine the best location from multiple possible locations.

[0100] Fig.14 An example of a computing device 1400 configured to implement one or more example embodiments is shown according to an example embodiment. The computing device 1400 may be configured to perform any aspect of the methods described herein.

[0101] The computing device 1400 may include at least one processor 1401. The at least one processor 1401 may include, for example, one or more of various processing devices, such as a coprocessor, a microprocessor, a controller, a digital signal processor (DSP), a processing circuit with or without an attached DSP, or various other processing devices, including integrated circuits, such as application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), microcontroller units (MCUs), hardware accelerators, dedicated computer chips, etc.

[0102] The computing device 1400 may also include at least one memory 1402. The memory 1402 may be configured to store, for example, computer program code 1403, such as operating system software and application software. In an embodiment, the memory 1402 may include, for example, EV charging station data obtained based on public POI data. The memory 1402 may include one or more volatile memory devices, one or more non-volatile memory devices, and / or a combination thereof. For example, the memory may be implemented as a magnetic storage device (such as a hard drive, a magnetic tape, etc.), an optical magnetic storage device, or a semiconductor memory (such as a mask ROM, a PROM (programmable ROM), an EPROM (erasable PROM), a flash ROM, a RAM (random access memory), etc.).

[0103] The computing device 1400 may also include a communication interface 1405, which is configured to enable the computing device 1400 to send and / or receive information to / from other devices. The communication interface 1405 may be configured to provide at least one radio connection, such as a 3GPP mobile broadband connection (e.g., 3G, 4G, 5G). However, the communication interface 1405 may be configured to provide one or more other types of connections, such as a wireless local area network (WLAN) connection, such as a WLAN connection standardized by the IEEE 802.11 series or the Wi-Fi Alliance; a short-range wireless network connection, such as Bluetooth, NFC (near field communication) or RFID connection; a wired connection, such as a local area network (LAN) connection, a universal serial bus (USB) connection, or an optical network connection, etc.; or a wired Internet connection. The communication interface 1405 may include or be configured to be coupled to at least one antenna to send and / or receive radio frequency signals. One or more of the various types of connections may also be implemented as a separate communication interface, which may be coupled or configured to be coupled to multiple antennas.

[0104] The computing device 1400 may be configured to obtain data from a variety of data sources, such as Fig.15 As shown in . The computing device 1400 may be configured to obtain data associated with a plurality of electric vehicle charging stations 1500, for example, from one or more POI data sources 1501. POI data may be obtained, for example, based on a pull method or a push method. In the pull method, data may be taken out periodically from a database. Data may be retrieved at configurable intervals (e.g., once every minute, every 5 minutes, or every 15 minutes), and the retrieved data may be compared with a previous version of the same data to list the differences in the data. In the push method, data may be obtained from a provider of data, such as a CPO of a corresponding charging station. For example, data may be obtained as an update each time a change occurs in the data. In addition to public data, the computing device 1400 may also be configured to obtain data from a charging station 1502 set, including data corresponding to public data from other charging stations 1500 and additional data having accurate information about charging sessions from the charging station 1502 set. The additional data may include, for example, the energy used for each charge, the duration of the charge, the income from the charge, and / or the utilization of the charging station.

[0105] The computing device 1400 may also include a user interface 1404, which includes an input device and / or an output device. The input device may take various forms, such as a keyboard, a touch screen, or one or more embedded control buttons. The output device may include, for example, a display, etc.

[0106] When the computing device 1400 is configured to implement a certain function, a certain component and / or several components (e.g., at least one processor 1401 and / or memory 1402) of the computing device 1400 may be configured to implement this function. In addition, when the at least one processor 1401 is configured to implement some functions, the functions may be implemented using, for example, the program code 1403 included in the memory 1402.

[0107] The functions described herein may be performed at least in part by one or more computer program product components such as software components. According to an embodiment, the computing device 1400 includes a processor 1401 or a processor circuit, such as a microcontroller, which is configured by program code 1403 when executed to perform the described operations and embodiments of the functions. Alternatively or additionally, the functions described herein may be performed at least in part by one or more hardware logic components. For example, but not limited to, illustrative types of hardware logic components that may be used include field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and graphics processing units (GPUs).

[0108] The computing device 1400 includes means for performing at least one method described herein. In one example, the means includes at least one processor 1401 and at least one memory 1402, the at least one memory 1402 containing program code 1403, the program code 1403 being configured to cause the computing device 1400 to perform the method when executed by the at least one processor 1401.

[0109] The computing device 1400 may include, for example, a server device, a client device, a mobile phone, a tablet computer, a laptop computer, etc. Although the computing device 1400 is shown as a single device, it should be understood that the functionality of the computing device 1400 may be distributed across multiple devices, where applicable.

[0110] It is obvious to a person skilled in the art that, as technology advances, the basic idea can be implemented in various ways.Thus, the aspects and their embodiments are not limited to the examples described above, but instead they may vary within the scope of the claims.

[0111] Any range or device value given herein may be expanded or altered without losing the effect sought. Furthermore, any embodiment may be combined with another embodiment unless expressly prohibited.

[0112] Although the claimed subject matter has been described in language specific to structural features and / or acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Instead, the specific features and acts described above are disclosed as examples of implementing the claims, and other equivalent features and acts are intended to fall within the scope of the claims.

[0113] It should be understood that the above benefits and advantages may relate to one embodiment or may relate to several embodiments. These embodiments are not limited to those embodiments that solve any or all of the problems described, or those embodiments that have any or all of the benefits and advantages described. It will also be understood that references to "one", "at least one" and "one or more" items may refer to one or more of those items or a plurality of those items.

[0114] The operations of the methods described herein may be performed in any appropriate order, or may be performed simultaneously where appropriate. In addition, individual blocks may be deleted from any method without departing from the scope of the subject matter described herein. Aspects of any of the embodiments described above may be combined with aspects of any other embodiment described to form additional embodiments without losing the effects sought.

[0115] The term "comprising" is used herein to mean including the identified methods, blocks or elements, but such blocks or elements do not include an exclusive list and the method or apparatus may contain additional blocks or elements.

[0116] Although an object may be referred to as the "first" or "second" object, this does not necessarily indicate any order or importance of these objects. Instead, these attributes may be used solely for the purpose of creating a distinction between objects.

Claims

1. A computer-implemented method comprising: obtaining, from a publicly available data source (100, 101, 102, 200), attributes of a plurality of electric vehicle charging stations of different operators, the attributes comprising state changes, maximum power, and location information associated with charging of an electric vehicle at each of the plurality of electric vehicle charging stations; obtaining reference data (202) comprising known values ​​of a plurality of utilization metrics obtained from the set of the plurality of electric vehicle charging stations and an average charging power calculated based on the plurality of utilization metrics for each category of the plurality of charging stations selected based on their maximum power, the plurality of utilization metrics comprising energy used and charging duration for the set of electric vehicle charging stations; determining (302, 400) an estimated charging detail record (201, 203) comprising a plurality of estimated utilization metrics (303) for each of the plurality of electric vehicle charging stations based on the attributes of the plurality of electric vehicle charging stations and the reference data, the estimated utilization metrics (303) comprising a charging duration based on consecutive state changes and timestamps of the state changes and an energy used based on the charging duration and the average charging power value for the category of the respective electric vehicle charging station; determining ( 304 ) a combined value of at least one of the energy used or the duration for the set of the plurality of electric vehicle charging stations based on the reference data and based on the estimated charging detail records for the set of the plurality of electric vehicle charging stations; determining (305) an adjustment factor for at least one estimated utilization metric based on a difference between the combined value determined based on the estimated charging detail records for the set of the plurality of electric vehicle charging stations and the combined value determined based on the reference data; as well as updating ( 306 ) each of the estimated charging detail records for the plurality of electric vehicle charging stations based on the at least one adjustment factor; determining one or more location types for each of the estimated charging detail records for each of the plurality of electric vehicle charging stations based on location information of the corresponding charging station; obtaining from a user criteria for installing a new electric vehicle charging station (501, 1200), the criteria including at least one location type where the new electric vehicle charging station is considered for installation; determining an optimal location for installing the new electric vehicle charging station based on the estimated charging detail records for each of the plurality of electric vehicle charging stations and the criteria (505, 600, 701, 801, 802, 1000, 1101, 1201, 1202, 1301); as well as outputting data to display the determined optimal location (505, 600, 701, 801, 802, 1000, 1101, 1201, 1202, 1301) for the user to install the new electric vehicle charging station, wherein the data indicates utilization of electric vehicle charging stations by location type based on the estimated charging detail records for each of the electric vehicle charging stations and the criteria; as well as Installing the new electric vehicle charging station.

2. The computer-implemented method of claim 1 , wherein the attribute comprises a maximum power, and the plurality of electric vehicle charging stations are sorted based on their maximum power; and The reference data (202) includes an average charging power for each category; and in, The used energy in the estimated charging detail record is determined based on the average power of the category of the corresponding electric vehicle charging station.

3. The computer-implemented method of claim 1 or 2, wherein: The estimated utilization metric includes an income indicative of a user's needs, the income being determined based on the energy used, the duration, a session fee, a price per minute, and a price per kilowatt-hour.

4. The computer-implemented method of claim 1 , wherein: The plurality of estimated utilization metrics includes a number of charges based on the number of consecutive state changes.

5. The computer-implemented method of claim 1 , wherein: The plurality of estimated utilization metrics includes a utilization rate based on a total charging duration for a day divided by minutes in a day.

6. The computer-implemented method of claim 1 , wherein: Each estimated charge detail record includes a timestamp; and Wherein the criteria comprises at least one time interval and the outputted data comprises a growth factor for at least one of the estimated utilization metrics, the growth factor being based on the criteria and the timestamp of the estimated charging detail record.

7. The computer-implemented method of claim 1 , wherein: The attributes include model information for the electric vehicle charging station, and the output data includes a preferred type of charging station based on the estimated utilization metric.

8. The computer-implemented method of claim 6, wherein: The criteria include one or more thresholds for at least one of the estimated utilization metric or the growth factor, and the outputted data includes a recommendation for a location to install a new charging station based on the one or more thresholds (1202).

9. The computer-implemented method of claim 1 , wherein: The criteria include at least one of: a geographic location, a group of geographic locations, one or more location types, one or more thresholds for optimal locations, or one or more estimated utilization metrics.

10. The computer-implemented method of claim 1 , wherein the method comprises: determining a location score for a particular location based on the criteria and the estimated charging detail record associated with the location; as well as The output data includes the location score.

11. A computing device (1400), the computing device (1400) comprising: at least one processor (1401); as well as at least one memory (1402) comprising computer program code (1403); The at least one memory (1402) and the computer code (1403) are configured to, together with the at least one processor (1401), cause the computing device (1400) to perform the method according to any one of claims 1 to 10.

12. A computer program product (1403) comprising instructions which, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 10.

13. A computer readable medium comprising instructions which, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 10.

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