Network Optimization for Electric Vehicle Charging Stations

Abstract

Various exemplary embodiments relate to optimizing an electric vehicle charging station network. A computer-implemented method may be provided that is applicable to a network with multiple electric vehicle charging stations operated by different parties. In one embodiment, public data including general information related to charging stations is used to estimate the utilization of the network at various locations. Advantageously, new locations suitable for charging stations can be determined based on the estimates without requiring detailed data from each party. Computing devices, methods, and computer programs are disclosed.

Description

[Technical field]

[0001] FIELD OF THE DISCLOSURE This application relates generally to electric vehicle charging stations. In particular, some example embodiments of this application relate to a method for optimizing a network of electric vehicle charging stations. [Background technology]

[0002] As electric vehicles become more popular, the electric vehicle charging station network is expanding. To provide an extensive charging network to users, new charging stations can be built in places where there are no charging stations yet, based on appropriate location guesses. However, it is not efficient to place charging stations in places that do not serve the needs of electric vehicle users. Summary of the Invention [Means for solving the problem]

[0003] This Summary is provided to introduce a selection of 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] Exemplary embodiments may provide more accurate and comprehensive information about the utilization of a network of electric vehicle charging stations when accurate charging data from each charging station of the network is not available. A network may consist of multiple electric vehicle charging stations with different characteristics and operators may not share detailed data of their own charging station usage with others. Thus, network optimization is improved if decisions regarding new locations of charging stations can be made based on estimates of utilization of the entire network. This and other advantages may be realized by the features of the independent claims. Further embodiments 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 including the steps of acquiring attributes of a plurality of electric vehicle charging stations, the attributes including location information and state changes associated with charging of an electric vehicle at each of the plurality of electric charging stations, acquiring reference data including accurate values ​​of a plurality of utilization indicators including a charging power of a set of the plurality of electric vehicle charging stations, determining an estimated charging detail record including a plurality of estimated utilization indicators for each of the plurality of electric vehicle charging stations based on the attributes and the reference data, the estimated charging detail record including a plurality of estimated utilization indicators including a duration of charging based on successive state changes and timestamps of the state changes and an energy used based on the duration and the reference data, acquiring an installation criterion for a new electric vehicle charging station, and outputting data indicative of a utilization of the electric vehicle charging station by location based on the estimated charging detail record and the criterion.

[0006] In one example, the attribute includes a maximum power, the plurality of electric vehicle charging stations are categorized based on the maximum power of the electric vehicle charging stations, the reference data includes an average charging power for each category, and the energy used is determined based on the average power for each electric vehicle charging station category.

[0007] In one embodiment, additionally or alternatively, the estimated utilization metrics include revenue indicative of user demand, determined based on energy used, duration, session fee, price per minute, and price per kilowatt hour.

[0008] In one example, additionally or alternatively, the plurality of estimated utilization indicators of the reference data include at least one of energy used, duration, or revenue for charging the set of electric vehicle charging stations, and the method further includes determining a combined value for at least one of energy used, duration, or revenue for the set of the plurality of electric vehicle charging stations based on the reference data and the estimated charging detail records, determining an adjustment factor for the at least one estimated utilization indicator based on a difference between the combined value determined based on the estimated charging detail records 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, the plurality of estimated utilization metrics additionally or alternatively includes a charge number based on a number of consecutive state changes.

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

[0011] In one embodiment, additionally or alternatively, the method includes determining one or more location types for each of the estimated charging detail records based on location information of the respective charging stations, the determination criteria including at least one location type for which installation of the new electric vehicle charging station is to be considered.

[0012] In one embodiment, additionally or alternatively, each estimated charging detail record includes a timestamp, the criteria include at least one time interval, and the output data includes an increase factor for at least one of the estimated utilization indicators based on the criteria and the timestamp of the estimated charging detail record.

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

[0014] In one embodiment, additionally or alternatively, the criteria include one or more thresholds for at least one of the estimated utilization index or the increase factor, and the output data includes recommended locations for installing new charging stations based on the one or more thresholds.

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

[0016] In one embodiment, the method additionally or alternatively includes determining a location score for a particular location based on the criteria and an estimated charging detail record associated with the location, and the output data includes the location score.

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

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

[0020] Many of the attendant features will be more readily appreciated as the same becomes better understood by reference to the following detailed description considered in conjunction with the accompanying drawings, in which:

[0021] The accompanying drawings, which are incorporated in and constitute a part of this specification to provide a further understanding of the exemplary embodiments, illustrate exemplary embodiments and, together with the description, help to explain the principles of the exemplary embodiments. [Brief description of the drawings]

[0022] [Figure 1] 1 is an example flow diagram of a method for generating improved estimates of electric vehicle charging station utilization rates for optimizing a network of electric vehicle charging stations, according to one illustrative embodiment. [Diagram 2] FIG. 1 illustrates an example of a method for optimizing a network of electric vehicle charging stations based on data indicative of current network utilization to locate new electric vehicle charging stations, according to one illustrative embodiment. [Diagram 3]FIG. 1 illustrates an example of captured attributes of an electric vehicle charging station and generated inferred charging detail records according to an illustrative embodiment. [Figure 4] FIG. 13 illustrates an example of an estimated charging detail record updated based on a reference charging detail record according to an exemplary embodiment. [Diagram 5] FIG. 13 illustrates an example of a method for updating an estimated charging detail record based on an adjustment factor, according to an exemplary embodiment. [Figure 6] FIG. 2 illustrates an example of output data showing utilization of a plurality of electric vehicle charging stations according to an illustrative embodiment. [Figure 7] FIG. 13 illustrates an example of a user interface configured to output data indicative of utilization rates of a plurality of electric vehicle charging stations based on configured criteria, according to one exemplary embodiment. [Figure 8] FIG. 13 illustrates another example of a user interface configured to output data indicative of utilization rates of a plurality of electric vehicle charging stations based on configured criteria, according to an exemplary embodiment. [Figure 9] FIG. 1 illustrates an example of the rate of increase in utilization of electric vehicle charging stations based on location, according to one illustrative embodiment. [Figure 10] FIG. 1 illustrates an example calculation of a growth rate for a set of electric vehicle charging stations, according to one illustrative embodiment. [Figure 11] FIG. 13 illustrates an example of output data including a rate of increase in utilization of electric vehicle charging stations based on established criteria, according to one illustrative embodiment. [Figure 12] FIG. 13 illustrates an example of a user interface and output data for optimizing a network of electric vehicle charging stations based on acquired attributes, generated data, and set criteria according to one illustrative embodiment. [Figure 13] FIG. 13 illustrates an example of data presented to estimate the location of a new charging station based on location scores, according to one exemplary embodiment. [Figure 14]FIG. 1 illustrates an example of a computing device configured to execute one or more exemplary embodiments, according to an exemplary embodiment. [Figure 15] FIG. 1 illustrates an example of a computing device configured to obtain data from multiple data sources to optimize a network of electric vehicle charging stations, according to one illustrative embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] Reference will now be made in detail to the illustrative embodiments, examples of which are illustrated in the accompanying drawings. The detailed description presented below in conjunction with the accompanying drawings is intended to describe the examples, and is not intended to represent the only manner in which the examples may be constructed or utilized. The description sets forth functions of the examples, and possible sequences of acts for constructing and operating the examples, although the same or equivalent functions and sequences may be accomplished in different examples.

[0024] Charging stations (CS) are used to charge electric vehicles (EV). There are many different charging station operator (CPO) companies that own and operate different charging stations in different locations.

[0025] Charging stations may have various Point of Interest (POI) data publicly available. A POI may generally refer to the location of a particular geographic point that someone may find useful or interesting. POI data may include location information and additional data such as name, time information, or other data that may be useful to an interested party.

[0026] The POI data may have both static and dynamic attributes. Static POI attributes may include, for example, location (coordinates), connector (Type 2, CCS, CHaDeMo, etc.), maximum charging power (kW), address (city, street name, number, etc.), country, etc. Static POI data generally includes various attributes of the charging station that typically cannot be changed.

[0027] The dynamic POI data may represent the current status of the charging station. The dynamic status may include, for example, available, out of order, charging, unavailable, offline, and / or reserved. However, there is no one standard for the dynamic POI data or status and it may vary depending on the CPO and POI data. Additionally, the dynamic POI data may include the charging cost.

[0028] Both static and dynamic POI data may be available from various different public POI data sources. POI data may be obtained, for example, from various charging station operators. Many companies working on EV charging need various data to optimize the structure of their charging network. Data of interest may include, for example, how many charges there are at a particular location, what the utilization rate is, and how much energy is charged at various stations. However, the required data may not be public information. On the contrary, many companies would prefer to keep this information private and do not share their data publicly.

[0029] An exemplary embodiment allows optimal decisions regarding the placement of electric vehicle charging stations. Advantageously, it provides an easy way to obtain improved data for determining the location of new charging stations, allowing for efficient use of the new charging stations. The optimization can be performed using publicly available data, which may not require detailed data from the charging station owners. This means that the utilization index of a CS network can be accurately estimated without having access to actual measurements of the utilization index. Furthermore, the estimation can be performed for the entire network, based on public data, even if the network is composed of charging stations of different manufacturers and different operators, located anywhere in the world.

[0030] The data is not based on charging stations of a single operator or geographic area, but data from multiple sources is aggregated to make better decisions, which may improve optimization. Positioning decisions can be made based on generated information regarding the worst locations for charging stations, where there may already be a large number of charging stations with low utilization, and the best locations with high utilization. The data can further provide insight into how various attributes associated with charging stations, such as type of connection, maximum power, or type of pricing, affect the utilization of the charging stations.

[0031] In one exemplary embodiment, the at least one dynamic operating state is used to calculate the number of charging sessions. Furthermore, the POI data can be used to generate estimated ratings, for example, of the duration of charging at a particular charging station, the energy used for charging, and the utilization rate of the charging station. The estimates can indicate user demand for charging stations at different types of locations. The attractiveness of different charging station types at different locations can be estimated based on the POI data.

[0032] FIG. 1 illustrates an example of obtained attributes of an electric vehicle charging station and generated inferred charging detail records according to one illustrative embodiment.

[0033] 1 illustrates an example of a first acquired POI data set 100 and a second acquired POI data set 101. The POI data sets 100, 101 may include various types of information related to one or more charging stations. The POI data may include multiple attributes of the charging station, such as the charging station's identification, address, status, model, maximum power, etc.

[0034] In the second POI data set 101, one of the charging stations may have a status change from available (at 100) to in use. The status change may be indicated by a succession of dynamic operating states that include changed data. Thus, charging at the respective station (VRT_0001) may have started.

[0035] In response to the status change, a new CDR record 103_1 may be created. The CDR record 103_1 may include, for example, the station's identification information and the start time of the charging event, i.e., a timestamp of the data update related to the status change. Subsequently, another POI dataset 102 may again indicate a status change of the charging station. The status may now have changed from in use to available, indicating that charging has stopped. In response, the CDR record 103_2 may be updated with a new status change timestamp indicating the time when charging stopped. Additionally or alternatively, the total duration of charging (from start to stop) may be updated in the CDR based on the timestamp.

[0036] 2 illustrates an example of an estimated charging detail record 203 updated based on reference data, according to an exemplary embodiment. The charging detail record may generally include multiple utilization metrics of the CS, such as state changes, timestamps of state changes, number of charge attempts, duration of each charge, energy used per charge, and / or utilization percentage of the charging station. The charging detail record may further include attributes related to the CS, such as CS ID, model information, connector type, geographic location, maximum power, and / or operator.

[0037] Based on attributes from POI data 200, the maximum power of the charging station is known to be 10 kW. The estimated rating CDR 201 may include an identification of the charging station, and a start time, end time, and duration of charging at the charging station. The reference data 202 may include an average charging power value for a category selected based on the maximum power of the charging station.

[0038] Because an electric vehicle may not be charged to full power from start to stop, an average power relative to a maximum power may be determined based on the reference data 202. The reference data set may include detailed data, including known values ​​of at least one utilization metric. The reference data 202 may be obtained from a set of electric vehicle charging stations.

[0039] Simply calculating E=Pmax×t in terms of the energy used will produce erroneous results. The average power of the reference data 202 is calculated based on all the charges in the reference data and the power per charge.

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[0040] In one exemplary embodiment, the duration of charging and the maximum power of the charging station used for charging can be obtained from public data, such as the POI data shown in Figure 1. Then, instead of using E = Pmax x t, which would give an erroneous result, the energy used for charging can be calculated as follows: E=P category_avg ×t Thus, a more accurate value of the energy used can be obtained with respect to the estimated CDR.

[0041] FIG. 3 illustrates an example flow diagram of a method 300 for generating improved estimates of electric vehicle charging station utilization rates to optimize a network of electric vehicle charging stations, according to one illustrative embodiment.

[0042] At 301, a set of charge detail records (CDRs) is selected from a reference dataset. The reference dataset may include detailed data, including known values ​​of parameters of interest, obtained from a set of charging stations.

[0043] At 302, an inferred rating CDR dataset can be created based on the public data, such as POI data. The inferred rating CDR dataset can include CDRs created for the same charging stations from which the set of CDRs was selected from the reference dataset.

[0044] At 303, a plurality of estimated utilization metrics may be determined based on the estimated evaluation CDR. The utilization metrics may include, for example, an estimated evaluation of at least one of a duration of charging, an amount of energy usage, and a utilization percentage. Each of the plurality of utilization metrics determined for the same set of charging stations from which the criteria set was selected may be aggregated into a combined estimated utilization metric value.

[0045] In addition to the energy usage, duration, and utilization rate, other metrics can be calculated to help optimize the charging network. One of the metrics can be the revenue of a charging session. The revenue can be used as a measure of the demand and attractiveness of a charging location to users. Thus, the revenue can be used to estimate the development needs of the charging network. The charging prices of the charging stations can be known from the POI dataset. Charging can be priced using, for example, a fixed session fee, a time-based fee, or an energy-based fee. The total revenue of one charging can be calculated as follows: Revenue=(1×session fee)+(time×time-based fee)+(energy×energy-based fee) In this formula, time is the duration of charging, and energy is the energy used, calculated based on the duration of charging and the average power from the reference set.

[0046] A plurality of utilization metrics may be calculated based on the baseline data set at 304. The respective baseline utilization metrics associated with the set of charging stations may be aggregated into a respective baseline utilization metric combination value.

[0047] An adjustment factor may be calculated for each of the multiple utilization metrics at 305. The adjustment factor may include a percentage value indicating how much the estimated utilization metrics should be adjusted.

[0048] The difference between the estimated utilization index and the baseline-based utilization index can be calculated to determine an adjustment factor. For example, the actual duration may be 3.2% shorter than estimated, 4.1% more energy may be used, and 2.5% more revenue may be generated. Detailed and accurate data including actual values ​​obtained from the baseline database along with the adjustment factor can be used to update the estimated utilization index for any charging station.

[0049] At 306, a final estimated utilization index can be determined based on the adjustment factor and the estimated CDR. For example, if a normal estimation evaluation method indicates that the duration of charging is 100 minutes, a duration value of 100 x 1.032 = 103.2 minutes can be used. At 307, the correction of the estimate can be repeated periodically, and the latest value can then be used for the estimated evaluation of the CDR.

[0050] FIG. 4 illustrates an example of determining an improved CDR using an adjustment factor, according to one exemplary embodiment.

[0051] 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 chargings including charging station identifiers, and estimated utilization metrics and reference utilization metrics, such as duration, energy usage, and revenue for a single charging. The estimated evaluation utilization metrics for the CDR may be calculated using the algorithms described above in the description of Figures 1-3.

[0052] At 401, the difference between the estimated utilization index and the reference utilization index may be calculated based on the total value of the single charges. At 402, the CDR of the data set may be updated using the adjustment factors by multiplying the estimated utilization index of each single charge by the respective adjustment factor, thereby improving the accuracy of the estimated values.

[0053] The improved data of charging station utilization can be used to develop the charging network. In one embodiment, the optimal location of a new charging station 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 CPO charging stations in the same location, but one of them has a significantly higher utilization rate than the others, it can be determined whether there is a demand for more charging stations with certain technical or user experience related design features.

[0054] 5 illustrates an example of a flow diagram 500 for determining optimal locations of electric vehicle charging stations based on the generated CDRs, according to an exemplary embodiment. Criteria for optimal locations may include, for example, few charging stations with high utilization rates and / or high demand for charging stations. Demand may be estimated based, for example, on high energy used or high revenue from charging stations. Charging station locations may be classified based on location type to obtain broader insights into the development needs of the charging station network.

[0055] At 501, one or more target location types can be selected for locating electric vehicle charging stations. Location types may include, for example, coordinates, city, street, street type, gas station, shopping center, office building, proximity to a particular location or building, etc. Location type can be a dynamic attribute such that new location types can be added as needed. A single charging station can be associated with multiple location types in the CDR.

[0056] At 502, each CDR of an existing charging station associated with a selected location type can be retrieved.

[0057] At 503, an aggregate of key attributes can be calculated from the CRDs of the charging stations associated with each of the location types. The key attributes can be user determined or pre-defined. The key attributes can include, for example, the number of charging sessions, the utilization percentage, the total energy used, or any of a number of estimated utilization metrics.

[0058] At 504, the primary attributes can be sorted based on location type.

[0059] At 505, a report of the data sorted by location type may be displayed. The data, in one embodiment, may be presented based on any criteria set by a user, which may include, for example, location types or groups of geographic locations, charging station types, estimated utilization metric thresholds, or combinations thereof.

[0060] The optimal location of the charging station can be displayed on a map based on the collected location type data. The optimal location may already have a station present or may be an entirely new location.

[0061] In one example, data collected from charging stations in shopping centers can reveal estimated good utilization indicators, such as an average utilization of 40%, daily energy of 239 kWh, and daily revenue of 235 euros. Based on this data, shopping centers that do not yet have a charging station can be searched for and proposed to install a new station there. The same logic can be used to search for inappropriate location types, e.g. public roads with low utilization rates. Thus, it is possible to avoid installing a charging station on a public road where a charging station is not yet installed, but to search for shopping centers that are close to a public road and install a charging station there.

[0062] If a charging station needs to be built in an entirely new location, such as a small city that did not have a charging station before, geographic planning cannot be used since there is no baseline data for the city. However, the location type and the generated CDRs can be used to estimate usage, as shown in Figures 6 and 7.

[0063] The output data 600 may include a list of charging stations, associated location types, and multiple estimated utilization metrics for each of the charging stations. The data 600 may, for example, indicate that there are a total of 3290 charging stations with location type "small city" and that those stations have an average of 38 charging sessions per month. Using the user interface 700, the user may change the set criteria of the output data to, for example, check the location type combination of charging stations with location type "small city". The data 701 may indicate that the combination "small city, parking lot, office" has 20 sessions per month, while "small city, gas station" has 39 sessions per month. Based on the presented data, the user may make better decisions when developing a network of charging stations. Based on the above, the user may conclude that a suitable location for a new charging station in the small city in this case is a gas station in the city.

[0064] In one exemplary embodiment, it may be considered whether to install a new charging station at a gas station or at a shopping center. Again, based on the location type, it may be calculated that charging stations with a location type of "gas station" have an average of 32 sessions per month, whereas charging stations with a location type of "shopping center" have an average of 42 sessions per month. In this case, a decision may be made to first find a shopping center that does not yet have a charging station and install the new station there. This decision may be based on an estimated utilization index of a wide network of charging stations based on publicly available data, rather than just guesswork or a small sample of the network based on data from only one operator's charging stations.

[0065] In one exemplary embodiment, the geographic attributes of the POI data may be structured hierarchically, for example as follows: Continents (Europe, Asia, etc.) Country (Germany, France, etc.) Region (Bavaria, Hesse, etc.) Cities (Munich, Nuremberg, etc.) Districts (Altstadt, Lehel, etc.) Streets (Etstrasse, Löwengrübe, etc.)

[0066] If the POI data includes these attributes and they form a hierarchy, the POI data can be used in analysis to generate reports and partition the data, as shown in FIG. 8. For example, a European company may be interested in installing a new charging station somewhere in Europe. The company can use the method presented above to optimize the location of the charging station. This method may allow to generate and output summary reports 801, 802 for comparing all European countries. The interface shown in 800 allows the user to set different criteria for the report, for example to select a country and see statistics for different cities in that country. The criteria may include, for example, one or more cities to see statistics for different districts in the country. In addition, the criteria may include, for example, one or more districts to see statistics for different streets in the district. The user can thus search for the best new location by filtering and trying different options based on the estimated CDRs generated for the current charging station network.

[0067] The provided method may allow for easy search of suitable locations for installing new charging stations and early generation of estimates of charging station usage at the locations. In one exemplary embodiment, geographic data may be combined with location-type analysis. For example, a user may first search for a city based on geographic data and then switch to location-type data to see statistics for various locations (gas stations, shopping centers, etc.) in the city.

[0068] The previous examples showed snapshots of the current situation based on various criteria set by the user, but when planning locations for new charging stations, it can be important to understand trends rather than just the current situation.

[0069] If we simply look at a snapshot of the situation today, we might see that Berlin has a total of 5500 chargings per month, while Munich only has 4300 chargings per month. We might therefore draw the conclusion that Berlin is a more suitable location for a new charging station. However, our time series data might look something like that shown in Figure 9. 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 much faster, making Munich a more attractive location.

[0070] Each CDR may have a timestamp indicating when the CDR was created. Based on the timestamp, a growth factor may be calculated as a percentage (%) over a particular time span (e.g., 2019-2021). Additionally or alternatively, growth factors may be calculated over particular time intervals (e.g., monthly or so) grouped by geography or location type, as shown in an example of generated data 1000 in FIG. 10.

[0071] In one example embodiment, a user can select a first period of a timespan (e.g., January 2021). The key measurements (one or more utilization metrics, such as number of charging sessions, utilization %, energy, etc.) for that timespan can be calculated. The user can then select a second period (e.g., February 2021). The same measurements can be calculated for the next period. The measurements for the second period can be compared to the previous period to calculate a percentage growth per period as follows: Growth=Measurement current / Measurement previous

[0072] The location of the new charging station can be determined based on the estimated utilization index and the growth factor. Criteria for a suitable location may include, for example, a large growth rate of charging sessions and a fairly high utilization percentage, which can mean that the location may have too few charging stations. One or more thresholds can be determined for the growth rate and the estimated utilization index that indicate a suitable or unsuitable location.

[0073] FIG. 11 shows an example of a user interface 1100 and data 1101 presented based on a selection made by a user from the user interface. Data can be configured to be displayed based on criteria set by the user, for example, based on the growth rate and the selected utilization metric. The example data in FIG. 11 shows that Frankfurt has the most charging sessions, but station utilization is quite low and the growth trend is downwards. At the same time, Berlin has the least charging sessions, but station utilization is very high and growing fast, which means that Berlin may soon need more charging stations. For this reason, Berlin is an attractive location for new stations. The user can determine the location (based on the set geography or location type) with respect to the optimal location.

[0074] FIG. 12 shows another example of a user interface and data presented based on a user selection, where the data is displayed based on location type. Based on an analysis of the location type selected by the user at 1200, the data shows that at 1201, the utilization rate of a gas station is fairly low, but a large amount of energy is being charged there and the amount of energy charged is increasing. At the same time, the energy being charged at an office building is fairly low, despite its high utilization rate. Based on this data, the user can draw a conclusion that the gas station is a good location for a new charging station because a large amount of energy is being charged there, even though the utilization rate of the gas station is fairly low.

[0075] However, this kind of data can be a bit difficult to read and requires some data analysis expertise to understand. In one embodiment, the data may include new location recommendations 1202 that are easy to understand for non-experts. The point is that more accurate and quicker decisions on network construction can be made. The recommendations can be based on a location score. The location score can be calculated by combining some statistics from the estimated charging detail records with set criteria. The location score can further be based on an increase factor.

[0076] The location score can be limited to an area (e.g., Europe, Germany, etc.) and / or a location type. As shown in Fig. 13, the determined data 1301 can be used to test how suitable a particular location is assessed to be. There need not be an existing charging station at the location, and the recommendation or location score can provide information on whether the location is optimal for installing a new charging station based on the estimated CDR and location type.

[0077] In one exemplary embodiment, a user is planning to install a new charging station in a shopping center in Munich city center. A location score can be calculated using multiple estimated utilization metrics, such as current number of charging sessions, current utilization, current energy, and a 12-month growth factor for each of the current metrics, based on set criteria.

[0078] The location score can be calculated by determining the average value of the estimated utilization index and the growth factor based on the estimated CDRs linked to the selected geographic location and / or location type (e.g., a specific city and gas station in Germany). The optimal values ​​of the estimated utilization index and the growth factor can be determined based on all CDRs of the area (e.g., Germany and all location types) for reference. The average value of the selected location can be divided by the optimal value of the area and multiplied by a maximum point set for the estimated utilization index and the growth factor.

[0079] For example, at an average station in an office location in Berlin, there may be 18 chargings per month, and at an optimal charging station in Germany, there may be 54 chargings per month. The maximum points for the number of charging sessions may be selected to be 15. Thus, the location score for the number of charging sessions may be (18 / 54) x 15 = 5 points. The location score may be calculated for each estimated utilization index and growth factor in a similar manner. The location scores may be aggregated to obtain a total location score for the configured location / location type. The total location score is 73 out of a maximum total score of 100 for the selected location criteria in the example of FIG. 13. The user may experiment with how the score changes with different criteria to determine the optimal location from multiple possible locations.

[0080] 14 illustrates an example of a computing device 1400 configured to perform one or more exemplary embodiments, according to an exemplary embodiment. The computing device 1400 may be configured to perform any aspect of the methods described herein.

[0081] The computing device 1400 may include at least one processor 1401. The at least one processor 1401 may include one or more of a variety of processing devices, such as a coprocessor, a microprocessor, a controller, a digital signal processor (DSP), processing circuitry with or without an associated DSP, or a variety of other processing devices, including integrated circuits, such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a microcontroller unit (MCU), a hardware accelerator, a special purpose computer chip, or the like.

[0082] The computing device 1400 may further comprise at least one memory 1402. The memory 1402 may be configured to store, for example, operating system software and application software, such as, for example, computer program code 1403. The memory 1402, in one embodiment, may include EV charging station data obtained, for example, based on the 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. The memory may be embodied, for example, as a magnetic storage device (such as a hard disk drive, magnetic tape, etc.), a magneto-optical storage device, or a semiconductor memory (such as a mask ROM, a programmable ROM (PROM), an erasable PROM (EPROM), a flash ROM, a random access memory (RAM), etc.).

[0083] The computing device 1400 may further comprise a communication interface 1405 configured to enable the transmission and / or reception of information to and from other devices of the computing device 1400. The communication interface 1405 may be configured to enable at least one high frequency wireless connection, such as a 3GPP mobile broadband connection (e.g., 3G, 4G, 5G), etc. However, the communication interface 1405 may also be configured to enable one or more other types of connections, such as a wireless local area network (WLAN) connection, such as those standardized by the IEEE 802.11 series or the Wi-Fi Alliance, a short-range wireless network connection, such as a Bluetooth, NFC (near-field communication), or RFID connection, a wired connection, such as a local area network (LAN), universal serial bus (USB), or optical network connection, or a wired Internet connection. The communication interface 1405 may comprise or be configured to be coupled to at least one antenna for transmitting and / or receiving radio frequency signals. One or more of the various types of connections may be implemented as a separate communication interface that may be coupled or configured to be coupled to multiple antennas.

[0084] The computing device 1400 may be configured to obtain data from multiple data sources, as shown in FIG. 15. The computing device 1400 may be configured to obtain data related to multiple electric vehicle charging stations 1500, for example, from one or more POI data sources 1501. The POI data may be obtained, for example, based on a pull or push approach. In a pull approach, the data may be fetched periodically from a database. The data may be retrieved at configurable intervals, such as every minute, every five minutes, or every fifteen minutes, and the retrieved data may be compared to a previous version of the same data to tabulate the differences in the data. In a push approach, the data may be obtained from a provider of the data, such as the CPO of the respective charging station. The data may be obtained, for example, as updates whenever there is a change in the data. The computing device 1400 may be configured to obtain data from the set of charging stations 1502, including data corresponding to the public data from other charging stations 1500, and additional data having precise information from charging sessions at the set of charging stations 1502, in addition to the public data. The additional data may include, for example, energy used per charge, charging duration, revenue from charging, and / or utilization rate of charging stations.

[0085] The computing device 1400 may further include a user interface 1404 including input and / or output devices. The input devices may take a variety of forms, such as a keyboard, a touch screen, or one or more built-in control buttons. The output devices may include, for example, a display.

[0086] If the computing device 1400 is configured to perform a certain function, then any component and / or multiple components of the computing device 1400 may be configured to perform that function, such as, for example, the at least one processor 1401 and / or the memory 1402. Further, if the at least one processor 1401 is configured to perform a certain function, then the function may be performed using, for example, program code 1403 carried by the memory 1402.

[0087] The functionality described herein may be performed, at least in part, by one or more computer program product components, such as software components. Computing device 1400, according to one embodiment, includes processor 1401 or processor circuitry, such as a microcontroller, that, when executed, is configured by program code 1403 to perform embodiments of the operations and functions described herein. Alternatively or additionally, the functionality described herein may be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that may be used include, but are not limited to, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-a-chip systems (SOCs), complex programmable logic devices (CPLDs), and graphics processing units (GPUs).

[0088] The computing device 1400 comprises means for performing at least one of the methods described herein, which in one example comprises at least one processor 1401 and at least one memory 1402 having program code 1403 configured, when executed by the at least one processor 1401, to cause the computing device 1400 to perform the method.

[0089] Computing device 1400 may include, for example, a server device, a client device, a mobile phone, a tablet computer, a laptop, etc. Although computing device 1400 is shown as a single device, it should be understood that whenever applicable, the functionality of computing device 1400 may be distributed across multiple devices.

[0090] It is obvious to a person skilled in the art that with the advancement of technology, the basic idea can be realized in various ways, therefore the present aspect and its embodiments are not limited to the examples described above, but rather can be modified within the scope of the claims.

[0091] Any ranges or device values ​​set forth herein may be expanded or modified without losing the effect sought, and any embodiment may be combined with another embodiment unless expressly prohibited.

[0092] Although the subject matter has been described in language specific to structural features and / or acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example implementations of the claims, and other equivalent features and acts are intended to be within the scope of the claims.

[0093] It will be understood that the benefits and advantages described above may pertain to one embodiment or to several embodiments. The embodiments are not limited to those that solve some or all of the problems described or those that have some or all of the benefits and advantages described. Furthermore, it will be understood that references to "an" item, "at least one" item, and "one or more" items may refer to one or more of these items, or to multiple of these items.

[0094] The actions of the methods described herein may be performed in any suitable order, or simultaneously where appropriate. Additionally, individual blocks may be deleted from any of the methods 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 of the other embodiments described to form further embodiments without losing the desired effect.

[0095] As used herein, the term "comprising" means including the specified methods, blocks, or elements, but such blocks or elements do not constitute an exclusive list and the method or apparatus may include additional blocks or elements.

[0096] Although an object may be referred to as a "first" object or a "second" object, this does not necessarily indicate an order or importance of the objects. Such conjunctions may instead be used only to distinguish between the objects.

Claims

1. acquiring attributes of a plurality of electric vehicle charging stations based on publicly available data sources (100, 101, 102, 200), the attributes including location information of each of the plurality of electric charging stations and state changes related to charging of electric vehicles; obtaining reference data (202) including accurate values ​​of a plurality of utilization metrics, including charging power, of the set of a plurality of electric vehicle charging stations; determining (302, 400) an estimated charging detail record (201, 203) for each of the plurality of electric vehicle charging stations based on the attributes and the reference data, the estimated charging detail record (201, 203) including a plurality of estimated utilization indicators (303) for each of the plurality of electric vehicle charging stations, the estimated utilization indicators (303) including duration of the charging based on successive state changes and timestamps of the state changes, and energy used based on the duration and the reference data; Obtaining (501, 1200) installation criteria for a new electric vehicle charging station; and determining an optimal location for installing the new electric vehicle charging station (505, 600, 701, 801, 802, 1000, 1101, 1201, 1202, 1301), outputting data indicative of utilization of electric vehicle charging stations by location based on the estimated charging detail records and the criteria; A computer-implemented method comprising:

2. the attribute includes a maximum power, and the plurality of electric vehicle charging stations are categorized based on a maximum power of the plurality of electric vehicle charging stations; The reference data (202) includes an average charging power for each category; The computer-implemented method of claim 1 , wherein the energy used is determined based on the average power of the category of the respective electric vehicle charging stations.

3. 3. The computer-implemented method of claim 1 or 2, wherein the estimated utilization metrics include revenue indicative of user demand determined based on the energy used, the duration, session fee, price per minute, and price per kilowatt hour.

4. the plurality of estimated utilization metrics (303) of the reference data include at least one of energy used, duration, or revenue for charging the set of electric vehicle charging stations; The method further comprises determining (304) a combined value for at least one of the energy used, the duration, or the revenue for the set of the plurality of electric vehicle charging stations based on the reference data and the estimated charging detail records. determining (305) an adjustment factor for at least one of the estimated utilization indicators based on a 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 (306) each of the estimated charging detail records based on the at least one adjustment factor; 4. The computer-implemented method of claim 1, further comprising:

5. 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.

6. 6. The computer-implemented method of claim 1, wherein the plurality of estimated utilization metrics includes a utilization percentage based on a total duration of charging for a day divided by minutes for that day.

7. 7. The computer-implemented method of claim 1, further comprising determining one or more location types for each of the estimated charging detail records based on location information of each charging station, the determination criteria including at least one location type for which a new electric vehicle charging station is to be considered for installation.

8. Each estimated charge detail record includes a timestamp and 8. The computer-implemented method of claim 1, wherein the criteria include at least one time interval, and the output data includes an increase factor (1000) for at least one of the estimated utilization indicators based on the criteria and the timestamp of the estimated charging detail record.

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

10. 10. The computer-implemented method of claim 1, wherein the criteria include one or more thresholds for at least one of the estimated utilization index or the increase factor, and the output data includes recommended locations (1202) for installing new charging stations based on the one or more thresholds.

11. 11. 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 an optimal location, or one or more estimated utilization metrics.

12. The method further comprising: determining a location score for a particular location based on the criteria and the estimated charging detail record associated with the location.

12. The computer-implemented method of claim 1, comprising:

13. At least one processor (1401); At least one memory (1402) having computer program code (1403); 13. A computing device (1400) comprising: said at least one memory (1402) and said computer code (1403), together with said at least one processor (1401), configured to cause said computing device (1400) to perform a method according to any one of claims 1 to 12.

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

15. A computer readable medium having instructions which, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 12.

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