Method, apparatus, and computer program product for monitoring energy consumption in an electric vehicle charging network

Abstract

The present disclosure generally relates to electric vehicles, and more particularly, to methods, devices, and computer program products for monitoring energy consumption in an electric vehicle (EV) charging network. The methods, devices, and computer program products allow for obtaining a more accurate energy consumption profile in real time at each electric vehicle charging station of interest. The total average energy consumed by an electric vehicle at a given moment at an electric vehicle charging station of interest can then be calculated using the energy consumption profile.

Description

Technical Field

[0001] The present disclosure generally relates to electric vehicles, and more particularly, to methods, apparatuses, and computer program products for monitoring energy consumption in an electric vehicle (EV) charging network (100). Background Art

[0002] Currently, electric vehicles are increasingly being used for land, sea, and air travel. However, due to limited battery capacity, electric vehicles often need to be charged one or more times at electric vehicle charging stations along the way to the destination. The location and density of electric vehicle charging stations directly depend on the usage rate of electric vehicles in the relevant geographical area. Therefore, it is very important to know the frequency at which electric vehicle owners use a specific electric vehicle charging station during the day for optimizing the charging of electric vehicles and energy production at these charging stations.

[0003] In summary, each electric vehicle charging station is usually equipped with an electricity meter, which is configured to perform energy measurements at a given time and send the energy measurement information to a dedicated data center for further processing. Different electric vehicle charging stations may send this information through different communication protocols and time intervals. In addition, errors such as data communication problems may occur, resulting in the electric vehicle charging station not sending the above information to the data center. These do not timely obtain the accuracy of the appropriate energy consumption distribution, making it difficult or even impossible to calculate the total average energy consumed by an electric vehicle at an electric vehicle charging station of interest at a certain moment. Summary of the Invention

[0004] Some concepts are introduced herein in a simplified form, which will be further described in the detailed description below. This document is not intended to identify the key features or essential features of the claimed subject matter, nor is it intended to limit the scope of the claimed subject matter.

[0005] The main objective of the present disclosure is to provide a technical solution that allows for obtaining a more accurate energy consumption distribution in real time at each electric vehicle charging station of interest, and on this basis, calculating the total average energy consumed by an electric vehicle at an electric vehicle charging station of interest at a specific time.

[0006] The above objective is achieved by the features of the independent claims in the appended claims. Further embodiments and examples are apparent from the relevant claims, detailed description, and drawings.

[0007] According to a first aspect, a method for monitoring energy consumption in an electric vehicle (EV) charging network (100) is proposed. The electric vehicle charging network includes a plurality of electric vehicle charging stations, each equipped with an electricity meter. The method first receives new information from at least one electric vehicle charging station. The new information includes a timestamp, a station identifier (ID), a charging period ID unique to each charging period, and an electricity meter reading. Next, the method continues to store the new information in a database that includes similar old information from the electric vehicle charging stations. The method also includes a step of checking whether the database includes older information having the same station ID and charging period ID as the new information. If such older information exists in the database, the method proceeds to calculate the time difference and energy difference between the new information and the older information. The method also includes a step of selecting a time granularity level based on the calculated time difference, and obtaining an energy consumption distribution between the timestamps of the new information and the older information based on the time granularity level and the energy difference. This makes it possible to obtain information on how the energy consumption of the electric vehicle charging station changes over time, which in turn can be used to optimize electric vehicle charging and energy production at the electric vehicle charging station.

[0008] In one implementation form of the first aspect, the station ID includes the name, manufacturer information, and / or geographical location of the electric vehicle charging station. This allows for more detailed information about the electric vehicle charging station of interest to be obtained.

[0009] In one implementation form of the first aspect, the charging period ID is represented by one or more alphabetic and / or numeric characters. This simplifies the distinction of charging periods at each electric vehicle charging station.

[0010] In one implementation form of the first aspect, the timestamp is represented according to the ISO 8601 standard. This makes the method more flexible in use.

[0011] In one implementation form of the first aspect, the method further includes storing the energy consumption distribution in the database. By doing so, it becomes easier and more effective to monitor the energy consumption time history of a specific electric vehicle charging station.

[0012] In one implementation form of the first aspect, the step of obtaining the energy consumption distribution includes: dividing the time difference between the new information and the older information into a plurality of equal time intervals according to the time granularity level; calculating the average energy consumption for each time interval by dividing the energy difference by the number of the equal time intervals. This allows for controlling how the average energy consumption changes over time.

[0013] In one implementation form of the first aspect, the electricity meter reading and the average energy consumption are expressed in watt-hours or multiples of watt-hours. This makes the method more flexible in use.

[0014] In one implementation of the first aspect, the method further includes the following steps: converting the average energy consumption expressed in watt-hours or multiples of watt-hours to watts, calculating the average energy consumption for each time interval; obtaining the time energy consumption distribution between new information and older information based on the time granularity level and the average energy consumption; and storing the energy consumption distribution in a database. This power consumption distribution helps to more effectively optimize the electric vehicle charging and energy production of a specific electric vehicle charging station.

[0015] In one implementation of the first aspect, for the new information received from other electric vehicle charging stations, the above method steps are repeated. In this case, the method further includes calculating the total energy consumption of the electric vehicle charging network within a given time by comparing the energy consumption distributions of other electric vehicle charging stations. This helps to more effectively optimize the electric vehicle charging and energy production of the electric vehicle charging station of interest.

[0016] According to the second aspect, there is provided a device for monitoring energy consumption in an electric vehicle charging network. The electric vehicle charging network includes a plurality of electric vehicle charging stations, and each charging station is equipped with an electricity meter. The device includes at least one processor and a memory coupled to the at least one processor, and the memory stores a database including older information from the electric vehicle charging stations. Each piece of older information includes a timestamp, a site identifier (ID), a charging period ID unique to each charging period, and an electricity meter reading. The memory further stores processor-executable instructions that, when executed by the at least one processor, cause the at least one processor to: receive new information from at least one electric vehicle charging station; store the new information in the database; check whether the database includes older information having the same site ID and charging period ID as the new information; if older information exists in the database, calculate the time difference and energy difference between the new information and the older information; select a time granularity level according to the calculated time difference; and obtain the energy consumption distribution between the timestamps of the new information and the older information based on the time granularity level and the energy difference. In this way, it is possible to obtain information on the change in energy consumption of the electric vehicle charging station over time, which in turn can be used to optimize the electric vehicle charging and electric vehicle production of the electric vehicle charging station.

[0017] In one implementation of the second aspect, the site ID includes the name, manufacturer information, and / or geographical location of the electric vehicle charging station. In this way, more detailed information about the electric vehicle charging station can be obtained.

[0018] In one implementation of the second aspect, the charging period ID is represented by one or more letters and / or numeric characters. This simplifies the distinction of the charging periods of each electric vehicle charging station.

[0019] In one implementation of the second aspect, the timestamp is represented according to the ISO 8601 standard. This makes the instrument more flexible in use.

[0020] In one implementation of the second aspect, the memory further stores instructions that cause at least one processor to store the energy consumption distribution in a database. By doing so, it is possible to more easily and effectively monitor the energy consumption time history of a specific electric vehicle charging station.

[0021] In one implementation of the second aspect, at least one processor is configured to obtain the energy consumption distribution by: dividing the time difference between new information and older information into a number of equal time intervals according to a time granularity level; and calculating the average energy consumption for each time interval by dividing the energy difference by the number of equal time intervals. This allows an understanding of how the average energy consumption changes over time.

[0022] In one implementation of the second aspect, the electricity meter readings and the average energy consumption are expressed in watt-hours or multiples of watt-hours. This makes the instrument more flexible in use.

[0023] In one implementation of the second aspect, the memory further stores instructions that cause at least one processor to: calculate the average energy consumption for each time interval by converting the average energy consumption expressed in watt-hours or multiples of watt-hours to watts; obtain the time energy consumption distribution between new information and older information according to the time granularity level and the average energy consumption; and store the energy consumption distribution in a database. This power consumption allocation can more effectively help optimize the electric vehicle charging and energy production of a specific electric vehicle charging station.

[0024] In one implementation of the second aspect, at least one processor is configured to repeat the above operations for new information received from other electric vehicle charging stations. In this case, the memory further stores instructions that cause at least one processor to calculate the total energy consumption of the electric vehicle charging network at a given time by comparing the energy consumption distributions of other electric vehicle charging stations. This helps to more effectively optimize the electric vehicle charging and energy production of electric vehicle charging stations.

[0025] According to a third aspect, there is provided a computer program product including a computer-readable storage medium storing a computer program. The computer program executed by at least one processor causes at least one processor to perform the method according to the first aspect. In this way, the method according to the first aspect can be embodied in the form of a computer program, thus providing flexibility in use.

[0026] Other features and advantages of the present disclosure will become apparent upon reading the following detailed description and referring to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The essence of the present disclosure is explained with reference to the following drawings:

[0028] Figure 1 An example showing a typical electric vehicle charging network;

[0029] Figure 2 A block diagram of a device for monitoring energy consumption in an electric vehicle charging network according to one aspect of the present disclosure;

[0030] Figure 3 A flowchart of a method for monitoring energy consumption in an electric vehicle charging network according to another aspect of the present disclosure;

[0031] Figure 4 Showing the energy consumption correlation relationship of an electric vehicle charging station in a day obtained by adopting the method of Figure 3 ; Detailed Description of Specific Embodiments

[0032] Various embodiments of the present disclosure are further described in detail with reference to the accompanying drawings. However, the present disclosure can be embodied in many other forms and should not be construed as limited to any structure or function disclosed in the following description. Instead, these embodiments are provided to make the description of the present disclosure more detailed and complete.

[0033] According to the present disclosure, for those skilled in the art, the scope of the present disclosure clearly includes any embodiment of the present disclosure, regardless of whether the embodiment is implemented independently or in combination with any other embodiment of the present disclosure. For example, the devices and methods disclosed herein can be implemented by using any number of the embodiments provided herein. In addition, it should be understood that any embodiment of the present disclosure can be implemented by using one or more elements or steps set forth in the appended claims.

[0034] As used herein, "energy" refers to the electrical energy generated by one or more generators installed in an electric vehicle charging station or a remote power plant. In the latter case, the electrical energy can be provided to the electric vehicle charging station through cables or removable batteries. Correspondingly, "energy consumption" refers to the consumption of the electrical energy thus generated and stored in the electric vehicle charging station.

[0035] Figure 1 A typical electric vehicle charging network 100 is shown, which is usually deployed in a geographical area of interest. Among them, the electric vehicle charging network 100 includes four electric vehicle charging stations 102, 104, 106, 108 and a data center 110. Correspondingly, the geographical area of interest is exemplified by three roads 112, 114 and 116, on which four electric vehicles 118, 120, 122 and 124 are traveling. The positions of the respective electric vehicle charging stations are schematically shown as Figure 1The solid circles in. It can be seen that the electric vehicle charging station 102 is located along Road 112, the electric vehicle charging station 104 is located at the intersection of Roads 114 and 116, the electric vehicle charging station 106 is located at the intersection of Roads 112 and 116, and the electric vehicle charging station 108 is located along Road 116. In addition, each of the electric vehicle charging stations 102, 104, 106, and 108 is intended to be equipped with an electricity meter configured to measure energy at a certain time and send information about the energy measurement to the data center 110, as Figure 1 shown by the dashed arrow in. This information may include a timestamp, a station identifier (ID), a charging session ID unique to each charging session, and an electricity meter reading, so that the data center 110 can correctly distinguish the information sent by the electric vehicle charging stations 102, 104, 106, and 108.

[0036] Now let's consider a situation where car 118 is charging at the electric vehicle charging station 102, car 120 is charging at the electric vehicle charging station 104, car 122 is charging at the electric vehicle charging station 108, and car 124 is charging at the electric vehicle charging station 106. In this case, the electricity meters of the electric vehicle charging stations 102, 104, 106, and 108 can send the following text information to the data center 110:

[0037] · 2018-05-10 11:00:05 - Station 102 - Charging ID 10 - Electricity meter reading: 1100 Wh;

[0038] · 2018-05-10 11:05:23 - Station 104 - Charging ID 13 - Electricity meter reading: 436 Wh;

[0039] · 2018-05-10 11:06:25 - Station 102 - Charging ID 10 - Electricity meter reading: 1145 Wh;

[0040] · 2018-05-10 11:10:15 - Station 106 - Charging ID 27 - Electricity meter reading: 9923 Wh; · 2018-05-10 11:11:45 - Station 104 - Charging ID 13 - Electricity meter reading: 550 Wh;

[0041] · 2018-05-10 11:12:30 - Station 108 - Charging ID 30 - Electricity meter reading: 12340 Wh;

[0042] · 2018-05-10 11:16:23 - Station 102 - Charging ID 10 - Electricity meter reading: 1240 Wh.

[0043] From the above information, the following conclusions can be easily drawn. First, the electricity meters of the electric vehicle charging stations 102, 104, 106, and 108 can send information at different times. Second, the electricity meters of each electric vehicle charging station can send information irregularly, which can be easily seen from the timestamps of the information sent by the electric vehicle charging station 102. This irregularity may be caused by errors such as data communication problems, resulting in the electric vehicle charging station 102 sometimes not sending information. Given these conclusions, a problem is exposed, that is, it is impossible to calculate the total energy consumption of the electric vehicle charging network 100 at a certain time. For example, based on the above information, it is impossible to determine what the total energy consumption is at 11:10:00 on May 10, 2018.

[0044] The electricity meters of the electric vehicle charging stations 102, 104, 106, and 108 may send energy readings to the data center 110 using different information formats, which may cause another problem. For example, the energy readings sent by the electricity meters of the electric vehicle charging stations 102 and 104 are "enrg = XXX", while the energy readings sent by the electricity meters of the electric vehicle charging stations 106 and 108 are expressed as " <energy>XXX< / energy> ". This may complicate the information processing of the electric vehicle charging stations.

[0045] The present disclosure provides a technical solution for monitoring energy consumption in an electric vehicle charging network, which can alleviate or even eliminate the above problems. Although all aspects of the technical solution discussed below are related to electric vehicles, it should not be considered as any limitation. In other words, the aspects of the technical solution disclosed herein can be equally applied to other types of electric transportation means, such as electric aircraft and ships. For electric aircraft, the electric charging stations 102, 104, 106, and 108 may be located at different airports. The electric charging stations 102, 104, 106, and 108 are applicable to electric ships and can be located at different ports respectively.

[0046] Figure 2 A block diagram of a device 200 for monitoring energy consumption in an electric vehicle charging network is shown according to an aspect of the present disclosure. For simplicity, the electric vehicle charging network is intended to be the same as that shown in Figure 1 In this embodiment, the device 200 is intended to be arranged in the data center 110 of the electric vehicle charging network 100. As shown in Figure 2As shown, the apparatus 200 includes a memory 202 and a processor 204 coupled to the memory 202. The memory 202 stores processor-executable instructions 206, and at least one processor 204 will execute the instructions 206 to monitor the energy consumption of the electric vehicle charging network 100. The memory 202 also includes a database 208 that stores historical information from the electricity meters of the electric vehicle charging stations 102, 104, 106, and 108.

[0047] The memory 202 may be implemented as non-volatile or volatile memory used in modern electronic computers. For example, non-volatile memory may include read-only memory (ROM), ferroelectric random access memory (RAM), programmable memory (PROM), electrically erasable memory (EEPROM), solid state drives (SSD), flash memory, magnetic disk storage (such as hard disks and magnetic tapes), optical disk storage (such as CDs, DVDs, and Blu-ray discs), and so on. As for volatile memory, examples include dynamic RAM, synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), static RAM, and so on.

[0048] The processor 204 may be implemented as a central processing unit (CPU), general-purpose processor, single-purpose processor, microcontroller, microprocessor, application-specific integrated circuit (ASIC), field programmable gate array (FPGA), digital signal processor (DSP), complex programmable logic device, and so on. It should be noted that the processor 204 may be implemented as any combination of the above. For example, the processor 204 may be a combination of two or more CPUs, general-purpose processors, and so on.

[0049] The processor-executable instructions 206 stored in the memory 202 may be configured as computer-executable code, enabling the processor 204 to execute various aspects of the present disclosure. The computer-executable code for performing the operations or steps of various aspects of the present disclosure may be written in any combination of one or more programming languages (such as Java, C, C++, Python, or similar languages). In some examples, the computer-executable code may be in the form of a high-level language, or in a pre-compiled form, and is dynamically generated by an interpreter (also pre-stored in the memory 202).

[0050] The database 208 may be structured in tabular form, with each row corresponding to a piece of information received from an electric vehicle charging station, such as Figure 1As shown, and each column corresponds to information for a specific attribute, such as a timestamp, a station ID, a charging session ID, or a meter reading. In another embodiment, the database 208 can be configured as a set of tables, each table associated with information received from the same electric vehicle charging station. Meanwhile, the processor 204 can be configured to query and maintain the database 208 using any suitable programming language, for example, Structured Query Language (SQL).

[0051] Figure 3 FIG. 4 shows a flowchart of a method 300 for monitoring energy consumption in an electric vehicle charging network according to another aspect of the present disclosure. When causing the processor 204 to execute the processor-executable instructions 206, the method 300 will be executed by the processor 204 of the device 200.

[0052] Specifically, the method 300 begins at step S302, where the processor 204 receives new information from at least one of the electric vehicle charging stations 102, 104, 106, and 108. Similar to the old information, the new information includes a timestamp, a station ID, a charging session ID, and a meter reading. It should be noted that the representation of the attributes of each such information may be different. For example, the charging station ID may include the name of the electric vehicle charging station, manufacturer information, and / or geographical location. As for the charging session ID, it can be represented by one or more alphabetic and / or numeric characters. Apparently, the charging session ID is selected in ascending order for each next charging session. The timestamp can be presented according to the ISO 8601 standard, which covers the exchange of date and time-related data. The meter reading can be expressed in different energy units, but watt-hours (Wh) and its multiples, for example, kilowatt-hours (kWh), are the most common in electrical applications.

[0053] Once the new information is received, the method 300 proceeds to step S304, where the processor 204 accesses the database 208 in order to create a new entry for the new information therein. This means that the processor 204 creates a new row in the database 208 and fills the new row with the attribute values indicated in the new information.

[0054] Next, the method 300 proceeds to step S306, where the processor 204 checks whether the database 208 includes older information with the same station ID and charging session ID as the new information. To this end, the processor 204 can use an SQR query to retrieve the corresponding data from the database 208. If there is no older information with the information attributes of the above request, the method 300 ends. However, if the database 208 includes such older information, the method will continue.

[0055] In step S308, the processor 204 calculates the time difference and energy difference between the new information and the appropriate old information found in the database 208. The time difference is calculated by comparing the timestamps of the new information and the older information. For example, if the new information and the older information are received within one minute, the time difference may be given in seconds. As for the energy difference, it is calculated based on the electricity meter readings of the new information and the older information.

[0056] Further, the method proceeds to step S310, where the processor 204 selects a time granularity level based on the calculated time difference. Next, in step S312 of method 300, the processor 204 evenly divides the energy difference between the two timestamps of the new information and the older information according to the time granularity level. By doing so, the processor 204 finally obtains the energy consumption distribution between the two timestamps of the new information and the older information. After that, method 300 ends.

[0057] Now let's take an example to illustrate how method 300 is applied to information from an electric vehicle charging station. Suppose the data center 110 of the electric vehicle charging network 100 receives new information from the electric vehicle charging station 102. Accordingly, the reading of the electricity meter at 10:05 is 110 Wh. It is also assumed that the database 108 stored in the memory 202 of the device 200 includes old information with the same site ID and charging period ID as the new information, and the reading of the electricity meter at 10:00 is equal to 100 Wh. Given these initial data, method 300 can be continued as follows. First, the time difference is determined to be 5 minutes and the energy difference is 10 Wh. In this case, it is reasonable to select one minute as the time granularity level. With the selected time granularity level, there are 5 one-minute intervals between the timestamps of the new information and the older information. Next, the energy difference is evenly distributed over the 5 one-minute intervals, that is, 10 / 5 = 2 Wh / min. In other words, this example creates the following 5 data fields: 10:01 - 2 Wh, 10:02 - 2 Wh, 10:03 - 2 Wh, 10:04 - 2 Wh, 10:05 - 2 Wh. Then, the data fields can be stored in the memory 202 of the device 200, specifically stored in the database 208 as an additional attribute of the new information (i.e., its association with the old information). Thus, the energy consumption distribution between 10:00 and 10:05 is obtained. Although this energy consumption distribution presents the average energy consumption data of the electric vehicle charging station 102, it can enable the charging station operator to understand the usage rate of the electric vehicle charging station 102 at a certain moment.

[0058] If the data center 110 of the electric vehicle charging network 100 receives new information from all the electric vehicle charging stations 102, 104, 106, and 108, then the method 300 is performed on each piece of new information. Thus, assuming that the database 208 includes a suitable older piece of information for each piece of new information, an energy consumption distribution associated with each of the electric vehicle charging stations 102, 104, 106, and 108 can be obtained, as described above. In this way, the average total energy consumption of the electric vehicle charging network 100 at a certain moment can be determined. For example, if the average energy consumption of the electric vehicle charging station 102 at 10:03 is equal to 2 Wh, the electric vehicle charging station 104 at 10:03 is 4 Wh, the electric vehicle charging station 106 at 10:03 is 6 Wh, and the electric vehicle charging station 108 at 10:03 is 8 Wh, then the total average energy consumption of the electric vehicle charging network 100 at 10:03 is 2 + 4 + 6 + 8 = 20 Wh.

[0059] In one embodiment, the method 300 may further include the step of converting the average energy consumption of each electric vehicle charging station at a specific time, calculated in watt-hours or multiples of watt-hours (or any other energy unit), into an average energy consumption calculated in watts (W). As is well known, energy (E) is equal to power (P) multiplied by time (t), i.e., E = P·t. As shown in the above example, this means that the average power consumption of the electric vehicle charging station 102 at 10:03 is equal to P = 2 Wh / (1 / 60 h) = 120 W. Through such a conversion, an average energy consumption distribution between the time stamps of the new information and the older information received from the electric vehicle charging station 102 can be obtained. Similar to the energy consumption distribution, the energy consumption distribution can subsequently be stored in the memory 202 of the device 200, particularly in the database 208. Of course, if necessary, other electric vehicle charging stations 104, 106, and 108 can also obtain similar energy consumption distributions.

[0060] Accordingly, by comparing the energy consumption distributions of the electric vehicle charging stations 102, 104, 106, and 108, the total average energy consumption of the electric vehicle charging network 100 at a certain moment can be calculated. To this end, the processor 204 of the device 200 can correctly query the database 208. For example, a database query may be as follows (assuming that the information attribute "timestamp" is divided into sub-attributes "year", "month", "day", "hour", "minute", etc.):

[0061] Calculate the sum of all energy consumption values that meet the following conditions:

[0062] site ID_location = "Finland"

[0063] and timestamp_year = 2018

[0064] and timestamp_month = 10

[0065] and timestamp_day = 22

[0066] and timestamp_hour = 13

[0067] and timestamp_minute = 4".

[0068] Through the above query, the processor 204 can easily obtain the average total energy consumption information of all electric vehicle charging stations in Finland at 13:04:00 on October 22, 2018.

[0069] Figure 4 Illustrates multiple correlation relationships in time of the average energy consumption of multiple electric vehicle charging stations in an electric vehicle charging network. Each correlation relationship corresponds to an electric vehicle charging station and is obtained by method 300. Each point on the correlation relationship represents the sum of all energy consumption values corresponding to different charging period IDs of the same charging station ID within a certain period of time. Using this correlation relationship, we can understand the usage rate of any available electric vehicle charging station and determine the time period with the highest usage rate of any one or all available electric vehicle charging stations. This information can be used to optimize the electric vehicle charging and energy production of electric vehicle charging stations.

[0070] Those skilled in the art should understand that each module or step in method 300, or any combination of these modules or steps, can be implemented in various ways, such as hardware, firmware, and / or software. For example, one or more of the blocks or steps described above can be embodied by computer-executable instructions, data structures, program modules, and other suitable data representations. In addition, the computer-executable instructions including the above modules or steps can be stored on a corresponding data carrier and executed by at least one processor similar to the processor 204 in the device 200. This data carrier can be implemented as any computer-readable storage medium configured to be readable by at least one processor to execute computer-executable instructions. Such computer-readable storage media can include volatile and non-volatile media, removable and non-removable media. For example (but not limited to), computer-readable media include any medium implemented by any method or technology suitable for storing information. Specifically, examples of practical computer-readable media include, but are not limited to, information transfer media, RAM, ROM, EEPROM, flash memory, or other memory technologies, CD–ROM, digital versatile disc (DVD), holographic media, or other optical disc storage, magnetic tape, magnetic tape, disk storage, and other magnetic storage devices.

[0071] Although the exemplary embodiments of the present disclosure are described herein, it should be noted that various changes and modifications can be made to the embodiments of the present disclosure without departing from the scope of legal protection defined by the appended claims. In the appended claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. The fact that certain measures are recited only in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

Claims

1. A method (300) for monitoring energy consumption in an electric vehicle charging network (100), wherein the electric vehicle charging network (100) comprises a plurality of electric vehicle charging stations (102, 104, 106, 108), each of said charging stations being equipped with an electricity meter, and wherein, The method (300) includes: by using at least one processor (204): a) receiving (S302) new information from at least one electric vehicle charging station (102, 104, 106, 108), the new information including a timestamp, a station ID, a unique charging period ID for each charging period, and a watt-hour meter reading; b) storing (S304) the new information in a database (208), the database including old information from electric vehicle charging stations (102, 104, 106, 108); c) checking (S306) whether the database (208) includes older information, the older information including the same station ID and charging period ID as the new information; d) if the older information exists in the database (208), calculating (S308) the time difference and energy difference between the new information and the older information; e) selecting (S310) a time granularity level according to the calculated time difference; f) obtaining (S312) the energy consumption distribution between the timestamps of the new information and the older information based on the time granularity level and the energy difference; and g) providing the energy consumption distribution to the power station operator.

2. The method (300) according to claim 1, wherein The station ID includes the name, manufacturer information, and / or geographical location of the electric vehicle charging station.

3. The method (300) according to claim 1 or 2, wherein, The charging period ID is represented by one or more letters and / or numerical characters.

4. The method (300) according to claim 1 or 2, wherein, The timestamp is represented according to the ISO 8601 standard.

5. The method (300) according to claim 1 or 2, further comprising storing the energy consumption distribution in the database (208).

6. The method (300) according to claim 1 or 2, wherein, Obtaining (S312) the energy consumption distribution includes: - dividing the time difference between the new information and the older information into a plurality of equal time intervals according to the time granularity level; and - calculating the average energy consumption for each time interval by dividing the energy difference by the number of the equal time intervals.

7. The method (300) according to claim 6, wherein, The watt-hour meter reading and the average energy consumption are represented in watt-hours or multiples of watt-hours.

8. The method (300) according to claim 7, further comprising: - calculating the average energy consumption for each time interval by converting the average energy consumption represented in watt-hours or multiples of watt-hours to watts; - obtaining the energy consumption distribution between the timestamps of the new information and the older information based on the time granularity level and the average energy consumption; and - storing the energy consumption distribution in the database (208).

9. The method (300) according to claim 1 or 2, wherein, Steps a)-g) are repeated for new information received from other electric vehicle charging stations (102, 104, 106, 108), and wherein the method further includes calculating the total energy consumption of the electric vehicle charging network (100) at a given time by comparing the obtained energy consumption distributions of the electric vehicle charging stations (102, 104, 106, 108).

10. A device (200) for monitoring energy consumption in an electric vehicle charging network (100), wherein the electric vehicle charging network (100) includes a plurality of electric vehicle charging stations (102, 104, 106, 108), each of the charging stations being equipped with an electricity meter, and the device (200) includes: At least one processor (204), and A memory (202), coupled to the at least one processor (204), Wherein the memory (202) stores a database (208), the database including old information from the electric vehicle charging stations (102, 104, 106, 108), each piece of old information including a timestamp, a station ID, a charging period ID unique to each charging period, and an electricity meter reading, and The memory further stores processor-executable instructions (206), which when executed by the at least one processor (204), cause the at least one processor (204) to: a) Receive (S302) new information from at least one of the electric vehicle charging stations; b) Store (S304) the new information in the database (208); c) Check (S306) whether the database (208) includes older information, the older information including the same station ID and charging period ID as the new information; d) If the older message exists in the database (208), calculate (S308) the time difference and energy difference between the new information and the older information; e) Select (S310) a time granularity level according to the calculated time difference; f) Obtain (S312) the energy consumption distribution between the timestamps of the new information and the older information based on the time granularity level and the energy difference; and g) Provide the energy consumption distribution to the power station operator.

11. The apparatus (200) as claimed in claim 10, wherein, The station ID includes the name, manufacturer information, and / or geographical location of the electric vehicle charging station.

12. The device (200) according to claim 10 or 11, wherein, The charging period ID is represented by one or more letters and / or numerical characters.

13. The device (200) according to claim 10 or 11, wherein the timestamp is represented according to the ISO 8601 standard.

14. The device (200) according to claim 10 or 11, wherein the memory (202) further includes computer-executable instructions (206) that cause the at least one processor to store the energy consumption distribution in the memory (202) or the database (208).

15. The device (200) according to claim 10 or 11, wherein, The at least one processor (204) is configured to obtain the energy consumption distribution by: - Dividing the time difference between the new information and the older information into a plurality of equal time intervals according to the time granularity level; And - Calculating the average energy consumption of each time interval by dividing the energy difference by the number of the equal time intervals.

16. The apparatus (200) according to claim 15, wherein, The electricity meter reading and the average energy consumption are represented in watt-hours or multiples of watt-hours.

17. The apparatus (200) according to claim 16, wherein, The memory (202) further includes computer-executable instructions (206) that cause the at least one processor (204) to: - Calculate the average energy consumption for each time interval by converting the average energy consumption expressed in watt-hours or multiples of watt-hours to watts; - Based on the time granularity level and the average energy consumption, obtain the energy consumption distribution between the timestamps of the new information and the older information; and - Store the energy consumption distribution in the database (208).

18. The apparatus (200) according to claim 10 or 11, wherein the at least one processor (204) is configured to repeat operations a)-g) on new information received from other electric vehicle charging stations (102, 104, 106, 108), and wherein, The memory (202) further includes computer-executable instructions (206) that cause the at least one processor (204) to calculate the total energy consumption of the electric vehicle charging network (100) at a given time by comparing the energy consumption distributions of all electric vehicle charging stations (102, 104, 106, 108) obtained within the given time.

19. A computer program product comprising a computer-readable storage medium, wherein the storage medium stores computer-executable instructions that, when executed by at least one processor (204), cause the at least one processor (204) to perform the method (300) according to any one of claims 1 to 9.

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