How to determine the status of an electric vehicle charging station

Abstract

Various exemplary embodiments relate to determining the online / offline status of an electric vehicle charging station. A charging station may be determined to be online for a period of time after receiving a message from the charging station. Additionally, an online probability (100) of the charging station may be determined, and one or more actions may be initiated in response to the online probability dropping below a predefined threshold. Computer devices (200, 300), methods, systems, and computer programs (306) are disclosed.

Description

[Technical field]

[0001] Various illustrative embodiments relate generally to the field of electric vehicle charging stations, and more particularly, to determining whether an electric vehicle charging station is online or offline. [Background technology]

[0002] Electric vehicle charging stations may be connected to a charging station management system (CSMS) via the Internet using a variety of different communication protocols. Due to a variety of causes, charging stations may lose connection and go offline. It is important to quickly and reliably determine when a charging station is offline. However, with tens of thousands of charging stations, hundreds of different models, and a variety of different communication protocols, all operating in different ways, it is not easy to identify what the status of a charging station is. Summary of the Invention

[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 limit the scope of the claimed subject matter.

[0004] The present invention aims to provide a method and an apparatus for determining whether an electric vehicle charging station is online or offline. The electric vehicle charging station may be determined to be offline by default. A temporary online time of the electric vehicle charging station may be determined. An end time for the temporary online period may be set after a message, such as a heartbeat, is received from the electric vehicle charging station. In one embodiment, a probability that the electric vehicle charging station is online may be determined. The temporary online period may last until the probability function is, for example, 0%. A predefined threshold may be set for the probability to trigger one or more actions, for example to alert a maintenance person or to perform a measurement on the electric vehicle charging station. A more reliable and simpler method for determining the online / offline status of the charging station may be provided. The above object is achieved by the features of the independent claims. Some embodiments are described in the dependent claims.

[0005] According to a first aspect, a method is provided that may include determining an online duration of an electric vehicle charging station based on a most recent message received from the electric vehicle charging station, determining an online end time of the online duration based on a timestamp of the most recent message and a message interval of the electric vehicle charging station during which at least one message is expected to be received from the electric vehicle charging station, and determining that the electric vehicle charging station is online based on the online end time being greater than a current time.

[0006] In one embodiment, the online end time is determined based on the elapsed time from the most recent message timestamp being equal to the number of messages not received from the charging station within one or more message intervals since the most recent message timestamp multiplied by the message interval.

[0007] In one embodiment, additionally or alternatively, the message includes a heartbeat.

[0008] In one embodiment, additionally or alternatively, the method may further include determining an online probability of the electric vehicle charging station based on a time difference between an elapsed time from a timestamp of the most recent message and a number of messages not received from the electric vehicle charging station within one or more message intervals multiplied by the message interval, and initiating at least one action if the online probability is below a predetermined threshold.

[0009] In one embodiment, the online probability is 100% at the timestamp of the latest message, and decreases after the timestamp such that the online probability is 0% at the online end time.

[0010] In one embodiment, additionally or alternatively, the predetermined threshold is 50%.

[0011] In one embodiment, additionally or alternatively, the online probability is checked at predetermined intervals.

[0012] In one embodiment, additionally or alternatively, the at least one action includes transmitting a message to the electric vehicle charging station.

[0013] In one embodiment, additionally or alternatively, the at least one action includes attempting to restart the electric vehicle charging station.

[0014] In one embodiment, additionally or alternatively, the at least one action includes alerting a maintenance personnel.

[0015] In one embodiment, additionally or alternatively, if no response is received from the electric vehicle charging station in response to at least one of a message sent to the electric vehicle charging station or an attempted restart, an alert is issued to a maintenance personnel.

[0016] In one embodiment, additionally or alternatively, in response to a closed socket connection, the online probability is determined to be 0%.

[0017] According to a second aspect, a computer device is provided. The computer device may include at least one processor and at least one memory including computer program code configured to cause the computer device to perform at least the steps of: determining an online duration of the electric vehicle charging station based on a most recent message received from the electric vehicle charging station; determining an online end time of the online duration based on a timestamp of the most recent message and a message interval of the electric vehicle charging station during which at least one message is expected to be received from the electric vehicle charging station; and determining that the electric vehicle charging station is online based on the online end time being greater than a current time. The computer device may be further configured to perform any embodiment of the method of the first aspect.

[0018] According to a third aspect, there is provided an electric vehicle charging station management system including the computing device according to the second aspect and at least one electric vehicle charging station configured to be communicatively coupled to the computing device.

[0019] According to a fourth aspect, a computer program, when executed by a computing device, is configured to cause the computing device to perform at least the steps of: determining an online duration of an electric vehicle charging station based on a most recent message received from the electric vehicle charging station, determining an online end time of the online duration based on a timestamp of the most recent message and a message interval of the electric vehicle charging station during which at least one message is expected to be received from the electric vehicle charging station, and determining that the electric vehicle charging station is online based on the online end time being greater than a current time. The computer program, when executed by a computing device, may be further configured to cause the computing device to perform any embodiment of 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 connection with the accompanying drawings. [Brief description of the drawings]

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

[0022] [Figure 1] 1 illustrates an example of an online probability function for an electric vehicle charging station according to an illustrative embodiment. [Diagram 2] 1 illustrates an example of a management system for electric vehicle charging stations according to an illustrative embodiment. [Diagram 3] 1 illustrates an example of an apparatus configured to implement one or more exemplary embodiments, in accordance with an exemplary embodiment. [Figure 4] 1 illustrates an example of a flowchart for calculating an online end time according to an exemplary embodiment. [Diagram 5]1 illustrates an example of a flowchart for determining whether an electric vehicle charging station is offline or online according to an illustrative embodiment. [Figure 6] 1 illustrates an example of a flow chart for determining if an electric vehicle charging station is offline to enable early warning according to an exemplary embodiment.

[0023] In the following, like reference numbers are used to denote like parts in the accompanying drawings. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0024] Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. The detailed description provided below in connection 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 the sequence of steps for constructing and operating the examples. However, the same or equivalent functions and sequences may be accomplished by different examples.

[0025] The offline status of the electric vehicle charging station may be determined based on, for example, a heartbeat or a socket status. A heartbeat may be a protocol data unit configured to ensure that the CSMS knows that the electric vehicle charging station is still operational, i.e., online and / or functional. A heartbeat may be transmitted by the electric vehicle charging station based on a configured heartbeat interval. The electric vehicle charging station may transmit a heartbeat to the CSMS, for example, every 30 seconds, once a minute, every 5 minutes, or every 15 minutes.

[0026] The CSMS may monitor whether a heartbeat has been recently received from the electric vehicle charging station. The lack of a received heartbeat means that the electric vehicle charging station has gone offline. The offline status of the electric vehicle charging station may be determined based on two configurations, for example, the heartbeat interval (e.g., seconds between heartbeats) or the number of missed heartbeats. A heartbeat is missed if it is not received by the CSMS, for example, in the configured heartbeat interval. The CSMS may record the latest heartbeat if it receives one. If the time elapsed since the timestamp of the latest heartbeat is less than or equal to the heartbeat interval multiplied by the missed heartbeats (time since latest heartbeat≦heartbeat interval×number of missed heartbeats), the electric vehicle charging station may be set to offline.

[0027] In other words, an electric vehicle charging station may be determined to be online by default and may be updated to offline depending on the configuration if it does not send a heartbeat. In an alternative configuration, a socket connection (e.g., a web socket or TCP socket) may be opened from the electric vehicle charging station to the CSMS and the connection may indicate the status of the electric vehicle charging station. If the socket connection is opened, the electric vehicle charging station may be online. If the socket connection is closed, the electric vehicle charging station may be offline. Furthermore, a combination of heartbeats and socket connections may be used. If the socket connection is opened and a heartbeat is received, the electric vehicle charging station may be determined to be online, otherwise the electric vehicle charging station may be offline.

[0028] However, for example, socket-based monitoring may not be reliable on its own: even if a socket connection is opened, the electric vehicle charging station's software may fail, and the electric vehicle charging station may not be able to process the message.

[0029] The heartbeat monitoring mentioned above may also be unreliable. For example, if an electric vehicle charging station should send a heartbeat once per minute, and no heartbeat is received for 1.5 minutes, it is unclear whether the electric vehicle charging station is offline or not. Also, the online / offline status monitoring by the CSMS requires a lot of processing power. For example, when monitoring tens of thousands of electric vehicle charging stations with different configurations, it always requires tens of thousands of calculations to determine whether the time since the last heartbeat is too long. The reliability of the monitoring may depend on a running CSMS monitoring program that calculates the time since the last heartbeat. If there is a malfunction in the CSMS monitoring system, the electric vehicle charging station may not be set to offline even if it is actually offline.

[0030] Exemplary embodiments may provide a more reliable method for determining the online and offline status of an electric vehicle charging station, and may require less processing power to determine the status.

[0031] In one embodiment, the offline state of an electric vehicle charging station may be determined based on a reliability function rather than a binary state of whether the charging station is online or offline. The electric vehicle charging station may be determined to be offline by default. Additionally, a message received from the electric vehicle charging station may trigger a period during which the probability of the charging station being online is calculated. The electric vehicle charging station may be temporarily online, for example, when the online probability is higher than 0%. In one embodiment, the electric vehicle charging station may be determined to be offline when the probability falls below a certain threshold. This may allow maintenance personnel and users to be notified in advance of a possible offline state of the electric vehicle charging station based on the probability.

[0032] FIG. 1 illustrates an example of an online probability function of an online status of an electric vehicle charging station according to an exemplary embodiment. The probability may be calculated and monitored by a computing device. FIG. 2 illustrates an exemplary embodiment of an electric vehicle charging station management system 206 including a computing device 200. The system 206 may further include one or more electric vehicle charging stations 202. An electric vehicle charging station may refer to an apparatus that can be used to charge an electric vehicle, such as an electric car. The electric vehicle charging station 202 may be configured to send a message to the computing device at a predetermined message interval to indicate that it is operational. The computing device 200 may be communicatively coupled to the electric vehicle charging station 202. The computing device 200 may further be configured to communicate with one or more users 204 via user equipment. The user equipment may include other computing devices, e.g., mobile devices such as mobile phones. The users 204 may be, for example, drivers of electric vehicles or operators of the electric vehicle charging station 202. The computing device 200 may communicate with the electric vehicle charging stations 202 and / or the users 204 using, for example, a data connection. The data connection may include, for example, a wireless connection or a wired connection. The computing device 200 may be implemented as part of a back-end system. The back-end system may include, for example, multiple servers, and the computing device 200 may correspond to one or more of these servers.

[0033] In one embodiment, the probability 100 that electric vehicle charging station 202 is online may be determined to be 100% at the moment a heartbeat or some other message is received by computing device 200 from electric vehicle charging station 202. After receiving the heartbeat or other message, the online probability 100 of electric vehicle charging station 202 may begin to decrease until a new message is received from electric vehicle charging station 202. In one embodiment, the online probability 100 may decrease linearly to 0% if no messages are received from electric vehicle charging station 202 in a particular online period t.

[0034] The online period t may begin according to a timestamp of the most recently received message, such as a heartbeat. In one embodiment, the online period t may end at an online end time (expiration time) determined based on the timestamp of the most recently received message, a configured message interval, and the number of messages not received within one or more message intervals after the timestamp. If the online end time is met, the online probability 100 may be determined to be 0%.

[0035] In one embodiment, the online end time may correspond to a time after receipt of the most recently received message equal to the message interval multiplied by the number of missed messages. In one embodiment, the message interval may be a heartbeat interval of electric vehicle charging station 202, and the missed message may include a heartbeat. The online probability of electric vehicle charging station 202 may also be determined to be 0% if the socket-based protocol has closed the socket connection between electric vehicle charging station 202 and computing device 200.

[0036] In an exemplary embodiment, the online probability function may be:

[0037] x=((hbi*mh)-thb) / (hbi*mh) y=100%*x

[0038] where hbi is the heartbeat interval, mh is the missed heartbeat, and thb is the time since the most recent heartbeat (or some other message). Alternatively, the probability function may be calculated based on the message interval and the number of missed messages, with one message configured to be sent during one message interval. In an example embodiment, the message interval may be 60 seconds, the number of missed messages may be 3, and the time since the most recent received message may be 120 seconds. Thus, the online probability of a charging station may be:

[0039] =100%*(60*3-120) / 60*3 =100%*60 / 180 =100%*0.33=33%.

[0040] The advantage of the online probability function for estimating the online state is that it requires significantly less processing power. Furthermore, it provides a higher reliability for estimating the online / offline state of the electric vehicle charging station. This can be achieved because the method is less vulnerable to malfunctions. Based on the online probability, the computing device can react earlier to problems at the electric vehicle charging station.

[0041] In one embodiment, a message from electric vehicle charging station 202 may be received at 12:00. Based on the online probability function calculated by computing device 200, it is known that electric vehicle charging station 202 reaches a 0% online probability at 12:08. Therefore, the online end time of online period t may be set to 12:08. After determining the online end time, no further calculations may need to be performed. To determine which electric vehicle charging stations are online, one can simply check which electric vehicle charging stations have online end times that are later than the current time. Electric vehicle charging station 202 may be automatically determined to be offline after the end time of 12:08. Computing device 200 may be configured to alert a maintenance person in response to the offline status of charging station 202.

[0042] In this solution, when a problem occurs, for example when there is a failure in the computation by the computing device 200, it can be determined based on an online probability function that the electric vehicle charging station 202 is offline, thereby increasing reliability. If the electric vehicle charging station 202 is determined to be online by default, the electric vehicle charging station 202 may remain online in the event of a failure and provide false information to the user. Also, the offline status may be updated immediately without significant delay and notified to the user, for example in a mobile app. In one embodiment, the driver of the electric vehicle may be notified that the offline station is unavailable. Furthermore, a notification indicating that there is some abnormality in the offline station may be sent to a technical maintenance person.

[0043] 3 illustrates an example of an apparatus 300 configured to implement one or more exemplary embodiments, in accordance with an exemplary embodiment. The apparatus 300 may be, for example, the computing device 200.

[0044] The device 300 may include at least one processor 302. The at least one processor may include one or more of a variety of processing devices, such as, for example, a co-processor, a microprocessor, a controller, a digital signal processor (DSP), processing circuitry with or without a 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 dedicated computer chip, or the like.

[0045] The device 300 may further include at least one memory 304. The memory 304 may be configured to store, for example, computer program code operating system software or application software. The memory may include one or more volatile memory devices, one or more non-volatile memory devices, and / or a combination thereof. For example, the memory may be embodied as a magnetic storage device (e.g., a hard disk drive, a floppy disk, a magnetic tape, etc.), a magneto-optical storage device, or a semiconductor memory (e.g., a mask ROM, a PROM (programmable ROM), an EPROM (erasable PROM), a flash ROM, a RAM (random access memory), etc.).

[0046] The device 300 may further include a communication interface 308 configured to enable the device 300 to transmit and / or receive information from / to other devices. The communication interface 308 may be configured to provide at least one wireless communication connection, such as, for example, a 3GPP mobile broadband connection (e.g., 3G, 4G, 5G). However, the communication interface 208 may also be configured to provide one or more other types of connections, such as a wireless local area network (WLAN) connection standardized in 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) connection, a universal serial bus (USB) connection, or an optical network connection, or a wired Internet connection. The communication interface 308 may include at least one antenna and may be configured to be coupled to at least one antenna to transmit and / or receive radio frequency signals. One or more of the various types of connections may also be implemented as a separate communication interface that may include, and be configured to be coupled to, multiple antennas.

[0047] The device 300 may further include a user interface 310 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 embedded control buttons. The output devices may include, for example, a display, a speaker, a vibration motor, etc.

[0048] When the device 300 is configured to implement certain functions, some and / or several components of the device are configured to implement this functionality, such as the at least one processor 302 and / or the memory 204. Furthermore, when the at least one processor 302 is configured to implement certain functions, these functions may be implemented, for example, using program code 306 contained in the memory 304.

[0049] The functions described herein may be performed at least in part by computer program product components, such as software components. In one embodiment, an apparatus includes a processor or processor circuitry, such as a microcontroller, that is configured with program code and that, when executed, performs embodiments of the described operations and functions. Alternatively or additionally, the functions described herein may be performed at least in part by one or more hardware logic components. For example, but not limited to, exemplary types of hardware logic components that may be used include field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SoCs), composite programmable logic devices (CPLDs), graphics processing units (GPUs), and the like.

[0050] The apparatus 300 includes means for performing at least one of the methods described herein, in one example including at least one processor 302 and at least one memory 304 including program code 306 configured, when executed by the at least one processor 302, to cause the apparatus 300 to perform the method.

[0051] The device 300 may include, for example, a server device, a client device, a computing device such as a mobile phone, a tablet computer, a laptop, etc. In one example, the device 300 may include an electric vehicle charging station. Although the device 300 is shown as a single device, it should be understood that the functionality of the device 300 may be distributed across multiple devices, where applicable. In one embodiment, the device 300 may include a CSMS.

[0052] 4 shows an example of a flow chart for calculating an online end time according to an exemplary embodiment. The procedure of FIG. 4 may be performed, for example, by computing device 200.

[0053] At 400, a new message may be received from an electric vehicle charging station. The message may be, for example, a heartbeat.

[0054] At 402, a probability that the electric vehicle charging station is online may be determined to be 100%. Immediately after receiving a new message, the online probability may be 100%. The online probability may decrease linearly as time increases since receipt of the message.

[0055] At 404, an online end time may be set. The online end time may be determined based on a reception time of the most recently received message. The online end time may be determined based on a message interval of the electric vehicle charging station. The online end time may further be determined based on a number of missed messages from the electric vehicle charging station. The electric vehicle charging station may be configured to send messages to the computing device 200 at regular intervals. A missed message is a message that was expected from the electric vehicle charging station but was not received by the computing device within the message interval. For example, within three message intervals of the electric vehicle charging station, three messages may be expected. If the computing device 200 received only one message within the three message intervals, the number of missed messages may be two. In one embodiment, the online end time may be the number of missed messages multiplied by the message interval.

[0056] 5 illustrates an example of a flow chart for determining whether an electric vehicle charging station is offline or online, according to an exemplary embodiment. The procedure of FIG. 5 may be performed, for example, by computing device 200.

[0057] At 500, it may be determined whether the electric vehicle charging station's online end time is greater than the current time. The online end time may be determined as described above. At 502, if the online end time is greater than the current time, it may be determined that the electric vehicle charging station is online. At 504, if the online end time is not greater than the current time, it may be determined that the electric vehicle charging station is offline. In one embodiment, a timestamp of a latest message received from the electric vehicle charging station is 15:05. Based on the timestamp and the configuration of the electric vehicle charging station, it may be determined that the online end time is 15:17. The electric vehicle charging station may be determined to be online from 15:05 to 15:17. If the current time exceeds the online end time of 15:17, it may be determined that the electric vehicle charging station is offline. If a new message is received, the online period may be restarted based on the timestamp of the new message. Thus, a simple and reliable method for determining the status of an electric vehicle charging station may be provided.

[0058] 6 illustrates an example of a flow chart for determining if an electric vehicle charging station is offline to enable early warning, according to an exemplary embodiment. The procedure of FIG. 6 may be performed, for example, by computing device 200. Operations 400, 402, and 404 correspond to the operations shown in FIG.

[0059] At 600, the reception time of the new message received from the electric vehicle charging station at 400 may be input to an online probability function. The online probability function may be implemented as described above. When a new message is received, at 402, the online probability of the electric vehicle charging station may be determined to be 100%. The online probability may be determined to decrease between the reception time of the latest message and the online end time determined at 404. The probability function may decrease linearly. At 602, the computing device 200 may check whether any new messages are received from the electric vehicle charging station. If "Yes", the process returns to 400.

[0060] If no new messages are received from the electric vehicle charging station, computing device 200 may be configured to check the online probability of the electric vehicle charging station at 604. The online probability may be checked, for example, every 20 seconds, every 30 seconds, once a minute, every 5 minutes, every 10 minutes, or any other time within a range of 0 to 30 minutes.

[0061] In one embodiment, computing device 200 may be configured to issue an alert if it is determined that an electric vehicle charging station is more likely to be offline than online. If the alert is issued after an electric vehicle charging station goes offline, rather than based on probability, latency may be increased in at least some cases. Additionally, computing device 200 may be configured to attempt some automatic repair of the charging station before sending the alert.

[0062] For example, an electric vehicle charging station may have a message interval of 10 minutes and a setting of 3 missed messages before the electric vehicle charging station is considered offline. Thus, after 30 minutes of no messages being received, maintenance may be alerted that something is wrong with the electric vehicle charging station. This means that it may be as long as 45 minutes after the problem first occurs before someone investigates the problem. A total time that may be too long for critical infrastructure.

[0063] At 606, computing device 200 may determine whether the online probability is less than 50%. In one embodiment, there may be no messages from the electric vehicle charging station for 15 minutes. Computing device 200 may determine that there is only a 50% probability that the charging station is still online based on the reception time of the most recently received message. If the online probability is greater than or equal to 50%, the process may return to 604. If the online probability decreases to less than 50%, computing device 200 may be configured to attempt to contact the electric vehicle charging station by sending a message to the electric vehicle charging station at 608.

[0064] Computing device 200 may then wait a predetermined amount of time for the electric vehicle charging station to respond to the message. Additionally or alternatively, computing device 200 may remotely reboot the electric vehicle charging station at 610 to attempt to see if a reboot resolves the situation. If rebooted, the electric vehicle charging station may be shut down and rebooted. If no response is received from the electric vehicle charging station in response to the message and / or the reboot, computing device 200 may be configured to send an alert to maintenance personnel at 612.

[0065] The warning may indicate that there may be something wrong with a particular charging station.

[0066] If the electric vehicle charging station sent a response at 608 or 610, the process may return to 400. The process may return to 400 any time a message is received by computing device 200 from an electric vehicle charging station.

[0067] The online probability function can be used to automatically solve various problems with electric vehicle charging stations, potentially providing early warning to maintainers of potential problems with the electric vehicle charging stations.

[0068] Further features of the method are directly obtained from the functions of the computer device described in the entire specification and the appended claims, and therefore will not be described here. Also, various modifications of the method may be applied as described in connection with various exemplary embodiments. It is obvious to those skilled in the art that the basic concept of the present disclosure may be implemented in various ways due to the advancement of technology. Therefore, the present disclosure and embodiments are not limited to the above examples, instead, they may be modified within the scope of the claims.

[0069] A computing device may perform or be configured to cause any aspect of the methods described herein. Further, a computer program may, for example, include instructions that, when executed, cause an apparatus to perform any aspect of the methods described herein. Further, an apparatus may include means for performing any aspect of the methods described herein. In an exemplary embodiment, the means includes at least one processor and a memory including program code, the at least one processor and the program code, when executed by the at least one processor, are configured to cause any aspect of the methods to be performed.

[0070] Any ranges or device values ​​given herein may be expanded or modified without losing the effect sought, and any embodiment may be combined with another embodiment, unless expressly prohibited.

[0071] Although the subject matter has been described in language specific to structural features and / or operations, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or operations described above. Rather, the specific features and operations described above are disclosed as example forms of implementing the claims, and other equivalent features and operations are intended to be within the scope of the claims.

[0072] It should be understood that the benefits and advantages described above may relate to one embodiment or to several embodiments. The embodiments are not limited to those that solve any or all of the problems described or those that have any or all of the benefits and advantages described. Further, it should be understood that references to "an" item may refer to one or more of those items.

[0073] The actions of the methods described herein may be performed in any suitable order, or simultaneously as needed. Additionally, individual blocks may be deleted from any method without departing from the scope of the subject matter described herein. Additionally, any aspect of any of the embodiments described above may be combined with any aspect of any of the other embodiments described above to form further embodiments without losing the desired effect.

[0074] As used herein, the term "comprising" is used to mean including the specified methods, blocks, or elements, but that such blocks or elements do not constitute an exclusive list and that the method or apparatus may include additional blocks or elements.

[0075] As used herein, the terms "automated," "automatically," and "automatic," and variations thereof, may refer to any process or action that occurs without human input when the process or action is performed. However, even if human input is used to perform the process or action, the process or action may be automatic if input is received prior to the execution of the process or action.

[0076] Although a subject matter may be referred to as a "primary" or "secondary" subject matter, this does not necessarily indicate the order or importance of the subject matter. Instead, such attributes may be used only to distinguish one subject matter from another.

[0077] It should be understood that the above description is given by way of example only, and that various modifications may be made by those skilled in the art. The above specification, examples, and data provide a complete description of the structure and use of the exemplary embodiments. Although various embodiments have been described above with a degree of particularity, or with reference to one or more individual embodiments, those skilled in the art may make numerous modifications to the disclosed embodiments without departing from the scope of the present specification.

Claims

1. determining a start of an online period for an electric vehicle charging station based on a most recent message received from the electric vehicle charging station; determining an online end time of the online period based on a timestamp of the latest message and a message interval of the electric vehicle charging station during which at least one message is expected to be received from the electric vehicle charging station; determining that the electric vehicle charging station is online based on the end online time being greater than a current time; determining an online probability of the electric vehicle charging station based on the latest message, the online probability having a value between 0% and 100% indicating that the electric vehicle charging station is online, the online probability being checked at a predetermined interval; and initiating at least one action based on the determined predetermined threshold of online probability.

2. determining an online probability of the electric vehicle charging station based on a time difference between a time elapsed since a timestamp of the most recent message and a number of messages not received from the electric vehicle charging station within one or more message intervals multiplied by the message interval; and initiating the at least one action if the online probability is below the predetermined threshold.

3. 2. The method of claim 1 , determining the online end time based on an elapsed time from a latest message timestamp being equal to a number of messages not received from the electric vehicle charging station within one or more message intervals since the latest message timestamp multiplied by the message interval.

4. The method of any one of claims 1 to 3, wherein the message comprises a heartbeat.

5. The method according to any one of claims 1 to 4, wherein the online probability is 100% at the timestamp of the latest message and decreases from said timestamp onwards to 0% at the time of the online end time.

6. The method of claim 2 or 5, wherein the predetermined threshold is 50%.

7. The method of any one of claims 1 to 6, wherein the at least one action includes sending a message to the electric vehicle charging station.

8. The method of claim 2 , wherein the at least one action includes attempting to restart the electric vehicle charging station.

9. The method of claim 2 , wherein the at least one action includes alerting a maintenance personnel.

10. 10. The method of claim 9, wherein if no response is received from the electric vehicle charging station in response to at least one of a message sent to the electric vehicle charging station or an attempted restart, an alert is issued to a maintenance personnel.

11. The method according to any one of claims 1 to 10, wherein in response to a closed socket connection, the online probability is determined to be 0%.

12. 1. A computing device comprising: At least one processor; at least one memory containing computer program code; The at least one memory and the computer program code are transmitted by the at least one processor to the computing device, at least: determining an online duration of an electric vehicle charging station based on a most recent message received from the electric vehicle charging station; determining an online end time of the online period based on a timestamp of the latest message and a message interval of the electric vehicle charging station during which at least one message is expected to be received from the electric vehicle charging station; determining that the electric vehicle charging station is online based on the end online time being greater than a current time, wherein the computer program code is configured to cause the computing device to execute, by the at least one processor, at least: determining an online probability of the electric vehicle charging station based on the latest message, the online probability having a value between 0% and 100% indicating that the electric vehicle charging station is online, the online probability being checked at a predetermined interval; and initiating at least one action based on the determined predetermined threshold of the online probability.

13. 13. An electric vehicle charging station management system comprising the computing device of claim 12 and at least one electric vehicle charging station configured to be communicatively coupled to the computing device.

14. When executed by a computing device, the computing device performs at least determining an online duration of an electric vehicle charging station based on a most recent message received from the electric vehicle charging station; determining an online end time of the online period based on a timestamp of the latest message and a message interval of the electric vehicle charging station during which at least one message is expected to be received from the electric vehicle charging station; determining that the electric vehicle charging station is online based on the end online time being greater than a current time; determining an online probability of the electric vehicle charging station based on the most recent message, the online probability having a value between 0% and 100% indicating that the electric vehicle charging station is online, the online probability being checked at predetermined intervals.

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