WO2024141803A1

WO2024141803A1 - Predicitve electrical load management of electric vehicle chargers

Info

Publication number: WO2024141803A1
Application number: PCT/IB2023/058581
Authority: WO
Inventors: Adi BARON; Ariela Blumer
Original assignee: Weev Energy B.F. Ltd
Priority date: 2022-12-29
Filing date: 2023-08-30
Publication date: 2024-07-04

Abstract

A method for charging electric vehicles (EVs) at an EV charging site having a plurality of EV chargers, the method comprising: collecting a first dataset indicative of EV charging properties of each of a plurality of EV users of the EVs, a second dataset indicative of electrical properties of the EV charging site, a third dataset indicative of electricity prices; determining a state of each EV charger; generating an EV charging plan based on the datasets and the real-time state of each EV charger; and developing a schedule for charging operation of each EV charger based on the EV charging plan; causing each EV charger to operate for charging according to the schedule; wherein the datasets and the state of each EV charger are continuously monitored and the charging plan and schedule for charging are updated based on changes in the datasets and the state of each EV charger.

Description

PREDICTIVE ELECTRICAL LOAD MANAGEMENT OF ELECTRIC VEHICLE CHARGERS

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application No. 63/477,676 filed on December 29, 2022, the contents of which are hereby incorporated by reference.

TECHNICAL FIELD

[0002] The disclosure generally relates to electric vehicles, (EVs), and more particularly to systems and methods for predicting and managing electrical load of EV chargers.

BACKGROUND

[0003] Over the last few years more and more people have started using electric vehicles. EV chargers are used for charging the batteries of the EVs and are usually installed in private houses, apartment buildings, shopping centers, charging centers and workplaces.

[0004] Installation and management of EV chargers on a large scale in apartment buildings, shopping centers and workplaces is extremely complicated due to power constraints, complex billing, and infrastructure updates that are typically required.

[0005] Any EV charging site has an electrical infrastructure which is always only able to provide a limited amount of electric power. When too many EVs connect to the EV chargers at a single site and try to start charging at the same time, the outcome is usually a relatively low charging rate for all of the EVs at the site. Therefore, it would be advantageous to provide a solution that overcomes the shortcomings of prior art solutions noted above.

SUMMARY

[0006] A summary of several example embodiments of the disclosure follows. This summary is provided for the convenience of the reader to provide a basic understanding of such embodiments and does not wholly define the breadth of the disclosure. This summary is not an extensive overview of all contemplated embodiments and is intended to neither identify key or critical elements of all embodiments nor to delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more embodiments in a simplified form as a prelude to the more detailed description that is presented later. For convenience, the term “certain embodiments” may be used herein to refer to a single embodiment or multiple embodiments of the disclosure.

[0007] Certain embodiments disclosed herein include a method for charging a plurality of electric vehicles (EV) at an EV charging site. The method comprises: collecting, by a management server, a first dataset that is indicative of electric vehicle charging properties of each EV user of a plurality of EV users, wherein each EV user is associated with at least one EV of the plurality of EVs; collecting, by the management server, a second dataset that is indicative of electrical properties of the EV charging site, wherein the EV charging site comprises a plurality of EV chargers connected to an electric infrastructure of the EV charging site, wherein each of the plurality of EV chargers is configured to charge a respective EV of the plurality of EVs; collecting, by the management server, a third dataset that is indicative of electricity prices in a region in which the EV charging site is located; determining, by the management server, a real-time state of each EV charger of the plurality of EV chargers; generating an EV charging plan for the EV charging site based on the first dataset, the second dataset, the third dataset and the real-time state of each EV charger of the plurality of EV chargers; and developing, by the management server, a schedule for charging operation of each of the EV chargers of the plurality of EV chargers in the EV charging site based on the EV charging plan; causing, by the management server, each of the EV chargers to operate for charging according to the schedule; wherein the first dataset, the second dataset, the third dataset and the state of each EV charger are continuously monitored by the management server in real time; and wherein the charging plan and schedule for charging are updated in real-time based on any changes detected by the management server in the first dataset, the second dataset, the third dataset and the state of each of the EV chargers.

[0008] Certain embodiments disclosed herein also include a system for charging a plurality of electric vehicles (EV) at an EV charging site. The system comprises a processing circuitry; and a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to: collect a first dataset that is indicative of electric vehicle charging properties of each EV user of a plurality of EV users, wherein each EV user is associated with at least one EV of the plurality of EVs; collect a second dataset that is indicative of electrical properties of the EV charging site, wherein the EV charging site comprises a plurality of EV chargers connected to an electric infrastructure of the EV charging site, wherein each of the plurality of EV chargers is configured to charge a respective EV of the plurality of EVs, wherein each of the plurality of EV chargers is configured to charge the at least one EV; collect a third dataset that is indicative of electricity prices in a region in which the EV charging site is located; determine a real-time state of each EV charger of the plurality of EV chargers; generate an EV charging plan for the EV charging site based on the first dataset, the second dataset, the third dataset and the real-time state of each EV charger of the plurality of EV chargers; and develop a schedule for charging operation of each of the EV chargers of the plurality of EV chargers in the EV charging site based on the EV charging plan; wherein the first dataset, the second dataset, the third dataset and the state of each EV charger are continuously monitored by the system in real time; and wherein the charging plan and schedule for charging are updated in real-time based on any changes detected by the system in the first dataset, the second dataset, the third dataset and the state of each of the EV chargers.

BRIEF DESCRIPTION OF THE DRAWING

[0009] In the drawing:

[00010] FIG. 1 shows an illustrative network diagram for use in implementing an embodiment of the disclosure;

[00011] FIG. 2 shows an illustrative embodiment of a management server of FIG. 1 ;

[00012] FIG. 3 shows a flowchart of an illustrative process for performing predictive electrical load management for a plurality of EV chargers, according to an embodiment;

[00013] FIG. 4A shows an illustrative optimal EV charging plan for a plurality of EV chargers of an EV charging site, according to an embodiment;

[00014] FIG. 4B is a diagram showing an example of collected data for a plurality of EV chargers of an EV charging site, according to an embodiment; and

[00015] FIG. 4C shows an illustrative traditional, i.e. , prior art, EV charging plan for a plurality of EV chargers.

DETAILED DESCRIPTION

[00016] It is important to note that the embodiments disclosed herein are only examples of the many advantageous uses of the innovative teachings herein. In general, statements made in the specification of the present application do not necessarily limit any of the various claimed embodiments. Moreover, some statements may apply to some inventive features but not to others. In general, unless otherwise indicated, singular elements may be in plural and vice versa with no loss of generality. In the drawings, like numerals refer to like parts through several views.

[00017] The disclosed system and methods are utilized for efficiently managing the electrical load of electric vehicles at a charging site by learning the EV users’ charging patterns. To that end, data that is indicative of EV users’ charging properties, the charging site’s electrical properties, and electricity prices, is collected. When conditions to start charging one or more EV chargers are met, the system generates an optimal EV charging plan enabling an efficient charging of the EVs connected to the EV chargers of the site. The optimal EV charging plan is generated based on the data collected with respect to the EV user’s properties, EV charging site electrical properties, and the electricity prices associated with the region in which the site is located. Then, the system schedules the operation of each active EV charger based on the optimal EV charging plan.

[00018] FIG. 1 shows an illustrative network diagram 100 for use in implementing an embodiment of the disclosure. FIG. 1 shows a management server 120 and a plurality of electric vehicle chargers 130-1 through 130-M, where M is an integer equal to or greater than 1 , hereinafter referred to as EV charger 130 or EV chargers 130, merely for simplicity, one or more user devices 160, a database 170, and one or more web sources 180 which are all communicatively coupled by a network 110. The network 110 may be a wireless network, a wired network, a wide area network (WAN), a local area network (LAN), or any other kind of applicable network, as well as any combination thereof.

[00019] The management server 120 may include hardware and software which enable the management server 120 to collect datasets, analyze data, receive information from the EV chargers, e.g., the EV chargers 130, send instructions to the EV chargers, and the like. The components of the management server 120 are further described with respect to FIG. 2. In an embodiment, the management server 120 is deployed in a cloud computing platform, such as Amazon® AWS or Microsoft® Azure.

[00020] The EV charger 130 is a piece of equipment that supplies electrical power for charging plug-in EVs. An EV charger is usually connected to a local electrical service panel, e.g., the local electrical service panel 140, while the local electrical service panel is connected to a grid power supply, e.g., the grid power supply 150, from which the electric power is provided to the EV charger 130. The local electrical service panel 140 is a central distribution point that connects the external wires coming from the grid and the internal electrical wires of the electrical system of the EV charging site. The grid power supply 150 is an interconnected network for electricity delivery from electricity producers to electricity consumers.

[00021] The EV chargers 130 may further include a network interface (not shown) by which the EV chargers 130 are able to communicate with, for example, the management server 120. EV chargers are usually located in shopping centers, government facilities, as well as at residences, workplaces, and hotels. In many cases there are multiple EV chargers that operate at the same time at such EV charging sites and therefore an efficient allocation of the electric power among the active EV chargers is required to enable an efficient charging of the EV that are connected to the EV chargers.

[00022] The user device 160 may be for example a smartphone, a tablet, a smart wearable device, and the like. The user device 160 may include an application (not shown) allowing it to collect data about the EV user’s EV charging habit and to communicate with the management server 120, the EV charger 130, other user devices of other users, and the like.

[00023] The database 170 is a data warehouse that is configured to store, for example, data regarding the EV user, the EV of the user, charging properties of users, users’ profiles, data regarding the charging site, e.g., electrical load capacity of the charging site, electricity prices, and so on. The database 170 may be a centralized database, a cloud database, and the like.

[00024] The web source, or web sources 180 may include a server, a website, a government website, a database, and the like. For example, the web source 180 may be a website of an official authority in which electricity prices are shown and updated from time to time.

[00025] FIG. 2 is an example schematic diagram of the management server 120 according to an embodiment. The management server 120 includes a processing circuitry 121 coupled to a memory 123, a storage 125, a scheduling engine 127 and a network interface 129. The components of the management server 120 may be communicatively connected via a bus 128.

[00026] The processing circuitry 121 may be realized as one or more hardware logic components and circuits. For example, and without limitation, illustrative types of hardware logic components that can be used, include field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), Application-specific standard products (ASSPs), system-on-a-chip systems (SOCs), general-purpose microprocessors, microcontrollers, digital signal processors (DSPs), and the like, or any other hardware logic components that can perform calculations or other manipulations of information.

[00027] The memory 123 may be volatile, e.g., RAM, etc., non-volatile, e.g., ROM, flash memory, etc., or a combination thereof. In one configuration, computer readable instructions to implement one or more embodiments disclosed herein may be stored in the storage 125.

[00028] In another embodiment, the memory 123 is configured to store software. Software shall be construed broadly to mean any type of instructions, whether referred to as software, firmware, middleware, microcode, or hardware description language. Instructions may include code in formats such as source code, binary code, executable code, or any other suitable format of code. The instructions, when executed by the one or more processing circuitry 121 , cause the processing circuitry 121 to perform the various processes described herein.

[00029] The storage 125 may be magnetic storage, optical storage, and the like, and may be realized, for example, as flash memory or other memory technology, or any other medium which can be used to store the desired information.

[00030] The scheduling engine 127 may include hardware and software which enable the scheduling engine 127 to collect and analyze data, generate outputs, and the like. The scheduling engine 127 manages at least the creation and cancellation of EV charging plans of an EV charging site. The scheduling engine 127 may be configured to receive data associated with, for example, EV charging properties of users, electrical capacity of the EV charging site, electricity prices, activated EV chargers, etc., and determine an optimal EV charging plan which considers all the active EV chargers in the EV charging site. To that end, the scheduling engine 127 may use a set of rules which may be stored in a memory, e.g., the memory 123, machine learning (ML) techniques, and so on.

[00031] The network interface 129 is configured to connect to a network, e.g., the network 110. The network interface 129 allows the management server 120 to communicate with at least the user devices 160, the EV chargers, the DB 170, and the like. The network interface 129 may include a wired port or a wireless port, e.g. , an 802.11 compliant Wi-Fi circuitry configured to connect to a network.

[00032] In an embodiment, the management server 120 collects a first dataset that is indicative of EV charging properties of each EV user of a plurality of EV users. Each EV user is associated with at least an identifier and at least one EV. An EV user is, for example, an owner of an EV. An EV user’s identifier may be, for example, an ID number. It should be noted that each user may be associated with more than one EV. Therefore, each EV has its own identifier, allowing to distinguish between a plurality of EVs. Also, an EV user may have its own private EV charger, e.g., in an apartment building which may be used as an identifier to identify the user and thus, the user’s EV charging properties may be collected and associated with the user. However, when the disclosed method is implemented in a public EV charging site, e.g., at a shopping center, the user’s identity may be detected using a designated application that runs on the user’s user device 160, radio-frequency identification (RFID) techniques, and so on. According to one embodiment, EV information for each EV of each EV user may be received by the management server 120. The EV information may be received as an input from the user device 160, e.g., through a designated application that is adapted to communicate with the management server 120 over the network 110. The EV information may indicate the type of EV charger the EV is compatible with, e.g., a one-phase charger, a three-phase charger, etc., the EV’s battery capacity, and so on. According to a further embodiment, the EV information may be part of the first dataset.

[00033] The first dataset indicating the EV charging properties of each EV user may specify the time at which the user usually connects the EV to the EV charger, the time at which the user usually disconnects the EV from the EV charger, the EV user’s average charging duration, the user’s EV type, the user’s EV properties, the EV battery capacity, charging speed of the user’s EV, and so on. It should be noted that the first dataset may be collected from one or more sources, such as, the user device 160, e.g., from a designated application, the DB 170, the EV charger 130, and the like. For example, information regarding the time at which the user usually connects and disconnects from the EV charger, e.g., the EV charger 130, as well as the EV charging duration, may be collected from the EV charger 130, the DB 170, an application installed on the user’s user device 160, and so on. For example, the user device 160 may be used for determining, e.g., using a global positioning system module, the time when the EV arrived at the EV charging site and when the EV left the EV charging site.

[00034] For example, in an apartment building, in which 30 EV chargers of 30 different users operate, the management server 120 collects the first dataset with respect to each user from the EV chargers 130 from the user devices 160 of the EV users, and so on. According to this example, the collected first dataset is utilized to determine the 30 users’ EV charging properties and patterns which allows to predict the future usage of each user in different time frames, as further described herein.

[00035] In an embodiment, the management server 120 collects a second dataset that is indicative of electrical properties of an EV charging site. The EV charging site includes a plurality of EV chargers, e.g., the EV chargers 130 that are connected to an electric infrastructure of the EV charging site. Each of the EV chargers 130 is configured to charge at least one EV. It should be noted that some EV chargers are configured to charge two EVs simultaneously. The charging site may be located in an apartment building, workplace, shopping centers, and so on. The electrical properties of the EV charging site may specify, for example, the electrical load capacity of the site, real-time electrical consumption data, and so on. The second dataset that is indicative of the electrical properties of the EV charging site may be collected from, for example, one or more web sources, e.g., the web source 180, database 170, the EV chargers 130, and the like. For example, the database may store therein information indicating the electrical load capacity of the site. As another example, the real-time electrical consumption data may be collected from the active EV chargers 130 in the EV charging site, or from an external one or more electrical meters installed on the EV charging site’s main or sub panels.

[00036] In an embodiment, the management server 120 collects a third dataset that is indicative of electricity prices in the region in which the EV charging site is located. Electricity prices may vary between different regions and countries. Also, the electricity prices may vary based on the time of day. For example, the price per 1 kWh could be cheaper at night between 10 pm and 6 am, compared to the price of 1 kWh at the rest of the day.

[00037] In an embodiment, the management server 120 determines whether at least a portion of the plurality of EV chargers 130 of the EV charging site was activated. It should be noted that each EV charger of the plurality of EV chargers 130 is activated by an EV user of the plurality of EV users. An activated EV charger is an EV charger for which all conditions to start charging are met. The conditions to start charging an EV may include one or more of the following: establishment of a physical connection between the EV charger and the EV through an EV charging cable, establishment of an authorized charging session for an authorized entity, e.g., user, e.g., by an authorization center, a combination thereof, and the like.

[00038] The management server 120 may be configured to monitor the state of each EV charger 130. To that end, the management server 120 may interface with all EV chargers 130 at the EV charging site to collect information such as the real-time state of each EV charger 130. The real-time state may indicate for example the number of kilowatts that is currently consumed by each EV charger 130 in the charging site. The management server 120 may use the network interface 129 to communicate with each EV charger 130 via the network 110. It should be noted that, each EV charger 130 may include, among other components, a network interface (not shown) that may be used for sending information, receiving information, and the like.

[00039] In an embodiment, the management server 120 generates an optimal EV charging plan for the EV charging site based on the first dataset, the second dataset, the third dataset and the activated EV chargers in the EV charging site. An activated EV charger 130 is an EV charger that is connected to a respective EV and has permission to start a charging session, e.g., for which all conditions to start charging are met. An optimal EV charging plan may have several goals such as: (a) to fulfill the charging requirements of each EV user of the plurality of EV users at the EV charging site by the time the EV user wishes to leave the EV charging site, (b) to charge all the EV at the lowest electricity price, (c) to prevent a power outage. In an embodiment, the management server 120 applies a set of rules to the collected datasets and the information regarding the active EV chargers to determine the optimal EV charging plan for the EV charging site considering the real-time state of each of the plurality of EV chargers at the EV charging site.

[00040] It should be noted that each of the first, second and third datasets includes at least one parameter, e.g., departure time - time at which the user usually disconnects the EV from the EV charger, and a respective parameter value, e.g., 8 am. Also, the state of each EV charger 130 may also be monitored using a set of parameters, e.g., charging speed and parameters values, e.g., 11 kWh, related to each EV charger 130. According to one embodiment, the management server 120 may be configured to assign a weight to each parameter, e.g., departure time, average amount of kWh the user needs, electricity prices, and so on. The weights are represented by numbers that correspond to the strength or weakness of a given parameter. The weights assigned to each parameter affect the EV charging plan. For example, in case the weight of the parameter associated with the number of kWh each EV user needs, is relatively high compared to weights of other parameters, the EV charging plan may determine that all EV chargers must leave the site with a full EV battery. Referring to the same example, it should be noted that by assigning the parameter of the number of kWhs each EV user needs with a relatively high weight, and the electricity price parameter with a relatively low weight, charging the EV of the EV users in the site may be charged when electricity is expensive. The strength or weakness of a parameter may correspond to its relative importance to the user, where a more important parameter has a greater strength.

[00041] In an embodiment, the management server 120 schedules the operation of each activated EV charger 130 in the EV charging site based on the optimal EV charging plan. Scheduling the operation of the active EV chargers 130 may be achieved by the management server 120 using the scheduling engine 127. To that end, the scheduling engine 127 may receive as an input the first dataset, the second dataset and the third dataset, as well as the real-time state of each active EV chargers 130 at the EV charging site and the identity of each user that is associated with an EV charger 130. The scheduling engine 127 may be adapted to apply a set of rules to the inputs, i.e. , collected data, to generate an optimal EV charging plan. According to further embodiment, the management server 120 uses the scheduling engine 127 to apply a supervised or unsupervised machine learning model to the collected inputs to generate the optimal EV charging plan for charging the activated EV chargers 130 in the EV charging site.

[00042] For example, the first dataset that is collected with respect to ten EV users of the same EV charging site indicates that the first user usually connects the EV to the EV charger at 7 pm and disconnects, i.e., leaves the EV charging site, at 10 am, the second user usually connects the EV to the EV charger at 11 pm and disconnects at 5 am, the third user usually connects the EV to the EV charger at 9 pm and disconnects at 2 pm, and seven other EV users usually connect their EVs to their EV chargers at 9 pm and disconnect at 7:30 am. In addition, the first dataset also indicates that the EV of the first user usually consumes 30 kWh, the EV of the second user usually consumes 28 kWh, the EV of the third user usually consumes 15 kWh, the EV of the fourth user usually consumes 33 kWh, the EV of the fifth user usually consumes 50 kWh, and so on. For this example, the second dataset indicates the electrical load capacity of the EV charging site, i.e., the maximum electrical power that can be provided by the electric infrastructure of the EV charging site at the same time, is 30 kW. Also, for this example, the third dataset indicates that the electricity price is the cheapest between 1 am and 4 am. Thereafter, by monitoring all the EV chargers 130 in the EV charging site, the management server 120 determines that EV chargers 1 through 5, and EV chargers 8 and 10, are each currently active, i.e., they are connected to an EV and have permission to start charging. In response, the management server 120 uses the scheduling engine 127 to generate an optimal EV charging plan for all the active EV chargers, e.g., all seven. Then, the management server 120 schedules the operation of each active EV charger 130 based on the optimal EV charging plan.

[00043] In an embodiment, the management server 120 continuously monitors the first dataset, the second dataset, the third dataset and the plurality of EV chargers 130 in real-time. As noted above, each dataset includes at least one parameter, e.g., time at which the user usually disconnects the EV form the EV charger, and a respective parameter value, e.g., 8 am. Thus, the management server 120 may be configured to continuously monitor the parameter values of each dataset, i.e. , of the first, second, and third datasets, as well as the state of each EV charger 130 at the EV charging site. The state of each EV charger 130 may also be monitored using a set of parameters, e.g., charging speed, and parameters values, e.g., 11 kWh, related to each EV charger 130.

[00044] When the management server 120 determines that one or more parameters values of the one or more datasets have been changed and/or the status of the active EV chargers in the EV charging site has been changed, the management server 120 adjusts the optimal EV charging plan in real-time. For example, an initial optimal EV charging plan is used for scheduling the charging of 10 active EV chargers. Then, one hour after the optimal plan was generated another three EVs connect to their EV chargers at the same EV charging site. According to the same example, the management server 120 adjusts the optimal EV charging plan in real-time to ensure that (a) the charging requirements of each EV user of the now 13 EV users are fulfilled by the time each of the EV users wish to leave the EV charging site, (b) all the EVs are charged at a time in which the electricity prices are cheapest, and (c) to prevent a power outage at the EV charging site. According to another embodiment, the management server 120 reschedules the charging of the EVs in the site based on the adjusted EV charging plan.

[00045] FIG. 3 shows a flowchart of an illustrative process for performing predictive electrical load management for a plurality of EV chargers, according to an embodiment. The disclosed method may be executed by the management server 120 of FIG. 2.

[00046] At S310, a first dataset that is indicative of EV charging properties of each EV user of a plurality of EV users, is collected. Each EV user is associated with at least an identifier and at least one EV. An EV user is, for example, an owner of an EV. An EV user’s identifier may be for example, an ID number. The first dataset indicating the EV charging properties of each EV user may specify the time at which the user usually connects the EV to the EV charger, the time at which the user usually disconnects the EV from the EV charger, the EV user’s average charging duration, the user’s EV type, the user’s EV properties, the EV battery capacity, charging speed of the user’s EV, and so on. According to one embodiment, information for each EV of each EV user may be received by the management server 120. The EV information may be received as an input from the user device 160, e.g., through an application that is adapted to communicate with the management server 120 over the network 110. The EV information may indicate the type of EV charger the EV is compatible with, e.g., a one-phase charger, a three-phase charger, etc., the EV’s battery capacity, and so on. According to further embodiment, the EV information may be part of the first dataset.

[00047] At S320, a second dataset that is indicative of electrical properties of an EV charging site, is collected. The EV charging site includes a plurality of EV chargers that are connected to an electric infrastructure of the EV charging site. Each of the EV chargers 130 is configured to charge at least one EV. The charging site may be located in an apartment building, workplace, shopping centers, and so on. The electrical properties of the EV charging site may specify, for example, the electrical load capacity of the site, real-time electrical consumption data, and so on.

[00048] At S330, a third dataset indicating electricity prices in the region in which the EV charging site is located, is collected. Electricity prices may vary between different regions and countries. Also, the electricity prices may vary based on the time of day. For example, the price per 1 kWh could be cheaper at night between 10 pm and 6 am, compared to the price of 1 kWh at the rest of the day.

[00049] At S340, real-time state of each EV charger in the site is determined. That is, each active EV charger, which is associated with a specific EV user, is detected and real-time parameters values may be collected with respect to each active EV charger. The real-time parameters values facilitate determination of real-time state of all the active EV charger at the EV charging site. The real-time state may indicate for example the number of kilowatts that is currently consumed by each EV charger 130 in the charging site, the number of EV charging, the identities of the EV users associated with each active EV charger, and so on.

[00050] At S350, an optimal EV charging plan is generated for the EV charging site based on the first dataset, the second dataset, the third dataset and the real-time state of the activated EV chargers in the EV charging site. An activated EV charger is an EV charger that is connected to a respective EV and has permission to start charging. An optimal EV charging plan has several goals such as: (a) to fulfill the charging requirements of each EV user of the plurality of EV users at the EV charging site by the time the EV user wishes to leave the EV charging site, (b) to charge all the EV at the cheapest electricity price, (c) to prevent a power outage in the EV charging site. In an embodiment, the management server 120 applies a set of rules to the collected datasets and the information regarding the real-time state of the active EV chargers to determine the optimal EV charging plan. According to another embodiment, a supervised or an unsupervised machine learning model may be applied to the collected inputs, e.g., the datasets and the information regarding the real-time state of the active EV chargers, to generate the optimal EV charging plan for charging the activated EV chargers in the EV charging site.

[00051] At S360, the operation of each activated EV charger in the EV charging site is scheduled based on the optimal EV charging plan.

[00052] FIG. 4A is an illustrative representation of an optimal EV charging plan for a plurality of EV chargers of an EV charging site, according to an embodiment. Based on the collected first, second and third datasets which were further discussed hereinabove, an optimal EV charging plan is generated. As noted above, an optimal EV charging plan has several goals such as: (a) to fulfill the charging requirements of each EV user of the plurality of EV users at the EV charging site by the time the EV user wishes to leave the EV charging site, (b) to charge all the EV at the lowest electricity price, (c) to prevent power outage. Reference is made to illustrative data shown in FIG. 4B, in which the number of each EV user is shown in column 420-1 , the predicted departure time of each EV user is shown in following column 420-2, the predicted amount of kWh that each EV user needs is shown in column 420-3, the EV charging speed is shown in column 420-4, the required number of charging units assigned to the EV user in the plan of FIG. 4A, where each charging unit is represented by a cell in the EV charging plan, is shown in column 420-5. Each charging unit, i.e., each cell in the EV charging plan, is a predetermined amount of electrical power, e.g., 3.6 kW, that is provided for a predetermined amount of time, e.g., one hour.

[00053] Referring back to FIG. 4A, the X axis represents the electric power, i.e., kW, and the Y axis represents the time intervals. The number shown in each of the cells is indicative of a unique EV to which the charging unit represented by the cell is allocated. In the example of FIG. 4A, each cell indicates a charging unit corresponding to charging 3.6 kW in one hour.

[00054] For example, the first EV, i.e., EV 1 , usually leaves at 5 am, needs 61 kWh, charges at 11 kw and therefore 17 charging units are required to complete the charging of EV 1 by 5 am which is represented in the plan of FIG. 4A by the 17 cells containing a 1 . The second EV, i.e., EV 2, usually leaves at 8 am, needs 33kWh, charges at 11 kw and therefore it requires 9 charging units represented by the 9 cells containing a 2 that are required to complete the charging of EV 2 by 8 am. The third EV, i.e., EV 3, usually leaves at 8 am, needs 33kWh, charges at 11 kw and therefore it requires 9 charging units represented by the 9 cells containing a 3 to complete the charging of EV 3 by 8 am.

[00055] The fourth EV, i.e., EV 4, usually leaves at 7 am, needs 33kWh, charges at 11 kw and therefore it requires 9 charging units represented by the 9 cells containing a 4 complete the charging of EV 4 by 7 am. The fifth EV, i.e., EV 5, usually leaves at 5 am, needs 11 kWh, charges at 11 kw and therefore it requires 3 charging units represented by the 3 cells containing a 5 to complete the charging of EV 5 by 5 am. The sixth EV, i.e., EV 6, usually leaves at 6 am, needs 11 kWh, charges at 11 kw and therefore it requires 3 charging units represented by the 3 cells containing a 6 to complete the charging of EV 6 by 6 am.

[00056] The seventh EV, i.e., EV 7, usually leaves at 8 am, needs 11 kWh, charges at 11 kw and therefore it requires 3 charging units represented by the 3 cells containing a 7 to complete the charging of EV 7 by 8 am. The eighth EV, i.e., EV 8, usually leaves at 8 am, needs 11 kWh, charges at 11 kw and therefore it requires 3 charging units represented by the 3 cells containing an 8 to complete the charging of EV 8 by 8 am.

[00057] It should be noted that when traditional load balancing systems, i.e., known prior art solutions, are required to provide more electric power than it possibly can, the traditional systems usually divide the electric power equally between the different EV chargers in the charging site, without taking into consideration the users’ charging behavior patterns. Therefore, the electric power provided to each EV charger is limited and relatively low, and the risk that charging of the EVs will not be completed by the time the EV users need to leave, i.e., their desired departure time, increases.

[00058] Reference is now made to FIG. 4C which shows an illustrative representation of a charging plan 400C based on a traditional EV charging process for a plurality of EV chargers. The same illustrative data shown in FIG. 4B is employed for the plan 400C of FIG. 4C. Thus, the first EV, i.e., EV 1 , usually leaves at 5 am, needs 61 kWh, chargers at 11 kw and therefore 17 charging units of 3.6 kW, that is provided an hour (i.e., cells) are required to complete the charging of EV 1 by 5 am. However, as noted above, when using a traditional load balancing system, the system divides the electric power equally between the EV chargers in the EV charging site. That is, the electrical capacity of the EV charging site is equally divided between the eight active EV chargers, such that instead of providing the first EV charger with 10.8 kWh at the beginning of the charging, as is shown in the illustrative plan of FIG. 4A, the traditional load balancing system provides to the first EV only 3.6 kWh for three hours. After few hours, when other EV chargers in the EV charging site complete their operation, e.g., the fifth EV charger, the eighth EV charger, the sixth EV charger, and the seventh EV charger, the first EV charger can start consuming 7.2 kWh, instead of 3.6 kWh, and when more EV chargers complete their operation, e.g. , six hours from the beginning of the charging, the first EV charger can consume 10.8 kWh.

[00059] Disadvantageously, under such a traditional plan, EV1 will not be fully charged and ready to leave by 5 AM as required.

[00060] The principles of the disclosure are implemented as hardware, firmware, software, or any combination thereof. Moreover, the software is preferably implemented as an application program tangibly embodied on a program storage unit or computer readable medium. The application program may be uploaded to, and executed by, a machine comprising any suitable architecture. Preferably, the machine is implemented on a computer platform having hardware such as one or more central processing units ("CPUs"), a memory, and input/output interfaces. The computer platform may also include an operating system and microinstruction code. The various processes and functions described herein may be either part of the microinstruction code or part of the application program, or any combination thereof, which may be executed by a CPU, whether or not such computer or processor is explicitly shown. In addition, various other peripheral units may be connected to the computer platform such as an additional data storage unit and a printing unit.

[00061] All examples and conditional language recited herein are intended for pedagogical purposes to aid the reader in understanding the principles of the disclosure and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the disclosure, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure.

Claims

CLAIMS What is claimed is:

1 . A method for charging a plurality of electric vehicles (EV) at an EV charging site, the method comprising: collecting, by a management server, a first dataset that is indicative of electric vehicle charging properties of each EV user of a plurality of EV users, wherein each EV user is associated with at least one EV of the plurality of EVs; collecting, by the management server, a second dataset that is indicative of electrical properties of the EV charging site, wherein the EV charging site comprises a plurality of EV chargers connected to an electric infrastructure of the EV charging site, wherein each of the plurality of EV chargers is configured to charge a respective EV of the plurality of EVs; collecting, by the management server, a third dataset that is indicative of electricity prices in a region in which the EV charging site is located; determining, by the management server, a real-time state of each EV charger of the plurality of EV chargers; generating an EV charging plan for the EV charging site based on the first dataset, the second dataset, the third dataset and the real-time state of each EV charger of the plurality of EV chargers; developing, by the management server, a schedule for charging operation of each of the EV chargers of the plurality of EV chargers in the EV charging site based on the EV charging plan; and causing, by the management server, each of the EV chargers to operate for charging according to the schedule; wherein the first dataset, the second dataset, the third dataset and the state of each EV charger are continuously monitored by the management server in real-time; and wherein the charging plan and schedule for charging are updated in real-time based on any changes detected by the management server in the first dataset, the second dataset, the third dataset and the state of each of the EV chargers.

2. The method of claim 1 , wherein the electrical properties of the site includes an electrical load capacity of the EV charging site.

3. The method of claim 1 , wherein the EV charging plan is generated to fulfill respective charging requirements of each EV user of the plurality of EV users at the EV charging site by the time each EV user desires to disconnect the user’s EV from the EV charging site.

4. The method of claim 1 , wherein the EV charging plan is generated to charge each of the EVs at the lowest total price for electricity.

5. The method of claim 1 , wherein the EV charging plan is generated to prevent a power outage due to overload at the EV charging site.

6. The method of claim 1 , wherein updating of the schedule further comprises rescheduling the charging of the at least one of the EVs based on an updated adjusted EV charging plan.

7. The method of claim 1 , wherein generating the EV charging plan the EV charging plan further comprises the management server applying in real-time a set of rules to the first dataset, the second dataset, the third dataset and the real-time state of each EV charger of the plurality of EV chargers.

8. The method of claim 1 , wherein generating the EV charging plan the EV charging plan further comprises applying in real-time a machine learning model to the first dataset, the second dataset, the third dataset and the real-time state of each EV charger of the plurality of EV chargers.

9. The method of claim 1 , further comprising: receiving EV information regarding the EV of each EV user of the plurality of EV users.

10. The method of claim 1 , wherein the real-time state of each EV charger includes an activation status.

11. A system for charging a plurality of electric vehicles (EV) at an EV charging site comprising: a processing circuitry; and a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to: collect a first dataset that is indicative of electric vehicle charging properties of each EV user of a plurality of EV users, wherein each EV user is associated with at least one EV of the plurality of EVs; collect a second dataset that is indicative of electrical properties of the EV charging site, wherein the EV charging site comprises a plurality of EV chargers connected to an electric infrastructure of the EV charging site, wherein each of the plurality of EV chargers is configured to charge a respective EV of the plurality of EVs; collect a third dataset that is indicative of electricity prices in a region in which the EV charging site is located; determine a real-time state of each EV charger of the plurality of EV chargers; generate an EV charging plan for the EV charging site based on the first dataset, the second dataset, the third dataset and the real-time state of each EV charger of the plurality of EV chargers; and develop a schedule for charging operation of each of the EV chargers of the plurality of EV chargers in the EV charging site based on the EV charging plan; wherein the first dataset, the second dataset, the third dataset and the state of each EV charger are continuously monitored by the system in real-time; and wherein the charging plan and schedule for charging are updated in real-time based on any changes detected by the system in the first dataset, the second dataset, the third dataset and the state of each of the EV chargers.

12. The system of claim 11 , wherein the electrical properties of the site include an electrical load capacity of the EV charging site.

13. The system of claim 11 , wherein the EV charging plan fulfills respective charging requirements of each EV user of the plurality of EV users at the EV charging site by the time each EV user desires to disconnect the user’s EV from the EV charging site.

14. The system of claim 11 , wherein the EV charging plan is such as to charge each of the EVs at the lowest total price for electricity.

15. The system of claim 11 , wherein the generated EV charging plan is adapted to prevent a power outage due to overload at the EV charging site.

16. The system of claim 11 , wherein the system is further configured to update the schedule by rescheduling the charging of the at least one of the EVs based on an updated adjusted EV charging plan.

17. The system of claim 11 , wherein the system is further configured to generate the EV charging plan by applying in real-time a set of rules to the first dataset, the second dataset, the third dataset and the real-time state of each EV charger of the plurality of EV chargers.

18. The system of claim 11 , wherein the system is further configured to generate the EV charging plan by applying in real-time a machine learning model to the first dataset, the second dataset, the third dataset and the real-time state of each EV charger of the plurality of EV chargers.

19. The system of claim 11 , wherein the system is further configured to: receive EV information regarding the EV of each EV user of the plurality of EV users.

20. The system of claim 11 , wherein the real-time state of each EV charger includes an activation status.