WO2023148620A1

WO2023148620A1 - Techniques for charging electric vehicles for ridesharing

Info

Publication number: WO2023148620A1
Application number: PCT/IB2023/050855
Authority: WO
Inventors: Daniel Feldman; Vincent Schachter; Jenya Kirshtein; Amanpreet Kaur
Original assignee: ENEL X Way S.r.l.
Priority date: 2022-02-01
Filing date: 2023-02-01
Publication date: 2023-08-10

Abstract

Techniques for charging one or more electric vehicles (EVs) are disclosed. In one aspect, a system can include at least two charging stations to charge the EVs. The at least two charging stations include a first charging station in a first location. The first charging station is configured to supply a first amount of electric charge during a first duration of time. The at least two charging stations also include a second charging station in a second location. The second charging station is configured to supply a second amount of electric charge during a second duration of time. The system then determines a first instance of communication connectivity between the system and the one or more EVs, communicates with the one or more EVs using a communication protocol, and receives ridesharing characteristics of at least one trip. Based on the ridesharing characteristics, the system selectively transmits to the one or more EVs the first or the second location.

Description

TECHNIQUES FOR CHARGING ELECTRIC VEHICLES FOR RIDESHARING

RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Patent Application No. 63/267,414, titled “TECHNIQUES FOR CHARGING ELECTRIC VEHICLES FOR RIDESHARING,” filed February 1 , 2022, which is hereby incorporated herein by reference in its entirety.

TECHNICAL FIELD

[0002] The present disclosure generally relates to the field of configuring an electric vehicle supply equipment (EVSE). More particularly, the present disclosure describes techniques and systems for configuring two or more EVSEs to plan for charging an electric vehicle (EV).

BACKGROUND

[0003] Generally, a vehicle with a combustion engine can refuel during a relatively short time (e.g., five minutes). Further, the vehicle with the combustion engine may include a large-enough fuel tank that enables this vehicle to drive relatively-long distances (e.g., 600, 700 kilometers) without needing to refuel. By contrast, an electric vehicle (EV) may require a considerably-longer time to recharge. In part, the time to recharge the EV depends on a type of an electric vehicle supply equipment (EVSE) that supplies charge to the EV and/or a power load capacity of or at the EVSE. Further, the EV may be unable to drive the relatively-long distances without needing to recharge. However, driving the EV includes some remarkable benefits compared to driving the vehicle with the combustion engine. The benefits can be multiplied in ridesharing scenarios. Therefore, it is desirable to have EVSEs capable of increasing the benefits of driving the EV.

SUMMARY

[0004] This disclosure describes techniques and systems for enhancing the benefits of driving at least one EV. In more detail, a system may include at least two EVSEs (or charging stations) for charging the at least one EV. The system may analyze ridesharing characteristics of the at least one EV to aid a driver of the at least one EV to enhance the benefits of driving the at least one EV.

BRIEF DESCRIPTION OF THE DRAWINGS

[0005] The present embodiments will become more fully apparent from the following description, taken in conjunction with the accompanying drawings. Understanding that the accompanying drawings depict only typical embodiments, and are, therefore, not to be considered limiting of the scope of the disclosure, the embodiments will be described and explained with specificity and detail in reference to the accompanying drawings.

[0006] FIG. 1 shows a system for selectively enabling a charging station to charge an electric vehicle (EV), according to one embodiment.

[0007] FIG. 2 is a diagram of example techniques to estimate a state of charge (SoC) of the EV, according to one embodiment.

[0008] FIG. 3 is a diagram of a system, according to one embodiment, that includes a power grid and a network of charging stations.

[0009] FIG. 4 shows inputs and outputs of a model used to selectively enable the charging station to charge the EV, according to one embodiment.

[0010] FIG. 5 is a flow diagram of a process for selectively enabling the charging station to charge the EV, according to one embodiment. DETAILED DESCRIPTION

[0011] Systems, methods, and techniques for enhancing the benefits of driving an EV are disclosed. A system, according to one embodiment, may include multiple EVSEs (or charging stations) for charging at least one EV. The embodiments described herein may analyze ridesharing characteristics of the at least one EV to aid a driver of the at least one EV to enhance the benefits of driving the at least one EV. The benefits may include lower transportation costs; lower energy rates; reduced emissions of greenhouse gases; using more renewable energy sources (instead of nonrenewable) used to generate an electric power to charge the EV; lower opportunity costs; higher revenues; faster charging times; fewer charging occurrences during peak power load hours; lower occurrences of blackouts and/or brownouts of a power grid; reduced traffic by encouraging and incentivizing ridesharing and/or carpooling; and additional benefits to the driver, the environment, and/or the community, as further described below. In some embodiments, the ridesharing characteristics of an EV can be utilized or otherwise considered to prioritize or influence one or more of schedule, location, equipment, timing, cost, or other aspects of charging the EV or of guiding the EV in charging.

[0012] In some embodiments, a system for charging at least one EV comprises a first charging station in a first location and a second charging station in a second location. The first charging station may be configured to supply a first amount of electric power during a first duration of time. The second charging station may be configured to supply a second amount of electric power during a second duration of time. The system can also include at least one processor and/or at least one computer- readable medium. The computer-readable medium includes instructions that, responsive to execution by at least one processor, can cause the system to determine a first instance of communication connectivity between the system and the at least one EV. Upon determining the first instance of communication, the system can communicate with the at least one EV using a communication protocol. Using the communication protocol, the system can receive ridesharing characteristics of at least one trip of the at least one EV. The ridesharing characteristics may include, for example, a state of charge (SoC) of the at least one EV and a current location of the at least one EV. The ridesharing characteristics may include ride information (or ride data), which may include current rides the EV may be providing, prospective rides the EV may be scheduled or otherwise engaged to provide, potential rides the EV may be offered to provide, and/or past rides the EV may have provided. The ride information may be received from a third-party ridesharing application or other system for coordinating rideshare rides. The system may then analyze the ridesharing characteristics. Based on the analysis of the ridesharing characteristics, the system can selectively transmit to the at least one EV the first and/or the second location, prioritized or otherwise selected to enhance benefits of driving the EV and/or ridesharing. The system may also guide the at least one EV to the first or the second location, for example, by prompting a driver of the at least one EV to follow turn-by-turn navigation instructions using a navigation application software. Finally, the system enables the at least one EV to receive a third amount of electric power. [0013] In some embodiments, a computer-implemented method includes determining a first instance of communication connectivity between a system and at least one EV. The method may be implemented by or in conjunction with a system that can include a first charging station in a first location and a second charging station in a second location. The first charging station may supply a first amount of electric power during a first duration of time, and the second charging may supply a second amount of electric power during a second duration of time. Then, the method includes the system communicating with the at least one EV using a communication protocol. In more detail, by using the communication protocol, the method includes the system receiving ridesharing characteristics of at least one trip. The ridesharing characteristics may include, for example, an SoC and a current location of the at least one EV. Finally, the method includes selectively transmitting to the at least one EV the first or the second location based on the ridesharing characteristics.

[0014] In some embodiments, a system, an apparatus, a software, an algorithm, a model, and/or means include performing the computer-implemented method mentioned above.

[0015] This disclosure includes simplified concepts for using EVSEs (or charging stations) to charge at least one EV, which is further described below. For brevity and ease of description, the disclosure focuses on the EVSEs charging the at least one EV. However, the techniques and systems described herein are not limited to transportation needs and/or charging EVs.

[0016] It will be readily understood that the components of the embodiments, as generally described and illustrated in the figures herein, may and/or can be arranged and designed in a wide variety of different configurations. Thus, the following more detailed description of various embodiments, as represented in the figures, is not intended to limit the scope of the disclosure, as claimed, but is merely representative of various embodiments. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

[0017] Moreover, the phrases “connected to” and “coupled to” are used herein in their ordinary sense and are broad enough to refer to any suitable coupling or other forms of interaction between two or more entities, including electrical, mechanical, fluid, and/or thermal interaction. Two components may be coupled to each other even though they are not in direct contact with each other. The phrase “attached to” refers to interaction between two or more entities that are in direct contact with each other and/or are separated from each other only by a fastener of any suitable variety (e.g., an adhesive).

[0018] The terms “a” and “an” can be described as one, but not limited to one. For example, although the disclosure may recite an element having, e.g., “a processor,” the disclosure also contemplates that the element can have two or more processors.

[0019] Unless otherwise stated, all ranges include both endpoints and all numbers between the endpoints.

[0020] For consistency and broad international understanding, throughout this disclosure, units of measurements may be expressed using le Systeme International d’unites (the International System of Units, abbreviated from the French, the “SI” units), or may be colloquially referred to as the “metric system.” In addition to, or alternatively of, it is to be understood that the techniques and systems described herein may operate using other units, for example, units defined in the United States Customary System (USCS).

[0021] The terms “charge,” “energy,” and “power,” for example, “electric charge,” “electric energy,” and “electric power,” may be used interchangeably, in part, because these terms may be related. Further, the terms “power” and/or “electric power” may be expressed in units of Watts (W) and/or a derivative thereof, for example, kilowatt-hour (kWh). Persons having ordinary skill in art can infer and/or differentiate these terms based on context, industry usage, academic usage, linguistic choice, and/or other factors. [0022] For decimal separators and thousand(s) separators, this disclosure generally uses an English-speaking (e.g., the United States of America) number formatting instead of, for example, a Continental-European number formatting. As such, two dollars and thirty-two cents may be written as “$2.32.” Similarly, two euros and thirty-two cents may also be written as “€2.32.” Also, when using the USCS units, one million and ninety-two pounds (e.g., weight units in USCS) may be written as “1 ,000,092 lb.” Likewise, even when using the SI units, one million and ninety-two kilograms may also be written as “1 ,000,092 kg.”

[0023] Reference throughout this specification to “an embodiment” or “the embodiment” means that a particular feature, structure, or characteristic described in connection with that embodiment is included in at least one embodiment. Thus, the quoted phrases, or variations thereof, as recited throughout this specification are not necessarily all referring to the same embodiment. Not every embodiment is shown in the accompanying illustrations; however, at least a preferred embodiment is shown. At least some of the features described for a shown preferred embodiment are present in other embodiments.

[0024] Alternatively of, or in addition to, the terms “an embodiment” or “the embodiment,” this disclosure may also include the terms “an aspect” or “the aspect,” depending on a linguistic choice, for example, for lowering repetitiveness of the terms “an embodiment” or “the embodiment.” Therefore, the terms “an aspect” and “an embodiment” may be synonymous with each other.

[0025] The term electric vehicle (EV), as used herein, refers to a motorized vehicle deriving locomotive power, either full-time or part-time, from an electric system on board the motorized vehicle. By way of non-limiting examples, an EV may be an electrically powered passenger vehicle for road use; an electric scooter; an electric forklift; a cargo-carrying vehicle powered, full-time or part-time, by electricity; an offroad electrically powered vehicle; an electrically powered watercraft; and so forth. The EV may also utilize an autonomous-driving application software and/or driverassistance application software.

[0026] The term electric vehicle supply equipment (EVSE), as used herein, refers to equipment by which an EV may be charged or recharged. An EVSE may comprise or be coupled to a computing system whereby service to the EV is provisioned, optionally, according to parameters (e.g., operator-selectable parameters). Also, an EVSE may comprise a means of providing cost accounting and may further comprise a payment acceptance component. An EVSE may be installed at a home of an owner/operator of an EV, at a place of business for an owner/operator of an EV, at a fleet facility for a fleet comprising one or more EVs, at a public charging station, etc. The present disclosure uses the terms EVSE and “charging station,” interchangeably.

[0027] According to some embodiments, techniques that estimate parameters relating to EV charging using location information obtained from a user’s computing device (e.g., cell phone, tablet, mobile device, etc.) are disclosed. Determining how far an EV travels between charging sessions, which EV receives priority charging privileges during a certain time of a certain day (e.g., rush hour), which EV can use a fast-charging station, and other factors pose a challenge. According to some embodiments, an estimation of the distance an EV travels between charging sessions is determined, which, among other things, may provide a basis for estimating the SoC for the EV, determining when to limit charging of the EV (e.g., when the EV is approaching full charge or a desired charge level that is less than full charge), and/or determining how to allocate power to the EV during periods of multivehicle charging.

[0028] For example, a user may drive an EV to a charging station (e.g., at home or a public location, such as a shopping mall or business) and direct charging of the EV using an in-vehicle infotainment (IVI) system with its associated user interface (III) or a portable computing device (e.g., a driver’s smartphone, an EV’s occupant’s smartphone) storing the EV’s media access control (MAC) address. Embodiments of the present disclosure include application software that associates this MAC address of the EV with a profile of the EV stored in a database. The application software may recognize that the computing device stores the EV MAC address and automatically selects the EV as the vehicle that was driven to the charging station and is to be charged. This recognition and automatic selection may occur because an EV MAC address is stored on a computing device when the device is connected to an EV, for example, via Bluetooth Classic® for handsfree functionality such as music playback, navigation, or the like. Thus, it may be likely that when a MAC address corresponding to an EV is stored on the computing device directing charging, the vehicle to be charged corresponds to the stored MAC address. In some embodiments, the EV selection is automatic (as discussed above). In other embodiments, the EV selection is semi-automatic, where, for example, when the user has previously indicated that a different EV is subject to charging functions, the user is asked (e.g., via an application on the computer device) whether the user is instead driving the vehicle recognized using the stored MAC address.

[0029] In some embodiments, location information of the smartphone is accessed. For example, one or more “trips,” identified by instances since a vehicle’s last charge where the selected vehicle and the computing device were connected to each other (e.g., via Bluetooth Classic®), are determined, where the estimated distance traveled during each trip may be further determined. In some embodiments, each trip may be defined by determining a location of the computing device when it was initially connected to the selected EV and stored the EV’s MAC address (e.g., when a Bluetooth Classic® connection is established), determining a trip end location (e.g., a current location at the end of a trip), and determining a difference between the trip end location and the location where the initial connectivity took place. A distance (e.g., in kilometers) driven by the EV during the trip may be estimated using the determined difference. The total estimated distance traveled during all of the one or more trips between charging sessions may be determined and used to estimate the distance driven by the vehicle since the last charge. The EV’s SoC may be determined at the end of each trip.

[0030] In some embodiments, the EV’s SoC is estimated using the estimated distance driven since the last charge. In some embodiments, in addition or alternatively to the estimated distance driven since the last charge, one or more of an estimated SoC at the end of the EV’s previous trip and vehicle characteristics (e.g., mileage per kWh, battery size) may be used to estimate the EV’s SoC. A user may select the EV characteristics to specify a certain EV configuration. The EV characteristics may be obtained from a third party (e.g., a database of the EV manufacturer) or a first party (e.g., input from a driver or a rider).

[0031] By estimating an SoC of the EV, allocation of power between multiple EVs being charged by different power sources or a same power source can be improved. Moreover, a user may be provided with an improved indication of travel range of the EV and may make a more informed and improved selection of charging limits during charging. For example, the driver can select a charging limit of a certain percentage of the total battery capacity of the EV (e.g., 10%, 25%, 50%, 75%, 90%).

[0032] FIG. 1 is a diagram of a system 100 for selectively enabling a charging station to charge an EV 102, according to some embodiments of the present disclosure. In some embodiments, the system 100 includes multiple charging stations, including a first charging station 104 (charging station 104) and a second charging station 106 (charging station 106). The EV 102 may be charged using the charging station 104, the charging station 106, and/or another charging station (not illustrated), where the charging stations may provide electricity (e.g., electric charge, electric energy, electric power) to a battery of the EV 102.

[0033] The system 100 may include a computing device 108. In some embodiments, the computing device 108 may be an IVI system, where the IVI system and its associated user interface may enhance a driving or riding experience by incorporating features, such as navigation, directions to the nearest charging station, directions to a fast-charging station, traffic information, ridesharing characteristics or information, an SoC of the EV 102, a rear dashcam, parking assistance, handsfree phone, radio stations, and/or other features. For these features, the computing device 108 may utilize ridesharing, navigation, autonomous-driving, driverassistance, and/or other application software.

[0034] The computing device 108, however, may be implemented as any suitable computing or other electronic device. In some embodiments, the computing device 108 may be or may include a smartphone, a navigation device, a media device, a laptop computer, a network-attached storage (NAS) device, a desktop computer, a tablet computer, a computer server, a smart appliance, a cellular base station, a broadband router, an access point, a gaming device, an internet-of-things (loT) device, a sensor, a security device, an asset tracker, a fitness management device, a wearable device, a wireless power device, and so forth.

[0035] In some embodiments, the computing device 108 includes at least one application processor (processor) and at least one computer-readable medium. The processor may include any type of processor, such as a central processing unit (CPU) or a multi-core processor configured to execute instructions (e.g., code, algorithms, application software), that may be stored in the computer-readable medium. The computer-readable medium may include any suitable data storage media, for example, non-volatile memory (e.g., flash memory), volatile memory (e.g., random-access memory (RAM)), optical media, magnetic media (e.g., disc or tape), and so forth. Moreover, the computer-readable medium does not include transitory propagating signals or carrier waves.

[0036] In some embodiments, the system 100 includes one or more databases 110. For example, the database 110 may store data from or used by one or more of the EV 102, the charging station 104, the charging station 106, the computing device 108, and/or another computing device 112. The data may be profile data for a driver of the EV 102 reflecting information (e.g., make, model, vehicle identification number (VIN), MAC address) of the EV 102 operated by, owned by, or otherwise associated with the driver.

[0037] In some embodiments, the system 100 may include a ridesharing system 111. The ridesharing system 111 may provide ridesharing characteristics, which may include ride information (or ride data). The ridesharing system 111 may include a mobile app that enables user (e.g. riders) to coordinate rides with other users (e.g., drivers). The ridesharing system 111 may be an interface with a third-party system. The ridesharing system 111 may be provided by an EV original equipment manufacturer (OEM). The ridesharing system 111 may be integral to or a subsystem of the system 100. Although one ridesharing system 111 is depicted in FIG. 1 , the system 100 may comprise a plurality of ridesharing systems 111.

[0038] In some embodiments, the computing device 112 may be a remote computing device (e.g., a cloud computer or the like) that communicates with one or more of the EV 102, the charging station 104, the charging station 106, the computing device 108, and/or the database 110 directly and/or via a network 114. Like the computing device 108, the computing device 112 may include another computer-readable medium, where the other computer-readable medium may store the instructions. In some embodiments, the computing device 112 determines whether a particular user (e.g., EV driver, occupant, rider, or person associated with the EV) is authorized to charge or have the EV 102 charged at a particular charging station (e.g., 104, 106). For example, the computing device 112 may process data (e.g., identification data), security token data, SoC data, make, model, driving efficiency, traffic information, power load capacity, trip data, past driving behavior data, a time of a day, a day of a week, a week of a month, a month of a year, a count of riders, greenhouse gas data, carbon dioxide (CO2) data, and so forth from the EV 102, the charging station 104, the charging station 106, the computing device 108, and/or the database 110 to determine whether a user is authorized to charge or have the EV 102 charged at the charging station (e.g., 104, 106), as is further described below. The computing device 112 may process ride data (e.g., current rides the EV may be providing, prospective rides the EV may be scheduled or otherwise engaged to provide, potential rides the EV may be offered to provide, and/or past rides the EV may have provided). The ride data may be received from the EV 102. The ride data may be received from the ridesharing application 111 or other system for coordinating rideshare rides.

[0039] In some embodiments, the computing device 112 may be configured to control charging of the EV 102, determine an estimated SoC of the EV 102, guide the driver of the EV 102 to the charging station 104 having a first location, the charging station 106 having a second location, or another charging station having a third location. For example, the computing device 112 may receive one or more of EV location data, planned trip data, a count of riders and other ride data, SoC data, EV characteristics, and the like from the EV 102, the charging station 104, the charging station 106, the computing device 108, and/or the database 110, and the computing device 112 may selectively transmit the first, the second, and so forth, location of a particular charging station (e.g., 104, 106) to the EV 102. Further, the computing device 112 may guide the EV 102 (e.g., using navigation application software) to drive to the particular charging station (e.g., 104, 106) and enable the EV 102 to receive a charge.

[0040] In some embodiments, the network 114 may facilitate communication between the EV 102, the charging station 104, the charging station 106, the database 110, the computing devices 108, 112, a satellite(s) 116, and/or a base station(s) 118. Communication(s) in the system 100 may be performed using various protocols and/or standards. Examples of such protocols and standards include a 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE) standard, such as a 4th Generation (4G) or a 5th Generation (5G) cellular standard; an Institute of Electrical and Electronics (IEEE) 802.11 standard, such as IEEE 802.11g, ac, ax, ad, aj, or ay (e.g., Wi-Fi 6® or WiGig®); an IEEE 802.16 standard (e.g., WiMAX®); a Bluetooth Classic® standard; a Bluetooth Low Energy® or BLE® standard; an IEEE 802.15.4 (e.g., Thread® or ZigBee®); other protocols and standards established or maintained by various governmental, industry, and/or academia consortiums, organizations, and/or agencies; and so forth. Therefore, the network 114 may be a cellular network, the Internet, a wide area network (WAN), a local area network (LAN), a wireless LAN (WLAN), a wireless personal-area-network (WPAN), a mesh network, a wireless wide area network (WWAN), a peer-to-peer (P2P) network, and/or a Global Navigation Satellite System (GNSS) (e.g., Global Positioning System (GPS), Galileo, Quasi-Zenith Satellite System (QZSS), BeiDou, GLObal NAvigation Satellite System (GLONASS), Indian Regional Navigation Satellite System (IRNSS), and so forth).

[0041] In addition to, or alternatively of, the communications illustrated in FIG. 1 , the system 100 may facilitate other unidirectional, bidirectional, wired, wireless, direct, and/or indirect communications utilizing one or more communication protocols. In some embodiments, the computing device 108 communicates with the EV 102, the charging station 104, and the charging station 106 directly (e.g., via Bluetooth Classic® or a different short-range communication protocol) and/or indirectly (e.g., via the network 114). In some embodiments, the EV 102 and a charging station (e.g., 104, 106) communicate with each other directly (e.g., via Bluetooth Classic® or a different short-range communication protocol) and/or indirectly (e.g., via the network 114, the satellite 116, the base station 118, and so forth). It is to be understood that the EV 102, the charging stations (e.g., 104, 106), the network 114, the satellite 116, the base station 118, and other elements in the system 100 that may not be explicitly illustrated in FIG. 1 include appropriate wired and/or wireless interfaces to accommodate the abovementioned communication protocols and/or standards. Next, the disclosure covers example techniques for estimating an SoC of an EV (e.g., 102).

[0042] FIG. 2 is a diagram 200 of example techniques for estimating an SoC of an EV, according to some embodiments of the disclosure. Partly, FIG. 2 is described in the context of FIG. 1 . As such, an EV 202 of FIG. 2 may be similar and/or analogous to the EV 102; a computing device 204 may be similar and/or analogous to the computing device 108 and/or the computing device 112; and charging stations 206 and 208 may be similar and/or analogous the charging stations 104 and 106, respectively. In some embodiments, the computing device 204 is a device of a driver of, occupant of, or individual otherwise associated with the EV 202.

[0043] In FIG. 2, at a location L0, the computing device 204 and the EV 202 may establish or already have established connectivity or communication with each other. For example, the connectivity or communication may be direct, indirect, wired, and/or wireless. Also, regardless of wired or wireless communication, the computing device 204 safely enables a user (e.g., driver, rider, passenger) to make call(s), send voice-assistant messages (e.g., sending text messages using a voice-activated feature), navigate, control media, and so forth, while driving the EV 202.

[0044] In some embodiments, the computing device 204 obtains or otherwise receives and/or stores a MAC address of the EV 202 when communicating with the EV 202. For example, the computing device 204 may store a time at the location L0 when the computing device 204 first receives the MAC address of the EV 202. The computing device 204 may further store initial location data (e.g., data reflecting GNSS coordinates and/or a street address) of the computing device 204 when it first obtains the MAC address of the EV 202, reflecting the location L0. For example, the computing device 204 may store the initial location data of the computing device 204 at a time when connectivity with the EV 202 was first initiated. The computing device 204 determines the location L0 by using GNSS, cellular, or other location circuitry of the computing device 204 or the EV 202. In some embodiments, the location may correspond to the location of the EV 202 at location L0, for example, if the computing device 204 is in short-range communication and therefore close in proximity to the EV 202.

[0045] At location L0, the EV 202 may be located at a charging station 206. In some embodiments, the computing device 204 communicates directly with the charging station 206 using a short-range or a longer-range wireless communication protocol. In some embodiments, the computing device 204 communicates with the charging station 206 via a network (e.g., the network 114 of FIG. 1). In some embodiments, the computing device 204 communicates with the charging station 206 via the network 114 and/or another computing device (e.g., 112 of FIG. 1 ). For example, the computing device 112 may communicate with both the computing device 204 and the charging station 206 via the network 114, and the computing device 112 may facilitate communication between the computing device 204 and the charging station 206. In some embodiments, the computing device 112 may provide authentication for one or both of the computing device 204 and the charging station 206. In some embodiments, the computing device 112 may control charging of the EV 202 by the charging station 206 and/or determine an estimated SoC of the EV 202 for use by the charging station 206 during the charging.

[0046] At location L0, the SoC of the EV 202 may be determined, for example, by determining the SoC of a battery of the EV 202. In one aspect, the computing device 204 may obtain or receive the estimated SoC. In another aspect, based on prior SoC data for the EV 202, the computing device 204 or another computing device (e.g., computing device 112) may determine an estimated SoC of the EV 202. The prior SoC data for the EV 202 may be, for example, an estimated SoC of the EV 202 at the end of an immediately previous trip and/or an SoC of the EV 202 at the end of one or more previous trips. Additionally, or alternatively, the estimated SoC of the EV 202 may be based on one or more vehicle characteristics.

[0047] In one aspect, the initial location data at a location L0 may reflect a location of the charging station 206. For example, the charging station 206 may be used to charge the battery of the EV 202. The estimated SoC for the EV 202 may also be determined at the location L0 based on charging by the charging station 206. For example, after the charging station 206 charges the battery of the EV 202, the estimated SoC of the battery of the EV 202 may be determined by one or more of the charging station 206, the EV 202, the computing device 204, and/or the computing device 112. The estimated SoC of the EV 202 may be transmitted to or received by the computing device 204 and/or the computing device 112. Note that the estimated SoC at the location L0 may be determined using the same or similar processes discussed below for SoC estimating at a location L1 .

[0048] The EV 202 may be driven along a route 210 to a new location L1 . The EV 202 and the computing device 204 may be located at the location L1 . For example, the route 210 may be a trip from the location L0 to the location L1. At the location L1 , a distance traveled by the EV 202 along the route 210 may be estimated. In some embodiments, the stored initial location data of the computing device 204 reflecting where the computing device 204 was located when it was initially connected to a selected EV (e.g., 202) and stored in the selected EV’s MAC address (e.g., at location L0) is determined. The current location data (e.g., street address and/or GNSS coordinates) of the computing device 204 (e.g., at location L1 ) may also be determined and stored by the computing device 204. The storage may be locally on the computing device 204, the IVI of the EV 202, or at a remote location (e.g., the database 110, the computing device 112, on a cloud, and so forth).

[0049] The current location of the computing device 204 may correspond or be proximate to the current location of the EV 202. In some embodiments, the current location of the computing device 204 may be conditioned on present connectivity with the EV 202. For example, to ensure that a determined current location of the computing device 204 corresponds or at least approximates the current location of the EV 202, the determined current location may only be determined if a short-range wireless (e.g., Bluetooth Classic®, BLE®) connectivity with the EV 202 is active and/or enabled. It should be noted that a connectivity between the computing device 204 and the EV 202 need not be constant. In some embodiments, the computing device 204 and the EV 202 may be connected for a first time period when determining the current location of the computing device 204 but are not connected for a second time period when the current location of the computing device 204 is not being determined.

[0050] In some embodiments, a distance between the current location and an initial location may be determined by the computing device 204 and/or the computing device 112. The distance between the current location and the initial location may be determined by analyzing one or more map routes between the current location and the initial location (e.g., between L0 and L1 ) and determining a distance of a more-likely route. The more-likely route may be, for example, a shortest-distance route, a route avoiding one or more tolls, a route avoiding or using freeways, or a route having the shortest estimated travel time. The determined distance may be used as an estimation of a distance traveled by the EV 202 along the route 210 between the location L0 and the location L1 .

[0051] In some embodiments, the EV 202 SoC at the location L1 is estimated by the computing device 204 using the determined distance. In some embodiments, the SoC of the EV 202 is estimated using the determined distance as well as one or more of an estimated SoC at an end of a previous trip of the EV 202, an estimated SoC at a previous location (e.g., location L0), and/or one or more characteristics (e.g., kilometers per kWh), battery size, battery charge capacity) of the EV 202. For example, a user (e.g., a driver or a passenger) of the computing device 204 may select one or more characteristics of the EV 202 to specify a certain EV 202 configuration that impacts the SoC determination. In another example, the characteristics of an EV may be preselected for a particular EV (e.g., 202) and/or obtained from a third party (e.g., a database of a manufacturer of the EV). In some embodiments, historical data for the EV 202 is used alone or in conjunction with one or more of the determined distance, the estimated SoC at the end of the previous trip of the EV 202 or at the previous location (e.g., at the location L0), and the one or more characteristics of the EV 202, to estimate the SoC of the EV 202. The historical data may include one or more of distances traveled on one or more previous trips, kilometers per kWh for the one or more previous trips, energy usage of the EV 202 from operating air conditioning or heating for the one or more previous trips, weather conditions for the one or more previous trips, energy expenditure of the EV 202 for one or more previous city driving trips, energy expenditure of the EV 202 for one or more previous highway and/or freeway driving trips, and the like. In some embodiments, the SoC estimation of the EV 202 uses one or more of a trip distance, route, terrain information, time of day, day of the week, week of the month, traffic, road conditions, EV information (e.g., make, model), power load capacity (e.g., at a charging station), planned trip data, weather, past driving behavior data, the count of riders in the planned trip data, greenhouse gas data (e.g., CO2 data), and/or other data and/or factors. In some embodiments, a model (e.g., an algorithm, a machine-learned model) estimates the SoC of the EV 202 by using data received by a computing device (e.g., 204, 108, 112) from the EV 202, as is further described below.

[0052] With reference again to FIG. 2, in some embodiments, the SoC of the EV 202 is determined immediately after the EV 202 arrives at the location L1 . In some embodiments, the SoC of the EV 202 is determined after a certain time period after arriving at the location L1. Here, for example, the battery of the EV 202 may experience battery drain or loss. Therefore, the SoC of the EV 202 may need to account for or determine such battery drain or loss. In some embodiments, data regarding battery drain or loss in EVs having similar characteristics (e.g., similar or same kilometer per kWh, battery size, battery charge capacity, etc.) to the EV 202 is used to estimate battery drain or loss in the EV 202, which may then be used to determine the SoC of the EV 202 at the location L1 . In some embodiments, the data regarding battery drain or loss may be obtained from the EVs having similar characteristics to the EV 202. The EVs may have at least one application programming interface (API) connection with/to one or more of the database 110 and/or the computing device 112. The APIs may be associated with one or more application software, such as ridesharing, navigation, autonomous-driving, driverassistance, and/or other application software.

[0053] After the location L1 , the EV 202 may travel to one or more of a location L2 along a route 212 and to one or more of a subsequent location Ln, and then arrive at a location Lf, where the charging station 208 is located. Similar or the same processes as those that have been described as occurring at the location L1 above may also occur at one or more of the locations L2 to Ln, as well as at the location Lf, such that an estimated SoC of EV 202 is determined at the location Lf. It should also be noted that a vehicle may travel directly from the location L0 to the location Lf without intermediate trips to the locations L1 , L2, and Ln.

[0054]At the location Lf, in some embodiments, the computing device 204 communicates with the charging station 208 using a shorter-range wireless communication protocol (e.g., Bluetooth Classic®, BLE®) or a longer-range wireless communication (e.g., GNSS, cellular). In some embodiments, the computing device 204 communicates with the charging station 208 via a network (e.g., 114). In some embodiments, the computing device 204 communicates with the charging station 208 via the network 114 and/or another computing device (e.g., 112).

[0055] Regardless of the SoC of the EV 202 and/or other abovementioned factors, the charging station 206, the charging station 208, and/or another charging station that may not be explicitly illustrated in FIGs. 1 and 2 may be unable to charge the EV 202 without proper planning and without considering a power load capacity at a certain location (e.g., L1 , Lf) of a charging station. Next, the description partly focuses on factors that affect the power load capacity at a certain charging station. [0056] FIG. 3 shows a diagram of a system 300, according to one embodiment, with a power grid 302 (illustrated as a dashed-line box) that may be connected to a network of charging stations 304 (illustrated as a dashed-line box). In some embodiments, the network of charging stations 304 may provide electric charge (or electric power, or electric energy) to at least one electric vehicle 305 (EV 305). FIG. 3 may be described in the context of FIGS. 1 and 2. As such, the EV 305 may be similar to, or the same as, the EV 102 of FIG. 1 and the EV 202 of FIG. 2.

[0057] In some embodiments, the power grid 302 may be a local (e.g., county, city) power grid, a regional (e.g., Southern Idaho) power grid, a state-wide (e.g., Utah) power grid, a country-wide (e.g., United States of America) power grid, a continentwide (e.g., Continental Europe, North America) power grid, and/or so forth. In some embodiments, the power grid 302 may be privately-owned (e.g., a privately-owned company, a privately-owned corporation, a publicly-traded corporation), government- owned, privately-owned and government-regulated, government-owned and internationally-regulated, privately-owned and internationally-regulated, and/or a combination thereof. In some embodiments, the regulations may include voltage(s), current(s), phase(s), grid protection, system protection, electric energy rates (e.g., cost), equipment protection, power industry employee protection, consumer protection, environmental protection, and/or other regulations defined by local, regional, country, international, power industry, and/or so forth entities. In some embodiments, the regulations may include an amount of a power generation capacity, energy trading, and/or an amount of power consumption (e.g., power demand, power load capacity).

[0058] Power generation may utilize renewable and/or nonrenewable energy sources. Examples of renewable energy sources include solar energy from the Sun, geothermal energy from heat inside the Earth, wind energy, biomass from plants, hydropower from flowing water, and/or so forth. On the other hand, nonrenewable energy sources include petroleum, hydrocarbon gas liquids, natural gas, coal, nuclear energy, and/or so forth. [0059] In some aspects, renewable energy sources may be more desirable than nonrenewable energy sources because, by definition, the nonrenewable energy sources are limited (e.g., with an end-of-life). On the other hand, the renewable energy source may be utilized nearly perpetually, according to some embodiments. However, using certain energy sources to produce electricity may have certain drawbacks regarding worker conditions (e.g., safety), greenhouse gases, generation capacity, baseload capacity, overall power load capacity, cost of producing electric energy, environmental impact (e.g., air quality, waste, mining), geographic availability, scarcity (e.g., nonrenewable energy sources), and/or so forth.

[0060] In some embodiments, using renewable (e.g., hydropower, solar, biomass, geothermal, wind, etc.) and/or nonrenewable (e.g., nuclear) energy sources to produce electric energy may lower the amount of greenhouse gases released in the atmosphere.

[0061] In some embodiments, using only solar energy and/or wind energy to produce electric energy may not meet a desired baseload capacity of the power grid 302, where the desired baseload capacity is a minimum level of power demand on the power grid 302 over a duration of time, for example, one day, one week, one month, one year, and/or so forth. Consequently, relying only on solar energy and/or wind energy to produce electric power may cause blackouts and/or brownouts of the power grid 302, unless, for example, excess electric energy is stored to be used when solar energy is not available (e.g., during nighttime) and/or when wind energy is not available.

[0062] In some embodiments, shifting from nonrenewable energy sources (e.g., coal) to renewable energy sources (e.g., wind energy, solar energy) to produce electric energy may temporarily increase electric energy rates. Nevertheless, when using renewable energy sources to produce electric energy, the electric energy rates may decrease over time. For example, a levelized cost of energy (LCOE) of a solar- powered power plant may be lower than the LCOE of a coal-powered power plant. Note that the LCOE is a measure of the average net present cost of electricity for a power plant over a lifetime of the power plant.

[0063] In some embodiments, using renewable and/or nonrenewable sources for producing electric energy may lower the greenhouse gases while meeting the desired baseload of the power grid 302. For example, using hydropower, a renewable energy source, and/or using e energy, a nonrenewable energy source, to produce electric energy may lower the greenhouse gases and may meet the desired baseload of the power grid 302. However, in one aspect, using hydropower may have an adverse environmental impact on wildlife (e.g., fish(es)), rivers, and/or humanity (e.g., building large dams may displace towns and/or villages). Also, unfortunately, accidents involving nuclear energy may have devastating effects on humanity, the environment, and/or the wildlife. Further, currently, only select countries have adequate resources (e.g., nuclear material, nuclear engineers and/or scientists) to use nuclear energy to produce electric energy.

[0064] In some embodiments, an availability of a type of energy source

(e.g., renewable and/or nonrenewable) may limit the use of such type of energy source for producing electric energy. For example, Norway receives less solar energy than Egypt; therefore, Norway may not be able to produce enough electric energy by using solar energy to meet the desired baseload of a power grid (e.g., 302). Therefore, in addition to, or alternatively of, the solar energy, Norway may use another energy source to produce enough electric energy to meet the desired baseload. [0065] In some embodiments, it is desirable to use energy sources that lower the electric energy rates, lower the impact on the environment, lower greenhouse gases, are plentiful, are renewable, and/or pose little danger to humanity. To this end, this disclosure focuses on meeting some of these desirable usages of the energy sources for charging EVs, as is further described below. However, the techniques and systems described herein are not limited to transportation needs and/or charging EVs.

[0066] Continuing with the power grid 302, a utility company may purchase (e.g., in an energy marketplace) and/or generate electric energy using at least one power plant(s) 306 (power plant 306). The power plant 306 may be centralized (e.g., in a particular location), decentralized in various locations, and may utilize renewable and/or nonrenewable energy sources to produce electric energy. The power plant 306 may generate a first electric power 308 (electric power 308). The utility company may then utilize at least one first transformer(s) 310 (transformer 310) to transform the electric power 308 to a second electric power 312 (electric power 312). The electric power 312 may have an accompanying set of characteristics, such as an alternating current (AC) power with three phases that is transmitted using a high voltage line and/or an extremely-high voltage line (e.g., for voltages 50,000 V to 200,000 V), and/or other characteristics. In some embodiments, the electric power 312 may be part of a transmission network (not explicitly illustrated in FIG. 3). The transmission network may be regulated by local, regional, country, international, power industry, and/or other entities. It is to be understood, however, that for the high voltage lines and/or the extremely-high voltage lines, some regulations may allow transmission of the AC power, a direct current (DC) power, and/or a combination thereof that may be referred to as “hybrid” power. [0067] In some embodiments, the power grid 302 uses the electric power 312 for transmitting electric power over a first range of distances, for example, from a country to another country, from a state to another second state, from a region to another second region, from a city to another city, and/or so forth.

[0068] In some embodiments, the power grid 302 may also include at least one second transform er(s) 314 (transformer 314) to transform the electric power 312 to a third electric power 316 (electric power 316). The electric power 316 may have another accompanying set of characteristics, such as an AC power with three phases transmitted using a medium voltage line (e.g., for voltages 1 ,000 V to 50,000 V) and/or other power characteristics. In some embodiments, the electric power 316 may be part of a distribution network (not explicitly illustrated in Figure 3). For example, the distribution network may provide the electric power 316 to a small country, a small principality, a small city-state, a small state, a county, a municipality, a city, a town, a village, and/or so forth.

[0069] Given that the baseload of the power grid 302 may change over a duration of time, for example, one day, one week, one month, one year, and/or so forth, the utility company may also utilize a first peaking power plant(s) 318 (peaking power plant 318) and/or a second peaking power plant(s) 320 (peaking power plant 320) during a high power consumption, a high power demand, a high power load, and/or a peak power load. For example, the high power load may be during a particular time duration or period of a weekday, such as Monday through Friday from 7:00 AM to 9:00 AM, when some residents get ready for work; Monday through Friday from 5:00 PM to 7:00 PM, when the some residents come back from work; and/or so forth.

As another example, the high power load may be during a certain period of a year, for example, at the end of July, when some farmers may increase the use of water pumps to water their crops and/or so forth.

[0070] In some embodiments, the peaking power plant 318 may generate a fourth electric power 322 (electric power 322). The utility company may then use at least one third transformer(s) 324 (transformer 324) to transform the electric power 322 to the electric power 312. Therefore, the power grid 302 may utilize the peaking power plant 318 to supply electric power to the transmission network.

[0071] In some embodiments, the peaking power plant 320 may generate a fifth electric power 326 (electric power 326). The utility company may then use at least one fourth transform er(s) 328 (transformer 328) to transform the electric power 326 to the electric power 316. Therefore, the power grid 302 may utilize the peaking power plant 320 to supply electric power to the distribution network.

[0072] Although not illustrated in FIG. 3, the distribution network of the power grid 302 may also include other transformers to transform the electric power 316 to other electric powers having, for example, lower voltages, and/or sometimes fewer phases (e.g., two phases, one phase) to supply electric power to various establishments. The various establishments may include charging stations, residential homes, apartment complexes, offices, stores, educational institutions, government buildings, factories, and/or so forth.

[0073] Generally, utility-scale generation, storage, transmission, and/or distribution of electric power may be referred to as a front-of-the-meter (FTM) electric power (and/or FTM electric energy). Therefore, as is illustrated in FIG. 3, the electric power (e.g., 308, 312, 316, 322, 326) of the power grid 302 may be referred to as an FTM electric power. Energy rates of the FTM electric power may change depending on an amount of electric power used by an establishment during a time of a day, a day of a week, a month of a year, and/or any combination thereof. For example, an establishment may pay a first energy rate for a first amount of the FTM electric power (e.g., the first 400 kWh), a second energy rate for a second amount of the FTM electric power (e.g., 400 kWh to 800 kWh), and/or a third energy rate for a third amount of the FTM electric power (e.g., over 800 kWh), wherein the third energy rate may be higher than the second energy rate, and the second energy rate may be higher than the first energy rate. As another example, an establishment may pay a fourth energy rate of the FTM electric power during non-peak power load hours (e.g., at 11 :00 AM) and a fifth energy rate of the FTM electric power during peak power load hours (e.g., 7:00 AM to 9:00 AM, 5:00 PM to 7:00 PM), wherein the fifth energy rate may be higher than the fourth energy rate. As yet another example, an establishment may pay a sixth energy rate of the FTM electric power during a month of a year (e.g., March) and a seventh energy rate of the FTM electric power during another month of the year (e.g., July), wherein the seventh energy rate may be higher than the sixth energy rate.

[0074] Fortunately, the various establishments, including charging stations of the network of charging stations 304, are increasingly utilizing renewable energy sources to generate electric energy, in part, to reduce their greenhouse gas emissions and/or carbon (e.g., CO2) footprints and to lower their cost of electric power. Alternatively, or in addition, the charging stations may also utilize nonrenewable energy sources (e.g., fossil fuels) to generate electric energy, for example, for backup generation in cases of blackouts, brownouts, and/or staying “off the grid.” The electric energy and/or electric power generated by the charging stations of the network of charging stations 304 may be referred to as a behind-the-meter (BTM) electric power (and/or BTM electric energy). [0075] In aspects, BTM resources (e.g., solar panels, on-site batteries) may be distributed energy resources (DERs). In addition to the network of charging stations 304, the BTM resources may provide numerous benefits to communities and other establishments because they may help provide alternative means to using peaking power plants (e.g., 318, 320). Specifically, the peaking power plants 318 and 320 may be costly to operate, and the utility company may transfer operating costs to establishments with BTM resources and/or without BTM resources.

Therefore, even establishments without BTM resources may benefit from less usage of the peaking power plants 318 and 320. Further, the peaking power plants 318 and 320 may use fossil fuels (e.g., natural gas) that increase greenhouse gases emitted to the atmosphere. To this end, numerous entities (e.g., countries, states, cities) may offer incentives (e.g., financial incentives) to the network of charging stations 304 to increase the BTM resources. The incentives may include lower borrowing rates to build more BTM resources, monetary credits for using less FTM electric power, carbon credits, ease of integrating BTM-generated electric power to the power grid 302, and/or other incentives.

[0076] In some embodiments, the power grid 302 may partly support a decentralized system of generating and/or transferring electric power, whether the electric power is an FTM electric power and/or a BTM electric power. However, sustaining a stable power grid (e.g., without blackouts and/or brownouts) poses some challenges. One of many challenges may include storing a decentralized energy. In some embodiments, the decentralized energy may be stored in various forms, including chemically, potentially, gravitationally, electrically, thermally, and/or kinetically. For example, the network of charging stations 304 may use batteries (e.g., lithium-ion batteries) to store electric energy (electric charge) generated during the daytime using solar panels. EVs (e.g., 306) can then use the stored energy in the batteries of the charging stations during nighttime, peak power load hours, and/or anytime when necessary.

[0077] FIG. 3 also illustrates how the network of charging stations 304 may utilize the BTM and/or the FTM electric power to charge the EVs (e.g., EV 305), according to some embodiments. The network of charging stations 304 may include two (e.g., FIGs. 1 and 2) or more (e.g., FIG. 3) charging stations. FIG. 3 illustrates that the network of charging stations 304 includes a first charging station 330 (charging station 330), a second charging station 332 (charging station 332), a third charging station 334 (charging station 334), and a fourth charging station 336 (charging station 336).

[0078]As is illustrated in FIG. 3, the charging station 330 is coupled to the power grid 302 using an accompanying power meter 330-1 . The charging station 332 is coupled to the power grid 302 using an accompanying power meter 332-1 . The charging station 334 is coupled to the power grid 302 using an accompanying power meter 334-1. Lastly, the charging station 336 is not coupled to the power grid 302; therefore, the charging station 336 is off the grid.

[0079] Since typically, utility companies own the power meters, therefore, the power meters 330-1 , 332-1 , and 334-1 are illustrated as being inside the power grid 302. Nevertheless, as it will become apparent, the power meters (330-1 , 332-1 , and 334- 1 ) delineate the FTM electric power from the BTM electric power. Therefore, even though not illustrated as such in FIG. 3, in one aspect, the power meters 330-1 , 332- 1 , and 334-1 may define a separation (e.g., an abstract electric power border) of the power grid 302 from the network of charging stations 304, and the FTM electric power from the BTM electric power. [0080] In some embodiments, the charging stations (e.g., 330 to 336) may supply electric power using different charging speeds. For example, a charging station in a location may be capable of supplying a first amount of electric charge and/or electric power (e.g., 3 kW) during a first duration of time (e.g., one hour). As another example, another charging station in another location may be capable of supplying a second amount of electric charge and/or electric power (e.g., 40 kW to 50 kW) during a second duration of time (e.g., 30 minutes). Differently stated, the first amount of electric charge during the first amount of time may be a faster charging time for a same electric charge compared to the second amount of electric charge during the second amount of time. Understandably, a driver of the EV 305 may prefer to spend as little time as possible at a charging station (e.g., 330 to 336).

[0081] In some aspects, charging speeds may depend on an input AC power at a charger and an ability of an AC-to-DC converter to convert the AC power to DC power to charge a battery of the EV 305. For example, typically, home chargers utilize an AC power from the power grid 302, for example, the distribution network. Also, a relatively small transformer (e.g., a 15 kVA transformer, not illustrated) may transform the AC power from the distribution network (e.g., 316) to a home with the home charger. The relatively small transformer, however, supplies an AC power with a relatively low AC current. Further, an EV 305 may use an AC-to-DC converter located inside the EV (e.g., an onboard charger) to charge a battery of the EV 305. Due to physical constraints, the onboard AC-to-DC converter of the EV 305 is relatively small. Therefore, the charging speed of the home charger is relatively low. This home-style charging approach works well if a driver (e.g., owner, family member, an authorized person to charge) of the EV 305 spends a considerable amount of time (e.g., multiple hours) to charge the battery of the EV 305 while they may be doing something else (e.g., sleeping). This home-style charging approach, however, may not be convenient to be used in charging stations.

[0082] To lower time spent at a charging station, charging stations 330 to 336 offer higher charging speeds than home chargers. Further, the charging stations 330 to 336 may offer different charging speeds. In some aspects, the charging speeds of the charging stations 330 to 336, in part, may depend on whether the charging stations are AC charging stations or DC charging stations. The AC charging stations may operate similarly to the home charger and may utilize the onboard AC-to-DC converter of the EV 305. However, the AC charging stations can usually supply a higher AC current than the home charging station, for example, by using a bigger than 15 kVA transformer to receive power from the power grid 302. Therefore, the AC charging stations can often charge the battery of the EV 305 faster than the home charging stations. To increase the charging speeds even further, the DC charging stations utilize DC charging. To do so, the DC charging stations perform an AC-to-DC power conversion before power enters the EV 305. Therefore, the DC charging stations may have an on-site AC-to-DC converter, which enables the DC charging station to bypass the onboard AC-to-DC converter of the EV 305 and charge the battery of the EV 305 directly. Regardless, the AC and the DC charging stations have a considerable associated cost to produce, install, and operate the charging stations.

[0083] In some embodiments, the operating costs of the charging stations 330 to 336 partly depend on the electric energy rates of the FTM electric power. For example, assume the charging station 330 does not have BTM resources. As such, the charging station 330 may rely solely on the AC power of the power grid 302 to supply electric charge to the EV 305. Consequently, depending on the time of the day, the day of the week, or the month of the year, a driver of the EV 305 may pay different electric energy rates that may increase a cost to charge and/or to operate (e.g., drive) the EV 305. Furthermore, as is illustrated in FIG. 3, power flow concerning the charging station 330 is unidirectional, from the power grid 302 to the power meter 330-1 .

[0084] In some embodiments, the charging station 332 includes a BTM resource. Specifically, the charging station 332 may include an associated storage device 332-2 (storage device 332-2) and an on-site AC-to-DC converter (not illustrated) to charge the storage device 332-2 (e.g., an on-site battery). In addition, or alternatively, the on-site AC-to-DC converter may provide DC power to the EV 305, increasing the charging speed. The charging station 332, however, does not include BTM resources that generate electric power (e.g., solar panels). Therefore, like the charging station 330, power flow regarding the charging station 332 is unidirectional, from the power grid 302 to the power meter 332-1 , as illustrated in FIG. 3. Further, unlike the charging station 330, the charging station 332 can use the storage device 332-2 to supply electric power to the EV 305 during, for example, peak power load hours, avoiding higher electric energy rates. In one aspect, the charging station 332 may be configured to charge the storage device 332-2 during non-peak load hours and use the stored energy (or charge) in the storage device 322-2 to supply electric power to the EV 305 during the peak power load hours.

[0085] In some embodiments, the charging station 334 includes BTM resources. For example, the charging station 334 includes an associated energy storage device 334-2 (storage device 334-2) and an on-site AC-to-DC converter (not illustrated) to charge the storage device 334-2. In addition, or alternatively, the on-site AC-to-DC converter can provide DC power to the EV 305, increasing the charging speeds. As another example, the charging station 334 may also include an associated energy generating device 334-3 (energy generating device 334-3). The energy generating device 334-3 may utilize renewable (e.g., solar) and/or renewable (e.g., petroleum) energy sources. Unlike the charging stations 330 and 332, power flow regarding the charging station 334 may be bidirectional, from the power grid 302 to the power meter 334-1 and/or from the power meter 334-1 to the power grid 302, as is illustrated in FIG. 3. It may be assumed that the network of charging stations 304 strives to lower the amount of greenhouse gases released to the atmosphere; to this end, the energy generating device 334-3 may include solar panels. In some aspects, the charging station 334 is configured to supply energy generated from the solar panels (e.g., 334-3) to the power grid 302, the storage device 334-2, and/or the EV 305. DERs, such as a combination of energy generating devices (e.g., 334-3) and energy storage devices (e.g., 334-2), may enable the charging station 334 to decrease the amount of the FTM power used to charge the EV 305; off-set the amount of the FTM power used to charge the EV 305 during, for example, peak power load hours; use mainly the BTM power to charge the EV 305 and supply excess BTM power to the power grid 302; and/or any combination thereof. Further, the storage device 334-2 and the energy generating device 334-3 may increase a power load capacity at the charging station 334.

[0086] In some embodiments, the network of charging stations 304 may also include the charging station 336, which, as is illustrated in FIG. 3, is not coupled to the power grid 302 (e.g., off the grid). The charging station 336 may also include an associated storage device 336-1 (storage device 336-1 ) and an associated energy generating device 336-2 (energy generating device 336-2). Depending on a type of energy generating device, the energy generating device 336-2 may generate AC or DC power. If the energy generating device 336-2 generates AC power, the charging station 336 may also include an on-site AC-to-DC converter (not illustrated) to charge the storage device 336-1 . In addition, or alternatively, the on-site AC-to-DC converter can provide DC power to the EV 305, increasing the charging speed. Advantages of using off-the-grid charging stations include an independence from the power grid 302. The independence from the power grid 302 allows the network of charging stations 304 to build at least one charging station off the grid, for example, in a remote location without FTM electric power. Also, in such a case, a cost of electric energy is independent of the FTM electric power. Therefore, all power associated with the charging station 336 is BTM electric power.

[0087] In some embodiments, the network of charging stations 304 may also utilize a power load capacity algorithm to measure, monitor, and/or predict the power load capacity of at least one location (e.g., the location of the charging station 334). To predict the power load capacity, the algorithm may use data of FTM power and BTM power availability at the location of the charging station 334. In one aspect, the network of charging stations 304 (e.g., utilizing the power load capacity algorithm) may predict the FTM power availability at the charging station 334 by considering the size of the transformer (not illustrated) used to supply AC power from the power grid 302 to the charging station 334. In one aspect, the network of charging stations 304 may communicate with the utility company to gather FTM electric power data. The FTM electric power data may be communicated in real-time or at time intervals. In one aspect, the network of charging stations 304 may predict the BTM power availability by considering past measurements and/or current measurements of electric energy produced and/or stored using the BTM resources (e.g., 334-2, 334-3) at the charging station 334, for example, during a time of a day, a day of a week, a week of a month, a month of a year, meteorological events (e.g., a cloudy day, a rainy day, a clear and sunny day), and/or so forth. Next, this disclosure partly describes techniques and/or apparatuses used by the system 100 of FIG. 1 and/or the network of charging stations 304 of FIG. 3 to increase revenue, incentivize virtuous driving behavior (e.g., promote carpooling, ridesharing), lower greenhouse gases, lower energy rates, and help a community.

[0088] FIG. 4 illustrates a diagram 400 of a model 402 used to selectively enable a first or a second charging station of at least two charging stations to charge one or more EVs. The one or more EVs may include the EV 102 of FIG. 1 , the EV 202 of FIG. 2, the EV 305 of FIG. 3, and/or so forth. FIG. 4 may be described in the context of FIGs. 1 to 3. Therefore, at least two charging stations may include the charging stations 104 and 106 of FIG. 1 , the charging stations 206 and 208 of FIG. 2, and the network of charging stations 304 of FIG. 3. For brevity and clarity, the following description of FIG. 4, in part, may be explained in the context of the system 100, the network of charging stations 304, and the power grid 302. Assume the network of charging stations 304 supports charging the one or more EVs. In one aspect, a merchandise-transportation company may own a count of, or all, the EVs. In that case, the merchandise-transportation company may employ drivers to transport merchandise. In one aspect, the EVs include one or more ridesharing EVs. The ridesharing EVs may be owned by respective drivers, a ridesharing company, a taxi company, or a combination thereof. Depending on a business model, the drivers of the EVs may be contractors working part-time or full-time, owners of a business, employees of the business, and/or a combination thereof. Further, the EVs may transport people, merchandise, mail, packages, food, livestock, and/or so forth. [0089] Regardless of a business model or a personal use of the EV, it is desirable to lower transportation costs to increase a profit of a business and/or any financially- driven activity that requires transportation (e.g., ridesharing) and/or decrease personal transportation budgets. When using EVs, the transportation costs include energy costs of charging the EVs; energy costs and opportunity costs associated with a driver driving the EV to a charging station; opportunity costs of a time the driver spends at the charging station while charging their EV; opportunity costs of a time the driver spends in heavy traffic while driving their EV; environmental costs (e.g., greenhouse gases) associated with a production of electric energy used by the EV; and/or other costs that may be associated with, for example, purchasing, maintaining, parking, and/or so forth the EV. In microeconomics, an opportunity cost of a particular activity option (e.g., a driver spending time at a charging station, the driver spending time in heavy traffic) is a loss of a value or benefit (e.g., making money by driving passengers) that would be incurred by engaging in that particular activity option, relative to engaging in an alternative activity (e.g., driving passengers) offering a higher return or benefit. To this end, the model 402 may use ridesharing characteristics to increase revenue, incentivize virtuous driving behavior (e.g., promote carpooling, increase ridesharing), lower greenhouse gases, lower energy rates, and/or help a community, as is further described below.

[0090] To do so, the model 402 may analyze and/or use one or more inputs 404 to 418 to generate one or more outputs 420 to 432. As is described herein, information associated with the one or more inputs 404 to 418 may be referred to as “ridesharing characteristics.” The model 402 may be a ridesharing charging algorithm that may be installed on the computing device 108 and/or the computing device 112. The system 100 and/or the network of charging stations 304 may utilize processor (e.g., CPU) and/or memory resources (e.g., at least one computer-readable medium) of the computing device 108, the computing device 112, and/or the database 110 to store data (e.g., the ridesharing characteristics) and to execute the ridesharing charging algorithm. Additionally, or alternatively, the model 402 may be a machine- learned model, such as a neural network, a support vector machine, a recurrent neural network (RNN), a convolutional neural network (CNN), a dense neural network (DNN), heuristics, or a combination thereof.

[0091] In some embodiments, the model 402 may use a first input 404 that may include information of an SoC of the EV. To determine the SoC of the EV, the system 100 and/or the network of charging stations 304 may use any of the techniques and apparatuses described in FIGs. 1 to 3. Additionally, or alternatively, the driver of the EV may utilize the ridesharing application software, navigation application software, autonomous-driving application software, driver-assistance application software, ridesharing charging algorithm, and/or another application software, model, and/or algorithm to initiate a communication with the system 100 and/or the network of charging stations 304. The EV may communicate with the network of charging stations 304 directly, indirectly (e.g., using the network 114), with a wired connection, and/or wirelessly using short-range and/or longer-range communications protocols and standards, such as the 3GPP LTE standard, the IEEE 802.11 standard, the IEEE 802.16 standard, the IEEE 802.15.4 standard, the Bluetooth Classic® standard, the BLE® standard, and/or so forth. For example, the IVI of the EV may report the SoC of the EV to the system 100 and/or the network of charging stations 304.

[0092] In some embodiments, the model 402 may use a second input 406 (input 406) that may include information of a make, model, and/or VIN of the EV. Based on the make, model, and/or VIN, the system 100 and/or the network of charging stations 304 may obtain an energy efficiency of the EV. Additionally, or alternatively, the IVI of the EV may report (e.g., to the system 100 and/or the network of charging stations 304) data, including how many kilometers can the EV drive with a current SoC and/or an efficiency (e.g., in kilometers per kWh) of the EV.

[0093] In some embodiments, the model 402 may use a third input 408 (input 408) that may include traffic information. The system 100 and/or the network of charging stations 304 may utilize a database (e.g., 110) of GNSS locations of various EVs, accident reports, traffic jams, historical traffic patterns, and/or so forth to monitor and/or predict the traffic information. Additionally, or alternatively, the system 100 may use a third-party traffic-related application software to monitor and/or predict the traffic information.

[0094] In some embodiments, the model 402 may use a fourth input 410 (input 410) that may include information of a power load capacity, a BTM electric power capacity, and/or an FTM electric power capacity at a charging station (e.g., 332) of the network of charging stations 304. Note that the power load capacity may be a sum of the BTM power load capacity and the FTM power load capacity. The system 100 and/or the network of charging stations 304 may determine the input 410 using any techniques and/or apparatuses described in FIGs. 1 to 3. As such, the input 410 may include information of the power load capacity, the BTM electric power capacity, and/or the FTM electric power capacity at a charging station during a time of a day, a day of a week, a week of a month, a month of a year, meteorological events (e.g., a cloudy day, a rainy day, a clear and sunny day), and/or so forth. The input 410 may also include information regarding the energy source used to generate the power load capacity, the BTM electric power capacity, and/or the FTM electric power capacity at a charging station. Consequently, the input 410 may include greenhouse data, energy rates (e.g., cost), peak power load hours, non-peak power load hours, and/or any information described in relation to FIG. 3.

[0095] In some embodiments, the model 402 may use a fifth input 412 (input 412) that may include information of a charging speed at a charging station of the network of charging stations 304. As described in relation to FIG. 3, the charging stations may supply electric energy at different rates (or speeds). Consequently, the driver of the EV may spend less time charging at a faster-charging station than at a slower- charging station. The input 412 may also include the energy cost and the opportunity cost associated with the driver driving the EV to the charging station, and the opportunity cost of the time the driver spends at the charging station. For example, a slower-charging station may be considerably closer to a current location of the EV than a faster-charging station; therefore, the faster-charging station may not always be a preferred option to charge the EV. Further, the faster-charging station may include an additional cost (e.g., convenience cost) compared to the slower-charging station.

[0096] In some embodiments, the model 402 may use a sixth input 414 (input 414) that may include a count of persons (e.g., riders, passengers, and/or driver) in the EV. To do so, the system 100 and/or the network of charging stations 304 may use information from the ridesharing application software. For example, the ridesharing application software may prompt a passenger to enter a current location, a destination location, an initial time (e.g., a pickup time and date), and/or a count of passengers in at least one trip. As another example, the input 414 may include information from various sensors embedded in and/or on the EV, such as pressure sensors embedded in seats, motion sensors, proximity sensors, and/or cameras to determine the count of persons in the EV. As yet another example, the input 414 may include GNSS coordinates of personal devices of the count of persons in the EV. Alternatively, or additionally, the driver of the EV may be prompted (e.g., on a screen of the computing device 108) to report the count of persons in the EV. [0097] In some embodiments, the model 402 may use a seventh input 416 (input 416) that may include information regarding a business and/or a business model. For example, assuming the driver utilizes the EV to transport passengers that may use the ridesharing application software, the input 416 may include information from the ridesharing application software. The information from the ridesharing application software may used to estimate a count of trip requests the driver may receive during a time period (e.g., a time duration, one hour, one day, etc.). Also, the input 416 may include information on an amount of money the driver may earn during the time period. As another example, assuming the driver may drive the EV to deliver food, merchandise, mail, packages, livestock, and/or so forth, based on past data and/or current delivery orders, the input 416 may include an estimated count of deliveries and/or an estimated amount of money the driver may earn in the time period. The information from the ridesharing application software may include current rides the EV may be providing (e.g., passengers presently in the EV), prospective rides the EV may be scheduled or otherwise engaged to provide, potential rides the EV may be offered to provide, and/or past rides the EV may have already provided.

[0098] In some embodiments, the model 402 may use an eighth input 418

(input 418) that includes information regarding special events in a community. For example, assume the driver of the EV drives passengers in Salt Lake City, and the Utah Jazz professional basketball team is playing the Houston Rockets. Due to the popularity of this game, many residents of Salt Lake City may use the ridesharing application software to take a trip to and/or from the home court of the Utah Jazz. In such special events, it is desirable to complete these trips quickly and/or efficiently, and perhaps during a window of time, so attendees of the game can arrive before or at the start of the game and/or so the residents of Salt Lake City can resume their daily lives and/or activities. Further, the special events may predict transportation costs using the ridesharing application software (e.g., higher costs due to a higher service demand).

[0099] Although not illustrated in FIG. 4, the model 402 may analyze additional inputs (e.g., inputs in addition to the one or more inputs 404 to 418 discussed). In some embodiments, if the model 402 utilizes the machine-learned model, the machine- learned model may analyze each input (e.g., 404) of the one or more inputs 404 to 418 separately and/or in relation to other inputs (e.g., from the set of one or more inputs 404 to 418). Combining and analyzing the one or more inputs 404 to 418 may enhance a capability of the machine-learned model to better predict a desired output value of each of the one or more outputs 420 to 432. In addition to the one or more inputs 404 to 418 and the one or more outputs 420 to 432, the machine-learned model may include pre-trained hidden layers (not illustrated) with neurons in each hidden layer required for processing input data. For example, assuming the one or more inputs 404 to 418 may contain data with arbitrary lengths and strings, an RNN may be appropriately and successfully used to better predict the desired output values of the one or more outputs 420 to 432.

[00100] In addition to, or alternatively of, using the pre-trained hidden layers, in some embodiments, during initial stages of model training of the model 402, the system 100 may ask for driver input. For example, using a display screen or a voice- activated feature of the computing device 108, the system 100 may ask the driver of the EV “Do you want to charge your EV in the next closest charging station?” “Are you currently driving passengers?” “What is the count of persons in your EV?” “Do you want to lower transportation costs?” “Do you want to increase your revenue?” “Do you want to lower the greenhouse gases?” “Do you want to charge the EV before the special event(s) in the community?” “Do you want to avoid peak power load hours?” “Do you want to reserve a lower-cost slower-charging station?” “Do you want to reserve a higher-cost faster-charging station?” “Do you want to avoid high electric energy rates?” “Do you have a preference for using FTM electric power or BTM electric power?” In addition, the system 100 may ask other questions, a combination of the above questions, and/or a derivative thereof.

[00101] In some embodiments, the model 402 may be personalized depending on a driver’s preferences and/or a business model. For example, more affluent drivers may prefer the faster-charging stations most of the time and may be willing to pay higher rates for such a service. The system 100 and/or the network of charging stations 304 may then transfer profits from these higher rates to less affluent drivers (e.g., college students). In more detail, the system 100 and/or the network of charging stations 304 may offer incentives to the driver of the EV to use slower- charging stations; charge their EV during non-peak power load hours; lower greenhouse gases; use charging stations with BTM resources; increase the count of the persons in each trip; and/or so forth. The incentives may be monetary (e.g., lower energy rates) and/or may include earned privileges to make a reservation at a faster-charging station. Given the sizeable computational power that machine learning may use to train the model 402, the model training may be performed on a cloud, server, or other capable computing devices (e.g., 112). In addition, periodic model updates may be sent to each EV or an associated computing device (e.g., 108).

[00102] Further, the system 100 may communicate periodic and/or nearly real-time reports to the driver of the EV. The reports may include information, such as an amount of greenhouse gases emitted per trip, an amount of greenhouse gases emitted per person riding in the EV, a cost of electric power per person riding in the EV, a cost of electric power per trip, a cost of electric power per kilometer, efficiency of the EV in kilometers per kWh, an average amount of time spent charging the EV at a charging station, and/or other reports. The reports may help the driver understand how to lower transportation costs, increase revenue, lower greenhouse gases, lower energy rates, avoid increasing the power load of the grid 302 during peak power load hours, promote carpooling, increase ridesharing, promote using renewable energy sources to produce electric power (e.g., using BTM resources), decrease time spent at a charging station, and/or help the community.

[00103] Based on at least the abovementioned ridesharing characteristics, the model 402 generates an output (e.g., value, result) as or represented by the one or more outputs 420 to 432, which may cause the system 100 and/or the network of charging stations 304 to selectively transmit to at least one EV a first location of a first charging station or a second location of a second charging station.

[00104] In some embodiments, the model 402 may generate a first output 420 (output 420) that may be partly, heavily, and/or mainly driven by a threshold SoC of the EV and a current location of the EV. For example, for certain EVs, the output 420 may prompt or otherwise signal the system 100 and/or the network of charging stations 304 to selectively transmit a location of a nearby (e.g., the closest) charging station to the current location of the EV when, for example, the SoC of the battery of the EV is equal to or less than a predetermined percentage of the total battery capacity of the EV (e.g., 5%, 10%). As another example, in a less-than- common case, if a ridesharing driver of an EV is currently driving a passenger, and the ridesharing driver may risk draining the battery of the EV before the EV completes the trip, the output 420 may prompt or otherwise signal the system 100 and/or the network of charging stations 304 to selectively transmit a location of a nearby (e.g., the closest) available faster-charging station (e.g., a faster-charging station alongside a same route as the current trip). In this less-than-common case, the ridesharing driver may spend a brief time (e.g., five minutes, ten minutes) at the faster-charging station to charge their EV with enough charge to complete the trip. The driver may then charge their EV again after dropping off the passenger or otherwise completing the trip. However, it behooves the ridesharing driver of the EV to avoid this less-than-common case due to passenger dissatisfaction and/or inconvenience. Therefore, in general, the system may assist the ridesharing driver of the EV in making certain to charge the EV before accepting a trip request from a prospective passenger. For example, the first output 420 may include prioritized scheduling or other enhanced aspect of charging of the EV at a location, based on ridesharing characteristics.

[00105] In some embodiments, the model 402 may generate a second output 422 (output 422) for selectively enabling a driver to reserve a faster-charging station. The output 422 may be partly, heavily, and/or mainly driven by a business model, such as a ridesharing company. The ridesharing company may employ drivers to transport people, merchandise, packages, mail, food, and/or so forth. For example, assume a ridesharing driver may drive their EV part-time as a contractor for the ridesharing company. Using the ridesharing application software, the ridesharing driver may usually drive passengers from 10:00 PM to 2:00 AM. The model 402 may generate the output 422 to give the ridesharing driver a higher priority than nonridesharing drivers to reserve a faster-charging station outside of, proximate to, and/or during the period between 10:00 PM to 2:00 AM. Note that the model 402 and the associated output 422 may consider the power load capacity at the faster- charging station during the reservation time, for example, by utilizing any of the techniques and apparatuses described in relation to FIG. 3. By so doing, the model 402 and the output 422 may help increase revenues of the ridesharing driver and/or the ridesharing company. The output 422 may prompt the system 100 and/or the network of charging stations 304 to allow the ridesharing driver to reserve the faster- charging station when they may not usually drive passengers. Alternatively or in addition, the output 422 may prompt the system and/or the network of charging stations 304 in certain situation to allow the ridesharing driver to reserve the faster- charging station for shorter durations when the EV is currently driving passengers. The second output 422 may include prioritized scheduling or other enhanced aspect of charging of the EV at a location, based on ridesharing characteristics.

[00106] In some embodiments, the model 402 may generate a third output 424 (output 424) for selectively transmitting locations of faster-charging stations to EVs with a higher count of passengers. The count of passengers may be current, historical, or committed, such as during a time window. The count of passenger may be speculative, based on ridesharing demand present on a ridesharing application. The EVs may be ridesharing EVs, EVs of a private company, EVs of a publicly- traded corporation, government EVs, school EVs, or privately-owned EVs. For example, assume a first EV that is transporting two people (or perhaps previously transported two people during an immediately preceding trip) and a second EV that is transporting five people (or perhaps previously transported five people during an immediately preceding trip) are at an approximately same location, and the first and the second EV communicate with the system 100 and/or the network of charging stations 304 to find a closest available faster-charging station. Then, the model 402 and the associated output 424 may prompt the system 100 and/or the network of charging stations 304 to selectively transmit the location of the closest available faster-charging station to the second EV. By so doing, the model 402 and the output 424 may help incentivize virtuous driving behavior (e.g., promote carpooling, increase ridesharing), and/or help the community (e.g., less traffic, cleaner air, less greenhouse gases, efficient transportation of residents of the community). The third output 424 may include prioritized scheduling or other enhanced aspect of charging of the EV at a location, based on ridesharing characteristics.

[00107] In some embodiments, the model 402 may generate a fourth output 426 (output 426) for selectively transmitting locations of charging stations that lower greenhouse gas emissions per person in the EV when driving and/or charging the EV. As partly illustrated in FIG. 3 and discussed in the associated description, electric energy, charge, and/or power used to charge the EVs may be generated from various energy sources. Specific power grids may generate and/or transfer FTM electric power from energy sources with negligible greenhouse gas emissions. For example, countries like Albania, Norway, Paraguay, and Nepal generate most of their FTM electric power using hydropower. As another example, countries such as France, Slovakia, and Ukraine generate most of their FTM electric power using nuclear energy. However, these countries and their respective power grids are exceptions compared to the rest of the World, in part, due to their physical geographies, resources (e.g., rivers, nuclear material, nuclear engineers), a willingness to build dams on their respective rivers, and/or a willingness to use nuclear energy to produce electric power. Note that, as described in relation to FIG. 3, the use of hydropower and/or nuclear power to generate electric energy has drawbacks such that some governments and/or peoples may be unable to justify their extensive use. Regardless, most people live in areas with power grids that generate and/or transfer electric energy generated from energy sources with a relatively-high amount of greenhouse gases (e.g., coal). Therefore, the model 402 and the output 426 may change depending on a residence of the driver of the EV. For drivers living near power grids that generate and/or transfer electric energy from energy sources with higher greenhouse emissions, the model and the output 426 may selectively transmit locations of charging stations with BTM resources that store (e.g., using batteries) and/or generate electric energy using energy sources with low (or no) greenhouse gases, such as solar-powered energy generating devices. By so doing, the model 402 and the output 426 may increase a utilization of the BTM instead of the FTM resources, increase a utilization of renewable instead of nonrenewable energy sources, lower energy rates, and help the community (e.g., less greenhouse gas emissions, cleaner air). The fourth output 426 may include prioritized scheduling or other enhanced aspect of charging of the EV at a location, based on ridesharing characteristics.

[00108] In some embodiments, the model 402 may generate a fifth output 428 (output 428) for selectively transmitting locations of charging stations that may lower a cost for charging the EV. In one aspect, these charging stations may be slower- charging stations that some drivers of EVs may be willing to use so they can pay lower energy rates to charge their EVs. For example, assume the driver of the EV is retired from their full-time profession and/or has a fixed income. As a result, the driver of the EV may be willing to spend a little extra time at the charging station to pay lower energy rates. As another example, assume the SoC of the EV may not be low, and the driver of the EV may be willing to stop at a charging station and charge their EV whenever energy rates are low. In such a case, the model 402 and the output 428 may prompt the system 100 and/or the network of charging stations 304 to selectively transmit a charging station location that offers lower energy rates, for example, during non-peak power load hours. By so doing, the model 402 and the output 428 may lower energy rates, avoid or lower charging during peak power load hours, offer incentives to drivers to use available electric charge stored in BTM resources, and/or so forth. The fifth output 428 may include prioritized scheduling or other enhanced aspect of charging of the EV at a location, based on ridesharing characteristics.

[00109] In some embodiments, the model 402 may generate a sixth output 430 (output 430) for selectively transmitting locations of charging stations with lower charging times (e.g., with faster-charging stations). For example, as previously described, some drivers of the EVs (e.g., affluent drivers) may be willing to pay higher energy rates, costs, and/or fees for the convenience of using the faster- charging stations. As another example, the system 100 and/or the network of charging stations 304 may strive to increase a utilization of available faster-charging stations. In such a case, the model 402 and the output 430 may prompt the system 100 and/or the network of charging stations 304 to selectively transmit a location of an available faster-charging station instead of a slower-charging station. The sixth output 430 may include prioritized scheduling or other enhanced aspect of charging of the EV at a location, based on ridesharing characteristics. [00110] In some embodiments, the model 402 may generate a seventh output 432 (output 432) for selectively transmitting a location of a charging station that may decrease the opportunity cost of driving to the charging station and/or decrease the opportunity cost of the EV and increase revenue of a business, for example, revenue from ridesharing. For example, a slower-charging station may be considerably closer to a current location of the EV than a faster-charging station; therefore, the faster-charging station may not always be a preferred option to charge the EV.

Further, the faster-charging station may include an additional cost (e.g., convenience cost) compared to the slower-charging station. The seventh output 432 may include prioritized scheduling or other enhanced aspect of charging of the EV at a location, based on ridesharing characteristics.

[00111] As described in relation to FIG. 4, benefits of specific outputs of the outputs 420 to 432 may not be mutually exclusive. For example, the output 424 and the output 426 may help lower the amount of greenhouse gases released into the atmosphere. Nevertheless, in part, the model 402 may enable the system 100 and/or the network of charging stations 304 to place a different emphasis on outcomes and/or benefits of the described techniques and apparatuses for charging the EVs. Further the model 402 may be capable of generating additional outputs (e.g., in addition to the one or more outputs 420 to 432 described), based on the one or more inputs 404 to 418.

[00112] FIG. 5 shows a flow diagram of a process 500 for selectively enabling a charging station of at least two charging stations to charge an EV. FIG. 5 is described in the context of FIGs. 1 to 4. For example, the charging station may be any charging station of the network of charging stations 304. For brevity and clarity, it is assumed that each charging station (e.g., 330, 332, 334, or 336) of the network of charging stations 304 is located at a particular location. As such, the charging stations 330, 332, 334, and 336 are located at various locations (instead of a same location). However, it is to be understood that the process 500 may also be applied and valuable for charging stations located at the same location (e.g., charging stations being adjacent to each other). For example, in a case of at least two adjacent charging stations: a first charging station may be a faster-charging station, and a second charging station may be a slower-charging station; a first charging station may be available, and a second charging station may be unavailable; a first charging station may be a DC charging station, and a second charging station may be an AC charging station; a first charging station may include BTM resources, and the second charging station may not include BTM resources; a first charging station may charge using higher energy rates, and a second charging station may charge using lower energy rates; a first charging station may be off the grid, and a second charging station may be coupled to the grid (e.g., 302); or other combinations, as, for example, described in FIGs. 1 to 4.

[00113] Nevertheless, assuming a system 100 and/or the network of charging stations 304 having at least two charging stations includes a first charging station in a first location; the first charging station may be configured to supply a first amount of electric power during a first duration of time. The system 100 and/or the network of charging stations 304 also includes a second charging station in a second location; the second charging station may be configured to supply a second amount of electric power during a second duration of time. At stage (or block) 502, the system 100 and/or the network of the charging stations 304 determines a first instance of communication connectivity between the system 100 (and/or the network of charging stations 304) and with at least one EV. To do so, a driver of the at least one EV may utilize the ridesharing application software, navigation application software, autonomous-driving application software, driver-assistance application software, ridesharing charging algorithm, and/or another application software, model, and/or algorithm to initiate a communication with the system 100 and/or the network of charging stations 304.

[00114] Consequently, at stage 504, the system 100 and/or the network of charging stations 304 communicates with the at least one EV (and/or an associated computing device of the EV) using a communication protocol and/or standard, as described in FIGs. 1 to 4. The system 100 and/or the network of charging stations 304 may communicate with the EV directly, indirectly (e.g., using the network 114), with a wired connection, and/or wirelessly using short-range and/or longer-range communications protocols and standards, such as the 3GPP LTE standard, the IEEE 802.11 standard, the IEEE 802.16 standard, the IEEE 802.15.4 standard, the Bluetooth Classic® standard, the BLE® standard, and/or so forth.

[00115] At stage 506, the system 100 and/or the network of charging stations 304 receives ridesharing characteristics of at least one trip of the at least one EV. As described in FIG. 4, the ridesharing characteristics may include two of, some of, all of, or more than the inputs 404 to 418. Note that the two of, some of, all of, or more than the inputs 404 to 418 may be EV-related inputs and/or system 100-related inputs. However, for clarity, it may be assumed, in some embodiments, that at least one input can be an EV-related input (e.g., current location, SoC), and at least one input can be a system 100-related input (e.g., first location, second location, power load capacity, and/or so forth).

[00116] At stage 508, based on the ridesharing characteristics, the system 100 and/or the network of charging stations 304 can selectively transmit to the at least one EV a first or a second location of at least two charging stations. To do so, as described in FIG. 4, at stage 508, the system 100 and/or the network of charging stations 304 may utilize the model 402 to analyze two of, some of, all of, or more than the inputs 404 to 418 to generate at least one of the outputs 420-432. The model 402 may be a ridesharing charging algorithm or a machine-learned model, as described in FIG. 4. Depending on the output of the model 402 and driver preferences, the driver of the at least one EV may, for example, lower transportation costs, increase revenue, lower greenhouse gases, lower energy rates, avoid increasing the power load of the grid 302 during peak power load hours, promote carpooling, increase ridesharing, promote using renewable energy sources to produce electric power (e.g., using BTM resources), decrease the time spent at a charging station, and/or help the community.

[00117] For clarity, based on the output of the model 402, the system 100 and/or the network of charging stations 304 selectively transmits to the at least one EV the first or the second location of at least two charging stations. The driver of the EV may then consider the transmitted location and drive to the transmitted location to charge their EV.

[00118] In detail, as previously discussed, the ridesharing characteristics may include information of the SoC of the EV (e.g., input 404) and vehicle characteristics based on the make, mode, and/or VIN of the EV (e.g., input 406). The inputs 404 and 406 enable the process 500, the system 100, and/or the network of charging stations 304 to determine the energy efficiency of the EV (e.g., in kilometers per kWh) and a distance the EV can travel with a current SoC.

[00119] The ridesharing characteristics may also include distances between the EV and each charging station the network of charging stations 304, together with traffic information (e.g., input 408). For example, the EV 305 may be closer to the charging station 334 than the charging station 336, but due to heavy traffic between the EV 305 and the charging station 334 and light to no traffic between the EV 305 and the charging station 336, the process 500 at stage 508 may determine that it behooves the driver of the EV 305 to drive to the charging station 336.

[00120] The ridesharing characteristics may also include information of the power load capacity, the BTM electric power capacity, and/or the FTM electric power capacity at a charging station of the network of charging stations 304 (e.g., input 410). For example, if the driver of the EV prefers and emphasizes driving that decreases greenhouse gas emissions, at stage 508, the system 100 and/or the network of charging stations 304 may selectively transmit a charging station that uses more BTM resources and less FTM resources.

[00121] The ridesharing characteristics may also include information of a charging speed at a charging station of the network of charging stations 304 (e.g., input 412). For example, if the driver of the EV prefers and emphasizes spending as little time as possible while charging regardless of cost, at stage 508, the system 100 and/or the network of charging stations 304 may selectively transmit the fastest charging station, in part depending on a current location of the EV.

[00122] The ridesharing characteristics may also include the count of persons (e.g., riders, passengers, and/or driver) in the EV (e.g., input 414), information on the business and/or the business model (e.g., input 416), information regarding special events in a community (e.g., input 418), and/or other ridesharing characteristics not that are not explicitly described herein. The ride sharing characteristics may include current ride(s) (e.g., number of passengers) the EV may be providing, prospective rides the EV may be scheduled or otherwise engaged to provide, potential rides the EV may be offered to provide, and/or past rides the EV may have provided, such as during a time frame. The time frame may be a current ridesharing session. The time frame may be the current day. The time frame may be a week, month, year, pay cycle, or any other period of time. The time frame may be the life of the EV.

[00123] In some embodiments, the process 500, the model 402, and/or the ridesharing charging algorithm may aim to optimize a primary choice and/or a goal of the driver of the EV. As previously discussed, the ridesharing driver driving their EV part-time and/or full-time as a contractor for the ridesharing company may aim to optimize (e.g., increase) profitability for driving passengers. To this end, the process 500 and/or the ridesharing charging algorithm may optimize factors that affect (e.g., increase) the profitability for driving the passengers. For example, the process 500 and/or the ridesharing charging algorithm may: decrease the forementioned opportunity costs; decrease and/or avoid the high energy rates; utilize the planned trip data to select a shorter (or shortest) distance from the current location to the destination location; utilize the planned trip data; utilize the traffic information to minimize (e.g., lower) a time the driver spends driving below a speed limit and/or stopped behind other vehicles; lower a wait time at a charging station by, for example, selectively transmitting a location of an available charging station; predict transportation costs using the ridesharing application software (e.g., higher costs due to a higher service demand) and communicate to the driver the predicted transportation costs; take into account available charging stations along a route of the planned trip; aid the ridesharing driver of the EV to make certain to charge their EV with enough SoC to before accepting a trip request from the prospective passenger; and/or other factors that can aid the driver to increase the profitability for driving the passengers. [00124] In some embodiments, a primary choice and/or goal of the driver of the EV, a city, a state, a country, a utility company, the passengers, the ridesharing company, the society, and/or so forth may be to lower the greenhouse gas emissions. Governments, the ridesharing company, and/or other entities may also offer incentive(s) to lower the amount of the greenhouse gases (e.g., CO2). To this end, the process 500, the model 402, and/or the ridesharing charging algorithm may optimize factors that affect (e.g., decrease) the amount of the greenhouse gases. For example, the process 500 and/or the ridesharing charging algorithm may: maximize (or increase) a use of charging stations with more BTM resources (e.g., solar panels, on-site batteries); may maximize a use of charging stations that receive electric energy generated from renewable energy sources; minimize (or decrease) a use of charging stations that receive electric energy generating from petroleum, hydrocarbon gas liquids, natural gas, and/or coal; favor the driver of the EV with a higher count of riders by selectively transmitting locations of the faster-charging stations; and/or other forementioned factors that help decrease the amount of the greenhouse gases.

[00125] As previously discussed, however, regardless of the primary choice and goal of the driver of the EV, outcomes of the process 500, the model 402, and/or the ridesharing charging algorithm may not be mutually exclusive. As such, additionally, or alternatively, the process 500 and/or the ridesharing charging algorithm may cooptimize more than one choice and/or goal of the driver of the EV. In detail, the process 500, the model 402, and/or the ridesharing charging algorithm may use inputs 404 to 418 of FIG. 4, to co-optimize for more than one output of FIG. 4. For example, the process 500, the model 402, and/or the ridesharing charging algorithm may help the ridesharing driver to increase revenues (e.g., output 422) and lower the greenhouse gas emissions (e.g., output 426), simultaneously. The process 500, the model 402, and/or the ridesharing charging algorithm may do so by assigning relative weights to each desired output. Then, the process 500, the model 402, and/or the ridesharing charging algorithm may use the relative weights of the desired outputs and/or goals to reach a co-optimized goal and/or output.

[00126] It is to be understood, however, that a user (e.g., a driver, a rider, a passenger) may be provided with controls that allow the user to choose as to what information can be collected from the user and/or the EV and what information may be sent to the user and/or the EV. Also, certain data may be treated in one or more ways that remove personally identifiable information. For example, the system 100 and/or the network of charging stations 304 may identify someone with the name “John Smith” with a randomly assigned alphanumeric code. Similarly, the model, make, color, VIN, and/or license plate of an EV may also be identified with a randomly assigned alphanumeric code. Further, identities of the drivers, riders, and/or passengers in a ridesharing EV may be identified as a count (e.g., person one, person two, person three), instead of, for example, names, ages, genders, and/or so forth. The user may also be provided with controls as to how long information regarding ridesharing characteristics of the user, the EV, and/or trips may be stored, even though the information may be encrypted and may not contain personal identifiable information. Lastly, the user may elect not to utilize the techniques, apparatus, application software, and/or models described herein and still receive a charge at a charging station. In such a case, however, the user may be unable to, for example, receive notifications of lower charging rates, reserve a charging station, and so forth. [00127] Next, the description includes additional example embodiments of the described techniques and systems for charging EVs.

Example Embodiments

[00128] Example 1 . A system for charging at least one electric vehicle (EV), the system comprising: a first charging station in a first location, the first charging station configured to supply a first amount of electric power during a first duration of time; a second charging station in a second location, the second charging station configured to supply a second amount of electric power during a second duration of time; at least one processor; at least one computer-readable medium having instructions that, responsive to execution by the at least one processor, cause the system to: determine a first instance of communication connectivity between the system and the at least one EV, and communicate with the at least one EV using a communication protocol; responsive to communicating with the at least one EV, receive ridesharing characteristics of at least one trip, the ridesharing characteristics including: a state of charge of the at least one EV; and a current location of the at least one EV; and responsive to receiving the ridesharing characteristics, selectively transmit to the at least one EV the first or the second location.

[00129] Example 2. The system of Example 1 , wherein selectively transmitting the first or the second location to the at least one EV, further cause the system to: guide the at least one EV to the first or the second location; and enable the at least one EV to receive a third amount of electric power.

[00130] Example 3. The system of Example 1 , wherein the first amount of electric power during the first amount of time comprises a faster charging time for a same electric power compared to the second amount of electric power during the second amount of time.

[00131] Example 4. The system of Example 2, wherein guiding the at least one EV comprises using Global Navigation Satellite System (GNSS) coordinates and enabling a driver of the at least one EV to follow turn-by-turn navigation instructions using a navigation application software.

[00132] Example 5. The system of Example 1 , wherein selectively transmitting the first or the second location comprises a location having a shortest distance between: the first location and the current location; and the second location and the current location.

[00133] Example 6. The system of Example 1 , wherein the state of charge is less than or equal to a low threshold state of charge, and selectively transmitting the first or the second location comprises transmitting a location having a shortest distance between: the first location and the current location; and the second location and the current location.

[00134] Example 7. The system of Example 3, wherein: the ridesharing characteristics of the at least one trip further include a count of persons; the at least one EV comprises: a first EV with a first count of persons; and a second EV with a second count of persons, and the first count of persons is greater than the second count of persons; and the instructions, responsive to execution by the at least one processor, further cause the system to selectively transmit: the first location to the first EV; and the second location to the second EV.

[00135] Example 8. The system of Example 3, wherein: the at least one trip is a current trip; the state of charge is less than or equal to a low threshold state of charge; the at least one EV being unable to complete the current trip; and the instructions, responsive to execution by the at least one processor, further cause the system to selectively transmit the first location.

[00136] Example 9. The system of Example 1 , wherein the at least one trip is a future trip, and wherein the instructions, responsive to execution by the at least one processor, further cause the system to: make a reservation at a future time for a selectively transmitted location; and enable the at least one EV to receive a third amount of electric power at the future time to complete the future trip.

[00137] Example 10. The system of Example 1 , wherein supplying the first amount of electric power during the first duration of time, and supplying the second amount of electric power during the second duration of time, comprises determining a power load capacity at the first and the second locations.

[00138] Example 11 . The system of Example 10, wherein determining the power load capacity comprises measuring, monitoring, or predicting a front-of-the-meter (FTM) electric power and a behind-the-meter (BTM) electric power at the first and the second locations.

[00139] Example 12. The system of Example 11 , wherein: the FTM electric power is generated from a first energy source, the first energy source emitting a first amount of greenhouse gases; the BTM electric power is generated from a second energy source, the second energy source emitting a second amount of greenhouse gases; and the first amount is greater than the second amount.

[00140] Example 13. The system of Example 12, wherein: the first amount of electric power includes a first amount of the FTM electric power and a first amount of the BTM electric power; the second amount of electric power includes a second amount of the FTM electric power and a second amount of the BTM electric power; the first amount of the BTM electric power is greater than the second amount of the BTM electric power; and selectively transmitting the first or the second location comprises transmitting the first location.

[00141] Example 14. The system of Example 1 , wherein: the first amount of electric power costs a first monetary rate per a kilowatt-hour; the second amount of electric power costs a second monetary rate per the kilowatt-hour; the first monetary rate being less than the second monetary rate; and selectively transmitting the first or the second location comprises transmitting the first location.

[00142] Example 15. The system of Example 1 , wherein: charging at the first location comprises a first total cost, the first total cost including: a first monetary value for the first amount of electric power; and a second monetary value for a first opportunity cost of the first duration of time; charging at the second location comprises a second total cost, the second total cost including: a third monetary value for the second amount of electric power; and a fourth monetary value for a second opportunity cost of the second duration of time; the first total cost being less than the second total cost; and selectively transmitting the first or the second location comprises transmitting the first location.

[00143] Example 16. The system of Example 3, wherein the instructions, responsive to execution by the at least one processor, further cause the system to utilize a model to analyze the ridesharing characteristics to generate an output of the model, and responsive to generating the output, selectively transmit to the at least one EV the first or the second location.

[00144] Example 17. The system of Example 16, wherein the model comprises a machine-learned model, the machine-learned model being a neural network, a support vector machine, a recurrent neural network (RNN), a convolutional neural network (CNN), a dense neural network (DNN), heuristics, and/or a combination thereof.

[00145] Example 18. The system of Example 16, wherein selectively transmitting to the at least one EV the first or the second location is partly based on transportation costs, energy rates, emissions of greenhouse gases, a count of persons in the at least one EV, a percentage of renewable energy sources used to generate the first and the second amounts of electric power, opportunity costs, the faster charging time, peak power load hours, and/or a power load capacity at the first and the second locations.

[00146] Example 19. The system of Example 1 , wherein the communication protocol comprises: a Third Generation Partnership Project (3GPP) Long-Term Evolution (LTE) standard; an Institute of Electrical and Electronics (IEEE) 802.11 standard; an IEEE 802.16 standard; an IEEE 802.15.4 standard; a Bluetooth Classic® standard; and/or a Bluetooth Low Energy® (BLE®) standard.

[00147] Example 20. A computer-implemented method comprising: determining a first instance of communication connectivity between a system and at least one EV, the system comprising: a first charging station in a first location, the first charging station supplying a first amount of electric power during a first duration of time; and a second charging station in a second location, the second charging supplying a second amount of electric power during a second duration of time; responsive to determining, the system communicating with the at least one EV using a communication protocol; responsive to communicating with the at least one EV, the system receiving ridesharing characteristics of at least one trip, the ridesharing characteristics including: a state of charge of the at least one EV; and a current location of the at least one EV; and responsive to receiving the ridesharing characteristics, the system selectively transmitting to the at least one EV the first or the second location.

[00148] Furthermore, the described features, operations, or characteristics may be arranged and designed in a wide variety of different configurations and/or combined in any suitable manner in one or more embodiments. Thus, the detailed description of the embodiments of the systems and methods is not intended to limit the scope of the disclosure, as claimed, but is merely representative of possible embodiments of the disclosure. In addition, it will also be readily understood that the order of the steps or actions of the methods described in connection with the embodiments disclosed may be changed as would be apparent to those skilled in the art. Thus, any order in the drawings or Detailed Descriptions is for illustrative purposes only and is not meant to imply a required order, unless specified to require an order.

[00149] Embodiments may include various steps, which may be embodied in machine-executable instructions to be executed by a general-purpose or specialpurpose computer (or other electronic device). Alternatively, the steps may be performed by hardware components that include specific logic for performing the steps, or by a combination of hardware, software, and/or firmware.

[00150] A software module, module, or component may include any type of computer instruction or computer executable code located within a memory device and/or computer-readable storage medium, as is well known in the art.

[00151] It will be obvious to those having skill in the art that many changes may be made to the details of the above described embodiments without departing from the underlying principles of the invention. The scope of the present invention should, therefore, be determined only by the following claims.

Claims

1 . A system for charging at least one electric vehicle (EV), the system comprising: a first charging station in a first location, the first charging station configured to supply a first amount of electric power during a first duration of time; a second charging station in a second location, the second charging station configured to supply a second amount of electric power during a second duration of time; at least one processor; at least one computer-readable medium having instructions that, responsive to execution by the at least one processor, cause the system to: determine a first instance of communication connectivity between the system and the at least one EV, and communicate with the at least one EV using a communication protocol; responsive to communicating with the at least one EV, receive ridesharing characteristics of at least one trip, the ridesharing characteristics including: ride information; a state of charge of the at least one EV; a current location of the at least one EV; and based on the ridesharing characteristics, selectively transmit to the at least one EV the first location or the second location.

2. The system of claim 1 , wherein selectively transmitting the first or the second location to the at least one EV includes transmitting information to guide the at least one EV to the first or the second location.

3. The system of claim 1 , wherein the first amount of electric power during the first amount of time comprises a faster charging time for a same amount of electric power compared to the second amount of electric power during the second amount of time.

4. The system of claim 2, wherein the information to guide the at least one EV comprises Global Navigation Satellite System (GNSS) coordinates.

5. The system of claim 1 , wherein selectively transmitting the first location or the second location comprises selecting a shortest distance between: the first location and the current location; and the second location and the current location.

6. The system of claim 1 , wherein the state of charge is less than or equal to a low threshold state of charge, and selectively transmitting the first location or the second location comprises selecting a shortest distance between: the first location and the current location; and the second location and the current location.

7. The system of claim 1 , wherein: the ridesharing characteristics of the at least one trip include a count of persons..

8. The system of claim 1 , wherein: the at least one trip is a current trip; the state of charge is less than or equal to a low threshold state of charge; and the at least one EV is unable to complete the current trip.

9. The system of claim 1 , wherein the at least one trip is a future trip, and wherein the instructions, responsive to execution by the at least one processor, further cause the system to: make a reservation at a future time for the transmitted first or second location; and enable the at least one EV to receive a third amount of electric power at the future time to complete the future trip.

10. The system of claim 1 , wherein supplying the first amount of electric power during the first duration of time, and supplying the second amount of electric power during the second duration of time, comprises determining a power load capacity at the first and the second locations.

11. The system of claim 10, wherein determining the power load capacity comprises measuring, monitoring, or predicting a front-of-the-meter (FTM) electric power and a behind-the-meter (BTM) electric power at the first and the second locations.

12. The system of claim 11 , wherein: the FTM electric power is generated from a first energy source emitting a first amount of greenhouse gases; the BTM electric power is generated from a second energy source emitting a second amount of greenhouse gases.

13. The system of claim 12, wherein: the first amount of electric power includes a first amount of the FTM electric power and a first amount of the BTM electric power.

14. The system of claim 1 , wherein: the first amount of electric power costs a first monetary rate per a kilowatt- hour; the second amount of electric power costs a second monetary rate per the kilowatt-hour; the first monetary rate is less than the second monetary rate; and selectively transmitting the first or the second location comprises transmitting the first location.

15. The system of claim 1 , wherein: charging at the first location comprises a first total cost, the first total cost including: a first monetary value for the first amount of electric power; and a second monetary value for a first opportunity cost of the first duration of time; charging at the second location comprises a second total cost, the second total cost including: a third monetary value for the second amount of electric power; and a fourth monetary value for a second opportunity cost of the second duration of time; the first total cost being less than the second total cost; and selectively transmitting the first or the second location comprises transmitting the first location.

16. The system of claim 1 , wherein the instructions, responsive to execution by the at least one processor, further cause the system to utilize a model to analyze the ridesharing characteristics to generate an output of the model, and responsive to generating the output, selectively transmit to the at least one EV the first or the second location.

17. The system of claim 16, wherein the model comprises a machine-learned model, the machine-learned model being a neural network, a support vector machine, a recurrent neural network (RNN), a convolutional neural network (CNN), a dense neural network (DNN), heuristics, or a combination thereof.

18. The system of claim 16, wherein the ridesharing characteristics further include one or more of transportation costs, energy rates, emissions of greenhouse gases, a percentage of renewable energy sources used to generate the first and the second amounts of electric power, opportunity costs, the faster charging time, peak power load hours, and a power load capacity at the first and the second locations.

19. A computer-implemented method comprising: determining a first instance of communication connectivity between a system and an EV, the system comprising: a plurality of charging stations at a plurality of locations, each charging station of the plurality of charging stations supplying an amount of electric power during a duration of time; responsive to determining the first instance, the system communicating with the EV using a communication protocol to receive ridesharing characteristics of at least one trip; and responsive to receiving the ridesharing characteristics, the system selectively transmitting to the EV a selected charging station of the plurality of charging stations, the selected charging station selected based on the ridesharing characteristics.

20. The computer-implemented method of claim 19, wherein the ridesharing characteristics include one or more of: ride information; state of charge of the EV; and a current location of the EV.