WO2023154391A1

WO2023154391A1 - Systems and methods for battery capacity tracking using vehicle charging metrics

Info

Publication number: WO2023154391A1
Application number: PCT/US2023/012697
Authority: WO
Inventors: Chris SUKUMPANTANASARN; Amanpreet Kaur; Daniel Feldman
Original assignee: ENEL X Way S.r.l.
Priority date: 2022-02-11
Filing date: 2023-02-09
Publication date: 2023-08-17

Abstract

Systems and methods for calculating and tracking a capacity of a battery ( BATTCAP) of an electric vehicle (EV) throughout a system having multiple EV chargers are described herein. An EV charger may not be capable of determining BATTCAP via direct communication with the EV. Accordingly, a charging management server in network communication with the EV charger may provide a BATTCAP for the EV charger to use to determine, for example, an amount of energy and/or a duration of time it will take for the battery of the EV to charge. The BATTCAP may be calculated at the charging management server based on charging metrics of a previous charging of the battery at an EV charger that returns such metrics to the charging management server. The charging management server may then calculate the BATTCAP in a way that accounts for non-charging energy uses during the previous charging. Methods for updating the BATTCAP at the charging management server are discussed.

Description

SYSTEMS AND METHODS FOR BATTERY CAPACITY TRACKING USING VEHICLE CHARGING METRICS RELATED APPLICATIONS [0001] This application claims priority to U.S. Provisional Patent Application No. 63/267,902, titled “SYSTEMS AND METHODS FOR BATTERY CAPACITY TRACKING USING VEHICLE CHARGING METRICS,” filed February 11, 2022, which is hereby incorporated herein by reference to the extent such subject matter is not inconsistent herewith. TECHNICAL FIELD [0002] The present disclosure is generally directed to electric vehicle (EV) charging, and more directly to systems and methods of calculating and tracking a capacity of a battery of an EV across/throughout a system having multiple EV chargers (e.g., electric vehicle supply equipment (EVSE)). BACKGROUND [0003] Applications used to control electric vehicle charging may determine EV parameters such as maximum charging power for a battery of the EV and/or battery size of the battery, etc., based on user input. For example, in some cases (such as when using an EV charger (e.g., EVSE) that is an alternating current (AC) level 2 charger) it may be that there is no available information about the state of charge (SoC) sent from the EV to the EV charger, meaning that in order to predict the duration of charging, the user may be required to enter, among other things, the SoC of the battery and/or the total battery size of the battery. [0004] In these (and other) cases, with humans entering this information, it is possible for mistakes to be made, which, in the case of battery charging, may prevent an accurate determination of, for example, an amount of energy and/or a duration of time that it will take for the battery of the EV to charge. [0005] Further, persons familiar with EVs understand that a capacity of a battery of an EV changes over time. For example, EV batteries naturally degrade over time and/or with use. As an effect of this degradation, the capacity of the battery may correspondingly decrease over time. [0006] Accordingly, due to variations in this battery degradation across different EVs, an actual capacity of any given battery of a particular EV may not be determinable by merely identifying the EV using the battery (or even by identifying the particular battery itself). Further, mere estimates of battery capacity (such as merely using a (pre-degradation) specification for the battery capacity and/or using a guess as to the battery capacity based on assumed degradation, etc.) may not themselves be accurate. Accordingly, the use of such estimations does not enable an accurate calculation of, for example, an amount of energy and/or a duration of time that it will take for the battery of the EV to charge. BRIEF SUMMARY [0007] The present disclosure provides systems and methods for using vehicle charging metrics to calculate and/or track battery capacity of a battery of an EV, such that the battery capacity is accurately known and available for use within the system at a later time. BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS [0008] To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced. [0009] FIG.1 illustrates a present state of a system that uses a charging management server to track the energy capacity of a battery of an EV, according to an embodiment of the present disclosure. [0010] FIG.2 illustrates a past state of the system that uses the charging management server to track the energy capacity of a battery of an EV, according to an embodiment of the present disclosure. [0011] FIG.3 illustrates a method of a charging management server, according to an embodiment of the present disclosure. [0012] FIG.4 illustrates an expanded view of the system for using the charging management server with an emphasis on the charging management server, according to an embodiment of the present disclosure. DETAILED DESCRIPTION [0013] The present embodiments will become more fully apparent from the following description and appended claims, taken in conjunction with the accompanying drawings. Understanding that the accompanying drawings depict only typical embodiments and are, therefore, not be considered limiting of the scope of the disclosure, the embodiments will be described and explained with specificity and detail in reference to the following accompanying drawings. [0014] FIG. 1 illustrates a present state of a system 102 that uses a charging management server 104 to track the energy capacity of a battery of an EV, according to an embodiment of the present disclosure. The energy capacity of a battery of an EV may be referred to herein as a

of the EV. The system 102 includes the charging management server 104, which is connected via a network 106 to a first EV charger 108, a second EV charger 110, a third EV charger 112, and a fourth EV charger 114. [0015] At the time (present state) illustrated in FIG. 1, the second EV charger 110 is connected to the first EV 116, and the fourth EV charger 114 is connected to the second EV 118. The connection between the second EV charger 110 and the first EV 116 allows the second EV charger 110 to deliver energy (e.g., provide a “charging”) to a battery of the first EV 116. The first EV 116 may then draw this energy from the battery and use it to operate one or more of its functions. As illustrated, at the time illustrated in FIG. 1, the fourth EV charger 114 may be similarly providing a charging to a battery of the second EV 118. [0016] This connection may also allow the second EV charger 110 (and subsequently the charging management server 104) to identify the first EV 116. For example, upon connection, the second EV charger 110 may identify the first EV 116 as corresponding to a unique identifier for the first EV 116 within the system 102 (e.g., that is provided to the second EV charger 110 by the first EV 116). This identifier may then be sent by the second EV charger 110 to the charging management server 104, thereby allowing the charging management server 104 to uniquely identify the first EV 116 within the system 102. [0017] In alternative embodiments, an operator of the first EV 116 may instead identify the first EV 116 to the charging management server 104. For example, the operator may use a user device 120 (such as a smartphone) that is in communication with the charging management server 104 via the network 106 to identify the first EV 116 to the charging management server 104, or may identify the first EV 116 at an input device present on the second EV charger 110 (which data is then forwarded from the second EV charger 110 to the charging management server 104) to identify the first EV 116 to the charging management server 104, etc. [0018] Each of the EV chargers 108 through 114 may be in different locations, and/or some subset (or all) of the EV chargers 108 through 114 may be located at the same location. For example, the first EV charger 108 may be located in a first parking lot, the second EV charger 110 may be located in a parking garage, the third EV charger 112 may be located in a second parking lot, and the fourth EV charger 114 may be located at a private residence. Or, the second EV charger 110 may be located at a private residence while the first EV charger 108, the third EV charger 112, and the fourth EV charger 114 are all located in the same public parking structure. Other physical/geographical arrangements of EV chargers 108 through 114 are possible. [0019] Note that the use of four EV chargers 108 through 114 in the system 102 is given by way of example and not by way of limitation, and that a system like the system 102 as contemplated herein may include more or fewer than the EV chargers 108 through 114 illustrated here. [0020] The EV chargers 108 through 114 of the system 102 may be of different types. For example, it may be that the second EV charger 110 is an AC level 2 EV charger, the first EV charger 108 and the fourth EV charger 114 are direct current (DC) EV chargers, while the third EV charger 112 may be either of these types (or another type altogether). Other combinations of EV charger types within analogous systems other than the system 102 illustrated are contemplated. [0021] The charging management server 104 may be configured to communicate with each of the EV chargers 108 through 114. For example, the charging management server 104 may receive information from an EV charger. This information may include information about the identity of an EV connected to the EV charger, charging metrics associated with a charging of an EV connected to the EV charger, etc. Further, the charging management server 104 may send information to an EV charger. This information may include, for example, a of an EV that is connected to the EV charger that is known to the charging management server 104. [0022] Each of the EV chargers 108 through 114 may include or be associated with a temperature sensor. For example, the first EV charger 108 may include or be associated with a first temperature sensor 122, the second EV charger 110 may include or be associated with a second temperature sensor 124, the third EV charger 112 may include or be associated with a third temperature sensor 126, and/or the fourth EV charger 114 may include or be associated with a fourth temperature sensor 128, as illustrated. The uses of one or more of these temperature sensors 122 through 128 will be discussed more fully below. [0023] In some cases, one or more of the EV chargers 108 through 114 that is connected to an EV in order to provide a charging to that EV may not be capable of directly querying the EV for its

. This may be due to a limitation of the particular EV charger. For example, the second EV charger 110 may be an AC level 2 EV charger. In such a circumstance, there may be no communication protocol compatible with and/or used by the second EV charger 110 that is capable of passing a battery capacity metric from the first EV 116 to the second EV charger 110. Such a limitation may (also, or alternatively) occur because an EV that is connected to an EV charger is not capable of performing this communication (even if the EV charger is). For example, the fourth EV charger 114 may be a DC EV charger that operates according to one or more communications protocols that are capable of passing a battery capacity metric from the second EV 118 to the fourth EV charger 114. However, it may be that the second EV 118 does not use any of these one or more communications protocols. [0024] Accordingly, in the system 102, the charging management server 104 may be configured to provide the respective EV charger with a

of the respective connected EV over the network 106, such that the

of the EV is in that way known at the EV charger. Thus the EV charger may be provided with the

of the EV (as known to the charging management server 104) regardless of whether (or not) it has been capable of receiving battery capacity information from the EV directly. [0025] This

may then be used by the EV charger to make charging determinations (e.g., such as a determination of an amount of energy and/or a duration of time needed to fully charge the battery). These determinations may be made relative to a current SoC reported by the battery to the EV charger. Or, in the event that the EV charger cannot query the EV for the current SoC directly, the current SoC may be provided to the EV charger by the user (e.g., via the user device 120 and the network 106, or an input device on the EV charger). [0026] The

of an EV that is being charged by one of the EV chargers 108 through 114 of the system 102 may have been previously determined at the charging management server 104. This may be done using previously collected charging metrics that were provided by a previous charging of the battery performed by any one of the EV chargers 108 through 114. [0027] FIG. 2 illustrates a past state of the system 102 that uses the charging management server 104 to track the energy capacity of a battery of an EV, according to an embodiment. As illustrated in FIG. 2, previous to the state illustrated in FIG. 1, the first EV 116 was (previously) connected to the first EV charger 108, which then performed a (previous) charging of the battery of the first EV 116. (Note that the selection of the first EV charger 108 as the previously used charger is arbitrary, and that another of the EV chargers 108 through 114 that is capable of receiving charging metrics from the first EV 116 could have been used as the previously used charger instead). [0028] During the past state represented in FIG. 2, the first EV charger 108 sends charging metrics for that previous charging of the battery of the first EV 116 to the charging management server 104. These charging metrics may include, but are not limited to, a starting

of the battery (a SoC of the battery when the charging began) and an ending

of the battery (a SoC of the battery when the charging ended). Other possible charging metrics that may be sent from the first EV charger 108 to the charging management server 104 include a total amount of energy dispensed by the first EV charger 108 during the charge, a power provided at the battery of the first EV 116 during the charge, and/or a duration of the charge. [0029] Based on the provided charging metrics, the charging management server 104 may determine the total amount of energy dispensed by the first EV charger during the charge. A total amount of energy dispensed by an EV charger during a charging may be referred to herein as “ In cases where is already provided by the first EV charger 108 to the

charging management server 104 as a charging metric, the charging management server 104 determines that the total amount of energy is as directly provided. In cases where is not provided to the charging management server 104 from the first EV charger 108, the charging management server 104 may use other charging metrics provided from the first EV charger 108 to determine . For example, the charging management server 104 may multiply a reported power provided at the battery of the first EV 116 (by the first EV charger 108) by the duration of the charging to arrive at

[0030] Then, the charging management server 104 may determine a non-charging energy amount used by the first EV 116 during the charging for non-charging purposes. For various reasons, it may be that during a charge, energy is being used by the first EV 116 for non-charging purposes (e.g., for purposes not directly used to affect (increase) the SoC of the battery). [0031] One example of energy use for a non-charging purpose is energy used to provide battery conditioning during the charge. It may be that a battery that is charged when it is cold may not ultimately hold the same capacity as compared to a case of charging within an environment having a more typical or standard temperature. Further, it may be that a battery that is charged when it is hot experiences additional degradation during the charging as compared to a case of charging within an environment having a more typical or standard temperature. Accordingly, it may be that the first EV 116 is configured to provide battery conditioning to the battery while it is being charged. This battery conditioning may contemplate the use of a blower and an associated heater and/or condenser unit of the first EV 116 to blow heated/cooled air at the battery during the charge, which may act to promote and/or maintain a more typical or standard temperature for charging in the immediate vicinity of the battery. [0032] In some cases, it may be that the amount of energy used by the first EV 116 during the charging for battery conditioning is not known at the first EV charger 108 (and thus cannot be reported as such to the charging management server 104). It may be instead that the first EV charger 108 sends the charging management server 104 an ambient temperature at the first EV charger 108 (e.g., that the first EV charger 108 detects using the first temperature sensor 122). Then, the charging management server 104 uses the ambient temperature information to determine the amount of energy used by the first EV 116 during the charging to condition the battery. For example, the charging management server 104 may be aware of an amount of power that is used by the first EV 116 to condition its battery (e.g., corresponding to its make and model), relative to the provided ambient temperature. This amount of power may then be multiplied by the duration of the charging in order to arrive at the amount of energy used by the first EV 116 during the charging for battery conditioning. [0033] The amount of power used to condition the battery relative to the ambient temperature may be determined according to historical data collected for the first EV 116 and/or according to historical data for that particular model of the first EV 116. This historical data may be collected by the charging management server 104 even prior to the time of the past state illustrated in FIG.2, as will now be described. [0034] For example, it may be that such historical data is collected during or otherwise corresponding to one or more charges of the first EV 116 that occur within the system 102 when the first EV 116 (and thus the battery of the first EV 116) is new. When the battery of the first EV 116 is new, the charging management server 104 may appropriately assume that the battery has not yet degraded, and thus is being charged to its full rated capacity. The charging management server 104 may be able to determine that the battery is new based on, for example, a report of a number of charges that the battery has experienced made by the EV 116 to an EVSE of the system 102, and/or by tracking the number of times that the first EV 116 has been charged within the system 102. Because the actual capacity of the battery of the first EV 116 is effectively known to equal the battery’s rated capacity at this early stage in the lifecycle of the battery of the first EV 116, data collected during this time can be appropriately extrapolated from with at least one aspect (the actual capacity of the battery) accurately anchored at a known value. [0035] The removal of the degraded battery variable during these early charge cycles may allow the charging management server 104 to build a model for the first EV 116 that records the power use of the first EV 116 for battery conditioning according to temperature. One example of this process is now described. Suppose that the battery of the first EV 116, prior to any degradation, has a capacity of 100 kWh, and the temperature to which first EV 116 keeps the battery at via the use of battery conditioning is set to 20 degrees C. Then, the charging management server 104 may record during a first such charging (e.g., at a DC charger) that the battery of the first EV 116 was provided with 100 kW of power for 62 minutes to go from a 0 SoC to a 100 SoC when the ambient temperature was 21 degrees C. Then, the charging management server 104 determines that because 100 kW was dispensed for 62/60 = 1.033 hours, a total of 100 kW * 1.033 hours = 103.3 kWh of energy was dispensed to the first EV 116 during this first charge. [0036] Further, the charging management server 104 may record during a second such charging (e.g., at a DC charger) that the battery of the first EV 116 was provided with 100 kW of power for 64 minutes to go from a 0 SoC to a 100 SoC when the ambient temperature was 22 degrees C. Then, the charging management server 104 determines that because 100 kW was dispensed for 64/60 = 1.066 hours, a total of 100 kW * 1.066 hours = 106.6 kWh of energy was dispensed to the first EV 116 during this second charge. [0037] Because the total capacity of the battery is known to be 100 kWh (because the battery is rated for 100 kWh and has not yet been used enough to have experienced meaningful degradation, the charging management server 104 is accordingly aware that, in the first case, when the battery conditioning system of the first EV 116 worked to lower the battery temperature one degree C (21 degrees C – 20 degrees C = 1 degree C), it used 3.3 kWh (103.3 kWh total dispensed – 100 kWh into the battery = 3.3 kWh) to perform the battery conditioning over 62 minutes (or 1.033 hours). Then, the charging management server 104 can accordingly calculate the amount of power used to cool the battery 1 degree C as 3.3kWh / 1.033 hours = 3.19 kW. The data point (-1, 3.19) may accordingly be generated (with the negative sign representing the cooling (as opposed to heating) by 1 degree). [0038] The information that 3.19 kW of power is used to cool the battery by one degree during conditioning may be then used in association with future charges of the first EV 116 within the system 102 (e.g., during future charges of the first EV 116 occurring after the battery has experienced degradation and the charging management server 104 can no longer determine this power value directly based on a non-degradation assumption). [0039] The charging management server 104 is further aware that, in the second case, when the battery conditioning system of the first EV 116 worked to lower the battery temperature two degrees C (22 degrees C – 20 degrees C = 2 degrees C) it used 6.6 kWh (106.6 kWh total dispensed – 100 kWh into the battery = 6.6 kWh) to perform the battery conditioning over 64 minutes (or 1.066 hours). Then, the charging management server 104 can accordingly calculate the amount of power used to cool the battery 2 degrees C as 6.6 kWh / 1.066 hours = 6.19 kW. The data point (-2, 6.19) may accordingly be generated (with the negative sign representing the cooling (as opposed to heating) by 1 degree). [0040] The information that 6.19 kW of power is used to cool the battery by two degrees C during conditioning may be then used in association with future charges of the first EV 116 within the system 102 (e.g., during future charges of the first EV 116 occurring after the battery has experienced degradation and the charging management server 104 can no longer determine this power value directly based on a non-degradation assumption). [0041] Further, additional data points similarly generated within the system 102 during charges of another EV that is the same model as the first EV 116 prior to significant battery degradation of the battery of that EV may also be so collected and used in relation to the first EV 116. [0042] It is also noted that while the above example data points involve cooling the battery to 20 degrees C, a similar process would be used to generate data points in the case where the battery conditioning used power to heat the battery to 20 degrees C instead, and that these data points could similarly/also be used to generate the mathematical model for the first EV 116 for battery conditioning energy use relative to ambient temperature. In some cases, the degrees coordinate in such heating cases would be positive rather than negative (such that data corresponding to operation of cooling equipment is differentiated from data corresponding to operation of heating equipment). [0043] Then, a mathematical model that maps a number of degrees temperature difference (whether positive or negative) versus the amount of power used for battery conditioning may be constructed using the collected data points. For example, the two data points explicitly described above ((-1, 3.19), (-2, 6.19)), could be used in conjunction with additional data points similarly gathered in order to develop the mathematical model. The mathematical model may be a polynomial model, a linear model, etc., based on the accuracy/granularity desired by an implementer or operator of the charging management server 104. [0044] This mathematical model could be generated using known mathematical modeling methods applied to the data points. Additionally or alternatively, this mathematical model may be generated by applying machine learning methods familiar to those of ordinary skill to the collected data points. Note that using these machine learning methods, the model may be improved over time as additional data points (e.g., from the first EV 116 and/or other EV of the same model) are collected and incorporated into the machine learning process. [0045] The mathematical model so generated can then be used to determine power use by the first EV 116 for battery conditioning during later charges (e.g., once it is no longer reasonable to assume that the first EV 116 has a non-degraded battery, and thus the 100 kWh anchor point for battery capacity can no longer be assumed relative to then-present charging metrics). [0046] Another example of energy use for a non-charging purpose is energy used to provide cabin conditioning during the charge. It may be that an operator of the first EV 116 is present within the cabin of the first EV 116 while the battery of the first EV 116 is being charged by the first EV charger 108. Accordingly, it may be that the first EV 116 is configured to provide cabin conditioning to the cabin while the battery of the first EV 116 is being charged. This cabin conditioning may contemplate the use of a blower and an associated heater and/or condenser unit of the first EV 116 to blow heated/cooled air into the cabin during the charge, which may act to promote and/or maintain a desired cabin temperature during the charge. [0047] In some cases, it may be that the amount of energy used by the first EV 116 during the charging for cabin conditioning is not known at the first EV charger 108 (and thus cannot be reported as such to the charging management server 104). It may be instead that the first EV charger 108 sends the charging management server 104 an ambient temperature at the first EV charger 108 (e.g., that the first EV charger 108 detects using the first temperature sensor 122). Then, the charging management server 104 uses the ambient temperature information to determine the amount of energy used by the first EV 116 during the charging to perform cabin conditioning. For example, the charging management server 104 may be aware of an amount of power that is used by the first EV 116 to condition its cabin to a given cabin temperature (e.g., corresponding to its make and model), relative to the provided ambient temperature. It may be that the charging management server 104 uses historical data that reflects the user preferences for cabin temperature setting to determine this amount of power relative to the current ambient temperature. This amount of power may then be multiplied by the duration of the charging in order to arrive at the amount of energy used by the EV during the charging for cabin conditioning. [0048] A method of using historical data to determine an amount of power used for cabin conditioning relative to the current ambient temperature during a charging is now described. The charging management server 104 may be able to collect historical data points relating an ambient temperature, a total amount of energy used to perform a charge, and a duration of the charge, and identify data points where there are differences in total energy used to perform a charging but that correspond to same or similar ambient temperatures and charging durations. In such cases, the charging management server 104 may assume that some or all of this discrepancy can be attributed to the use/non-use of cabin conditioning at these instances (e.g., assuming that the data points being so compared are close enough in time that the difference is not explainable by battery degradation instead). Accordingly, the differences in the energy use found in these data points may be used by the charging management server 104 to recognize patterns in (1) an amount of power that is consumed by cabin conditioning relative to ambient temperature during a charging of the first EV 116 and (2) the frequency with which cabin conditioning is used across charges of the first EV 116. [0049] For example, it may be determined that the energy difference of 2.0 kWh found in two such data points of (21 degrees, 106.19 kWh, 1 hour) and (21 degrees, 108.19 kWh, 1 hour) is because of a non-use of cabin conditioning when generating the first data point and a use of cabin conditioning when generating the second point. This may be recognized at least in part because the value of 2.0 kWh is within the range of expected cabin conditioning energy use when performing a charging around 106 kWh for 1 hour, as known to the charging management server 104. Where multiple such data points for the same ambient temperature are available corresponding to the first EV 116, mathematical modeling methods and/or machine learning methods may be used to determine a precise value of energy use difference as between the points. This energy use difference can then be divided by the time corresponding to the points to arrive at the power use corresponding to the ambient temperature. In the example points provided, the 2.0 kWh may be divided by the 1 hour to arrive at 2.0 kW used for cabin conditioning by the EV 116 when the ambient temperature is 21 degrees. [0050] Further, the frequency with which the cabin conditioning is used may be determined by identifying a number of data points for which the cabin conditioning was apparently not used as compared to a number of data points for which the cabin conditioning was apparently used (e.g., based on comparisons across groups of data points for the same/similar ambient temperatures in the x-coordinate and same/similar durations in the z-coordinate, as in the provided example points), and using mathematical modeling methods and/or machine learning methods to arrive at a determined frequency using this data. [0051] It may be that the charging management server 104 determines the amount of power that is consumed by cabin conditioning relative to ambient temperature in the manner described by using data points generated relative to multiple EVs that are the same make and model of the first EV 116 (as these EVs may be assumed to have the same/similar cabin conditioning characteristics, and thus have similar power usages corresponding to a given ambient temperature). Alternatively, it may be that only data points corresponding to specifically the first EV 116 are used (in recognition of the fact that individual users may use different cabin temperature settings). [0052] It may also be that the charging management server 104 determines the frequency with which cabin conditioning is used across charges using data points corresponding to specifically the first EV 116 (in recognition of the fact that different users may use/not use cabin conditioning during charges with different frequencies). [0053] Once determined by the charging management server 104, this historical data regarding power use and frequency of cabin conditioning during a charging may then be used during later charges of the first EV 116 to inform how much power may (or may not) have been used for cabin conditioning during that later charging of the first EV 116. For example, if the historical data represents that the user frequently uses cabin conditioning while charging, it may be assumed that the power amount for cabin conditioning corresponding to the current ambient temperature was used during the charging of the first EV 116. In other cases, if the historical data represents that the user almost never uses cabin conditioning while charging, it may be assumed that no power was used for cabin conditioning during the charge. [0054] Another example of energy use for a non-charging purpose is energy used to provide cabin lighting during the charge. It may be that an operator of the first EV 116 is present within the cabin of the first EV 116 while the battery of the first EV 116 is being charged by the first EV charger 108. Accordingly, it may be that the first EV 116 is configured to provide cabin lighting while the battery of the first EV 116 is being charged. In such cases, it may be that the charging management server 104 uses historical data reflecting the preferences of the user regarding cabin lighting to determine whether or not the lights are being used. In alternative embodiments, the first EV charger 108 may communicate with the first EV 116 to determine whether (and for what length of time) the cabin lights were on during the charge. The charging management server 104 may be aware of an amount of power that is used by the first EV 116 for cabin lighting (e.g., corresponding to its make and model). This amount of power may then be multiplied by the duration of the charge/the length of time that the cabin lights were on during the charging (as the case may be) in order to arrive at the amount of energy used by the first EV 116 during the charging for cabin lighting. [0055] A method of using historical data to determine whether the lights are on during a charging is now described. It may be that the charging management server 104 knows the amount of power that is consumed by cabin lighting according to pre-established information at the charging management server 104 for the make and model of the first EV 116. [0056] The charging management server 104 may be able to collect historical data points relating an ambient temperature, a total amount of energy used to perform a charge, and a duration of the charge, and identify data points where there are differences in total energy used to perform a charging but that correspond to same or similar ambient temperatures and charging durations. In such cases, the charging management server 104 may assume that some or all of this discrepancy can be attributed to the use of cabin lighting at these instances (e.g., assuming that the data points being so compared are close enough in time that the difference is not explainable by battery degradation instead). Accordingly, the differences in the energy use found in these data points may be used by the charging management server 104 to recognize patterns in the frequency with which cabin lighting is used across charges of the first EV 116. [0057] For example, it may be determined that the difference of 0.10 kWh found in two such data points of (21 degrees, 106.19 kWh, 1 hour) and (21 degrees, 106.29 kWh, 1 hour) is because of a non-use of cabin lights when generating the first data point and a use of cabin lights when generating the second point. This may be recognized at least in part because the value of 0.10 kWh is within the range of expected cabin lighting energy use when performing a charging around 106 kWh for 1 hour, as known to the charging management server 104. Then, the frequency with which the cabin lighting is used may be determined by identifying a number of data points for which the cabin lighting was apparently not used as compared to a number of data points for which the cabin lighting was apparently used (e.g., based on comparisons across groups of data points for the same/similar ambient temperatures in the x-coordinate and same/similar durations in the z- coordinate, as described previously), and using mathematical modeling methods and/or machine learning methods to arrive at a determined frequency using this data. [0058] Once determined by the charging management server 104, this historical data regarding frequency of cabin lighting use during a charging may then be used during later charges of the first EV 116 to inform how much power may (or may not) have been used for cabin conditioning during that later charging of the first EV 116. For example, if the historical data represents that the user frequently uses cabin lighting while charging, it may be assumed that the power amount for cabin lighting (as known to the charging management server 104) was used during the charging of the first EV 116. In other cases, if the historical data represents that the user almost never uses cabin lighting while charging, it may be assumed that no power was used for cabin lighting during the charge. [0059] Another example of energy use for a non-charging purpose is energy used to energize one or more auxiliary devices of the first EV 116. Examples of auxiliary devices of the first EV 116 may include, for example a radio of the first EV 116, a separate user device (such as a smartphone, tablet, or laptop) that is connected to a power port in the cabin of the first EV 116, and the like. Accordingly, it may be that the first EV 116 energizes and/or charges these devices while the battery of the first EV 116 is being charged. [0060] In some cases, it may be that the amount of energy used by the first EV 116 during the charging for energizing auxiliary devices is not known at the first EV charger 108 (and thus cannot be reported as such to the charging management server 104). In such cases, historical data reflecting the customs of the user(s) of the first EV 116 as to auxiliary device use during the charging may be used to estimate an energy use by one or more auxiliary devices during the charge. For example, this historical data may reflect whether auxiliary devices are typically energized during the charge, and/or an amount of power used by those auxiliary devices during the charge. [0061] A method of using historical data to determine an amount of power used for auxiliary devices during a charging is now described. The charging management server 104 may be able to collect historical data points relating an ambient temperature, a total amount of energy used to perform a charge, and a duration of the charge, and identify data points where there are differences in total energy used to perform a charging but that correspond to same or similar ambient temperatures and charging durations. In such cases, the charging management server 104 may assume that some or all of this discrepancy can be attributed to the use/non-use of auxiliary devices at these instances (e.g., assuming that the data points being so compared are close enough in time that the difference is not explainable by battery degradation instead). Accordingly, the differences in the energy use found in these data points may be used by the charging management server 104 to recognize patterns in (1) an amount of power that is consumed by auxiliary devices during a charging of the first EV 116 and (2) the frequency with which auxiliary devices are used across charges of the first EV 116. [0062] For example, it may be determined that the difference of 4.0 kWh found in two such data points of (21 degrees, 106.19 kWh, 1 hour) and (21 degrees, 110.19 kWh, 1 hour) is because of a non-use of auxiliary devices when generating the first data point and a use of auxiliary devices when generating the second point. This may be recognized at least in part because the value of 4.0 kWh is within the range of potential auxiliary device energy use when performing a charging around 106 kWh for 1 hour, as known to the charging management server 104. Where multiple such data points for the same ambient temperature are available corresponding to the first EV 116, mathematical modeling methods and/or machine learning methods may be used to determine a precise value of energy use difference as between the points. This energy use difference can then be divided by the time corresponding to the points to arrive at the power use by auxiliary devices. In the example points provided, the 4.0 kWh may be divided by the 1 hour to arrive at 4.0 kW used for auxiliary devices by the EV 116. [0063] Further, the frequency with which the cabin conditioning is used may be determined by identifying a number of data points for which auxiliary devices were apparently not used as compared to a number of data points for which the auxiliary devices were apparently used (e.g., based on comparisons across groups of data points for the same/similar ambient temperatures in the x-coordinate and same/similar durations in the z- coordinate, as in the provided example points), and using mathematical modeling methods and/or machine learning methods to arrive at a determined frequency using this data. [0064] Once determined by the charging management server 104, this historical data regarding power use by and frequency of auxiliary device use during a charging may then be used during later charges of the first EV 116 to inform how much power may (or may not) have been used for auxiliary devices during that later charging of the first EV 116. For example, if the historical data represents that the user frequently uses auxiliary devices while charging, it may be assumed that the established power amount for auxiliary devices was used during the charging of the first EV 116. In other cases, if the historical data represents that the user almost never uses auxiliary devices while charging, it may be assumed that no power was used for auxiliary devices during the charge. [0065] The charging management server 104 calculates each of the energy use(s) due to non-charging purposes (e.g., as determined as described above) and totals them. A total non-charging energy amount used by an EV during a charging for one or more non-charging purposes (e.g., as in the examples provided above) may be referred to herein as

Accordingly, during a charging where the first EV 116 uses energy for one or more non- charging purposes, an amount of energy

is used up rather than being/remaining stored at the battery. It therefore follows that in circumstances where energy uses for non-charging purposes occur during the charge, an ultimate value of

(a total amount of energy dispensed by the first EV charger 108 to the battery of the first EV 116 during the charge) is higher than merely the difference between

and the energy stored at the battery at the beginning of the charging (e.g., by an amount

, in order to make up for the use of during the charge). [0066] The charging management server 104 then proceeds to calculate the

of the first EV 116 based on

It may be that can be calculated by dividing a total amount of energy that was used (directly) to charge the battery during the charging by a change in the SoC of the battery due to the charge. Accordingly, the charging management server 104 may determine the total amount of energy that was used (directly) to charge the battery by subtracting (the total non-charging energy amount used by the first EV 116 during the charge) from

(the total amount of energy dispensed by the first EV charger 108 to the first EV 116 during the charge). The change in the SoC of the battery due to the charging may be calculated by subtracting

(the SoC of the battery at the beginning of the charge) by

(the SoC of the battery at the end of the charge). Accordingly, the calculation for

may be written as

[0067] Once has been calculated, this value may be stored at the charging management server 104. [0068] In some embodiments, it may be that the charging management server 104 is configured to determine whether

is different than a previous energy capacity for that same battery that was previously determined and stored by the charging management server 104 (e.g., during a charging of the battery of the first EV 116 that occurred prior to the time of the state of the system 102 as illustrated in FIG. 2, using the method just described). Such a previous value for

stored at the charging management server 104 may be referred to herein as For example, the charging

management server 104 may determine a difference between

and in terms of a percentage, and then compare that

difference to a threshold percentage. If the difference is greater than (or greater than or equal to) the threshold percentage, the charging management server 104 may proceed to update with (e.g., store in place of

If this is not the case, the charging management server 104 may instead continue to use as the battery capacity for the battery within the charging

management server 104. [0069] In some cases, an update of with

may occur as

described above in further response to input to the charging management server 104 provided by the user via the user device 120. In these cases, the charging management server 104, upon determining that the difference between and exceeds (or meets, as the case may be) the

threshold percentage, sends a request to update with

to the

user device 120. The user device 120 may be a device used by an operator of the first EV 116. In some cases, the request may include

(e.g., such that this newly proposed value may be displayed by the user device 120 and thereby reviewed by the user). The user may then operate the user device 120 to cause the user device 120 to send a response to the charging management server 104 that contains an approval of the change. Upon receiving this approval, is updated to

within the

charging management server 104. [0070] It is also contemplated that, upon any update of with

in the charging management server 104, the charging management server 104 may send a notification to the user device 120 that has been so updated. This

notification may include

such that this value may be reviewed by the user of the user device 120. [0071] Returning to the present state of the system 102 as illustrated in FIG. 1: When the first EV 116 is connected to the second EV charger 110 at the time of the present state, it may be that the first EV 116 does not or cannot report a

for the first EV 116 to the second EV charger 110 (e.g., for reasons discussed above). Accordingly, after the first EV 116 is identified to the charging management server 104, the charging management server 104 may provide its stored value of

for the first EV 116 (e.g., as previously calculated by the charging management server 104 as described in relation to the past state of the system 102 illustrated in FIG. 2) to the second EV charger 110. In this way, the second EV charger 110 is aware of the of the first EV 116 (as known

within the system 102) and is therefore enabled to accurately determine, for example, an amount of energy and/or a duration of time that it will take for the battery of the first EV 116 to charge at the second EV charger 110. [0072] FIG. 3 illustrates a method 300 of a charging management server, according to an embodiment. The method 300 includes receiving 302, from a first EV charger in communication with the charging management server, charging metrics for a charging of a battery of an EV performed by the first EV charger, wherein the charging metrics include a starting SoC of the battery,

, and an ending SoC of the battery,

[0073] The method 300 further includes determining 304, based on the charging metrics, a total energy amount,

dispensed by the first EV charger during the charge. [0074] The method 300 further includes determining 306 a non-charging energy amount, , used by the EV during the charging for non-charging purposes. [0075] The method 300 further includes calculating 308 an energy capacity of the battery, based on

[0076] The method 300 further includes sending 310

to a second EV charger in communication with the charging management server that is to charge the battery. [0077] In some embodiments of the method 300,

is calculated using

[0078] In some embodiments of the method 300, the non-charging purposes include battery conditioning, and at least a portion of includes an amount of energy used by the EV for the battery conditioning. Some of these embodiments of the method 300 further include receiving, from the first EV charger, an ambient temperature at the first EV charger during the charge, wherein the amount of energy used by the EV for the battery conditioning during the charging is determined by the charging management server using the ambient temperature. [0079] In some embodiments of the method 300, the non-charging purposes include cabin conditioning, and wherein at least a portion of

includes an amount of energy used by the EV for the cabin conditioning during the charge. Some of these embodiments of the method 300 further include receiving from the first EV charger an ambient temperature at the first EV charger during the charge, wherein the amount of energy used by the EV for the cabin conditioning during the charging is determined by the charging management server using the ambient temperature. [0080] In some embodiments of the method 300, the non-charging purposes include cabin lighting, and at least a portion of

includes an amount of energy used by the EV for the cabin lighting during the charge. [0081] In some embodiments of the method 300, the non-charging purposes include energizing one or more auxiliary devices of the EV, and wherein a portion of includes an amount of energy used by the EV for energizing the one or more auxiliary devices. [0082] In some embodiments of the method 300, the charging metrics include a power provided at the battery by the first EV charger during the charging and a duration of the charge, and the charging management server determines

by multiplying the power provided at the battery by the first EV charger during the charging by the duration of the charge. [0083] In some embodiments of the method 300, the charging metrics include

[0084] In some embodiments, the method 300 further includes storing

at the charging management server. [0085] In some embodiments, the method 300 further includes determining that

is different than a previous energy capacity of the battery,

, that is stored at the charging management server by at least a threshold percentage, and updating with

at the charging management server. In some

cases of updating with

the method 300 further comprises

sending, to a user device in communication with the charging management server, a request to update with

at the charging management server, and

receiving a response to the request from the user device, wherein the updating of with

at the charging management server is performed

according to an approval in the response. This request may, in some instances, include

[0086] In some cases of updating with

the method 300

further comprises sending, to a user device in communication with the charging management server, a notification that has been updated

with

at the charging management server. This notification may include

[0087] In some embodiments of the method 300, the first EV charger comprises a DC charger, and the second EV charger comprises an AC charger. [0088] FIG. 4 illustrates an expanded view of the system 102 for using the charging management server 104 with an emphasis on the charging management server 104, according to an embodiment. The charging management server 104 may include a memory 402, one or more processor(s) 404, a network/COM interface 406, and an I/O interface 408, which all may communicate with each other using a system bus 410. [0089] The network/COM interface 406 of the charging management server 104 may be connected to the network 106 and may act as a reception and/or distribution device for computer-readable instructions. This connection may facilitate the transfer of information (e.g., computer-readable instructions) as between the charging management server 104, the first EV charger 108, the second EV charger 110, the third EV charger 112, the fourth EV charger 114, and the user device 120 (via the network 106). [0090] The memory 402 of the charging management server 104 may include a data store 412 and engine instructions 414. [0091] The data store 412 may include the charging metrics data 416, the ambient temperature data 418, the non-charging power use data 420, and the battery capacity data 422. [0092] The charging metrics data 416 may include metrics of a charging provided to an EV as determined by, for example, one of the EV chargers 108 through 114 performing the charging of the EV that then provides these charging metrics to the charging management server 104. The charging metrics data 416 may include (but are not limited to) some or all of: an

of the battery when the charging began, an

of the battery when the charging ended, a total amount of energy dispensed by the EV charger during the charge, a power provided at the battery of the EV during the charge, and/or a duration of the charge. [0093] The ambient temperature data 418 may include ambient temperatures at one or more of the EV chargers 108 through 114 (e.g., that the respective EV charger detects using its associated temperature sensor) as provided to the charging management server 104 by one or more of the EV chargers 108 through 114. [0094] The non-charging power use data 420 may include information that allows the charging management server 104 to calculate one or more non-charging energy uses during a charging of an EV by one of the EV chargers 108 through 114. For example, this information may include an amount of power used by an EV to condition its battery during a charging, relative to an ambient temperature. This information may include historical data regarding the preferences (e.g., cabin temperature setting) of a user of the EV and/or an amount of power used by an EV to perform cabin conditioning, relative to an ambient temperature and the user’s preferences. This information may include historical data regarding the preferences of a user to determine whether cabin lighting is used during a charging, and/or data reflecting an amount of power used by the EV for the cabin lighting. This information may include historical data regarding the customs of a user to determine whether power provision to auxiliary devices occurs during a charging, and/or an amount of power used by the EV for the auxiliary devices during a charging. A total non-charging energy amount used by an EV during a charging for one or more non-charging purposes may also be stored here once it is determined by the charging management server 104 (e.g., by multiplying these power uses, as applicable, by an amount of time of the charging (or amount of time for that power use) and then summing the results). [0095] The battery capacity data 422 may include

values for one or more EVs that are charged at one or more of the EV chargers 108 through 114 of the system 102 at some (previous) point in time. As described above, when a new

is calculated for an EV, an old

stored at the charging management server 104 (e.g., a for that EV may be replaced by (the new) in the battery

capacity data 422. [0096] The engine instructions 414 may include the non-charging energy calculation instructions 424, the battery capacity calculation engine instructions 426, and the EV charger communication engine instructions 428. Each of the engine instructions 414 may be operable with the processor(s) 404 to implement an engine of the charging management server 104 that performs the functions instructed by those instructions. Accordingly, the non-charging energy calculation instructions 424 may be operable with the processor(s) 404 to implement a non-charging energy calculation engine, the battery capacity calculation engine instructions 426 may be operable with the processor(s) 404 to implement a battery capacity calculation engine, and the EV charger communication engine instructions 428 may be operable with the processor(s) 404 to implement an EV charger communication engine. [0097] The non-charging energy calculation instructions 424 may include instructions for determining a total non-charging energy amount used by an EV during a charging for one or more non-charging purposes

The non-charging energy calculation instructions 424 may include instructions for using the ambient temperature data 418 and/or the non- charging power use data 420 associated with the EV to make the determination of corresponding to the charging, using methods discussed above. [0098] The battery capacity calculation engine instructions 426 may include instructions for calculating a

value for an EV, storing comparing a difference

between

and to a percentage threshold, updating a

with

and/or sending requests and/or notifications and/or

receiving responses from a user device (such as the user device 120) attendant to these updates, etc., in the manner described above. These instructions may instruct in the use of the charging metrics data 416, the non-charging power use data 420, and/or the battery capacity data 422 for these purposes, using methods discussed above. [0099] The EV charger communication engine instructions 428 may include instructions for communicating with one or more of the EV chargers 108 through 114. For example, the instructions may instruct in receiving the charging metrics data 416 from one or more of the EV chargers 108 through 114, receiving the ambient temperature data 418 from one or more of the EV chargers 108 through 114, receiving an identification of a connected EV from one or more of the EV chargers 108 through 114, sending

for the EV to the one or more of the EV chargers 108 through 114, etc. [0100] The one or more processor(s) 404 of the charging management server 104 may operate one or more of the engine instructions 414 of the data store 412 to implement corresponding engines, as just described. In addition, processor(s) 404 may perform other system control tasks, such as controlling data flows on the system bus 410 between the memory 402, the network/COM interface 406, and the I/O interface 408. The details of these (and other) background operations may be defined in operating system instructions (not shown) upon which the one or more processor(s) 404 operate. [0101] The I/O interface 408 may include any mechanism allowing an operator to interact with and/or provide data to the charging management server 104. For example, the I/O interface 408 may include a keyboard, a mouse, a monitor, and/or a data transfer mechanism, such as a disk drive or a flash memory drive. The I/O interface 408 may allow an operator to place information in the memory 402, or to issue instructions to the charging management server 104 to perform any of the functions described herein. [0102] As illustrated, the first EV charger 108 may use a first temperature sensor 122, the second EV charger 110 may use a second temperature sensor 124, the third EV charger 112 may use a third temperature sensor 126, and/or the fourth EV charger 114 may use a fourth temperature sensor 128 for determining ambient temperatures the respective EV charger, in the manner discussed above. [0103] While the charging management server 104 has been illustrated in various figures (including FIG. 1) as a physical device, it is contemplated that it could exist instead as a virtual device operating on a virtual machine. While the charging management server 104 has been illustrated in various figures (including FIG. 1) as a single device, it is contemplated that a charging management server performing the same functions as the charging management server 104 could be a virtualized device that is instantiated across one or more physical and/or virtual machines. [0104] Examples [0105] Example 1. A method of a charging management server, comprising: receiving, from a first EV charger in communication with the charging management server, charging metrics for a charging of a battery of an EV performed by the first EV charger, wherein the charging metrics include a starting SoC of the battery,

and an ending SoC of the battery,

; determining, based on the charging metrics, a total energy amount,

dispensed by the first EV charger during the charging; determining a non-charging energy amount, , used by the EV during the charging for non-charging purposes; calculating an energy capacity of the battery,

based on

and sending to a second EV charger in communication with the charging management server that is to charge the battery. [0106] Example 2. The method of the charging management server of Example 1, wherein is calculated using

[0107] Example 3. The method of the charging management server of Example 1, wherein the non-charging purposes include battery conditioning, and wherein at least a portion of includes an amount of energy used by the EV for the battery conditioning. [0108] Example 4. The method of the charging management server of Example 3, further comprising receiving, from the first EV charger, an ambient temperature at the first EV charger during the charging, wherein the amount of energy used by the EV for the battery conditioning during the charging is determined by the charging management server using the ambient temperature. [0109] Example 5. The method of the charging management server of Example 1, wherein the non-charging purposes include cabin conditioning, and wherein at least a portion of

includes an amount of energy used by the EV for the cabin conditioning during the charging. [0110] Example 6. The method of the charging management server of Example 5, further comprising receiving, from the first EV charger, an ambient temperature at the first EV charger during the charging, wherein the amount of energy used by the EV for the cabin conditioning during the charging is determined by the charging management server using the ambient temperature. [0111] Example 7. The method of the charging management server of Example 1, wherein the non-charging purposes include cabin lighting, and wherein at least a portion of includes an amount of energy used by the EV for the cabin lighting during the charging. [0112] Example. 8. The method of the charging management server of Example 1, wherein the non-charging purposes include energizing one or more auxiliary devices of the EV, and wherein a portion of

includes an amount of energy used by the EV for energizing the one or more auxiliary devices. [0113] Example 9. The method of the charging management server of Example 1, wherein the charging metrics include a power provided at the battery by the first EV charger during the charging and a duration of the charging, and wherein the charging management server determines by multiplying the power provided at the battery by the first EV charger during the charging by the duration of the charging. [0114] Example 10. The method of the charging management server of Example 1, wherein the charging metrics include

[0115] Example 11. The method of the charging management server of Example 1, further comprising storing at the charging management server.

[0116] Example 12. The method of the charging management server of Example 1, further comprising: determining that is different than a previous energy capacity of the

battery, that is stored at the charging management server by at least a

threshold percentage; and updating with

at the charging

management server. [0117] Example 13. The method of the charging management server of Example 12, further comprising: sending, to a user device in communication with the charging management server, a request to update with

at the charging

management server; and receiving a response to the request from the user device, wherein the updating of with

at the charging management server is

performed according to an approval in the response. [0118] Example 14. The method of the charging management server of Example 13, wherein the request includes

[0119] Example 15. The method of the charging management server of Example 12, further comprising sending, to a user device in communication with the charging management server, a notification that has been updated with

at the charging management server. [0120] Example 16. The method of the charging management server of Example 15, wherein the notification includes

[0121] Example 17. The method of the charging management server of Example 1, wherein the first EV charger comprises a DC charger, and wherein the second EV charger comprises an AC charger. [0122] Example 18. A charging management server, comprising: one or more processors; a network interface to communicate with a first EV charger and a second EV charger; and a memory comprising instructions that, when executed by the one or more processors, configure the charging management server to: receive, from the first EV charger, charging metrics for a charging of a battery of an EV performed by the first EV charger, wherein the charging metrics include a starting SoC of the battery,

, and an ending SoC of the battery,

determine, based on the charging metrics, a total energy amount,

dispensed by the first EV charger during the charging; determine a non-charging energy amount, used by the EV during the charging for non-charging purposes; calculate an energy capacity of the battery,

based on and

send to the second EV charger.

[0123] Example 19. The charging management server of Example 18, wherein is calculated using

[0124] Example 20. The charging management server of Example 18, wherein the non- charging purposes include battery conditioning, and wherein at least a portion of includes an amount of energy used by the EV for the battery conditioning. [0125] Example 21. The charging management server of Example 20, wherein the instructions, when executed by the one or more processors, further configure the charging management server to receive, from the first EV charger, an ambient temperature at the first EV charger during the charging, wherein the amount of energy used by the EV for the battery conditioning during the charging is determined by the charging management server using the ambient temperature. [0126] Example 22. The charging management server of Example 18, wherein the non- charging purposes include cabin conditioning, and wherein at least a portion of includes an amount of energy used by the EV for the cabin conditioning during the charging. [0127] Example 23. The charging management server of Example 22, wherein the instructions, when executed by the one or more processors, further configure the charging management server to receive, from the first EV charger, an ambient temperature at the first EV charger during the charging, wherein the amount of energy used by the EV for the cabin conditioning during the charging is determined by the charging management server using the ambient temperature. [0128] Example 24. The charging management server of Example 18, wherein the non- charging purposes include cabin lighting, and wherein at least a portion of includes an amount of energy used by the EV for the cabin lighting during the charging. [0129] Example 25. The charging management server of Example 18, wherein the non- charging purposes include energizing one or more auxiliary devices of the EV, and wherein a portion of includes an amount of energy used by the EV for energizing the one or more auxiliary devices. [0130] Example 26. The charging management server of Example 18, wherein the charging metrics include a power provided at the battery by the first EV charger during the charging and a duration of the charging, and wherein the charging management server determines by multiplying the power provided at the battery by the first EV charger during the charging by the duration of the charging. [0131] Example 27. The charging management server of Example 18, wherein the charging metrics include

[0132] Example 28. The charging management server of Example 18, wherein the instructions, when executed by the one or more processors, further configure the charging management server to store at the charging management server.

[0133] Example 29. The charging management server of Example 18, wherein the instructions, when executed by the one or more processors, further configure the charging management server to: determine that is different than a previous energy

capacity of the battery, that is stored at the charging management server

by at least a threshold percentage; and update with at the

charging management server. [0134] Example 30. The charging management server of Example 29, wherein the instructions, when executed by the one or more processors, further configure the charging management server to: send, to a user device in communication with the charging management server, a request to update with

at the charging

management server; and receive a response to the request from the user device, wherein the updating of with

at the charging management server is

performed according to an approval in the response. [0135] Example 31. The charging management server of Example 30, wherein the request includes

[0136] Example 32. The charging management server of Example 29, wherein the instructions, when executed by the one or more processors, further configure the charging management server to send, to a user device in communication with the charging management server, a notification that has been updated

with

at the charging management server. [0137] Example 33. The charging management server of Example 32, wherein the notification includes

[0138] Example 34. The charging management server of Example 18, wherein the first EV charger comprises a DC charger, and wherein the second EV charger comprises an AC charger. [0139] Example 35. A non-transitory computer-readable storage medium including instructions that, when executed by one or more processors of a computing device, cause the computing device to: receive, from a first EV charger in communication with the computing device, charging metrics for a charging of a battery of an EV performed by the first EV charger, wherein the charging metrics include a starting SoC of the battery,

, and an ending SoC of the battery,

determine, based on the charging metrics, a total energy amount, dispensed by the first EV charger during the charging; determine a non- charging energy amount, , used by the EV during the charging for non-charging purposes; calculate an energy capacity of the battery,

based on

, and ; and send to a second EV charger in communication with the

computing device that is to charge the battery. [0140] Example 36. The non-transitory computer-readable storage medium of Example 35, wherein the computing device calculates

using

[0141] Example 37. The non-transitory computer-readable storage medium of Example 35, wherein the non-charging purposes include battery conditioning, and wherein at least a portion of includes an amount of energy used by the EV for the battery conditioning. [0142] Example 38. The non-transitory computer-readable storage medium of Example 37, wherein the instructions, when executed by one or more processors, further cause the computing device to receive, from the first EV charger, an ambient temperature at the first EV charger during the charging; and wherein the computing device determines the amount of energy used by the EV for the battery conditioning during the charging using the ambient temperature. [0143] Example 39. The non-transitory computer-readable storage medium of Example 35, wherein the non-charging purposes include cabin conditioning, and wherein at least a portion of includes an amount of energy used by the EV for the cabin conditioning during the charging. [0144] Example 40. The non-transitory computer-readable storage medium of Example 39, wherein the instructions, when executed by one or more processors, further cause the computing device to receive, from the first EV charger, an ambient temperature at the first EV charger during the charging; and wherein the computing device determines the amount of energy used by the EV for the cabin conditioning during the charging using the ambient temperature. [0145] Example 41. The non-transitory computer-readable storage medium of Example 35, wherein the non-charging purposes include cabin lighting, and wherein at least a portion includes an amount of energy used by the EV for the cabin lighting during the charging. [0146] Example 42. The non-transitory computer-readable storage medium of Example 35, wherein the non-charging purposes include energizing one or more auxiliary devices of the EV, and wherein a portion of

includes an amount of energy used by the EV for energizing the one or more auxiliary devices. [0147] Example 43. The non-transitory computer-readable storage medium of Example 35, wherein the charging metrics include a power provided at the battery by the first EV charger during the charging and a duration of the charging; and the computing device determines by multiplying the power provided at the battery by the first EV charger during the charging by the duration of the charging. [0148] Example 44. The non-transitory computer-readable storage medium of Example 35, wherein the charging metrics include . [0149] Example 45. The non-transitory computer-readable storage medium of Example 35, wherein the instructions, when executed by one or more processors, further cause the computing device to store

at the charging management server. [0150] Example 46. The non-transitory computer-readable storage medium of Example 35, wherein the instructions, when executed by one or more processors, further cause the computing device to: determine that

is different than a previous energy capacity of the battery, that is stored at the charging management server by at least

a threshold percentage; and update with

at the charging

management server. [0151] Example 47. The non-transitory computer-readable storage medium of Example 46, wherein the instructions, when executed by one or more processors, further cause the computing device to: send, to a user device in communication with the charging management server, a request to update with at the charging

at the charging management server is

performed according to an approval in the response. [0152] Example 48. The non-transitory computer-readable storage medium of Example 47, wherein the request includes

[0153] Example 49. The non-transitory computer-readable storage medium of Example 46, wherein the instructions, when executed by one or more processors, further cause the computing device to send, to a user device in communication with the charging management server, a notification that has been updated with at the

charging management server. [0154] Example 50. The non-transitory computer-readable storage medium of Example 49, wherein the notification includes

[0155] Example 51. The non-transitory computer-readable storage medium of Example 35, wherein the first EV charger comprises a DC charger, and wherein the second EV charger comprises an AC charger. [0156] The foregoing specification has been described with reference to various embodiments, including the best mode. However, those skilled in the art appreciate that various modifications and changes can be made without departing from the scope of the present disclosure and the underlying principles of the invention. Accordingly, this disclosure is to be regarded in an illustrative rather than a restrictive sense, and all such modifications are intended to be included within the scope thereof. Likewise, benefits, other advantages, and solutions to problems have been described above with regard to various embodiments. However, benefits, advantages, solutions to problems, and any element(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or element. [0157] As used herein, the terms “comprises,” “comprising,” or any other variation thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. [0158] Embodiments herein may include various engines, which may be embodied in machine-executable instructions to be executed by a general-purpose or special-purpose computer (or other electronic device). Alternatively, the engine functionality may be performed by hardware components that include specific logic for performing the function(s) of the engines, or by a combination of hardware, software, and/or firmware. [0159] Principles of the present disclosure may be reflected in a computer program product on a tangible computer-readable storage medium having stored instructions thereon that may be used to program a computer (or other electronic device) to perform processes described herein. Any suitable computer-readable storage medium may be utilized, including magnetic storage devices (hard disks, floppy disks, and the like), optical storage devices (CD-ROMs, DVDs, Blu-ray discs, and the like), flash memory, and/or other types of medium/machine readable medium suitable for storing electronic instructions. These instructions may be loaded onto a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions that execute on the computer or other programmable data processing apparatus create means for implementing the functions specified. These instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the function specified. The instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified. [0160] Principles of the present disclosure may be reflected in a computer program implemented as one or more software modules or components. As used herein, a software 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. A software module may, for instance, comprise one or more physical or logical blocks of computer instructions, which may be organized as a routine, a program, an object, a component, a data structure, etc., that perform one or more tasks or implement particular data types. [0161] In certain embodiments, a particular software module may comprise disparate instructions stored in different locations of a memory device, which together implement the described functionality of the module. Indeed, a module may comprise a single instruction or many instructions, and may be distributed over several different code segments, among different programs, and across several memory devices. Some embodiments may be practiced in a distributed computing environment where tasks are performed by a remote processing device linked through a communications network. In a distributed computing environment, software modules may be located in local and/or remote memory storage devices. In addition, data being tied or rendered together in a database record may be resident in the same memory device, or across several memory devices, and may be linked together in fields of a record in a database across a network. [0162] Suitable software to assist in implementing the invention is readily provided by those of skill in the pertinent art(s) using the teachings presented here and programming languages and tools, such as Java, JavaScript, Pascal, C++, C, database languages, APIs, SDKs, assembly, firmware, microcode, and/or other languages and tools. [0163] Embodiments as disclosed herein may be computer-implemented in whole or in part on a digital computer. The digital computer includes a processor performing the required computations. The computer further includes a memory in electronic communication with the processor to store a computer operating system. The computer operating systems may include, but are not limited to, MS-DOS, Windows, Linux, Unix, AIX, CLIX, QNX, OS/2, and MacOS. Alternatively, it is expected that future embodiments will be adapted to execute on other future operating systems. [0164] 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

CLAIMS 1. A method of a charging management server, comprising: receiving, from a first electrical vehicle (EV) charger in communication with the charging management server, charging metrics for a charging of a battery of an EV performed by the first EV charger, wherein the charging metrics include a starting state of charge (SoC) of the battery,

and an ending SoC of the battery,

determining, based on the charging metrics, a total energy amount, , dispensed by the first EV charger during the charging; determining a non-charging energy amount,

used by the EV during the charging for non-charging purposes; calculating an energy capacity of the battery, based on

, and ; and sending

to a second EV charger in communication with the charging management server that is to charge the battery.

2. The method of the charging management server of claim 1, wherein is

calculated using

3. The method of the charging management server of claim 1, wherein the non- charging purposes include battery conditioning, and wherein at least a portion of includes an amount of energy used by the EV for the battery conditioning.

4. The method of the charging management server of claim 3, further comprising receiving, from the first EV charger, an ambient temperature at the first EV charger during the charging, wherein the amount of energy used by the EV for the battery conditioning during the charging is determined by the charging management server using the ambient temperature.

5. The method of the charging management server of claim 1, wherein the non- charging purposes include cabin conditioning, and wherein at least a portion of includes an amount of energy used by the EV for the cabin conditioning during the charging.

6. The method of the charging management server of claim 5, further comprising receiving, from the first EV charger, an ambient temperature at the first EV charger during the charging, wherein the amount of energy used by the EV for the cabin conditioning during the charging is determined by the charging management server using the ambient temperature.

7. The method of the charging management server of claim 1, wherein the non- charging purposes include cabin lighting, and wherein at least a portion of

includes an amount of energy used by the EV for the cabin lighting during the charging.

8. The method of the charging management server of claim 1, wherein the non- charging purposes include energizing one or more auxiliary devices of the EV, and wherein a portion of includes an amount of energy used by the EV for energizing the one or more auxiliary devices.

9. The method of the charging management server of claim 1, wherein the charging metrics include a power provided at the battery by the first EV charger during the charging and a duration of the charging, and wherein the charging management server determines

by multiplying the power provided at the battery by the first EV charger during the charging by the duration of the charging.

10. The method of the charging management server of claim 1, wherein the charging metrics include

11. The method of the charging management server of claim 1, further comprising storing at the charging management server.

12. The method of the charging management server of claim 1, further comprising: determining that

is different than a previous energy capacity of the battery, that is stored at the charging management server by at least a

threshold percentage; and updating with

at the charging management server.

13. The method of the charging management server of claim 12, further comprising: sending, to a user device in communication with the charging management server, a request to update with at the charging management server; and

at the charging management server is performed

according to an approval in the response.

14. The method of the charging management server of claim 13, wherein the request includes

15. The method of the charging management server of claim 12, further comprising sending, to a user device in communication with the charging management server, a notification that has been updated with

at the charging management server.

16. The method of the charging management server of claim 15, wherein the notification includes .

17. The method of the charging management server of claim 1, wherein the first EV charger comprises a direct current (DC) charger, and wherein the second EV charger comprises an alternating current (AC) charger.

18. A charging management server, comprising: one or more processors; a network interface to communicate with a first electric vehicle (EV) charger and a second EV charger; and a memory comprising instructions that, when executed by the one or more processors, configure the charging management server to: receive, from the first EV charger, charging metrics for a charging of a battery of an EV performed by the first EV charger, wherein the charging metrics include a starting state of charge (SoC) of the battery, , and an ending SoC of

the battery,

determine, based on the charging metrics, a total energy amount, dispensed by the first EV charger during the charging; determine a non-charging energy amount, used by the EV during the charging for non-charging purposes; calculate an energy capacity of the battery, based on

and

send

to the second EV charger.

19. The charging management server of claim 18, wherein is calculated

using

20. The charging management server of claim 18, wherein the instructions, when executed by the one or more processors, further configure the charging management server to store

at the charging management server.

21. The charging management server of claim 18, wherein the instructions, when executed by the one or more processors, further configure the charging management server to: determine that

threshold percentage; and update with

at the charging management server.

22. The charging management server of claim 21, wherein the instructions, when executed by the one or more processors, further configure the charging management server to: send, to a user device in communication with the charging management server, a request to update with

at the charging management server; and

receive a response to the request from the user device, wherein the updating of with

at the charging management server is performed

according to an approval in the response.

23. The charging management server of claim 22, wherein the request includes

24. The charging management server of claim 21, wherein the instructions, when executed by the one or more processors, further configure the charging management server to send, to a user device in communication with the charging management server, a notification that has been updated with

at the charging management server.

25. The charging management server of claim 24, wherein the notification includes

26. The charging management server of claim 18, wherein the first EV charger comprises a direct current (DC) charger, and wherein the second EV charger comprises an alternating current (AC) charger.

27. A non-transitory computer-readable storage medium including instructions that, when executed by one or more processors of a computing device, cause the computing device to: receive, from a first electrical vehicle (EV) charger in communication with the computing device, charging metrics for a charging of a battery of an EV performed by the first EV charger, wherein the charging metrics include a starting state of charge (SoC) of the battery,

and an ending SoC of the battery,

determine, based on the charging metrics, a total energy amount,

, dispensed by the first EV charger during the charging; determine a non-charging energy amount,

, used by the EV during the charging for non-charging purposes; calculate an energy capacity of the battery,

based on

and and

send

to a second EV charger in communication with the computing device that is to charge the battery.

28. The non-transitory computer-readable storage medium of claim 27, wherein the computing device calculates