Charging station recommendation for electric vehicles

Abstract

The subject matter disclosed herein may relate to systems, devices, and / or methods for extracting one or more characteristics of a current coupled to an electric vehicle in response to initiating a charging process at a first charging station; determining one or more performance characteristics of the electric vehicle based at least in part on the charging process; determining a charging performance model based at least in part on the determined one or more performance characteristics of the electric vehicle; and displaying a recommendation for at least a second charging station based at least in part on a comparison between one or more characteristics of the second charging station and the charging performance model.

Description

Technical Field

[0001] The subject matter disclosed herein may relate to systems, devices, and / or processes for recommending a charging station for charging an electric vehicle based at least in part on features extracted from a charging current, for example, during the process of charging the electric vehicle. Background Art

[0002] An operator of an electric vehicle may charge the energy storage battery of the electric vehicle via a home-based charger that operates to convert ordinary household voltage and current to a voltage and current suitable for charging the electric vehicle, for example. However, at times, such as when the electric vehicle is traveling over long distances, the electric vehicle may need to be charged at charging stations spaced at various locations along highways and other avenues. In some cases, particularly when traveling over considerable distances, the electric vehicle may need to be charged at several intervals (such as, for example, at 200 km-400 km intervals) throughout the day. Therefore, in response to the need to visit a charging station several times, which may occur during a single day, it may become apparent that electric vehicle charging stations differ in available facilities, amenities, charging parameter options, and overall customer experience.

[0003] Furthermore, while charging stations may make certain claims regarding available charging waveforms and charge quality, such claims may be unverifiable or may be general in nature and thus at least partially inconsistent with respect to a particular electric vehicle. As a result, electric vehicle operators may find themselves at electric vehicle charging stations that are unable to charge the operator's electric vehicle as advertised or that offer charging options that are not fully compatible with the operator's particular electric vehicle. Thus, improving systems and processes for selecting and / or recommending charging services that are suitable (or even optimized) for a particular electric vehicle remains an active area of ​​research. Summary of the invention

[0004] One general aspect includes a method that includes extracting one or more characteristics of an electric current coupled to an electric vehicle in response to initiating a charging process at a first charging station. The method also includes determining one or more performance characteristics of the electric vehicle based at least in part on the one or more characteristics of the electric current coupled to the electric vehicle in response to initiating the charging process at the first charging station. The method also includes determining a charging performance model based at least in part on the determined one or more performance characteristics of the electric vehicle. The method also includes displaying a recommendation for at least a second charging station based at least in part on the charging performance model and one or more characteristics of at least a second charging station.

[0005] In a particular embodiment, extracting one or more features of the current includes detecting at least one of: an indication of an abnormality in the coupled current, an indication of an abnormality in the voltage, an indication of an abnormality in the power factor, an indication of the noise content of the coupled current, or an indication of the total harmonic distortion of the coupled current. In a particular embodiment, determining one or more performance characteristics of the electric vehicle includes determining the time period required to substantially fully charge the electric vehicle. In a particular embodiment, determining one or more performance characteristics of the electric vehicle includes determining a measure of electrical stress or thermal stress on one or more energy storage elements of the electric vehicle. In a particular embodiment, displaying a recommendation for at least a second charging station is further based on calculating a predicted time for at least partially charging the electric vehicle via at least a second charging station. In a particular embodiment, displaying a recommendation for at least a second charging station is further based on calculating an estimated travel time to at least a second charging station. In a particular embodiment, the method may also include displaying at least a portion of a route map from the current location to at least a second charging station. In a particular embodiment, the method may also include displaying a second recommendation for at least a second charging station based at least in part on parameters specifically related to at least a second charging station. In a particular embodiment, determining a charging performance model includes assigning at least one weighting factor to one or more features extracted during the charging process. In certain embodiments, at least one assigned weighting factor relates to one or more of: electrical or thermal stress of a battery utilized by the electric vehicle, lifespan of the battery utilized by the electric vehicle, and age of the battery utilized by the electric vehicle.

[0006] Another general aspect includes an apparatus for recommending a charging station, the apparatus comprising one or more processors coupled to at least one memory device, the one or more processors responsive to executing instructions to extract one or more characteristics of an electric current to be coupled to an electric vehicle in response to initiation of a charging process at a first charging station. The one or more processors are further operable to determine one or more performance characteristics of the electric vehicle based at least in part on the one or more characteristics of the electric current coupled to the electric vehicle in response to initiating the charging process at the first charging station. The one or more processors are further operable to determine a charging performance model based at least in part on the determined one or more performance characteristics of the electric vehicle. The one or more processors coupled to at least one memory device may further display, or cause a display device of the electric vehicle to display, a recommendation for at least a second charging station based at least in part on the charging performance model and one or more characteristics of at least a second charging station.

[0007] In a particular embodiment, the one or more features to be extracted from the coupled current include one or more of: an indication of an abnormality of the coupled current, an indication of an abnormality of the voltage of the coupled current, an indication of an abnormality of the power factor of the coupled current, an indication of the noise content of the coupled current, and an indication of the total harmonic distortion of the coupled current. In a particular embodiment, the one or more performance characteristics of the electric vehicle determined include a time period for substantially fully charging the electric vehicle or a measure of electrical or thermal stress on one or more energy storage elements of the electric vehicle. In a particular embodiment, one or more processors coupled to at least one memory device display or cause the display device to display a recommendation for at least a second charging station in addition to displaying or causing the display device to display a predicted time for at least partially charging the electric vehicle via at least a second charging station, and / or display an estimated travel time to at least a second charging station. In a particular embodiment, one or more processors coupled to at least one memory device display or cause the display device to display a recommendation for at least a second charging station in addition to displaying or causing the display device to display a location of at least a second charging station based at least in part on parameters specifically related to at least a second charging station. In certain embodiments, the one or more processors coupled to the at least one memory device additionally determine the charging performance model via assigning at least one weighting factor to one or more features extracted during the process of charging the electric vehicle.

[0008] Another general aspect includes an article of manufacture comprising a non-transitory computer-readable medium having instructions encoded thereon that, in response to execution by a processor coupled to at least one memory device, are operable to extract one or more characteristics of an electric current to be coupled to an electric vehicle in response to initiation of a charging process at a first charging station. The encoded instructions are further operable to determine one or more performance characteristics of the electric vehicle in response to the one or more characteristics of the electric current coupled to the electric vehicle during the charging process at the first charging station. The encoded instructions are further operable to determine a charging performance model based at least in part on the determined one or more performance characteristics of the electric vehicle. The encoded instructions are further operable to display a recommendation for at least a second charging station based at least in part on the charging performance model and one or more characteristics of at least a second charging station.

[0009] In a particular embodiment, the one or more features to be extracted from the coupled current include one or more of the following: an indication of an abnormality of the coupled current. In a particular embodiment, the extracted features may include an indication of an abnormality of the voltage of the coupled current, an indication of an abnormality of the power factor of the coupled current, an indication of the noise content of the coupled current, and an indication of the total harmonic distortion of the coupled current. In a particular embodiment, the one or more performance characteristics of the electric vehicle determined may include one or more of the following: a measure of the time period to substantially fully charge the electric vehicle, a measure of electrical or thermal stress on one or more energy storage elements of the electric vehicle, a measure of the life of one or more energy storage elements of the electric vehicle, and a measure of the age of one or more storage elements of the electric vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The claimed subject matter is particularly pointed out and distinctly claimed in the concluding portion of the specification. However, as to the organization and / or method of operation, together with objects, features, and / or advantages thereof, if used in conjunction with the accompanying Figure 1 The claimed subject matter may be best understood by reference to the following detailed description, read in conjunction with the accompanying drawings:

[0011] Figure 1 is a diagram depicting an electric vehicle module that facilitates an electric vehicle charging process according to an embodiment;

[0012] Figure 2 and Figure 3 is a diagram depicting certain features of voltage / current waveforms encountered at an electric vehicle charging station, according to an embodiment;

[0013] Figure 4 is a diagram of a coupling to a Figure 1 A schematic representation of the performance characterization module's energy storage module and vehicle systems;

[0014] Figure 5 is a diagram depicting portions of an instrument panel display for an electric vehicle according to an embodiment;

[0015] Figure 6 is a diagram depicting certain electric vehicle modules and an electric vehicle station selector involved in a feature extraction process according to an embodiment;

[0016] Figure 7 depicts a flow chart of an example process for feature extraction during the process of charging an electric vehicle according to an embodiment; and

[0017] Figure 8 is a schematic block diagram illustrating an example computing system environment according to an embodiment.

[0018] In the following detailed description, reference is made to the accompanying drawings, which form part of this description, wherein similar reference numerals may refer to corresponding and / or similar similar parts throughout the text. It should be understood that the drawings are not necessarily drawn to scale, such as for the simplicity and / or clarity of illustration. For example, the dimensions of some aspects may be exaggerated relative to other aspects. In addition, it should be understood that other embodiments may be utilized. In addition, without departing from the claimed subject matter, structural and / or other changes may be made. References throughout this specification to "claimed subject matter" refer to the subject matter intended to be covered by one or more claims or any part thereof, and are not necessarily intended to refer to a complete set of claims, a specific combination of claim sets (e.g., method claims, device claims, etc.), or a specific claim. It should also be noted that directions and / or other reference words (e.g., such as upper, lower, top, bottom, etc.) can be used to facilitate discussion of the drawings without intending to limit the application of the claimed subject matter. Therefore, the following specific embodiments should not be considered to limit the claimed subject matter and / or equivalents. DETAILED DESCRIPTION

[0019] Throughout this specification, references to an implementation, an implementation, an embodiment, an embodiment, etc. mean that the specific features, structures, and / or characteristics described in conjunction with a particular implementation and / or embodiment are included in at least one implementation and / or embodiment of the claimed subject matter. Therefore, the appearance of such phrases, for example, in various places throughout this specification is not necessarily intended to refer to the same implementation or to any one of the specific implementations described. In addition, it should be understood that the specific features, structures, and / or characteristics described can be combined in one or more implementations in various ways, and therefore, for example, within the scope of the intended claims. Of course, in general, these and other issues vary with the context. Therefore, the specific context of description and / or use provides guidance on the inferences to be drawn.

[0020] As previously mentioned, an operator of an electric vehicle may charge the energy storage battery of the electric vehicle via a home-based charger that operates to convert ordinary household voltage and current to, for example, a voltage and current suitable for use with an electric vehicle. Some electric vehicle manufacturers may offer different charging voltage options, such as options for charging with a 120 volt power source, a 240 volt power source, or a 480 volt power source. However, when traveling away from a home-based charger, such as when traveling over a relatively long distance, the electric vehicle may be charged at a charging station that may be located along a highway, a freeway, or other type of avenue. In some cases, particularly when traveling over considerable distances, an electric vehicle may need to be charged at several intervals (such as, for example, intervals that may be between about 200 kilometers and about 400 kilometers) throughout the day. Therefore, in response to the need to visit a charging station several times during a day, it may become apparent that electric vehicle charging stations differ in available facilities, amenities, available charging voltages, and overall customer experience.

[0021] Furthermore, while a charging station may advertise certain aspects of the charging station, such as available charging voltage, overvoltage protection, overcurrent protection, etc., such statements may be questionable, unverifiable, outdated, or may be of a general nature that is not representative of the actual parameters of the charging signals provided by the charging station. As a result, an electric vehicle operator may find themselves at an electric vehicle charging station that provides charging services that are not fully compatible with the operator's particular electric vehicle or that operate in a manner that is not optimal with respect to the particular electric vehicle. In such circumstances, for example, the operator of the electric vehicle may wish to cancel the charging operation in favor of driving to a secondary charging location. Furthermore, particularly if the energy storage unit (e.g., battery) of the electric vehicle is in a fairly depleted state, driving the vehicle to a potentially more desirable secondary charging location that may be located several kilometers from the charging station may be a less than ideal option.

[0022] Although electric vehicle user groups may from time to time provide crowdsourced electric vehicle charging station reports, weblogs, ratings, or other forms of feedback, such reports may be general and may not be relevant to certain specific, higher quality electric vehicles. For example, even though the general rating of a particular electric vehicle charging station may be relatively low, a driver of a particular electric vehicle may find that the vehicle performs satisfactorily or even excellently in response to charging from a relatively low-rated charging station. Thus, a driver of a particular electric vehicle need not be prevented from using service from a relatively low-rated charging station despite the relatively low general rating of the electric vehicle charging station.

[0023] In addition, the energy storage devices (e.g., batteries) used by electric vehicles may vary so that some types of energy storage devices may be more tolerant of charging voltage and current anomalies than other types of energy storage devices. Therefore, the general ratings of certain charging stations may come from a user group that uses a specific type of battery technology. For example, users who use a specific type of energy storage device may report that the charging parameters of a specific charging station have a negative impact on the performance of the electric vehicle. However, operators of electric vehicles that include more advanced energy storage devices and / or energy storage management systems may not experience any negative effects when using a specific charging station. Therefore, in many cases, it may be advantageous for the computing device (or other device) of the electric vehicle to extract parameters or characteristics of the current coupled from the charging station to the electric vehicle, or coupled to the charging station. (In this context, the term "coupled to" refers to the current sent from the charging station in the direction of the electric vehicle.) The computing device can monitor or determine the performance characteristics of the electric vehicle in response to the charging process, thereby allowing the computing device to determine or generate a charging performance model, which can include or reflect quality data such as charging quality indicators based on the charging station and / or the electric vehicle. The charging performance model is operable to associate the performance characteristics of the electric vehicle with features extracted from the current coupled to the electric vehicle from the charging station. In this context, the term "extraction" refers to one or more features generated, tracked and / or followed over a period of time by monitoring the current coupled from the charging station. The term "extraction" may also refer to the manner in which the electric vehicle or its appropriate charging system responds to the current waveform coupled from the charging station to the electric vehicle. Therefore, features extracted from the charging current waveform can be used to describe the current or describe how the electric vehicle is being charged. Based on the charging performance model and any history of the performance characteristics of a particular electric vehicle, the computing device may be able to display (or at least initiate or otherwise cause a display) a recommendation as to whether a particular charging station can be expected to perform well after being charged by the particular charging station. For example, the charging performance model can determine the charging duration based on the performance of the charging station, and determine a recommended charging station that can be within the driving range of the electric vehicle and provide faster charging capabilities.

[0024] As the term is used herein, a "charging performance model" refers to a computer model relating to various charging parameters related to performance aspects of an electric vehicle. Thus, for example, a charging performance model may correlate a charging current and / or a charging voltage (which may be coupled to an electric vehicle charging port for a threshold duration) with a particular expected electric vehicle range. In another example, a charging performance model may correlate radiated or conducted electrical noise and distortion present in the charging current with the likelihood that the noise or distortion will produce or generate interference with an infotainment system, an in-vehicle stereo system, or the like. In certain examples, a charging performance model may include quality data, such as a charging quality indicator based on the charging station and / or the electric vehicle.

[0025] Thus, for example, a computing device of an electric vehicle may determine that a charging station may operate satisfactorily with an energy storage unit of the electric vehicle regardless of, for example, the rating of the charging station. Additionally, operators of newer (potentially more advanced) electric vehicles may not experience disadvantages identified by operators of other (potentially less advanced) electric vehicles in response to different energy storage device technologies. Thus, operators of electric vehicles may have access to recommendations for electric vehicle charging stations that may be more accurate with respect to the operator's specific electric vehicle than general recommendations applicable to a population that includes all types of electric vehicles.

[0026] The computing device may guide the driver to a recommended charging station based on parameters from the charging process, a user's request via a navigation system, and / or the age of the electric vehicle. In addition, as the electric vehicle ages, the vehicle may exhibit certain trends in changes in the charging process (such as the state of the battery and / or the charging process) relative to the energy storage device (e.g., battery). For example, based at least in part on the increased internal resistance of the energy storage device of the electric vehicle as the device ages, a computing device coupled to the battery management system may recommend an optimal charging station that can consistently / consistently maintain a (stable) voltage and current waveform without voltage and current sags, voltage and current spikes, distortion of the charging waveform, and other issues. In addition, in view of the increased internal resistance, the computing device of the electric vehicle may recommend that although charging at an increased voltage is possible, such charging may not be recommended for electric vehicles of a certain age because the internal battery resistance may cause excessive thermal and / or electrical stresses within the energy storage device. It will be appreciated that such charging station recommendations may be operable to extend the useful life of the energy storage device by directing the electric vehicle operator to select a charging station that provides the optimal current and voltage waveforms, which may be more suitable for older storage devices.

[0027] Thus, in certain embodiments, the computing device of the electric vehicle may guide the operator of the electric vehicle to a charging station that has been recommended as optimal for a particular electric vehicle. The recommendation of the optimal charging station for use with a particular electric vehicle may be made empirically, such as via real-time current and voltage measurements performed by one or more computing devices of the electric vehicle during past charging processes. Such an indication may appear via an audio message and / or via one or more indicators overlaid on a digitized map presented to the operator of the electric vehicle. Thus, at least in certain embodiments, the electric vehicle operator may ensure that the recommended charging station is at least highly likely to provide the optimal charging current and voltage waveform for the particular electric vehicle. In an example applicable to an aging electric vehicle, one or more processors of the electric vehicle may recommend a charging station based at least in part on the performance of the charging station in providing a lower voltage, lower current charging waveform, which may be more suitable for use with an older onboard energy storage device. Such a recommendation may be inconsistent with a recommendation for a charging station, which may be based on the performance of the charging station in providing a higher voltage or higher current charging waveform. The charging station recommendation may be based on a charging performance model, which may include quality data, such as a charging quality indicator based on the charging station and / or the electric vehicle.

[0028] In some cases, the selection of the best charging station for a particular electric vehicle may be based on the operator's desire to access the charging station and arrive at a particular destination within a specific time period. In one possible example, the operator of the electric vehicle may wish to obtain a charge for the electric vehicle and arrive at the destination within an hour. Therefore, in a particular embodiment, the computing device of the electric vehicle may determine that although the vehicle is close to a first charging station that allows charging at a relatively slow rate, it may be advantageous to travel a certain distance to a second charging station so that the electric vehicle can be charged at a relatively fast rate, thereby ensuring that the operator can charge the vehicle and arrive at the destination within a specified time. In another example, the computing device of the electric vehicle may recommend a charging station that may be suboptimal for a particular electric vehicle but is located on the route to the destination. Therefore, in such a case, the selection of a charging station for a particular electric vehicle may represent a balance between the desire to optimally charge the particular electric vehicle and arriving at the destination within the desired time period.

[0029] In certain embodiments, charging station recommendations for electric vehicles can be implemented by utilizing a charging management system and / or a battery management system. Systems such as battery management systems, which may include battery monitoring devices that have been installed as standard equipment on electric vehicles, can operate via real-time monitoring of charging waveforms. As described below, such real-time monitoring may include monitoring current and / or voltage transients, current and / or voltage sags, current and / or voltage signal amplitudes relative to upper and lower thresholds, noise content of current and / or voltage waveforms, relative phasing of current and / or voltage waveforms (e.g., for AC charging signals), and many other possible measurable anomalies / abnormalities present on charging waveforms. It should be noted that the claimed subject matter is intended to include any and all anomalies / abnormalities on charging waveforms almost without limitation. Also as described below, measurements may be compiled over a period of time to facilitate determining charging trends relative to a specific charging station operating with a specific electric vehicle. In addition, measurements may be uploaded or otherwise communicated to a cloud-based repository, which may include measurements from other potentially similar electric vehicles (e.g., electric vehicles of a specific brand and model) to facilitate customized recommendations for charging stations suitable for specific electric vehicles. The cloud-based repository may utilize various processing methods, such as using neural networks, which may, for example, facilitate machine learning to provide recommendations for optimal charging stations for various electric vehicles.

[0030] In an embodiment, an electric vehicle may include one or more computing devices for controlling various functions on the vehicle. Examples of computing devices (also referred to as controllers) include at least one of the following: electronic control unit (ECU), infotainment system controller (e.g., infotainment host unit), telematics control unit (TCU), central transmission controller (CPC), central driving and charging controller (CDCC), centralized vehicle computer, regional controller, or any other controller. In some cases, the computing device may include one or more processors (also referred to as processing circuits). These processors may include one or more microprocessors, one or more processing cores, programmable logic circuits (PLCs) or programmable logic / gate arrays (PLA / PGA), field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), system on chip (SoC) controllers with one or more chiplets, or any other control circuits.

[0031] In some implementations, a computing device may include or may be in communication with a memory, which may also be referred to as a memory device or a non-transitory computer-readable medium or a non-transitory storage mechanism. Figure 8 Memory devices are discussed in more detail below. In some cases, non-transitory computer-readable media may store computer-executable instructions or computer-readable instructions (also more simply referred to as instructions), such as for executing Figure 7 The method instruction.

[0032] In various embodiments, the terms "computer-readable instructions" and "computer-executable instructions" are used to describe software instructions or computer code that are configured to perform various tasks and operations. In various embodiments, if the computer-readable or computer-executable instructions form a software module, then the term software module broadly refers to a collection of software instructions or codes that are configured to cause a processor of a computing device to perform one or more functional tasks. When a processor or other hardware component is executing the software module or computer-readable instructions, the software module and computer-readable / executable instructions may be described as performing various operations or tasks. When the computing device is part of an electric vehicle, the software module may also be referred to as an electric vehicle software module.

[0033] Figure 1 1 is a diagram depicting electric vehicle software and / or hardware modules that facilitate the electric vehicle charging process according to an embodiment 100. The modules include a conversion module 110, a feature extraction module 125, an energy storage module 115, a performance characterization module 130, and a charging station recommendation module 145. Figure 1 In certain embodiments, charging station 105 represents a source of current (e.g., alternating current, direct current), or may be capable of providing both types of charging current, and the claimed subject matter is not limited in this respect. Charging station 105 may provide the ability to couple current to electric vehicle 102 via conductors that mate with or at least electrically contact corresponding conductors at a socket of electric vehicle 102. In certain embodiments, charging station 105 may provide different voltage levels, such as levels corresponding to 120 volts, 240 volts, 480 volts, or other voltages, which may enable electric vehicle 102 to be charged at a relatively slow rate, a medium rate, or a relatively fast rate. In certain embodiments, charging station 105 may provide charge to electric vehicle 102 for a fee, wherein the fee is assessed based at least in part on the level or value of the provided charging voltage and / or charging current.

[0034] exist Figure 1, the conversion module 110 can obtain power (e.g., Vin, Ln) from the charging station 105. It should be noted that the conversion module 110 is operable to convert a DC signal of a first voltage into a DC signal of a second voltage, or is operable to convert an AC signal of a first voltage into a DC signal of a second voltage, and the claimed subject matter is not limited in this regard. The current conducted from the charging station 105 can be conveyed to the energy storage module 115, which can include any type of energy storage technology. In a specific embodiment, the energy storage module 115 can represent a plurality of cylindrical batteries or a plurality of prismatic batteries, such as batteries including a right-angled prism shape. In a specific embodiment, the energy storage module 115 can incorporate any type of energy storage technology, such as batteries including lithium ion, lead acid, or batteries / chemical energy storage devices including any other materials and / or compounds, and the claimed subject matter is not limited in this regard. In certain embodiments, the energy storage device 115 may also include a low voltage storage device, such as an energy storage unit that provides a voltage output signal between about 10 volts and about 14 volts (e.g., a nominal 12 volt output), to name a few non-limiting examples. In certain embodiments, the energy storage device 115 may additionally include a higher voltage storage device, such as an energy storage unit that provides an output voltage between about 12 volts and about 60 volts (e.g., a nominal 36 volts or 48 volts), to name a few non-limiting examples.

[0035] The energy storage module 115 can provide Figure 1 The vehicle system 120 in FIG. 1 provides main power to various electric vehicle systems represented by the vehicle system 120. The vehicle system may include a drive system, an infotainment system, a lighting system, a battery management system, an anti-lock braking system, an autonomous driving system, sensors and measurement equipment, a heating, ventilation and air conditioning system, etc. These systems may be controlled by one or more computing devices described above, wherein one or more computing devices may be a controller embedded in the electric vehicle 102. In some specific implementations, one or more computing devices may also execute and / or control Figure 1 Various modules in, such as feature extraction module 125 or performance characterization module 130. Figure 1 Although not explicitly shown in FIG. 1 , the vehicle system 120 may additionally include a power conditioning module, such as a DC to AC conversion module, which may be used to drive various machines on the electric vehicle 102, such as a coolant pump, a fan, an air conditioning compressor, etc. The feature extraction module 125 may sample the voltage and current inputs coupled to the conversion module 110. Such sampling may determine various parameters of the voltage and current input signals to the conversion module 110, which may be referenced to Figures 2 to 4 Describe in more detail.

[0036] The performance characterization module 130 is operable to characterize performance parameters of various vehicle systems and components (such as those corresponding to the vehicle system 120) in response to the charging of the electric vehicle 102. Thus, for example, the performance characterization module 130 can characterize the performance of the electric vehicle 102, such as the range performance of one or more drive systems and the battery charge level as a function of time. The performance characterization module 130 may additionally receive signals from the energy storage module 115, such as during the process of charging one or more energy storage modules. Thus, for example, in response to a charging process that causes thermal stress (e.g., an increase in battery temperature), the performance characterization module 130 may record the occurrence of such an event. The performance characterization module 130 may additionally record the occurrence of unstable or abnormal operation of one or more other components of the vehicle system 120, which may occur in response to anomalies / abnormalities in the charging signals (e.g., Vin, Ln) from the charging station 105.

[0037] The performance characterization module 130 may communicate with a charging performance model 140 that is operable to construct a computer-implemented model of the electric vehicle 102. In this context, a computer-implemented "model" refers to an arrangement of parameters, such as encoded within a two-dimensional array of a computer memory device, that describes the performance of a charging operation relative to a charging waveform and / or the response of the electric vehicle to the charging waveform. Thus, for example, a computer-implemented model may describe a relationship or correlation between a first grouping of one or more parameters of a current and / or voltage waveform coupled from a charging device to the electric vehicle and, for example, charging performance and / or battery performance, which may include battery electrical / thermal stress in response to the charging waveform. In another example, a computer-implemented model may describe overall vehicle performance characteristics, such as driving range associated with a first grouping of one or more parameters of a current and voltage waveform coupled from a charging device to the electric vehicle or the first grouping.

[0038] In certain embodiments, the computer-implemented model may include input parameters, such as features extracted from the charging signals Vin, Ln, which may generate output parameters, such as indicators of battery electrical / thermal stress, vehicle range, indicators of unstable / abnormal operation of one or more of the vehicle systems 120, etc. For example, the charging performance model may indicate that a particular electric vehicle charged with a relatively low voltage (e.g., about 120 volts) produces a vehicle range of 300 kilometers, while using a slightly higher voltage (e.g., about 240 volts) produces a vehicle range of 285 kilometers. The charging performance model 140 may determine or indicate quality data such as charging quality indicators based on the charging station 105 and / or the electric vehicle 102. Based at least in part on the charging performance model 140, a charging station recommendation 145 may be determined by the computing device, which may cause the recommendation 145 to be displayed (e.g., via the display 150). The charging station recommendation may include an alphanumeric message and / or a graphic and / or other content, such as through a vehicle display, to indicate a nearby charging station capable of providing a 120 volt charging signal.

[0039] In some implementations, a computing device in an electric vehicle may extract features of the current by monitoring the current waveform of the electric vehicle in response to the vehicle being charged at a charging station. For example, the computing device may execute a feature extraction module to extract features of the current. Figure 2 and Figure 3 is a diagram depicting certain features of the current waveform encountered at an electric vehicle charging station according to an embodiment. It should be noted that although Figure 2 A conduction current signal is depicted, but a voltage signal may be substituted for the conduction current signal, and the claimed subject matter is intended to cover anomalies and other types of characteristics of the conduction current signal or the applied voltage signal from the charging station 105. Figure 2 In the embodiment 200 of FIG. 1 , the charging signal Ln is shown as at time t o The current peak 203 may represent the current level at which the activation ( Figure 1 However, it will be appreciated that a large surge current may cause damage to the conversion module 110 and / or the energy storage module 115. After rising to the current peak 203, the conduction current may decrease to a more nominal / stable level, such as by Figure 2 t in 1 Given at.

[0040] In t 1 With t 2During the time between the charging station 105 and the current peak 203, the current conducted from the charging station 105 to the electric vehicle 102 may experience a momentary dip / sag 204, followed by a second current peak 205. Although the second current peak 205 may not include an amplitude equivalent to the amplitude of the current peak 203, the second current peak may still cause damage to the electronic components and systems of the electric vehicle 102. After returning to the nominal current level, the current conducted from the charging station 105 may include a current spike 210. Figure 2 In an embodiment of the present invention, the current spike 210 that may occur at b may be caused by an anomaly of the charging station 105, such as a relatively sudden decrease in current conducted by other charging stations of the electric vehicle charging facility.

[0041] Likewise Figure 2 As shown, from time t 4 To time t 5 , the conducted current may include noise signal 215. Noise signal 215 may be generated by insufficient and / or inadequate filtering of the DC signal provided by charging station 105. In certain embodiments, noise signal 215, if of sufficient amplitude, may couple into precision electronic systems in the form of radiated or conducted interference signals.

[0042] Figure 3 3 is a diagram depicting certain features of an alternating current waveform supplied by a charging station to an electric vehicle charging station according to an embodiment 300. Figure 3 As shown, the charging station can supply a voltage waveform as indicated by Vin and a current waveform as indicated by Ln. It will be appreciated that for a substantially resistive load, the voltage waveform Vin and the current waveform Ln can be in phase with each other. However, in response to the electric vehicle 102 including an inductive mode, and / or in response to an abnormality of the charging station 105, the voltage waveform Vin and the current waveform Ln can be out of phase with each other. In such cases, it will be appreciated that the power coupled from the charging station 105 to the electric vehicle 102 may not represent the maximum power transfer (or at least the power transfer above the threshold) from the charging station 105 to the electric vehicle 102. In a particular embodiment, the voltage waveform Vin and the current waveform Ln can be measured by the time difference (t o to 1 ) (such as in seconds or in phase angles). In certain embodiments, the out-of-phase current and voltage signals can help reduce the power factor of the charging signal coupled to the electric vehicle 102. Figure 3 It is not explicitly shown in FIG. 1 , but the voltage waveform Vin and the current waveform Ln may additionally include a noise signal component, which may include, for example, harmonics of the voltage waveform Vin.

[0043] Thus, in certain embodiments, Figure 2 and Figure 3 As indicated in , the feature extraction module 125 is operable to detect undercurrent (i.e., a conduction current less than a predetermined threshold), overcurrent (i.e., a conduction current greater than a predetermined threshold), undervoltage (i.e., an applied voltage less than a predetermined threshold), and overvoltage (i.e., an applied voltage greater than a predetermined threshold) in the charging signal from the charging station. The feature extraction module 125 is additionally operable to detect voltage sags (i.e., a momentary significant drop in voltage) in the charging signal from the charging station and is operable to detect current sags (i.e., a momentary significant drop in current) therein. The feature extraction module 125 is additionally operable to detect voltage spikes / surges (e.g., a momentary significant increase in voltage) in the charging signal from the charging station and is operable to detect current spikes (i.e., a momentary significant increase in current). The feature extraction module 125 is additionally operable to detect noise signals coupled onto the charging signal from the charging station. The feature extraction module 125 is additionally operable to detect voltage / current waveform distortions, voltage / current waveform out-of-phase conditions, voltage / current waveform noise content, and voltage / current undervoltage / overvoltage in the AC signal. The feature extraction module 125 may be operable to detect additional features of the AC current signal or the DC current signal coupled from the charging station to the electric vehicle 102, and the claimed subject matter is not limited in this respect. The feature extraction module 125 may assign weighting factors that may facilitate the emphasis of certain parameters relative to other parameters. For example, electrical or thermal stress on one or more energy storage devices (e.g., batteries) may be assigned a higher weight than mileage because, for example, excessive electrical or thermal stress may result in permanent damage to the energy storage module. In contrast, a small decrease in mileage (which may be assigned a lower weight) may merely represent an inconvenience.

[0044] Figure 4 is a diagram illustrating a method of coupling to a Figure 1 1 and 120. Figure 4 As depicted in FIG. 1 , a characteristic associated with the energy storage device 115 may be operated as an input signal transmitted to the performance characterization module 130, which characteristic may convey a parameter indicating, for example, battery thermal stress, battery electrical stress, or a parameter indicating other potentially damaging stresses. Figure 4 As depicted in FIG. 1 , the performance characterization module 130 may utilize characteristics associated with the vehicle system 120, such as a driving range achieved based at least in part on a particular charging operation, a duration or time period required to perform the charging process, and any announcements / alerts provided by one or more computing devices of the electric vehicle 102 during the charging process, to determine ( Figure 1 The performance characterization module 130 may determine one or more performance characteristics of the electric vehicle based at least in part on the one or more extracted features of the current.

[0045] Figure 5 is a diagram depicting portions of a dashboard display for an electric vehicle according to embodiment 500. Figure 5 In an embodiment of the present invention, the dashboard 505 may include various instruments and gauges, such as a speedometer, a tachometer, warning indicators, etc., and a multi-function display 510. The multi-function display 510 may display a digitized map, which may indicate, among other things, charging stations (such as Figure 1 The displayed location of the charging station 105 may correspond to the best charging station for a particular electric vehicle. Figure 5 As shown, a particular charging station 105 (e.g., a "Main Street EV Charger") may include a general rating, which may include a rating achieved via feedback from operators of various types of electric vehicles. Figure 5 As shown, such a general rating includes 3 stars (e.g., general rating: ***), which may correspond to a slightly above average rating. Figure 5 As shown, for a particular electric vehicle such as electric vehicle 102, charging station 105 may include a rating of 5 stars (e.g., Rating for this car: *****), which may indicate that the charger at the location corresponding to the Main Street EV charger may perform well above average when operating with the particular electric vehicle 102. For example, the rating of electric vehicle 102 may be based on charging quality indications from charging quality data collected from the electric vehicle and / or the operator. Such operation may include factors such as increased driving range, minimal thermal and / or electrical stress on the energy storage device, a relatively short period of time to obtain a full charge, and potentially numerous other factors. Similarly, as Figure 5 As shown, display 510 may display the predicted charging time. Display 510 may additionally display the predicted travel time to the selected charging station.

[0046] In an implementation scheme, Figure 1 The charging station recommendation module 145 may be configured to generate recommendations for charging stations, as discussed above. Figure 6 6 shows an example in which the charging station recommendation module 145 is implemented as an electric vehicle station selector 630. More specifically, Figure 6 6 is a diagram depicting certain electric vehicle modules and an electric vehicle station selector 630 involved in the feature extraction process according to an embodiment 600. Figure 6 In the embodiment shown in FIG. 1 , the feature extraction module 125 can communicate with the telematics module 605 to wirelessly transmit features extracted from the charging current / voltage signal coupled from the charging station 105 to the electric vehicle 102. In certain embodiments, such communications may include parameters of the DC current and / or AC current waveforms, such as those described herein with reference to FIG. Figures 2 to 4 Telematics module 605 is operable to communicate other parameters related to electric vehicle 102, such as current location, charging status, vehicle operating parameters, and the claimed subject matter is intended to cover all such additional parameters.

[0047] Wireless communication signals may be received via a wireless transceiver 610, which may represent a cellular base station of a cellular network. Examples of cellular network technologies that may facilitate a wireless communication link between the electric vehicle 102 and the wireless transceiver 610 may include GSM, Code Division Multiple Access (CDMA), Wideband CDMA (WCDMA), Long Term Evolution (LTE), High Rate Packet Data (HRPD). GSM, WCDMA, and 5G. Alternatively, the wireless transceiver 610 may represent a Wireless Fidelity (Wi-Fi) access point, a femtocell, a home base station, a small cell base station, a Home Node B (HNB), or a Home eNodeB (HeNB), which may provide access to, for example, a wireless local area network (WLAN, such as an IEEE 802.11 network), a wireless personal area network (WPAN, such as network), and the claimed subject matter is not limited in this respect.

[0048] The wireless transceiver 610 may communicate with an electric vehicle station selector 630, such as through a network 620, which may include a computing platform such as a server, or any other type of computer including at least one processor coupled to one or more memory devices. The network 620 may include any number of sub-networks, a localized Ethernet, or may include any other networking technology now developed or to be developed in the future.

[0049] In certain embodiments, the electric vehicle station selector 630 may be configured to generate charging station recommendations via machine learning operations. For example, the selector 630 may include, for example, a neural network that may perform one or more machine learning processes as represented by the machine learning module 634. In some implementations, the neural network may be a convolutional neural network including convolutional layers (such as 5 convolutional layers) and fully connected layers (such as 3 fully connected layers) and rectification layers. The neural network may be operable to provide a heuristic that may be operable to utilize historical examples of charging station parameters and their effects on the charging performance model 140 of vehicles that are at least similar to the electric vehicle 102 in some respects. The neural network of the electric vehicle station selector 630 may be operable to receive parameters from the history module 632 and expand such parameters to apply to the electric vehicle 102.

[0050] The electric vehicle station selector 630 may additionally include a location coordinate module 636 that is operable to obtain location coordinates (such as positioning signals) from GPS satellites and / or from elements of a cellular communication infrastructure (such as a wireless transceiver 610). Based at least in part on such positioning signals and based on input signals from the machine learning module 634, the electric vehicle station solver 638 may determine an optimal charging location for the electric vehicle 102, which may include a charging quality indicator based on one or more characteristics of the charging station and / or the electric vehicle. The determination of the optimal charging location for a particular electric vehicle may be based on the need and / or desire of the electric vehicle operator to visit the charging station and arrive at a particular destination within a predetermined time period. Thus, in a particular embodiment, the machine learning module 634 of the electric vehicle station selector 630 may determine that although the vehicle is close to a first charging station that allows charging at a relatively slow rate, it may be advantageous to travel a certain distance to a second charging station so that the electric vehicle 102 can be charged at a relatively faster rate, thereby ensuring that the operator can charge the electric vehicle 102 and arrive at the destination within the specified time. In another example, the computing device of the electric vehicle 102 may recommend a charging station that may be suboptimal for the particular electric vehicle but is located on the route to the destination. Thus, in such a case, the electric vehicle station solver 638 may select a charging station for the particular electric vehicle 102 that represents a balance between the desire to optimally charge the particular electric vehicle and to reach the destination within a desired time period.

[0051] Figure 7 A flow chart of an example process for feature extraction during the process of charging an electric vehicle is depicted according to embodiment 700. It should be noted that the claimed subject matter is intended to encompass all of the actions depicted at 705-720, fewer actions than those depicted at 705-720, and / or more actions than those depicted at 705-720. In some cases, the method may be performed by a computing device or other apparatus, such as when a processor of a computing device executes instructions stored on a non-transitory computer-readable medium. Figure 7 Embodiments of the present invention may begin at 705, which may include extracting one or more characteristics of a current coupled to an electric vehicle in response to the initiation of a charging process at a first charging station. The characteristics of the current may include characteristics of the conducted current and / or the applied voltage. The characteristics of the conducted DC current may include, for example, current sags, current spikes, current noise and distortion, and, as described herein, such as relative to Figures 2 to 4 Other anomalies / abnormalities described herein. Characteristics of the applied voltage may include, for example, voltage sags, voltage spikes, voltage noise and distortion, as well as, for example, voltage fluctuations relative to Figures 2 to 4Other anomalies described. Characteristics of the conducted alternating current may include a phase lag of the current waveform relative to the voltage waveform or a phase lag of the voltage waveform relative to the current waveform. Other characteristics of the conducted alternating current may include harmonic distortion or any other current anomalies, and the claimed subject matter is not limited in this respect.

[0052] Figure 7 The method may continue at 710, which may include the computing device determining, in response to initiating a charging process at the first charging station, one or more performance characteristics of the electric vehicle based at least in part on one or more characteristics of the electrical current coupled to the electric vehicle, such as those determined at 705. In certain embodiments, the performance characteristics may include, for example, an electric vehicle range achieved in response to the charging process, battery thermal and / or electrical stress in response to battery charging, anomalies in the operation of an infotainment system, a navigation system, or any other vehicle system.

[0053] The method may continue at 715, which may include determining a charging performance model based at least in part on one or more determined performance characteristics of the electric vehicle, such as those determined at 710. In certain embodiments, the charging performance model may include input signal descriptions that may correspond to features extracted from the charging current / voltage and output signal descriptions that may correspond to operating characteristics of a particular electric vehicle and / or its component vehicle systems. For example, the charging performance model may include quality data, such as charging quality indicators based on the charging station and / or the electric vehicle.

[0054] The method may continue at 720, which may include displaying, or causing a display device to display, a recommendation for at least a second charging station. The recommendation may be generated (e.g., by a computing device) based at least in part on the charging performance model and one or more characteristics of at least the second charging station. In certain embodiments, 720 may include recommending a charging station that may include a relatively low general rating as assigned by a general population of electric vehicle operators, such as Figure 5 As discussed previously herein, a charging station with a relatively low rating for some electric vehicles may operate satisfactorily for other, potentially higher quality electric vehicles. Also as discussed previously herein, a charging station with a relatively high rating for some electric vehicles may cause battery electrical and / or thermal stress when used to charge an older electric vehicle that may have a higher internal battery resistance. Thus, despite the charging station having a relatively high rating, the charging station may not be fully suitable for certain (e.g., older) electric vehicles.

[0055] Figure 8 is a schematic block diagram illustrating an example computing system environment according to an embodiment. Figure 8 is a diagram illustrating a computing environment according to implementation 800 . Figure 8 An embodiment may correspond to performing as relative to Figure 1 The functional computing environment of the feature extraction module 125, the performance characterization module 130, the charging performance model 140 and the charging station recommendation 145. Figure 8 In an embodiment of the present invention, the first device 802 and the third device 806 may be capable of presenting a graphical user interface (GUI) for displaying recommendations for charging stations. Figure 8 , computing device 802 ( Figure 8 The "first device" in the example may be connected to the computing device 804 ( Figure 8 In the embodiment, the computing device 804 may also include, for example, features of a client computing device and / or a server computing device. The processor (e.g., processing device) 820 and memory 822 (which may include main memory 825 and auxiliary memory 826) may communicate, for example, via a communication interface 830 and / or an input / output module 832. The term "computing device" or "computing resource" in this patent application refers to a system and / or device, such as a computing device, that includes the ability to process (e.g., perform calculations) and / or store digital content (such as electronic files, electronic documents, measurements, text, images, video, audio, etc.) in the form of signals and / or states. Therefore, in the context or environment of the present patent application, a computing device may include hardware, software, firmware, or any combination thereof (except the software itself). As Figure 8 The computing device 804 depicted in FIG. 8 is merely an example, and the scope of claimed subject matter is not limited to this particular example.

[0056] exist Figure 8 8, computing device 802 may provide one or more executable computer instruction sources, for example, in the form of physical states and / or signals (e.g., stored in memory states). Computing device 802 may communicate with computing device 804 via a wired or wireless network connection, such as, for example, via network 808. As previously mentioned, the connection, while physical, may be virtual and need not be tangible. Figure 8 The computing device 804 of illustrative embodiments shows various tangible physical components, but the claimed subject matter is not limited to computing devices having only these tangible components, as other implementations and / or embodiments may include, for example, alternative arrangements that function differently in achieving similar results, which may include additional tangible components or fewer tangible components. Rather, the examples are provided merely as illustrations. It is not intended that the scope of the claimed subject matter be limited to the illustrative examples.

[0057] The memory 822 may include any non-transitory storage mechanism. The memory 822 may include, for example, a main memory 825 and an auxiliary memory 826, and additional memory circuits, mechanisms, or combinations thereof may be used. The memory 822 may include, for example, random access memory, read-only memory, etc., such as in the form of one or more storage devices and / or systems, such as, for example, a disk drive including an optical drive, a tape drive, a solid-state memory drive, etc. (just to name a few examples). Examples of memory include a hard disk drive (HDD), a solid-state drive (SOD) or a solid-state integrated memory, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a dynamic random access memory (DRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), and / or a memory stick.

[0058] The memory 822 may include one or more articles of manufacture for storing a program of executable computer instructions. For example, the processor 820 may retrieve executable instructions from the memory and continue to execute the retrieved instructions. The memory 822 may also include a memory controller for accessing a device-readable medium 840, which may carry digital content and / or make digital content accessible, which may include, for example, codes and / or instructions that can be executed by the processor 820 and / or some other device (such as a controller, as an example) that can execute, for example, computer instructions. Under the direction of the processor 820, a non-transitory memory (such as a memory unit storing a physical state (e.g., a memory state)) including, for example, a program of executable computer instructions may be executed by the processor 820 and may be capable of generating a signal to be communicated via, for example, a network, as previously described. The generated signal may also be stored in the memory, also as previously mentioned.

[0059] The memory 822 may store electronic files and / or electronic documents, such as those related to one or more users, and may also include machine-readable media that may carry content and / or make content accessible, including, for example, code and / or instructions that may be executed by the processor 820 and / or some other device (such as a controller, as an example) that is capable of executing, for example, computer instructions. As previously mentioned, the term electronic file and / or the term electronic document are used throughout the document to refer to a group of stored memory states and / or a group of physical signals that are associated in some way so as to form an electronic file and / or electronic document. That is, it is not meant to implicitly refer to, for example, a specific syntax, format, and / or method used with respect to a group of associated memory states and / or a group of associated physical signals. It should also be noted that the association of memory states may be, for example, in a logical sense, and not necessarily in a tangible, physical sense. Therefore, in a preferred embodiment, although the signal and / or state components of an electronic file and / or electronic document are logically associated, their storage may reside, for example, in one or more different locations in a tangible physical memory.

[0060] In an implementation scheme, Figure 8 The example devices in may include features of, for example, a client computing device and / or a remote / server computing device. It should also be noted that the term computing device generally refers to at least a processor and memory connected by the communication bus 815, whether used as a client and / or server or otherwise. For example, "processor" is understood to refer to a specific structure, such as a central processing unit (CPU) of a computing device that may include a control unit and an execution unit, and / or any other examples discussed above (e.g., SoC, ASIC). In one aspect, a processor may include a device that interprets and executes instructions to process input signals to provide output signals. Thus, at least in the context of this patent application, computing device and / or processor is understood to refer to sufficient structure within the meaning of 35 USC § 112(f) such that 35 USC § 112(f) is specifically intended not to be implied through use of the terms "computing device," "processor," and / or similar terms; however, if, for some reason that is not immediately obvious, it is determined that the foregoing understanding does not hold, and therefore 35 USC § 112(f) is necessarily implied through use of the terms "computing device," "processor," and / or similar terms, then, under that statutory section, it is intended that the corresponding structure, material, and / or actions for performing one or more functions be understood and interpreted as being intended to be at least in part to be implied. Figures 1 to 7 Neutralization is described in the text associated with the aforementioned figures of this patent application.

[0061] In some implementations, obtaining measurements from a sensor and / or measurement device may include obtaining a particular set of parameters based on one or more specified parameters may include, for example, sorting a particular set of data elements relative to a distance from a specified point along a specified axis and / or trajectory within a specific spatial coordinate system. Additionally, in implementations, obtaining output signals from a sensor and / or measurement device may include selecting a particular set of data elements based at least in part on a specified range of distances from a specified point along a specified axis and / or trajectory within a specific spatial coordinate system.

[0062] In addition, for example, obtaining output signals from sensors and / or measuring devices based on one or more specified parameters may include grouping individual parameters of a particular set of data elements into multiple subsets based at least in part on specific individual parameters describing or characterizing the signal from the charging station. In addition, in specific implementations, processing of output signals from sensors and / or measuring devices may include, for example, sorting multiple subsets relative to the distance of corresponding grid cells from a specified point along a specified axis and / or trajectory within a specific spatial coordinate system. In specific implementations, obtaining output signals from sensors and / or measuring devices may include selecting specific data elements based at least in part on a specified time period, and may also include, for example, sorting a particular set of data elements relative to temporal proximity to a specified point in time.

[0063] In the context of this patent application, the term "connection", the term "component" and / or similar terms are intended to be physical, but not necessarily always tangible. Therefore, whether or not these terms refer to a tangible subject matter can vary in a specific context of use. As an example, a tangible connection and / or a tangible connection path can be formed, such as by a tangible electrical connection (such as a conductive path including a metal or other conductor) that can conduct current between two tangible components. Similarly, the tangible connection path can be at least partially influenced and / or controlled so that, as is typical, sometimes due to the influence of one or more externally derived signals (such as external currents and / or voltages of an electrical switch), the tangible connection path can be opened or closed. Non-limiting descriptions of electrical switches include transistors, diodes, etc. However, in a specific context of use, "connection" and / or "component" can also be non-tangible, such as a connection between a client and a server through a network (particularly a wireless network), which generally refers to the ability of the client and server to send, receive and / or exchange communications, as discussed in more detail later.

[0064] Therefore, in a specific context of use (such as a specific context of discussing tangible components), the terms "coupled" and "connected" are used in a way that makes these terms non-meaningful. Similar terms can also be used in a way that shows similar intentions. Therefore, "connected" is used to indicate that, for example, two or more tangible components, etc., are in direct physical contact in a tangible manner. Therefore, using the previous example, two tangible components that are electrically connected are physically connected via a tangible electrical connection, as previously discussed. However, "coupled" is used to mean that potentially two or more tangible components are in direct physical contact in a tangible manner. However, "coupled" is also used to mean that two or more tangible components, etc. are not necessarily in direct physical contact in a tangible manner, but can collaborate, contact and / or interact, for example, through "optical coupling". Similarly, the term "coupled" is also understood to mean an indirect connection. Further note that in the context of the present patent application, since the memory (such as a memory component and / or a memory state) is intended to be non-transient, the term physical (at least in the case of memory use) necessarily implies that such memory components and / or memory states (continuing this example) are tangible.

[0065] Unless otherwise indicated, in the context of this patent application, the term "or" (when used in an associative list such as A, B, or C) is intended to mean "A, B, and C" (used herein in an inclusive sense), as well as "A, B, or C" (used herein in an exclusive sense). With this understanding, "and" is used in an inclusive sense and is intended to mean A, B, and C; while "and / or" may be used with caution to make it clear that all of the foregoing meanings are intended, although such usage is not required. In addition, the terms "one or more" and / or similar terms are used to describe any feature, structure, characteristic, etc. in the singular, and "and / or" is also used to describe multiple features, structures, characteristics, etc. and / or some other combination of features, structures, characteristics. Similarly, the terms "based on" and / or similar terms are understood to not necessarily be intended to convey an exhaustive list of factors, but rather allow for the presence of additional factors that are not necessarily explicitly described.

[0066] In addition, for situations involving specific implementations of the claimed subject matter and subject to testing, measurement, and / or specification of degree, it is intended to understand the specific situation in the following manner. As an example, in a given case, it is assumed that the value of a physical property is to be measured. If, at least for specific implementation purposes, a person of ordinary skill in the art reasonably might think of alternative reasonable methods for testing, measuring, and / or specification of degree (at least relative to the property) (continuing this example), the claimed subject matter is intended to cover those alternative reasonable methods, unless otherwise expressly indicated. As an example, if a drawing of measurements on an area is generated and the specific implementation of the claimed subject matter refers to the measurement of the slope on the area, but there are various reasonable and alternative techniques for estimating the slope on the area, the claimed subject matter is intended to cover those reasonable alternative techniques, unless otherwise expressly indicated.

[0067] To the extent that claimed subject matter relates to one or more specific measurements, such as with respect to physical manifestations capable of being physically measured, such as, but not limited to, temperature, pressure, voltage, current, electromagnetic radiation, etc., it is believed that claimed subject matter does not fall within the abstract concept judicial exception to statutory subject matter. In contrast, it is asserted that physical measurements are not mental steps and are likewise not abstract concepts.

[0068] However, it is noted that the typical measurement model employed is that one or more measurements may each include the sum of at least two components. Thus, for a given measurement, for example, one component may include a deterministic component, which in an ideal sense may include a physical value (e.g., found via one or more measurements) often in the form of one or more signals, signal samples, and / or states, and one component may include a stochastic component, which may have various sources that may be difficult to quantify. Sometimes, for example, lack of measurement precision may affect a given measurement. Thus, for the claimed subject matter, in addition to deterministic models, statistical or stochastic models may also be used as methods for identifying and / or predicting one or more measurement values ​​that may be related to the claimed subject matter.

[0069] For example, a relatively large number of measurements may be collected to better estimate the deterministic component. Likewise, if the measurements vary (which may typically occur), it is possible that some portion of the variance may be interpreted as the deterministic component, while some portion of the variance may be interpreted as the random component. Typically, if feasible, it is desirable that the random variance associated with the measurements is relatively small. That is, typically, it may be preferred to be able to account for a reasonable portion of the measurement variation in a deterministic manner rather than in a random manner, as an aid to identification and / or predictability.

[0070] As above relative to Figure 6As discussed, a wireless network may be used to transmit information such as features extracted by a feature extraction module. A network may include two or more devices such as a network device and / or a computing device, and / or may couple devices such as a network device and / or a computing device, such that signal communications such as in the form of signal packets and / or signal frames (e.g., including one or more signal samples) may be exchanged between, for example, server devices and / or client devices and other types of devices, including, for example, between wired and / or wireless devices coupled via a wired and / or wireless network.

[0071] In the context of this patent application, the term network device refers to any device capable of communicating via a network and / or as part of a network, and may include a computing device. Although network devices may be capable of transmitting signals (e.g., signal packets and / or frames) such as via a wired and / or wireless network, they may also be capable of performing operations associated with computing devices, such as arithmetic and / or logical operations, processing and / or storage operations (e.g., storing signal samples), such as in a memory as a tangible physical memory state, and / or may be, for example, in various embodiments, operated as a server device and / or client device. As an example, a network device capable of operating as a server device, a client device, and / or in other ways may include a dedicated rack server, a desktop computer, a laptop computer, a set-top box, a tablet computer, a netbook, a smart phone, a wearable device, an integrated device combining two or more features of the aforementioned devices, etc., or any combination thereof. As mentioned, for example, signal packets and / or frames may be exchanged between server devices and / or client devices and other types of devices, including, for example, exchanged between wired and / or wireless devices coupled via a wired and / or wireless network, or any combination thereof. It is noted that the terms server, server device, server computing device, server computing platform, and / or similar terms may be used interchangeably. Similarly, the terms "client," "client device," "client computing device," "client computing platform," and / or similar terms may also be used interchangeably. Although in some cases, for ease of description, these terms may be used in the singular, such as by reference to a "client device" or a "server device," the description is intended to cover one or more client devices and / or one or more server devices, as the case may be. Similarly, references to a "database" are understood to mean one or more databases and / or portions thereof, as the case may be.

[0072] The computing and / or network device may include and / or may execute various currently known and / or to be developed operating systems, derivatives and / or versions thereof, including computer operating systems, mobile operating systems, etc. The computing and / or network device may include and / or may execute various possible applications, such as client software applications that enable communication with other devices. For example, one or more messages (e.g., content) may be transmitted, such as via one or more protocols (now known and / or later to be developed, suitable for the transmission of email, short message service (SMS), and / or multimedia message service (MMS)), including via a network (such as a social network) formed at least in part by a portion of the computing and / or communication network. The computing and / or network device may also include executable computer instructions for processing and / or transmitting digital content (such as, for example, text content, digital multimedia content, etc.). The computing and / or network device may also include executable computer instructions for performing various possible tasks, such as operating a vehicle, browsing, searching, playing various forms of digital content (including locally stored and / or streamed videos), and / or games. The foregoing is provided merely to illustrate that claimed subject matter is intended to include a wide range of possible features and / or capabilities.

[0073] In the foregoing description, various aspects of the claimed subject matter have been described. For the purpose of explanation, details such as amounts, systems and / or configurations are set forth as examples. In other cases, well-known features are omitted and / or simplified so as not to confuse the claimed subject matter. Although certain features have been illustrated and / or described herein, many modifications, substitutions, changes and / or equivalents will now occur to those skilled in the art. Therefore, it should be understood that the appended claims are intended to cover all modifications and / or changes that fall within the claimed subject matter.

Claims

1. A method, include: extracting one or more characteristics of an electrical current coupled to the electric vehicle in response to initiating a charging process at a first charging station; In response to initiating the charging process at the first charging station, determining one or more performance characteristics of the electric vehicle based at least in part on one or more characteristics of the electrical current coupled to the electric vehicle; determining a charging performance model based at least in part on the determined one or more performance characteristics of the electric vehicle; as well as A recommendation for the at least second charging station is displayed or caused to be displayed on a display device of the electric vehicle based at least in part on the charging performance model and one or more characteristics of the at least second charging station.

2. The method according to claim 1, in, Extracting one or more characteristics of the current includes detecting at least one of: an indication of a coupled current anomaly, an indication of a voltage anomaly, an indication of a power factor anomaly, an indication of noise content of the coupled current, or an indication of total harmonic distortion of the coupled current.

3. The method according to claim 1, in, Determining one or more performance characteristics of the electric vehicle includes determining a period of time required to substantially fully charge the electric vehicle.

4. The method according to claim 1, in, Determining one or more performance characteristics of the electric vehicle includes determining a measure of electrical or thermal stress on one or more energy storage elements of the electric vehicle.

5. The method according to claim 1, in, Displaying the recommendation for at least a second charging station is further based on calculating a predicted time to at least partially charge the electric vehicle via the at least second charging station.

6. The method according to claim 1, in, Displaying the recommendation for the at least second charging station is further based on calculating an estimated travel time to the at least second charging station. 7 . The method of claim 6 , further comprising displaying at least a portion of a route map from a current location to the at least second charging station. 8 . The method of claim 1 , further comprising displaying a second recommendation for the at least second charging station based at least in part on parameters specifically related to the at least second charging station.

9. The method according to claim 1, in, Determining the charging performance model includes assigning at least one weighting factor to the one or more features extracted during the charging process.

10. The method according to claim 9, in, At least one assigned weighting factor relates to one or more of the following: electrical stress or thermal stress of a battery used by the electric vehicle, life span of the battery used by the electric vehicle, and age of the battery used by the electric vehicle.

11. A device for recommending charging stations, include: at least one memory device having instructions encoded thereon; one or more processors coupled to at least one memory device, the one or more processors, in response to executing the instructions: extracting one or more characteristics of an electric current to be coupled to the electric vehicle in response to initiation of a charging process at a first charging station; In response to initiating the charging process at the first charging station, determining one or more performance characteristics of the electric vehicle based at least in part on one or more characteristics of the electrical current coupled to the electric vehicle; determining a charging performance model based at least in part on the determined one or more performance characteristics of the electric vehicle; as well as A recommendation for at least a second charging station is displayed, or caused to be displayed, on a display device of the electric vehicle based at least in part on the charging performance model and the one or more characteristics of at least a second charging station.

12. The device according to claim 11, in, The one or more features to be extracted from the coupled current include one or more of the following: Indication of abnormalities in coupling current; Indication of abnormalities in the voltage of the coupled current; Indication of abnormalities in the power factor of the coupled current; An indication of the noise content of the coupled current; as well as An indication of the total harmonic distortion of the coupled current.

13. The device according to claim 11, in, The one or more performance characteristics of the electric vehicle determined include a period of time required to substantially fully charge the electric vehicle or a measure of electrical or thermal stress on one or more energy storage elements of the electric vehicle.

14. The device according to claim 11, in, The one or more processors coupled to the at least one memory device, when displaying or causing the display device to display the recommendation for the at least second charging station, are further configured to: Displaying or causing the display device to display a predicted time for at least partially charging the electric vehicle via the at least second charging station.

15. The device according to claim 14, in, The one or more processors coupled to the at least one memory device, when displaying or causing the display device to display the recommendation for the at least second charging station, are further configured to: An estimated travel time to the at least second charging station is displayed.

16. The device according to claim 11, in, The one or more processors coupled to the at least one memory device, when displaying or causing the display device to display a recommendation for the at least second charging station, are further configured to: The location of the at least second charging station is displayed based at least in part on parameters specifically associated with the at least second charging station.

17. The device according to claim 11, in, The one or more processors coupled to the at least one memory device are further configured to determine the charging performance model via assigning at least one weighting factor to the one or more features extracted during a process of charging the electric vehicle.

18. A product, include: A non-transitory computer readable medium having encoded thereon instructions operable in response to execution by a processor coupled to at least one memory device to: extracting one or more characteristics of an electric current to be coupled to the electric vehicle in response to initiation of a charging process at a first charging station; determining one or more performance characteristics of the electric vehicle in response to one or more characteristics of electrical current coupled to the electric vehicle during the charging process at the first charging station; determining a charging performance model based at least in part on the determined one or more performance characteristics of the electric vehicle; and A recommendation for the at least second charging station is displayed based at least in part on the charging performance model and one or more characteristics of the at least second charging station.

19. The article according to claim 18, in, The one or more features to be extracted from the coupled current include one or more of the following: Indication of abnormalities in coupling current; Indication of abnormalities in the voltage of the coupled current; Indication of abnormalities in the power factor of the coupled current; An indication of the noise content of the coupled current; as well as An indication of the total harmonic distortion of the coupled current.

20. The article according to claim 18, in, The one or more performance characteristics of the electric vehicle determined include one or more of: a measure of the time period required to substantially fully charge the electric vehicle, a measure of electrical or thermal stress on one or more energy storage elements of the electric vehicle, a measure of the life of one or more energy storage elements of the electric vehicle, and a measure of the age of one or more storage elements of the electric vehicle.

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