Adaptive fast charging of vehicle battery

Abstract

Systems / techniques are provided that facilitate adaptive fast charging of a vehicle battery. The system has a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components include: an adaptive charging system that receives one or more factors that affect use of the battery; a cell allocation determination section that determines a cell allocation scheme based on the one or more factors; and a display section displaying a cell allocation scheme including the adjustment of the battery and the number of cells affected by the adjustment of the battery.

Description

Technical Field

[0001] The present disclosure generally relates to charging of a vehicle, and more particularly to adaptive fast charging of a vehicle battery. Background Art

[0002] Many modern vehicles implement a fully or partially electric propulsion system. Such vehicles can be charged via normal charging or fast charging. To save time, drivers often use fast charging extensively, at the cost of accelerated battery degradation.

[0003] Therefore, a system or technology that can solve one or more of these technical problems may be desirable. Summary of the Invention

[0004] The following presents a summary of the invention to provide a basic understanding of one or more embodiments of the invention. This summary is not intended to identify key or critical elements, or to delineate any scope of particular embodiments or any scope of the claims. Its sole purpose is to present concepts in a simplified form as a prelude to the more detailed description presented later. In one or more embodiments described herein, devices, systems, computer-implemented methods, apparatuses, or computer program products that facilitate adaptive fast charging of a vehicle battery are described.

[0005] According to one or more embodiments, a method includes: receiving, by an adaptive charging system, one or more factors affecting the use of a battery; determining, by the system, a cell allocation scheme based on the one or more factors; and displaying, by the system, the cell allocation scheme, the cell allocation scheme including an adjustment to the battery and the number of cells affected by the adjustment to the battery.

[0006] In another embodiment, a system includes: a memory storing computer-executable components; and a processor executing the computer-executable components stored in the memory, wherein the computer-executable components include: an adaptive charging system that receives one or more factors affecting the use of a battery; a cell allocation determination component that determines a cell allocation scheme based on the one or more factors; and a display component that displays the cell allocation scheme, the cell allocation scheme including an adjustment to the battery and the number of cells affected by the adjustment to the battery.

[0007] In yet another embodiment, a non-transitory machine-readable medium includes executable instructions that, when executed by a processor, facilitate performance of operations including: receiving one or more factors affecting the use of a battery; determining a cell allocation scheme based on the one or more factors; and displaying the cell allocation scheme, the cell allocation scheme including an adjustment to the battery and the number of cells affected by the adjustment to the battery.

[0008] According to one or more embodiments, the above system can be implemented as a computer-implemented method or a computer program product. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A block diagram showing an example non-limiting system that facilitates adaptive fast charging of a vehicle battery, according to one or more embodiments described herein.

[0010] Figure 2 A block diagram showing an example non-limiting system that includes fast charging instructions that facilitate adaptive fast charging of a vehicle battery, according to one or more embodiments described herein.

[0011] Figure 3 A block diagram showing an example non-limiting system, according to one or more embodiments described herein, that includes a vehicle context, a machine learning model, and cell allocation determination that facilitate adaptive fast charging of a vehicle battery.

[0012] Figure 4 An example non-limiting block diagram showing how a machine learning model can generate cell allocation determination, according to one or more embodiments described herein.

[0013] Figure 5 A block diagram showing an example non-limiting system that includes an allocation alert that facilitates adaptive fast charging of a vehicle battery, according to one or more embodiments described herein.

[0014] Figure 6 A block diagram showing an example non-limiting system that includes a recommended time or date that facilitates adaptive fast charging of a vehicle battery, according to one or more embodiments described herein.

[0015] Figure 7 An example non-limiting block diagram showing how a machine learning model can generate cell allocation determination and a recommended time or date for fast charging, according to one or more embodiments described herein.

[0016] Figure 8 An example non-limiting block diagram showing a training data set, according to one or more embodiments described herein.

[0017] Figure 9 An example non-limiting block diagram showing how a machine learning model can be trained, according to one or more embodiments described herein.

[0018] Figure 10A A flowchart showing an example non-limiting computer-implemented method that facilitates adaptive fast charging of a vehicle battery, according to one or more embodiments described herein.

[0019] Figure 10B A flow chart illustrating an example non-limiting computer-implemented method for facilitating adaptive fast charging of a vehicle battery in accordance with one or more embodiments described herein.

[0020] Figure 11 Block diagram illustrating an example non-limiting operating environment that can facilitate one or more embodiments described herein.

[0021] Figure 12 An example network environment is shown that is operable to perform various implementations described herein. DETAILED DESCRIPTION

[0022] The following detailed description is illustrative only and is not intended to limit the embodiments or the application / use of the embodiments. In addition, it is not intended to be bound by any explicit or implicit information presented in the previous background technology or summary section or in the detailed description section.

[0023] One or more embodiments are now described with reference to the accompanying drawings, wherein like reference numerals are used throughout to refer to like elements. In the following description, for the purpose of explanation, numerous specific details are set forth in order to provide a more thorough understanding of one or more embodiments. However, it is apparent that in various circumstances, one or more embodiments may be practiced without these specific details.

[0024] Many modern vehicles (e.g., cars, trucks, buses, motorcycles, boats, aircraft) implement fully or partially electric propulsion systems. For example, various vehicles are fully electric, such that they are propelled by an electric motor rather than an internal combustion engine. As another example, various other vehicles are hybrid, such that they are propelled by both an electric motor and an internal combustion engine working together simultaneously or alternately.

[0025] A vehicle having a full or partial electric propulsion system may have an on-board battery that powers the electric propulsion system. Such an on-board battery may be charged via normal charging or rapid charging. Normal charging may involve routing power to the on-board battery at any suitable baseline transfer rate (e.g., measured in watts or kilowatts). In contrast, rapid charging may alternatively involve routing power to the on-board battery at an increased transfer rate (e.g., one or more multiples or orders of magnitude higher than the baseline transfer rate). In practice, recharging an on-board battery from 10% capacity to 80% capacity via normal charging may take several hours (e.g., 6 to 8 hours), while recharging the on-board battery from 10% capacity to 80% capacity via rapid charging may alternatively take a period of one hour (e.g., 15 to 30 minutes). However, when the on-board battery is exposed to rapid charging, the on-board battery experiences accelerated or increased wear (e.g., physical, chemical, or electrical decomposition or breakdown of the electrolyte or other hardware components of the on-board battery) compared to when the on-board battery is alternatively exposed to normal charging.

[0026] To save time in the hustle and bustle of modern life, the operator of a vehicle will typically use rapid charging heavily or even exclusively. While this reduces the time spent charging the on-board battery (and thus reduces the time the operator spends waiting), such heavy or exclusive use of rapid charging may lead to premature failure of the on-board battery.

[0027] Accordingly, a system or technique that can address one or more of these technical problems may be desirable.

[0028] One or more of the described embodiments herein can address one or more of these technical problems. One or more embodiments described herein include systems, computer-implemented methods, devices, or computer program products that can facilitate adaptive fast charging of a vehicle battery. In other words, the different embodiments described herein can utilize artificial intelligence to reduce the wear of a vehicle battery caused by fast charging. In particular, when fast charging of a vehicle battery is requested or commanded, such artificial intelligence can determine which portions (e.g., which individual cells) of the vehicle battery are subject to fast charging and which other portions (e.g., which other individual cells) of the vehicle battery are alternatively subject to normal charging (or no charging at all) based on the current health of the vehicle battery and based on driving habits or driving plans associated with the vehicle battery. In various cases, such artificial intelligence can be considered to perform wear leveling within the vehicle battery in order to increase or extend the usable life of the vehicle battery, where such wear leveling can be informed or customized by the way the vehicle battery is actually used (e.g., different batteries can be used differently, meaning that their usable life can be optimally extended via different wear leveling). Accordingly, the various embodiments described herein can be considered to help reduce or mitigate the excessive wear of a vehicle battery caused by heavy or exclusive fast charging.

[0029] The various embodiments described herein can be considered computerized tools (e.g., any suitable combination of computer-executable hardware or computer-executable software) that can facilitate adaptive fast charging of a vehicle battery. In various aspects, the computerized tool can include an access component, an allocation component, or a charging component.

[0030] In various embodiments, there can be a vehicle. In various aspects, the vehicle can include an on-board battery that can power all or part of the vehicle's electric propulsion system. In various examples, the on-board battery can include a set of normal battery cells and a set of fast battery cells. In various cases, the set of normal battery cells can include any suitable number of normal battery cells, and the set of fast battery cells can include any suitable number of fast battery cells. In various aspects, the normal battery cells can be any suitable battery cells configured or designed for normal charging, while the fast battery cells can alternatively be any suitable battery cells configured or designed for fast charging. That is, the normal battery cells can experience a baseline level of degradation when undergoing normal charging and an elevated level of degradation when undergoing fast charging, while the fast battery cells can alternatively experience a baseline level of degradation when undergoing fast charging and even less degradation when undergoing normal charging. In other words, if the normal battery cells and the fast battery cells undergo fast charging, the normal battery cells will experience significantly more degradation or loss than the fast battery cells. In various examples, the normal battery cells can exhibit any suitable construction or composition designed for normal charging (e.g., can be chemical battery cells, can be solid-state battery cells), while the fast battery cells can exhibit any suitable construction or composition designed for fast charging (e.g., can be advanced chemical battery cells, can be advanced solid-state battery cells).

[0031] In various embodiments, the charging component of the computerized tool can be electronically controlled, electronically managed, or otherwise electronically accessed to any suitable vehicle charging station where the vehicle can dock. More specifically, the vehicle charging station can include a charging cable that can be plugged or inserted into the vehicle's charging port to supply power to the on-board battery. Thus, when the vehicle is docked at the vehicle charging station, the charging component can control, manage, or otherwise affect how the vehicle's on-board battery is charged (e.g., how much power is delivered to the on-board battery, how quickly the power is delivered to the on-board battery, which individual battery cells the power is delivered to).

[0032] In various embodiments, the access component of the computerized tool can electronically receive or otherwise electronically access a fast charging instruction. In some aspects, the access component can electronically retrieve the fast charging instruction from any suitable centralized or decentralized data structure (e.g., a graphical data structure, a relational data structure, a hybrid data structure), whether remote from the access component or local to the access component. In any case, the access component can electronically obtain or access the fast charging instruction such that other components of the computerized tool can electronically interact with the fast charging instruction (e.g., read, write, edit, copy, manipulate).

[0033] In various aspects, a fast charge instruction can be any suitable electronic message that indicates, specifies, or commands an on-board battery to undergo fast charging. In some cases, a vehicle can electronically broadcast a fast charge instruction in response to docking (e.g., plugging into) a vehicle charging station controlled by a charging component. In other cases, a fast charge instruction can be manually provided by an operator of the vehicle via a touchscreen, keypad, or voice control system of the vehicle, or via a touchscreen, keypad, or voice control system of the vehicle charging station.

[0034] In various embodiments, a distribution component of a computerized tool can electronically record, measure, or otherwise capture the current environment of a vehicle.

[0035] In various aspects, the current environment can include the current time or date. In various cases, the current time or date can be the time or date at which the fast charge instruction is received. In various examples, the current time or date can be provided by any suitable electronic clock or electronic calendar accessible to the distribution component.

[0036] In various cases, the current environment can include a current health report of the battery. In various aspects, the current health report of the battery can indicate or otherwise represent the respective health states (e.g., the total amount of cumulative degradation, the total reduction in maximum storage capacity) of each cell of the on-board battery (e.g., each of the set of normal battery cells and each of the set of fast battery cells) at the current time or date or at the current time or date instance. In various examples, the current battery health report can be provided to the distribution component by any suitable voltmeter or ammeter integrated with the on-board battery, or can otherwise be derived from any suitable voltmeter or ammeter integrated with the on-board battery.

[0037] In various examples, the current environment can include the driving history of the vehicle. In various cases, the driving history of the vehicle can be time series data that indicates how the vehicle was or has been driven at various past times or dates (e.g., how fast the vehicle was moving, how sharply the vehicle was turning, or how far the vehicle has traveled since its most recent recharge at each of such various past times or dates). In various aspects, the driving history of the vehicle can have been previously recorded by any suitable motion sensor integrated with the vehicle.

[0038] In various situations, the current environment can include the current destination or route of a vehicle. In various aspects, the current destination or route can indicate the geographical destination to which the vehicle is traveling or is scheduled to travel at the current time or date, or can indicate the geographical route along which the vehicle is traveling or is scheduled to travel at the current time or date. In various examples, the current destination or route can be provided to the allocation component by any suitable electronic navigation system of the vehicle.

[0039] In various aspects, the current environment can include the current weather forecast associated with the current destination or route. In various examples, the current weather forecast can be time series data indicating external temperature, external pressure, external precipitation level, or external wind speed, which is measured at the current time or date and at or along the current destination or route, or is predicted to occur at or along the current destination or route at a future time or date. In various situations, the current weather forecast can be provided to the allocation component by any suitable computing device associated with a weather forecasting service.

[0040] In various aspects, the current environment can include one or more upcoming scheduling events associated with the vehicle. In various examples, the one or more upcoming scheduled events can indicate any suitable activities related to the vehicle or otherwise likely to involve the vehicle, and in which the operator of the vehicle is scheduled to participate at the current time or date (e.g., a scheduled dragstrip race, a scheduled off-road trip, a scheduled trailer tow, a scheduled road trip). In various situations, the one or more upcoming scheduling events can be provided to the allocation component by any suitable electronic calendar associated with the vehicle.

[0041] In various aspects, the current environment can include one or more current vehicle sensor measurements. In various examples, the one or more current vehicle sensor measurements can indicate the values of any suitable potential transient characteristics or properties of the vehicle (e.g., current tire pressure, current total weight, current coolant temperature) at the current time or date. In various situations, the one or more vehicle sensor measurements can be provided to the allocation component via any suitable electronic sensor integrated with the vehicle.

[0042] These are merely non-limiting examples of the current context of the vehicle. In various situations, the current environment can include any other suitable information related to the vehicle or the on-board battery.

[0043] In various embodiments, the allocation component may electronically store, maintain, control, or otherwise access a machine learning model. In various aspects, the machine learning model may exhibit any suitable internal architecture, such as a deep learning neural network internal architecture. For example, the machine learning model may include any suitable number of any suitable type of layers (e.g., an input layer, one or more hidden layers, an output layer, any of which may be a convolutional layer, a dense layer, a non-linear layer, a pooling layer, a batch normalization layer, or a padding layer). As another example, the machine learning model may include any suitable number of neurons in various layers (e.g., different layers may have the same or different numbers of neurons from each other). As yet another example, the machine learning model may include any suitable activation function among various neurons (e.g., different neurons may have the same or different activation functions from each other) (e.g., a normalized exponential function, a Sigmoid function, a hyperbolic tangent, a rectified linear unit). As yet another example, the machine learning model may include any suitable intermediate neuron connections or inter-layer connections (e.g., forward connections, skip connections, recurrent connections). However, these are merely non-limiting examples of machine learning models. In other cases, the machine learning model may exhibit any other suitable internal architecture (e.g., a support vector machine, a naive Bayes, a linear regression, a logistic regression, a decision tree, a random forest).

[0044] In any case, the machine learning model may be configured to receive the current environment as input and in response to a fast charging instruction, and produce a battery allocation determination as output.

[0045] In various aspects, the cell allocation determination may include a respective classification label for each cell of the on-board battery (e.g., for each of the set of normal battery cells and for each of the set of fast battery cells). In various examples, the classification label of the cell allocation determination may indicate whether the respective cell of the on-board battery should (from the perspective of the machine learning model) undergo fast charging. In other words, the fast charging instruction may indicate that the on-board battery is to be fast charged, and the cell allocation determination may be considered a type of segment mask that indicates which cells of the on-board battery should undergo such fast charging and which cells should not (e.g., which cells should instead undergo normal charging or no charging at all).

[0046] In various cases, as described herein, the machine learning model may be trained such that the cell allocation determination balances or otherwise distributes losses across or between the respective cells of the on-board battery in order to extend or increase the usable life of the on-board battery while also adequately preparing the on-board battery for the actual use that the on-board battery has seen or will soon see.

[0047] For example, assume that the current environment of the vehicle indicates that a particular fast-charging battery cell has a disproportionately high amount of wear, and the vehicle is likely to only experience non-severe driving conditions (e.g., a history of gentle acceleration or turning; no currently marked distant destination or long route). In this case, the cell allocation determination may indicate that the particular fast-charging battery cell should not undergo fast charging at the current time or date. In other words, the machine learning model may have determined that the on-board battery will adequately prepare for the non-severe driving conditions that may be encountered, even though the particular fast-charging battery cell is not fast-charged at the current time or date.

[0048] As another example, assume that the current environment of the vehicle indicates that a particular fast-charging battery cell has a disproportionately high amount of wear, and the vehicle is likely to experience moderate-intensity driving conditions (e.g., a history of moderate acceleration or turning; one or more currently marked distant destinations or long routes; some slightly adverse weather forecasts). In this case, the cell allocation determination may indicate that the particular fast-charging battery cell should undergo fast charging at the current time or date, despite its disproportionately high wear. In other words, the machine learning model may have determined that the on-board battery will not be adequately prepared for the moderate-intensity driving conditions that may be encountered unless the particular fast-charging battery cell is fast-charged at the current time or date.

[0049] As yet another example, assume that the current environment of the vehicle indicates that a particular fast-charging battery cell has a disproportionately high amount of wear, a particular normal-charging battery cell has a disproportionately low amount of wear, and the vehicle is likely to experience moderate-intensity driving conditions (e.g., a history of moderate acceleration or turning; one or more currently marked distant destinations or long routes; some slightly adverse weather forecasts). In this case, the cell allocation determination may indicate that the particular fast-charging battery cell should not undergo fast charging at the current time or date, and the cell allocation determination may indicate that the particular normal-charging battery cell should undergo fast charging at the current time or date. In other words, the machine learning model may have determined that, given the already severe degradation of the particular fast-charging battery cell and given the moderate-intensity driving conditions that may be encountered, it is preferable not to fast-charge the particular fast-charging battery cell, but instead to fast-charge the particular normal-charging battery cell, even though the particular normal-charging battery cell is not configured or designed for fast charging.

[0050] As yet another example, assume that the current environment of the vehicle indicates that the vehicle may be subject to extremely intense driving conditions (e.g., a history of sudden acceleration or turning; an upcoming resistance band moving quickly; a freezing weather forecast). In such a case, the cell allocation determination may indicate that, in addition to all of the group of fast-charging battery cells, a plurality of normal-charging battery cells should be fast-charged at the current time or date. In other words, the machine learning model may have determined that the on-board battery will not be adequately prepared for the extremely intense driving conditions that may be encountered unless the plurality of normal-charging battery cells are fast-charged in combination with the group of fast-charging battery cells at the current time or date.

[0051] In some cases, if the cell allocation determination indicates that a particular battery cell is not to be fast-charged at the current time or date, this may indicate that the particular battery cell is instead assumed to be normally charged at the current time or date (e.g., each classification label of the cell allocation determination may be a binary label indicating: the corresponding cell is to be fast-charged; or the corresponding cell is to be normally charged). In other cases, if the cell allocation determination indicates that a particular battery cell is not to be fast-charged at the current time or date, this may indicate that the particular battery cell is instead assumed not to be charged at all at the current time or date (e.g., each classification label of the cell allocation determination may be a binary label indicating: the corresponding cell is to be fast-charged; or the corresponding cell is not to be charged). In even other cases, the cell allocation determination may indicate that some cells are to be fast-charged, while other cells are to be normally charged, and still other cells are not to be charged at the same time (e.g., each classification label of the cell allocation determination may be a ternary label indicating: the corresponding cell is to be fast-charged, the corresponding cell is to be normally charged, or the corresponding cell is not to be charged).

[0052] In any case, the allocation component may be considered to be intelligently and adaptively determining, based on the current environment of the vehicle, which individual battery cells of the on-board battery are to be fast-charged and which are not.

[0053] In various embodiments, the charging component may electronically charge the on-board battery at the current time or date according to the cell allocation determination. That is, the charging component may perform or otherwise cause to be performed at the current time or date: fast-charging of any battery cell designated as fast-charged by the cell allocation determination; normal-charging of any battery cell (if any) designated as normally charged by the cell allocation determination; and non-charging of any battery cell (if any) designated as not to be charged by the cell allocation determination.

[0054] In various embodiments, if the monomer allocation determination indicates that any of the set of normal charging battery monomers should undergo fast charging at the current time or date, the allocation component may electronically generate any suitable alert or warning indicating such. In various cases, the allocation component may transmit the alert or warning to any suitable computing device. In various other cases, the allocation component may visually present the alert or warning on any suitable electronic display (e.g., on a computer screen of a vehicle or a vehicle charging station controlled by the charging component).

[0055] So far, the present disclosure has mainly described various embodiments in which the allocation component determines in real time or instantaneously which monomers of the on-board battery should be fast charged and which should not be fast charged in response to a fast charging notification. However, in other embodiments, in the absence of a fast charging notification, the allocation component may alternatively proactively recommend which monomers of the on-board battery should be fast charged and which should not be fast charged. In this case, the vehicle cannot dock or plug into a vehicle charging station at the current time or date. Additionally, in this case, a machine learning model may be configured to receive the current environment of the vehicle as an input and produce not only a monomer allocation determination as an output but also a recommended future time or date for implementing the monomer allocation determination as an output. In other words, the machine learning model may be configured to not only determine which individual monomers of the on-board battery should undergo fast charging, normal charging, or no charging at all, given the current context of the vehicle, but the machine learning model may also be configured to further predict a future time or date at which, given the current context of the vehicle, the execution of such charging would be appropriate or convenient.

[0056] To help make the monomer allocation determination accurate, as described herein, the machine learning model may undergo any suitable type or paradigm of training (e.g., supervised training, unsupervised training, reinforcement learning).

[0057] The various embodiments described herein may be employed to solve problems that are highly technical in nature (e.g., to facilitate adaptive fast charging of vehicle batteries) using hardware or software, which are not abstract and cannot be performed as a set of mental acts of a human. Further, some of the processes performed may be executed by a special-purpose computer (e.g., a deep learning neural network having internal parameters such as convolutional kernels) for performing defined tasks related to adaptive fast charging of vehicle batteries.

[0058] For example, a task defined in this way may include: accessing an instruction to perform fast charging of a vehicle's battery by means operatively coupled to a processor; determining, by the means in response to the instruction and via a machine learning model executed on the vehicle's environment, an area of the battery to be allocated for fast charging; and performing fast charging on the determined area of the battery and normal charging or no charging on the remainder of the battery by the means.

[0059] A task defined in this way is not performed manually by a human. In fact, neither the human brain nor a person with a pen and paper can electronically allocate some cells of a vehicle's battery for fast charging, and allocate other cells of the vehicle's battery to normal charging or no charging, and electronically charge the vehicle's battery according to the allocation, by executing a machine learning model (e.g., a deep learning neural network) on vehicle environment data (e.g., battery health reports, driving history, driving destination / route, weather forecast). In fact, the vehicle, the machine learning model (e.g., a deep learning neural network), and the battery are inherently computerized, hardware-based devices that cannot be realized by the human brain in any way without a computer. Thus, a computerized tool that can determine, via artificial intelligence, how to allocate individual cells of a vehicle's battery for fast charging is similarly inherently computerized and hardware-based and cannot be realized in any practical or reasonable way without a computer.

[0060] In addition, various embodiments described herein may integrate various teachings related to adaptive fast charging of a vehicle's battery into practical applications. As explained above, normal charging may result in low battery wear, but may consume too much time, while fast charging may consume little time, but may accelerate battery wear. To save time, vehicle owners often use fast charging heavily or exclusively, which may result in premature battery failure.

[0061] The various embodiments described herein can address or improve various ones of these technical problems. Specifically, the various embodiments described herein can utilize artificial intelligence to allocate individual cells of a vehicle battery to fast charging, normal charging, or no charging depending on the real-time environment of the vehicle battery. In particular, the real-time environment can indicate any suitable information related to the vehicle battery, such as: the current-time health state of each cell of the vehicle battery; the history of driving modes or driving conditions that the vehicle battery has experienced; the current-time destination or route to which the vehicle battery is supposed to travel or along which it is traveling; or the weather conditions that currently affect or will affect the vehicle battery. In various aspects, a machine learning model can be executed on the real-time environment, resulting in a cell allocation determination. As described herein, depending on the real-time vehicle environment, the cell allocation determination can indicate which specific or individual cells of the vehicle battery should be fast charged and which should not. In various cases, the machine learning model can be trained or configured such that the cell allocation determination helps to extend the useful service life of the vehicle battery while also helping to ensure that the vehicle battery is ready to handle the possible upcoming usage indicated by the real-time environment. In various cases, the vehicle battery can then be charged according to the cell allocation determination. In this way, the various embodiments described herein can be considered to actively adapt or modify which cells of the vehicle battery are subjected to fast charging based on the actual usage that the vehicle battery has experienced. Thus, the various embodiments described herein can help to improve premature battery failure. That is, the various embodiments described herein can address the various disadvantages suffered by the prior art. Consequently, the various embodiments described herein certainly constitute a specific and practical technical improvement in the field of vehicle charging. Thus, the various embodiments described herein clearly qualify as useful and practical applications of a computer.

[0062] In addition, the various embodiments described herein can control real-world tangible devices based on the disclosed teachings. For example, the various embodiments described herein can electronically control (e.g., charge, discharge) real-world vehicle charging stations and real-world vehicle batteries.

[0063] It should be understood that the figures and descriptions herein provide non-limiting examples of the various embodiments and are not necessarily drawn to scale.

[0064] Figure 1 A block diagram showing an example non-limiting system 100 that can facilitate adaptive fast charging of a vehicle battery according to one or more embodiments described herein. As shown, an adaptive charging system 102 can be electronically integrated with a vehicle 104 via any suitable wired or wireless electronic connection.

[0065] In various embodiments, vehicle 104 can be any suitable vehicle having a fully electric propulsion system or a partially electric propulsion system. In various aspects, the propulsion system can be powered by a battery 106 that is onboard the vehicle 104 (e.g., physically located on or within it).

[0066] In various cases, battery 106 can include a set of normal cells 108. In various cases, the set of normal cells 108 can include n cells, for any suitable positive integer n: normal cell 108(1) to normal cell 108(n). In various aspects, any one of the set of normal cells 108 can be any suitable battery cell exhibiting any suitable composition, constitution, or physical configuration that is configured, designed, or otherwise adapted to undergo habitual or frequent normal charging. As a non-limiting example, any one of the set of normal cells 108 can be any suitable rechargeable chemical battery cell, such as: a lithium-ion battery cell; an aluminum-ion battery cell; a calcium battery cell; a flow battery cell; or a lead-acid battery cell. As another non-limiting example, any one of the set of normal cells 108 can be any suitable solid-state battery cell, such as: an inorganic solid electrolyte battery cell; a solid polymer electrolyte battery cell; or a quasi-solid-state electrolyte battery cell. In various cases, when exposed to normal charging, any one of the set of normal cells 108 can accumulate any suitable baseline, threshold, or otherwise non-accelerated amount of additional loss or degradation (e.g., loss or degradation of the anode, cathode, or electrolyte of the normal cell). In contrast, when exposed to rapid charging, any one of the set of normal cells 108 can accumulate more (e.g., significantly more in some cases) than the baseline, threshold, or otherwise non-accelerated amount of additional loss or degradation.

[0067] In various aspects, the battery 106 can include a set of fast cells 110. In various cases, the set of fast cells 110 can include m cells, for any suitable positive integer m: fast cell 110(1) to fast cell 110(m). In various examples, any one of the set of fast cells 110 can be any suitable battery cell exhibiting any suitable composition, constitution, or physical configuration that is configured, designed, or otherwise adapted to undergo habitual or frequent rapid charging. As a non-limiting example, any one of the set of fast cells 110 can be any suitable advanced chemistry cell (ACC). As another non-limiting example, any one of the set of fast cells 110 can be any suitable advanced solid-state battery cell. In various cases, when exposed to rapid charging, any one of the set of fast cells 110 can accumulate any suitable baseline, threshold, or otherwise non-accelerated amount of additional loss or degradation (e.g., loss or degradation of the anode, cathode, or electrolyte of the fast cell). In contrast, when exposed to normal charging, any one of the set of fast cells 110 can accumulate less than the baseline, threshold, or otherwise non-accelerated amount of additional loss or degradation. In other words, assume that both fast cells and normal cells are exposed to a rapid charging scenario. In this case, the fast cells will experience some limited amount of additional loss or degradation due to the rapid charging scenario, and the normal cells will experience significantly more (e.g., twice or more times) additional loss or degradation than this limited amount due to the rapid charging scenario.

[0068] In various aspects, the set of normal cells 108 and the set of fast cells 110 can exhibit any suitable connection layout or connection topology. As a non-limiting example, various ones of the set of normal cells 108 and the set of fast cells 110 can be connected in series with each other. As another non-limiting example, various ones of the set of normal cells 108 and the set of fast cells 110 can be connected in parallel with each other. As yet another non-limiting example, any suitable combination of parallel or series connections can be implemented in the battery 106.

[0069] In various aspects, it may be desirable to perform adaptive rapid charging on the battery 106. As described herein, the adaptive charging system 102 can facilitate such adaptive rapid charging.

[0070] In various embodiments, the adaptive charging system 102 can include a processor 112 (e.g., a computer processing unit, a microprocessor) and a non-transitory computer-readable memory 114 that is operatively or communicatively connected or coupled to the processor 112. The non-transitory computer-readable memory 114 can store computer-executable instructions that, when executed by the processor 112, can cause the processor 112 or other components of the adaptive charging system 102 (e.g., the access component 116, the distribution component 118, the charging component 120) to perform one or more actions. In various embodiments, the non-transitory computer-readable memory 114 can store computer-executable components (e.g., the access component 116, the distribution component 118, the charging component 120), and the processor 112 can execute the computer-executable components.

[0071] In various embodiments, the adaptive charging system 102 can include a charging component 120. In various aspects, the charging component 120 can electronically control, manage, direct, or otherwise electronically regulate any suitable vehicle charging station. In various examples, the vehicle charging station can be any suitable station, booth, kiosk, shed, or outlet where the vehicle 104 can charge the battery 106. In other words, the vehicle charging station can be considered a distribution access point of any suitable power grid. In various cases, the vehicle 104 can dock at the vehicle charging station. That is, the vehicle 104 can physically drive to the vehicle charging station and park beside or at the vehicle charging station, the charging cable of the vehicle charging station can be inserted into the charging port of the vehicle 104 (e.g., or equivalently, the charging cable of the vehicle 104 can be inserted into the discharge port of the vehicle charging station), and the vehicle charging station can accordingly route power to the battery 106 to charge the battery 106. In various cases, the charging component 120 can electronically control how much power or how quickly power is routed from the vehicle charging station to the battery 106. Additionally, in various aspects, the charging component 120 can electronically control where this power is routed within the battery 106. As a non-limiting example, the charging component 120 can electronically cause the vehicle charging station to route power to some of the normal cells 108 or the fast cells 110 and not route power to other of the normal cells 108 or the fast cells 110. As another non-limiting example, the charging component 120 can electronically cause the vehicle charging station to route power to some of the normal cells 108 or the fast cells 110 at a given transfer rate and route power to other of the normal cells 108 or the fast cells 110 at a different transfer rate.

[0072] In various embodiments, the adaptive charging system 102 can include an access component 116. In various examples, as described herein, the access component 116 can electronically access a fast charging notification that can request or command the battery 106 to undergo fast charging.

[0073] In various embodiments, the adaptive charging system 102 can include an allocation component 118. In various cases, as described herein, the allocation component 118 can electronically determine, via machine learning and in response to the fast charging notification, which of the set of normal cells 108 and the set of fast cells 110 should undergo fast charging.

[0074] In various embodiments, the charging component 120 can cause a vehicle charging station to charge the battery 106 based on the determination of the allocation component 118.

[0075] Figure 2 A block diagram of an example non - limiting system 200 including a fast charging instruction that can facilitate adaptive fast charging of a vehicle battery, in accordance with one or more embodiments described herein, is shown. As shown, in some cases, the system 200 can include the same components as the system 100 and can further include a fast charging instruction 202.

[0076] In various embodiments, access component 116 may electronically receive or otherwise electronically access fast charge instruction 202. In various aspects, fast charge instruction 202 may be any suitable electronic data (e.g., may be one or more scalars, one or more vectors, one or more matrices, one or more tensors, one or more strings, or any suitable combination thereof) that indicates, represents, or otherwise conveys a request or command to perform fast charging on battery 106. In various examples, access component 116 may electronically retrieve fast charge instruction 202 from any suitable centralized or decentralized data structure (not shown) or from any suitable centralized or decentralized computing device (not shown). As a non-limiting example, vehicle 104 may automatically generate fast charge instruction 202 in response to docking at or plugging into a vehicle charging station. In such a case, vehicle 104 may electronically broadcast fast charge instruction 202 to access component 116. As another non-limiting example, an operator of vehicle 104 may manually cause fast charge instruction 202 to be generated by interacting with any suitable human-machine interface device of vehicle 104 (e.g., a touchscreen, keyboard, or voice command system of vehicle 104) or by interacting with any suitable human-machine interface device of the vehicle charging station (e.g., a touchscreen, keyboard, or voice command system of the vehicle charging station). In any case, access component 116 may electronically obtain or access fast charge instruction 202 such that other components of adaptive charging system 102 may electronically interact with fast charge instruction 202.

[0077] Figure 3 A block diagram of an example non-limiting system 300 is shown in accordance with one or more embodiments described herein, which includes a vehicle environment, a machine learning model, and cell allocation determination, which may facilitate adaptive fast charging of a vehicle battery. As shown, in some cases, system 300 may include the same components as system 200 and may further include vehicle environment 302, machine learning model 304, and cell allocation determination 306.

[0078] In various embodiments, allocation component 118 may electronically access (e.g., via communication with vehicle 104) vehicle environment 302 in response to receiving fast charge instruction 202. In various aspects, vehicle environment 302 may be any suitable electronic data that indicates or otherwise pertains to the current time state or operation of vehicle 104 or battery 106.

[0079] In various embodiments, the allocation component 118 may electronically store, electronically maintain, electronically control, or otherwise electronically access the machine learning model 304. In various aspects, the machine learning model 304 may have or otherwise exhibit any suitable internal architecture. As a non-limiting example, the machine learning model 304 may have or otherwise exhibit a deep learning internal architecture. For example, the machine learning model 304 may have an input layer, one or more hidden layers, and an output layer. In various examples, any such layer may be coupled together by any suitable intermediate neuron connections or inter-layer connections (e.g., forward connections, skip connections, or recurrent connections). Additionally, in various cases, any such layer may be any suitable type of neural network layer having any suitable learnable or trainable internal parameters. For example, any such input layer, one or more hidden layers, or output layer may be a convolutional layer, and its learnable or trainable parameters may be convolutional kernels. As another example, any such input layer, one or more hidden layers, or output layer may be a dense layer, and its learnable or trainable parameters may be a weight matrix or a bias value. As yet another example, any such input layer, one or more hidden layers, or output layer may be a batch normalization layer, and its learnable or trainable parameters may be a shift factor or a scaling factor. Still further, in various cases, any such layer may be any suitable type of neural network layer having any suitable fixed or non-trainable internal parameters. For example, any such input layer, one or more hidden layers, or output layer may be a non-linear layer, a padding layer, a pooling layer, or a concatenation layer. However, these are merely non-limiting examples. In other aspects, the machine learning model 304 may alternatively have any other suitable internal architecture, such as a support vector machine architecture, a naive Bayes architecture, or a random forest architecture.

[0080] In various aspects, the allocation component 118 may electronically execute the machine learning model 304 on the vehicle environment 302. In various examples, such execution may cause the machine learning model 304 to generate a monomer allocation determination 306. In various cases, the monomer allocation determination 306 may be any suitable electronic data indicating which of the set of normal monomers 108 or the set of fast monomers 110 should undergo fast charging in response to the fast charging instruction 202, given or according to the vehicle environment 302. Refer to Figure 4 Non-limiting aspects are described.

[0081] In various aspects, the adaptive charging system 102 can make recommendations to the operator to physically swap out a portion of the battery 106. For example, the user provides at the vehicle one or more factors that may affect the use of the battery 106 (e.g., weather effects on the battery, driving distance value, route information of the desired route, charging station configuration of one or more charging stations associated with the desired route, tire pressure, total weight of the car including additional items and passengers, desired driving mode (standard, sport, off-road, etc.)). In one aspect, the adaptive charging system 102 receives these factors from a combination of the vehicle environment 302, an infotainment system (not shown), a machine learning model 304, or a communication component (not shown) that is electronically or wirelessly connected to the processor 112.

[0082] In one aspect, the cell allocation determination 306 (e.g., a module or component) of the allocation component 118 determines a cell allocation scheme for the battery 106 based on one or more factors received from the operator. The cell allocation scheme includes an adjustment to the battery and the number of cells affected by the adjustment (recommendations for the number of fast cells and normal cells of the battery 106). The cell allocation scheme includes cell allocation data and a digital representation of the battery 106 showing the number of fast cells and normal cells to be used, and the identified cells that should be swapped out (e.g., swapping 15 fast cells for 15 normal cells, swapping 15 normal cells for 15 fast cells, swapping normal cells for fast cells 1-4, or swapping fast cells for normal cells 1-4). It should be noted that the chemistry of the fast cells allows for a fast charging rate, while the normal cells only allow a standard slow charging rate. In one aspect, the vehicle user / operator is warned of any recommended modifications to the cell allocation scheme for the battery 106.

[0083] When determining the cell allocation scheme, a display component (not shown) of the adaptively charged system 102 that is electronically or wirelessly connected provides / displays data associated with the cell allocation scheme, such as a digital representation of the battery cell layout, which is displayed on the infotainment system of the vehicle 104 or on a mobile device. The digital representation can show the exact cells that need to be swapped out to conform to the cell allocation scheme.

[0084] In one aspect, if the user accepts the recommendation, the machine learning model 304 can determine the best facility for swapping out the cells and can schedule an appointment and transmit the cell allocation scheme to the facility for faster service. In another aspect, the battery 106 is implemented to allow for easy swapping of cells, which can be performed without having to go to an expert or mechanic. If the user does not accept the recommended cell allocation scheme, the machine learning model 304 can determine adjustments to the driving mode, route information, and / or tire pressure and warn the user of the adjustment.

[0085] In one aspect, the cell allocation scheme is adaptive based on driving habits or modification of the value of one or more factors that can affect the use of battery 106. For example, if the driver is not driving as quickly as initially indicated, the system can adaptively determine a new cell allocation scheme and alert the user. This will allow the user to make adjustments to achieve maximum performance throughout the trip.

[0086] Figure 4 FIG. 400 shows an example non - limiting block diagram according to one or more embodiments described herein, which shows how machine - learning model 304 can generate cell allocation determination 306.

[0087] In various embodiments, as shown, vehicle environment 302 can include current time / date 402. In various aspects, current time / date 402 can be specified at any suitable level of granularity. As a non - limiting example, current time / date 402 can be specified according to the current or present year, current or present month, current or present week, current or present day, current or present hour, current or present minute, fraction of the current or present second, or any suitable combination thereof. In various examples, current time / date 402 can be read or otherwise measured by any suitable electronic clock, electronic calendar, or electronic timer of adaptive charging system 102. In various cases, current time / date 402 can be the time or date at which fast - charging instruction 202 is received (e.g., can be the time or date when vehicle 104 is docked at or near a vehicle charging station controlled by charging component 120).

[0088] In various aspects, as shown, the vehicle environment 302 can include a current battery health report 404. In various cases, the current battery health report 404 can be one or more scalars, one or more vectors, one or more matrices, one or more tensors, one or more strings, or any suitable combination thereof, which can indicate or otherwise represent the respective health states of each cell of the battery 106 at the current time / date 402 or otherwise up to the current time / date. As a non-limiting example, the current battery health report 404 can indicate or specify how much total loss or degradation has been accumulated or accrued by the normal cell 108(1) up to the current time / date 402, how much total reduction in the maximum power storage capacity, or how much total performance corrosion. As another non-limiting example, the current battery health report 404 can indicate or specify how much total loss or degradation has been accumulated or accrued by the normal cell 108(n) at the current time / date 402, how much total reduction in the maximum power storage capacity, or how much total performance corrosion. As yet another non-limiting example, the current battery health report 404 can indicate or specify how much total loss or degradation has been accumulated or accrued by the fast cell 110(1) at the current time / date 402, how much total reduction in the maximum power storage capacity, or how much total performance corrosion. As still another non-limiting example, the current battery health report 404 can indicate or specify how much total loss or degradation has been accumulated or accrued by the fast cell 110(m) at the current time / date 402, how much total reduction in the maximum power storage capacity, or how much total performance corrosion. In some aspects, the current battery health report 404 can indicate or otherwise represent how much power is stored respectively in each cell of the battery 106 at the current time / date 402. In various cases, the current battery health report 404 can be read, measured, captured, or otherwise electronically quantified by any suitable voltmeter, ammeter, or other electronic sensor integrated with the battery 106.

[0089] In various aspects, as shown, the vehicle environment 302 can include a driving history 406. In various examples, the driving history 406 can be one or more scalars, one or more vectors, one or more matrices, one or more tensors, one or more strings, or any suitable combination thereof, which can indicate or otherwise represent how gently or how aggressively the vehicle 104 was driven at each of various past or previous times / dates or otherwise at each of various past or previous times / dates. As a non-limiting example, the driving history 406 can be or include a time series showing how fast the vehicle 104 was traveling at each of any suitable number of previous times / dates. As another non-limiting example, the driving history 406 can be or include a time series showing how fast the vehicle 104 was accelerating or decelerating at each of any suitable number of previous times / dates. As yet another non-limiting example, the driving history 406 can be or include a time series showing how sharp or how gradual the turns were that the vehicle 104 executed at each of any suitable number of previous times / dates. As still another non-limiting example, the driving history 406 can be or include a time series showing how much distance was covered or how much time has elapsed since the battery 106 was last recharged at each of any suitable number of previous times / dates. As yet another non-limiting example, the driving history 406 can be or include a time series showing what operating mode (e.g., economy mode, sport mode, off-road mode) the vehicle 104 was in at each of any suitable number of previous times / dates. In various cases, the driving history 406 can be recorded or otherwise electronically generated by any suitable motion sensors of the vehicle 104 (e.g., via the speedometer of the vehicle 104, via the odometer of the vehicle 104, via the accelerometer of the vehicle 104, via the steering angle sensor of the vehicle 104, via the gyroscopic sensor of the vehicle 104, via the global positioning sensor of the vehicle 104).

[0090] In various aspects, as shown, the vehicle environment 302 can include a current destination / route 408. In various examples, the current destination / route 408 can be one or more scalars, one or more vectors, one or more matrices, one or more tensors, one or more strings, or any suitable combination thereof, which can indicate or otherwise represent any of the following: a geolocation or address to which the vehicle 104 expects or plans to travel; or a geographic path composed of segmented sections of roads or streets along which the vehicle 104 expects or plans to travel. In various cases, the current destination / route 408 can indicate the total driving distance that the vehicle 104 has not traveled as of the current time / date 402 but will soon travel. In various aspects, the current destination / route 408 can be electronically identified or electronically recommended by any suitable navigation system of the vehicle 104. In other aspects, the current destination / route 408 can be electronically input into the navigation system of the vehicle 104 by an operator of the vehicle 104 (e.g., via any suitable touch screen, keyboard, or voice command system of the vehicle 104 or the navigation system).

[0091] In various aspects, as shown, the vehicle environment 302 can include a current weather forecast 410. In various examples, the current weather forecast 410 can be one or more scalars, one or more vectors, one or more matrices, one or more tensors, one or more strings, or any suitable combination thereof, which can indicate or otherwise represent the weather occurring at or along the current destination / route 408 at the current time / date 402, or the weather forecast to occur at or along the current destination / route 408 in the near future (e.g., in the next few hours) at the current time / date 402. As a non-limiting example, the current weather forecast 410 can be or include the atmospheric temperature at or along the current destination / route 408 at the current time / date 402, or the atmospheric temperature forecast to occur at or along the current destination / route 408 in the near future at the current time / date 402. As another non-limiting example, the current weather forecast 410 can be or include the atmospheric pressure at or along the current destination / route 408 at the current time / date 402, or the atmospheric pressure forecast to occur at or along the current destination / route 408 in the near future at the current time / date 402. As yet another non-limiting example, the current weather forecast 410 can be or include the level of atmospheric precipitation at or along the current destination / route 408 at the current time / date 402, or the level of atmospheric precipitation forecast to occur at or along the current destination / route 408 in the near future at the current time / date 402. As still another non-limiting example, the current weather forecast 410 can be or include the atmospheric wind speed at or along the current destination / route 408 at the current time / date 402, or the atmospheric wind speed forecast to occur at or along the current destination / route 408 in the near future at the current time / date 402. In various cases, the current weather forecast 410 can be supplied or provided by any suitable weather sensor or any suitable computing device of a weather service.

[0092] In various aspects, as shown, the vehicle environment 302 can include one or more upcoming scheduling events 412. In various examples, the one or more upcoming scheduling events 412 can be one or more scalars, one or more vectors, one or more matrices, one or more tensors, one or more strings, or any suitable combination thereof, which can indicate or otherwise represent one or more activities that are planned by an operator of the vehicle 104 starting from the current time / date 402, where such activities are inherently automatic or otherwise may involve driving or using the vehicle 104. As a non-limiting example, the one or more upcoming scheduling events 412 can indicate that the operator of the vehicle 104 is scheduled at the current time / date 402 to take the vehicle 104 on a road trip in the near future. As another non-limiting example, the one or more upcoming scheduling events 412 can indicate that the operator of the vehicle 104 is scheduled at the current time / date 402 to take the vehicle 104 to a drag strip for a quick run in the near future. As yet another non-limiting example, the one or more upcoming scheduling events 412 can indicate that the operator of the vehicle 104 is scheduled at the current time / date 402 to take the vehicle 104 off-road (e.g., to an off-road park or obstacle course) in the near future. As still another non-limiting example, the one or more upcoming scheduling events 412 can indicate that the operator of the vehicle 104 is scheduled at the current time / date 402 to perform trailer towing using the vehicle 104 in the near future. In various cases, the one or more upcoming scheduling events 412 can be supplied or provided by any suitable electronic calendar associated with or otherwise accessible to the vehicle 104. For example, the operator of the vehicle 104 may have marked one or more upcoming scheduling events 412 in an electronic calendar via any suitable touchscreen, keyboard, or voice command system of the vehicle 104. Alternatively, the operator of the vehicle 104 may have marked one or more upcoming scheduling events 412 in an electronic calendar via any suitable touchscreen, keyboard, or voice command system of any other suitable computing device (e.g., a mobile phone) of the operator, and the vehicle 104 can electronically access the electronic calendar.

[0093] In various aspects, as shown, the vehicle environment 302 can include one or more current vehicle sensor measurements 414. In various examples, the one or more current vehicle sensor measurements 414 can be one or more scalars, one or more vectors, one or more matrices, one or more tensors, one or more strings, or any suitable combination thereof, which can indicate or otherwise represent the values of any suitable measurable characteristics, features, or attributes of the vehicle 104 at or up to the current time / date 402. As a non-limiting example, the one or more current vehicle sensor measurements 414 can indicate one or more tire pressures of the vehicle 104 at the current time / date 402. As another non-limiting example, the one or more current vehicle sensor measurements 414 can indicate the total weight (or weight distribution) of the vehicle 104 at the current time / date 402. As yet another non-limiting example, the one or more current vehicle sensor measurements 414 can indicate one or more hot fluid temperatures (e.g., coolant temperature, oil temperature) of the vehicle 104 at the current time / date 402. In various cases, the one or more current vehicle sensor measurements 414 can be read, measured, captured, or otherwise electronically quantified by any suitable pressure gauge, weight gauge, thermometer, or other electronic sensor integrated with the vehicle 104.

[0094] In various aspects, the vehicle environment 302 can include any other suitable information related to the state, operation, or conditions experienced by the vehicle 104 or the battery 106.

[0095] In various examples, the allocation component 118 can execute the machine learning model 304 on the vehicle environment 302, and such execution can cause the machine learning model 304 to generate a cell allocation determination 306. As a non-limiting example, the allocation component 118 can feed the vehicle environment 302 (e.g., the current time / date 402, the current battery health report 404, the driving history 406, the current destination / route 408, the current weather forecast 410, one or more upcoming scheduling events 412, one or more current vehicle sensor measurements 414) to the input layer of the machine learning model 304. In various examples, the vehicle environment 302 can complete a forward pass through one or more hidden layers of the machine learning model 304. In various cases, the output layer of the machine learning model 304 can calculate the cell allocation determination 306 based on the activation maps or intermediate features generated by one or more hidden layers of the machine learning model 304. In any case, the cell allocation determination 306 can be any suitable electronic data that can be considered to specify or indicate which cells of the battery 106 will undergo fast charging in response to the fast charging instruction 202.

[0096] More specifically, as shown, the monomer allocation determination 306 can include a set of assigned or unassigned classification labels 416. In various aspects, the set of assigned or unassigned classification labels 416 can respectively correspond (e.g., in a one-to-one manner) to the set of normal monomers 108. Thus, since the set of normal monomers 108 can include n monomers, the set of assigned or unassigned classification labels 416 can similarly include n labels: assigned or unassigned classification label 416(1) to assigned or unassigned classification label 416(n). In various examples, each of the assigned or unassigned classification labels 416 can be any suitable electronic data indicating or specifying whether the corresponding one of the set of normal monomers 108 should undergo fast charging at the current time / date 402.

[0097] As a non-limiting example, the assigned or unassigned classification label 416(1) can correspond to the normal monomer 108(1). Thus, the assigned or unassigned classification label 416(1) can indicate whether the normal monomer 108(1) should (from the perspective of the machine learning model 304) undergo fast charging at the current time / date 402 according to the vehicle environment 302. In some cases, the assigned or unassigned classification label 416(1) can be a binary or dichotomous label indicating one of two possibilities: the normal monomer 108(1) should undergo fast charging at the current time / date 402; or the normal monomer 108(1) should instead undergo normal charging at the current time / date 402. In other cases, the assigned or unassigned classification label 416(1) can be a ternary or trichotomous label indicating one of three possibilities: the normal monomer 108(1) should undergo fast charging at the current time / date 402; the normal monomer 108(1) should undergo normal charging at the current time / date 402; or the normal monomer 108(1) should not undergo any charging at the current time / date 402.

[0098] As another non-limiting example, an assigned or unassigned classification label 416(n) may correspond to a normal cell 108(n). Thus, the assigned or unassigned classification label 416(n) may indicate whether the normal cell 108(n) should (from the perspective of the machine learning model 304) undergo fast charging at the current time / date 402, depending on the vehicle environment 302. As described above, in some cases, the assigned or unassigned classification label 416(n) may be a binary or dichotomous label that indicates one of two possibilities (e.g., fast charging vs. normal charging) for the normal battery cell 108(n) at the current time / date 402. However, in other cases, the assigned or unassigned classification label 416(n) may be a ternary or trichotomous label that indicates one of three possibilities (e.g., fast charging vs. normal charging vs. no charging) for the normal cell 108(n) at the current time / date 402.

[0099] In various aspects, the cell assignment determination 306 may include a set of assigned or unassigned classification labels 418. In various aspects, the set of assigned or unassigned classification labels 418 may respectively correspond to (e.g., in a one-to-one manner) the set of fast cells 110. Thus, since the set of fast cells 110 may include m cells, the set of assigned or unassigned classification labels 418 may similarly include m labels: assigned or unassigned classification label 418(1) through assigned or unassigned classification label 418(m). In various examples, each of the assigned or unassigned classification labels 418 may be any suitable electronic data that indicates or specifies whether the corresponding one of the set of fast cells 110 should undergo fast charging at the current time / date 402.

[0100] As a non-limiting example, the assigned or unassigned classification label 418(1) may correspond to the fast cell 110(1). Thus, the assigned or unassigned classification label 418(1) may indicate, based on the vehicle environment 302, whether the fast cell 110(1) should (from the perspective of the machine learning model 304) undergo fast charging at the current time / date 402. In some cases, the assigned or unassigned classification label 418(1) may be a binary or dichotomous label that indicates one of two possibilities: the fast cell 110(1) should undergo fast charging at the current time / date 402; or the fast cell 110(1) should instead undergo normal charging at the current time / date 402. In other cases, the assigned or unassigned classification label 418(1) may be a ternary or trichotomous label that indicates one of three possibilities: the fast cell 110(1) should undergo fast charging at the current time / date 402; the fast cell 110(1) should undergo normal charging at the current time / date 402; or the fast cell 110(1) should not undergo any charging at the current time / date 402.

[0101] As another non-limiting example, an assigned or unassigned classification label 418(m) may correspond to a fast cell 110(m). Thus, the assigned or unassigned classification label 418(m) may indicate whether the fast cell 110(m) should (from the perspective of the machine learning model 304) undergo fast charging at the current time / date 402, depending on the vehicle environment 302. As described above, in some cases, the assigned or unassigned classification label 418(m) may be a binary or dichotomous label that indicates one of two possibilities (e.g., fast charging vs. normal charging) for the fast cell 110(m) at the current time / date 402. However, in other cases, the assigned or unassigned classification label 418(m) may be a ternary or trichotomous label that indicates one of three possibilities (e.g., fast charging vs. normal charging vs. no charging) for the fast cell 110(m) at the current time / date 402.

[0102] Accordingly, in some cases, the cell assignment determination 306 may be considered a per-cell piecewise mask that indicates which specific cells of the battery 106 should undergo fast charging at the current time / date 402 and which specific cells of the battery 106 should not undergo fast charging at the current time / date 402. In various aspects, the machine learning model 304 may be trained such that the cell assignment determination 306 achieves (or attempts to achieve) two competing goals: extending the useful life of the battery 106 by preventing any one cell from experiencing disproportionately high wear; and preparing the battery 106 or preparing for any usage that the vehicle environment 302 implies the vehicle 104 may encounter soon or shortly. In other words, the cell assignment determination 306 may be considered to indicate how each individual cell of the battery 106 should be charged (e.g., fast, normal, or not at all) at the current time / date 402 such that the battery 106 can appropriately handle any usage it may see and also such that the total amount of accumulated wear or degradation of the battery 106 is minimized or balanced across the cells of the battery 106.

[0103] As a non-limiting example, assume that the vehicle environment 302 indicates that a particular one of the set of fast monomers 110 has a disproportionately large accumulation of wear at the current time / date 402 compared to the other fast monomers in the set of fast monomers 110. Additionally, assume that the vehicle environment 302 indicates that the vehicle 104 may encounter easy, non-stressful driving conditions in the near future (e.g., in the next few hours or days after the current time / date 402) (e.g., the driving history 406 may indicate that the vehicle 104 is typically driven very gently, with slow acceleration or deceleration; the current destination / route 408 may indicate that the vehicle 104 is not going to drive a very long distance or in an area where few or no vehicle charging stations are available). In such a case, the cell allocation determination 306 may indicate that the particular fast cell should not undergo fast charging at the current time / date 402. That is, the machine learning model 304 may infer that the already high wear of the particular fast cell should not be exacerbated and that the battery 106 will still be able to appropriately handle any upcoming use that the battery 106 may experience even if the particular fast cell is not fast charged at the current time / date 402.

[0104] As another non-limiting example, again assume that the vehicle environment 302 indicates that a particular one of the set of fast cells 110 has a disproportionately large amount of accumulated wear at the current time / date 402 compared to the other fast cells in the set of fast cells 110. Additionally, assume that the vehicle environment 302 indicates that a particular one of the set of normal cells 108 has a disproportionately low amount of accumulated wear at the current time / date 402 compared to the other cells in the set of normal cells 108. Further still, assume that the vehicle environment 302 indicates that the vehicle 104 may encounter moderate, somewhat strenuous driving conditions in the near future (e.g., the driving history 406 may indicate that the vehicle 104 is typically driven moderately, with moderate acceleration or deceleration; the current destination / route 408 may indicate that the vehicle 104 is about to drive a long distance or in an area where few or no vehicle charging stations are available). In such a case, the cell allocation determination 306 may indicate that a particular fast cell should not undergo fast charging at the current time / date 402, but rather that a particular normal cell should undergo fast charging at the current time / date 402. That is, the machine learning model 304 may infer that the already high wear of a particular fast cell should not be exacerbated, but that if the particular fast cell is not fast charged at the current time / date 402 and no other cell picks up its slack, the battery 106 will not be able to properly handle any upcoming use that the battery 106 may experience. Thus, the machine learning model 304 may be considered to compensate for the current disproportionately high wear of a particular fast cell with the current disproportionately low wear of a particular normal cell. In other words, the machine learning model 304 may determine that, given the vehicle environment 302, fast charging a particular normal cell is more beneficial than fast charging a particular fast cell, even though the particular normal cell is not configured or designed for fast charging.

[0105] As yet another non-limiting example, again assume that the vehicle environment 302 indicates that a particular one of the set of fast cells 110 has a disproportionately large amount of accumulated wear at the current time / date 402 compared to the other fast cells in the set of fast cells 110. Additionally, assume that the vehicle environment 302 indicates that a particular one of the set of normal cells 108 has a disproportionately high amount of accumulated wear at the current time / date 402 compared to the other cells in the set of normal cells 108. Still further, assume that the vehicle environment 302 indicates that the vehicle 104 may encounter demanding, harsh driving conditions in the near future (e.g., the driving history 406 may indicate that the vehicle 104 is typically driven aggressively, with sudden accelerations or decelerations; one or more upcoming dispatch events 412 may indicate that the vehicle 104 is about to participate in a drag strip run; the current weather forecast 410 may indicate that the vehicle 104 is about to experience inclement weather). In such a case, the cell allocation determination 306 may indicate that both the particular fast cell and the particular normal cell should undergo fast charging at the current time / date 402. That is, the machine learning model 304 may infer that, although it is not preferred to exacerbate the already high wear of the particular fast cell and the particular normal cell, the battery 106 will not be able to properly handle any upcoming usage that the battery 106 may experience without fast charging both the particular fast cell and the particular normal cell.

[0106] In this manner, the allocation component 118 may be considered to intelligently or smartly determine, based on the vehicle environment 302, which individual cells of the battery 106 should undergo fast charging at the current time / date 402.

[0107] In various embodiments, the charging component 120 may electronically implement or otherwise comply with the cell allocation determination 306. That is, the charging component 120 may charge the battery 106 according to the cell allocation determination 306 (e.g., via a vehicle charging station to which the vehicle 104 may dock). In other words, whichever battery cells should undergo fast charging (as indicated by the cell allocation determination 306) may be fast charged by the charging component 120 at the current time / date 402, whichever cells should undergo normal charging (as indicated by the cell allocation determination 306) may be normally charged by the charging component 120 at the current time / date 402, and whichever cells should not undergo charging (as indicated by the cell allocation determination 306) may not be charged by the charging component 120 at the current time / date 402.

[0108] Figure 5A block diagram showing an example non - limiting system 500 that includes an allocation alert, which can facilitate adaptive fast charging of a vehicle battery, in accordance with one or more embodiments described herein. As shown, in some cases, system 500 can include the same components as system 300 and can further include an allocation alert 502.

[0109] In various embodiments, the allocation component 118 can electronically generate an allocation alert 502 in response to a cell allocation determination 306 that indicates that any one of the set of normal cells 108 should be fast charged. In various aspects, the allocation alert 502 can be any suitable electronic data that designates or otherwise warns that at least one of the set of normal cells has been allocated for fast charging at the current time / date 402. In various examples, the allocation component 118 can transmit the allocation alert 502 to any suitable computing device. In various other examples, the allocation component 118 can visually present the allocation alert 502 on any suitable electronic display (e.g., on a computer screen of the vehicle 104 or on a computer screen of a vehicle charging station to which the vehicle 104 is docked). Thus, the operator of the vehicle 104 can become aware that at least one of the set of normal cells 108 has been allocated for fast charging. In some cases, after transmitting or presenting the allocation alert 502, the allocation component 118 can prohibit the charging component 120 from charging the battery 106 according to the cell allocation determination 306 until after receiving an acknowledgment message from the vehicle 104 (e.g., until the operator of the vehicle 104 acknowledges that such charging should be performed despite the allocation alert 502).

[0110] Figure 6 A block diagram showing an example non - limiting system 600 that includes a recommended time or date that can facilitate adaptive fast charging of a vehicle battery, in accordance with one or more embodiments described herein. As shown, in some cases, system 600 can include the same components as system 500, except for the fast - charging instruction 202, and can further include a recommended time / date 602.

[0111] In various embodiments, it can be the case that the vehicle 104 is not docked at a vehicle charging station controlled by the charging component 120 at the current time / date 402. In such a scenario, the fast - charging instruction 202 may not be present, and the charging component 120 may not be able to cause the battery 106 to be charged. In such a scenario, the machine - learning model 304 can be configured to not only generate a cell allocation determination 306 but also generate a recommended time / date 602. In various examples, the recommended time / date 602 can be a future time or date at which the cell allocation determination 306 should be implemented. Regarding Figure 7 Non - limiting aspects are described.

[0112] Figure 7 Shows an example non - restrictive block diagram 700 in accordance with one or more embodiments described herein, which shows how a machine learning model 304 can generate a cell allocation determination 306 and a recommended time / date 602.

[0113] In various embodiments, the allocation component 118 may execute the machine learning model 304 on the vehicle environment 302. In various aspects, such execution may cause the machine learning model 304 to generate not only the cell allocation determination 306, but also the recommended time / date 602. As a non - restrictive example, the allocation component 118 may feed the vehicle environment 302 (e.g., the current time / date 402, the current battery health report 404, the driving history 406, the current destination / route 408, the current weather forecast 410, one or more upcoming scheduling events 412, one or more current vehicle sensor measurements 414) to the input layer of the machine learning model 304. In various examples, the vehicle environment 302 may pass forward through one or more hidden layers of the machine learning model 304. In various cases, the output layer of the machine learning model 304 may calculate both the cell allocation determination 306 and the recommended time / date 602 based on the activation maps or intermediate features generated by one or more hidden layers of the machine learning model 304.

[0114] In any case, the recommended time / date 602 may be in the future (e.g., after the current time / date 402) and any suitable time or date at which the machine learning model 304 recommends charging the battery 106 based on the cell allocation determination 306. In other words, the machine learning model 304 may be configured to not only determine which individual cells of the battery 106 should undergo fast charging, normal charging, or no charging given the vehicle environment 302, but also predict a future time or date at which it would be appropriate or convenient to perform such charging based on the vehicle environment 302.

[0115] In various aspects, the allocation component 118 may electronically transmit the cell allocation determination 306 and the recommended time / date 602 to any suitable computing device (e.g., transmit to the vehicle 104), or may electronically present the cell allocation determination 306 and the recommended time / date 602 on any suitable electronic display (e.g., the computer screen of the vehicle 104). Thus, the operator of the vehicle 104 can become aware not only of which specific cells are recommended for fast charging (or normal charging, or no charging), but also of when such charging should be performed.

[0116] Now, in order for the monomer allocation determination 306 (or the recommended time / date 602, if applicable) to be accurate, the machine learning model 304 may first undergo training. As a non-limiting example, the machine learning model 304 may undergo supervised training, such as with respect to Figures 8 to 9 as described.

[0117] Figure 8 FIG. 800 is an example non-limiting block diagram showing a training data set 802 that can be used to train a machine learning model 304 in accordance with one or more embodiments described herein.

[0118] In various aspects, the training data set 802 may include a set of training inputs 804. In various examples, the set of training inputs 804 may include q inputs for any suitable positive integer q: training inputs 804(1) through training inputs 804(q). In various examples, each of the set of training inputs 804 may be a training vehicle environment having the same format, size, or dimensions as the vehicle environment 302.

[0119] In various aspects, the training data set 802 may include a set of ground-truth annotations 806 that may correspond, respectively, to the set of training inputs 804. Thus, since the set of training inputs 804 may have q inputs, the set of ground-truth annotations 806 may have q annotations: ground-truth annotations 806(1) through ground-truth annotations 806(q). In various examples, if the machine learning model 304 is not configured to produce a recommended time / date 602, each of the set of ground-truth annotations 806 may be a known or assumed correct or accurate monomer allocation determination (having the same format, size, or dimensions as the monomer allocation determination 306) corresponding to the respective one of the set of training inputs 804. On the other hand, if the machine learning model 304 is configured to produce a recommended time / date 602, each of the set of ground-truth annotations 806 may be a concatenation between a known or assumed correct or accurate monomer allocation determination (having the same format, size, or dimensions as the monomer allocation determination 306) corresponding to the respective one of the set of training inputs 804 and a known or assumed correct or accurate recommended time / date (having the same format, size, or dimensions as the recommended time / date 602) corresponding to such correct or accurate monomer allocation determination.

[0120] Figure 9 FIG. 900 is an example non-limiting block diagram showing how a machine learning model 304 can be trained in accordance with one or more embodiments described herein.

[0121] In various aspects, before training begins, the trainable internal parameters of the machine learning model 304 (e.g., convolutional kernels, weight matrices, bias values) can be initialized in any suitable manner (e.g., via random initialization).

[0122] In various aspects, the training input 902 and the ground truth annotation 904 corresponding to the training input 902 can be selected from the training dataset 802. In various examples, the machine learning model 304 can be executed on the training input 902, thereby causing the machine learning model 304 to produce an output 906. More specifically, in some cases, the input layer of the machine learning model 304 can receive the training input 902, the training input 902 can complete a forward pass through one or more hidden layers of the machine learning model 304, and the output layer of the machine learning model 304 can calculate the output 906 based on the activation maps or intermediate features provided by one or more hidden layers of the machine learning model 304.

[0123] Note that the format, size, or dimension of the output 906 can be specified by the number, arrangement, size, or other characteristics of the neurons, convolutional kernels, or other internal parameters of the output layer (or any other layer) of the machine learning model 304. Thus, by adding, removing, or otherwise adjusting the characteristics of the output layer (or any other layer) of the machine learning model 304, the output 906 can be forced to have any desired format, size, or dimension.

[0124] In various aspects, the output 906 can be considered a single-assignment determination of the prediction or inference that the machine learning model 304 believes should correspond to the training input 902 (and the recommended time / date of the prediction or inference, if so configured). On the other hand, the ground truth annotation 904 can be considered any correct or accurate single-assignment determination that is known or believed to correspond to the training input 902 (and the correct or accurate recommended time / date, if so configured). Note that if the machine learning model 304 has not experienced or has experienced very little training so far, the output 906 may be highly inaccurate. In other words, the output 906 can be very different from the ground truth annotation 904.

[0125] In various aspects, an error 908 (e.g., mean absolute error, mean squared error, cross-entropy error) between the output 906 and the ground truth annotation 904 can be computed. In various examples, the trainable internal parameters of the machine learning model 304 can be incrementally updated based on the error 908 via backpropagation (e.g., stochastic gradient descent).

[0126] In various cases, such an execution and update process can be repeated for any suitable number of training inputs (e.g., for each training input in the training dataset 802). This can ultimately cause the trainable internal parameters of the machine learning model 304 to become iteratively optimized to accurately generate monomer allocation determinations (or recommended times / dates, as appropriate). In various aspects, any suitable training batch size, any suitable error / loss function, or any suitable training termination criterion can be implemented.

[0127] Although the above description mainly describes the machine learning model 304 as being trained in a supervised manner, this is merely a non - restrictive example for ease of illustration and explanation. In various cases, any other suitable training paradigm (e.g., unsupervised training, reinforcement learning) can be implemented to train the machine learning model 304.

[0128] Although the present disclosure mainly describes various embodiments in which the battery 106 includes discrete battery monomers (e.g., 108 and 110) that can be independently fast - charged or normally charged, this is merely a non - restrictive example for ease of explanation and illustration. In various other embodiments, the battery 106 may lack discretely identifiable battery monomers, but still have independently controllable portions (which may be amorphous) that can be independently fast - charged or normally charged. It should be understood and appreciated that the various aspects described herein are equally applicable to embodiments having such independently controllable portions as they are to embodiments having discrete battery monomers.

[0129] Figure 10A A flowchart of an example non - restrictive computer - implemented method 1000 that can facilitate adaptive fast - charging of a vehicle battery in accordance with one or more embodiments described herein is shown. In various cases, the adaptive charging system 102 can facilitate the computer - implemented method 1000.

[0130] In various embodiments, the action 1002 can include a device (e.g., via 116) operatively coupled to a processor (e.g., 112) accessing instructions (e.g., 202) to perform a fast - charge on a battery (e.g., 106) of a vehicle (e.g., 104).

[0131] In various aspects, the action 1004 can include the device (e.g., via 118) determining, in response to the instructions and by executing a machine learning model (e.g., 304) on the environment of the vehicle (e.g., 302), a region of the battery to be allocated for fast - charging (e.g., as indicated by 306).

[0132] In various examples, operation 1006 may include the device (e.g., via 120) performing fast charging on a determined region of the battery (e.g., as indicated by 306, regardless of which cells are allocated for fast charging) and performing normal charging on the remainder of the battery (e.g., as indicated by 306, regardless of which cells are not allocated for fast charging).

[0133] Figure 10B A flowchart illustrating an example non - limiting computer - implemented method 1010 that may facilitate adaptive fast charging of a vehicle battery in accordance with one or more embodiments described herein. In various cases, an adaptive charging system 102 may facilitate the computer - implemented method 1010.

[0134] In various embodiments, operation 1012 may include the adaptive charging system receiving one or more factors that affect the use of the battery. In various aspects, operation 1014 may include the system determining a cell allocation scheme based on the one or more factors. In various examples, operation 1016 may include the system displaying a cell allocation scheme that includes adjustments to the battery and the number of cells affected by the adjustments to the battery.

[0135] Although not explicitly shown in Figure 10A and Figure 10B the context of the vehicle may include: current health data of the battery (e.g., 404); driving history of the vehicle (e.g., 406); current planned destination or route of the vehicle (e.g., 408); current weather forecast associated with the current planned destination or route (e.g., 410); upcoming events recorded in the vehicle's electronic calendar (e.g., 412); current weight of the vehicle (e.g., 414); current tire pressure of the vehicle (e.g., 414); or current thermal fluid temperature of the vehicle (e.g., 414).

[0136] Although not explicitly shown in Figure 10A and Figure 10B the battery may include one or more first cells (e.g., 110) configured to handle fast charging without accelerated degradation and one or more second cells (e.g., 108) not configured to handle fast charging without accelerated degradation, and the computer - implemented method 1000 may further include: the device (e.g., via 118) generating an alert (e.g., 502) in response to the determined region including any of the one or more second cells.

[0137] In various examples, a machine learning algorithm or model may be implemented in any suitable manner to facilitate any suitable aspect described herein. To facilitate some of the foregoing machine learning aspects of the various embodiments, consider the following discussion of artificial intelligence (AI). The various embodiments described herein may employ artificial intelligence to facilitate the automation of one or more features or functions. The various components may employ various AI-based schemes to perform the various embodiments / examples disclosed herein. To provide or assist with the numerous determinations (e.g., determine, ascertain, infer, compute, predict, estimate, evaluate, derive, forecast, detect, calculate) described herein, the various components described herein may examine all or a subset of the data to which they are permitted access and may provide inferences or determinations of the state of a system or environment from a set of observations captured via events or data. For example, a determination may be used to identify a particular environment or action, or a probability distribution of a state may be generated. The determination may be probabilistic; i.e., a probability distribution of a state of interest is calculated based on a consideration of data and events. A determination may also refer to techniques employed to construct higher-level events from a set of events or data.

[0138] Such a determination may result in the construction of new events or actions from a set of observed events or stored event data, regardless of whether the events are temporally close and regardless of whether the events and data are from one or several events and data sources. The various components disclosed herein may employ various classification (explicit training (e.g., via training data) and implicit training (e.g., via observed behavior, preferences, historical information, received external information, etc.)) schemes or systems (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, data fusion engines, etc.) to perform automated or determined actions related to the claimed subject matter. Thus, classification schemes or systems may be used to automatically learn and perform a number of functions, actions, or determinations.

[0139] A classifier may take an input attribute vector z = (z 1 , z 2 , z 3 , z 4 , z n)Mapped to the confidence that the input belongs to a class, such as by f(z) = confidence(class). Such classification can employ probability- or statistics-based analysis (e.g., taking into account analysis utility and cost) to determine the action to be automatically performed. A support vector machine (SVM) can be an example of a classifier that can be employed. The SVM operates by finding a hypersurface in the space of possible inputs, where the hypersurface attempts to separate the trigger criterion from non-trigger events. Intuitively, this enables the classification to be correct for test data that is close to but not identical to the training data. Other directed and non-directed model classification methods include, for example, naive Bayes, Bayesian networks, decision trees, neural networks, fuzzy logic models, or probability classification models that provide different independent modes, any of which can be employed. The classification used herein also includes the use of statistical regression for developing a priority model.

[0140] To provide an additional context for the various embodiments described herein, Figure 11 and the following discussion is intended to provide a brief, general description of a suitable computing environment 1100 in which the embodiments described herein can be implemented. Although the embodiments have been described above in the general context of computer-executable instructions that can run on one or more computers, those skilled in the art will recognize that the embodiments can also be implemented in combination with other program modules or as a combination of hardware and software.

[0141] In general, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In addition, those skilled in the art will understand that the methods of the present invention can be practiced with other computer system configurations, including single-processor or multi-processor computer systems, minicomputers, mainframe computers, Internet of Things (IoT) devices, distributed computing systems, and personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which can be operatively coupled to one or more associated devices.

[0142] The illustrated embodiments of the embodiments herein can also be practiced in a distributed computing environment where certain tasks are performed by remote processing devices linked through a communication network. In a distributed computing environment, program modules can be located in local and remote memory storage devices.

[0143] Computing devices typically include various media, which may include computer-readable storage media, machine-readable storage media, or communication media, where these two terms are used differently from each other as described below in this document. Computer-readable storage media or machine-readable storage media can be any available storage media accessible by a computer, and include volatile and non-volatile media, removable and non-removable media. By way of example and not limitation, computer-readable storage media or machine-readable storage media can be implemented in conjunction with any method or technology for storing information such as computer-readable or machine-readable instructions, program modules, structured data, or unstructured data.

[0144] Computer-readable storage media can include, but are not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD ROM), digital versatile disc (DVD), Blu-ray disc (BD) or other optical disc storage, magnetic tape cartridges, magnetic tape, magnetic disk storage or other magnetic storage devices, solid state drives or other solid state storage devices, or other tangible or non-transitory media that can be used to store the desired information. In this regard, the terms "tangible" or "non-transitory" as used herein with respect to storage devices, memory, or computer-readable media are to be understood to exclude only propagating transitory signals per se as a modifier, and do not relinquish rights to all standard storage devices, memory, or computer-readable media that do not only propagate transitory signals per se.

[0145] Computer-readable storage media can be accessed by one or more local or remote computing devices, for example, via an access request, query, or other data retrieval protocol, to perform various operations on the information stored by the media.

[0146] Communication media typically embody computer-readable instructions, data structures, program modules, or other structured or unstructured data in a data signal, such as a modulated data signal, for example, a carrier wave or other transmission mechanism, and include any information delivery or transmission media. The term "modulated data signal" or signals refers to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in one or more signals. By way of example and not limitation, communication media includes wired media, such as a wired network or direct-wired connection, and wireless media, such as acoustic, RF, infrared, and other wireless media.

[0147] Refer again to Figure 11, An example environment 1100 for various embodiments for implementing aspects described herein includes a computer 1102 that includes a processing unit 1104, a system memory 1106, and a system bus 1108. The system bus 1108 couples system components including, but not limited to, the system memory 1106 to the processing unit 1104. The processing unit 1104 can be any of a variety of commercially available processors. Dual microprocessors and other multi-processor architectures can also be employed as the processing unit 1104.

[0148] The system bus 1108 can be any of several types of bus structures that can further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory 1106 includes a ROM 1110 and a RAM 1112. A basic input / output system (BIOS) can be stored in non-volatile memory such as ROM, erasable programmable read-only memory (EPROM), EEPROM, where the BIOS contains basic routines that help transfer information between elements within the computer 1102, such as during startup. The RAM 1112 can also include high-speed RAM such as static RAM for caching data.

[0149] The computer 1102 further includes an internal hard disk drive (HDD) 1114 (e.g., EIDE, SATA), one or more external storage devices 1116 (e.g., a magnetic floppy disk drive (FDD) 1116, a memory stick or flash drive reader, a memory card reader, etc.), and a drive 1120, such as a solid-state drive, an optical disk drive, which can read from or write to a disk 1122 such as a CD-ROM disk, a DVD, a BD, etc. Alternatively, in the case of a solid-state drive, the disk 1122 will not be included unless separate. Although the internal HDD 1114 is shown as being located within the computer 1102, the internal HDD 1114 can also be configured for external use in a suitable chassis (not shown). Additionally, although not shown in the environment 1100, a solid-state drive (SSD) can be used in addition to or in place of the HDD 1114. The HDD 1114, the external storage device(s) 1116, and the drive 1120 can be connected to the system bus 1108 via an HDD interface 1124, an external storage interface 1126, and a drive interface 1128, respectively. The interface 1124 for external drive implementation can include at least one or both of the universal serial bus (USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394 interface technologies. Other external drive connection technologies are within the scope of the embodiments described herein.

[0150] The drive and its associated computer-readable storage medium provide non-volatile storage of data, data structures, computer-executable instructions, and the like. For computer 1102, the drive and storage medium accommodate storage of any data in a suitable digital format. Although the above description of the computer-readable storage medium refers to a corresponding type of storage device, those skilled in the art should understand that other types of computer-readable storage media, whether currently existing or to be developed in the future, may also be used in the exemplary operating environment, and furthermore, any such storage medium may contain computer-executable instructions for performing the methods described herein.

[0151] A number of program modules may be stored in the drive and RAM 1112, including operating system 1130, one or more application programs 1132, other program modules 1134, and program data 1136. All or part of the operating system, application programs, modules, or data may also be cached in RAM 1112. The systems and methods described herein may be implemented using a variety of commercially available operating systems or combinations of operating systems.

[0152] Computer 1102 may optionally include emulation technology. For example, a hypervisor (not shown) or other intermediary may emulate a hardware environment for operating system 1130, and the emulated hardware may optionally be different from Figure 11 the hardware shown in. In such an embodiment, operating system 1130 may include a virtual machine (VM) among multiple VMs hosted at computer 1102. Additionally, operating system 1130 may provide a runtime environment, such as a Java runtime environment or a.NET framework, for application programs 1132. A runtime environment is a consistent execution environment that allows application 1132 to run on any operating system that includes the runtime environment. Similarly, operating system 1130 may support containers, and application 1132 may be in the form of a container, which is a lightweight, independent, executable software package that includes, for example, code, runtime, system tools, system libraries, and settings for the application.

[0153] Furthermore, computer 1102 may be enabled with a security module, such as a Trusted Platform Module (TPM). For example, for a TPM, the boot component hashes the next one in time for the boot component and waits for the result to match a security value before loading the next boot component. This process may occur at any layer in the code execution stack of computer 1102, such as an application at the application execution level or the operating system (OS) kernel level, thereby implementing security at any code execution level.

[0154] A user can input commands and information into the computer 1102 through one or more wired / wireless input devices, such as a keyboard 1138, a touch screen 1140, and a pointing device such as a mouse 1142. Other input devices (not shown) may include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control or other remote controls, a joystick, a virtual reality controller or a virtual reality headset, a game pad, a stylus, an image input device such as a camera (multiple cameras), a gesture sensor input device, a visual motion sensor input device, an emotion or face detection device, a biometric input device (e.g., a fingerprint or iris scanner), or the like. These and other input devices are typically connected to the processing unit 1104 through an input device interface 1144 that can be coupled to the system bus 1108, but may also be connected through other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, interfaces, etc.

[0155] A monitor 1146 or other type of display device may also be connected to the system bus 1108 through an interface such as a video adapter 1148. In addition to the monitor 1146, a computer typically includes other peripheral output devices (not shown), such as speakers, printers, etc.

[0156] The computer 1102 can operate in a networked environment using a logical connection to one or more remote computers (e.g., remote computer(s) 1150) through wired or wireless communication. The remote computer(s) 1150 can be a workstation, a server computer, a router, a personal computer, a portable computer, a microprocessor-based entertainment facility, a peer device, or other common network nodes, and typically includes many or all of the elements described with respect to the computer 1102, but only the memory / storage device 1152 is shown for simplicity. The depicted logical connection includes a wired / wireless connection to a local area network (LAN) 1154 or a larger network such as a wide area network (WAN) 1156. Such LAN and WAN network environments are common in offices and companies and facilitate enterprise-wide computer networks such as intranets, all of which can be connected to a global communication network such as the Internet.

[0157] When used in a LAN network environment, the computer 1102 can be connected to the local area network 1154 through a wired or wireless communication network interface or adapter 1158. The adapter 1158 can facilitate wired or wireless communication with the LAN 1154, which may also include a wireless access point (AP) disposed thereon for communicating with the adapter 1158 in a wireless mode.

[0158] When used in a WAN network environment, computer 1102 may include a modem 1160, or may be connected to a communication server on WAN 1156 via other means, to establish communication through WAN 1156, such as through the Internet. The modem 1160, which can be an internal or external and wired or wireless device, can be connected to the system bus 1108 via the input device interface 1144. In a networked environment, program modules depicted relative to computer 1102 or portions thereof can be stored in the remote memory / storage device 1152. It will be understood that the network connections shown are examples, and other means of establishing a communication link between computers can be used.

[0159] When used in a LAN or WAN network environment, computer 1102 can access a cloud storage system or other network-based storage systems, as a supplement or alternative to the external storage device 1116 described above, such as, but not limited to, network virtual machines that provide one or more aspects of information storage or processing. Generally, the connection between computer 1102 and the cloud storage system can be established, for example, over LAN 1154 or WAN 1156 via adapter 1158 or modem 1160, respectively. When connecting computer 1102 to an associated cloud storage system, the external storage interface 1126 can manage the storage provided by the cloud storage system with the help of adapter 1158 or modem 1160, just as it manages other types of external storage. For example, the external storage interface 1126 can be configured to provide access to cloud storage sources as if these sources were physically connected to computer 1102.

[0160] Computer 1102 is operable to communicate with any wireless device or entity operatively disposed in wireless communication, such as a printer, scanner, desktop or portable computer, portable data assistant, communication satellite, any device or location associated with a wirelessly detectable tag (e.g., kiosk, newsstand, store shelf, etc.), and a telephone. This can include Wi-Fi and wireless technologies. Thus, the communication can be a predefined structure such as a conventional network, or simply an ad hoc communication between at least two devices.

[0161] Figure 12FIG. 0 is a schematic block diagram of an example computing environment 1200 with which the disclosed subject matter may interact. The example computing environment 1200 includes one or more clients 1210. The client(s) 1210 can be hardware or software (e.g., threads, processes, computing devices). The example computing environment 1200 also includes one or more servers 1230. The server(s) 1230 can also be hardware or software (e.g., threads, processes, computing devices). The server 1230 can house threads to perform transformations by adopting, for example, one or more embodiments described herein. A possible communication between the client 1210 and the server 1230 can be in the form of data packets suitable for transfer between two or more computer processes. The example computing environment 1200 includes a communication framework 1250 that can be employed to facilitate communication between the client(s) 1210 and the server(s) 1230. The client(s) 1210 are operatively connected to one or more client data stores 1220 that can be employed to store information local to the client(s) 1210. Similarly, the server(s) 1230 are operatively connected to one or more server data stores 1240 that can be employed to store information local to the server 1230.

[0162] Various embodiments can be a system, a method, an apparatus, or a computer program product at any possible technical detail integration level. The computer program product can include a computer-readable storage medium (or media) having computer-readable program instructions thereon for causing a processor to execute various aspects of the various embodiments. The computer-readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer-readable storage medium can be (but is not limited to) an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer-readable storage medium can also include the following: a portable computer floppy disk, a hard disk, 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 portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device such as a punch card or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. The computer-readable storage medium used herein should not be construed as a transitory signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (such as an optical pulse through an optical fiber) or an electrical signal transmitted through a wire.

[0163] The computer-readable program instructions described herein can be downloaded to respective computing / processing devices from a computer-readable storage medium or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network (LAN), a wide area network (WAN), or a wireless network. The network may include copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to a computer-readable storage medium within the respective computing / processing device for storage. The computer-readable program instructions for performing the operations of the various embodiments may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, or the like, and procedural programming languages such as the "C" programming language or the like. The computer-readable program instructions may execute entirely on the user's computer, may execute partly on the user's computer as a stand-alone software package, may execute partly on the user's computer and partly on a remote computer, or may execute entirely on the remote computer or server. In the latter case, the remote computer may be connected to the user's computer through any type of network, including a LAN or a WAN, or may be connected to an external computer (e.g., via the Internet of an Internet service provider). In some embodiments, an electronic circuit, including, for example, a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), can execute the computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuit, thereby performing various aspects.

[0164] In this document, various aspects are described with reference to flowcharts illustrations or block diagrams of methods, apparatus (systems), and computer program products according to various embodiments. It will be understood that each block in the flowchart illustrations or block diagrams, and combinations of blocks in the flowchart illustrations or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a method for implementing the functions / acts specified in one or more blocks of the flowchart or block diagram. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, or other device to function in a particular manner, such that the computer-readable storage medium containing the instructions comprises a manufacture including instructions for implementing the functions / acts specified in one or more blocks of the flowchart or block diagram. The computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other device to produce a computer-implemented process, such that the instructions executed on the computer, other programmable apparatus, or other device implement the functions / acts specified in one or more blocks of the flowchart or block diagram.

[0165] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of systems, methods, and computer program products that may be implemented according to various embodiments. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of instructions, including one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession in the figures may in fact be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It should also be noted that each block of the block diagrams or flowchart illustrations, and combinations of blocks in the block diagrams or flowchart illustrations, can be implemented by systems based on dedicated hardware that perform the specified functions or acts, or combinations of dedicated hardware and computer instructions.

[0166] Although the subject matter has been described above in the general context of computer-executable instructions of a computer program product that runs on a computer, those skilled in the art will recognize that the present disclosure may also or can be implemented in combination with other program modules. In general, program modules include routines, programs, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In addition, those skilled in the art will understand that aspects can be implemented by other computer system configurations, including single-processor or multi-processor computer systems, microcomputing devices, mainframe computers, and computers, handheld computing devices (such as PDAs, telephones), microprocessor-based or programmable consumer or industrial electronic products, etc. The illustrated content can also be implemented in a distributed computing environment, in which tasks are performed by remote processing devices connected through a communication network. However, some aspects of the present disclosure (if not all) can also be implemented on a stand-alone computer. In a distributed computing environment, program modules can be located in local and remote memory storage devices.

[0167] As used in this application, the terms "component", "system", "platform", "interface", etc. can refer to or include an entity related to a computer or an entity related to an operating machine with one or more specific functions. The entities disclosed herein can be hardware, a combination of hardware and software, software, or software in execution. For example, a component can be (but is not limited to) a process running on a processor, a processor, an object, an executable file, an execution thread, a program, or a computer. For example, an application running on a server and the server can both be components. One or more components can reside in a process or execution thread, and the components can be located on a computer or distributed between two or more computers. In another example, the various components can be executed from various computer-readable media that store various data structures. These components can communicate through local or remote processes, for example, communicate according to a signal having one or more data packets (for example, data for a component to interact with another component in a local system or a distributed system, or data for a component to interact with other systems across a network such as the Internet through a signal). Another example is that a component can be a device with a specific function, whose mechanical components are operated by an electrical or electronic circuit, and the electrical or electronic circuit is operated by a software or firmware application executed by a processor. In this case, the processor can be inside or outside the device and can execute at least part of the software or firmware. As another example, a component can be a device that provides a specific function through electronic components without mechanical components, where the electronic components can include a processor or other methods of executing software or firmware, and these software or firmware at least partially endow the electronic components with functions. In one aspect, a component can simulate an electronic component through a virtual machine, for example, within a cloud computing system.

[0168] Additionally, the term "or" means inclusive "or" rather than exclusive "or". That is, unless otherwise specified or clearly indicated by the context, "X employs A or B" means any natural inclusive arrangement. That is, if X employs A; X employs B; or X employs both A and B, then in any of these cases, "X employs A or B" holds. The term "and / or" used herein has the same meaning as "or". Further, the articles "a" and "an" used in the specification and drawings generally should be understood to mean "one or more", unless otherwise stated or clearly understood to be in the singular form according to the context. The terms "example" or "exemplary" used herein refer to serving as an example, instance, or illustration. To avoid ambiguity, the subject matter disclosed herein is not limited by these examples. Additionally, any aspect or design described herein as "example" or "exemplary" is not necessarily to be construed as superior or better than other aspects or designs, nor does it imply exclusion of equivalent exemplary structures and techniques known to those of ordinary skill in the art.

[0169] This disclosure describes non - limiting examples. For ease of description or explanation, various parts of this disclosure use the terms "every", "each", or "all" when discussing various examples. The use of the terms "every", "each", or "all" is non - limiting. In other words, when the description provided by this disclosure applies to "every", "each", or "all" of certain specific objects or components, it should be understood that this is a non - limiting example, and it should be further understood that in various other examples, this description may apply to less than "every", "each", or "all" of that specific object or component.

[0170] As used in this subject specification, the term "processor" can generally refer to any computing processing unit or device, including but not limited to a single-core processor; a single-core processor with software multithreading execution capabilities; a multi-core processor; a multi-core processor with software multithreading execution capabilities; a multi-core processor with hardware multithreading technology; a parallel platform; and a parallel platform with distributed shared memory. Additionally, the processor can also refer to an integrated circuit, an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a field-programmable gate array (FPGA), a programmable logic controller (PLC), a complex programmable logic device (CPLD), discrete gate or transistor logic, discrete hardware components, or any combination thereof, designed to perform the functions described herein. Furthermore, the processor can utilize nanoscale architectures, such as but not limited to molecule- and quantum-dot-based transistors, switches, and gates, to optimize space usage or improve the performance of user equipment. The processor can also be implemented as a combination of computing processing units. In this disclosure, terms such as "store", "storage", "data storage", "data store", "database", and any other information storage components related to the operation and functions of components are used to refer to "memory components", entities embodied as "memory", or components containing memory. It can be understood that the memory or memory components described herein can be volatile memory or non-volatile memory, and can also include volatile memory and non-volatile memory. By way of example and not limitation, non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, or non-volatile random access memory (RAM) (such as ferroelectric RAM (FeRAM)). Volatile memory can include RAM, such as can be used as an external cache memory. By way of illustration and not limitation, RAM has various forms, such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM). Additionally, the memory components of the systems or computer-implemented methods disclosed herein are intended to include but not limited to including these and any other suitable types of memory.

[0171] The above are merely examples of systems and computer-implemented methods. Of course, it is not possible to describe every conceivable combination of components or computer-implemented method for the purpose of describing the present disclosure, but many further combinations and permutations of the present disclosure are possible. In addition, the terms "comprising", "having", "owning", etc. used in the detailed description, claims, appendices, and drawings have meanings similar to the term "including", and when "including" is used as a transitional word in the claims, it means including.

[0172] The description of the various embodiments is for illustrative purposes but is not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent without departing from the scope and spirit of the described embodiments. The terms used herein are intended to best explain the principles of the embodiments of the present invention, practical applications, or technical improvements over the prior art in the market, or to enable those of ordinary skill in the art to understand the embodiments disclosed herein.

[0173] The various non-limiting aspects of the various embodiments described herein are presented in the following clauses.

[0174] 1. A method, comprising: receiving, by an adaptive charging system, one or more factors affecting the use of a battery; determining, by the system, a cell allocation scheme based on the one or more factors; and displaying, by the system, the cell allocation scheme, the cell allocation scheme including an adjustment to the battery and the number of cells affected by the adjustment to the battery.

[0175] 2. The method according to any one of the preceding clauses, wherein the one or more factors include route information for a desired route and a desired driving mode.

[0176] 3. The method according to any one of the preceding clauses, wherein the one or more factors include an indication of the impact of weather on the battery, a driving distance value, a charging station configuration for one or more charging stations associated with the desired route, a tire pressure value, and the total weight of the sedan.

[0177] 4. The method according to any one of the preceding clauses, wherein the cell allocation scheme includes the number of fast cells and the number of normal cells to be used.

[0178] 5. The method according to any one of the preceding clauses, wherein the cell allocation scheme includes the identification of one or more cells to be swapped out.

[0179] 6. The method according to any one of the preceding clauses, further comprising: transmitting, by the system, the cell allocation scheme having the identification of the fast cells.

[0180] 7. The method according to any one of the preceding clauses, further comprising: determining, by the system, adjustments to the driving mode, route information, and tire pressure; and

[0181] The system warns the user of the adjustment.

[0182] In various cases, any suitable combination or sub - combination of Clauses 1 - 7 can be implemented.

[0183] 8. A system comprising: a memory storing computer - executable components; and a processor executing the computer - executable components stored in the memory, wherein the computer - executable components include: an adaptive charging system that receives one or more factors affecting the use of a battery; a cell allocation determination component that determines a cell allocation scheme based on the one or more factors; and a display component that displays the cell allocation scheme, the cell allocation scheme including an adjustment to the battery and the number of cells affected by the adjustment to the battery.

[0184] 9. The system according to any of the preceding clauses, wherein the one or more factors include route information for a desired route and a desired driving mode.

[0185] 10. The system according to any of the preceding clauses, wherein the one or more factors include an indication of the effect of weather on the battery, a driving distance value, a charging station configuration for one or more charging stations associated with a desired route, a tire pressure value, and the total weight of the sedan.

[0186] 11. The system according to any of the preceding clauses, wherein the cell allocation scheme includes the number of fast cells and the number of normal cells to be used.

[0187] 12. The system according to any of the preceding clauses, wherein the cell allocation scheme includes the identification of one or more cells to be swapped out.

[0188] 13. The system according to any of the preceding clauses, wherein the computer - executable components further include: a machine - learning model that transmits the cell allocation scheme with the identification of the fast cells.

[0189] 14. The system according to any of the preceding clauses, wherein the adaptive charging system further determines adjustments to the driving mode, route information, and tire pressure and warns the user of the adjustments.

[0190] In various cases, any suitable combination or sub - combination of Clauses 8 - 14 can be implemented.

[0191] 15. A non - transitory machine - readable medium comprising executable instructions that, when executed by a processor, facilitate the execution of operations including:

[0192] Receive one or more factors that affect the use of the battery; determine a cell allocation plan based on the one or more factors; and display the cell allocation plan, which includes adjustments to the battery and the number of cells affected by the adjustments to the battery.

[0193] 16. The non-transitory machine-readable medium according to any of the preceding clauses, wherein the one or more factors include route information for a desired route and a desired driving mode.

[0194] 17. The non-transitory machine-readable medium according to any of the preceding clauses, wherein the one or more factors include an indication of the impact of weather on the battery, a driving distance value, a charging station configuration for one or more charging stations associated with the desired route, a tire pressure value, and the total weight of the sedan.

[0195] 18. The non-transitory machine-readable medium according to any of the preceding clauses, wherein the cell allocation plan includes the number of fast cells and the number of normal cells to be used.

[0196] 19. The non-transitory machine-readable medium according to any of the preceding clauses, wherein the cell allocation plan includes the identification of one or more cells to be swapped out.

[0197] 20. The non-transitory machine-readable medium according to any of the preceding clauses, further comprising: transmitting the cell allocation plan with the identification of the fast cells; determining adjustments to the driving mode, route information, and tire pressure; and warning the user of the adjustments.

[0198] In various cases, any suitable combination or sub-combination of clauses 17-20 can be implemented.

[0199] In various cases, any suitable combination or sub-combination of clauses 1-20 can be implemented.

Claims

1. A method comprising: receiving, by the adaptive charging system, one or more factors affecting usage of the battery; determining, by the system, a monomer allocation plan based on the one or more factors; as well as The cell allocation plan is displayed by the system, the cell allocation plan including the adjustment to the battery and the number of cells affected by the adjustment to the battery. 2 . The method of claim 1 , wherein the one or more factors include route information for a desired route and a desired driving pattern.

3. The method of claim 1 , wherein the one or more factors include an indication of weather effects on the battery, a driving distance value, a charging station configuration for one or more charging stations associated with the desired route, a tire pressure value, and a gross weight of the sedan. 4 . The method of claim 3 , wherein the monomer allocation scheme includes the number of fast monomers and the number of normal monomers to be used. The method of claim 4 , wherein the cell allocation scheme includes an identification of one or more cells to be swapped out.

6. The method according to claim 1, further comprising: The cell allocation plan is transmitted by the system with identification of the fast cell.

7. The method according to claim 1, further comprising: determining, by the system, adjustments to the driving mode, the route information, and the tire pressure; as well as The user is alerted by the system to the adjustment.

8. A system comprising: Memory that stores computer executable components; as well as a processor that executes the computer executable components stored in the memory, wherein the computer executable components include: an adaptive charging system receiving one or more factors affecting usage of the battery; A cell allocation determining component that determines a cell allocation plan based on the one or more factors; and A display component displays the cell allocation scheme, the cell allocation scheme including adjustments to the battery and the number of cells affected by the adjustments to the battery. 9 . The system of claim 8 , wherein the one or more factors include route information for a desired route and a desired driving pattern.

10. The system of claim 9, wherein the one or more factors include an indication of weather effects on the battery, a driving distance value, a charging station configuration for one or more charging stations associated with the desired route, a tire pressure value, and a gross weight of the sedan.

11. The system of claim 8, wherein the monomer allocation scheme includes a number of fast monomers and a number of normal monomers to be used.

12. The system of claim 11, wherein the cell allocation scheme includes an identification of one or more cells to be swapped out.

13. The system of claim 8, wherein the computer executable components further comprise: A machine learning model is provided that transmits the monomer allocation plan with an identification of the fast monomer.

14. The system of claim 8, wherein the adaptive charging system further determines adjustments to the driving mode, the route information, and the tire pressure and alerts the user of the adjustments.

15. A non-transitory machine-readable medium comprising executable instructions that, when executed by a processor, facilitate performance of operations comprising: receiving one or more factors affecting battery usage; determining a monomer allocation scheme based on the one or more factors; and The cell allocation plan is displayed, the cell allocation plan including the adjustment to the battery and the number of cells affected by the adjustment to the battery. 16 . The non-transitory machine-readable medium of claim 15 , wherein the one or more factors include route information for a desired route and a desired driving pattern.

17. The non-transitory machine-readable medium of claim 16, wherein the one or more factors include an indication of an effect of weather on the battery, a driving distance value, a charging station configuration for one or more charging stations associated with the desired route, a tire pressure value, and a gross weight of the sedan.

18. The non-transitory machine-readable medium of claim 15, wherein the cell allocation scheme includes a number of fast cells and a number of normal cells to be used.

19. The non-transitory machine-readable medium of claim 15, wherein the cell allocation scheme includes an identification of one or more cells to be swapped out.

20. The non-transitory machine-readable medium of claim 15, further comprising: transmitting a monomer allocation plan having an identification of the fast monomer; determining adjustments to the driving mode, the route information, and the tire pressure; as well as The user is warned of the adjustment.

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