Optimized charge limit

Abstract

In some embodiments, an electronic device may include a power system including a battery and one or more processors programmed to: detect that the electronic device has been connected to a power source, previous usage data of the electronic device is used to predict whether a battery usage between an expected time to disconnect from the power supply and a next expected time to connect to the power supply exceeds a threshold, and charging the battery to a state of charge less than a fully charged state of the battery if the predicted battery usage between an expected time to disconnect from the power supply and a next expected time to connect to the power supply does not exceed the threshold.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. application No. 63 / 374,662, filed on September 6, 2022, entitled “OPTIMIZED CHARGE LIMIT,” which is hereby incorporated by reference in its entirety for all purposes. Background Art

[0003] Users may use a wide variety of electronic devices that rely on battery power. Users typically want these batteries to have sufficient capacity and charge to power the use of the devices between charging opportunities. Therefore, whenever possible, such devices may be plugged into an external power source, such as an AC mains power source. However, at least some battery technologies may exhibit increased wear when charged to a high state of charge relative to their full charge capacity. Therefore, battery wear may be reduced, and battery capacity may be maintained by reducing the amount of time the battery spends in a high state of charge. Summary of the invention

[0004] In some applications, a device may reduce the amount of time its battery spends in a high state of charge (relative to the battery's full charge capacity) by an optimized battery charging routine that delays charging the battery to its full state of charge until just before the battery is expected to be disconnected from the power source. However, even in such cases, if the user requires less than substantially all of the battery's full charge capacity, the battery may be charged to a lower state of charge that is less than the battery's full capacity but still sufficient to accommodate the expected use before the next time the battery is recharged.

[0005] An electronic device may include a power system including a battery and one or more processors programmed to: detect that the electronic device is connected to a power source, use previous usage data of the electronic device to predict whether battery usage between an expected time of disconnection from the power source and a next expected time of connection to the power source exceeds a threshold, and if the predicted battery usage between the expected time of disconnection from the power source and the next expected time of connection to the power source does not exceed the threshold, charge the battery to a charge state that is less than a full charge state of the battery.

[0006] The one or more processors may be further programmed to use previous usage data of the electronic device, using a machine learning model that analyzes the previous usage data to predict whether battery usage between an expected time of disconnection from the power source and a next expected time of connection to the power source exceeds a threshold. The previous usage data may include a time series of previous charge and discharge intervals. The previous usage data may also include at least one of the following items: user calendar data, an alarm clock, a pass to an event associated with a mobile wallet, or data extracted from an email. The charge state that is less than the full charge state of the battery may be selected to reduce wear on the battery caused by time in a high charge state. The one or more processors may be further programmed to dynamically select the charge state that is less than the full charge state based on the predicted battery usage between the expected time of disconnection from the power source and the next expected time of connection to the power source.

[0007] The one or more processors may be further programmed to predict whether a long charge event is expected. If a long charge event is not expected, the one or more processors may be further programmed to immediately charge the battery to a full charge state. Alternatively, if a long charge event is expected, the one or more processors may be further programmed to delay charging the battery to a full charge state until a time before the expected disconnection time.

[0008] The one or more processors may be programmed to delay charging the battery to a fully charged state by reducing the rate at which the battery is charged. The one or more processors may be programmed to delay charging the battery to a fully charged state by temporarily pausing the charging of the battery.

[0009] The electronic device may further include a display. The one or more processors may be further programmed to communicate information about at least one of the time before the expected disconnection time or the state of charge less than the full state of charge of the battery to the user via the display. The electronic device may also include an input device. The one or more processors may be further programmed to receive user input about at least one of the time before the expected disconnection time or the state of charge less than the full state of charge of the battery from the user via the input device.

[0010] A method for operating an electronic device, the method being performed by a processor of the electronic device, and the method may include detecting that the electronic device has been connected to a power source; using previous usage data of the electronic device to predict whether battery usage between an expected time of disconnection from the power source and a next expected time of connection to the power source exceeds a threshold; and if the predicted battery usage between the expected time of disconnection from the power source and the next expected time of connection to the power source does not exceed the threshold, charging a battery of the electronic device to a charge state that is less than a full charge state of the battery.

[0011] Using previous usage data of the electronic device to predict whether battery usage between an expected time of disconnection from the power source and a next expected time of connection to the power source exceeds a threshold may include analyzing the previous usage data using a machine learning model. The previous usage data may include a time series of previous charge and discharge intervals. The previous usage data may also include at least one of the following: user calendar data, an alarm clock, a pass to an event associated with a mobile wallet, or data extracted from an email. The state of charge that is less than the full state of charge of the battery may be selected to reduce wear on the battery caused by time in a high state of charge.

[0012] The method may also include dynamically selecting the state of charge that is less than the full state of charge based on predicted battery usage between an expected time of disconnection from the power source and a next expected time of connection to the power source. The method may also include communicating information about the state of charge that is less than the full state of charge of the battery to a user via a display of the electronic device. The method may also include receiving user input from the user about the state of charge that is less than the full state of charge of the battery via an input device of the electronic device. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A block diagram of an electronic device is shown.

[0014] Figure 2 An optimized battery charging routine for an electronic device is shown.

[0015] Figure 3 A flow chart of an optimized charging routine is shown.

[0016] Figure 4 A flow chart of an optimized charging routine with dynamic charge termination (also referred to as optimized charging limit) is shown.

[0017] Figure 5 A block diagram of an optimized charging routine with dynamic charge termination or optimized charging limits is shown.

[0018] Figure 6 An electronic device that communicates parameters of an optimized battery charging routine to a user is shown. DETAILED DESCRIPTION

[0019] In the following description, for the purpose of explanation, many specific details are set forth in order to provide a thorough understanding of the disclosed concepts. As part of this description, some of the drawings in the drawings of the present disclosure represent structures and devices in block diagram form for simplicity. For the sake of clarity, not all features of actual implementations are described in the present disclosure. In addition, the language used in the present disclosure is selected for readability and instructional purposes and has not been selected to describe or limit the disclosed subject matter. Instead, the attached claims are intended for this purpose.

[0020] Various embodiments of the disclosed concepts are illustrated by way of example, and are not limited to individual drawings, in which similar reference numerals indicate similar elements. For simplicity and clear illustration, reference numerals are repeated in different drawings to indicate corresponding or similar elements, where appropriate. In addition, many specific details are shown in order to provide a full understanding of the specific implementation described herein. In other cases, methods, procedures, and components are not described in detail to avoid blurring the related functions described. Mentioning "one", "a kind of" or "another" embodiment in this disclosure is not necessarily the same or different embodiments, and this means at least one. A given drawing may be used to illustrate more than one embodiment or more than one type of features of the present disclosure, and all elements in the drawings may not be required for a given embodiment or type. When provided in the drawings, the reference numerals refer to the same element throughout several drawings, but they may not be repeated in each drawing. Unless otherwise specified, the drawings are not drawn to scale, and the proportions of certain components may be enlarged to better illustrate the details and features of the present disclosure.

[0021] Figure 1 1 is a block diagram of an electronic device 100 according to an embodiment of the present disclosure. The electronic device 100 may include, among other things, one or more processors 101 (collectively referred to herein as a single processor for convenience, which may be implemented as any suitable form of processing circuit), a memory 102, a non-volatile storage device 103, a display 104, an input device 105, an input / output (I / O) interface 106, a network interface 107, and a power system 108. Figure 1 The various functional blocks shown may include hardware elements (including circuits), software elements (including machine executable instructions), or a combination of hardware and software elements (which may be referred to as logic). Processor 101, memory 102, non-volatile storage device 103, display 104, input device 105, input / output (I / O) interface 106, network interface 107 and / or power system 108 may each be directly or indirectly communicatively coupled to each other (e.g., through or via another component, a communication bus, a network, etc.) to transmit and / or receive data between each other. It should be noted that Figure 1This is merely one example of a particular implementation and is intended to illustrate the types of components that may be present in electronic device 100 .

[0022] For example, electronic device 100 may include any suitable computing device, including a desktop computer or a laptop / notebook computer (such as the Apple Computer available from Apple Inc. of Cupertino, California). Pro, MacBook mini or Mac ), a portable electronic device or a handheld electronic device such as a wireless electronic device or a smart phone (such as available from Apple Inc. in Cupertino, California ), tablet computers (such as those available from Apple Inc. in Cupertino, California ), wearable electronic devices (such as the Apple ) and other similar devices.

[0023] Figure 1 The processor 101 and other related items in the embodiment may be completely embodied by hardware or by hardware programmed to execute appropriate software instructions. Figure 1 Other related items in the electronic device 100 may be single independent processing modules, or may be fully or partially combined in any of the other elements in the electronic device 100. The processor 101 may be implemented with a combination of a general-purpose microprocessor, a microcontroller, a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic device (PLD), a controller, a state machine, a gated logic, a discrete hardware component, a dedicated hardware finite state machine, or any other suitable entity that can perform calculations or other manipulations of information. The processor 101 may include one or more application processors, one or more baseband processors, or both, and perform various functions described herein.

[0024] exist Figure 1In the electronic device 100, the processor 101 may be operably coupled to the memory 102 and the non-volatile storage device 103 to execute various algorithms. Such programs or instructions executed by the processor 101 may be stored in any suitable article including one or more tangible computer-readable media. The tangible computer-readable medium may include the memory 102 and / or the non-volatile storage device 103, individually or collectively, to store instructions or routines. The memory 102 and the non-volatile storage device 103 may include any suitable article for storing data and executable instructions, such as random access memory, read-only memory, rewritable flash memory, hard drive, and optical disk. In addition, the program (e.g., operating system) encoded on such a computer program product may also include instructions that can be executed by the processor 101 to enable the electronic device 100 to provide various functions.

[0025] In some embodiments, the display 104 can facilitate a user viewing images generated on the electronic device 100. In some embodiments, the display 104 can include a touch screen that can facilitate a user to interact with a user interface of the electronic device 100. In addition, it should be understood that in some embodiments, the display 104 can include one or more liquid crystal displays (LCDs), light emitting diode (LED) displays, organic light emitting diode (OLED) displays, active matrix organic light emitting diode (AMOLED) displays, or some combination of these and / or other display technologies.

[0026] The input device 105 of the electronic device 100 can enable a user to interact with the electronic device 100 (e.g., pressing a button to increase or decrease the volume level). As with the network interface 107, the I / O interface 106 can enable the electronic device 100 to interact with various other electronic devices. In some embodiments, the I / O interface 106 may include an I / O port for a hardwired connection for charging and / or content manipulation using a standard connector and protocol such as the Lightning connector provided by Apple Inc. of Cupertino, California, a universal serial bus (USB), or other similar connectors and protocols. The network interface 107 may include, for example, one or more interfaces for a personal area network (PAN) such as an ultra-wideband (UWB) or Network; Local Area Network (LAN) or Wireless Local Area Network (WLAN), such as one of the IEEE 802.11x family of protocols (e.g. ) networks; and / or wide area networks (WANs), such as any standards associated with the 3rd Generation Partnership Project (3GPP), including, for example, 3rd Generation (3G) cellular networks, Universal Mobile Telecommunications System (UMTS), 4th Generation (4G) cellular networks, Long Term Evolution (LET), and / or 4G cellular networks. Cellular network, Long Term Evolution Licensed Assisted Access (LTE-LAA) cellular network, 5th generation (5G) cellular network and / or new radio (NR) cellular network, 6th generation (6G) or beyond 6G cellular network, satellite network, non-terrestrial network, etc. Specifically, the network interface 107 may include, for example, one or more interfaces for using a cellular communication standard of the 5G specification including a millimeter wave (mmWave) frequency range (e.g., 24.25-300 gigahertz (GHz)) that defines and / or implements a frequency range for wireless communication. The network interface 107 of the electronic device 100 may allow communication through the aforementioned networks (e.g., 5G, Wi-Fi, LTE-LAA, etc.).

[0027] The network interface 107 may also include, for example, one or more interfaces for: a broadband fixed wireless access network (eg, ), mobile broadband wireless network (mobile ), asynchronous digital subscriber line (e.g., ADSL, VDSL), digital video terrestrial broadcasting Network and its extension DVB handheld equipment Networks, ultra-wideband (UWB) networks, alternating current (AC) power lines, etc.

[0028] The power system 108 of the electronic device 100 may include any suitable power source, such as a rechargeable battery (e.g., a lithium-ion or lithium-polymer (Li-poly) battery) and / or a power converter, including a DC / DC power converter, an AC / DC power converter, a power adapter (which may be external), etc.

[0029] Figure 2 An optimized battery charging routine for an electronic device is shown. More specifically, Figure 2 A graph 200 of battery charge state on the vertical axis versus time on the horizontal axis is shown. Various events in the charging sequence are identified as times on the time axis. For example, before the device is plugged into an external power source (such as a mains power source, an external battery pack, a car accessory adapter, etc.), the battery may be discharging as indicated by curve segment 211. After the device is plugged into the external power source, the battery may begin charging as indicated by curve segment 212. When plugged into the external power source, the device may determine an estimated disconnect time 220 at which it is expected to be disconnected from the external power source, which can be used to intelligently adjust the charging sequence, as described in more detail below.

[0030] The estimated disconnect time 220 may be determined in a variety of ways based on sensor inputs (e.g., time of day, location, etc.) and machine learning models or other program structures that take into account previous activity or otherwise infer possible disconnect times. For example, if a user typically plugs in a device at home around 10 p.m. and unplugs it around 6 a.m., the device may infer (if it is plugged in at the user's home location around 10 p.m.) that it will remain on the charger until around 6 a.m., and the charging sequence may be adjusted accordingly. The same is true if the user plugs in at the user's work location around 8 a.m. and unplugs around 12 p.m., which may allow the system to infer that the user is typically sitting at their desk at this time. Inferences may be based on inputs other than time and location. For example, if a user is plugged in and the device can detect that they are moving at a high rate, the device may infer that they are in a car. More or less detailed inferences may be drawn depending on the particular implementation of the machine learning model or other program structure. In any case, the inference may be used to modify the charging sequence as follows.

[0031] In the first example of the previous paragraph, where the device infers that it is at the user's home location and has been plugged in at around 10 PM, the device may assume that it will remain connected to the main power source until around 6 AM. Therefore, the device does not need to charge at a maximum rate (represented by the slope of curve segment 212) to reach a full charge as quickly as possible. In fact, it may be desirable to slow the charging rate and / or pause charging for a period of time (depicted by curve segment 213) to delay full charging of the battery. For example, the useful life of a battery (i.e., the battery health or number of charging cycles that can maintain substantially full capacity) may be extended by reducing the amount of time the battery spends at full charge. Thus, in Figure 2 In the optimized battery charging example, battery charging may be suspended during interval 213 .

[0032] The estimated disconnection time 220 may further be used by the device to resume charging at a time selected to ensure that the battery reaches a fully charged state (represented by the charge level / curve segment 215) before the estimated disconnection time 220. This resumed charging is represented by curve segment 214. This charging segment may occur at a reduced rate, for example because the battery is closer to a full charge, as indicated by the reduced slope of the resumed charging segment 214 relative to the original charging segment 212. In any case, the machine learning model or other program structure that controls the optimized battery charging sequence may take into account the expected charging rate and the estimated disconnection time to ensure that the battery is fully charged before the user disconnects the device from the external power source. This may be achieved by selecting an estimated disconnection time that is prior to a possible or typical disconnection time and / or by ensuring that the battery is fully charged before the estimated disconnection time 220. Once the user disconnects the device from the external power source, the battery may begin to discharge, as shown by curve segment 216.

[0033] Relative to Figure 2 The optimized battery charging techniques described are "optimized" in the sense that they may improve battery health by reducing the amount of time the battery spends in a fully charged state. Figure 3 A flowchart 300 is shown that further depicts such an optimized charging routine. The various processes described in flowchart 300 may be performed by a processor of electronic device 100, a microcontroller or other control circuit associated with power system 108, or any other suitable component of device 100. In some cases, different parts of the processes may be performed by different components of device 100. Beginning at block 321, the device may detect that it has been plugged into an external power source.

[0034] Once a plug-in event is detected, the optimized charging system may determine whether a "long charge" is expected (block 322). In this sense, a long charge means a charging event that will take longer than the time it takes to bring the battery to a fully charged state. As an example, if a user often plugs in his device around 10 p.m. when located at home, and usually unplugs the device around 6 a.m., being plugged in at 10 p.m. while located at home may trigger detection of a long charging event. Similarly, if a user often plugs in his device around 8 a.m. when arriving at the office, and unplugs the device around 12 p.m. when leaving for lunch, this may also be interpreted as an expected long charging event. However, if the device is plugged in at an unusual time or location, the device may not have an expectation of how long the charging event will last. The device may be equipped with a variety of programming, criteria, algorithms, etc. to determine whether an event is expected to be a long charging event. This may include using machine learning models, heuristics, and other data processing techniques to analyze historical data (such as previous charging and discharging events), current sensor data (such as charging status, location, etc.), and external data (such as information from the user's calendar related to upcoming events and their associated locations).

[0035] If a long charging event is not expected, the device can continue to charge the battery to 100% (box 326), which can provide an expected user experience. Alternatively, if a long charging event is detected, an optimized battery charging routine can be triggered (box 323). The basic concept of the optimized battery charging routine is as described above. That is, the battery is charged to a certain threshold below the full charge, for example, 80% (box 324). The threshold does not have to be 80% and can be any suitable value. However, minimizing the amount of time spent when the battery is above such a threshold can reduce battery wear or aging that causes the battery capacity to decrease. Then, in box 325, the device can determine whether an unplug event is about to occur. In this sense, the event is about to occur in the sense that the remaining time before the unplug event is approximately equal to the amount of time required to charge the battery from an 80% state (or other pause threshold) to a full charge (e.g., 100%). If an impending unplug event is not expected, the system can keep the battery at a charge state of 80% or other threshold (box 324). Otherwise, if an impending unplug is expected, the system can complete charging the battery to 100%.

[0036] As can be inferred from the foregoing description, part of the routine of the optimized battery charging routine, such as performed by block 322 and / or block 323, can be to determine when the device is expected to be unplugged. This can be determined using various algorithms, models, data analysis, etc. as discussed above. In one example embodiment, upon detecting the plug-in, the device can estimate the unplugging or disconnecting time. The OBC routine 323 can then specify the time before the estimated unplugging time as the target time to complete the battery charging. For example, if the user typically plugs in at 10 pm and unplugs at 6 am, 5 am can be used as the target time to complete the battery charging to ensure that the battery is fully charged when the user needs it. Then, if it takes 1 hour to charge the battery from 80% to 100%, the impending unplugging detection of block 325 can be set to occur at 4 am. These times, time periods, and charging threshold states are only examples, and other values ​​can be used as needed.

[0037] An optimized battery charging routine as described above can improve battery health and life by reducing the time that the battery spends in a highly charged state. However, using such an approach, the battery will remain in a highly charged state for significant periods of time. For example, using the times discussed above, the battery may be in a highly charged state (e.g., above 80%) from 4 a.m. (when restored to 100%) until sometime in the morning, when user usage of the phone causes the charge state to return to below 80% (or other appropriate threshold). However, if the user's usage pattern between insertion events does not use all or a significant portion of the battery's entire full charge capacity, these several hours above the charging threshold may still cause unnecessary wear on the battery.

[0038] Figure 4 A flow chart 400 of an optimized charging routine with dynamic charge termination (also referred to as optimized charging limit) is shown. Figure 3 As with the optimized battery charging routine discussed above, portions of the routine may be performed by any suitable processor, microcontroller, or other control circuitry of device 100 . Figure 4 The optimized charging routine and Figure 3 The optimized charging routine of is different in at least one respect that, upon detecting an insertion event (block 321), the system may determine whether the user's expected battery consumption is expected to be low before the next insertion (block 427). In other words, the system may determine whether the user will use all or a substantial portion of the battery's capacity before inserting it again. This determination may be made in a variety of ways, but may generally be considered to rely on an analysis of past usage data. The past usage data may include a time series of previous battery charge and discharge cycles. This data may be combined with related data (such as the time of day at which such charge and discharge cycles begin and end, the day of the week associated with the charge cycles, location data associated with the charge and discharge cycles, and any other data related to the user's device usage patterns that may be appropriately associated with the battery charge and discharge patterns) to reliably predict the amount of charge required by the user to store sufficient energy in the battery for the device to last until the next insertion event.

[0039] Determining whether the user's expected battery consumption before the next plug-in event is low consumption can use a variety of data analysis algorithms, types, heuristics, etc. As an example, the machine learning model can be configured to predict whether the user's discharge is expected to be greater than a threshold before the next plug-in event based on previous usage data. An alternative framework for this problem is whether the user's predicted use before the expected next plug-in event will cause the battery state of charge to be below a certain minimum threshold. As an example, it may be desirable to keep the battery state of charge above 10%, 20%, or some other suitable threshold, which can allow for unexpected use and prevent user concerns associated with a low state of charge. For example, if analysis of previous usage data indicates that the expected battery usage is 50% before the next expected plug-in event, it may not be necessary to charge to 100% before the next unplug event to meet the user's battery needs. On the other hand, if the expected use before the next plug-in event is 80% or more of the battery capacity before the next plug-in event, it may be preferred to charge the battery to full capacity (e.g., using an optimized battery charging routine as described above) to ensure that the user has sufficient available power. The anticipated usage of 50% and 80% described above are provided as examples, and other suitable thresholds may be used as desired.

[0040] In any case, if low consumption is expected before the next charging cycle, the dynamic charge end process 428 may be called. In the dynamic charge end process, a state of charge less than 100% may be selected as the end point of the charging process. In some cases, this may be 80%, but it may be any other suitable value. Similarly, in some applications, the charge end threshold may be a fixed value, i.e., if the low consumption expected state is triggered, the battery is always charged to a fixed state of charge less than 100%. In other applications, the dynamic charge end threshold may vary based on the actual expected consumption before the next insertion. For example, if a low discharge margin of 20% is selected and it is expected that the user will use 40% of the battery capacity before the next insertion, the dynamic charge end process 428 may charge the battery to 60%. Alternatively, in the case of a low discharge margin of 10% and an expected discharge of 60% before the next insertion event, a state of charge of 70% may be selected as the dynamic charge end threshold. In other words, the dynamic charge end threshold may be fixed (e.g., 80% in the first example above) or may vary based on the expected discharge. This variable dynamic end-of-charge threshold may also be affected by other conditions.

[0041] Alternatively, if low consumption is not expected before the next insertion event (i.e., higher consumption is expected), the optimized battery charging system represented by flowchart 400 may employ a method similar to that described above with respect to Figure 3 An optimized battery charging routine is discussed that controls the time it takes for a battery to reach a full charge to reduce the amount of time the battery spends above a threshold corresponding to a high state of charge (eg, 80%). Figure 4 These parts of the algorithm are similar to those above with respect to Figure 3 The discussion is the same, so the details of the process are not repeated here.

[0042] One other addition to the optimized charging routine depicted by flowchart 400 relates to the case where the device remains connected to the charger after reaching a 100% state of charge (as depicted in block 417). In this case, the system can stop battery charging and allow the battery to begin discharging. The device can also resume optimized battery charging (block 418), in which case the system can determine the next expected unplug time and operate the charging system to reach full charge before the next unplug time.

[0043] Figure 5A block diagram of various processing elements for an optimized charging routine with dynamic end of charge or optimized charging limits is shown. Block 531 may be an unplug estimation model. This may be implemented by programming one or more processors of device 100 (including a processor of processor complex 101 or a microcontroller or other suitable circuit associated with power system 108) to estimate the disconnect time when the device is plugged into an external power source. In some embodiments, unplug estimation model 531 may be a machine learning model that analyzes previous usage patterns to estimate predicted unplug times.

[0044] The unplugging estimation model 531 may take a variety of inputs. These inputs may include previous plugging and unplugging times, which may be advantageously processed by a machine learning model. Additional parameters may include locations associated with or derived from the plugging and disconnecting times, time of day, day of the week, plugging duration, unplugging duration, etc. The inputs may also include other usage data, including but not limited to user calendar data, alarm clocks, passes for events associated with mobile wallets or similar applications, data extracted from emails, or other user data. As outlined below, although measures may be taken to protect user privacy when analyzing such data to predict estimated values, it may be recommended to restrict access or not access certain types of data for privacy reasons. In any case, the predicted unplugging or disconnecting time may be provided to the dynamic charging end / optimized battery charging processor 535 (described in more detail below), and optionally provided to the usage estimation model 532 via path 533.

[0045] Continue to refer Figure 5 , block 532 may be to use an estimation model. Figure 4 As described above, this can be used to estimate the user's battery usage before the next insertion event and / or predict whether the user's battery usage will be above or below a threshold before the next insertion event. This prediction can be used to drive the above reference Figure 4 The dynamic end-of-charge limit described above. The usage estimation model 532 may be implemented by programming one or more processors of the device 100 (including a processor of the processor complex 101 or a microcontroller or other suitable circuit associated with the power system 108) to estimate the device usage before the next time the device is expected to be plugged into an external power source. The next expected plug-in time may be calculated by the usage estimation model 532 or may be received from the unplugging estimation model 531 via path 533. In some embodiments, the usage estimation model 532 may be a machine learning model that analyzes previous usage patterns to determine an estimated usage before the next plug-in time.

[0046] The unplugging estimation model 531 may take a variety of inputs. These inputs may include previous plug-in and unplugging times, which may be advantageously processed by a machine learning model. The inputs may also include usage data corresponding to device usage and / or battery discharge between plug-in intervals. Additional parameters may include locations associated with or derived from plug-in and disconnection times, time of day, day of the week, plug-in duration, unplugging duration, etc. The inputs may also include other usage data, including but not limited to user calendar data, alarm clocks, passes for events associated with mobile wallets or similar applications, data extracted from emails or other user data or other user activities. As outlined below, although measures may be taken to protect user privacy when analyzing such data to predict estimated values, it may be recommended to restrict access to or not access certain types of data for privacy reasons. In any case, the predicted use before the next plug-in or an indication of whether such use will be above or below a properly selected threshold may be provided to the dynamic charging end / optimized battery charging processor 535 (described in more detail below).

[0047] Continue to refer Figure 5 , block 534 may be a dynamic charge termination / optimized battery charging enable / disable module. This module may be used to selectively enable or disable the dynamic charge termination system and / or the optimized battery charging system when selected inputs indicate that use of these systems may be detrimental to the user. At the simplest level, enable / disable module 534 may be manually triggered by a user to disable as described below with respect to Figure 6 Thus, inputs to the enable / disable module 534 may be bound to one or more user interface elements that allow such user-driven actions. Additionally or alternatively, these inputs may include access to any of a variety of data that indicates that charging should be accelerated and that dynamic charge termination or optimized battery charging should not be used.

[0048] Additional inputs may include location associated with or derived from plug-in and disconnection times, time of day, day of the week, plug-in duration, unplugging duration, etc. Inputs may also include other usage data, including but not limited to user calendar data, alarms, passes for events associated with mobile wallets or similar applications, data extracted from emails, or other user data or other user activities. Different types of data may be more suitable for certain types of devices. For example, data indicating a user's exercise time may be more relevant to the user's watch because smart watch activity may increase during exercise. However, the data may not be very relevant to a smartphone or laptop. As another example, a calendar entry indicating a location outside the user's office may be more relevant to a laptop or notebook computer because it can be assumed that the computer will not be plugged in during this time. In some cases, the frequency with which the user plugs in and / or the frequency with which the user puts the device in low power mode can be used as a hint to infer the user's concern about battery power. These signals can also be used to enable or disable dynamic charge termination and / or optimized battery charging. As outlined below, while steps can be taken to protect user privacy when analyzing such data to predict estimates, it may be advisable to limit or exclude access to certain types of data due to privacy concerns.

[0049] In any case, the enable / disable block 534 may include appropriate programming to disable dynamic charge termination and / or optimized battery charging routines as desired. An example of such a situation may be when the confidence level in the accuracy of the prediction of the unplug time (by the unplug estimation model 531) or the estimated usage (by using the estimation model 534) drops below a threshold.

[0050] Each of the above modules 531, 532 and 534 has been described as a separate module, but in at least some embodiments, two or more of these models may be combined into an integrated model. Alternatively, although the estimation models 531 and 532 are described above in terms of machine learning models, other non-machine learning models may also be combined, which may be used in conjunction with the machine learning models or as an alternative to the machine learning models. In any case, the output of the model predicting unplugging time, usage before the next plug-in, and whether dynamic charge termination and / or optimized battery charging should be enabled or disabled may be provided to the dynamic charge termination / optimized battery charging box 535. The dynamic charge termination / optimized battery charging box 535 may broadly correspond to the above with respect to Figure 3 and Figure 4The optimized battery charging block 323 and the dynamic charge termination block 428 discussed above. The dynamic charge termination / optimized battery charging block 535 can be programmed or configured to generate a charging characteristic 536 based on the output of the models 531, 532, and 534 discussed above. Such charging characteristics can, for example, generally correspond to the above with respect to Figure 2 The battery charging profile discussed. Thus, charging characteristics may include selection of charging time, charging rate, charging target, holding threshold, and / or other parameters that allow the battery to charge to an acceptable level in an appropriate time while attempting to reduce the amount of time the battery spends in a high state of charge.

[0051] Figure 6 An electronic device 100 is shown communicating parameters of an optimized battery charging routine to a user. Figure 6 The electronic device 100 is shown as a smart phone, but in fact it can be any of a variety of electronic devices, such as a smart watch, a tablet computer, a notebook or laptop computer, etc. As an example, the electronic device 100 can use the display 104 to present a message 641 to the user, which indicates the time when the charging is planned to be completed and the level to which the battery can be charged. The time when the charging is planned to be completed can be associated with the optimized battery charging routine as described above, and the level to which the battery is to be charged can be associated with the dynamic charge end system as described above. In some cases, the electronic device 100 can present a control element 642 (such as a UI button) that the user can use to bypass the optimized charging routine and the dynamic charge end threshold to complete the charging to 100% charge state as quickly as possible. In some cases, the electronic device 100 can also present a control element 643 (such as a UI button) that the user can use to bypass the dynamic charge end routine (charging to a full charge state), but allow the optimized battery charging system to delay reaching a full charge until shortly before the predicted unplug / disconnect time. In some embodiments, control elements 642 and 643 and message 641 may be part of the same UI element, such as a GUI message / button, etc. In other cases, one or more other output devices of electronic device 100 may be used to provide an indication to the user, and one or more other input devices of electronic device 100 may be used to receive input from the user indicating that the user wishes to bypass the optimized charging settings and complete charging as quickly as possible.

[0052] As used throughout this disclosure, machine learning may refer to algorithms, statistical models, and the like that a computer system (such as electronic device 100) can use to perform a specific task with or without explicit instructions. For example, a machine learning process may generate a mathematical model based on a data sample (referred to as "training data") to make predictions or decisions without being explicitly programmed to perform a task. Depending on the inference to be made, the electronic device 100 (or its subsystems or associated devices) may implement different forms of machine learning. For example, in some embodiments (e.g., when there are specific known examples related to future predictions or estimates that a machine learning engine may be delegated to generate), the machine learning engine may implement supervised machine learning. In supervised machine learning, a mathematical model of a set of data contains both inputs and expected outputs. This data is referred to as "training data" and may include a set of training examples. Each training example may have one or more inputs and expected outputs, also referred to as a supervisory signal. In the mathematical model, each training example is represented by an array or vector (sometimes referred to as a feature vector), and the training data is represented by a matrix. By iteratively optimizing the objective function, a supervised learning algorithm can learn a function that can be used to predict an output associated with a new input. The optimal function may allow the algorithm to correctly determine outputs for inputs that were not part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task.

[0053] Supervised learning algorithms can include classification and regression techniques. Classification algorithms are used when the output is restricted to a finite set of values, while regression algorithms are used when the output has values ​​within a certain range. Similarity learning is a closely related area of ​​supervised machine learning that is related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. Similarity learning has applications in ranking, recommendation systems, visual identity tracking, face verification, and speaker verification.

[0054] Additionally and / or alternatively, in some cases, it may be advantageous for a machine learning engine to utilize unsupervised learning (e.g., when a particular output type is unknown). An unsupervised learning algorithm takes a set of data containing only inputs and looks for structure in the data, such as groupings or clusters of data points. Thus, the algorithm learns from test data that has not yet been labeled, classified, or categorized. Instead of responding to feedback, an unsupervised learning algorithm identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data.

[0055] That is, the machine learning engine may implement cluster analysis, which is the assignment of a set of observations into subsets (called clusters) such that observations within the same cluster are similar according to one or more predetermined criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions about the structure of the data, which are typically defined by some similarity measure and evaluated, for example, by internal compactness (or similarity between members of the same cluster) and separability (dissimilarity between clusters). In additional or alternative embodiments, the machine learning engine may implement other machine learning techniques, such as those based on estimated density and graph connectivity.

[0056] The above describes an exemplary embodiment of transferring certain tasks performed by an electronic device based on the power grid conditions associated with an external power source connected to the electronic device. Although many specific features and various embodiments have been described, it should be understood that unless otherwise indicated to be mutually exclusive, the various features and embodiments may be combined into various arrangements in a specific implementation. Therefore, the various embodiments described above are provided only by way of example and should not be interpreted as limiting the scope of the present disclosure. Various modifications and changes may be made to the principles and embodiments herein without departing from the scope of the present disclosure and without departing from the scope of the claims.

[0057] The foregoing describes exemplary embodiments of electronic systems that are capable of sending certain information between other systems and devices. The present disclosure contemplates that this information transfer will improve the functionality of the device. Entities implementing the present technology, to the extent that any sensitive information is used in a particular implementation, should take care to ensure compliance with established privacy policies and / or privacy practices. Specifically, such entities will be expected to implement and consistently apply privacy practices that are generally recognized as meeting or exceeding industry or government requirements for maintaining user privacy. Implementers should inform users where personally identifiable information is expected to be sent, and allow users to "opt in" or "opt out" to participate.

[0058] Once the data is no longer needed, risks can be minimized by limiting the collection of data and deleting the data. In addition, and when applicable, data de-identification can be used to protect the privacy of users. For example, a device identifier can be partially masked to convey the power characteristics of the device without uniquely identifying the device. De-identification can be promoted when appropriate by removing identifiers, controlling the amount or specificity of stored data (e.g., collecting location data at the city level rather than at the address level), controlling how data is stored (e.g., aggregating data across users), and / or other methods such as differential privacy. Robust encryption can also be used to reduce the likelihood that communications between devices can be intercepted, spoofed, or otherwise tampered with.

Claims

1. An electronic device, comprising: electrical systems including batteries; and One or more processors programmed to: detecting that the electronic device is connected to a power source; using previous usage data of the electronic device to predict whether battery usage between an expected time of disconnection from the power source and a next expected time of connection to the power source exceeds a threshold; and If the predicted battery usage between an expected time of disconnection from the power source and a next expected time of connection to the power source does not exceed the threshold, the battery is charged to a state of charge that is less than a full state of charge of the battery.

2. An electronic device according to claim 1, wherein the one or more processors are programmed to use previous usage data of the electronic device and use a machine learning model that analyzes the previous usage data to predict whether battery usage between an expected time of disconnection from the power source and a next expected time of connection to the power source exceeds a threshold.

3. The electronic device of claim 2, wherein the previous usage data comprises a time series of previous charging and discharging intervals.

4. The electronic device of claim 3, wherein the previous usage data further comprises at least one of: user calendar data, an alarm clock, a pass to an event associated with a mobile wallet, or data extracted from an email.

5. The electronic device of claim 1, wherein the state of charge that is less than the full state of charge of the battery is selected to reduce wear on the battery caused by time in a high state of charge.

6. An electronic device according to claim 1, wherein the one or more processors are further programmed to dynamically select the charge state that is less than the full charge state based on predicted battery usage between an expected time of disconnection from the power source and a next expected time of connection to the power source.

7. The electronic device of claim 1, wherein the one or more processors are further programmed to: predicting whether a long charging event is expected; and If a long charging event is not expected, immediately charging the battery to the full charge state; or If a long charging event is anticipated, charging of the battery to the full state of charge is delayed until a time prior to the anticipated disconnect time.

8. The electronic device of claim 1, wherein the one or more processors are programmed to delay charging the battery to the fully charged state by reducing a rate at which the battery is charged.

9. The electronic device of claim 1, wherein the one or more processors are programmed to delay charging the battery to the fully charged state by temporarily pausing battery charging.

10. An electronic device according to claim 6, further comprising a display, wherein the one or more processors are further programmed to convey to a user via the display information about at least one of a time before an expected disconnection time or a charging state that is less than the full charge state of the battery.

11. An electronic device according to claim 10, further comprising an input device, wherein the one or more processors are further programmed to receive user input from the user via the input device regarding at least one of the time before the expected disconnection time or the charging state that is less than the full charge state of the battery.

12. The electronic device of claim 1, further comprising a display, wherein the one or more processors are further programmed to convey information about the charge state that is less than the full charge state of the battery to a user via the display.

13. An electronic device according to claim 12, further comprising an input device, wherein the one or more processors are further programmed to receive user input from the user via the input device regarding the charge state that is less than the full charge state of the battery.

14. A method of operating an electronic device, the method being executed by a processor of the electronic device, and the method comprising: detecting that the electronic device is connected to a power source; using previous usage data of the electronic device to predict whether battery usage between an expected time of disconnection from the power source and a next expected time of connection to the power source exceeds a threshold; and If the predicted battery usage between an expected time of disconnection from the power source and a next expected time of connection to the power source does not exceed the threshold, charging a battery of the electronic device to a state of charge that is less than a full state of charge of the battery.

15. A method according to claim 14, wherein using previous usage data of the electronic device to predict whether battery usage between an expected time of disconnection from the power source and a next expected time of connection to the power source exceeds a threshold includes using a machine learning model to analyze the previous usage data.

16. The method of claim 15, wherein the previous usage data comprises a time series of previous charging and discharging intervals.

17. The method of claim 16, wherein the previous usage data further comprises at least one of: user calendar data, an alarm clock, a pass to an event associated with a mobile wallet, or data extracted from an email.

18. The method of claim 14, wherein the state of charge that is less than the full state of charge of the battery is selected to reduce wear on the battery caused by time in a high state of charge.

19. The method of claim 14, comprising dynamically selecting the state of charge that is less than the full state of charge based on predicted battery usage between an expected time of disconnection from the power source and a next expected time of connection to the power source.

20. The method of claim 14, further comprising conveying information to a user via a display of the electronic device regarding the state of charge that is less than the full state of charge of the battery.

21. The method of claim 20, further comprising receiving user input from the user via an input device of the electronic device regarding the state of charge that is less than the full state of charge of the battery.

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