ENERGY-BASED TASK SHIFTING

Abstract

An electronic device may include a power system including a battery and a processor programmed to detect connection of an external power source to the electronic device, determine an estimated disconnection time at which the external power source is expected to be disconnected from the electronic device, analyze power grid data about the external power source to identify one or more desired battery charging intervals and one or more undesired battery charging intervals prior to the estimated disconnection time, and operate the power system to charge the battery from the external power source during the identified one or more desired battery charging intervals and inhibit charging of the battery during the one or more undesired battery charging intervals.The processor may be programmed to inhibit battery charging by slowing the battery charging rate or preventing the battery from charging.

Description

BACKGROUND

[0001] Electronic devices can perform tasks that are not time-critical. For example, users of battery-operated electronic devices may plug them in overnight to charge, even though the time required to charge the battery is shorter than the time the device is plugged in. Likewise, electronic devices can perform background tasks that are not time-critical. SUMMARY

[0002] In some cases, it may be desirable to postpone non-time-critical tasks performed by an electronic device, such as battery charging and / or background processing, to specific time windows depending on the characteristics of a power source connected to the electronic device.

[0003] An electronic device may include a power system including a battery and a processor programmed to detect connection of an external power source to the electronic device, determine an estimated disconnection time at which the external power source is expected to be disconnected from the electronic device, analyze power grid data about the external power source to identify one or more desired battery charging intervals and one or more undesired battery charging intervals prior to the estimated disconnection time, and operate the power system to charge the battery from the external power source during the identified desired battery charging interval(s) and inhibit charging of the battery during the undesired battery charging interval(s).The processor may be programmed to determine an estimated disconnection time using a machine learning model. The processor may be programmed to inhibit battery charging by reducing the battery charging rate or preventing the battery from charging.

[0004] The processor may be further programmed to retrieve the power grid data corresponding to the external power source from a grid data server. The power grid data corresponding to the external power source may include carbon intensity data. The one or more desired battery charging intervals may be lower carbon intensity intervals, and the one or more undesired battery charging intervals may be higher carbon intensity intervals. The power grid data corresponding to the external power source may include cost data. The one or more desired battery charging intervals may be lower cost intervals, and the one or more undesired battery charging intervals may be higher cost intervals.

[0005] The electronic device may further include a display, and the processor may be further programmed to communicate information about the one or more desired battery charging intervals or the one or more undesired battery charging intervals to a user via the display. The electronic device may further include an input device, and the processor may be further programmed to receive user inputs for charging via the input device.

[0006] A method of operating an electronic device, performed by a processor of the electronic device, may include detecting the connection of an external power source to the electronic device, determining an estimated disconnection point at which the external power source is expected to be disconnected from the electronic device, analyzing power grid data corresponding to the external power source to identify one or more desired battery charging intervals and one or more undesired battery charging intervals prior to the estimated disconnection time, and operating the power system including charging the battery from the external power source during the identified one or more desired battery charging intervals and inhibiting battery charging during the one or more undesired battery charging intervals.Determining the estimated disconnection time may involve using a machine learning model. Inhibiting battery charging may involve reducing the charging rate or preventing battery charging.

[0007] The method may further include retrieving the power grid data corresponding to the external power source from a grid data server. The power grid data corresponding to the external power source may include carbon intensity data. The one or more desired battery charging intervals are lower carbon intensity intervals, and the one or more undesired battery charging intervals are higher carbon intensity intervals. The power grid data corresponding to the external power source may include cost data. The one or more desired battery charging intervals may be lower cost intervals, and the one or more undesired battery charging intervals may be higher cost intervals.

[0008] The method may further include communicating information about the one or more desired battery charging intervals or the one or more undesired battery charging intervals to a user via a display of the electronic device. The method may further include receiving user inputs for charging via an input device of the electronic device. BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 illustrates a block diagram of an electronic device. Fig. Figure 2 illustrates an optimized battery charging routine for an electronic device. Fig. Figure 3 illustrates an optimized battery charging routine for an electronic device that adapts battery charging to grid conditions. Fig. Figure 4 illustrates a flowchart of an optimized battery charging routine for an electronic device that adapts battery charging to grid conditions. Fig. Figure 5 illustrates a system including an electronic device that implements an optimized battery charging routine that adapts to grid conditions and external data sources that support the optimized battery charging routine. Fig. Figure 6 illustrates an electronic device that communicates parameters of an optimized battery charging routine to a user. Fig. Figure 7 illustrates an optimization routine for background processing in an electronic device that adapts the background processing to the network conditions. Fig. Figure 8 illustrates a flowchart of a background processing adaptation routine in an electronic device that adapts background processing to network conditions. DETAILED DESCRIPTION

[0009] In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the disclosed concepts. For purposes of this description, some drawings of this disclosure illustrate structures and devices in block diagram form for convenience. For clarity, this disclosure does not describe all features of an actual implementation. Furthermore, the language used in this disclosure is chosen for readability and teaching purposes and not to define or limit the disclosed subject matter. Rather, the appended claims are provided for that purpose.

[0010] Various embodiments of the disclosed concepts are illustrated by way of example and not limitation in the accompanying drawings, wherein like references indicate like elements. For simplicity and clarity of illustration, reference numerals have been repeated where appropriate throughout the several figures to indicate corresponding or analogous elements. Furthermore, numerous specific details are set forth in order to provide a thorough understanding of the implementations described herein. In other instances, methods, procedures, and components have not been described in detail in order not to obscure the associated relevant function being described. References in this disclosure to "a," "a particular," or "another" embodiment do not necessarily refer to the same or different embodiment, and mean at least one.A given figure may be used to illustrate the features of more than one embodiment or more than one type of disclosure, and not all elements in the figure may be required for a given embodiment or type. A reference number, when provided in one drawing, refers to the same element in each of the different drawings, although it may not be repeated in every drawing. The drawings are not to scale unless otherwise indicated, and the proportions of certain parts may be exaggerated to better illustrate details and features of the present disclosure.

[0011] Fig. 1 is a block diagram of an electronic device 100 according to embodiments of the present disclosure. The electronic device 100 may include, among other things, one or more processors 101 (collectively referred to herein for convenience as a single processor, which may be implemented in any suitable form of processing circuitry), a memory 102, a non-volatile memory 103, a display 104, input devices 105, an input / output (I / O) interface 106, a network interface 107, and a power system 108. The various functional blocks shown in Fig. 1 may include hardware elements (including circuit logic), software elements (including machine-executable instructions), or a combination of both hardware and software elements (which may be referred to as logic). The processor 101, the memory 102, the non-volatile memory 103, the display 104, the input devices 105, the input / output (I / O) interface 106, the network interface 107, and / or the power system 108 may each be communicatively coupled to one another directly or indirectly (e.g., through or via another component, a communication bus, a network) to send and / or receive data between one another. It should be noted that Fig. 1 is merely an example of a particular implementation and is intended to illustrate the types of components that may be present in the electronic device 100.

[0012] By way of example, the electronic device 100 may include any suitable computing device, including a desktop or notebook computer (e.g., in the form of a MacBook®, MacBook® Pro, MacBook Air®, iMac®, Mac® mini, or Mac Pro®, available from Apple Inc. of Cupertino, California, USA), a portable or handheld electronic device, such as a wireless electronic device or a smartphone (e.g., in the form of an iPhone® model, available from Apple Inc. of Cupertino, California, USA), a tablet (e.g., in the form of an iPad® model, available from Apple Inc. of Cupertino, California, USA), a wearable electronic device (e.g., in the form of an Apple Watch® from Apple Inc. of Cupertino, California, USA), and other similar devices.

[0013] The processor 101 and other associated elements in Fig. 1 may be implemented entirely in hardware or by hardware programmed to execute appropriate software instructions. Furthermore, the processor 101 and other associated elements may be Fig. 1 may be a single, self-contained processing module or may be fully or partially integrated within any of the other elements within the electronic device 100. The processor 101 may be implemented with any combination of general-purpose microprocessors, microcontrollers, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), programmable logic devices (PLDs), controllers, state machines, gated logic, separate hardware components, dedicated hardware finite state machines, or any other suitable entities capable of performing computations or other processing of information. The processor 101 may include one or more application processors, one or more baseband processors, or both, and may perform the various functions described herein.

[0014] In the electronic device 100 of Fig. 1, the processor 101 may be operatively coupled to a memory 102 and non-volatile storage 103 to execute various algorithms. Such programs or instructions executed by the processor 101 may be stored in any suitable article of manufacture including one or more tangible, computer-readable media. The tangible, computer-readable medium may include the memory 102 and / or the non-volatile memory 103 individually or collectively to store the instructions or routines. The memory 102 and the non-volatile memory 103 may include any suitable articles of manufacture for storing data and executable instructions, such as random access memory, read-only memory, rewritable flash memory, hard disks, and optical disks. In addition, programs (e.g.,an operating system) may also include instructions that can be executed by the processor 101 to enable the electronic device 100 to provide various functionalities.

[0015] In certain embodiments, display 104 may enable users to view images generated on electronic device 100. In some embodiments, display 104 may include a touchscreen that may facilitate user interaction with a user interface of electronic device 100. Further, it should be understood that in some embodiments, display 104 may 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 a combination of these and / or other display technologies.

[0016] Input devices 105 of electronic device 100 may enable a user to interact with electronic device 100 (e.g., press a button to increase or decrease a volume level). I / O interface 106 may enable electronic device 100 to interface with various other electronic devices, as may network interface 107. In some embodiments, I / O interface 106 may include an I / O port for a hardwired connection for charging and / or manipulating content using a standard connector and protocol, such as the Lightning connector provided by Apple Inc. of Cupertino, California, USA, a Universal Serial Bus (USB) connector, or another similar connector and protocol.The network interface 107 may, for example, include one or more interfaces for a personal area network (PAN), such as an ultra-wideband (UWB) or a BLUETOOTH® network, a local area network (LAN) or a wireless local area network (WLAN), such as a network employing one of the protocols of the IEEE 802.11x family (e.g., WI-FI®), and / or a wide area network (WAN), such as any Third Generation Partnership Project (3GPP) cellular network standards, including, for example, a 3rd Generation (3G) cellular network, a Universal Mobile Telecommunications System (UMTS), a 4th Generation (4G) cellular network, a Long Term Evolution (LTE®) cellular network, a Long Term Evolution License Assisted Access (LTE-LAA) cellular network, a 5th Generation (5G) cellular network, and / or a New Radio cellular network. (NR mobile network), a 6th generation mobile network.Generation (6G) or higher than 6G, a satellite network, a non-terrestrial network, and so on. In particular, the network interface 107 may include, for example, one or more interfaces for using a cellular communication standard of the 5G specifications, including the millimeter-wave (mmWave) frequency range (e.g., 24.25 to 300 gigahertz (GHz)), which defines and / or enables frequency ranges used for wireless communication. The network interface 107 of the electronic device 100 may enable communication over the aforementioned networks (e.g., 5G, Wi-Fi, LTE-LAA, and the like).

[0017] The network interface 107 may also include one or more interfaces, for example, for Broadband Fixed Wireless Access networks (e.g., WiMAX®), Mobile Broadband Wireless Networks (Mobile WiMAX®), Asynchronous Digital Subscriber Lines (e.g., ADSL, VDSL), a Digital Video Broadcasting Terrestrial (DVB-T®) network and its DVB Handheld Extension (DVB-H®) network, an Ultra Wideband (UWB) network, AC power lines (AC power lines), and the like.

[0018] 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.

[0019] Fig. Figure 2 illustrates an optimized battery charging routine for an electronic device. More specifically, Fig. 2 shows a graph 200 of the battery state of charge on the vertical axis versus time on the horizontal axis. Various events in the charging sequence are labeled as points in time on the time axis. For example, before the device is connected to an external power source (such as AC power, an external battery pack, a car accessory adapter, etc.), the battery may discharge, as indicated by curve segment 211. After the device is connected to the external power source, the battery charging process may begin, as indicated by curve segment 212. Upon connecting to the external power source, the device may determine an estimated disconnection time 220 at which it expects to be disconnected from the external power source, which can be used to intelligently adjust the charging sequence, as described in more detail below.

[0020] Determining the estimated disconnection time 220 can be done in various ways based on sensor inputs (e.g., time of day, location, etc.) and a machine learning model or other program structure that considers previous activity or otherwise infers a likely disconnection time. For example, if a user typically plugs in a device at home around 10:00 PM and unplugs it around 6:00 AM, the device can infer that if plugged in at the user's residence around 10:00 PM, it will remain connected to the charging device until approximately 6:00 AM and can adjust the charging schedule accordingly. The same applies if a user plugs in the device at their workplace around 8:00 AM and unplugs it around 12:00 PM, from which the system could infer that the user is typically at their desk at that time.The inferences can be based on inputs other than time and location. For example, if a user plugs in their device and it detects that they are moving at a relatively high speed, the device could conclude that they are in a car. Depending on the specific implementation of the machine learning model or other program structure, more or less detailed inferences can be derived. In any case, the inference can be used to modify the charging process as follows.

[0021] In the first example of the previous paragraph, where the device concludes that it is located at the user's residence and was plugged in at approximately 10:00 PM, the device may assume that it will remain connected to mains power until approximately 6:00 AM. Therefore, the device does not need to be charged at the maximum rate (represented by the slope of curve segment 212) to achieve a full charge as quickly as possible. In fact, it may be desirable to reduce the charging rate and / or pause the charging process for a period of time (see curve segment 213) to delay the battery from reaching full charge. For example, the useful life of a battery (i.e., battery condition or number of charging cycles over which substantially full capacity is maintained) can be extended by reducing the time the battery is fully charged.Therefore, in the example of optimized battery charging of . Fig. 2 Battery charging can be paused during interval 213.

[0022] The estimated disconnection time 220 may further be used by the device to resume charging at a selected time to ensure that the battery reaches a fully charged state (represented by state of charge / curve segment 215) prior to the estimated disconnection time 220. This resumption of charging is represented by curve segment 214. This charging segment may occur at a reduced rate, for example, because the battery is closer to full charge, as indicated by the reduced slope of the resumed charging segment 214 compared to the original charging segment 212.In any case, the machine learning model or other program structure controlling the optimized battery charging schedule may consider 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 choosing an estimated disconnection time that is earlier than the likely 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, battery discharge may begin, as represented by curve segment 216.

[0023] The information relating to Fig. The optimized battery charging technique described in paragraph 2 is “optimized” in the sense that it can improve the battery condition by reducing the time the battery spends in a fully charged state. Fig. Figure 3 illustrates an optimized battery charging routine for an electronic device, where the "optimization" may further adapt battery charging to "grid conditions." In one embodiment, the grid conditions may include a carbon emission characteristic of the power grid. In many regions, the relative proportion of more carbon-intensive energy sources compared to less carbon-intensive energy sources may fluctuate throughout the day. For example, in regions where grid-sourced solar power is available, a greater proportion of solar power may be available during the day than at night when the grid relies more heavily on fossil fuels. In some regions with wind power, more wind power may be available at night, potentially resulting in a greater reliance on fossil fuels during the day. As a result, the carbon intensity of the grid-sourced electricity supply may vary.If current and / or forecast carbon intensity data is available to the device, the optimized battery charging routine can adjust battery charging based on whether the current composition of the grid power provided from the power grid has a relatively high or relatively low carbon intensity.

[0024] In some applications, "grid conditions" may be based on characteristics of the supplied energy that are not based on the carbon intensity of the grid. For example, a user may have some type of local power generation capability that is separate from or complementary to the grid's power supply, such as a solar power system, a local wind power system, a geothermal power system, a battery system, etc. In these cases, the device may be able to obtain data from these local power generation sources to adjust the optimized charging routine.Although the following description primarily focuses on adapting battery charging to grid conditions in terms of current and / or forecast carbon intensity, such a system can advantageously adapt battery charging to a variety of characteristics of the supplied electricity, whether from a grid or another, more local source.

[0025] Fig. Figure 3 illustrates an optimized battery charging routine for an electronic device that can additionally adapt to grid conditions. More specifically, Fig. 3 shows a graph 300 of the battery state of charge on the vertical axis versus time on the horizontal axis. Various events in the charging process are labeled as points in time on the time axis. For example, before the device is connected to an external power source (in this example, utility power), the battery may discharge, as indicated by curve segment 311. At a particular point in time, the device may receive data on grid conditions. This point in time may be before, during, or after the device is connected. The device may receive updated data on grid conditions at regular intervals; it may be advantageous for the data to be relatively recent so that the adaptive charging techniques described below can be more precise. For example, the data on grid conditions may include the carbon intensity data described above.In some cases, these dates may correspond to different time intervals, as indicated by the vertical bars 321 / 322 / 323 / 324 in . Fig. 3. In addition, the device can analyze the grid data to determine time windows during which battery charging should be performed or paused. For example, it may be desirable to forgo charging during time windows where carbon intensity (or other grid data) indicates that charging would be less favorable. (Another example would be a system with a variable tariff. In this case, it might be desirable to forgo charging during periods when electricity is more expensive.) The process could also work in reverse, e.g., by selecting time windows during which charging is preferred. In any case, the device can use the received grid data to determine which time windows are more or less preferred for charging.The device can identify such more or less preferred time windows to inform the optimized time windows for charging the battery. As shown in . Fig. 3, the less preferred time intervals 321 / 323 (darker shading) may correspond to periods of relatively higher carbon intensity (or other grid conditions) and the more preferred time intervals 322 / 324 (lighter shading) may correspond to periods of relatively lower carbon intensity (or other grid conditions).

[0026] Thus, upon connecting to the mains power, the device can determine an estimated disconnection time 320 at which it is expected to be disconnected from the mains power, which can be used to intelligently adjust the charging process. Determining the estimated disconnection time 320 can be done in various ways based on sensor inputs (e.g., time of day, location, etc.) and a machine learning model or other program structure that considers previous activities or otherwise infers a likely disconnection time, as described above with respect to Fig. 2. Referring again to the overnight charging example, where the device assumes that it is at the user's home and was plugged into the power outlet at approximately 10:00 PM (or at another time, depending on previous usage), the device may assume that it will remain plugged into the mains until approximately 6:00 AM (or another time). The device may identify certain periods within this period when mains conditions are less favorable for charging, as described above. Therefore, the device may not begin charging immediately after plugging in, as indicated by curve segment 312, as these correspond to the identified less favorable time intervals 321.Once grid conditions improve, as indicated by the received (and optionally updated) grid data, charging may begin, as indicated by curve segment 313 corresponding to the more preferred time intervals 322. Subsequently, another time window with less preferred time intervals 323 may cause the optimized battery charging routine to pause, as indicated by curve segment 314. Charging may then resume upon an improvement in grid conditions, as indicated by curve segment 315 corresponding to improved grid conditions indicated by more preferred time intervals 324.

[0027] Otherwise, the Fig. 3 adaptive charging technology as described above with respect to Fig. 2. Therefore, in the example of optimized battery charging of Fig. 3, the charging of the battery may be paused during the interval corresponding to curve segment 316. The charging of the battery may be resumed during the interval corresponding to curve segment 317 to ensure that the battery is fully charged (indicated by curve segment 318) before the estimated disconnection time 320 and the actual disconnection time at which the battery may begin discharging (indicated by curve segment 319).

[0028] Under certain operating conditions, it may be desirable to disable or otherwise forego optimization of battery charging based on grid conditions. For example, if the battery's state of charge is below a certain discharge threshold (e.g., below 30%, 25%, etc.), it may be desirable to begin charging immediately to ensure that power is available to the device regardless of grid conditions. Once the battery reaches a state of charge corresponding to a certain state of charge threshold (e.g., 75%, 80%, etc.), it may be desirable to allow the battery to fully charge immediately before the estimated disconnection time, regardless of grid conditions, to ensure that the device is fully charged when it is disconnected from the user.In addition, it may be preferable to allow optimized battery charging adapted to grid conditions only if grid conditions are expected to improve within the forecast period.

[0029] Fig. 4 shows a flowchart of an optimized battery charging routine 400 for an electronic device that adapts the charging of the battery to the grid conditions. This flowchart illustrates functionality that can be implemented by the electronic device 100, including, for example, by the processor 101, which can include one or more processors as described above. In some cases, various steps described in the flowchart of Fig. 4 may be implemented by various processors of a multiprocessor system 100. As just one example, a processor 101 including a CPU, a GPU, and an NPU (Neural Processing Unit) may utilize the NPU to perform certain machine learning computational operations. In any case, an optimized battery charging routine 400 adapted to grid conditions may include a machine learning model and a signal processing block 431 capable of detecting when the electronic device is connected to an external power source so that the optimized charging routine can be initiated as described above. These signals may include an indication of connection to an external power source, as well as other signals that enable the processor to identify the power source.For example, a location signal may indicate that the device is at the user's home, work, or other frequently visited location, in which case the external power source would be assumed to be mains power. Alternatively, a changing location signal or a speed sensor may indicate that the device is connected to the vehicle's auxiliary power source. In some cases, an external power source may provide another signal that allows its identification, as is often the case with wireless power systems. These and other signals may be processed by a machine learning model to attempt to identify the power source and predict a disconnection time, as indicated in block 432.

[0030] As indicated above, the estimated disconnection time may be derived from the external signals. For example, a time signal corresponding to late evening and a location signal corresponding to a user's residence, along with a pattern of previous usage, may infer that a nighttime charging cycle is beginning. Alternatively, plugging in the device at an unusual time or location, as well as a low battery charge level, may serve as an indication that the user needs to charge the device as soon as possible. In cases where the estimated disconnection time provides a sufficient time window for optimized charging, the processor may retrieve network data (block 433) and find desired and / or undesired charging windows (block 434), which may correspond to the preferred time intervals 322 and less preferred time intervals 321 discussed above.As mentioned above, the retrieval of the grid data in block 433 need not occur immediately after connecting the device to an external power source, but may be part of an ongoing process that is regularly updated both before and during connection to an external power source so that the grid data is available and / or updated as desired. Once the desired and / or undesired charging time windows are identified (block 434), the processor may operate the device's charging system to charge the battery during the desired time windows and inhibit charging during the undesired time windows, as illustrated in block 435. In some cases, the processor may also communicate information about the charging process and the desired / undesired charging time windows to the user (block 436). Examples of such communication and optional user interaction are discussed further below with respect to . Fig. 6 is explained in more detail.

[0031] Fig. 5 shows a system including an electronic device 100 implementing an optimized battery charging routine that adapts to grid conditions, along with external data sources 543, 544 that can be used to inform the optimized battery charging routine. More specifically, the electronic device 100 may include a charging controller 541, which may be part of the power system 108. The charging controller may include electronic circuitry (including analog, digital, and / or programmable circuitry) configured to control whether, when, and at what rate a battery of the electronic device 100 is charged. It should be noted that, in addition to or instead of allowing or prohibiting charging as described above, the optimized battery charging routine could also be used to control the rate at which the battery is charged in response to grid conditions.Therefore, instead of completely preventing battery charging during the less preferred time intervals 321, the system could be configured to slow battery charging during those intervals and increase the battery charging rate during the more preferred time intervals 322.

[0032] To achieve this, the charging controller 541 may be operably coupled to a grid data framework 542, which may include programming structures, application programming interfaces, etc., executed by the processor 101 and enabling the electronic device to retrieve grid data from a grid data server 543. The grid data server 543 may be part of or associated with a user-premises device, such as a solar power system, a grid battery, etc., that supplies power to the user's home, office, or other location. In other cases, the grid data server could be part of a home automation server in the user's home (or other location). In still other cases, the grid data server could be an internet-accessible device that provides one or more users with data about power conditions in their region.In some cases, the grid data server 543 may obtain third-party data from a third-party data server 544. For example, the third-party data server 543 may retrieve grid data for a number of grid regions from one or more third-party servers 544 and provide grid data to the electronic device 100 based on the current location of the electronic device 100. In such a case, the third-party servers 544 may be a server of a grid regulator or other entity that has access to carbon intensity data, pricing data, or other grid data.

[0033] Fig. 6 illustrates an electronic device 100 that communicates parameters of an optimized battery charging routine to a user. Fig. 6 illustrates the electronic device 100 as a smartphone, but could in fact be any electronic device, such as a tablet computer, a notebook or laptop computer, etc. For example, the electronic device 100 could use a display 104 to present the user with a message 551 indicating a time at which charging is expected to be completed. In some cases, the electronic device 100 could include a control 552, such as a UI button, that would allow the user to bypass the optimized charging routine and complete charging as quickly as possible. In some embodiments, the control 552 and the message 551 could be part of the same UI element, such as a GUI message / button, etc.In other cases, one or more other output devices of the electronic device 100 could be used to provide the display to the user, and one or more other input devices of the electronic device 100 could be used to receive input from the user indicating that the user wishes to bypass the optimized charging device and complete the charging process as quickly as possible.

[0034] Message 551 could also present additional information to the user. For example, message 551 could specifically indicate that battery charging is paused due to grid conditions and / or paused to optimize battery health. Additionally or alternatively, electronic device 100 may continuously monitor grid conditions and provide an indication to the user when grid conditions are more desirable for charging, such as lower carbon intensity, lower cost, the availability of a preferred source such as a local solar or grid-battery system, etc. This type of communication can be further controlled with respect to battery state of charge, grid conditions, and other parameters.For example, the electronic device 100 may be configured to inform the user of available advantageous charging time windows only when the battery state of charge is below a certain threshold, e.g., below 50%, and / or when the battery charge is low compared to a typical state of charge for that time of day, indicating that opportunistic charging may be desirable.

[0035] In addition to battery charging, there are other functions of electronic devices that can be adjusted based on data on grid conditions, such as carbon intensity, electricity costs, available power sources, etc. One example of such a function is background processing. Many portable or battery-operated electronic devices can be configured to perform certain tasks as background tasks when the device is connected to an external power source, but not when the device is relying on its internal battery power. Backups are an example of such a background process, which preferably only runs when connected to an external power source. Such tasks might include, for example, analyzing a photo library or other media to perform indexing or similar functions.As with battery charging, it may be desirable to adapt the timing or intensity of such tasks to the grid conditions of the external power source.

[0036] Fig. 7 shows a diagram 700 of a background processing optimization routine for an electronic device that adapts the background processing to the network conditions. Fig. 7 resembles Fig. 3 and the battery charge level curve 710 is for continuity. As described above, the electronic device 100 may retrieve network data from which preferred and less preferred time intervals may be identified. In some cases, a time window may be more preferred or less preferred. For example, more preferred intervals 722 and 724 (lighter shading) may allow for substantially unhindered background processing. Slightly less preferred intervals 721 (medium shading) may result in the inhibition of some less important background tasks (e.g., software updates) while allowing more important background tasks (e.g., backups) to perform. Additionally or alternatively, the power allocated to such background processing may be reduced less during such an interval compared to a significantly less preferred interval 723.Significantly lower preferred intervals 723 may result in inhibition of most or all backup tasks and / or a greater reduction in the power allocated to background processing. Like the charging optimization adjustments described above, these can be based on any suitable grid data, such as carbon intensity, cost, desired availability or unavailability of the power source, etc.

[0037] As in the charging case discussed above, after connecting to an external power source, the electronic device 100 may estimate a disconnection time 720. Subsequently, the network data for the inserted interval may be analyzed to identify less preferred intervals 721 / 723 and more preferred intervals 722 / 724 for background processing. Then, as described above, background processing during such intervals may be regulated accordingly. Fig. Figure 8 illustrates a flowchart of a background processing adaptation routine 800 for an electronic device that adapts background processing to grid conditions. The background processing adaptation routine 800 may be similar to the optimized battery charging routine 400 described above with reference to Fig. 4. Thus, the flowchart of Fig. 8 illustrates functionality that may be implemented by the electronic device 100, including, for example, by the processor 101, which may include one or more processors as described above. In some cases, various functions described in the flowchart of Fig. 8 may be implemented by various processors of a multiprocessor system 100. As just one example, a processor 101 including a CPU, a GPU, and an NPU (Neural Processing Unit) may use the NPU to perform certain machine learning computational operations.

[0038] In any case, a background processing routine 800 adapted to the grid conditions may include a machine learning model and a signal processing block 831 that can detect when the electronic device is connected to an external power source and initiate the optimized charging routine as described above. These signals may include an indication of connection to an external power source, as well as other signals that allow the processor to identify the power source. For example, a location signal may indicate that the device is at the user's home, work, or other frequently visited location, in which case the external power source would be assumed to be grid power. Alternatively, a changing location signal or a speed sensor may indicate that the device is connected to the vehicle's auxiliary power source.In some cases, an external power source may provide another signal that allows its identification, as is often the case with wireless power systems. These and other signals may be processed by a machine learning model to attempt to identify the power source and predict a disconnection time, as indicated in block 832.

[0039] As indicated above, the estimated disconnection time may be derived from the external signals. In cases where the estimated disconnection time provides a sufficient time window for optimized background processing, the processor may retrieve grid data (block 833) and find desired and / or undesired background processing windows (block 834), which may correspond to the preferred time intervals 722 / 724 and less preferred time intervals 721 / 723 discussed above. As mentioned above, retrieving the grid data in block 833 need not occur immediately after connecting the device to an external source, but may be part of an ongoing process that is regularly updated both before and during connection to an external power source so that the grid data is available and / or updated as desired.Once the desired and undesired background processing windows are identified (block 834), the processor may perform or inhibit background processing during those windows to the desired extent, as illustrated in block 835. This may include reducing the amount of background processing performed during the undesired intervals or even stopping background processing during the undesired intervals. In some cases, the processor may also communicate information about the charging process and the desired charging time windows to the user (block 835). Such communication and optional user interaction may be performed as described above with respect to . Fig. 6. In addition, a system for retrieving network data to optimize background processing as described above with respect to Fig. 5 described.

[0040] As used in various places throughout this disclosure, the term "machine learning" may refer to algorithms, statistical models, and the like that computer systems (such as electronic device 100) may use to perform a particular task with or without the use of explicit instructions. For example, a machine learning process may generate a mathematical model based on a sample of data, known as "training data," to make predictions or decisions without being explicitly programmed to perform that task. Depending on the conclusions to be drawn, electronic device 100 (or a subsystem or associated device thereof) may implement different forms of machine learning. For example, in some embodiments (e.g.,For example, if certain known examples exist that correlate with future predictions or estimates that the machine learning engine may be required to make, a machine learning engine can implement supervised machine learning. In supervised machine learning, a mathematical model of a dataset contains both inputs and desired outputs. This data is called "training data" and may contain a set of training examples. Each training example may have one or more inputs and a desired output, also known as a supervision signal. In a mathematical model, each training example is represented by an array or vector, sometimes called a feature vector, and the training data is represented by a matrix.By iteratively optimizing an objective function, supervised learning algorithms can learn a function that can be used to predict an output associated with new inputs. An optimal function can allow the algorithm to correctly determine the output 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 this task.

[0041] Supervised learning algorithms can include classification and regression techniques. Classification algorithms can be used when the outputs are restricted to a finite number of values, and regression algorithms can be used when the outputs have a numerical value within a range. Similarity learning is an area of ​​supervised machine learning closely 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, recommender systems, visual identity tracking, facial verification, and speaker verification.

[0042] Additionally and / or alternatively, in some situations, it may be advantageous for the machine learning engine to use unsupervised learning (e.g., when certain output types are unknown). Unsupervised learning algorithms take a dataset containing only inputs and find structure in the data, such as the grouping or clustering of data points. The algorithms therefore learn from test data that has not been labeled, classified, or categorized. Instead of reacting to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new data piece.

[0043] That is, the machine learning engine may perform cluster analysis, which involves assigning a set of observations into subsets (called clusters) such that observations within the same cluster are similar with respect to one or more pre-specified criteria, while observations from different clusters are different. Different clustering techniques make different assumptions about the structure of the data, which is often defined by a similarity metric and evaluated, for example, by internal compactness, or the similarity between members of the same cluster, and separation, the difference 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.

[0044] The foregoing describes exemplary embodiments of shifting certain tasks performed by an electronic device based on grid conditions associated with an external power source connected to the electronic device. It should be understood that while numerous specific features and various embodiments have been described, unless otherwise stated as mutually exclusive, the various features and embodiments may be combined in various implementations within a particular implementation. Therefore, the various embodiments described above are provided for illustrative purposes only and should not be construed as limiting the scope of the disclosure.Various modifications and changes may be made to the principles and embodiments contained herein without departing from the scope of the disclosure and without departing from the scope of the claims.

[0045] The foregoing describes exemplary embodiments of electronic systems capable of transferring certain information between other systems and devices. The present disclosure contemplates that this information sharing enhances the functionality of the devices. Entities implementing the present technology should take care to ensure that, to the extent sensitive information is used in particular implementations, established data protection policies and / or practices are adhered to. In particular, such entities are expected to implement and consistently apply data protection practices that are generally recognized as meeting or exceeding industry or government requirements for preserving user privacy.Implementers should inform users when identifiable personal information is likely to be transferred and allow users to consent or decline to participate.

[0046] Risk can be minimized by limiting data collection and deleting data when no longer needed. Additionally, and where applicable, data de-identification can be used to protect a user's privacy. For example, a device identifier can be partially masked to convey device performance characteristics without uniquely identifying the device. De-identification can be facilitated, where appropriate, by removing identifiers, controlling the amount or specificity of stored data (e.g., collecting location data at the city level rather than the address level), controlling how data is stored (e.g., aggregating data across users), and / or through other techniques such as differential privacy.Robust encryption can also be used to reduce the likelihood that communication between devices will be intercepted, spoofed, or otherwise tampered with. QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited non-patent literature

[0000] Cupertino, California, USA

[0012] Apple Watch® from Apple Inc. of Cupertino, California, USA

[0012]

Claims

[1] Electronic device comprising: a power supply system including a battery; and a processor programmed to: Detecting the connection of an external power source to the electronic device; Determining an estimated disconnection time at which the external power source is expected to be disconnected from the electronic device; Analyzing power grid data corresponding to the external power source to identify one or more desired battery charging intervals and one or more undesired battery charging intervals prior to the estimated disconnection time; and Operating the power system to charge the battery from the external power source during the identified one or more desired battery charging intervals, and inhibiting charging of the battery during the one or more undesired battery charging intervals. [2] The electronic device of claim 1, wherein the processor is programmed to determine an estimated separation time using a machine learning model. [3] The electronic device of claim 1, wherein: the power grid data corresponding to the external power source comprises carbon intensity data, the one or more desired battery charging intervals are lower carbon intensity intervals, and the one or more undesired battery charging intervals are higher carbon intensity intervals. [4] The electronic device of claim 1, wherein: the power grid data corresponding to the external power source comprises cost data, the one or more desired battery charging intervals are lower cost intervals, and the one or more undesired battery charging intervals are higher cost intervals. [5] The electronic device of claim 1, wherein the processor is programmed to inhibit charging of the battery by reducing the charging rate of the battery. [6] The electronic device of claim 1, wherein the processor is programmed to inhibit charging of the battery by preventing charging of the battery. [7] The electronic device of claim 1, wherein the processor is further programmed to retrieve the power grid data corresponding to the external power source from a grid data server. [8] The electronic device of claim 1, further comprising a display, wherein the processor is further programmed to communicate to a user, via the display, information about the one or more desired battery charging intervals or the one or more undesired battery charging intervals. [9] The electronic device of claim 8, further comprising an input device, wherein the processor is further configured to receive user input regarding charging via the input device. [10] A method for operating an electronic device, the method being executed by a processor of the electronic device and comprising: Detecting the connection of an external power source to the electronic device; Determining an estimated disconnection time at which the external power source is expected to be disconnected from the electronic device; Analyzing power grid data corresponding to the external power source to identify one or more desired battery charging intervals and one or more undesired battery charging intervals prior to the estimated disconnection time; and Operating the power system including charging the battery from the external power source during the identified one or more desired battery charging intervals and preventing battery charging during the one or more undesired battery charging intervals. [11] The method of claim 10, wherein determining an estimated separation time comprises using a machine learning model. [12] The method of claim 10, wherein: the power grid data corresponding to the external power source comprises carbon intensity data, the one or more desired battery charging intervals are lower carbon intensity intervals, and the one or more undesired battery charging intervals are higher carbon intensity intervals. [13] The method of claim 10, wherein: the power grid data corresponding to the external power source comprises cost data, the one or more desired battery charging intervals are lower cost intervals, and the one or more undesired battery charging intervals are higher cost intervals. [14] The method of claim 10, wherein inhibiting charging of the battery comprises reducing the charging rate of the battery. [15] The method of claim 10, wherein inhibiting charging of the battery comprises preventing charging of the battery. [16] The method of claim 10, further comprising retrieving the power grid data corresponding to the external power source from a grid data server. [17] The method of claim 10, further comprising communicating information about the one or more desired battery charging intervals or the one or more undesired battery charging intervals to a user via a display of the electronic device. [18] The method of claim 17, further comprising receiving user inputs regarding the charging via an input device of the electronic device.

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