System and method for tracking and controlling battery consumption in a battery-powered electronic device from

Abstract

Systems and methods for tracking and controlling battery consumption in a fleet of battery-powered electronic devices are disclosed herein. An example method includes receiving power consumption and usage information of a device for a duration; determining a first power usage value of the device based on the power consumption and usage information over the duration; analyzing the power consumption and usage information to determine attributes associated with excessive power usage of the device over the duration; identifying and applying mitigation measures to process the attribute and reduce the first power usage value for the duration; determining a second power usage value of the device based on the mitigation action implemented over the duration; determining a difference between the first power usage value and the second power usage value for the duration; and determining at least one indicator indicative of energy conservation for the duration based on the determined difference.

Description

BACKGROUND

[0001] Environmental, social, and corporate governance (ESG) impact metrics are becoming an important factor in customer and consumer choices. Improving the environmental impact component involves, for example, moving towards carbon neutrality and reducing emissions and use of landfills. However, this environmental impact is often assessed qualitatively rather than quantitatively, so it can be difficult to determine where an organization stands with respect to these metrics. Therefore, there is a need for a method to assess these metrics quantitatively, particularly with respect to the impact on the environment of battery consumption and disposal for mobile devices, such as mobile computers and symbol readers, and robotic devices. SUMMARY

[0002] In an embodiment, the invention is a method comprising: receiving power consumption information for a device and usage information for the device for a duration of time; determining a first power usage value for the device for the duration of time based on the power consumption information and the usage information for the duration of time; analyzing the power consumption information and the usage information to determine at least one attribute associated with excess power usage for the device for the duration of time; identifying and applying at least one mitigation measure to address the at least one attribute and reduce the first power usage value for the duration of time; determining a second power usage value for the device based on the at least one mitigation measure implemented for the duration of time; determining a difference between the first power usage value and the second power usage value for the duration of time; and determining at least one metric indicative of energy savings for the duration of time based on the determined difference.

[0003] In a variation of this embodiment, the power consumption information includes battery charging data for the duration of time.

[0004] Further, in a variation of this embodiment, the usage information includes one or more of the following for the duration of time: battery discharge rate, screen on time, screen brightness data, scan rate, usage data associated with one or more business applications installed on the device, battery cycle, battery charge level, battery temperature, battery current capacity, battery total cumulative charge, battery rated capacity, average current, average power, voltage, charge on / off events, battery charging source, or battery replacement data.

[0005] Further, in a variation of this embodiment, determining a first power usage value for the device for the duration of time is further based on one or more of the following associated with the device: battery type, battery health, device type, device screen timeout setting, device brightness setting, battery cycle, battery charge level, battery temperature, battery current capacity, battery total cumulative charge, battery rated capacity, average current, average power, voltage, charge on / off events, battery charging source, or site name.

[0006] Additionally, in variations of this embodiment, the first power usage value is a minimum battery charge required for the duration.

[0007] Further, in variations of this embodiment, the at least one attribute associated with the excess power usage of the device for the duration includes one or more of: a screen brightness attribute, a screen on time attribute, a wireless signal strength attribute, a device location attribute, a device physical memory attribute, an application physical memory utilization attribute, a battery utilization per application attribute, a scanner usage attribute, a data transfer attribute associated with the device, a data transfer per application attribute, a reception usage attribute associated with the device, and a reception usage per application attribute.

[0008] Further, in variations of this embodiment, the at least one mitigation measure includes one or more of: a screen brightness reduction, a screen on time reduction, an increase in wireless signal strength quality, an improvement in memory utilization associated with the device, an improvement in memory utilization per application, a reduction in battery usage per application, an optimization of data transfer, an optimization of reception, or an optimization of scanner usage.

[0009] Additionally, in variations of this embodiment, the at least one indicator of energy savings for the duration includes one or more of: a battery life indicator, a battery purchase indicator, a battery disposal indicator, a power consumption indicator, a CO2 emission rate indicator, or a precious metal mining rate indicator.

[0010] Further, in variations of this embodiment, the duration is a duration associated with a work shift.

[0011] In another embodiment, the application is a device comprising: a smart battery including a battery memory and one or more battery processors storing first computer readable instructions that cause the one or more battery processors to store battery usage and state of charge (SOC) data on the battery memory; a clock, a device communication system, one or more device processors, and a device memory storing second computer readable instructions; and a server including a server communication system, one or more server processors, and a server memory storing third computer readable instructions; wherein, when executed by the one or more device processors, the second computer readable instructions cause the one or more device processors to: detect events associated with the battery; compile event data based on the detected events, the event data including, for each of one or more detected events, one or more of: battery usage data stored on the battery memory associated with the event, SOC data stored on the memory associated with the event, an indication of an event type associated with the event, a time associated with the event, an indication of a backup voltage level during the event, a battery temperature associated with the event, a cumulative charge of the battery at the time of the event, or a battery charging source associated with the event; and transmit the compiled event data to the server via the device communication system; wherein, when executed by the one or more server processors, the third computer readable instructions cause the one or more server processors to: receive the compiled event data from the device via the server communication system; determine device activity data associated with the device based on the compiled event data; and analyze the device activity data using a power consumption model to estimate power consumption of the device based on battery consumption associated with the device.

[0012] In a variation of this embodiment, the device is one of: a mobile computing device, a mobile printer, a scanner device, and a robotic device.

[0013] Further, in variations of this embodiment, when executed by the one or more device processors, the second computer-readable instructions further cause the one or more device processors to: capture additional device data, the additional device data comprising one or more of: power usage data associated with the device, screen on time associated with the device, screen brightness data associated with the device, scan rate associated with the device, usage data associated with one or more business applications installed on the device, wireless signal strength associated with the device, device location, an indication of device physical memory utilization, an indication of physical memory utilization per application, an indication of battery utilization per application, an indication of data transfer associated with the device, an indication of data transfer per application, an indication of reception usage associated with the device, or an indication of reception usage per application; and transmit the additional device data to the server via the device communication system.

[0014] Further, in variations of this embodiment, when executed by the one or more server processors, the third computer-readable instructions further cause the one or more server processors to: receive the additional device data via the server communication system; and determine the device activity data further based on the additional device data.

[0015] Additionally, in variations of this embodiment, when executed by the one or more server processors, the third computer-readable instructions further cause the one or more server processors to: determine at least one metric associated with the estimated power consumption, the at least one metric comprising one or more of: an emission rate, a precious metal mining rate, or a landfill rate.

[0016] Further, in variations of this embodiment, the detected events associated with the battery comprise one or more of: a low power event, a battery replacement mode event, a battery replacement entry event, a battery replacement exit event, a battery charging on event, a battery charging off event, a battery status event, a battery temperature event, a battery cumulative charge event, a device hibernation event, a device hibernation recovery event, or a device shutdown event.

[0017] In still another embodiment, the invention is a system comprising: a mobile device comprising a battery, a clock, a mobile device communication system, one or more mobile device processors, and a mobile device memory storing first computer readable instructions that, when executed by the one or more mobile device processors, cause the one or more mobile device processors to collect and transmit, via the mobile device communication system, mobile device data comprising battery and usage state data; a server comprising a server communication system, one or more server processors, and a server memory storing second computer readable instructions that, when executed by the one or more server processors, cause the one or more server processors to: receive, via the server communication system, mobile device data; analyze the mobile device data to determine one or more of: a duration of one or more shifts associated with the mobile device, a required power consumption associated with the mobile device, a measured power consumption associated with the mobile device, a predicted power consumption associated with the mobile device over time, a predicted rate of battery purchases associated with the mobile device, a predicted rate of battery disposal associated with the mobile device, a predicted impact of power consumption of the mobile device on an emissions rate, or a predicted impact of power consumption of the mobile device on a landfill rate.

[0018] In a variation of this embodiment, the mobile device is one of: a mobile computing device, a mobile printer, a scanner device, or a robotic device.

[0019] Further, in a variation of this embodiment, the battery is a lithium-ion battery.

[0020] Further, in a variation of this embodiment, analyzing the mobile device data comprises analyzing the mobile device data using one or more machine learning algorithms.

[0021] Additionally, in a variation of this embodiment, analyzing the mobile device data comprises analyzing the mobile device data using one or more simulations.

[0022] Further, in a variation of this embodiment, the one or more simulations comprise Monte Carlo simulations.

[0023] Further, in a variation of this embodiment, the second computer readable instructions, when executed by the one or more server processors, further cause the one or more server processors to: identify one or more of: a mobile device setting change, a wireless coverage improvement, an application update, an application memory usage change, an application battery usage change, or a behavior change that impacts the power consumption associated with the mobile device. BRIEF DESCRIPTION OF DRAWINGS

[0024] The accompanying drawings, where like reference numerals refer to identical or functionally similar elements throughout the various figures, which together with the detailed description below are incorporated in and form part of the specification, and wherein:

[0025] Figure 1 is a block diagram of an example system for tracking and controlling battery consumption in a fleet of battery-powered electronic devices in accordance with some embodiments.

[0026] Figure 2 is a block diagram of an example electronic device in the system of Figure 1

[0027] Figure 3 is a block diagram of an example server that can be used in the system of Figure 1

[0028] Figures 4-7 is a flow diagram of an example process for implementing the example methods and / or operations described herein including techniques for tracking and controlling battery consumption in a fleet of battery-powered electronic devices as can be performed by the system of Figure 1

[0029] Those skilled in the art will appreciate that the elements in the figures are shown for the purpose of simplifying the drawings and are not necessarily drawn to scale. For example, the dimensions of some of the elements in the figures can be exaggerated relative to other elements to help improve understanding of the embodiments of the application.

[0030] The apparatus and method configurations have been represented in the drawings by conventional symbols, and specific functional details have been set forth in order to provide a thorough understanding of the embodiments of the application. However, it will be apparent to those skilled in the art that the application can be practiced without the specific details presented above. DETAILED DESCRIPTION

[0031] SUMMARY

[0032] As discussed above, environmental, social, and corporate governance (ESG) impact metrics are becoming an important factor in customer and consumer choices. Improving the environmental impact component involves, for example, moving towards carbon neutrality and reducing emissions and use of landfills. However, this environmental impact is often assessed qualitatively rather than quantitatively, so it can be difficult to determine the organization’s standing on these metrics. Thus, there is a need for a method to assess these metrics quantitatively, particularly with respect to the impact on the environment of battery consumption and disposal of mobile devices, such as mobile computers and symbol readers, and robotic devices.​​​

[0033] The present disclosure provides techniques for determining battery-related power consumption of devices in use of a benchmark, measuring changes in battery-related power consumption when various power saving methods are implemented, and providing mechanisms that increase battery life, thereby reducing the number of batteries that need to be recycled or disposed. Further, the present disclosure determines and provides metrics that show the impact of these changes, such as, for example, a reduction in the weight of batteries disposed in a landfill when such changes are made. Numerically determining such metrics can allow organizations to make quantitative ESG impact statements related to the power consumption of mobile device batteries, particularly as to how reducing the power consumption requirements of devices can reduce the carbon footprint of an organization, and how increasing the health and useful life of batteries used by such devices reduces the impact of battery disposal on landfills.

[0034] Example system

[0035] Figure 1 An example system 100 for tracking and controlling battery consumption in a fleet of battery-powered electronic devices is shown. In the illustrated example, the system 100 includes one or more electronic devices 102 that can communicate with a server device 104 via a network 106 (and / or via a wired interface, not shown). Figure 2 And Figure 3 Additional details related to the electronic devices 102 and the server device 104 are provided.

[0036] The electronic devices 102 can be mobile computing devices such as smartphones, smartwatches, tablets, and laptops, as well as specialized mobile computing devices such as barcode readers, two-dimensional code scanners, RFID readers, robotic devices, and the like. In general, each of the electronic devices 102 can include a network interface (not shown) that represents any suitable type(s) of communication interface (e.g., wired interfaces such as Ethernet or USB, and / or any suitable wireless interface) configured to operate according to any suitable protocol(s) for communicating with the server 104 over the network 106. Further, each of the devices 102 can include a battery 108. In some examples, the battery 108 can be a lithium-ion battery. Further, in some examples, the battery 108 can be a smart battery that includes a battery memory and one or more battery processors that store computer-readable instructions that cause the one or more battery processors to store battery usage and state of charge (SOC) data on the battery memory.

[0037] Further, each of the devices 102 can include one or more processors 110, such as one or more microprocessors, controllers, and / or any other suitable type of processors, and storage 112 that is accessible by the one or more processors 110, e.g., via a memory controller. The example processor(s) 110 can interact with the storage 112 to obtain, for example, machine-readable instructions stored in the storage 112 corresponding to operations represented by, for example, flowcharts of the present disclosure, including Figures 4-7 The instructions stored in the storage 112 can cause the processor(s) 110 to execute various applications stored in the storage 112, such as a power consumption diagnostic application 114, for example. The power consumption diagnostic application 114 can include instructions that cause the processor(s) 110 to perform operations represented by, for example, flowcharts of the present disclosure.

[0038] Executing the power consumption diagnostic application 114 can include causing the mobile device 102 to capture or otherwise obtain device data and transmit the device data to the server 104, e.g., via one of the above-described communication interfaces. For example, the mobile device data can include battery usage data, battery status data, etc. associated with the battery 108 of the mobile device. In some examples, the mobile device 102 can obtain some portions of the device data from the battery 108, e.g., when the battery 108 is a smart battery. Further, in some examples, executing the power consumption diagnostic application 114 can cause the mobile computing device 102 to capture additional device data and transmit the additional device data to the server 104. For example, the additional device data can include one or more of: power usage data associated with the device, screen on time associated with the device, screen brightness data associated with the device, scan rate associated with the device, usage data associated with one or more business applications installed on the device, wireless signal strength associated with the device, device location, an indication of device physical memory utilization, an indication of physical memory utilization per application, an indication of battery utilization per application, an indication of data transmission associated with the device, an indication of data transmission per application, an indication of reception usage associated with the device, or an indication of reception usage per application.

[0039] Further, in some examples, executing the power consumption diagnostic application 114 can cause the mobile computing device 102 to detect a battery-associated event, compile event data based on the detected event, and transmit the compiled event data to the server. For example, the detected battery-associated event can include one or more of a low-battery event, a battery replacement mode event, a battery replacement entry event, a battery replacement exit event, a battery charging on event, a battery charging off event, a battery status event, a battery temperature event, a battery cumulative charge event, a device hibernate event, a device hibernate resume event, or a device shutdown event. For a given event, the event data can include one or more of battery usage data stored on a battery memory associated with the event, SOC data stored on a memory associated with the event, an indication of an event type associated with the event, a time associated with the event, an indication of a backup voltage level during the event, a battery temperature associated with the event, a battery cumulative charge at the time of the event, or a battery charging source associated with the event.

[0040] The server 104 can include one or more processors 116, and a memory 118 that is accessible by the one or more processors 116 (e.g., via a memory controller), which can be one or more microprocessors, controllers, and / or any other suitable type of processors. The example processor(s) 116 can interact with the memory 118 to obtain, for example, machine-readable instructions stored in the memory 118 that correspond to operations represented by, for example, flowcharts of the present disclosure (including Figures 4-7 For example, when executed by the processor(s) 116, the instructions stored in the memory 118 can cause the processor(s) 116 to execute various applications stored in the memory 118, such as a power consumption diagnostic application 120, a power consumption diagnostic machine learning model training application 122, and a power consumption diagnostic machine learning model 124.

[0041] In an example, executing the power consumption diagnostic application 120 can include receiving device data, additional device data, and / or compiled event data from the mobile computing device 102 (e.g., via the network 106). For example, the power consumption diagnostic application 120 can analyze the device data and / or the additional device data (e.g., using a simulation such as a Monte-Carlo simulation, and / or using a machine learning model such as the power consumption diagnostic machine learning model 124 discussed in more detail below) to determine one or more of: a duration of one or more shifts associated with the mobile device, a required power consumption associated with the mobile device, a measured power consumption associated with the mobile device, a predicted power consumption associated with the mobile device over time, a predicted rate of battery purchases associated with the mobile device, a predicted rate of battery disposal associated with the mobile device, a predicted impact of power consumption of the mobile device on an emissions rate, or a predicted impact of power consumption of the mobile device on a landfill rate.

[0042] Further, in an example, the power consumption diagnostic application 120 can determine device activity data associated with the device 102 based on the compiled event data, and analyze the device activity data using a power consumption model to estimate power consumption of the device 102 based on battery consumption associated with the device 102. For example, the power consumption model can be a simulation such as a Monte-Carlo simulation, or can also be a machine learning model such as the machine learning model 124. Further, executing the power consumption diagnostic application 120 can include determining at least one metric associated with the estimated power consumption. For example, the metric can be a carbon emissions rate metric, a precious metal mining rate metric, or a landfill rate metric.

[0043] Further, in an example, the power consumption diagnostic application 120 can identify one or more mobile device setting changes, wireless coverage improvements, application updates, application memory usage changes, application battery usage changes, or behavior changes that impact power consumption associated with the mobile device.

[0044] Returning now to the trained power consumption diagnostic machine learning model 124, in some examples, the trained power consumption diagnostic machine learning model 124 can be executed on the server device 104, while in other examples, the power consumption diagnostic machine learning model 122 can be executed on another computing system that is independent of the server device 104. For example, the server device 104 can send the device data and / or additional device data to another computing system in which the trained power consumption diagnostic machine learning model 124 is applied to data corresponding to the device data and / or additional device data. Based on applying the trained power consumption diagnostic machine learning model 124 to the device data and / or additional device data, the other computing system can send to the server device 104 a prediction or identification of one or more of: a duration of a shift associated with the mobile device, a required power consumption associated with the mobile device, a measured power consumption associated with the mobile device, a predicted power consumption over time associated with the mobile device, a predicted rate of battery purchases associated with the mobile device, a predicted rate of battery disposal associated with the mobile device, a predicted impact of power consumption on an emissions rate of the mobile device, or a predicted impact of power consumption on a landfill rate of the mobile device. Moreover, in some examples, the power consumption diagnostic machine learning model 124 can be trained by the power consumption diagnostic machine learning model training application 122 executing on the server device 104, while in other examples, the power consumption diagnostic machine learning model 124 can be trained by a machine learning model training application executing on another computing system that is independent of the server device 104.

[0045] Regardless of whether the power consumption diagnostic machine learning model 124 is trained on the server device 104 or elsewhere, the power consumption diagnostic machine learning model 124 can be trained (e.g., by the power consumption diagnostic machine learning model training application 122) using training data from the server device 104, the device 102, and / or databases including: historical device data and / or historical additional device data corresponding to historical devices, and durations of one or more historical shifts, historical required power consumption, historical measured power consumption, historical power consumption over time, historical rate of battery purchases, historical rate of battery disposal, historical impact of power consumption on an emissions rate, historical impact of power consumption on a landfill rate, etc. for corresponding historical devices. The trained machine learning model 124 can then be applied to new device data and / or additional device data corresponding to a given device to identify or predict, for example, shift durations, required power consumption, amount of power consumption, power consumption over time, rate of battery purchases, rate of battery disposal, impact of power consumption on an emissions rate, impact of power consumption on a landfill rate, etc. for that device.

[0046] In various aspects, the power consumption diagnostic machine learning model 124 can include a machine learning program or algorithm that can be trained by and / or employ a neural network, which can be a deep learning neural network, or a combined learning module or program that learns one or more features or feature data sets in a particular field(s) of interest. The machine learning program or algorithm can also include natural language processing, semantic analysis, automated reasoning, regression analysis, support vector machine (SVM) analysis, decision tree analysis, random forest analysis, K- nearest neighbor analysis, Naive Bayes analysis, clustering, reinforcement learning, and / or other machine learning algorithms and / or techniques.

[0047] In some embodiments, the artificial intelligence and / or machine learning based algorithms used to train the power consumption diagnostic machine learning model 124 can include libraries or software packages executed on the server device 104 (or other computing devices not shown in FIG. 1). For example, such libraries can include, but are not limited to, TENSORFLOW based libraries, PYTORCH libraries, and / or SCIKIT-LEARN Python libraries. Figure 1

[0048] As referenced herein, machine learning can involve identifying and recognizing patterns in existing data (such as training the model 124 based on historical device data and / or additional device data corresponding to historical devices, and one or more historical shift durations, historical required power consumption, historical measured power consumption, historical power consumption over time, historical battery purchase rates, historical battery disposal rates, historical effects of power consumption on emissions rates, historical effects of power consumption on landfill rates, etc. for one or more historical shifts of corresponding historical devices) in order to facilitate predictions or identifications for subsequent data (such as new device data and / or additional device data corresponding to a given device using the machine learning model 124 to identify or predict, for example, shift durations, required power consumption, power consumption amounts, power consumption over time, battery purchase rates, battery disposal rates, effects of power consumption on emissions rates, effects of power consumption on landfill rates, etc. for that device).

[0049] ​The machine learning model(s) can be created and trained based on example data (e.g., “training data”) inputs or data (which can be referred to as “features” and “labels”) in order to make effective and reliable predictions on new inputs, such as test or production level data or inputs. In supervised machine learning, a machine learning program operating on a server, computing device, or other processor(s) can be provided with example inputs (e.g., “features”) and their associated or observed outputs (e.g., “labels”) in order for the machine learning program or algorithm to determine or discover rules, relationships, or other machine learning “models” that map such inputs (e.g., “features”) to outputs (e.g., labels), e.g., by determining and / or assigning weights or other metrics to individual feature categories of a model. Such rules, relationships, or other models can then be provided to subsequent inputs of the model executing on a server, computing device, or other processor(s) to predict an expected output based on the discovered rules, relationships, or models.

[0050] In unsupervised machine learning, a server, computing device, or other processor(s) can need to find structure in unlabeled example inputs, e.g., by performing multiple training iterations to train multiple generations of models until a satisfactory model is generated, e.g., a model that provides sufficient prediction accuracy when generating given test or production level data or inputs. The disclosures herein can use one or both of such supervised or unsupervised machine learning techniques.

[0051] Additionally, the memory 118 can also store additional machine-readable instructions, including any of one or more applications, one or more software components, and / or one or more application programming interfaces (APIs), which can be implemented to facilitate or perform the features, functions, or other disclosures described herein, such as any methods, processes, elements, or limitations as illustrated, depicted, or described with respect to the various flowcharts, illustrations, diagrams, figures, and / or other disclosures herein. It should be appreciated that one or more other applications that the processor(s) 116 execute can be contemplated. It should be appreciated that the processes, functions, and steps performed by the server device 104 described herein can be performed by a mobile computing device, such as the electronic device 102, or a smart battery, such as the battery 108, in view of the advanced state of mobile computing devices.

[0052] Example Electronic Device

[0053] Figure 2 is in accordance with some embodiments with which can be used Figure 1a block diagram associated with an example electronic device 102 in the system 100. As shown in block 202, the electronic device 102 (and / or the battery 108 of the electronic device 102) can capture / receive data associated with the battery 108 and / or the device 102. For example, in some examples, the smart battery 108 can include a fuel gauge to determine the health of the battery and can determine the state of charge (SOC) of the battery. In addition, the smart battery 108 can capture an indication of a battery event and can store an indication of the identification of the battery 108. Additionally, the electronic device 102 can capture all of the above data, as well as a timestamp (e.g., associated with the indication of the battery event), smart battery data, and other device data including screen brightness and screen timeout settings (in some cases with an associated timestamp), a measurement of screen on time, and other data for reducing power usage of the device 102. A communication system 204 can facilitate a wired or wireless connection between the electronic device 102 and the server 104, which can transmit the data associated with the battery 108 and / or the device 102 to the server 104.

[0054] As shown in block 206, the server 104 can analyze the data associated with the battery 108 and / or the device 102 to determine power usage and / or usage information of the device 102 to determine how much power the device 102 can need in a user shift.

[0055] In addition, as shown in block 208, the server 104 can analyze the data associated with the battery 108 and / or the device 102, as well as the determined power usage of the device 102 and / or the power needed by the device 102 in a user shift, to determine ways to reduce the battery purchase rate and / or the battery disposal rate. This can include, for example, determining a delta in benchmark consumption after the implementation of a power saving recommendation by the device 102, and / or determining a reduction in the battery rate acquired per year and / or the battery rate disposed per year after the implementation of a power saving recommendation by the device 102.

[0056] As shown in block 210, the server 104 can access published or projected data related to the production and / or disposal of smart batteries, including equivalent carbon emissions associated with the production of lithium ion batteries, and the impact of production and disposal on landfills.

[0057] As shown in box 212, using the data from box 210, server 104 can determine the incremental baseline consumption following the implementation of energy-saving recommendations by device 102 and / or the associated numerical indicators of the annual battery acquisition rate and / or annual battery disposal rate reduction following the implementation of energy-saving recommendations by device 102, as determined in box 208. Specifically, as shown in box 212, server 104 can determine reductions in carbon emission rates, reductions in precious metal mining, reductions in landfill usage, etc., which correspond to the incremental baseline consumption following the implementation of energy-saving recommendations by device 102, and / or the annual battery acquisition rate and / or annual battery disposal rate reduction following the implementation of energy-saving recommendations by device 102.

[0058] Example server device

[0059] Figure 3 According to some embodiments, and can be used Figure 1 A block diagram associated with example server device 104 in system 100. (See diagram below.) Figure 3 As shown, server device 104 may include a communication system 302 that facilitates wired or wireless connections between server 104 and electronic device 102, and can receive data associated with battery 108 and / or device 102 from battery 108 and / or device 102. For example, as shown in box 304, data associated with battery 108 and / or device 102 may include timestamps, smart battery metrics, device usage metrics, device metrics indicating characteristics affecting power output, etc.

[0060] As shown in box 306, server device 104 may further receive published or predicted data related to the production and / or disposal of the device's smart batteries. For example, this data may include equivalent carbon emissions in lithium-ion battery production, and / or the impact of such batteries in production and disposal.

[0061] Server device 104 may further store data from boxes 302 and 304 on memory 118. For example, this data may be stored as event data, battery data, usage data, emissions data, and / or disposal impact data associated with one or more electronic devices 102. Server device 104 may use one or more simulations (e.g., Monte Carlo simulations) or machine learning models (e.g., machine learning model 124 discussed in more detail above) to analyze the data stored on memory 118. As shown in box 308, server 104 may determine the power used by device 102 and the power increment if users of device 102 implement energy-saving recommendations, the reduction in battery purchase / disposal rate if users of device 102 implement energy-saving recommendations, and / or the reduction in carbon emissions and landfill if users of device 102 implement energy-saving recommendations.

[0062] Additionally, the server 104 can generate suggested device setting changes or behavior changes that affect battery power consumption, and can generate indications to transmit the suggested device setting changes or behavior changes directly to the corresponding device 102 or an intermediary party associated with the device 102. For example, the suggested device setting changes or behavior changes can be provided as a notification to a user of the device 102, or can be provided directly to the device 102 as a configuration file or update to the device.

[0063] Example method

[0064] Figure 4 is a flow diagram of an example process 400 for implementing the example methods and / or operations described herein, including as can be performed by Figure 1 the system 100 of FIG. 1 for tracking and controlling battery consumption in a fleet of battery-powered electronic devices. For example, instructions for performing the example process 400 can be stored on the memory of the smart battery 108, on the memory 112, and / or on the memory 118, and executed by the processor of the smart battery 108, the processor 110, and / or the processor 116, respectively.

[0065] At block 402, device metrics associated with the electronic device 102 can be captured to measure power consumption (e.g., total cumulative charge and timestamp), and usage information associated with the electronic device 102 can be captured (e.g., battery discharge rate, screen on time, scan rate, traffic application usage, etc.) to determine the power required by a user of the electronic device 102 to complete a work shift without interruption.

[0066] At block 404, an algorithm such as the simulation algorithm and machine learning algorithm discussed above can be used to determine a range of power consumption for a work shift using the electronic device 102, thereby defining a minimum charge of the battery 108 of the device 102 that is properly charged at the beginning of the work shift in order to complete the work shift without interruption.

[0067] At block 406, data associated with battery replacements of the device 102 can be analyzed to identify battery replacements in which a properly charged battery 108 did not allow the device 102 to complete a work shift without interruption (as compared to battery replacements due to a battery 108 that was not properly charged, and battery replacements that were completely unnecessary).

[0068] At block 408, a baseline power usage of the device 102 can be determined.

[0069] At block 410, data from block 406 can be analyzed using an algorithm, such as the simulation algorithm and machine learning algorithm discussed above, to determine the primary cause of the excessive usage impact (e.g., brightness, screen on time, etc.).

[0070] At block 412, one or more recommendations to reduce power usage can be implemented.

[0071] At block 414, the power usage of device 102 after implementing the recommendations at block 412 can be determined.

[0072] At block 416, the delta between the power usage determined at block 408 and the power usage determined at block 414 can be determined.

[0073] At block 418, the impact of the delta determined at block 416 on battery life, as well as battery purchase rate and battery disposal rate, can be determined.

[0074] At block 420, the impact of the delta determined at block 416 on reduced power consumption rate for power service providers can be determined.

[0075] At block 422, the impact on carbon emission rate, precious metal mining rate, landfill usage, etc. can be determined based on the impacts determined at blocks 418 and 420.

[0076] Figure 5 is a flow diagram of an example process 500 for implementing the example methods and / or operations described herein, including techniques for tracking and controlling battery consumption in a fleet of battery-powered electronic devices as can be performed by Figure 1 the system 100. For example, instructions for performing the example process 500 can be stored on the memory of the smart battery 108, on the memory 112, and / or on the memory 118, and executed by the processor of the smart battery 108, the processor 110, and / or the processor 116, respectively.

[0077] At block 502, power consumption of a device 102 having a battery can be measured.

[0078] At block 504, usage demands of the device 102 can be measured and compared to usage scenarios (e.g., work shift) to ensure that the battery of the device 102 lasts throughout the shift.

[0079] At block 506, energy saving recommendations for the device 102 can be identified and implemented

[0080] At block 508, possible setting changes (such as brightness changes or screen on time changes) or behavior changes can be identified and / or implemented.

[0081] At block 510, a power top off charge can be determined based on the power required for the work shift.

[0082] At block 512, the power consumption after the suggestions of blocks 506, 508, and 510 can be measured.

[0083] At block 514, a difference between the measured power consumption at block 512 and the measured power consumption in block 512 can be determined.

[0084] At block 516, an impact based on the power reduction rate can be determined and shown.

[0085] At block 518, a reduction in battery purchase and disposal can be determined based on the impact determined at block 516.

[0086] At block 520, a reduction rate of landfill impact can be determined based on the reduction in battery purchase and disposal determined at block 518.

[0087] At block 522, a reduction rate of rare earth metal mining and carbon emissions can be determined based on the reduction rate of battery purchase and disposal determined at block 518.

[0088] Figure 6 is a flow diagram of an example process for implementing example methods and / or operations described herein, including as can be performed by a system of Figure 1 Techniques for tracking and controlling battery consumption in a fleet of battery-powered electronic devices. For example, instructions for performing the example process 600 can be stored on the memory of the smart battery 108, on the memory 112, and / or on the memory 118, and executed by the processor of the smart battery 108, the processor 110, and / or the processor 116, respectively.

[0089] In particular, Figure 6 The process 600 is shown, including example inputs and outputs determined with respect to the above Figure 4 and Figure 5 discussed with respect to blocks 404-blocks 410 and blocks 414-blocks 420 of Figure 4 and blocks 514 and blocks 518-blocks 522 of Figure 5 .

[0090] Figure 7 is a flow diagram of an example process 700 for implementing example methods and / or operations described herein, including as can be performed by a system of Figure 1techniques performed by the system for tracking and controlling battery consumption in a fleet of battery-powered electronic devices. For example, instructions for performing the example process 700 can be stored on the memory of the smart battery 108, on the memory 112, and / or on the memory 118, and executed by the processor of the smart battery 108, the processor 110, and / or the processor 116, respectively.

[0091] At block 702, power consumption information for the device and usage information for the device can be received for a duration of time. In some examples, the duration of time is a duration of time associated with a work shift. For example, the power consumption information can include battery charge data for the duration of time. Additionally, for example, the usage information can include one or more of the following for the duration of time: battery discharge rate, screen on time, screen brightness data, scanner rate, usage data associated with one or more business applications installed on the device, battery cycle, battery charge level, battery temperature, battery current capacity, battery total cumulative charge, battery rated capacity, average current, average power, voltage, charge on / off events, battery charging source, or battery replacement data.

[0092] At block 704, a first power usage value for the device for the duration of time can be determined based on the power consumption information and the usage information for the duration of time. For example, the first power usage value can be a minimum battery charge required for the duration of time. In some examples, determining the first power usage value for the device for the duration of time can be further based on one or more of the following associated with the device: battery type, battery health, device type, device screen timeout setting, device brightness setting, battery cycle, battery charge level, battery temperature, battery current capacity, battery total cumulative charge, battery rated capacity, average current, average power, voltage, charge on / off events, battery charging source, or site name.

[0093] At block 706, the power consumption information and the usage information can be analyzed to determine at least one attribute associated with excess power usage by the device for the duration of time. For example, the at least one attribute associated with excess power usage by the device for the duration of time can include one or more of the following: a screen brightness attribute, a screen on time attribute, a wireless signal strength attribute, a device location attribute, a device physical memory attribute, an application physical memory utilization attribute, a battery utilization attribute for each application, a scanner usage attribute, a data transfer attribute associated with the device, a data transfer attribute for each application, a reception usage attribute associated with the device, and a reception usage attribute for each application.

[0094] At block 708, at least one mitigation measure is identified and applied to address the at least one attribute and reduce the first power usage value over the duration. For example, the at least one mitigation measure can include one or more of: a screen brightness reduction, a screen on time reduction, an increase in wireless signal strength quality, an improvement in memory utilization associated with the device, an improvement in memory utilization per application, a reduction in battery usage per application, an optimization of data transmission, an optimization of reception, or an optimization of scanner usage.

[0095] At block 710, a second power usage value for the device can be determined based on the at least one mitigation measure implemented over the duration.

[0096] At block 712, a difference between the first power usage value and the second power usage value over the duration can be determined.

[0097] At block 714, at least one indicator of energy savings over the duration can be determined based on the determined difference. The at least one indicator of energy savings over the duration includes one or more of: a battery life indicator, a battery purchase indicator, a battery disposal indicator, a power consumption indicator, a CO2 emission rate indicator, or a precious metal mining rate indicator.

[0098] Additional Considerations

[0099] The above description relates to block diagrams of the figures. Alternative implementations of the examples represented by the block diagrams include one or more additional or alternative elements, processes, and / or devices. Additionally or alternatively, one or more of the example blocks in the figures can be combined, divided, rearranged, or omitted. The components represented by the blocks in the figures are implemented by hardware, software, firmware, and / or any combination of hardware, software, and / or firmware. In some examples, at least one of the components represented by a block is implemented by a logic circuit. As used herein, the term“logic circuit” is expressly defined as a physical device that includes at least one hardware component configured (e.g., via operation based on a predetermined configuration and / or via execution of stored machine-readable instructions) to control one or more machines and / or perform operations of one or more machines. Examples of logic circuits include one or more processors, one or more coprocessors, one or more microprocessors, one or more controllers, one or more digital signal processors (DSPs), one or more application-specific integrated circuits (ASICs), one or more field-programmable gate arrays (FPGAs), one or more microcontroller units (MCUs), one or more hardware accelerators, one or more specialized computer chips, and one or more system-on-a-chip (SoC) devices. Some example logic circuits, such as ASICs or FPGAs, are specially configured hardware to perform operations (e.g., one or more of the operations described herein and represented by flowcharts of the present disclosure, if present). Some example logic circuits are hardware that executes machine-readable instructions to perform operations (e.g., one or more of the operations described herein and represented by flowcharts of the present disclosure, if present). Some example logic circuits include a combination of specially configured hardware and hardware that executes machine-readable instructions. The above description relates to the various operations described herein and flowcharts that can be appended herein to illustrate those operations. Any such flowcharts represent example methods disclosed herein. In some examples, the methods represented by the flowcharts implement the apparatus represented by the block diagrams. Alternative implementations of the example methods disclosed herein can include additional or alternative operations. Additionally or alternatively, the operations of alternative implementations of the methods disclosed herein can be combined, divided, rearranged, or omitted. In some examples, the operations described herein are implemented by machine-readable instructions (e.g., software and / or firmware) stored on a medium (e.g., a tangible machine-readable medium) for execution by one or more logic circuits (e.g., processors). In some examples, the operations described herein are implemented by one or more configurations of one or more specially designed logic circuits (e.g., ASIC(s)). In some examples, the operations described herein are implemented by a combination of specially designed logic circuits and machine-readable instructions stored on a medium (e.g., a tangible machine-readable medium) for execution by the logic circuit(s).

[0100] As used herein, each of the terms "tangible machine-readable medium," "non-transitory machine-readable medium," and "machine-readable storage device" is expressly defined as a storage medium (e.g., a platter of a hard disk drive, a disc of a digital versatile disc, an optical disc, a flash memory, a read-only memory, a random access memory, etc.) on which machine-readable instructions (e.g., program code in the form of software and / or firmware) are stored for any suitable duration of time (e.g., permanently, for an extended period of time (e.g., while a program associated with the machine-readable instructions is executing), and / or for a short period of time (e.g., while the machine-readable instructions are cached and / or in a buffering process)). Moreover, as used herein, each of the terms "tangible machine-readable medium," "non-transitory machine-readable medium," and "machine-readable storage device" is expressly defined to exclude propagating signals. That is, none of the terms "tangible machine-readable medium," "non-transitory machine-readable medium," and "machine-readable storage device" as used in any claim of this patent is to be interpreted as being realized by a propagating signal.

[0101] In the foregoing specification, specific embodiments have been described. However, one of ordinary skill in the art appreciates that various modifications and changes can be made without departing from the scope of the present application as set forth in the claims below. Accordingly, the specification and figures are to be regarded in an illustrative manner, rather than a restrictive one, and all such modifications are intended to be included within the scope of the present teachings. Additionally, the described embodiments / examples / implementations should not be interpreted as mutually exclusive, but rather as potentially combinable, if such combinations are in any way permissible. In other words, any feature disclosed in any of the foregoing embodiments / examples / implementations can be included in any of the other foregoing embodiments / examples / implementations.

[0102] These benefits, advantages, problem solutions, and any element(s) that can cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or element of any or all the claims. The application is solely defined by the appended claims including any modification made during the pendency of this application and all equivalents of the claims as issued. The use of the terms "estimate," "processing," "employ," "implement," "instantiate," "associate," "determine," and "identify," among others, can be "circuits" that manipulate and / or transform data

[0103] Furthermore, relational terms such as first and second, top and bottom, and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms "comprises," "comprising," "has," "having," "includes," "including," "contains," "containing," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises, has, includes, contains a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises a," "has a," "includes a," "contains a" does not, without more constraints, preclude the existence of additional identical elements in the process, method, article, or apparatus that comprises, has, includes, contains the element. The terms "a" and "an" are defined as one or more unless explicitly stated otherwise herein. The terms "substantially," "essentially," "approximately," "about" or any other version thereof, are defined as being close to as understood by one of ordinary skill in the art, and in one non-limiting embodiment the term is defined to be within 10%, in another embodiment within 5%, in another embodiment within 1% and in another embodiment within 0.5%. The term "coupled" as used herein is defined as connected, although not necessarily directly and not necessarily mechanically. A device or structure that is "configured" in a certain way is configured at least to that degree, but can also be configured in other ways other than those explicitly described.

[0104] The abstract of the disclosure is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or the meaning of the claims. In addition, in the above Detailed Description, various features are grouped together in various embodiments for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than the claims do. Rather, as the following claims reflect, inventive subject matter can lie in fewer than all features of a single disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description, where each claim stands on its own as indicating the subject matter creating ownership over the covered renderings.

Claims

1. A method comprising: Receive device power consumption information and device usage information over a period of time; The first power usage value of the device during the duration is determined based on the power consumption information and the usage information during the duration. Analyze the power consumption information and the usage information to determine at least one attribute associated with excessive power use of the device during the duration; Identify and apply at least one mitigation measure to address the at least one attribute and reduce the first power usage value during the duration; A second power usage value for the device is determined based on the at least one mitigation measure implemented during the duration; Determine the difference between the first power usage value and the second power usage value during the specified duration; as well as Based on the determined difference, at least one indicator indicating energy saving during the duration is determined.

2. The method of claim 1, wherein the power consumption information includes battery charging data over the duration.

3. The method of claim 1, wherein the usage information includes one or more of the following during the said duration: battery discharge rate, screen on time, screen brightness data, scan rate, usage data associated with one or more business applications installed on the device, battery cycle, battery charge level, battery temperature, current battery capacity, total accumulated battery charge, rated battery capacity, average current, average power, voltage, charging on / off events, battery charging source, or battery replacement data.

4. The method of claim 1, wherein determining the first power usage value of the device during the duration is further based on one or more of the following associated with the device: battery type, battery health, device type, device screen timeout setting, device brightness setting, battery cycle, battery charge level, battery temperature, current battery capacity, total accumulated battery charge, rated battery capacity, average current, average power, voltage, charging on / off event, battery charging source, or site name.

5. The method of claim 1, wherein the first power usage value is the minimum battery charge required during the duration.

6. The method of claim 1, wherein the at least one attribute associated with the excessive power use of the device during the duration includes one or more of the following: screen brightness attribute, screen on time attribute, wireless signal strength attribute, device location attribute, device physical memory attribute, application physical memory utilization attribute, battery utilization attribute for each application, scanner usage attribute, data transmission attribute associated with the device, data transmission attribute for each application, reception usage attribute associated with the device, and reception usage attribute for each application.

7. The method of claim 1, wherein the at least one mitigation measure comprises one or more of the following: reducing screen brightness, reducing screen on time, increasing wireless signal strength quality, improving memory utilization associated with the device, improving memory utilization for each application, reducing battery usage for each application, optimizing data transmission, optimizing reception, or optimizing scanner usage.

8. The method of claim 1, wherein the at least one indicator indicating energy saving during the duration includes one or more of the following: battery life indicator, battery purchase indicator, battery disposal indicator, power consumption indicator, CO2 emission rate indicator, or precious metal mining rate indicator.

9. The method of claim 1, wherein the duration is a duration associated with a work shift.

10. A system comprising: The equipment includes: A smart battery, the smart battery including a battery memory and one or more battery processors storing first computer-readable instructions, the first computer-readable instructions causing the one or more battery processors to store battery usage and state of charge (SOC) data on the battery memory. clock, Equipment communication system One or more device processors, and Device memory, the device memory storing second computer-readable instructions; and The server includes a server communication system, one or more server processors, and a server memory storing third computer-readable instructions; When executed by the one or more device processors, the second computer-readable instruction causes the one or more device processors to: Detect events associated with the battery; Event data is compiled based on detected events, the event data including, for each of one or more detected events, one or more of the following: battery usage data stored in the battery memory associated with the event, SOC data stored in the memory associated with the event, an indication of the event type associated with the event, the time associated with the event, an indication of the standby voltage level during the event, the battery temperature associated with the event, the battery charge at the time the event occurred, or the battery charging source associated with the event; and The compiled event data is transmitted to the server via the device communication system; When executed by the one or more server processors, the third computer-readable instruction causes the one or more server processors to: The server receives compiled event data from the device via the server communication system. Based on the compiled event data, determine the device activity data associated with the device; and The device activity data is analyzed using a power consumption model to estimate the device's power consumption based on the battery consumption associated with the device.

11. The system of claim 10, wherein the device is one of: a mobile computing device, a mobile printer, a scanner device, and a robotic device.

12. The system of claim 10, wherein the second computer-readable instruction, when executed by the one or more device processors, further causes the one or more device processors to: Capture additional device data, which includes one or more of the following: power usage data associated with the device, screen on-time associated with the device, screen brightness data associated with the device, scan rate associated with the device, usage data associated with one or more business applications installed on the device, wireless signal strength associated with the device, device location, indication of device physical memory utilization, indication of physical memory utilization for each application, indication of battery utilization for each application, indication of data transmission associated with the device, indication of data transmission for each application, indication of reception usage associated with the device, or indication of reception usage for each application; and The additional device data is transmitted to the server via the device communication system.

13. The system of claim 12, wherein, when executed by the one or more server processors, the third computer-readable instruction further causes the one or more server processors to: The additional device data is received via the server communication system; and the device activity data is further determined based on the additional device data.

14. The system of claim 10, wherein, when executed by the one or more server processors, the third computer-readable instruction further causes the one or more server processors to: Identify at least one indicator associated with the estimated electricity consumption, said at least one indicator including one or more of the following: emission rate, precious metal mining rate, or landfill rate.

15. The system of claim 10, wherein the detected events associated with the battery include one or more of the following: low battery event, battery replacement mode event, battery replacement entry event, battery replacement exit event, battery charging on event, battery charging off event, battery status event, battery temperature event, battery accumulated charge event, device hibernation event, device hibernation resume event, or device shutdown event.

16. A system comprising: A mobile device, the mobile device including a battery, a clock, a mobile device communication system, one or more mobile device processors, and a mobile device memory storing first computer-readable instructions, which, when executed by the one or more mobile device processors, cause the one or more mobile device processors to collect and transmit mobile device data via the mobile device communication system, the mobile device data including battery and usage status data; The server includes a server communication system, one or more server processors, and a server memory storing second computer-readable instructions, which, when executed by the one or more server processors, cause the one or more server processors to: Receive mobile device data via the server communication system; Analyze the mobile device data to determine one or more of the following: the duration of one or more shifts associated with the mobile device, the required power consumption associated with the mobile device, the measured power consumption associated with the mobile device, the predicted power consumption over time associated with the mobile device, the predicted rate of battery purchases associated with the mobile device, the predicted rate of battery disposal associated with the mobile device, the impact of the mobile device's power consumption on the prediction of emission rates, or the impact of the mobile device's power consumption on the prediction of landfill rates.

17. The system of claim 16, wherein the mobile device is one of: a mobile computing device, a mobile printer, a scanner device, or a robotic device.

18. The system of claim 16, wherein the battery is a lithium-ion battery.

19. The system of claim 16, wherein analyzing the mobile device data includes using one or more machine learning algorithms to analyze the mobile device data.

20. The system of claim 16, wherein analyzing the mobile device data includes using one or more simulations to analyze the mobile device data.

21. The system of claim 20, wherein the one or more simulations include Monte Carlo simulations.

22. The system of claim 16, wherein, when executed by the one or more server processors, the second computer-readable instructions further cause the one or more server processors to: Identify one or more mobile device settings changes, wireless coverage improvements, application updates, application memory usage changes, application battery usage changes, or behavior changes that affect the power consumption associated with the mobile device.

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