Method for characterizing and monitoring energy usage of battery-powered wireless connected devices

Abstract

A method for managing the battery of a remote wireless device powered by a wireless sensor or other battery comprises the steps of: pre-characterizing the energy consumption of the device during various activities and modes; placing the device in an operating state; opportunistically collecting operational data of the device acquired for purposes other than battery management; and estimating the state of the battery based on an analysis of operational data considering the pre-characterizing information. The method further comprises taking battery management activities based on the estimated battery state, such as recharging or replacing the battery when it is nearly depleted, and / or modifying the operation of the device to extend battery life, for example, by reducing or increasing the frequency of data transmission, measurement, calculation, and / or other dynamic current events. The state estimation may further consider measurements provided by a simple current measurement circuit included in the device.

Description

Technology Field

[0001] This application claims priority to U.S. application no. 17 / 380,901 filed on July 20, 2021, the entire contents of which are incorporated herein by reference for all purposes.

[0002] The present invention relates to a battery-powered wireless device, and more specifically, to a method for monitoring energy usage, estimating remaining battery life, optimizing battery management, and extending the battery life of a battery-powered wireless device. Background Technology

[0003] Battery-powered wirelessly linked devices have long been used in a wide range of applications, including portable communication devices such as pagers and mobile phones, remote sensing devices, devices for tracking valuable assets, and security applications such as monitoring commercial buildings and identifying suspicious behavior. Banks can replace wireless push buttons with panic buttons for employees, and retailers can install wireless window sensors at every building access point. Homeowners can also use wireless air sensors to detect harmful gases in the air, such as carbon monoxide.

[0004] In particular, battery-powered wireless sensors can be used to predict, detect, and / or mitigate current or future problems that may occur in various types of devices. For example, wireless rope sensors can be used to detect the presence of water near computer hardware in server rooms and data centers, and water leak sensors can be mounted on walls to detect plumbing failures or pipes that may burst in winter. Additionally, wireless sensors that monitor vibration, temperature, noise, pressure, and / or other environmental factors can be used to detect problems and failures in pumps, valves, and other operationally critical devices in refineries, factories, utilities, and other large-scale facilities.

[0005] In some cases, since battery-powered wireless sensors or other devices may be implemented in hazardous areas, using basic batteries—that is, non-rechargeable batteries—is preferred over using rechargeable batteries due to safety considerations. In the oil industry, wireless sensors are sometimes used to monitor the "down-hole" of oil wells, and are therefore exposed to a wide range of temperatures and pressures.

[0006] Where ease of installation is required, the option of implementing wireless sensors and / or other wireless devices is becoming increasingly attractive, instead of similar devices that supply power from wires and / or communicate over them. In particular, installing a large number of wireless sensors to monitor equipment across major facilities, such as large-scale factories, utilities, and refineries, can be highly desirable because sensors can be implemented without the need to install extensive power and / or data networks throughout the facility. Furthermore, the ease of installation of wireless devices can make them ideal for installation in hazardous areas.

[0007] It may sometimes be important to monitor the data communication patterns and other activities of a wireless device to detect excessive data collisions and / or other issues that could degrade the performance of the wireless device and / or network. For example, logs regarding the length and frequency of the wireless device's wireless transmissions, including retransmissions due to network collisions and other errors, may be maintained. This data can then be retrieved using a wired connection during battery replacement or other service events in the event of remote commands or non-periodic trigger events via wireless data transmission, or it can be retrieved periodically via wireless data transmission.

[0008] The utility of wireless sensors and many other wireless devices can be significantly increased through their integration into the Internet of Things (IoT), which provides protocols for low-energy, low-data-rate wireless transmission over very long distances, and also through the development of low-power wide area network (LPWAN) protocols such as LoRaWAN, SigFox, and NB-IoT. Along with improved battery technology, these advancements in low-power communication have helped increase the battery life of wireless battery-powered devices, thereby reducing the frequency of needing to recharge or replace batteries.

[0009] Nevertheless, for most types of battery-powered wireless devices, it is desirable to extend battery life as much as possible. For this reason, wireless sensors and other wireless devices often alternate between low-power or minimum-power "sleep periods" and maximum-power "active" periods to minimize total power consumption and maximize battery life. As used herein, the term "active" period refers to the period during which the wireless device performs tasks such as collecting data, sensing, measuring, calculating, and interpreting. Generally, during the active cycle, the device may experience "dynamic current" events that require bursts of current and energy usage, such as transmitting messages over a wireless network. A "sleep" period refers to a period during which most or all of the device is turned off, or a period during which it intentionally enters a state of significantly lower power consumption than during the "active" period. A "wake / sleep cycle" refers to the sleep period following the active period, or vice versa.

[0010] Many wireless sensors and other wireless devices also have the ability to enter one or more partial sleep "modes," which significantly reduce energy consumption while allowing some low-power activities to still be executed. For example, a device can be set to a relatively low-power partial sleep mode that monitors I / O pins connected to a sensing device or circuit. Upon the detection of an electrical threshold breach on an I / O pin, the device can wake from the low-power partial sleep mode and switch to a high-power sleep mode, which allows for more energy-consuming activities. The transition from low-power sleep mode to high-power sleep mode is also considered a transition from a "sleep" state to an "active" state.

[0011] Often, the duration of the active and / or sleep periods of wireless devices is variable. In some cases, the timing of wake / sleep cycles is automatically adjusted; for example, when a battery-powered wireless sensor detects anomalies and automatically changes the sequence and type of cycles performed to monitor them more closely. In other cases, operational changes, such as altering data transmission intervals, are made by the end user. Even if the wake / sleep cycle timing of a wireless device is strictly defined, the amount of energy consumed during each active period can vary, for instance, when the number of dynamic current events increases due to packet retransmission caused by network collisions. For all these reasons, it can be difficult to remotely estimate how much energy remains in the battery at any given time and to predict when the device is likely to require recharging or replacement.

[0012] While not all, many wireless devices of all types include some form of battery level display. In some cases, a series of bars is displayed to show the number decreasing as the battery discharges, or the remaining battery life is provided as a percentage. In large-scale installations where it is not convenient to frequently observe the battery indicator directly, the battery life display can sometimes be monitored remotely.

[0013] Most battery-powered wireless systems indicate their battery life based on measurements of the battery's voltage levels. To generate warnings and notify the end user when the battery needs to be recharged or replaced, many people set a simple voltage threshold. However, this approach is highly inaccurate due to the uniform voltage discharge profiles of lithium-based and other battery chemistries. In other words, changes in battery voltage are minimal until the battery is nearly completely discharged. Furthermore, this approach ignores the importance of battery performance trends under load—that is, during active and sleep periods—as battery voltage typically varies depending on how much current is consumed. Moreover, if a voltage warning is triggered by a wireless sensor only when the battery is nearly discharged, the result can be a battery “crisis,” as recharging or replacing the battery before the sensor is completely turned off can be inconvenient or costly. If this occurs, measured data may be lost, and the end user cannot receive additional information about the status of the device monitored by the wireless sensor until the battery can be recharged or replaced.

[0014] Of course, energy consumed from the battery can be monitored by adding additional dedicated circuitry, such as power measurement circuits, to the wireless device; however, this approach increases the device's cost, complexity, and potential failure modes, consumes additional power, and requires increasing the device size to integrate additional hardware.

[0015] Therefore, a stable and accurate method is required to remotely monitor the battery usage of a wireless device and predict its remaining lifespan, while minimizing or eliminating the need for additional monitoring hardware and using little to no additional energy, in order to utilize the battery's full capacity and ensure that it is exchanged or replaced at a convenient time before it is completely depleted. The problem to be solved

[0016] The present invention is a reliable and accurate method that remotely estimates the battery status of a wireless device, predicts its remaining battery life, and provides battery management, while minimizing or completely avoiding the implementation of any additional monitoring hardware and requiring almost no additional energy consumption, so as to ensure that the battery is exchanged or replaced at a convenient time before it is completely depleted and that the full capacity of the battery can be utilized. means of solving the problem

[0017] More specifically, the present invention estimates battery usage and remaining battery life by taking advantage of information already being collected for other purposes, thereby minimizing or eliminating any need for additional measurements and / or communications specifically implemented for battery management purposes.

[0018] According to the disclosed method, prior characterization information is obtained regarding a wireless device. The prior characterization information may include the power consumption of the wireless device during each of its various active modes. The prior characterization information may further include historical statistical data gathered from observations of multiple batteries or devices of similar or identical types, such as the average sensitivity of such batteries or devices to temperature, pressure and / or other environmental conditions, battery passivation behavior, battery chemistry life profile, etc. The historical data may also include information regarding the history of the wireless device itself, such as periods when the device is in storage or otherwise the battery is idle before the device enters service.

[0019] In an embodiment, the analysis of prior characterization information further includes developing one or more statistical models of battery life derived from historical data, for example, a statistical model that associates battery life with the density of sensors or other devices in various environments, or a model that associates passivation with environmental factors and usage patterns. In various embodiments, the analysis of prior characterization information includes training an artificial intelligence / machine learning algorithm using the prior characterization information for subsequent use in analyzing its observations while the device is in service.

[0020] When the device enters service, operational data collected for other purposes is opportunistically monitored and analyzed and used in conjunction with prior characterization information to make periodic estimates of the total amount of power consumed since the last recharge or replacement of the battery. In an embodiment, at least some of the operational data is combined with prior characterization information to be used for subsequent estimations. In various embodiments, the analysis of the opportunistically monitored operational data includes the application of machine learning / AI algorithms to the operational data, wherein the algorithms are "taught" using the historical data described above and / or observations of the device in service. For example, a machine learning algorithm may detect correlations between the battery's environmental conditions, the length of the sleep period, and passivation behavior based on historical information, and then apply those correlations to help predict the passivation behavior of the device currently being monitored.

[0021] As used herein, the term "monitored operational data" refers to data collected regarding the activity and status of a wireless device for purposes unrelated to battery life monitoring. Accordingly, the use of this information to estimate battery energy consumption is also referred to herein as "opportunistic." Examples of opportunistically monitored operational data of a wirelessly connected device are provided in the embodiments.

[0022] - Length of wake / sleep cycle (including length and type of arbitrary partial slip periods)

[0023] - Length and number of active periods,

[0024] - Number and duration of dynamic electric current events during the active period,

[0025] - Number of packets transmitted during the active period

[0026] - Frequency of the cycle from slip to wake,

[0027] - Number of measurements performed,

[0028] - Number of calculations performed,

[0029] - Internal and / or ambient temperature, and / or pressure profile, and

[0030] - Transient behavior of battery voltage during the application and / or removal of a load

[0031] Includes

[0032] Some embodiments derive their power and battery life estimates based solely on the analysis of prior characterization information and monitored operational data, but other embodiments further improve these estimates by implementing simple, low-cost, low-profile, and low-energy circuits of the wireless device, such as a current measurement circuit capable of measuring and reporting current flow during dynamic current events.

[0033] In some embodiments, a voltage sensor is implemented in a device for measuring the voltage of a battery, and the device monitors and reports the "start-up" load behavior of the battery voltage, that is, a transient voltage drop and voltage recovery that occur immediately after a load is applied to the battery when the active period begins or during dynamic current events such as wireless transmission. Likewise, in an embodiment, the device monitors and reports the behavior of the battery voltage when energy consumption suddenly decreases, for example, due to a transition from the active period to the sleep period.

[0034] The disclosed method further comprises battery management, and accordingly, power and battery life estimation, and passivation behavior also predicted in the embodiments are used to schedule the recharging or replacement of the battery comfortably but not excessively before the battery's energy is depleted. In some embodiments, if excessive energy consumption is detected or environmental conditions that are likely to accelerate battery death are detected, the method further comprises reducing the battery power consumption rate by increasing the length of the sleep period and / or reducing the number of dynamic current events required during each active period.

[0035] In the embodiments, battery management may further include minimizing battery passivation problems where unmanaged passivation of the battery can shorten battery life. For example, if energy is consumed infrequently from the battery, this can cause a large passivation layer to accumulate within the battery, thereby causing the battery to fail to provide sufficient dynamic power to operate the wireless sensor. In these embodiments, if analysis of pre-characterized and opportunistically monitored operational data indicates that current should be consumed more frequently from the battery, battery management may include increasing the frequency of active periods and / or dynamic current events.

[0036] The present invention is a method for managing a battery-powered remote, wireless device. The method is as follows:

[0037] - A) step of obtaining pre-characterized information regarding the wireless device - wherein the pre-characterized information includes substantially identifying all operational phases and / or activities that characterize the operation of the device, and determining the amount of energy consumed from the battery during each of the phases and / or activities,

[0038] - Step B) of placing the above wireless device in a service state,

[0039] - Step C) of receiving operational data regarding its actual steps and / or activities while the wireless device is in service - said operational data is “opportunistic” in that it is acquired for purposes unrelated to battery management,

[0040] - Step D, which analyzes the data of the aforementioned opportunistic operational situation considering prior characterization information,

[0041] - Step E) of estimating the state of the battery based on the analysis of Step D) - The estimated state includes the estimated total energy consumed by the battery since it was last recharged or replaced,

[0042] - Step F) which repeats steps C) through E) during the operation period of the above-mentioned remote wireless device, and

[0043] - Includes step G) of taking battery management action based on the estimated state of the battery.

[0044] In the embodiment, the remote wireless device is a remote wireless sensor.

[0045] In any of the embodiments described above, the remote wireless device may be configured to cycle between at least one active mode and at least one sleep mode, and the prior characterization information may include an energy usage profile of the active or sleep mode for each of the active and sleep modes.

[0046] In any of the embodiments described above, the prior characterization information may include energy usage associated with dynamic current events during which a relatively large amount of current is consumed from the battery compared to a smaller amount of current being consumed from the battery during most other operational phases and activities of the remote wireless device.

[0047] In any of the embodiments described above, the prior characterization information may include at least one of historical data regarding the wireless device, historical data regarding the battery usage and / or behavior of similar or identical batteries and / or devices, information regarding the estimated total battery idle time that will elapse before the device enters service, and information derived from any of steps C) to E) after steps C) to E) have been performed at least once, combined with the prior characterization information.

[0048] In any of the embodiments described above, the method may further include the step of analyzing prior characterization information. In some of these embodiments, the prior characterization information includes historical data regarding the wireless device, and step D) includes at least one of the steps of developing a statistical model of battery life based on the historical data, developing a statistical model of battery passivation based on the historical data, and training a machine learning / artificial intelligence algorithm using the historical data.

[0049] In some of these embodiments, at least one of the statistical model of battery life, the statistical model of battery passivation, and the machine learning / artificial intelligence training is updated periodically or continuously after step B) in consideration of the opportunistic operational data. In any of these embodiments, step E) may include the step of applying at least one of the statistical model and the machine learning / artificial intelligence algorithm to the opportunistic operational data.

[0050] In any of the embodiments described above, the opportunistic operational data may include the duration and number of active and sleep periods in which the remote wireless device enters.

[0051] In any of the embodiments described above, the data on the opportunistic operation may include the number of dynamic current events that occurred. In some of these embodiments, the dynamic current events may include wireless transmission by the remote wireless device. In some of these embodiments, the remote wireless device may be a wireless sensor, and the dynamic current events may include a measurement made by the wireless sensor and / or a calculation performed by the wireless sensor.

[0052] In any of the embodiments described above, the opportunistic operational data may include an amount of information that is wirelessly retransmitted by the remote wireless device.

[0053] In any of the embodiments described above, at least some of the opportunistic operational data may be obtained by supporting network management.

[0054] In any of the embodiments described above, at least some of the opportunistic operational data may be obtained through remote management of the wireless device.

[0055] In any of the embodiments described above, the remote wireless device may be a remote wireless sensor, and at least some of the opportunistic operational data may be obtained as the remote wireless sensor reports the data it has detected.

[0056] In any of the embodiments described above, the opportunistic operational data may further include environmental information regarding at least one of the interior of the remote wireless device and the adjacent environment of the remote wireless device.

[0057] In any of the embodiments described above, the step of estimating the total energy consumed by the battery after it has been last recharged or replaced may further follow current measurement data obtained by a current measurement circuit included in the remote wireless device.

[0058] In any of the embodiments described above, the step of estimating the state of the battery may further include the step of analyzing the transient behavior of the battery voltage during the application and / or removal of the load according to a transient voltage measurement obtained by a voltage measurement circuit included in the remote wireless device.

[0059] In any of the embodiments described above, the estimated state of the battery may further include an estimation of the degree of passivation of the battery.

[0060] In any of the embodiments described above, the battery management activity of step F) may include at least one of recharging or replacing the battery when the energy consumption estimate indicates that the battery is nearly depleted; reducing the degree of device activity to reduce the energy consumption of the device; increasing the degree of device activity to reduce the passivation of the battery; increasing the degree of device activity to prevent excessive cooling of the battery; reducing the degree of device activity to prevent excessive heating of the battery; and adjusting a temperature control device that is close to but independent of the wireless device to prevent excessive cooling or heating of the battery.

[0061] In another embodiment, if it is determined that the temperature of the environment near the wireless device has fallen below a specified minimum value or risen above a specified maximum value, battery management includes the step of increasing or decreasing the device temperature. This may be achieved by increasing or decreasing the rate of activity of the device so that the heat generated by the device itself increases or decreases. In some embodiments, if an external heater or other climate is available, battery management may include causing the climate to be activated or increasing the rate of heating or cooling.

[0062] The features and benefits described herein are not exhaustive, and many additional features and benefits will be apparent to those skilled in the art from the drawings, specification, and claims. Furthermore, it should be noted that the language used herein is chosen primarily for readability and educational purposes and does not limit the scope of the subject matter of the invention. Brief explanation of the drawing

[0063] FIG. 1 illustrates a typical awake / sleep cycle of a remote wireless device of the prior art, and FIG. 2a is a flowchart showing the steps of the present invention in an embodiment of the present invention, FIG. 2b is a block diagram showing the steps of FIG. 2a, indicating how prior characterization information and opportunistic operational data are combined when estimating the total energy consumption of the battery. FIG. 2c is a block diagram showing steps included in an embodiment as part of the preliminary characterization of FIG. 2b, FIG. 3 is a block diagram illustrating the opportunistic collection of operational data acquired for purposes unrelated to estimating battery usage, FIG. 4 is a flowchart showing steps included in an embodiment during the analysis of operational data, and FIG. 5 is a flowchart showing the steps included in the embodiment during battery management of a wireless device. Specific details for implementing the invention

[0064] The present invention is a reliable and accurate method that remotely estimates the battery status of a wireless device, predicts its remaining battery life, and provides battery management, while minimizing or completely avoiding the implementation of any additional monitoring hardware and requiring almost no additional energy consumption, so as to utilize the full capacity of the battery and ensure that the battery is exchanged or replaced at a convenient time before it is completely depleted.

[0065] Referring to FIGS. 2a and 2b, a wireless device according to the present invention is pre-characterized (200), then operational data is opportunistically monitored (202) and periodically analyzed (204), and battery management is implemented (206). This process is repeated periodically throughout the battery's life. As shown in FIG. 2b, in the embodiment, information derived from estimation (206) and battery management (208) is combined with pre-characterized information for use in subsequent analysis (204).

[0066] Referring to FIG. 2c, the preliminary characterization (200) may include the measurement (210) of the characteristics of the device itself, for example, determining the energy usage profile during the wake period (102) and during various partial and full sleep periods (100). In an embodiment, if the power consumption during the active period (102) and during various partial and full sleep periods (100) is relatively constant, the preliminary characterization is achieved by determining the power consumption profile during the active period (102) and the sleep period (100).

[0067] In another embodiment, specific operations that may be performed during an active period (102), including dynamic current events, are identified and individually pre-characterized (200) in terms of their energy consumption. For example, when a device is taking measurements, performing calculations, and / or transmitting data wirelessly, energy usage may be pre-characterized.

[0068] Referring further to FIG. 2c, in addition to measuring (210) the characteristics of the device itself, the preliminary characterization may further include collecting and analyzing historical statistical data (212) collected from previous observations of multiple batteries or devices of similar or identical types, such as the average sensitivity of these batteries or devices to environmental conditions such as temperature and / or pressure, the average passivation behavior of the batteries, and the battery chemistry life profile. The historical data may also include information regarding the history of the device itself, such as the period the device is in storage before entering service, or otherwise the period the battery is idle. In an embodiment, ongoing data regarding the operation of the device is combined with the historical data (212) for use in subsequent estimation.

[0069] The analysis of the prior characterization information may further include developing one or more statistical models of battery life and / or battery passivation derived from historical data, for example, a statistical model that associates battery life with the density of sensors or other devices in various environments, or a model that associates passivation with environmental factors and usage patterns (214). In various embodiments, the prior characterization (200) includes using historical data (216) to train an artificial intelligence / machine learning algorithm for subsequent use in analyzing opportunistic operational data acquired when the wireless device is in service.

[0070] When the device enters service, the present invention opportunistically monitors operational data already collected from the device for unrelated purposes (202), and combines prior characterization information with the opportunistically monitored operational data to estimate the state of the battery, which may include estimating the total energy consumed since the last recharge or replacement of the battery, the passivation of the device, and the remaining battery life of the device (206). These steps of opportunistically monitoring and estimating are repeated indefinitely.

[0071] As used herein, the term "monitored operational data" refers to data collected in relation to the activity and status of a wireless device for purposes unrelated to estimating energy consumption and remaining battery life. Accordingly, using this information to also estimate battery energy consumption is referred to herein as "opportunistic."

[0072] For example, referring to FIG. 3, it may be important for network management (302) purposes to collect data regarding the number of packets transmitted by the device (300) to detect excessive packet collisions caused by congestion and other network problems. This information includes retransmissions due to network collisions and is opportunistically used in embodiments of the invention to estimate the amount of energy consumed by the device when transmitting packets (306).

[0073] The monitored operational data may also include device usage information (304) collected as the device monitors and manages activities that occur when the device (300) performs its intended purpose. For example, it may be important to remotely monitor the active and sleep periods of the wireless sensor (300) because an increase in the frequency and / or duration of the active period may indicate that an abnormality or degradation in the performance of the monitored device is detected and is being closely monitored by the sensor (300). This information is opportunistically used in the embodiment to estimate energy usage based on pre-characterized energy consumption during the active and sleep periods (306).

[0074] During the operation of the wireless sensor (300), data will generally be transmitted by the sensor (300) to the receiving device. In the event of a message collision, the message may be retransmitted by the transmitting wireless sensor. Where the wireless sensor does not directly provide information regarding the number and duration of its active and sleep periods, the embodiment estimates the duration and number of active and sleep periods based on the timing of this received data in combination with commands and parameters given to the wireless sensor (300), which will generally be known a priori.

[0075] In an embodiment, to determine the total number of messages transmitted by the sensor (300), the number of messages received from the sensor by the receiving device is logged along with the number of retransmissions and opportunistically reviewed. This estimation can be combined with a prior characterization of the energy consumption per transmitted message and then used to estimate the total amount of energy consumed by message transmission.

[0076] Other examples of monitored operational data include the total number of measurements performed and the total number of calculations performed, all of which can be derived from sensor data reported to a receiving device by a wireless transmission device.

[0077] As another example, a wireless device may be configured to measure and report its internal temperature and / or pressure and to predict and prevent damage to the device as a means of monitoring the health and / or environment of the wireless device. In an embodiment of the method, such measured temperature and / or pressure is included in the monitored operational data and is used to further improve the accuracy of the estimated battery status.

[0078] In summary, examples of operational data opportunistically monitored by a wirelessly connected device in various embodiments include, but are not limited to, the following.

[0079] - Wake / slip period length,

[0080] - Length and number of active periods,

[0081] - Number and duration of dynamic electric current events during the active period,

[0082] - Number of messages sent during the active period

[0083] - Frequency of the cycle from slip to wake,

[0084] - Number of measurements performed;

[0085] - Number of calculations performed;

[0086] - Internal and ambient temperature and / or pressure profiles, and

[0087] - Transient behavior of battery voltage during the application and / or removal of a load.

[0088] Then, according to the present invention, operational data is analyzed by referring to prior characterization information (204). Referring to FIG. 4, this analysis (204) may include a direct comparison of the measured characteristics of the monitoring device (400) and operational data, a comparison of the historical data collected and analyzed during prior characterization (200) and operational data (402), and / or the application of a statistical model and / or machine learning / artificial intelligence (404) to the operational data.

[0089] For example, during the training of a machine learning algorithm, it can reveal correlations between environmental conditions, the length of the sleep period, and the battery's passivation behavior in historical data, or improve the performance of existing correlations, and then apply the correlations or improvements to help predict the passivation behavior of a currently monitored device.

[0090] As a result of the analysis (204) of the data on opportunistic operation, the battery condition including the total energy consumption of the battery, remaining battery life, and / or battery passivation can be estimated.

[0091] In some embodiments, power, battery life, and battery passivation estimates (206) are derived solely based on prior characterization information (200) and monitored operational data (204), whereas other embodiments can improve these estimates by implementing one or more simple, low-cost, and low-profile circuits of the device, such as a current measurement circuit capable of measuring and reporting current flow, particularly during dynamic current events.

[0092] In some embodiments, a voltage sensor is implemented in a device that monitors and reports the "start-up" load behavior of the battery voltage, that is, transient voltage drops and voltage recovery occurring immediately after a load is applied to the battery when the active period begins or during dynamic current events such as wireless transmission. Likewise, in embodiments, the device monitors and reports the behavior of the battery voltage when energy consumption suddenly decreases, for example, due to a transition from the active period to the sleep period.

[0093] Referring to FIG. 5, battery management (208) may include reducing the activity of the wireless device to increase battery life (500). For example, when excessive energy consumption is detected (510), battery management (208) may include reducing the rate of battery consumption (500) by increasing the length of the sleep period and / or reducing the number of dynamic current events required during each active period, for example, by reducing the number of measurements and / or calculations to be performed and / or the amount of data to be transmitted.

[0094] Similarly, battery management may include increasing the activity of the wireless device (502) in cases where un-managed passivation of the battery can shorten battery life (512). For example, if energy is consumed infrequently from the battery, this can cause a large passivation layer to accumulate within the battery and thus cause the battery to fail to provide sufficient dynamic power to operate the device. In this embodiment, if analysis of pre-characterized and opportunistically monitored operational data indicates that current should be consumed more frequently from the battery, battery management may include increasing the frequency of active periods and / or dynamic current events to reduce passivation (502).

[0095] In an embodiment, if the device becomes too cold or too hot (514), battery management includes taking steps (504) to prevent the device's temperature from falling below a specified minimum value or rising above a specified maximum value by, for example, increasing or decreasing its activity so that the device generates more or less heat, and / or adjusting an external temperature control device, such as a heater or cooler that has thermal communication with the device. By avoiding extreme temperatures, the device is protected from component failure and / or excessive passivation.

[0096] Battery management may also include using battery life estimation to determine when the battery is nearly depleted (516), and scheduling its recharging or replacement (506) comfortably but not excessively before the battery's energy is depleted.

[0097] The foregoing description of embodiments of the present invention is provided for illustrative and illustrative purposes only. Each and every page of this submission and all contents contained therein, regardless of how they are characterized, identified, or numbered, or in any form or arrangement within the application, shall be considered for all purposes to be a substantial part of this application. This specification is not intended to make the invention complete or limit it in the exact form disclosed. Many modifications and variations are possible in light of this disclosure.

[0098] Although this application is presented in a limited number of forms, the scope of the invention is not limited to these forms and various changes and modifications are possible. The disclosures set forth herein do not explicitly disclose all possible combinations of features that fall within the scope of the invention. Features disclosed herein for various embodiments may generally be interchanged and combined in any combination that is not self-contradictory and does not depart from the scope of the invention. In particular, the limitations set forth in the dependent claims below may be combined with the independent claims in any number and any order without departing from the scope of the disclosure, provided that the dependent claims are not logically incompatible with one another.

Claims

Claim 1 A method for managing a battery-powered remote wireless device comprises: A) obtaining pre-characterized information regarding said wireless device, wherein said pre-characterized information includes identifying operational phases and / or activities that characterize the operation of the device, and determining the amount of energy consumed from the battery during each of said phases and / or activities; B) placing said wireless device in a remote wireless communication state; C) receiving operational data regarding the operational phases and / or activities of said wireless device while said wireless device is in a remote wireless communication state, said operational data is obtained for purposes unrelated to battery management and is collected in relation to the activities and state of said wireless device; D) analyzing said operational data in consideration of the pre-characterized information; E) estimating the state of the battery according to the analysis of D), said estimated state includes the estimated total energy consumed by the battery since it was last recharged or replaced; and steps C) through E) during the operation of said wireless device A method comprising: step F) repeating; and step G) taking a battery management action based on the estimated state of the battery. Claim 2 In claim 1, the wireless device is a wireless sensor. Claim 3 A method according to claim 1, wherein the wireless device is configured to cycle between at least one active mode and at least one sleep mode, and the prior characterization information includes an energy usage profile of the active or sleep mode for each of the active and sleep modes. Claim 4 A method according to claim 1, wherein the prior characterization information comprises energy usage associated with dynamic current events during which a relatively large amount of current is consumed from the battery compared to a smaller amount of current being consumed from the battery during other operational phases and activities of the wireless device. Claim 5 A method according to claim 1, wherein the prior characterization information comprises at least one of the following: past data regarding the wireless device; past data regarding the battery usage and / or behavior of similar or identical batteries and / or devices; information regarding the estimated total battery idle time to be elapsed before step B); and information derived from any of steps C) to E) after steps C) to E) have been performed at least once, combined with the prior characterization information. Claim 6 In claim 1, the method further comprises the step of analyzing prior characterization information. Claim 7 In claim 6, the prior characterization information includes historical data regarding the wireless device, and step D) comprises at least one of the steps of: developing a statistical model of battery life based on the historical data; developing a statistical model of battery passivation based on the historical data; and training a machine learning / artificial intelligence algorithm using the historical data. Claim 8 In claim 7, at least one of the statistical model of the battery life, the statistical model of the battery passivation, and the machine learning / artificial intelligence training is periodically or continuously updated in consideration of the operational data after step B). Claim 9 In claim 7, the above step E) comprises the step of applying at least one of a statistical model and a machine learning / artificial intelligence algorithm to the operating data. Claim 10 A method according to claim 1, wherein the operational data includes the duration and number of times the wireless device operates in active mode, and the duration and number of times the wireless device operates in sleep mode. Claim 11 In claim 1, the method includes the number of dynamic current events that occurred in the operational data. Claim 12 In paragraph 11, the dynamic current event comprises a method including wireless transmission by the wireless device. Claim 13 In claim 11, the wireless device is a wireless sensor, and the dynamic current event includes a measurement made by the wireless sensor and / or a calculation performed by the processor of the wireless sensor. Claim 14 A method according to claim 1, wherein the operational data includes an amount of information that is wirelessly retransmitted by the wireless device. Claim 15 A method according to claim 1, wherein at least some of the operational data is data obtained for network management, and the data for network management includes data regarding the number of packets. Claim 16 A method according to claim 1, wherein at least some of the operational data is obtained through remote management of the wireless device. Claim 17 In claim 1, the wireless device is a wireless sensor, and at least some of the operational data is obtained as the wireless sensor reports the data it detects. Claim 18 A method according to claim 1, wherein the operational data further includes environmental information regarding at least one of the internal environment of the wireless device and the adjacent environment of the wireless device. Claim 19 In claim 1, the method of estimating the total energy consumed by the battery after the battery is last recharged or replaced follows the current measurement data obtained by the current measurement circuit included in the wireless device. Claim 20 A method according to claim 1, wherein the step of estimating the state of the battery further comprises the step of analyzing the transient behavior of the battery voltage during the application and / or removal of a load according to a transient voltage measurement obtained by a voltage measurement circuit included in the wireless device. Claim 21 A method according to claim 1, wherein the estimated state of the battery further includes an estimation of the degree of passivation of the battery. Claim 22 A method according to claim 1, wherein the battery management activity of step G) comprises at least one of recharging or replacing the battery, reducing the degree of device activity of the wireless device to reduce energy consumption of the wireless device, increasing the degree of device activity to reduce passivation of the battery, increasing the degree of device activity to prevent excessive cooling of the battery, reducing the degree of device activity to prevent excessive heating of the battery, and adjusting a temperature control device independent of the wireless device to prevent excessive cooling or heating of the battery.

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