CN115808623A - Method and device for predicting endurance time of equipment battery and computer readable medium - Google Patents

Abstract

The invention provides a device battery endurance prediction method and device capable of accurately predicting the endurance of a device battery, and a computer readable medium. The device battery endurance time prediction method comprises the following steps: a device power consumption testing step, in which power consumption data of the device is tested; a battery discharge test step, in which the discharge data of the battery is tested, and the weights of different discharge electric quantity sections are calculated; a step of calculating energy consumption used by a user, which is to calculate total energy consumption data generated by the operation behavior of the user in each unit time according to the use habit of the user and the power consumption data of the equipment; a weighted power consumption calculation step, wherein the weighted average calculation is carried out on the total power consumption data according to the weights of different discharging electric quantity sections of the battery to obtain weighted average power consumption; and a endurance calculating step, namely calculating the endurance of the battery according to the current residual capacity of the battery and the weighted average power consumption.

Description

Method and device for predicting endurance time of equipment battery and computer readable medium

Technical Field

The invention relates to the technical field of battery endurance prediction, in particular to a method and a device for predicting the battery endurance of equipment and a computer readable medium.

Background

With the development of internet technology, intelligent devices such as intelligent sound boxes and intelligent watches are widely applied. Therefore, it becomes very important to monitor the battery power of the smart device and realize accurate prediction of battery endurance. At present, three methods are generally adopted for monitoring electric quantity: a coulometer integral calculation mode, which detects the electric quantity loss in real time and judges low electric quantity by the loss accumulation reaching a certain threshold value; a battery voltage monitoring mode for detecting a battery voltage and determining a low battery level when the battery voltage is less than a threshold; the internal resistance monitoring mode of the battery judges whether the internal resistance of the battery is low or not by detecting that the internal resistance of the battery is higher than a certain threshold value because the internal resistance of the battery is increased along with the reduction of the electric quantity of the battery.

The three ways are to monitor the current state of the battery, and the result of the low battery cannot be predicted according to the use habit of the user. For example, in a battery-powered device, a low battery alarm is issued when the battery voltage reaches a threshold of 0.9V. However, some users operate frequently, and the low battery alarm threshold should be raised accordingly to meet the reserved time for charging before the battery is exhausted. And the daily operation frequency of some users is extremely low, so the low-battery alarm threshold value can be correspondingly reduced.

In contrast, patent document 1 proposes a method for predicting cruising information, in which an individualized power consumption weight is set for a terminal according to individualized usage habits of different terminals, so as to more accurately express the actual power consumption situation of the terminal and to more accurately predict cruising information of a battery.

Documents of the prior art

Patent document

Patent document 1: CN113268131A

Disclosure of Invention

Technical problem to be solved by the invention

However, in the above patent document 1, the discharge characteristics of the battery itself, the power conversion loss of the power converter, and the like are not considered at all, and there is a problem that the battery life is not sufficiently accurate.

The present invention has been made to solve the above problems, and an object of the present invention is to provide a method and an apparatus for predicting the battery life of a device, and a computer-readable medium, which can accurately predict the battery life of a device.

Technical scheme for solving technical problem

The invention provides a method for predicting the endurance time of a battery of equipment, which predicts the endurance time of the battery supplying power to the equipment through a power converter and comprises the following steps:

a device power consumption testing step, in which power consumption data of the device is tested;

a battery discharge testing step, in which the discharge data of the battery is tested, and the weights of different discharge electric quantity sections are calculated;

a step of calculating the energy consumption for the user, which is to calculate the total energy consumption data generated by the operation behavior of the user in each unit time according to the use habit of the user and the power consumption data of the equipment;

a weighted power consumption calculation step, wherein the weighted average calculation is carried out on the total power consumption data according to the weights of different discharging electric quantity sections of the battery to obtain weighted average power consumption; and

and a endurance calculating step, namely calculating the endurance of the battery according to the current residual capacity of the battery and the weighted average power consumption.

Preferably, in the method for predicting the battery life of the device, in the step of calculating the weighted power consumption, the weighted average power consumption is further calculated according to power conversion efficiencies of the power converter under different operating conditions.

Preferably, in the above device battery life prediction method, the operating condition includes at least one of a voltage, a current, and an ambient temperature condition.

Preferably, in the method for predicting battery life of a device, the power consumption data of the device includes action power consumption data of each action process of the device in an action mode and sleep power consumption data of the device in a standby mode, and the total power consumption data includes total action power consumption and total sleep power consumption.

Preferably, in the above method for predicting a battery life of an apparatus, the power converter includes different power converters corresponding to an operation mode and a standby mode of the apparatus, respectively.

Preferably, in the method for predicting the battery endurance of the device, in the battery discharge testing step, a discharge curve model of the battery voltage and the discharge time is constructed according to the discharge data of the battery.

Preferably, in the method for predicting battery life of an apparatus, the weight of different discharging capacity sections of the battery is a percentage of the discharging capacity in the corresponding battery voltage section in the total capacity of the battery.

The present invention also provides an apparatus for predicting a battery life of a device, which predicts a battery life of a battery that supplies power to the device via a power converter, including:

the equipment power consumption testing module is used for testing the power consumption data of the equipment;

the battery discharge testing module is used for testing discharge data of the battery and calculating weights of different discharge electric quantity sections;

the energy consumption calculation module is used by the user, and total energy consumption data generated by the operation behavior of the user in each unit time is calculated according to the use habit of the user and the power consumption data of the equipment;

the weighted power consumption calculation module is used for performing weighted average calculation on the total power consumption data according to the weights of different discharge electric quantity sections of the battery to obtain weighted average power consumption; and

and the endurance time calculation module is used for calculating the endurance time of the battery according to the current residual capacity of the battery and the weighted average power consumption.

Preferably, in the device battery endurance predicting apparatus, the weighted power consumption calculating module further calculates the weighted average power consumption according to power conversion efficiencies of the power converter under different operating conditions.

Preferably, in the device battery life prediction apparatus, the operating condition includes at least one of a voltage, a current, and an ambient temperature condition.

Preferably, in the device for predicting battery endurance, the battery discharge test module builds a discharge curve model of battery voltage and discharge time according to discharge data of the battery.

Preferably, in the device battery endurance predicting apparatus, the weights of the different discharged power sections of the battery are percentages of the discharged power in the corresponding battery voltage sections in the total battery power.

The present invention also provides a computer-readable medium storing a program for executing the above-described device battery life prediction method.

Effects of the invention

According to the present invention, the endurance of the device battery can be accurately predicted according to the user's usage habits and the discharge characteristics of the battery.

In addition, according to the invention, on the basis of the use habit of the user and the discharge characteristic of the battery, the endurance time of the equipment battery can be further accurately predicted according to the power conversion efficiency of the power converter.

Drawings

Fig. 1 is a flowchart illustrating a device battery duration prediction method according to an embodiment of the present invention.

Fig. 2 is a diagram showing one example of a battery discharge data curve.

FIG. 3 is a flow chart illustrating a user usage habit statistics action.

Fig. 4 is a block diagram showing a configuration of a device battery duration prediction apparatus according to an embodiment of the present invention.

Detailed Description

Other advantages and technical effects of the present application will become apparent to those skilled in the art from the present disclosure, which is described in the following detailed description. The present application is not limited to the following embodiments, and various other embodiments may be implemented or applied, and various modifications and changes may be made to the details of the present description without departing from the spirit of the present application.

Hereinafter, specific embodiments of the present application will be described in detail with reference to the drawings. The drawings are for simplicity and clarity and are not intended to be drawn to scale, reflecting the actual dimensions of the structures described. To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures. The figures are not drawn to scale and may be simplified for clarity. Elements and features of one embodiment may be advantageously incorporated in other embodiments without further recitation.

In the drawings, the arrows indicate the input and output relationships of voltages, electric powers and/or signals between the parts (the direction indicated by the arrows is from the output of one part to the input of another part), but the input and output relationships in the drawings are just an example, and those skilled in the art can also conceive that the input and output relationships between the parts are increased, decreased and/or changed as required, and these changes should fall within the protection scope defined by the claims of the present application without redundancy.

Fig. 1 is a flowchart illustrating a device battery duration prediction method according to an embodiment of the present invention. The device battery endurance prediction method predicts the endurance of a battery supplying power to a device via a power converter, as shown in fig. 1, which includes the following steps.

Step S1 of testing equipment power consumption: power consumption data of the test device.

Specifically, the power consumption data of the device may include action power consumption data of each action procedure of the device in the action mode and sleep power consumption data of the device in the standby mode. The test of the action power consumption and the dormancy power consumption can be carried out at the stage of equipment design, and all possible operation processes and standby power consumption data of a user in the using process are obtained.

For example, taking a smart speaker as an example, the tested power consumption data is shown in table 1 below.

TABLE 1 Power consumption data of Smart speakers

Battery discharge test step S2: and testing the discharge data of the battery, and calculating the weights of different discharge electric quantity sections.

For example, taking the smart speaker as an example, the discharge data of the battery selected for the smart speaker under the constant current of 2.2A is shown in table 2 below.

TABLE 2 discharge data of the cells

Here, a discharge curve model of the battery voltage and the discharge time as shown in fig. 2 may be constructed according to the discharge data of the battery. In fig. 2, the vertical axis represents the battery voltage, and the horizontal axis represents the discharge time.

Based on the discharge data of the battery, weights of different discharge capacity sections of the battery can be calculated. Specifically, the weights of the different discharge capacity segments may be set as the percentage of the discharge capacity in the corresponding battery voltage segment in the total battery capacity.

Taking the above-described battery as an example, the weight thereof was calculated as shown in table 3 below. Wherein, the total battery capacity is 2174.5mAh.

TABLE 3 weight of different discharged capacity sections of the battery

In the above table, the battery voltage range of the battery voltage discharged from 8V to 7.4V is taken as an example, and the discharged capacity reaches 586.3mAh from 18.3 mAh. Therefore, the percentage of the discharge capacity in the battery voltage section in the total battery capacity is (586.3-18.3)/2174.5 =26%.

User usage energy consumption calculation step S3: and calculating total energy consumption data generated by the operation behavior of the user in each unit time according to the use habits of the user and the power consumption data of the equipment.

For the statistics of the usage habits of the user, an action flow as shown in fig. 3 may be adopted.

In fig. 3, in step S31, user actions are classified into, for example, actions X, Y, Z \8230;. Next, in step S32, it is determined whether the user operates the device. If yes in step S32, the corresponding action count is incremented by 1 (for example, X = X +1 when the user operates the action X), and the time is accumulated to obtain a time accumulation T (in this example, the unit time is day) (step S33), otherwise, the determination of step S32 is continued. In step S34, it is determined whether or not the statistical time accumulation T is 7 days or more. If yes in step S34, the operation frequency of the user per day is calculated (for example, X = X/7 for the action X), and the time accumulation T is cleared (step S35), otherwise, the process returns to step S32.

An example of the unit time of day is shown in fig. 3, but the present invention is not limited thereto, and different unit times may be set as necessary for statistics.

By using the operation flow shown in fig. 3, taking the smart speaker as an example, energy consumption data generated by calculating the operation behavior of the user in each day is shown in table 4 below.

TABLE 4 energy consumption data generated by the user's daily operational behavior

As shown in table 4, the total energy consumption data generated by the user's operation behavior in each day may include total active energy consumption and total dormant energy consumption. The total active energy consumption is the sum of the individual power fractions, and the total sleep energy consumption is the sum of the power consumption consumed by the sleep current after each day, excluding the dynamic active time.

Weighted power consumption calculation step S4: and performing weighted average calculation on the total energy consumption data according to the weights of different discharging electric quantity sections of the battery to obtain weighted average power consumption.

In addition, since the battery supplies power to the device via the power converter, and a certain power conversion loss occurs when the power converter performs DC-DC conversion, it is preferable to calculate the weighted average power consumption also from the power conversion efficiency of the power converter under different operating conditions. Wherein the operating condition comprises at least one of a voltage, a current, and an ambient temperature condition.

Here, taking the smart speaker as an example, the total energy consumption data is weighted and averaged based on the power conversion efficiency of the power converter under different operating conditions and the weights of different discharge capacity sections of the battery.

In the intelligent sound box, the power converters are arranged to include different power converters (model numbers are SM8102H and JW5710, respectively) corresponding to the operation mode and the standby mode of the equipment respectively. The power conversion efficiency (hereinafter, referred to as efficiency) of the power converter under different operating conditions can be obtained from a product manual thereof, and can also be obtained by performing actual tests.

The calculation formula for performing weighted average calculation on the total energy consumption data to obtain the weighted average power consumption is as follows:

input power consumption = total power consumption/efficiency;

weighted power consumption = input power consumption — weight of different discharged electricity quantity sections of the battery (percentage of discharged electricity quantity in the total electricity quantity of the battery under the corresponding battery voltage section);

weighted power consumption = weighted power consumption addition of the respective input voltages.

Wherein the input voltage corresponds to a battery voltage, and the input power consumption is an input power consumption of the battery before the DC-DC conversion by the power converter.

In the above example of the smart speaker, the power converter in the active mode (model number SM 8102H) and the power converter in the standby mode (model number JW 5710) are calculated, respectively, to obtain the following table 5 indicating the weighted active power consumption and table 6 indicating the weighted sleep power consumption.

TABLE 5 calculation of weighted action Power consumption (constant Current 50mA condition)

TABLE 6 calculation of weighted sleep power consumption (constant current 10mA condition)

In the above calculation example, the case where the weighted average calculation is performed on the total energy consumption data in consideration of the power conversion efficiency of the power converter and the weights of the different discharged electricity amount sections of the battery is shown, but in the present invention, the weighted average calculation may be performed based on only the weights of the different discharged electricity amount sections of the battery. In this case, the total power consumption may be set as the input power consumption without considering the power conversion efficiency of the power converter.

Further, in the above calculation example, the case where the calculation is performed using the efficiencies of the two types of power converters under the conditions of the constant current of 50mA and the constant current of 10mA, respectively, is shown, but the present invention is not limited thereto, and the calculation may be performed using the efficiencies under other operating conditions, for example, different ambient temperatures.

A cruising time calculation step S5: and calculating the endurance time of the battery according to the current residual capacity and the weighted average power consumption of the battery.

In the above example of the smart sound box, the following formula is used for calculation.

Endurance = current remaining battery capacity/(weighted sleep power consumption + weighted action power consumption)

Through the above calculation, table 7 below showing the battery life is obtained.

TABLE 7 duration of battery

As described above, according to the device battery duration prediction method of the present embodiment, the duration of the device battery can be accurately predicted according to the user's use habits and the discharge characteristics of the battery.

In addition, on the basis of the use habits of users and the discharge characteristics of the batteries, the endurance time of the equipment batteries can be further accurately predicted according to the power conversion efficiency of the power converter.

Another embodiment of the present invention also provides an apparatus battery duration prediction device 10 that predicts the duration of a battery that supplies power to an apparatus via a power converter.

As shown in fig. 4, the device battery life prediction apparatus 10 includes: the equipment power consumption testing module 11 is used for testing the power consumption data of the equipment; the battery discharge testing module 12 is used for testing the discharge data of the battery and calculating the weights of different discharge electric quantity sections; the user consumption calculating module 13 calculates total consumption data generated by the operation behavior of the user in each unit time according to the user usage habit and the power consumption data of the equipment; the weighted power consumption calculation module 14 is used for performing weighted average calculation on the total power consumption data according to the weights of different discharging electric quantity sections of the battery to obtain weighted average power consumption; and a endurance calculation module 15 for calculating the endurance of the battery according to the current remaining capacity and the weighted average power consumption of the battery.

The actions executed by the device power consumption testing module 11, the battery discharge testing module 12, the user energy consumption calculating module 13, the weighted power consumption calculating module 14, and the endurance calculating module 15 correspond to the steps of the device battery endurance predicting method, and are not described herein again.

As with the above-described device battery duration prediction method, in the device battery duration prediction apparatus 10, preferably, the weighted power consumption calculation module further calculates the weighted average power consumption according to the power conversion efficiency of the power converter under different operating conditions. The operating conditions may include at least one of voltage, current, ambient temperature conditions.

In addition, in the battery discharge test module, a discharge curve model of the battery voltage and the discharge time can be built according to the discharge data of the battery. The weight of different discharge capacity sections of the battery can be set as the percentage of the discharge capacity in the corresponding battery voltage section in the total capacity of the battery.

Another embodiment of the present invention also provides a computer-readable storage medium, which stores instructions that, when executed on a computer, cause the computer to execute the method for predicting the battery life of a device according to an embodiment of the present invention.

Another embodiment of the present invention further provides a computer program product containing instructions, which when run on a computer, causes the computer to execute the method for predicting the battery life of a device according to an embodiment of the present invention.

It should be noted that, for the sake of simplicity, the embodiments of the mechanical resonance suppression method are described as a series of combinations of operations, but it should be understood by those skilled in the art that the present invention is not limited by the described operation sequence. Some steps may be performed in other orders or simultaneously according to the invention. Furthermore, those skilled in the art should also appreciate that the embodiments described in this specification are presently preferred and that no acts or modules are required by the invention.

It will be understood by those skilled in the art that all or part of the steps in the embodiments of the mechanical resonance suppression method described above may be implemented by hardware associated with program instructions, and the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, read-Only memories (ROMs), random Access Memories (RAMs), EPROMs, EEPROMs, magnetic or optical disks, etc.

The present invention has been described in detail, but the above embodiments are merely examples of all embodiments, and the present invention is not limited thereto. The present invention may freely combine the respective embodiments, may modify any of the components of the respective embodiments, or may omit any of the components of the respective embodiments within the scope of the present invention.

Claims (13)

1. A method for predicting a battery life of a device, the method predicting the life of a battery powering the device via a power converter, comprising the steps of:

a device power consumption testing step, which is used for testing the power consumption data of the device;

a battery discharge test step, in which the discharge data of the battery is tested, and the weights of different discharge electric quantity sections are calculated;

a step of calculating the energy consumption for the user, which is to calculate the total energy consumption data generated by the operation behavior of the user in each unit time according to the use habit of the user and the power consumption data of the equipment;

a weighted power consumption calculation step, wherein the weighted average calculation is carried out on the total power consumption data according to the weights of different discharging electric quantity sections of the battery to obtain weighted average power consumption; and

and a endurance calculating step, namely calculating the endurance of the battery according to the current residual capacity of the battery and the weighted average power consumption.

2. The device battery life prediction method of claim 1,

in the weighted power consumption calculating step, the weighted average power consumption is calculated according to the power conversion efficiency of the power converter under different operating conditions.

3. The device battery life prediction method of claim 2,

the operating conditions include at least one of voltage, current, ambient temperature conditions.

4. The device battery life prediction method according to any one of claims 1 to 3,

the power consumption data of the device comprises action power consumption data for each action process of the device in an action mode and hibernation power consumption data of the device in a standby mode,

the total energy consumption data includes total active energy consumption and total dormant energy consumption.

5. The device battery life prediction method of claim 4,

the power converters include different power converters corresponding to an operation mode and a standby mode of the device, respectively.

6. The device battery life prediction method according to any one of claims 1 to 3,

and in the battery discharge testing step, a discharge curve model of the battery voltage and the discharge time is established according to the discharge data of the battery.

7. The device battery endurance prediction method of claim 6,

the weight of different discharging electric quantity sections of the battery is the percentage of the discharging electric quantity in the total electric quantity of the battery under the corresponding battery voltage section.

8. An apparatus for predicting a duration of a battery for supplying power to a device via a power converter, comprising:

the equipment power consumption testing module is used for testing the power consumption data of the equipment;

the battery discharge testing module is used for testing discharge data of the battery and calculating weights of different discharge electric quantity sections;

the energy consumption calculation module is used by the user, and total energy consumption data generated by the operation behavior of the user in each unit time is calculated according to the use habit of the user and the power consumption data of the equipment;

the weighted power consumption calculation module is used for carrying out weighted average calculation on the total power consumption data according to the weights of different discharging electric quantity sections of the battery to obtain weighted average power consumption; and

and the endurance time calculation module is used for calculating the endurance time of the battery according to the current residual capacity of the battery and the weighted average power consumption.

9. The device battery endurance predicting apparatus according to claim 8,

in the weighted power consumption calculation module, the weighted average power consumption is calculated according to the power conversion efficiency of the power converter under different operating conditions.

10. The device battery life prediction apparatus according to claim 9,

the operating conditions include at least one of voltage, current, ambient temperature conditions.

11. The device battery endurance prediction apparatus according to any one of claims 8 to 10,

and in the battery discharge test module, a discharge curve model of the battery voltage and the discharge time is established according to the discharge data of the battery.

12. The device battery life prediction apparatus according to claim 11,

the weight of different discharging electric quantity sections of the battery is the percentage of the discharging electric quantity in the total electric quantity of the battery under the corresponding battery voltage section.

13. A computer-readable medium storing a program for executing the device battery life prediction method according to any one of claims 1 to 7.

Images