KR20160033588A - A method and a mobile communication terminal for estimating battery consumption state - Google Patents

A method and a mobile communication terminal for estimating battery consumption state Download PDF

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KR20160033588A
KR20160033588A KR1020150108341A KR20150108341A KR20160033588A KR 20160033588 A KR20160033588 A KR 20160033588A KR 1020150108341 A KR1020150108341 A KR 1020150108341A KR 20150108341 A KR20150108341 A KR 20150108341A KR 20160033588 A KR20160033588 A KR 20160033588A
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South Korea
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battery
load voltage
mobile communication
communication terminal
estimating
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KR1020150108341A
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Korean (ko)
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구종회
최성현
이원보
박용석
이옥선
홍영기
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삼성전자주식회사
서울대학교산학협력단
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Priority to US14/858,963 priority Critical patent/US9693308B2/en
Publication of KR20160033588A publication Critical patent/KR20160033588A/en

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    • G01R31/3606—
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R21/00—Arrangements for measuring electric power or power factor
    • G01R21/06—Arrangements for measuring electric power or power factor by measuring current and voltage
    • G01R31/3624—
    • G01R31/3682—
    • G—PHYSICS
    • G08—SIGNALLING
    • G08C—TRANSMISSION SYSTEMS FOR MEASURED VALUES, CONTROL OR SIMILAR SIGNALS
    • G08C17/00—Arrangements for transmitting signals characterised by the use of a wireless electrical link
    • G08C17/02—Arrangements for transmitting signals characterised by the use of a wireless electrical link using a radio link
    • G—PHYSICS
    • G08—SIGNALLING
    • G08C—TRANSMISSION SYSTEMS FOR MEASURED VALUES, CONTROL OR SIMILAR SIGNALS
    • G08C19/00—Electric signal transmission systems
    • G08C19/02—Electric signal transmission systems in which the signal transmitted is magnitude of current or voltage

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  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Secondary Cells (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

According to the present invention, A method of estimating a battery consumption state of a mobile communication terminal, comprising the steps of: databaseing characteristic information of a battery that changes based on at least one of a temperature and an aging characteristic of a battery providing power to the mobile communication terminal; Estimating consumed power and battery consumption rate of the mobile communication terminal based on the characteristic information of the mobile communication terminal, and displaying the estimated related information.

Figure P1020150108341

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to a method for estimating a battery consumption state,

The present disclosure relates to a method of estimating a consumption state of a battery and a mobile communication terminal thereof.

2. Description of the Related Art Generally, a method of estimating a capacity of a battery that provides power to a mobile communication terminal is mainly used for preventing a battery from being completely discharged to prevent damage to the battery or enabling a corresponding terminal in an emergency Has been used.

However, in accordance with the rapid development of mobile communication, the mobile communication terminal provides the user with data communication functions such as schedule management, facsimile transmission and reception, and Internet access, Audio devices, and the like. Also, the mobile communication terminal can use an online game and a streaming video and audio service by installing an application. As a result, the power consumption of the battery of the mobile communication terminal becomes different depending on the use or operation of the user. Also, in consideration of the portability and design of the mobile communication terminal, a small battery is used for the mobile communication terminal. In order to continuously use the mobile communication terminal, battery replacement and charging are required periodically. Therefore, it is one of factors to improve the sensory performance of the user by reducing the battery replacement or the number of charging times of the user terminal, and thus various low power terminal operation techniques have been studied. It is essential to estimate the power consumption of the terminal for the adaptive operation of the low-power terminal operation method by recognizing the current power consumption of the terminal.

Therefore, in order to improve the use time of the mobile communication terminal, the power consumption amount of the battery of the mobile communication terminal needs to be more accurately and specifically predicted.

The present disclosure proposes a method of estimating battery consumption and a mobile communication terminal.

A method according to an embodiment of the present disclosure includes: A method of estimating a battery consumption state of a mobile communication terminal, comprising the steps of: databaseing characteristic information of a battery that changes based on at least one of a temperature and an aging characteristic of a battery providing power to the mobile communication terminal; Estimating consumed power and battery consumption rate of the mobile communication terminal based on the characteristic information of the mobile communication terminal, and displaying the estimated related information.

An apparatus according to an embodiment of the present disclosure includes: A mobile communication terminal for estimating a consumption state of a battery, comprising: a training module for converting characteristics information of a battery, which varies based on at least one of a temperature and an aging characteristic of a battery providing power to the mobile communication terminal, into a database; And estimates consumption power and battery consumption rate of the mobile communication terminal based on the characteristic information of the mobile communication terminal and controls the display of the estimated related information on the display screen.

The present disclosure can more accurately control the power of the mobile communication terminal by estimating the battery consumption state more accurately.

1A is a graph showing a supply voltage of a Li-ion battery according to SoC.
1B is an example of a device configuration diagram for estimating consumed power, battery consumption rate, and duration of a mobile communication terminal according to an embodiment of the present disclosure;
2A is an example of a detailed configuration diagram of a battery characteristic training module according to an embodiment of the present disclosure;
FIG. 2B is a graph showing the relationship between the current flow pattern of the training operation according to the embodiment of the present disclosure and the voltage drop variation in the battery internal resistance measured in the current flow training according to the temperature,
2C is a graph of the relationship between SoC and operating time at various temperatures by operation of a training application according to an embodiment of the present disclosure,
FIG. 3A is a graph showing the relationship between the total battery capacity and the temperature estimated by the operation of the training application according to the embodiment of the present disclosure,
3B is a graph of the relationship between the internal resistance of the battery and the temperature estimated by the operation of the training application according to the embodiment of the present disclosure,
FIG. 3C is an example of the operational flow diagram of the estimator of FIG. 1B according to the embodiment of the present disclosure;
FIG. 4A is a diagram for specifically explaining an example of an operation of the estimation unit 108 according to the embodiment of the present disclosure to estimate information related to the battery consumption state of the terminal,
4B shows an equivalent circuit of the battery of FIG. 4A,
4C is a graph of the result of a voltage drop (V oc -V out ) in a battery internal resistance with temperature according to an embodiment of the present disclosure;
5 is an example of a detailed configuration diagram of the display unit 110 according to the embodiment of the present disclosure,
FIG. 6A is a diagram illustrating an example in which information related to battery consumption status according to the embodiment of the present disclosure is implemented on a display screen,
FIG. 6B is a view showing another example in which information related to the battery consumption state according to the embodiment of the present disclosure is implemented on a display screen,
FIG. 6C is a view showing another example in which information related to battery consumption status according to the embodiment of the present disclosure is implemented on a display screen,
FIG. 7A illustrates an example of a graph of battery consumption status information when performing an operation of receiving video data at a mobile communication terminal according to an embodiment of the present disclosure; FIG.
FIG. 7B is a graph showing an example of a graph comparing an estimated value of battery consumption status information with a measured value when performing an operation of receiving video data in a mobile communication terminal according to an embodiment of the present disclosure;
FIG. 7C illustrates an example of a graph of battery consumption status information when the mobile communication terminal performs web surfing according to an embodiment of the present disclosure;
FIG. 7D is an example of a graph comparing an estimated value and a measured value of battery consumption status information when performing web surfing,
FIGS. 8A and 8B are graphs showing an example of a graph obtained by estimating the available capacity and the internal resistance of the battery during a time period during which the fully charged battery is in the fully discharged state,
9A is an example of a device configuration diagram according to another embodiment of the present disclosure,
FIG. 9B is an example of the operation flow chart of the apparatus configuration diagram of FIG. 9A according to the embodiment of the present disclosure,
10 is an operation algorithm of the no-load voltage estimating unit according to the embodiment of the present disclosure,
11 is a diagram illustrating a battery parameter estimating operation algorithm according to an embodiment of the present disclosure,
FIG. 12 is a graph showing performance evaluations for six and four batteries in two types of smart devices according to an embodiment of the present disclosure; FIG.
13 is an example of a result of measuring battery consumption rate in real time while operating various applications for a smart device equipped with an aged battery according to an embodiment of the present disclosure.

Generally, the power of the mobile communication terminal is obtained by measuring the current flowing to the terminal using an external measuring instrument.

On the other hand, in the case of a mobile communication terminal using the Android operating system, there is an application program interface (API) for providing battery information, so that battery information can be acquired at an application terminal. However, the battery information provided by the API is about the temperature, the state, the voltage and the type of the battery. Accordingly, since the general mobile communication terminal can not acquire information on the current flowing to the terminal through the battery information, it is difficult to estimate the consumed power.

Accordingly, a method for estimating the consumed power of a mobile communication terminal without external measurement equipment has been studied. One of them is a method of using a chip that measures the state of charge (SoC) in the mobile communication terminal. The SoC estimated through such a chip is defined as the rated capacity for the maximum capacity of the battery and is provided as an integer between 0 and 100 in units of%. Accordingly, since the user can estimate the battery consumption speed only when the current capacity of the battery is reduced by 1%, the updating period of the consumed power becomes longer by several minutes. Further, in a situation where the consumed power of the battery is low, there is a problem that the update period of the predicted consumed electric power becomes longer.

As shown in the table of FIG. 1A, the other scheme uses a relation that a supply voltage (Open circuit voltage, V OC ) of a Li-ion battery mainly used in a smart phone or the like decreases as the SoC decreases.

Since the terminal can not directly measure the supply voltage of the battery, the measured load voltage can be approximated to the supply voltage of the battery by measuring the load voltage of the terminal. Practically, the load voltage is a value that is reduced by the voltage drop in the internal resistance of the battery at the supply voltage of the battery. Therefore, when the current flowing in the terminal increases, the voltage drop in the internal resistance of the battery also becomes large, so that the load voltage measured for the terminal becomes significantly smaller than the supply voltage of the battery. If the variation of the current of the terminal is large, it is difficult to accurately estimate the supply voltage of the battery, and there is a high possibility that the SoC is erroneously estimated.

Therefore, in one embodiment of the present disclosure, there is provided a method of estimating a current flowing in a terminal from a change in a load voltage applied to the terminal, which can be measured by a mobile communication terminal, estimating consumed power of the terminal using the estimated current, Configuration. Hereinafter, a mobile communication terminal using a Li-ion battery having a built-in battery interface for providing battery information such as battery temperature, load voltage, and SoC will be described as an example.

Specifically, in one embodiment of the present disclosure, in order to more accurately measure the consumed electric power of the mobile communication terminal, the battery internal resistance and the total capacity change of the battery are measured according to the temperature of the mobile communication terminal, , And estimates the power consumption of the battery.

1B is an example of a device configuration diagram for estimating consumed power, battery consumption rate, and duration of a mobile communication terminal according to an embodiment of the present disclosure.

Referring to FIG. 1B, the mobile communication terminal 100 may be configured as follows to estimate the consumed power of the battery according to the embodiment of the present disclosure. The mobile communication terminal 100 may include, for example, a battery interface 102, a battery characteristic training module 104, and a main estimation module. Here, the main estimation module may include a process detection unit 106, an estimation unit 108, and a display unit 110.

For convenience of explanation, it is assumed that the mobile communication terminal 100 of FIG. 1B is a terminal based on the Android operating system. However, embodiments of the present disclosure are also applicable to terminals based on other operating systems having an embedded battery interface that provides battery information such as battery temperature, load voltage, SoC, and the like. The battery interface 102 corresponds to the API for providing the battery information described above, and is one of modules installed in the terminal based on the Android operating system. The battery interface 102 transmits the battery information installed in the mobile communication terminal 100 to the estimation unit 108. [ Here, the battery information may include the temperature of the battery, the terminal voltage, and the like.

The battery characteristic training module 104 may include, for example, an analysis unit 104-1 and a storage unit 104-2.

The Li-on battery which the mobile communication terminal 100 can use decreases the chemical reaction capacity at a low temperature, thereby lowering battery performance. As a result, the battery of the corresponding terminal is discharged faster at room temperature in a low temperature environment such as a ski resort. Practically, as the temperature of the battery is lowered, the total capacity of the battery is reduced, and the internal resistance of the battery is increased. Therefore, when the consumed current of the terminal is estimated at a place where the external temperature is different using the internal resistance of the battery obtained by training at a certain temperature, a large error may occur.

Also, when estimating the battery consumption rate and duration, the accuracy of the full capacity of the battery can affect the accuracy of the consumption rate and the estimate of the duration. Therefore, in the embodiment of the present disclosure, in consideration of the portion of the environment in which the user uses the terminal affects the temperature of the battery, the consumed electric power of the terminal and the battery Estimate the consumption rate. Accordingly, the analyzer 104-1 measures the temperature of the battery according to the embodiment of the present disclosure, operates an application for training battery characteristics based on the measured temperature, and acquires battery characteristic data obtained as a result And stores it in the storage unit 104-2. The battery characteristic data may include a battery internal resistance according to the temperature of the mobile communication terminal 100, a maximum usable capacity according to temperature, and a battery supply voltage according to SoC. The application for training battery characteristics based on the measured temperature may be performed for each battery type.

2A is an example of a detailed configuration diagram of a battery characteristic training module according to an embodiment of the present disclosure. For convenience of explanation, the description will be made on the basis of the battery characteristic training module 104 of FIG. 1B.

Referring to FIG. 2A, the battery property training module 104 may operate based on the battery property training application 200. The battery characteristic training application 200 models the supply voltage of the battery according to the internal resistance of the mobile communication terminal 100, the maximum usable capacity of the battery according to temperature, and the SoC, .

Specifically, the battery characteristic training module 104 drives the battery characteristics training application 200 to train the characteristics of the mounted battery, and analyzes the battery information log data generated as a result of the training, Modules 202-206. ≪ / RTI > Here, the sub-modules include, for example, a temperature: battery internal resistance modeling unit 202 for modeling the internal resistance of the battery according to temperature, a temperature: maximum battery capacity modeling unit 204 for modeling the maximum battery capacity according to temperature, And corresponds to the remaining battery capacity modeling the battery supply voltage according to the SoC: no-load voltage modeling unit 206.

The trained battery characteristic data, which is a result modeled by each of the three sub-modules 202 to 206, is stored in the battery characteristic data storage unit 208 and then transmitted to the estimating unit 108. For example, the battery characteristic data may be represented as shown in FIGS. 2B and 2C.

FIG. 2B shows the current flow pattern of the training operation for modeling the internal resistance of the battery with temperature and the change in the voltage drop in the battery internal resistance measured during the current flow training according to the temperature. In this case, for example, when an external temperature is placed in a device capable of changing the temperature such as a freezing room, for example, a room temperature terminal is caused to flow in a constant current to the terminal, to be.

2C is a graph showing SoC according to temperature. In this case, as shown in Table 2b, when the training application for causing the current to flow is executed, the SoC with time is shown. The SoC has an integer value between 0 and 100, and the value between the integer values of the SoC on the graph is connected by a straight line. Here, in the graph of FIG. 2C, the numbers mapped to each of the straight lines and the dotted lines indicate the temperature of the battery. As shown in FIG. 2C, it can be seen that SoC decreases rapidly as the temperature is lowered, which can be interpreted as a decrease in the total usable capacity of the battery as the temperature of the battery decreases. Flowing a constant current for a period of time Tt to estimate the available capacity of the battery, it is possible to operate the application to repeat the training operation for rest during the time T b. Through the operation of the training application as described above, for example, a result graph of FIGS. 3A and 3B can be obtained.

Referring to FIGS. 3A and 3B, as the temperature decreases, the total capacity of the battery decreases and the size of the internal resistance increases.

3C is an example of an operation flow chart of the estimating unit of FIG. 1B according to the embodiment of the present disclosure.

Referring to FIG. 3C, in step 300, the process detection unit 106 detects that a specific application installed in the mobile communication terminal 100 or a specific function is operating for a predetermined time or more. Here, the specific function may include a voice call of the mobile communication terminal 100, an Internet search, a camera operation, and the like. Then, the process detector 106 triggers the estimator 108 to estimate the instantaneous power consumption of the terminal.

In step 302, the estimator 108 accesses the battery interface 102 to acquire battery information. That is, the estimator 108 may estimate battery consumption status information using the battery information (battery temperature, voltage, SoC value) and battery characteristic data. Here, the battery consumption status information may include the power consumption of the terminal, the moving average consumption power, the consumption speed and duration of the battery, and the like.

4A is a diagram for explaining an example of an operation in which the estimating unit 108 estimates battery consumption status related information according to the embodiment of the present disclosure.

4A, the estimation unit 108 includes a battery consumption state calculation unit 400, a V OC -SoC converter 402, a no-load voltage estimation unit 404, a battery parameter estimation unit 406).

FIG. 4B is a diagram showing a relationship between an equivalent circuit for looking at a terminal in a battery and a supply voltage V OC of the battery, a battery internal resistance R, a voltage drop V r in the battery internal resistance, and a load voltage V out . The load voltage V out can be calculated as a value reduced by the voltage drop V r in the battery internal resistance at the battery supply voltage V OC as shown in Equation (1) below.

Figure pat00001

The battery characteristic training module 104 described in FIG. 2A drives the battery characteristic training application 200 to control a constant current to flow to the terminal, as described with reference to FIG. 2A. For example, it is possible to control the mobile communication terminal such that a constant current, I t , flows by repeating the floating point operation. Alternatively, it is possible to operate the mobile communication terminal such that a constant current flows through another method. In this case, let us assume that I t is measured in advance through an external measuring instrument and is known. The battery characteristic training module 104 is a constant current to flow in the terminal during the time t T, it is possible to use the difference between V in and V out OC this case to estimate the internal resistance of the battery.

The V OC in the previous resting operation can be approximated to the V out in the resting operation while the current in the terminal flows. That is, when there is almost no current flowing in the terminal, such as a break time of the training process, V OC can be approximated to V out according to Equation (2).

Figure pat00002

The estimated V OC in the rest period can be regarded as V OC when the current of I t flows to the next terminal. The battery characteristic training module 104 divides the difference value between V out when the current of I t flows into the terminal and V OC estimated during the previous rest interval by I t and calculates the internal resistance of the battery according to Equation (3) .

Figure pat00003

Equation (3) is valid at an arbitrary temperature T. As an example, suppose that the terminal is placed in a temperature-changing device in which the temperature continuously increases or decreases in the range of 40 degrees to -10 degrees, and a battery characteristic training application is executed to flow the current of I t .

The difference between V oc and V out can be calculated by estimating V out and V oc at the terminal simultaneously with execution of the battery characteristic training application. Accordingly, a graph of the result of V oc -V out according to the temperature can be obtained as shown in FIG. 4c. Based on FIG. 4c and Equation (3), the internal resistance according to the temperature can be obtained as shown in FIG. 3b. Specifically, sufficient samples for V oc -V out according to the temperature are obtained, and the values of V oc -V out according to the value of y-axis for each sample, that is, temperature are divided by It as shown in Equation (3) A graph of temperature: battery internal resistance to be obtained by replacing with a resistor can be obtained. The internal resistance of the battery according to the temperature can be stored in a database. The internal resistance of each temperature unit can be stored in the database. The internal resistance of the battery can be stored in a predetermined temperature range of, for example, -10, 0, 10, 20, 30, Only the resistance value can be stored, and the internal resistance value for the remaining temperatures can be obtained by linear interpolation.

The battery information log data generated as a result of the training of the battery characteristics training application 200 is transmitted to the submodules 202 to 206 for modeling each characteristic of the battery as described above. Here, detailed descriptions of the sub-modules are omitted because they are duplicated in the previous description. The trained battery characteristic data, which is modeled by the three sub-modules 202 to 206, is classified as battery characteristic data and transmitted to the battery parameter estimator 406. The training operation of the battery characteristic training module 104 is performed once for the first time and is converted into a database. If it is determined that the condition that the number of times of full charge / discharge of the battery is greater than or equal to the predetermined threshold is satisfied and it is determined that the battery has aged, battery characteristic data may be changed. In this case, an additional training process may be performed after a predetermined period of time, e.g., about one month, for updating the database.

Then, the battery parameter estimating unit 406 estimates the internal resistance available battery capacity of the current battery corresponding to the current temperature based on the database of the battery characteristic data 408 and transmits it to the battery consumption state calculating unit 400 do.

The no-load voltage estimator 404 terminates the display and background operations of the mobile communication terminal 100 to make the terminal almost idle, estimates V OC as in Equation (2) The VOC can be estimated using the database of the battery capacity: the no-load voltage modeling unit 206 remaining in the SoC obtained in the battery 102 and the battery characteristic data 408. In addition, the estimated VOC can be corrected by estimating the amount of battery consumption accumulated at runtime.

As described above, the estimated initial V OC has a characteristic in which the value gradually decreases as the battery is discharged, as shown in Table 1 above. Thus, to prevent errors in current estimates that may occur over time, the no-load voltage estimator 404 according to the embodiment of the present disclosure compensates for V OC at run time based on the characteristics. Referring to Table 1, since the y value does not have a linear relation with respect to x in the entire section of the graph, it can be linearly regressed by intervals, so that the graph is approximated in a piecewise linear manner. On the other hand, becomes larger, the time resolution of the power consumption estimating the shorter the period (t s) for measuring the load voltage V out, to directly access a system file of the battery interface 102, power is additionally consumed in the case to obtain the SoC It is possible to adjust the period t s for measuring the voltage V out of the terminal.

The no-load voltage estimating unit 404 can correct V.sub.OC at run time by decreasing V.sub.OC by the consumed battery capacity using the consumption power and the battery consumption rate estimate of the mobile communication terminal at run time. Assuming that the sampling period is t s and the consumed power estimated at time i is P [i], the energy consumed from any time period, that is, the energy from time t 1 to time t k , .

Figure pat00004

The slope of the delta linear function of V oc and SoC between " SoC in any time interval " and " SoC smaller than 1 &

Figure pat00005
Can be expressed as Here, the physical meaning of the slope a indicates a decrease in the average V OC when the SoC of the battery is reduced by 1%. Therefore, 1 / a% of the total battery energy must be consumed for V OC to decrease by one. Therefore, cumulative consumption energy is calculated every ts from t1, and VOC is decreased by 1 when cumulative consumption energy becomes (C / 100) a (1 / a% of total battery energy). Here, C is the total energy of the battery. Thereafter, this point is referred to as t1 again, and the cumulative energy is initialized, and the above process can be repeated to correct the VOC.

Also, according to the relation between V OC and V out described above, V out = V OC - V r = V OC -IR, V OC always has a value larger than V out . Therefore, when the measured V out value is larger than V OC , since V OC is erroneously estimated, the measured V out is replaced with V OC .

Based on the above two schemes, the no-load voltage estimator 404 performs run-time V OC correction to continuously estimate the power consumption of the mobile communication terminal.

 The no-load voltage estimating unit 404 acquires the changed battery information every time the battery information is changed in the battery interface 102, for example, using the BroadCastReceiver component of the Android, or directly accesses the system file to read the battery information come. In this case, for example, the load voltage can be read by using the command "cat / sys / class / power_supply / sec-fuelgauge / voltage_now".

The V OC -SoC converter 402 estimates the SoC of the battery in units of a predetermined unit, for example, less than 1% through V OC , and calculates the battery consumption state calculation unit 400 and the battery parameter estimation unit 406).

The battery consumption state calculation unit 400 calculates a battery consumption state based on the temperature and load voltage of the battery read by the battery interface 102 and the internal resistance of the current temperature obtained from the battery parameter estimation unit 406, The instantaneous current I [n] (n is sampling time) flowing through the mobile communication terminal 100 using the V OC estimated by the government can be estimated as shown in Equation (5).

Figure pat00006

Then, the battery consumption state calculation unit 400 calculates an instantaneous consumption power P (mW) calculated by Equation (6) using the estimated instantaneous currents I and V out .

Figure pat00007

Then, the battery consumption state calculation unit 400 calculates the battery consumption state related power consumption P (mW) according to Equation (7)

Figure pat00008
Can be calculated.

Figure pat00009

here,

Figure pat00010
Is defined as a weight to be applied to the instantaneous power to obtain the moving average power consumption.
Figure pat00011
The larger the value of the instantaneous power consumption is, the larger the instantaneous power consumption is reflected in the moving average power consumption. Since instantaneous power consumption can be highly variable, mobile average power consumption is utilized. In other words, the moving average power consumption is used to reduce the fluctuation of the battery duration, the battery consumption rate, and the power consumption of the terminal to be provided to the future user and to show an average value. The battery consumption state calculation unit 400 calculates the battery consumption state calculation unit 400 based on the estimated current I and the current usable capacity
Figure pat00012
(mAh) to determine the battery's duration
Figure pat00013
(h) is estimated as shown in Equation (8) below.

Figure pat00014

Then, the battery consumption state calculation unit 400 calculates , The battery consumption rate R d (-% / h) is calculated. The battery consumption rate according to the embodiment of the present disclosure is defined as the rate of reduction of SoC per hour. Accordingly, the battery consumption rate can be calculated by dividing the duration of the battery capacity by 100 as shown in Equation (9).

Figure pat00016

In step 304a of FIG. 3, the battery consumption state calculation unit 400 calculates the instantaneous consumption power P, the battery consumption rate R d , and the duration time

Figure pat00017
And the like to the display unit 110. The display unit 110 displays the information on the battery consumption status of the battery.

5 is an example of a detailed configuration diagram of the display unit 110 according to the embodiment of the present disclosure.

5, the display unit 110 may include a display triggering module 502, a display type setting module 504, and a displayed value update period setting module 506, for example. The display triggering module 502 triggers information on the battery consumption status of the battery to be displayed on the display screen when a predetermined amount of energy consumption occurs for a predetermined time or satisfies a condition set by the user. Here, the condition set by the user can be set such that, for example, when the specific application or the specific function is operated for a predetermined time, information related to the battery consumption state is displayed. Alternatively, if the predetermined amount of energy consumption occurs according to the operation of the specific application or the specific function, the information related to the battery consumption state may be displayed. The user can also arbitrarily input the predetermined time and the predetermined amount. Alternatively, the user may be provided with a list of predetermined units of information, and the user may select a desired unit.

The display type setting module 504 can set a concrete form in which the battery consumption status related information is displayed on the display screen. The information on the battery consumption status according to the embodiment of the present disclosure may be displayed on the display screen by, for example, selecting one of the three schemes shown in FIGS. 6A to 6C.

FIG. 6A is a diagram illustrating an example of information on battery consumption status according to an embodiment of the present disclosure implemented on a display screen. FIG.

Referring to FIG. 6A, the display type setting module 504 can implement battery consumption status related information using a toast function. That is, the toasting function may be set so that the information related to the battery consumption state is displayed on the display screen as a temporary image in some areas and disappears.

FIG. 6B is a diagram showing another example in which information related to the battery consumption state according to the embodiment of the present disclosure is implemented on a display screen.

Referring to FIG. 6B, the display type setting module 504 may display information on battery consumption status using the status bar 602 on the display screen. For example, the status bar 602 displays the consumed power (W) of the current terminal and the duration of the battery.

FIG. 6C is a diagram illustrating another example in which information related to the battery consumption state according to the embodiment of the present disclosure is implemented on a display screen.

Referring to FIG. 6C, the display type setting module 504 may display a function of a user on a display screen, for example, information on battery consumption status on a screen on which a moving image is being reproduced, . 6C, for example, battery consumption status related information 606 is superimposed on the upper left corner of the display screen. However, according to the embodiment of the present disclosure, it is also possible to directly set or change the position at which the battery consumption status related information is superimposed according to user input.

The displayed value update period setting module 506 may set a period at which the battery consumption status related information is updated. The update period may also be set according to the user's input, or the user may select and set a pre-set period interval.

In step 304b, the application developer can access the battery consumption status related information. In this case, suppose that an application developer receives an estimate of instantaneous power consumption. Then, the received power consumption can be integrated and converted into an energy consumption amount. The average power amount of the battery according to the driving of the application can be estimated by dividing the energy consumption amount by the training time according to the driving start time and the ending time of the application. In this case, the application developer may develop an application that can adaptively operate on the power consumption of the mobile communication terminal using the average power amount.

FIG. 7A is an example of a graph of information on battery consumption status when performing an operation of receiving video data in a mobile communication terminal according to an embodiment of the present disclosure.

7A, it is assumed that a user plays streaming video data, for example, in a mobile communication terminal. That is, the power consumption of the streaming video is represented by the duration of the data, the received power consumption, and the weighted moving average (WMA). The WMA consumed power was weighted to 0.7. The time is expressed in units of seconds. It can be seen that the instantaneous consumption power decreases and the duration increases as the reception of the video being reproduced is terminated at the time corresponding to 15 seconds.

FIG. 7B is an example of a graph comparing an estimated value of battery consumption status information with a measured value when performing an operation of receiving video data in a mobile communication terminal according to an embodiment of the present disclosure.

7B, it is shown that the difference between the average value of the estimated average power consumption (Avg. Row in the power. Row) and the average value of the measured value (Avg. Row in the Measured power row) is not large for three experiments.

FIG. 7C is an example of a graph of battery consumption status information when the mobile communication terminal performs web surfing according to the embodiment of the present disclosure, FIG. 7D is an example of battery consumption status information when performing web surfing Is an example of a graph comparing the estimated value with the measured value.

The graphs of FIGS. 7C and 7D are also not significantly different from the case of receiving video data.

Hereinafter, another embodiment of the present disclosure considers not only the temperature of the mobile communication terminal but also the battery characteristic change according to the aging state of the battery mounted in the mobile communication terminal. As described above, when the temperature of the Li-on battery, which is one of the batteries that the mobile communication terminal can use, decreases or the aging progresses much, the chemical reaction capacity decreases and the battery performance decreases. Furthermore, Li-ion batteries with lower temperatures or more aging tend to have higher internal resistance and lower available capacity. The degree of aging of the battery becomes worse as the battery is fully charged / discharged. Hereinafter, the present invention will be described on the assumption that an aged battery is distinguished from a new battery by checking whether the number of fully charged / discharged batteries is greater than a predetermined number.

FIGS. 8A and 8B are graphs illustrating a result of estimating an available capacity and an internal resistance of the battery during a time period during which the fully charged battery is in a fully discharged state. In this case, the floating point operation consuming a constant current for the battery during the time period is repeatedly performed for a predetermined time, and then the operation of resting for a predetermined interval is repeated to measure the available capacity and the internal resistance. At this time, three new batteries that have not been used for the same smartphone, and three batteries that have been charged / discharged more than the predetermined number of times (hereinafter, referred to as 'aging battery') are measured. Each of the new batteries in the graph corresponds to "New 1", "New 2", "New 3", and each aging battery corresponds to "Old 1", "Old 2", "Old 3". Here, it is assumed that the number of charge / discharge cycles of the aging batteries is equal to or greater than the predetermined number and has different values. As the number of charge / discharge increases, the degree of aging becomes severe.

Referring to FIGS. 8A and 8B, as the temperature of the aged batteries is lowered as compared with the new battery, the total capacity decreases and the internal resistance becomes larger. Based on this estimation result, the battery total capacity and the internal resistance in the current state of the mobile communication terminal can be expressed as Equation (10).

Figure pat00018

In Equation (10), C (T, d H ) and r (T, d H ) denote the total capacity C of the battery and the internal resistance r considering the present temperature T and the present aging degree dH of the battery. C f and r f represent the total capacity and internal resistance of the new battery at room temperature. ε t and ε a are the ratios of the total capacity to the variation C f according to temperature and aging conditions, respectively, and μ t and μ a are the ratios to the variation r f of the internal resistance with temperature and aging, respectively.

Hereinafter, in the embodiment of the present disclosure, it is possible to estimate the total capacity of the battery and the internal resistance value according to the temperature and the aging state, The battery consumption rate and the sustainable operation time are estimated by reflecting the total capacity and internal resistance of the battery.

And, the embodiments of the present disclosure are based on operation in a smart device using a Li-ion battery having an embedded battery interface that provides battery temperature, battery voltage, remaining battery capacity values, and the like. The operation according to this embodiment of the present disclosure can be performed in the device configuration diagram of Fig. 9A, which is obtained by modifying the device configuration diagram of Fig. 4A.

9A, the estimating unit 108 is configured to include a battery consumption rate estimating unit 900, a no-load voltage estimating unit 904, and a battery parameter estimating unit 906 according to the embodiment, The configurations interact and operate. The period ts for estimating the battery consumption rate by the battery consumption rate estimating unit 900 may be set to be equal to the period for measuring the load voltage, and the period may be determined by the no-load voltage estimating unit 904, (906). The other constituent parts operate in the same manner as in FIG. 4A, and a duplicate description will be omitted.

In the embodiment of the present disclosure, the no-load voltage estimating unit 904 obtains the changed battery information every time the battery information is changed at the battery interface, or directly accesses the system file Read the battery information. Accordingly, the no-load voltage estimating unit 904 according to the present embodiment estimates the current no-load voltage using the load voltage and the battery consumption speed R d estimated by the battery consumption speed estimating unit 900 .

The battery parameter estimator 906 according to the embodiment of the present disclosure calculates the effective resistance (r e ) using the no-load voltage estimated by the no-load voltage estimator 904, the load voltage obtained from the battery interface, and SoC Can be estimated. The battery consumption rate estimating unit 900 estimates the battery consumption rate based on the no-load voltage estimated by the no-load voltage estimating unit 904, the load voltage obtained at the battery interface, and r e estimated from the battery parameter estimating unit 906, Can estimate the duration and the consumption rate.

The no-load voltage estimating unit 904 according to the embodiment of the present disclosure estimates the measured load voltage at a time when it is determined that the terminal is consuming almost no current as a no-load voltage. The viewpoint may be acquired by operating the background operation in the terminal or by stopping all applications in operation and minimizing power consumption.

In a state where the battery of the terminal is being discharged, the no-load voltage is reduced according to the amount of the used battery. Accordingly, the no-load voltage estimator 904 according to the embodiment of the present disclosure can estimate the amount of battery consumption accumulated during the time the embodiment of the present disclosure is operated, thereby correcting the degree to which the no-load voltage is reduced. At this time, the battery consumption rate estimated by the battery consumption rate estimating unit 900 is utilized to accumulate the battery consumption amount. The no-load voltage estimating unit 904 provides the current no-load voltage to the battery consumption rate estimating unit 900 in real time. Then, the battery consumption rate estimating unit 900 derives the battery consumption rate using the no-load voltage and provides the derived battery consumption rate to the no-load voltage estimating unit 904. The no-load voltage estimating unit 904 accumulates the supplied battery consumption rate and applies it to the SoC of the Li-ion battery and the no-load voltage relationship curve to perform an operation to correct in real time.

Specifically, in the embodiment of the present disclosure, in order to estimate the first no-load voltage, the above-mentioned point of view can be obtained by operating in a state in which the background operation in the terminal or the application in operation is halted to minimize power consumption. In this case, since the load voltage and the no-load voltage are almost the same, the no-load voltage estimator 904 can approximate the load voltage to a no-load voltage. Thereafter, the no-load voltage estimator 904 performs an operation of updating the no-load voltage in real time using the first no-load voltage. That is, the no-load voltage estimating unit 904 estimates a no-load voltage at a period of t s based on the first no-load voltage and delivers the estimated no-load voltage to the battery consumption rate estimating unit 900. In the embodiment of the present disclosure, the characteristic between the SoC and the no-load voltage is taken as an example. When the SoC is changed, the no-load voltage corresponding to the corresponding SoC is compared with the remaining battery capacity in the battery characteristic data 408 of FIG. Can be obtained from the database. Here, it is assumed that the no-load voltage estimating unit 904 stores the remaining battery capacity: the database of the no-load voltage modeling unit 206. Since the SoC is in units of 1%, the no-load voltage between two adjacent SoCs can be estimated by linear interpolation. Specifically, the no-load voltage at a specific sample time t k during run-

Figure pat00019
Can be expressed by Equation (11) below.

Figure pat00020

here,

Figure pat00021
Is the amount of no-load voltage that is estimated to have decreased between the previous sample time t k-1 and the current sample time t k . R d [t k-1 ] is the estimated battery consumption rate from the battery consumption rate estimating unit 900 at the previous sample time, and t s is the sample period. That is, R d [t k-1 ] * t s is the battery consumption (%) estimated to have consumed during the current sample time since the last sample time. S [t k ] represents the SoC at t k , and f (S [t k ]) represents the no-load voltage on the database mapped to the SoC value of t k . Thus, f (S [ tk ]) - f (S [ tk ] -1) is the slope of the VOC- SoC plot linearly interpolated in the current SoC.

That is, the no-load voltage estimating unit 904 estimates the amount of battery consumption for each sample time and calculates a linear interpolated SoC- By multiplying the no-load voltage slope by the battery consumption, a reduction in the no-load voltage can be estimated. The no-load voltage estimator 904 can estimate the current no-load voltage by subtracting the estimated decrease amount of the no-load voltage from the no-load voltage at the previous sample time. The no-load voltage is set to have a predetermined lower limit value of f (S [ tk ] -1), and it is possible to prevent the no-load voltage from being mistakenly predicted as a low value by setting the lower limit value.

The no-load voltage estimating unit 904 provides the no-load voltage derived by itself to the battery consumption rate estimating unit 900. The battery consumption rate estimating unit 900 estimates the battery consumption rate, Thereby estimating the no-load voltage. When the no-load voltage estimator 404 estimates the no-load voltage using the battery consumption rate, if the load voltage is greater than or equal to the no-load voltage, the no-load voltage is immediately replaced with the load voltage. The replacing operation is an operation for restoring to a no-load voltage which is close to the actual value quickly even when the estimation of the no-load voltage is wrong. The operation algorithm of the no-load voltage estimating unit 904 as described above is as shown in FIG.

The battery parameter estimator 906 according to the embodiment of the present invention defines the effective resistance r e as Equation (12), and estimates the effective resistance value and transmits it to the battery consumption rate estimator 900 .

Figure pat00022

Here, the effective resistance r e (T, d H ) is the current resistance value of the battery

Figure pat00023
) And the change in total capacity of the battery due to aging (
Figure pat00024
). r e is derived by the battery parameter estimator 906 using the algorithm described in FIG. First, the battery parameter estimator 906 receives the estimated no-load voltage from the no-load voltage estimator 904 in real time. Thereafter, while the battery is consumed by 1%, the value obtained by subtracting the load voltage from the no-load voltage is accumulated by multiplying the sample time t s . The cumulative value is then divided by the battery capacity C f and multiplied by 100 to estimate r e . The estimated r e at the moment when the SoC becomes (n)% is the accumulated value of the difference between the no-load voltage and the load voltage at the time when the battery capacity 1% is consumed recently, that is, the time when the SoC is (n + 1)% Can be calculated as shown in Equation (13).

Figure pat00025

Where, tn is the sample time instant of the SoC which n, means adds the V oc -V out with respect to the interval [t (n + 1), t n] is the time, which is maintained at a SoC n + 1 It means accumulating the value obtained by multiplying the difference between the no-load voltage and the load voltage by the sample period over time. That is, the SoC can estimate r e using the accumulated value and Cf for a certain period of time.

The r e according to the embodiment of the present disclosure is updated in real time at runtime and can reflect changes in the current temperature and aging state using moving averages and appropriate weights. By using this r e , this disclosure can reflect the effect of temperature and aging on the battery capacity and internal resistance modeling without performing the modeling. That is, r e is used to calculate the battery consumption rate R d and the battery duration L in the battery consumption rate estimating unit 900.

The training application 908 that repeats the operation of consuming a substantially constant level of power such as the operation performed by the battery characteristic training module 104 of FIG. 1B is defined and the current battery internal resistance r (T, d H ) can also be estimated. Using the estimated internal resistances r (T, d H ) and r e and C f in this way, the total available capacity of the current battery, C (T, d H ), can be estimated. In this way, the internal resistance and total capacity of the current battery can also be estimated. The estimated internal resistance is used to calculate instantaneous power consumption in the battery consumption rate estimating unit 900. [

According to the embodiment of the present disclosure, the battery consumption rate estimating section 900 provides, for example, the consumption rate of the battery in real time at a time t s . The battery consumption rate estimating unit 900 operates in real time in cooperation with the no-load voltage estimating unit 904 and the battery parameter estimating unit 906. The battery consumption rate R d (% / h) is defined as the reduction rate of SoC per unit time. The battery duration and the battery consumption rate are functions of the effective resistance, the current battery total capacity (C), the unit SoC, and the current consumed by the terminal (I), and can be expressed as Equation (14).

Figure pat00026

Here, C f represents the total capacity of the new battery at room temperature, r represents the internal resistance of the battery, V oc represents the no-load voltage, and V out represents the load voltage. The relationship between the voltage drop V r at the internal resistance of the battery, the current I consumed by the terminal, the no-load voltage and the load voltage

Figure pat00027
to be. By modifying the <Equation 14>, the battery consumption rate is calculated with the estimated no-load voltage and the load voltage, the estimated r e values, and C f value, C f value can be a value obtained through the battery specifications have.

If the battery parameter estimator 906 according to the embodiment of the present disclosure executes a training application 908 consuming a constant value of power and performs an additional measurement process through the internal resistance measurement application, the current internal resistance can be estimated , It is possible to estimate the current I flowing to the current terminal through the estimated internal resistance. Then, the instantaneous consumed power P can be obtained by multiplying the estimated current I by the load voltage. That is, the battery consumption rate estimating unit 900 receives the no-load voltage from the no-load voltage estimating unit 904 and receives r e from the battery parameter estimating unit 906 to estimate the battery consumption rate in real time . Then, the battery consumption rate estimated in real time is provided to the no-load voltage measurement unit 904 to help estimate the no-load voltage in the next sample.

As described above, the no-load voltage estimating unit 904, the battery parameter estimating unit 906, and the battery consumption speed estimating unit 900 according to the embodiment of the present invention interact with each other to estimate an accurate battery consumption speed in a period of ts .

Then, the consumed speed estimated by the battery consumption rate estimating unit 900 is displayed on the display of the terminal so that the user can confirm it or can be utilized by the developer through the API.

FIG. 9B is an example of an operation flow chart of the apparatus configuration diagram of FIG. 9A according to the embodiment of the present disclosure.

9B, in step 920, the no-load voltage estimating unit 904 estimates the load voltage by approximating the load voltage to the first no-load voltage by stopping the background operation of the terminal or all applications in operation to minimize the consumed power, -SoC database to estimate the first no-load voltage. In step 922, the no-load voltage estimating unit 904 predicts the current no-load voltage using the reduced no-load voltage for each sample time based on the first no-load voltage and the SoC mapped in units of 1% To the speed estimation unit 900 and the battery parameter estimation unit 906.

In step 924, the battery parameter estimator calculates the effective resistance value of the battery using the no-load voltage updated in real time from the no-load voltage estimator 904 according to Equation (12) and Equation (13). Then, the effective resistance value is transmitted to the battery consumption rate estimating unit 900.

Then, in step 926, the battery consumption estimating unit may calculate the battery consumption rate expressed by Equation (14) using the effective resistance value and the real-time no-load voltage received from the no-load voltage estimating unit 904 have. The operation of each step in FIG. 9B is the same as that of FIG. 9A, and thus detailed description thereof will be omitted.

The battery consumption rate of the smart device can be estimated in real time through the embodiment of the present disclosure as described above and the video streaming Operation is possible. For example, HTTP (Hyper Text Transfer Protocol) Adaptive Streaming over HTTP (DASH) adjusts the bit rate of video suitable for the link state between the video server and the terminal, Is an application-level technology that allows the user to view the highest-quality video that is not the highest quality. The DASH is combined with the battery consumption rate estimation technique of the present disclosure to compare the playback time, that is, the battery consumption amount with the current battery consumption speed, according to the image quality of the video to be played back by the user, The quality of the video can be provided. As a specific example, if the user's battery consumption rate is too high to allow the user to achieve a sustained playback time for video of the desired image quality, the user may request a lower quality video and lower the battery consumption rate. That is, a video streaming service that considers both the battery duration and the video quality can be realized by requesting the video of the highest image quality satisfying the target continuous playback time.

In addition, when the battery consumption rate estimation technique according to the embodiment of the present disclosure is used, the smart device application developer estimates the current battery consumption rate of the user to the application developer and provides the result through the API, It is possible to adaptively control the operation of the smart device by recognizing the consumption speed.

In the embodiments of the present disclosure, an apparatus and method for estimating a battery consumption speed considering an aging state even if the aging state of the battery is not accurately known is proposed. Accordingly, since the estimation method that does not consider the existing battery aging state does not consider the capacity decrease due to aging of the battery, the estimated sustainable time may be different from the actual sustainable time of the battery. In particular, in the embodiment of the present disclosure, it is possible to constantly update the aging state of the battery by estimating the battery consumption speed by reflecting the aging state without special training operation at run time. One of the existing technologies, the technology that deduces the battery consumption rate every 1% decrease of the SoC, provides the battery consumption rate with the long update period. Therefore, if the terminal frequently changes various operations, . On the other hand, according to another embodiment of the present disclosure, since the battery consumption rate is provided in a very short update period, even if the operation of the terminal changes frequently, the battery consumption rate can be accurately informed in real time.

12 is a graph illustrating performance of each of six batteries in a smart device according to an embodiment of the present disclosure. As described with reference to FIGS. 8A and 8B, performance evaluation was performed by attaching three new batteries and three aged batteries to the same apparatus. Assuming that aging progressed in the order of ol1 <old2 <old3 . In the legend, the SoC item is the average value of the battery consumption rate using the time consumed by 1% of the battery. That is, it represents the actual battery consumption rate as a result of dividing the decrease amount of the SoC value by the measurement time during the measurement. The BattTracker item is an index that evaluates the average consumption rate of the battery using the proposed algorithm. The algorithm shows the runtime consumption of the battery, which is the BattTracker result of averaging the measurement time of the battery consumption rate. Referring to FIG. 12, it can be seen that the actual battery consumption rate varies depending on the degree of aging of the battery and the resolution (360p and 720p) of the moving image. It can be seen that the SoC item and the BattTracker item are measured without significant errors. The actual measurement was performed without any special training operation, and it can be seen that the battery consumption rate can be derived with a small error by applying the aging state at runtime.

FIG. 13 is an example of a result of measuring battery consumption speed in real time while operating various applications for a smart device equipped with an aged battery according to an embodiment of the present disclosure.

Referring to FIG. 13, the Drain rate by SoC w / o time sync. Is the result of calculating the consumption rate of the battery by using the time while the battery is consumed at 1%. However, since this result is derived after 1% of the battery is actually consumed and then consumed at the previously estimated consumption rate until 1% is consumed thereafter, if the operation is changed at the terminal, One can not but show very inaccurate results. Drain rate by SoC w / time sync. Drain rate by SoC w / o time sync. This is the result of synchronizing the value of the item to the actual battery consumption time. That is, it is an index showing the battery consumption rate during 1% of the battery consumption in real time. Moving average of drain rate is a result obtained by setting a moving average weight to a value obtained by taking a moving average at a real time battery consumption speed derived from the algorithm proposed in the embodiment of the present disclosure. The average drain rate by effective resistance is the average of the real-time battery consumption rate derived from the present disclosure in terms of the time the battery is consumed by 1%. Drain rate by SoC w / time sync. It can be judged that the embodiment of the present disclosure can accurately inform the battery consumption speed regardless of the kind of the terminal and the type of the battery or even when the terminal is performing various operations. Also, the Drain rate by SoC w / o time sync, which uses the time it takes 1% The consumption speed of the battery can be determined with a very short update period by using the algorithm proposed in the present invention even when the operation of the terminal is changed frequently.

While the present invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiments, but is capable of various modifications within the scope of the invention. Therefore, the scope of the present invention should not be limited by the illustrated embodiments, but should be determined by the scope of the appended claims and equivalents thereof.

Claims (16)

A method for estimating a battery consumption state of a mobile communication terminal,
The method comprising the steps of: databaseizing characteristic information of a battery that changes based on at least one of a temperature and an aging characteristic of a battery that provides power to the mobile communication terminal;
Estimating consumption power and battery consumption rate of the mobile communication terminal based on the battery characteristic information, and displaying the estimated related information.
The method of claim 1,
And a change amount of a no-load voltage depending on a remaining amount of the battery and a variation amount of the maximum usable capacity of the battery according to the temperature.
3. The method of claim 2,
Wherein the step of estimating the battery consumption rate comprises:
Estimating a discharge current of the mobile communication terminal using a change amount of the internal resistance of the battery according to the temperature and a voltage drop due to the internal resistance; And calculating the battery consumption rate based on the consumed power calculated using the reduced load voltage by the voltage drop caused by the voltage drop.
3. The method of claim 2,
Wherein the magnitude of the internal resistance increases as the temperature decreases and the current available capacity decreases.
3. The method of claim 2,
The process of estimating the consumed power includes:
Estimating the first no-load voltage by approximating the load voltage to the first no-load voltage by minimizing the current consumption power of the mobile communication terminal or using the variation amount of the no-load voltage according to the remaining amount of the battery, Updating the current no-load voltage in real time;
Calculating an effective resistance of the battery using the no-load voltage updated in real time;
And estimating a consumption rate of the battery based on the effective resistance in real time.
6. The method of claim 5, wherein calculating the effective resistance comprises:
Wherein the current value is calculated by using a value obtained by multiplying a value obtained by subtracting the load voltage from the current no-load voltage by a sample time while the battery consumes a predetermined amount of electric charge.
The method as claimed in claim 1,
A condition for displaying the battery consumption status related information on the display screen of the mobile communication terminal, at least one of the location information on the battery consumption status related information and the update period setting information for the battery consumption status information Receiving a user input for the user;
And displaying the battery consumption status related information corresponding to the user input.
The method according to claim 1,
Further comprising the step of transmitting the estimated related information to a service provider for a function or application of the mobile communication terminal that has caused the power consumption of the mobile communication terminal.
A mobile communication terminal for estimating a consumption state of a battery,
A training module for converting characteristic information of a battery, which changes based on at least one of a temperature and an aging characteristic of a battery that provides power to the mobile communication terminal,
And a controller for estimating consumption power and battery consumption rate of the mobile communication terminal based on the battery characteristic information and controlling the display of the estimated related information on a display screen.
The method of claim 9,
And at least one of a change amount of the internal resistance of the battery according to the temperature, a change amount of the maximum usable capacity of the battery according to the temperature, and a change amount of the no-load voltage according to the remaining amount of the battery.
11. The apparatus according to claim 10,
Estimating a discharge current of the mobile communication terminal by using a change amount of the internal resistance of the battery according to the temperature and a voltage drop due to the internal resistance and comparing the discharge current and the voltage due to the internal resistance And calculates the battery consumption rate based on the consumed power calculated using the reduced load voltage.
11. The method of claim 10,
Wherein the size of the internal resistance increases as the temperature decreases, and the current usable capacity decreases.
10. The method of claim 9,
Wherein the estimating unit comprises:
Estimating the first no-load voltage by approximating the load voltage to the first no-load voltage by minimizing the power consumption of the mobile communication terminal or using a variation amount of the no-load voltage according to the remaining amount of the battery, A voltage estimating unit for updating the current no-load voltage in real time,
A parameter calculation unit for calculating an effective resistance of the battery using the real time updated no-load voltage, and
And a speed estimator for estimating a consumed speed of the battery based on the effective resistance in real time.
14. The apparatus according to claim 13,
Wherein the calculated value is calculated by using a cumulative value obtained by multiplying a value obtained by subtracting a load voltage from a no-load voltage with a sample time while the battery consumes a predetermined amount of power, and the drag of the battery.
10. The apparatus according to claim 9,
A condition for displaying the battery consumption status related information on the display screen of the mobile communication terminal, at least one of the location information on the battery consumption status related information and the update period setting information for the battery consumption status information And controls the display unit to display information related to the battery consumption state corresponding to the user input.
10. The apparatus according to claim 9,
And controls the transmitting / receiving unit to transmit the estimated related information to a service provider of the function or application of the mobile communication terminal that caused the power consumption of the mobile communication terminal.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20190056743A (en) * 2017-11-17 2019-05-27 주식회사 엘지화학 Apparatus and Method for Estimating Resistance of Secondary Battery
US11493826B2 (en) * 2019-12-11 2022-11-08 Gopro, Inc. Method and apparatus for sharing power between internal and external power sources
US11614495B2 (en) 2019-03-18 2023-03-28 Lg Energy Solution, Ltd. Battery state estimating apparatus

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20190056743A (en) * 2017-11-17 2019-05-27 주식회사 엘지화학 Apparatus and Method for Estimating Resistance of Secondary Battery
US11105861B2 (en) 2017-11-17 2021-08-31 Lg Chem, Ltd. Device and method for estimating battery resistance
US11614495B2 (en) 2019-03-18 2023-03-28 Lg Energy Solution, Ltd. Battery state estimating apparatus
US11493826B2 (en) * 2019-12-11 2022-11-08 Gopro, Inc. Method and apparatus for sharing power between internal and external power sources

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