WO2022116037A1 - 电量预测方法和设备 - Google Patents

电量预测方法和设备 Download PDF

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Publication number
WO2022116037A1
WO2022116037A1 PCT/CN2020/133314 CN2020133314W WO2022116037A1 WO 2022116037 A1 WO2022116037 A1 WO 2022116037A1 CN 2020133314 W CN2020133314 W CN 2020133314W WO 2022116037 A1 WO2022116037 A1 WO 2022116037A1
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Prior art keywords
temperature
charge
state
preset
battery
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PCT/CN2020/133314
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English (en)
French (fr)
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张亮
邓亚环
孙亚青
谢洪
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宁德新能源科技有限公司
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Priority to PCT/CN2020/133314 priority Critical patent/WO2022116037A1/zh
Priority to CN202080021012.6A priority patent/CN113748438B/zh
Publication of WO2022116037A1 publication Critical patent/WO2022116037A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Definitions

  • the present application relates to the technical field of intelligent terminals, and in particular, to a method and device for predicting electric quantity.
  • the battery temperature is predicted based on the linear and monotonous variation of the battery temperature with time or using an empirical formula. Since the internal resistance of the battery increases at low temperatures, the battery discharge temperature increases, which makes the current battery temperature increase. In some methods, the error of the predicted battery temperature is too large, for example, the temperature error can reach more than 5 °C, which makes the error of predicting the remaining power larger.
  • the error in predicting the remaining power is large, and the lower the temperature, the larger the error.
  • the error reaches about 8%, and at -10°C, the error reaches about 10%, so it is easy to cause the phenomenon of automatic shutdown at low temperature, which brings inconvenience to the user.
  • the present application provides a power prediction method and device, which can reduce the power prediction error, improve the accuracy of power display, and help reduce the occurrence of automatic shutdown at low temperatures.
  • the present application provides an electric quantity prediction method, including: A. Obtaining first data, where the first data includes a first state of charge, a first temperature, and a current; B. Based on the first electric charge The state is queried in a preset first relationship table to obtain a first entropy heat coefficient, wherein the first relationship table includes a mapping relationship between the state of charge and the entropy heat coefficient; C. Based on the preset time period, all The first data, the first entropy heat coefficient, the preset second relationship table, and the preset third relationship table are obtained to obtain second data, wherein the second data includes the second data after the preset time period.
  • the second relationship table includes the mapping relationship between the state of charge, temperature, current and internal resistance
  • the third relationship table includes the state of charge and the open circuit voltage.
  • mapping relationship among the state of charge, temperature, current and internal resistance in the second relationship table satisfies the formula: Among them, R cc (SOC, T, I) is the internal resistance, SOC is the state of charge, T is the temperature, I is the current, U(SOC, T) is the terminal voltage, and OCV(SOC, T) is the open circuit voltage.
  • the second relationship table further includes a preset magnification correction coefficient, and the internal resistance is determined by the state of charge, temperature, current, and the preset magnification correction coefficient.
  • the second data further includes a first heat generation power, where the first heat generation power is obtained by calculating the reversible heat, the current and the first internal resistance, and the reversible heat Determined from the first data and the first entropy thermal coefficient, the first internal resistance is obtained by querying the second relationship table based on the first state of charge, the first temperature, and the current .
  • the second temperature is calculated and obtained from the preset time period, the first heat generation power, the first heat dissipation power, the first temperature, the specific heat capacity and mass of the battery, wherein , the first heat dissipation power is calculated and obtained from the first temperature, the preset ambient temperature, the heat dissipation coefficient and the surface area of the battery.
  • the first heat generation power is determined by the formula Calculated, where Pin is the heat generation power, I is the current, Rcc is the internal resistance, T is the temperature, is the entropy thermal coefficient, OCV is the open circuit voltage, for reversible heat.
  • the second temperature is determined by the formula Calculated, where T2 is the second temperature, T1 is the first temperature, P in is the heat generation power, P out is the heat dissipation power, ⁇ t is the preset time period, c is the specific heat capacity of the battery, and m is the quality of the battery.
  • the second terminal voltage is obtained by calculation from the second internal resistance, the second open circuit voltage and the current, wherein the second internal resistance is based on the second state of charge, the The second temperature and the current are obtained by querying the second relationship table, and the second open circuit voltage is obtained by querying the third relationship table based on the second state of charge.
  • the second state of charge is obtained by calculation from the preset time period, the first state of charge, the current, and the capacity of the battery.
  • the second state of charge is determined by the formula The calculation is obtained, wherein, SOC 2 is the second state of charge, SOC 1 is the first state of charge, cap is the capacity of the battery, ⁇ t is the preset time period, and I is the current.
  • the preset time period is determined by the capacity of the battery, the current and the state of charge interval.
  • the preset time period is determined by the formula It is obtained by calculation, wherein, cap is the capacity of the battery, ⁇ SOC is the state of charge interval of the battery, and I is the current.
  • the step A, acquiring the first data includes: A1, acquiring the first terminal voltage and current; A2, detecting whether the current is less than or equal to a preset current; A3, if If the current is less than or equal to the preset current, query is performed in the third relationship table based on the first open-circuit voltage to obtain the first state of charge, wherein the first open-circuit voltage is determined by the first open-circuit voltage. The voltage at one end is determined.
  • the method further includes: F1, acquiring third data, where the third data includes a third state of charge, a third temperature, and a current; F2, based on the third data and a predetermined Set a discharge value to obtain a fourth temperature; F3, discharge from the third state of charge to the fourth state of charge based on the preset discharge value; F4, detect the fifth temperature in the fourth state of charge ; F5, obtain the difference between the fourth temperature and the fifth temperature; F6, if the difference is greater than a preset threshold, then based on the fourth temperature, the fifth temperature, the preset heat dissipation The initial value and the preset correction value are used to determine the heat dissipation coefficient of the battery.
  • the preset threshold is less than or equal to 2°C.
  • the preset correction value is in the range of 0.3 to 0.7.
  • the preset discharge value is 5% to 10% discharge percentage.
  • the preset voltage is determined based on the material system of the battery.
  • the battery is selected from: the material system is a lithium cobalt oxide system and the preset voltage is in the range of 3.0V to 3.4V, the material system is a ternary material system and the preset voltage In the range of 2.8V to 3.2V, the material system is one of a lithium iron phosphate system and the preset voltage is in the range of 2.5V to 2.9V.
  • the remaining power is determined by the formula The calculation is obtained, wherein, RM is the remaining power, SOC 1 is the first state of charge, and SOC 2 is the second state of charge.
  • the present application provides an electric quantity prediction device, comprising: a first data acquisition module for acquiring first data, the first data including a first state of charge, a first temperature and a current; an entropy thermal coefficient
  • the obtaining module is configured to perform a query in a preset first relationship table based on the first state of charge to obtain a first entropy heat coefficient, wherein the first relationship table includes a relationship between the state of charge and the entropy heat coefficient.
  • a mapping relationship a calculation module for obtaining second data based on a preset time period, the first data, the first entropy thermal coefficient, the preset second relationship table and the preset third relationship table, wherein the The second data includes the second temperature, the second state of charge and the second terminal voltage after the preset time period, and the second relationship table includes the mapping relationship between the state of charge, temperature, current and internal resistance , the third relationship table includes a mapping relationship between the state of charge and the open-circuit voltage; a detection module is used to detect whether the second terminal voltage is less than or equal to a preset voltage; a cycle judgment module is used to detect if the first The two-terminal voltage is less than or equal to the preset voltage, and the remaining power is obtained based on the first state of charge and the second state of charge, and if the second terminal voltage is greater than the preset voltage, make the The first state of charge is equal to the second state of charge, the first temperature is equal to the second temperature, and is determined by the entropy thermal coefficient obtaining module
  • the present application provides an electronic device, comprising: a display screen; one or more processors; a memory; a plurality of application programs; and one or more computer programs, wherein the one or more computer programs are stored In the memory, the one or more computer programs comprise instructions which, when executed by the apparatus, cause the apparatus to perform the method of the first aspect.
  • the present application provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, which, when executed on a computer, causes the computer to execute the method according to the first aspect.
  • the present application provides a computer program for executing the method of the first aspect when the computer program is executed by a computer.
  • the program in the fifth aspect may be stored in whole or in part on a storage medium packaged with the processor, and may also be stored in part or in part in a memory not packaged with the processor.
  • the power prediction method and device provided by the present application can reduce the power prediction error, improve the accuracy of power display, and help reduce the occurrence of automatic shutdown at low temperatures.
  • FIG. 1 is a schematic diagram of a method of an embodiment of an electric quantity prediction method of the present application
  • FIG. 2 is a schematic flowchart of an embodiment of an electric quantity prediction method of the present application
  • Fig. 3(a) is a table of precision deviations in the prior art that does not consider reversible heat to predict the amount of electricity;
  • Fig. 3(b) is a table of precision deviations of the predicted electric quantity considering reversible heat in the electric quantity prediction method of the application;
  • FIG. 4 is a schematic flowchart of a modified heat dissipation coefficient according to an embodiment of the electric quantity prediction method of the present application
  • FIG. 5 is a predicted temperature table for modifying the heat dissipation coefficient according to an embodiment of the electric quantity prediction method of the present application
  • Fig. 6(a) is a table showing the accuracy deviation of the predicted electric quantity when the ambient temperature changes from 40°C to -10°C in the prior art without correcting the heat dissipation coefficient;
  • Figure 6(b) is a table of the precision deviation of the predicted power quantity when the ambient temperature changes from 40°C to -10°C in the modified heat dissipation coefficient in the power quantity prediction method of the application;
  • FIG. 7 is an equivalent circuit model diagram of an embodiment of the electric quantity prediction method of the present application.
  • Figure 8(a) is a comparison between the predicted internal resistance and the test value in the electric quantity prediction method of the present application under the environment of -15°C without using the preset magnification correction coefficient to correct the internal resistance;
  • Figure 8(b) is a comparison between the predicted internal resistance and the test value in the power prediction method of the present application, which uses a preset magnification correction coefficient to correct the internal resistance at a temperature of -15°C;
  • FIG. 9 is a preset magnification correction coefficient table of an embodiment of the electric quantity prediction method of the present application.
  • Fig. 10(a) is a table of the accuracy deviation of predicted electric quantity in the environment of -20°C in the prior art
  • Fig. 10(b) is a table of precision deviations of predicted electric quantity in the environment of -20°C without using the preset magnification correction coefficient to correct the internal resistance in the electric quantity prediction method of the application;
  • Fig. 10(c) is a table of precision deviations of predicted electric quantity in the environment of -20°C using preset magnification correction coefficient to correct internal resistance in the electric quantity prediction method of the present application;
  • FIG. 11 is a schematic structural diagram of an embodiment of the electric quantity prediction device of the present application.
  • FIG. 12 is a schematic structural diagram of an embodiment of the electronic device of the present application.
  • the present application proposes a power prediction method and device, which can reduce the power prediction error, improve the accuracy of power display, and help reduce the occurrence of automatic shutdown at low temperatures.
  • the present application analyzes the factors affecting the error of the prediction of the remaining power.
  • the battery temperature is predicted based on the monotonous linear variation law of the battery temperature with time or using an empirical formula.
  • the factors that affect the temperature change of the battery include ambient temperature, current, heat dissipation, and state of charge, etc., rather than linearly and monotonically increasing or decreasing with time. Therefore, the existing technology cannot cover the battery in different environments.
  • the temperature change rule under working conditions leads to a large error in the prediction of battery temperature.
  • the factors affecting the temperature change law of the battery during charging and discharging are more reasonably and accurately considered, which is beneficial to reduce the prediction error of the battery temperature and reduce the error of predicting the remaining power.
  • FIG. 1 is a schematic diagram of an embodiment of the power prediction method of the present application. As shown in FIG. 1 and FIG. 2 , the above-mentioned power prediction method may include:
  • the first state of charge may include the initial state of charge of the battery (ie, the state of charge at time 0) or the state of charge at a certain time.
  • the first temperature may be acquired by using a temperature sensor
  • the current may be acquired by using a current sensor
  • the sampling frequency of the sensor may include 1 Hz.
  • the step S101 includes:
  • the first terminal voltage may be acquired by using a voltage sensor.
  • the preset current may include a 50mA current, that is, when the current is less than or equal to 50mA, it is determined that the first open circuit voltage is equal to the first terminal voltage, and the initial state of charge is based on the first open circuit voltage Perform a query in the third relational table to obtain.
  • the current is greater than 50mA, it is determined that the first open-circuit voltage is still equal to the first terminal voltage when the current is less than or equal to 50mA, and when the current is less than or equal to 50mA again, the first terminal voltage is recollected by the voltage sensor obtaining, it is determined that the first open-circuit voltage is equal to the first terminal voltage obtained by re-collecting.
  • the third relationship table may include a mapping relationship between the state of charge of the battery and the open circuit voltage.
  • the first state of charge can be calculated and obtained based on the current, the time t, the capacity of the battery and the initial state of charge.
  • the first state of charge can be expressed by the formula The calculation is obtained, wherein, SOC 1 is the first state of charge, SOC 0 is the initial state of charge, cap is the capacity of the battery, and I is the current.
  • the first open-circuit voltage is obtained by querying the first state of charge in the third relational table, that is, the first open-circuit voltage of the current state of the battery is obtained from the first open-circuit voltage of the current state
  • a state of charge is obtained by querying the third relational table.
  • the first state of charge is the state of charge of the current state of the battery
  • the first entropy heat coefficient is the entropy heat coefficient of the current state of the battery.
  • the first relationship table, the second relationship table, and the third relationship table can all be obtained through experimental tests, and are pre-stored in the electronic device.
  • the second data further includes a first heat generation power, where the first heat generation power is obtained by calculating the reversible heat, the current and the first internal resistance, and the reversible heat Determined from the first data and the first entropy thermal coefficient, the first internal resistance is obtained by querying the second relationship table based on the first state of charge, the first temperature, and the current .
  • the second temperature is calculated and obtained from the preset time period, the first heat generation power, the first heat dissipation power, the first temperature, the specific heat capacity and mass of the battery, wherein , the first heat dissipation power is calculated and obtained from the first temperature, the preset ambient temperature, the heat dissipation coefficient and the surface area of the battery.
  • the first heat generation power is determined by the formula Calculated, where Pin is the heat generation power, I is the current, Rcc is the internal resistance, T is the temperature, is the entropy thermal coefficient, OCV is the open circuit voltage, for reversible heat.
  • P in is the heat generation power
  • I is the charge and discharge current
  • the current sign is positive during charging
  • the current sign is negative during discharging
  • Rcc is the internal resistance
  • T is the temperature
  • the heat generation power of the battery can be divided into two parts: irreversible heat and reversible heat, wherein the irreversible heat part is I 2 R cc , and its value is always greater than 0, which is completely in the process of charging and discharging. Exothermic behavior, energy is completely wasted, and the reversible heat part is The reversible thermal part is related to the internal material structure change or entropy change of the battery (such as the material structure change or entropy change during the process of lithium deintercalation). If its value is positive during charging, its value is negative during discharge. During the charge-discharge cycle, the total reversible heat is 0, and the heat of the reversible heat part is reversible.
  • the heat dissipation coefficient It is the partial conductance of the battery open circuit voltage to the temperature. Specifically, at different temperatures, the open circuit voltage of the battery is obtained by testing, and then the difference is replaced by the differential to obtain the entropy thermal coefficient. The denser the temperature points, the more accurate the result.
  • the second temperature is determined by the formula Calculated, where T2 is the second temperature, T1 is the first temperature, P in is the heat generation power, P out is the heat dissipation power, ⁇ t is the preset time period, c is the specific heat capacity of the battery, and m is the quality of the battery.
  • the specific heat capacity c of the battery can be measured by an accelerated calorimeter.
  • the volume V of the battery can be calculated and obtained according to the length, width and height of the battery.
  • the mass m of the battery can be measured by a balance, and the density ⁇ of the battery can be obtained by dividing the mass m of the battery by the volume V.
  • c, ⁇ , V, k, P in and P out are the specific heat capacity, density, volume, thermal conductivity, heat generation power and heat dissipation power of the object, respectively.
  • the partial differential equation contains a Laplace operator
  • the second derivative of the space needs to be obtained in the actual solution. This step requires a large amount of calculation, and the calculation time of the processor (MCU) is generally more than 10s. Not good for practical application.
  • the heat generation during the charging and discharging process will also be relatively uniform. Therefore, the temperature distribution inside the battery will be relatively uniform, and the temperature difference everywhere is small (for example, consumer lithium-ion batteries are generally within 1°C, and power lithium-ion batteries are generally within 2°C). Therefore, in this embodiment, the temperature of the battery can be the same everywhere, then there are:
  • the iterative equation is obtained by shifting, and the iterative solution is performed to obtain
  • the second temperature can be calculated by substituting the first temperature into this formula.
  • the accuracy of predicting the battery temperature mainly depends on the accuracy of the heat generation power P in and the heat dissipation power P out of the battery.
  • a processor such as a single-chip microcomputer
  • the preset ambient temperature is the temperature measured when the battery is not in use.
  • the temperature T is the first temperature T 1 .
  • the heat dissipation of an object obeys Newton's law of cooling, that is, when there is a temperature difference between the surface of the object and the surrounding temperature, the heat dissipated from the unit area per unit time and the temperature difference
  • the proportional coefficient is the heat transfer coefficient or the heat dissipation coefficient. Therefore, the heat dissipation power of the battery can be calculated by the above formula, wherein the heat dissipation coefficient can be an empirical coefficient, which is mainly related to the air flow rate on the surface of the object.
  • the value of the heat dissipation coefficient h is generally 3 to 10W/(m 2 *K), while in a strong convection environment, the heat dissipation coefficient h can reach 30 to 50W/(m 2 *K).
  • the battery since the battery is installed inside the electronic device, it does not directly contact the outside air, but first conducts heat to the electronic device and then dissipates in the air. There is a certain error in the heat dissipation power.
  • the above formula is still used to obtain the heat dissipation power, and the heat dissipation coefficient h is corrected to reduce the error of the heat dissipation power.
  • the method further includes: S301, acquiring third data, where the third data includes a third state of charge, a third temperature, and a current; S302, based on the third data and a predetermined Set a discharge value to obtain a fourth temperature; S303, discharge from the third state of charge to a fourth state of charge based on the preset discharge value; S304, detect a fifth temperature in the fourth state of charge ; S305, obtain the difference between the fourth temperature and the fifth temperature; S306, if the difference is greater than a preset threshold, then based on the fourth temperature, the fifth temperature, the preset heat dissipation The initial value and the preset correction value are used to determine the heat dissipation coefficient of the battery.
  • the method in this embodiment can calculate the heat dissipation coefficient of the battery. Correction to adapt to changes in the environment to reduce the error of the heat dissipation power.
  • the preset initial heat dissipation value h 0 is determined based on the heat dissipation coefficient of the battery under common environmental conditions.
  • the common environmental conditions may include environmental conditions with an ambient temperature of 25°C and no wind. That is to say, the heat dissipation coefficient of the battery under the common environmental conditions is used as the preset heat dissipation initial value.
  • FIG. 4 is a schematic flowchart of a modified heat dissipation coefficient according to an embodiment of the electric quantity prediction method of the present application.
  • step S302 predict the temperature when the battery is discharged from the current state of charge (such as the third state of charge) to a certain state of charge (such as the fourth state of charge), and obtain the Fourth temperature.
  • the fourth temperature may be stored.
  • step S303 and the step S304 after the battery has been discharged from the third state of charge for a period of time and just reaches the fourth state of charge, a temperature sensor is used to collect the actual temperature of the battery at the moment is the fifth temperature.
  • the third temperature is set equal to the fifth temperature
  • the third state of charge is equal to the fourth state of charge
  • the initial heat dissipation is initially
  • the value h 0 is equal to the heat dissipation coefficient h
  • the steps S302 to S306 are repeatedly performed until the difference is less than or equal to the preset threshold, and the heat dissipation coefficient of the battery is output.
  • the preset threshold value is less than or equal to 2°C, or the preset threshold value may be in the range of 1°C to 2°C.
  • FIG. 5 is a predicted temperature table for correcting the heat dissipation coefficient according to an embodiment of the power prediction method of the present application.
  • the left ordinate is the voltage (voltage), the unit is (mV), and the right ordinate is the temperature (temperature), and the unit is (°C). )
  • the table includes the predicted temperature rise curve and the measured temperature rise curve. It can be seen that the error of the predicted temperature of the modified heat dissipation coefficient in this application can be reduced from 3 °C to 1.5 °C, which improves the accuracy of predicting the battery temperature. Reduced error in predicting remaining power.
  • the preset correction value is in the range of 0.3 to 0.7.
  • the preset discharge value is 5% to 10% discharge percentage
  • the discharge interval of the battery may be preset to be 2% to 10% discharge percentage
  • Fig. 3(a) is a table of accuracy deviations in the prior art that does not consider reversible heat for predicting the amount of electricity.
  • the abscissa is time (s), and the ordinate is the percentage (%) of residual power (SOC) deviation.
  • the error of the remaining power of the battery predicted by the existing TI algorithm is about 8%, as shown in Figure 3 (b).
  • Figure 3 (b) is the accuracy deviation table considering the reversible thermal predicted electric quantity in the electric quantity prediction method of the application, the abscissa is the time (s), and the ordinate is the residual electric quantity (SOC) deviation percentage (%).
  • the error of the method predicting the remaining capacity of the battery is reduced to about 3%.
  • the heat dissipation coefficient of the battery is continuously corrected, so as to continuously correct the predicted temperature (such as the second temperature), which improves the The accuracy of the heat dissipation power further reduces the error of predicting the temperature of the battery, thereby further reducing the error of predicting the remaining power of the battery.
  • the predicted temperature such as the second temperature
  • 6(a) is a table of the accuracy deviation of the predicted electric power when the ambient temperature changes from 40°C to -10°C without correction of the heat dissipation coefficient in the prior art, the abscissa is the time (s), and the ordinate is the remaining electric quantity ( SOC) deviation percentage (%), when the ambient temperature changes from 40°C to -10°C, the current technology (such as the method of predicting the remaining battery power by using the TI algorithm) and the power prediction method in this embodiment are used to carry out In contrast, the existing TI algorithm predicts that the error of the remaining battery power is about 7%.
  • Figure 6(b) shows the predicted power when the ambient temperature changes from 40°C to -10°C by correcting the heat dissipation coefficient in the power prediction method of the application.
  • the abscissa is time (s)
  • the ordinate is the residual power (SOC) deviation percentage (%).
  • the power prediction method predicts that the error of remaining battery power is reduced to within 3%.
  • the second terminal voltage is obtained by calculation from the second internal resistance, the second open circuit voltage and the current, wherein the second internal resistance is based on the second state of charge, the The second temperature and the current are obtained by querying the second relationship table, and the second open circuit voltage is obtained by querying the third relationship table based on the second state of charge.
  • FIG. 7 is an equivalent circuit model diagram of an embodiment of the electric quantity prediction method of the present application.
  • the second state of charge is obtained by calculation from the preset time period, the first state of charge, the current, and the capacity of the battery.
  • the second state of charge is determined by the formula The calculation is obtained, wherein, SOC 2 is the second state of charge, SOC 1 is the first state of charge, cap is the capacity of the battery, ⁇ t is the preset time period, and I is the current.
  • the preset time period is determined by the capacity of the battery, the current and the state of charge interval. Generally, if the preset time period is too large, the accuracy of predicting the battery temperature will be affected, and if the preset time period is too small, the calculation burden of the processor will be increased. Therefore, in this embodiment, the preset time period varies with the current.
  • the preset time period is determined by the formula It is obtained by calculation, wherein, cap is the capacity of the battery, ⁇ SOC is the state of charge interval of the battery, and I is the current.
  • the preset voltage is the discharge cut-off voltage U 0 of the battery, and the preset voltage is determined based on the material system of the battery.
  • the discharge cut-off voltage represents the voltage set by the minimum battery protection mechanism of the electronic device.
  • the battery is selected from: the material system is a lithium cobalt oxide system and the preset voltage is in the range of 3.0V to 3.4V, the material system is a ternary material system and the preset voltage In the range of 2.8V to 3.2V, the material system is one of a lithium iron phosphate system and the preset voltage is in the range of 2.5V to 2.9V.
  • the material system of the battery may also include other types of material systems, which are not limited herein.
  • the remaining power is determined by the formula The calculation is obtained, wherein, RM is the remaining power, SOC 1 is the first state of charge, and SOC 2 is the second state of charge.
  • the second relationship table includes the mapping relationship between the state of charge, temperature, current and internal resistance, The influence of different discharge currents on the internal resistance of the battery is considered to improve the accuracy of predicting the internal resistance of the battery and reduce the error of predicting the remaining power of the battery.
  • mapping relationship among the state of charge, temperature, current and internal resistance in the second relationship table satisfies the formula: Among them, R cc (SOC, T, I) is the internal resistance, SOC is the state of charge, T is the temperature, I is the current, U(SOC, T) is the terminal voltage, and OCV(SOC, T) is the open circuit voltage.
  • the present embodiment also provides a method for testing the internal resistance of the battery, including:
  • the current can also be preset to a discharge rate such as 0.2c or 0.5c.
  • the preset ambient temperature range may include -20°C to 55°C.
  • the preset test temperature interval is 3°C, that is, the internal resistance of the battery is obtained by testing every 3°C interval.
  • the preset test temperature interval is 5°C, that is, the internal resistance of the battery is obtained by testing at every 5°C interval
  • the preset test temperature interval is 10°C, that is, every interval The internal resistance of the battery is obtained by the 10°C test.
  • test method also includes: exposing the battery in a high and low temperature box, and using a strong convection environment to dissipate heat on the battery. It should be pointed out that the test method is not only applicable to testing the battery to obtain the internal resistance of the battery during the discharging process, but also applicable to testing the battery to obtain the internal resistance of the battery during the charging process.
  • the second relationship table further includes a preset magnification correction coefficient, and the internal resistance is determined by the state of charge, temperature, current, and the preset magnification correction coefficient.
  • the temperature is high (such as greater than or equal to 25°C)
  • the electrochemical reaction is easier to carry out, and the discharge current has less influence on the internal resistance of the battery.
  • the volt-ampere characteristic curve of the battery is (U-I curve) is a straight line (slope as a function of temperature).
  • the temperature is low (such as lower than 25°C)
  • the discharge current or discharge rate has a greater impact on the internal resistance of the battery. larger.
  • the preset rate correction coefficient is determined based on the test temperature and the discharge rate.
  • Figure 8(a) shows the comparison between the predicted internal resistance and the test value without using the preset magnification correction coefficient to correct the internal resistance in the power prediction method of the present application under the environment of -15 °C.
  • the ordinate is the internal resistance (unit is ohm).
  • the abscissa is the depth of discharge (ie DOD, which is opposite to the remaining power SOC)
  • 0.1C-test represents the measured internal resistance curve at 0.1c discharge rate
  • 0.1C-predic represents the predicted internal resistance curve at 0.1c discharge rate
  • 0.2C-test means the measured internal resistance curve at 0.2c discharge rate
  • 0.2C-predic means the predicted internal resistance curve at 0.2c discharge rate
  • 0.5C-test means the measured internal resistance curve at 0.5c discharge rate
  • 0.5 C-predic indicates the predicted internal resistance curve at 0.5c discharge rate.
  • Figure 8(b) shows the comparison between the predicted internal resistance and the test value in which the internal resistance is corrected by using the preset magnification correction coefficient in the environment of -15°C in the power prediction method of the present application, and the ordinate is the internal resistance (unit is ohm),
  • the abscissa is the depth of discharge (ie DOD, which is opposite to the remaining power SOC)
  • 0.1C-DC-test represents the measured internal resistance curve at a discharge rate of 0.1c
  • 0.1C-DC-predic represents the predicted internal resistance curve at a discharge rate of 0.1c Resistance curve
  • 0.2C-DC-test means measured internal resistance curve at 0.2c discharge rate
  • 0.2C-DC-predic means predicted internal resistance curve at 0.2c discharge rate
  • 0.5C-DC-test means at 0.5c discharge rate
  • the measured internal resistance curve at the rate, 0.5C-DC-predic indicates the predicted internal resistance curve at a discharge rate of 0.5c.
  • the electric quantity prediction method of this embodiment adopts the preset magnification correction coefficient to predict the internal resistance with higher accuracy and reliability.
  • FIG. 9 is a preset rate correction coefficient table of an embodiment of the electric quantity prediction method of the present application.
  • the discharge rate is 0.2c
  • different test temperatures such as -12°C, -11.5°C, -7.5°C, - 6°C, -3°C, 1.5°C, 4°C, 6°C, and 9°C
  • the preset magnification correction coefficients are 1.45, 1.45, 1.4, 1.3, 1.15, 1, 1, 1, and 1, respectively.
  • the pre- The magnification correction coefficients are set as 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.4, 1.3 and 1.15 respectively.
  • the discharge rate may include discharge rates of other rates, and the preset rate correction coefficient is not limited to the value provided in this embodiment.
  • the preset rate correction coefficient can be determined by a piecewise linear interpolation algorithm. If the discharge rate is greater than 0.5c or lower than 0.2c, the corresponding preset rate correction coefficient is obtained by linear extrapolation . Similarly, if the test temperature exceeds a certain temperature range, it can be determined by linear interpolation and extrapolation. It should be pointed out that for batteries of different material systems, the preset rate correction coefficients may be different or the same, and the above test methods can be used to test batteries of different material systems to determine the preset rate corresponding to batteries of different material systems.
  • the magnification correction factor is not limited here.
  • the second relationship table provided in this embodiment improves the accuracy of predicting the internal resistance of the battery, and reduces the error in predicting the remaining power of the battery.
  • Fig. 10(a) is the accuracy deviation table of predicted electric quantity in the environment of -20°C in the prior art, the abscissa is the time (s), the ordinate is the residual electric quantity (SOC) deviation percentage (%).
  • the existing technology (such as the method of predicting the remaining battery power by using the TI algorithm) is compared with the power prediction method in this embodiment. The error of the existing TI algorithm predicting the remaining battery power is about 13%.
  • the second relationship table further includes the preset rate correction coefficient to correct the predicted internal resistance of the battery, which further improves the accuracy of predicting the internal resistance of the battery and reduces the cost of predicting the remaining power of the battery. Error, for example, Fig.
  • 10(c) is the accuracy deviation table of predicted electric quantity in the environment of -20°C in which the preset magnification correction coefficient is used to correct the internal resistance in the electric quantity prediction method of the application, the abscissa is time (s), the ordinate is The coordinates are the residual power (SOC) deviation percentage (%). Under the environment of -20°C, the power prediction method in this embodiment predicts that the error of the remaining power of the battery is further reduced to about 3%.
  • FIG. 11 is a schematic structural diagram of an electric quantity prediction apparatus of the present application
  • the present application provides an electric quantity prediction apparatus, which includes: a first data acquisition module 10 for acquiring first data, and the first data includes a first data acquisition module 10 .
  • the entropy thermal coefficient obtaining module 20 is configured to query a preset first relationship table based on the first state of charge to obtain a first entropy thermal coefficient, wherein the The first relationship table includes a mapping relationship between the state of charge and the entropy thermal coefficient;
  • the calculation module 30 is configured to, based on a preset time period, the first data, the first entropy thermal coefficient, and a preset second relationship table and a preset third relationship table to obtain second data, wherein the second data includes the second temperature, the second state of charge and the second terminal voltage after the preset time period, the second relationship table Including the mapping relationship between the state of charge, temperature, current and internal resistance, the third relationship table includes the mapping relationship between the state of charge and the open circuit voltage; the voltage detection module 40 is used to detect the second terminal voltage Whether it is less than or equal to the preset voltage; the power obtaining module 50 is configured to obtain the remaining voltage based on the first state of charge and the second
  • mapping relationship among the state of charge, temperature, current and internal resistance in the second relationship table satisfies the formula: Among them, R cc (SOC, T, I) is the internal resistance, SOC is the state of charge, T is the temperature, I is the current, U(SOC, T) is the terminal voltage, and OCV(SOC, T) is the open circuit voltage.
  • the second relationship table further includes a preset magnification, and the internal resistance is determined by the state of charge, temperature, current, and the preset magnification.
  • the second data further includes a first heat generation power, where the first heat generation power is obtained by calculating the reversible heat, the current and the first internal resistance, and the reversible heat Determined from the first data and the first entropy thermal coefficient, the first internal resistance is obtained by querying the second relationship table based on the first state of charge, the first temperature, and the current .
  • the second temperature is calculated and obtained from the preset time period, the first heat generation power, the first heat dissipation power, the first temperature, the specific heat capacity and mass of the battery, wherein , the first heat dissipation power is calculated and obtained from the first temperature, the preset ambient temperature, the heat dissipation coefficient and the surface area of the battery.
  • the first heat generation power is determined by the formula Calculated, where Pin is the heat generation power, I is the current, Rcc is the internal resistance, T is the temperature, is the entropy thermal coefficient, OCV is the open circuit voltage, for reversible heat.
  • the second temperature is determined by the formula Calculated, where T2 is the second temperature, T1 is the first temperature, P in is the heat generation power, P out is the heat dissipation power, ⁇ t is the preset time period, c is the specific heat capacity of the battery, and m is the quality of the battery.
  • the second terminal voltage is obtained by calculation from the second internal resistance, the second open circuit voltage and the current, wherein the second internal resistance is based on the second state of charge, the The second temperature and the current are obtained by querying the second relationship table, and the second open circuit voltage is obtained by querying the third relationship table based on the second state of charge.
  • the second state of charge is obtained by calculation from the preset time period, the first state of charge, the current, and the capacity of the battery.
  • the second state of charge is determined by the formula The calculation is obtained, wherein, SOC 2 is the second state of charge, SOC 1 is the first state of charge, cap is the capacity of the battery, ⁇ t is the preset time period, and I is the current.
  • the preset time period is determined by the capacity of the battery, the current and the state of charge interval.
  • the preset time period is determined by the formula It is obtained by calculation, wherein, cap is the capacity of the battery, ⁇ SOC is the state of charge interval of the battery, and I is the current.
  • the first data acquisition module includes:
  • an acquisition module for acquiring the voltage and current of the first terminal
  • a current detection module for detecting whether the current is less than or equal to a preset current
  • a first state-of-charge determination module configured to query the third relationship table based on the first open-circuit voltage to obtain the first state of charge if the current is less than or equal to the preset current, wherein , the first open circuit voltage is determined by the first terminal voltage.
  • the device further includes:
  • a third data acquisition module configured to acquire third data, where the third data includes a third state of charge, a third temperature, and a current;
  • a fourth temperature obtaining module configured to obtain a fourth temperature based on the third data and the preset discharge value
  • a discharge module configured to discharge from the third state of charge to a fourth state of charge based on the preset discharge value
  • a temperature detection module for detecting a fifth temperature in the fourth state of charge
  • a difference calculation module for obtaining the difference between the fourth temperature and the fifth temperature
  • a heat dissipation coefficient determination module configured to determine a heat dissipation coefficient of the battery based on the fourth temperature, the fifth temperature, a preset heat dissipation initial value and a preset correction value if the difference is greater than a preset threshold.
  • the preset threshold is less than or equal to 2°C.
  • the preset correction value is in the range of 0.3 to 0.7.
  • the preset discharge value is 5% to 10% discharge percentage.
  • the preset voltage is determined based on the material system of the battery.
  • the battery is selected from: the material system is a lithium cobalt oxide system and the preset voltage is in the range of 3.0V to 3.4V, the material system is a ternary material system and the preset voltage In the range of 2.8V to 3.2V, the material system is one of a lithium iron phosphate system and the preset voltage is in the range of 2.5V to 2.9V.
  • the remaining power is determined by the formula The calculation is obtained, wherein, RM is the remaining power, SOC 1 is the first state of charge, and SOC 2 is the second state of charge.
  • the power prediction apparatus provided by the embodiment shown in FIG. 11 can be used to implement the technical solution of the method embodiment shown in FIG. 1 of the present application, and the implementation principle and technical effect can be further referred to the relevant description in the method embodiment.
  • circuit prediction apparatus may correspond to the electronic device 900 shown in FIG. 12 .
  • the functions of the entropy thermal coefficient acquisition module 20, the calculation module 30, the voltage detection module 40, the power acquisition module 50 and the sub-modules included therein can be implemented by the processor 910 in the electronic device 900 shown in FIG. 12, and the first data
  • the functions of the acquisition module 10 and the sub-modules it includes may be implemented by the sensors in the electronic device 900 shown in FIG. 12 .
  • each module of the electric quantity prediction apparatus shown in FIG. 11 above is only a division of logical functions, and in actual implementation, it may be fully or partially integrated into a physical entity, or may be physically separated.
  • these modules can all be implemented in the form of software calling through processing elements; they can also all be implemented in hardware; some modules can also be implemented in the form of software calling through processing elements, and some modules can be implemented in hardware.
  • the detection module may be a separately established processing element, or may be integrated in a certain chip of the electronic device.
  • the implementation of other modules is similar.
  • all or part of these modules can be integrated together, and can also be implemented independently.
  • each step of the above-mentioned method or each of the above-mentioned modules can be completed by an integrated logic circuit of hardware in the processor element or an instruction in the form of software.
  • the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more specific integrated circuits (Application Specific Integrated Circuit; hereinafter referred to as: ASIC), or, one or more microprocessors Digital Singnal Processor (hereinafter referred to as: DSP), or, one or more Field Programmable Gate Array (Field Programmable Gate Array; hereinafter referred to as: FPGA), etc.
  • ASIC Application Specific Integrated Circuit
  • DSP Digital Singnal Processor
  • FPGA Field Programmable Gate Array
  • these modules can be integrated together and implemented in the form of a system-on-a-chip (System-On-a-Chip; hereinafter referred to as: SOC).
  • FIG. 12 is a schematic structural diagram of an embodiment of an electronic device of the present application. As shown in FIG. 12 , the above-mentioned electronic device may include: a display screen; one or more processors; a memory; multiple application programs; and one or more computer programs .
  • the above-mentioned display screen may include a display screen of a vehicle-mounted computer (Mobile Data Center); the above-mentioned electronic equipment may be a mobile terminal (mobile phone), a smart screen, an unmanned aerial vehicle, an intelligent connected vehicle (Intelligent Connected Vehicle; hereinafter referred to as the : ICV), intelligent (car) car (smart/intelligent car) or in-vehicle equipment and other equipment.
  • ICV Intelligent Connected Vehicle
  • car smart/intelligent car
  • the above-mentioned one or more computer programs are stored in the above-mentioned memory, and the above-mentioned one or more computer programs include instructions, when the above-mentioned instructions are executed by the above-mentioned equipment, the above-mentioned equipment is caused to perform the following steps: A. Obtain the first data, the said The first data includes a first state of charge, a first temperature, and a current; B. Based on the first state of charge, query in a preset first relationship table to obtain a first entropy heat coefficient, wherein the first The relationship table includes a mapping relationship between the state of charge and the entropy thermal coefficient; C.
  • the first data includes the second temperature, the second state of charge and the second terminal voltage after the preset time period
  • the second relationship table includes the state of charge, the temperature , the mapping relationship between the current and the internal resistance
  • the third relationship table includes the mapping relationship between the state of charge and the open-circuit voltage
  • the second terminal voltage is less than or equal to the preset voltage, and the remaining power is obtained based on the first state of charge and the second state of charge, and if the second terminal voltage is greater than the preset voltage, The first state of charge is set equal to the second state of charge, the first temperature is equal to the second temperature, and the steps B-E are repeated.
  • mapping relationship among the state of charge, temperature, current and internal resistance in the second relationship table satisfies the formula: Among them, R cc (SOC, T, I) is the internal resistance, SOC is the state of charge, T is the temperature, I is the current, U(SOC, T) is the terminal voltage, and OCV(SOC, T) is the open circuit voltage.
  • the second relationship table further includes a preset magnification correction coefficient, and the internal resistance is determined by the state of charge, temperature, current, and the preset magnification correction coefficient.
  • the second data further includes a first heat generation power, where the first heat generation power is obtained by calculating the reversible heat, the current and the first internal resistance, and the reversible heat Determined from the first data and the first entropy thermal coefficient, the first internal resistance is obtained by querying the second relationship table based on the first state of charge, the first temperature, and the current .
  • the second temperature is calculated and obtained from the preset time period, the first heat generation power, the first heat dissipation power, the first temperature, the specific heat capacity and mass of the battery, wherein , the first heat dissipation power is calculated and obtained from the first temperature, the preset ambient temperature, the heat dissipation coefficient and the surface area of the battery.
  • the first heat generation power is determined by the formula Calculated, where Pin is the heat generation power, I is the current, Rcc is the internal resistance, T is the temperature, is the entropy thermal coefficient, OCV is the open circuit voltage, for reversible heat.
  • the second temperature is determined by the formula Calculated, where T2 is the second temperature, T1 is the first temperature, P in is the heat generation power, P out is the heat dissipation power, ⁇ t is the preset time period, c is the specific heat capacity of the battery, and m is the quality of the battery.
  • the second terminal voltage is obtained by calculation from the second internal resistance, the second open circuit voltage and the current, wherein the second internal resistance is based on the second state of charge, the The second temperature and the current are obtained by querying the second relationship table, and the second open circuit voltage is obtained by querying the third relationship table based on the second state of charge.
  • the second state of charge is obtained by calculation from the preset time period, the first state of charge, the current, and the capacity of the battery.
  • the second state of charge is determined by the formula The calculation is obtained, wherein, SOC 2 is the second state of charge, SOC 1 is the first state of charge, cap is the capacity of the battery, ⁇ t is the preset time period, and I is the current.
  • the preset time period is determined by the capacity of the battery, the current and the state of charge interval.
  • the preset time period is determined by the formula It is obtained by calculation, wherein, cap is the capacity of the battery, ⁇ SOC is the state of charge interval of the battery, and I is the current.
  • the device when the above-mentioned instruction is executed by the above-mentioned device, so that the above-mentioned device executes the step A and obtains the first data, the device further executes the following steps: A1. Obtain the first terminal voltage and current; A2. Detect whether the current is less than or equal to the preset current; A3. If the current is less than or equal to the preset current, query the third relationship table based on the first open-circuit voltage, and obtain the first state of charge, wherein the first open circuit voltage is determined by the first terminal voltage.
  • the above-mentioned device when executed by the above-mentioned device, the above-mentioned device is caused to further perform the following steps: F1. Obtain third data, where the third data includes a third state of charge, a third temperature and a current ; F2, obtain a fourth temperature based on the third data and the preset discharge value; F3, discharge from the third state of charge to the fourth state of charge based on the preset discharge value; F4, detect the the fifth temperature in the fourth state of charge; F5, obtain the difference between the fourth temperature and the fifth temperature; F6, if the difference is greater than a preset threshold, based on the fourth temperature The temperature, the fifth temperature, the preset heat dissipation initial value and the preset correction value determine the heat dissipation coefficient of the battery.
  • the preset threshold is less than or equal to 2°C.
  • the preset correction value is in the range of 0.3 to 0.7.
  • the preset discharge value is 5% to 10% discharge percentage.
  • the preset voltage is determined based on the material system of the battery.
  • the battery is selected from: the material system is a lithium cobalt oxide system and the preset voltage is in the range of 3.0V to 3.4V, the material system is a ternary material system and the preset voltage In the range of 2.8V to 3.2V, the material system is one of a lithium iron phosphate system and the preset voltage is in the range of 2.5V to 2.9V.
  • the remaining power is determined by the formula The calculation is obtained, wherein, RM is the remaining power, SOC 1 is the first state of charge, and SOC 2 is the second state of charge.
  • the electronic device shown in FIG. 12 may be a terminal device or a circuit device built in the above-mentioned terminal device.
  • the device can be used to execute the functions/steps in the method provided by the embodiment shown in FIG. 1 of the present application.
  • the electronic device 900 includes a processor 910 and a transceiver 920 .
  • the electronic device 900 may further include a memory 930 .
  • the processor 910, the transceiver 920 and the memory 930 can communicate with each other through an internal connection path to transmit control and/or data signals. Invoke and run the computer program.
  • the above-mentioned memory 930 can be a read-only memory (read-only memory, ROM), other types of static storage devices that can store static information and instructions, random access memory (random access memory, RAM), or other types of static storage devices that can store information and instructions.
  • types of dynamic storage devices which can also be electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), or other optical storage, CD-ROM storage (including compact discs, laser discs, optical discs, digital versatile discs, blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or may also be capable of carrying or storing desired program code in the form of instructions or data structures and any other medium that can be accessed by a computer, etc.
  • EEPROM electrically erasable programmable read-only memory
  • CD-ROM compact disc read-only memory
  • CD-ROM storage including compact discs, laser discs, optical discs, digital versatile discs, blu-ray discs, etc.
  • the electronic device 900 may further include an antenna 940 for sending out the wireless signal output by the transceiver 920 .
  • the above-mentioned processor 910 and the memory 930 can be combined into a processing device, and more commonly, they are independent components, and the processor 910 is used to execute the program codes stored in the memory 930 to realize the above-mentioned functions.
  • the memory 930 may also be integrated in the processor 910 , or be independent of the processor 910 .
  • the electronic device 900 may further include one or more of an input unit 960, a display unit 970, an audio circuit 980, a camera 990, a sensor 901, etc., the audio The circuit may also include a speaker 982, a microphone 984, and the like.
  • the display unit 970 may include a display screen.
  • the above electronic device 900 may further include a power supply 950 for providing power to various devices or circuits in the terminal device.
  • the electronic device 900 shown in FIG. 12 can implement each process of the method provided by the embodiment shown in FIG. 1 of the present application.
  • the operations and/or functions of each module in the electronic device 900 are respectively to implement the corresponding processes in the foregoing method embodiments.
  • processor 910 in the electronic device 900 shown in FIG. 12 may be a system-on-chip SOC, and the processor 910 may include a central processing unit (Central Processing Unit; hereinafter referred to as: CPU), and may further include other types of A processor, for example: a graphics processor (Graphics Processing Unit; hereinafter referred to as: GPU), etc.
  • CPU Central Processing Unit
  • GPU Graphics Processing Unit
  • each part of the processors or processing units inside the processor 910 can cooperate to implement the previous method process, and the corresponding software programs of each part of the processors or processing units can be stored in the memory 930 .
  • the present application also provides an electronic device, the device includes a storage medium and a central processing unit, the storage medium may be a non-volatile storage medium, and a computer-executable program is stored in the storage medium, and the central processing unit is connected to the central processing unit.
  • the non-volatile storage medium is connected, and the computer-executable program is executed to implement the method provided by the embodiment shown in FIG. 1 of the present application.
  • the involved processors may include, for example, a CPU, a DSP, a microcontroller or a digital signal processor, and may also include a GPU, an embedded neural-network process unit (Neural-network Process Units; hereinafter referred to as: NPU) and Image signal processor (Image Signal Processing; hereinafter referred to as: ISP), the processor may also include necessary hardware accelerators or logic processing hardware circuits, such as ASIC, or one or more integrated circuits for controlling the execution of the program of the technical solution of the present application circuit, etc. Furthermore, the processor may have the function of operating one or more software programs, which may be stored in a storage medium.
  • Embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when it runs on a computer, causes the computer to execute the method provided by the embodiment of the first aspect of the present application.
  • Embodiments of the present application further provide a computer program product, where the computer program product includes a computer program that, when running on a computer, causes the computer to execute the method provided by the embodiments of the first aspect of the present application.
  • “at least one” refers to one or more, and “multiple” refers to two or more.
  • “And/or”, which describes the association relationship of the associated objects means that there can be three kinds of relationships, for example, A and/or B, which can indicate the existence of A alone, the existence of A and B at the same time, and the existence of B alone. where A and B can be singular or plural.
  • the character “/” generally indicates that the associated objects are an “or” relationship.
  • “At least one of the following” and similar expressions refer to any combination of these items, including any combination of single or plural items.
  • At least one of a, b, and c may represent: a, b, c, a and b, a and c, b and c or a and b and c, where a, b, c may be single, or Can be multiple.
  • any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • the technical solution of the present application can be embodied in the form of a software product in essence, or the part that contributes to the prior art or the part of the technical solution, and the computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application.
  • the aforementioned storage medium includes: U disk, mobile hard disk, Read-Only Memory (Read-Only Memory; hereinafter referred to as: ROM), Random Access Memory (Random Access Memory; hereinafter referred to as: RAM), magnetic disk or optical disk and other various A medium on which program code can be stored.
  • ROM Read-Only Memory
  • RAM Random Access Memory
  • magnetic disk or optical disk and other various A medium on which program code can be stored.

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Abstract

本申请提供一种电量预测方法和设备，所述方法包括：A、获取第一数据，所述第一数据包括第一荷电状态、第一温度和电流；B、基于所述第一荷电状态在预设第一关系表中进行查询，获得第一熵热系数；C、基于预设时间段、所述第一数据、所述第一熵热系数、预设第二关系表和预设第三关系表，获得第二数据，所述第二数据包括第二温度、第二荷电状态和第二端电压；D、检测所述第二端电压是否小于或等于预设电压；E、若所述第二端电压小于或等于所述预设电压，基于所述第一荷电状态和所述第二荷电状态，获得剩余电量，若所述第二端电压大于所述预设电压，令所述第一荷电状态等于所述第二荷电状态，所述第一温度等于所述第二温度，重复所述步骤B-E。

Description

电量预测方法和设备 技术领域
本申请涉及智能终端技术领域，特别涉及一种电量预测方法和设备。
背景技术
目前电子设备如手机、电脑或电动车等普遍采用锂离子电池，其中，电量显示是电子设备的常用功能之一。目前，由于电量计算方法过于简单，电量预测的误差较高，准确度较低，影响电子设备剩余电量的显示，例如，电量显示发生跳变，电量显示在20％-30％时电子设备直接关机，或在低温环境下关机等，使得用户使用体验度较差。
现有的电量预测方法中，只考虑了电池内阻随温度和电荷状态的变化关系，并未考虑电池内阻随放电电流的变化关系，然而，随温度降低，电流对电池内阻的影响越大，因此导致得到的电池内阻数据不够准确，从而影响放电容量的估算和剩余电量的终端显示。
另外，在现有的电量预测方法中，基于电池温度随时间线性单调地变化规律或者采用经验公式，预测电池温度，由于在低温下，电池内阻增大，导致电池放电温度增大，使得现有方法中预测得到的电池温度的误差偏大，如温度误差可达5℃以上，使得预测剩余电量的误差较大。
目前，现有的电量预测方法中，由于获得的电池内阻数据不够准确和预测电池温度的误差较大，导致预测剩余电量的误差较大，且温度越低，该误差越大，例如，在0℃时，该误差达到8％左右，在-10℃时，该误差达到10％左右，因此易造成低温下自动关机的现象，给用户使用带来不便。
发明内容
本申请提供了一种电量预测方法和设备，能够降低电量预测误差，提高电量显示的准确度，有利于减少低温下自动关机的现象发生。
第一方面，本申请提供了一种电量预测方法，包括：A、获取第一数据，所述第一数据包括第一荷电状态、第一温度和电流；B、基于所述第一荷电状态在预设第一关系表中进行查询，获得第一熵热系数，其中，所述第一关系表包括荷电状态与熵热系数之间的映射关系；C、基于预设时间段、所述第一数据、所述第一熵热系数、预设第二关系表和预 设第三关系表，获得第二数据，其中，所述第二数据包括所述预设时间段后的第二温度、第二荷电状态和第二端电压，所述第二关系表包括荷电状态、温度、电流和内阻之间的映射关系，所述第三关系表包括荷电状态和开路电压之间的映射关系；D、检测所述第二端电压是否小于或等于预设电压；E、若所述第二端电压小于或等于所述预设电压，基于所述第一荷电状态和所述第二荷电状态，获得剩余电量，若所述第二端电压大于所述预设电压，令所述第一荷电状态等于所述第二荷电状态，所述第一温度等于所述第二温度，重复所述步骤B-E。
其中一种可能的实现方式中，所述第二关系表中的荷电状态、温度、电流和内阻之间的映射关系满足公式：
Figure PCTCN2020133314-appb-000001
其中，R cc(SOC,T,I)为内阻，SOC为荷电状态，T为温度，I为电流，U(SOC,T)为端电压，OCV(SOC,T)为开路电压。
其中一种可能的实现方式中，所述第二关系表还包括预设倍率修正系数，所述内阻由荷电状态、温度、电流和所述预设倍率修正系数确定。
其中一种可能的实现方式中，所述第二数据还包括第一产热功率，其中，所述第一产热功率由可逆热、所述电流和第一内阻计算获得，所述可逆热由所述第一数据和所述第一熵热系数确定，所述第一内阻基于所述第一荷电状态、所述第一温度、所述电流在所述第二关系表中查询获得。
其中一种可能的实现方式中，所述第二温度由所述预设时间段、所述第一产热功率、第一散热功率、所述第一温度、电池的比热容和质量计算获得，其中，所述第一散热功率由所述第一温度、预设环境温度、所述电池的散热系数和表面积计算获得。
其中一种可能的实现方式中，所述第一产热功率由公式
Figure PCTCN2020133314-appb-000002
计算获得，其中，Pin为产热功率，I为电流、Rcc为内阻，T为温度，
Figure PCTCN2020133314-appb-000003
为熵热系数，OCV为开路电压，
Figure PCTCN2020133314-appb-000004
为可逆热。
其中一种可能的实现方式中，所述第二温度由公式
Figure PCTCN2020133314-appb-000005
计算获得，其中，T 2为第二温度，T 1为第一温度，P in为产热功率，P out为散热功率，Δt为预设时间段，c为所述电池的比热容，m为所述电池的质量。
其中一种可能的实现方式中，所述第一散热功率由公式P out＝hS(T-T en)计算获得，其中，P out为散热功率，h为所述电池的散热系数，S为所述电池的表面积，T为温度，T en为预设环境温度。
其中一种可能的实现方式中，所述第二端电压由第二内阻、第二开路电压和所述电流 计算获得，其中，所述第二内阻基于所述第二荷电状态、所述第二温度和所述电流在所述第二关系表中查询获得，所述第二开路电压基于所述第二荷电状态在所述第三关系表中查询获得。
其中一种可能的实现方式中，所述第二端电压由公式U(SOC 2)＝OCV(SOC 2)+IR CC(SOC 2,T 2,I)计算获得，其中，U(SOC 2)为第二端电压，OCV(SOC 2)为第二开路电压，I为电流，R CC(SOC 2,T 2,I)为第二内阻，SOC 2为第二荷电状态，T 2为第二温度。
其中一种可能的实现方式中，所述第二荷电状态由所述预设时间段、所述第一荷电状态、所述电流、电池的容量计算获得。
其中一种可能的实现方式中，所述第二荷电状态由公式
Figure PCTCN2020133314-appb-000006
计算获得，其中，SOC 2为第二荷电状态，SOC 1为第一荷电状态，cap为所述电池的容量，Δt为预设时间段，I为电流。
其中一种可能的实现方式中，所述预设时间段由电池的容量、所述电流和荷电状态间隔确定。
其中一种可能的实现方式中，所述预设时间段由公式
Figure PCTCN2020133314-appb-000007
计算获得，其中，cap为所述电池的容量，ΔSOC为所述电池的荷电状态间隔，I为电流。
其中一种可能的实现方式中，所述步骤A、获取所述第一数据，包括：A1、获取第一端电压和电流；A2、检测所述电流是否小于或等于预设电流；A3、若所述电流小于或等于所述预设电流，则基于第一开路电压在所述第三关系表中进行查询，获得所述第一荷电状态，其中，所述第一开路电压由所述第一端电压确定。
其中一种可能的实现方式中，所述方法还包括：F1、获取第三数据，所述第三数据包括第三荷电状态、第三温度和电流；F2、基于所述第三数据和预设放电值，获得第四温度；F3、基于所述预设放电值，从所述第三荷电状态放电至第四荷电状态；F4、检测所述第四荷电状态下的第五温度；F5、获得所述第四温度和所述第五温度之间的差值；F6、若所述差值大于预设阈值，则基于所述第四温度、所述第五温度、预设散热初值和预设修正值，确定所述电池的散热系数。
其中一种可能的实现方式中，所述散热系数由公式h＝h 0+(T sim-T test)*b计算获得，其中，h为散热系数，h 0为预设散热初值，T sim为所述第四温度，T test为所述第五温度，b为预设修正值。
其中一种可能的实现方式中，所述预设阈值小于或等于2℃。
其中一种可能的实现方式中，所述预设修正值在0.3至0.7范围内。
其中一种可能的实现方式中，所述预设放电值为5％至10％放电百分比。
其中一种可能的实现方式中，所述预设电压基于电池的材料体系确定。
其中一种可能的实现方式中，所述电池选自：材料体系为钴酸锂体系和所述预设电压在3.0V至3.4V范围内，材料体系为三元材料体系和所述预设电压在2.8V至3.2V范围内，材料体系为磷酸铁锂体系和所述预设电压在2.5V至2.9V范围内中的其中一种。
其中一种可能的实现方式中，所述剩余电量由公式
Figure PCTCN2020133314-appb-000008
计算获得，其中，RM为剩余电量，SOC 1为第一荷电状态，SOC 2为第二荷电状态。
第二方面，本申请提供了一种电量预测装置，包括：第一数据获取模块，用于获取第一数据，所述第一数据包括第一荷电状态、第一温度和电流；熵热系数获得模块，用于基于所述第一荷电状态在预设第一关系表中进行查询，获得第一熵热系数，其中，所述第一关系表包括荷电状态与熵热系数之间的映射关系；计算模块，用于基于预设时间段、所述第一数据、所述第一熵热系数、预设第二关系表和预设第三关系表，获得第二数据，其中，所述第二数据包括所述预设时间段后的第二温度、第二荷电状态和第二端电压，所述第二关系表包括荷电状态、温度、电流和内阻之间的映射关系，所述第三关系表包括荷电状态和开路电压之间的映射关系；检测模块，用于检测所述第二端电压是否小于或等于预设电压；循环判断模块，用于若所述第二端电压小于或等于所述预设电压，基于所述第一荷电状态和所述第二荷电状态，获得剩余电量，若所述第二端电压大于所述预设电压，令所述第一荷电状态等于所述第二荷电状态，所述第一温度等于所述第二温度，并由所述熵热系数获得模块、所述计算模块、所述检测模块和所述循环判断模块进行循环处理。
第三方面，本申请提供一种电子设备，包括：显示屏；一个或多个处理器；存储器；多个应用程序；以及一个或多个计算机程序，其中所述一个或多个计算机程序被存储在所述存储器中，所述一个或多个计算机程序包括指令，当所述指令被所述设备执行时，使得所述设备执行如第一方面所述的方法。
第四方面，本申请提供一种计算机可读存储介质，该计算机可读存储介质中存储有计算机程序，当其在计算机上运行时，使得计算机执行如第一方面所述的方法。
第五方面，本申请提供一种计算机程序，当所述计算机程序被计算机执行时，用于执行第一方面所述的方法。
在一种可能的设计中，第五方面中的程序可以全部或者部分存储在与处理器封装在一起的存储介质上，也可以部分或者全部存储在不与处理器封装在一起的存储器上。
本申请提供的电量预测方法和设备，能够降低电量预测误差，提高电量显示的准确度， 有利于减少低温下自动关机的现象发生。
附图说明
图1为本申请电量预测方法一个实施例的方法示意图；
图2为本申请电量预测方法一个实施例的流程示意图；
图3(a)为现有技术中未考虑可逆热预测电量的精度偏差表；
图3(b)为本申请电量预测方法中考虑可逆热预测电量的精度偏差表；
图4为本申请电量预测方法一个实施例的修正散热系数的流程示意图；
图5为本申请电量预测方法一个实施例修正散热系数的预测温度表；
图6(a)为现有技术中未修正散热系数的在环境温度从40℃变到-10℃时预测电量的精度偏差表；
图6(b)为本申请电量预测方法中修正散热系数的在环境温度从40℃变到-10℃时预测电量的精度偏差表；
图7为本申请电量预测方法中一个实施例的等效电路模型图；
图8(a)为本申请电量预测方法中在-15℃环境下未采用预设倍率修正系数对内阻进行修正的预测内阻与测试值的比较；
图8(b)为本申请电量预测方法中在-15℃温度下采用预设倍率修正系数对内阻进行修正的预测内阻与测试值的比较；
图9为本申请电量预测方法中一个实施例的预设倍率修正系数表；
图10(a)为现有技术中在-20℃环境下预测电量的精度偏差表；
图10(b)为本申请电量预测方法中未采用预设倍率修正系数对内阻进行修正的在-20℃环境下预测电量的精度偏差表；
图10(c)为本申请电量预测方法中采用预设倍率修正系数对内阻进行修正的在-20℃环境下预测电量的精度偏差表；
图11为本申请电量预测装置一个实施例的结构示意图；
图12为本申请电子设备一个实施例的结构示意图。
具体实施方式
本申请的实施方式部分使用的术语仅用于对本申请的具体实施例进行解释，而非旨在限定本申请。
本申请提出一种电量预测方法和设备，能够降低电量预测误差，提高电量显示的准确度，有利于减少低温下自动关机的现象发生。
为降低对剩余电量的预测误差，本申请对影响预测剩余电量误差的因素作出了分析。
其一，在现有技术中，基于电池温度随时间单调地线性变化规律或者采用经验公式，预测电池温度。然而，由于在充放电时，影响电池温度变化的因素包括环境温度、电流、散热情况以及电荷状态等，而不是随时间线性单调地增加或降低，因此，现有技术中无法涵盖电池在不同环境工况下的温度变化规律，导致对电池温度的预测误差较大。特别地，在低温环境下，由于电池内阻较大，因而电池放电时温度上升也比较大，使得在低温下对电池温度的预测误差更大，从而导致预测剩余电量的误差增大。因此，在本申请中，更加合理地和准确地考虑了影响电池在充放电时温度变化规律的因素，有利于降低对电池温度的预测误差，以降低预测剩余电量的误差。
其二，在现有技术中，只考虑了电池内阻随温度和电荷状态的变化关系，并未考虑电池内阻随放电电流的变化关系。然而，由于电池内部发生的物理化学反应十分复杂，其伏安特性曲线并不像纯电阻一样的是一条直线，而是一条曲线，电池内阻会随着电流的变化而变化，且随温度降低，电流对电池内阻的影响越大，因此导致得到的电池内阻数据不够准确，从而影响放电容量的估算和剩余电量的终端显示。因此，在本申请中，考虑了电池内阻与电流、温度和荷电状态之间的关系，有利于降低预测电池内阻的误差，以降低预测剩余电量的误差。
图1为本申请电量预测方法一个实施例的方法示意图，如图1和图2所示，上述电量预测方法可以包括：
S101、获取第一数据，所述第一数据包括第一荷电状态、第一温度和电流。
在本实施例中，所述第一荷电状态可以包括电池的初始荷电状态(即在0时刻的荷电状态)或某一时刻的荷电状态。所述第一温度可以采用温度传感器采集获得，所述电流可以采用电流传感器采集获得，所述传感器的采样频率可以包括1Hz。
其中一种可能的实现方式中，所述步骤S101中，包括：
S201、获取第一端电压和电流；
S202、检测所述电流是否小于或等于预设电流；
S203、若所述电流小于或等于所述预设电流，则基于第一开路电压在所述第三关系表中进行查询，获得所述第一荷电状态，其中，所述第一开路电压由所述第一端电压确定。
在本实施例中，所述第一端电压可以采用电压传感器采集获得。所述预设电流可以包 括50mA电流，即，当所述电流小于或等于50mA时，判定所述第一开路电压等于所述第一端电压，所述初始荷电状态基于所述第一开路电压在所述第三关系表中进行查询获得。当所述电流大于50mA时，判定所述第一开路电压仍等于在电流小于或等于50mA时的第一端电压，当电流再次小于或等于50mA时，所述第一端电压由电压传感器重新采集获得，判定所述第一开路电压等于重新采集获得的所述第一端电压。
需要指出的是，所述第三关系表可以包括电池的荷电状态和开路电压之间的映射关系。
值得一提的是，在某一时刻t时，所述第一荷电状态可以基于所述电流、所述时刻t、所述电池的容量以及所述初始荷电状态计算获得，具体地，所述第一荷电状态可以由公式
Figure PCTCN2020133314-appb-000009
计算获得，其中，SOC 1为第一荷电状态，SOC 0为初始荷电状态，cap为电池的容量，I为电流。在所述时刻t时，所述第一开路电压由所述第一荷电状态在所述第三关系表中查询获得，即所述电池当前状态的所述第一开路电压由当前状态的第一荷电状态在所述第三关系表中查询获得。
S102、基于所述第一荷电状态在预设第一关系表中进行查询，获得第一熵热系数，其中，所述第一关系表包括荷电状态与熵热系数之间的映射关系。
可以理解的是，所述第一荷电状态为所述电池当前状态的荷电状态，所述第一熵热系数为所述电池当前状态的熵热系数。
S103、基于预设时间段、所述第一数据、所述第一熵热系数、预设第二关系表和预设第三关系表，获得第二数据，其中，所述第二数据包括所述预设时间段后的第二温度、第二荷电状态和第二端电压，所述第二关系表包括荷电状态、温度、电流和内阻之间的映射关系。
所述第一关系表、所述第二关系表以及所述第三关系表均可以通过实验测试获得，并预存于所述电子设备中。
其中一种可能的实现方式中，所述第二数据还包括第一产热功率，其中，所述第一产热功率由可逆热、所述电流和第一内阻计算获得，所述可逆热由所述第一数据和所述第一熵热系数确定，所述第一内阻基于所述第一荷电状态、所述第一温度、所述电流在所述第二关系表中查询获得。
其中一种可能的实现方式中，所述第二温度由所述预设时间段、所述第一产热功率、第一散热功率、所述第一温度、电池的比热容和质量计算获得，其中，所述第一散热功率由所述第一温度、预设环境温度、所述电池的散热系数和表面积计算获得。
其中一种可能的实现方式中，所述第一产热功率由公式
Figure PCTCN2020133314-appb-000010
计算获得， 其中，Pin为产热功率，I为电流，Rcc为内阻，T为温度，
Figure PCTCN2020133314-appb-000011
为熵热系数，OCV为开路电压，
Figure PCTCN2020133314-appb-000012
为可逆热。
具体地，由能量守恒定律和物理化学相关知识可知，电池正常充放电时，其总体产热功率可以用产热速率方程计算获得，即
Figure PCTCN2020133314-appb-000013
其中，P in为产热功率，I为充放电电流，在充电时电流符号为正，在放电时电流符号为负，Rcc为内阻，T为温度，
Figure PCTCN2020133314-appb-000014
为熵热系数。
可以看出的是，所述电池的产热功率可以分为不可逆热和可逆热两个部分，其中，不可逆热部分为I 2R cc，其值恒大于0，在充放电过程中表现为完全放热行为，能量被完全浪费掉，可逆热部分为
Figure PCTCN2020133314-appb-000015
所述可逆热部分与所述电池内部材料结构变化或熵变有关(如脱嵌锂过程中材料结构变化或熵变)，若充电时其值为正，则放电时其值为负，在一个充放电循环过程中，总的可逆热为0，所述可逆热部分的热量是可逆的。
需要指出的是，散热系数
Figure PCTCN2020133314-appb-000016
是电池开路电压对温度的偏导，具体地，在不同温度下，测试得到的电池开路电压，然后以差分代替微分求出所述熵热系数，温度点越密集，结果越精确。
其中一种可能的实现方式中，所述第二温度由公式
Figure PCTCN2020133314-appb-000017
计算获得，其中，T 2为第二温度，T 1为第一温度，P in为产热功率，P out为散热功率，Δt为预设时间段，c为所述电池的比热容，m为所述电池的质量。
所述电池的比热容c可以通过加速量热仪器测试得到。所述电池的体积V可以根据所述电池的长宽高计算获得。所述电池的质量m可以通过天平测得，所述电池的密度ρ可以根据所述电池的质量m除以体积V得到。
具体地，在物体受热后，其温升和传热过程服从傅里叶定律，即热传导偏微分方程为：
Figure PCTCN2020133314-appb-000018
其中，c，ρ，V，k，P in和P out分别为物体的比热容，密度，体积，导热系数，产热功率和散热功率。通过数值或解析方法求解所述偏微分方程后，即可得到物体在任意时刻任意位置的温度。
由于在所述偏微分方程中包含一个拉普拉斯算符，在实际求解时需要对空间求二阶导数，这一步的计算量较大，处理器(单片机)的计算时长一般在10s以上，不利实际应用。
因此，在本实施例中，考虑到电池内部的活性物质分布比较均匀，其在充放电过程中的产热也会比较均匀。因此，电池内部的温度分布将会比较均匀，各处温度差异较小(如消费类锂离子电池一般在1℃以内，功率型锂离子电池一般在2℃以内)，该差异并未对电池的剩余电量的预测产生的明显的影响，因此，在本实施例中，所述电池各处的温度可以相同，则有：
Figure PCTCN2020133314-appb-000019
因此，所述偏微分方程可以简化为：
Figure PCTCN2020133314-appb-000020
利用一阶差分法将所述偏微分方程转化为代数方程进行数值求解，得到
Figure PCTCN2020133314-appb-000021
移位得出迭代方程，进行迭代求解，得到
Figure PCTCN2020133314-appb-000022
可以看出的是，所述第二温度可以由所述第一温度代入该公式计算获得。预测电池温度的准确性主要取决于电池的产热功率P in和散热功率P out的准确性。在处理器(如单片机)计算电池温度时，无需对空间求二阶导数，运算时间可以达到10ms左右，甚至更低，有利于实际应用。
其中一种可能的实现方式中，所述第一散热功率由公式P out＝hS(T-T en)计算获得，其中，P out为散热功率，h为所述电池的散热系数，S为所述电池的表面积，T为温度，T en为预设环境温度。
所述预设环境温度为所述电池在未使用时测得的温度。所述温度T为所述第一温度T 1。
具体地，由传热学的基本理论可知，在一定的对流条件下，物体的散热服从牛顿冷却定律，即当物体表面与周围温度存在温度差时，单位时间从单位面积散失的热量与温度差成正比，比例系数为热传递系数或散热系数，因此，所述电池的散热功率可以采用上式计算获得，其中，散热系数可以是一个经验系数，主要与物体表面的空气流速有关。例如，在自然对流的情况下，散热系数h的值一般在3～10W/(m 2*K)，而在强对流环境下，散热系数h可达30～50W/(m 2*K)。
在本实施例中，由于电池被安装于电子设备的内部，并没有直接与外界空气接触，而是先把热量传导至电子设备，进而散失在空气中，因此，采用上式计算获得的所述散热功 率存在一定的误差。在本实施例中，为了不增加处理器(如单片机)的计算负担，仍采用上式计算获得所述散热功率，并对散热系数h进行修正，以降低所述散热功率的误差。
其中一种可能的实现方式中，所述方法还包括：S301、获取第三数据，所述第三数据包括第三荷电状态、第三温度和电流；S302、基于所述第三数据和预设放电值，获得第四温度；S303、基于所述预设放电值，从所述第三荷电状态放电至第四荷电状态；S304、检测所述第四荷电状态下的第五温度；S305、获得所述第四温度和所述第五温度之间的差值；S306、若所述差值大于预设阈值，则基于所述第四温度、所述第五温度、预设散热初值和预设修正值，确定所述电池的散热系数。
也就是说，由于电子设备所处的环境是复杂多变的，一般情况下很难预测电池的散热系数h的值，因此，本实施例中的所述方法能够对所述电池的散热系数进行修正，以适应环境的变化，以降低所述散热功率的误差。
其中一种可能的实现方式中，所述散热系数由公式h＝h 0+(T sim-T test)*b计算获得，其中，h为散热系数，h 0为预设散热初值，T sim为所述第四温度，T test为所述第五温度，b为预设修正值。
具体地，所述预设散热初值h 0基于所述电池在常见环境工况下的散热系数确定。所述常见环境工况可以包括环境温度为25℃和无风的环境工况。也就是说，所述电池在所述常见环境工况下的散热系数作为所述预设散热初值。
如图4为本申请电量预测方法一个实施例的修正散热系数的流程示意图。在所述步骤S302中，预测所述电池从当前荷电状态(如所述第三荷电状态)放电至某一荷电状态(如所述第四荷电状态)时的温度，得到所述第四温度。所述第四温度可以被存储。在所述步骤S303和所述步骤S304中，所述电池从所述第三荷电状态放电一段时间后，刚好达到所述第四荷电状态时，采用温度传感器采集所述电池此刻的实际温度为所述第五温度。所述第四温度和所述第五温度之间的差值error＝(T sim-T test)，所述差值可以取正值，如绝对值。
在所述步骤S306中，若所述第四温度和所述第五温度之间的差值error低于所述预设阈值，则所述散热系数无需修正，输出所述散热系数为所述电池的散热系数，若所述差值error大于所述预设阈值，则采用公式h＝h 0+(T sim-T test)*b对所述散热系数进行修正，并迭代求解确定所述电池的散热系数。
具体地，若所述差值大于所述预设阈值，则令所述第三温度等于所述第五温度，所述第三荷电状态等于所述第四荷电状态，所述初始散热初值h 0等于所述散热系数h，重复执行所述步骤S302至步骤S306，直到所述差值小于或等于所述预设阈值时，输出所述电池 的散热系数。
其中一种可能的实现方式中，所述预设阈值小于或等于2℃，或者，所述预设阈值可以在1℃至2℃范围内。如图5为本申请电量预测方法一个实施例修正散热系数的预测温度表，表中左侧纵坐标为电压(voltage)单位为(mV)，右侧纵坐标为温度(temperature)单位为(℃)，表中包括预测温升曲线和实测温升曲线，可以看出的是，本申请中修正散热系数的预测温度的误差可以由3℃降低到1.5℃，提高了预测电池温度的准确度，降低了预测剩余电量的误差。
其中一种可能的实现方式中，所述预设修正值在0.3至0.7范围内。
其中一种可能的实现方式中，所述预设放电值为5％至10％放电百分比，所述电池的放电间隔可以预设为2％至10％放电百分比。
值得一提的是，在本实施例中，在计算获得所述电池的产热功率中，考虑了电池可逆热的影响，提高所述产热功率的准确度，降低了预测所述电池温度的误差，从而降低了预测所述电池剩余电量的误差。例如，图3(a)为现有技术中未考虑可逆热预测电量的精度偏差表，横坐标为时间(s)，纵坐标为剩余电量(SOC)偏差百分比(％)，在-10℃环境下，通过现有技术(如利用TI算法预测电池剩余电量的方法)与本实施例中电量预测方法进行对比，现有的TI算法预测得到电池剩余电量的误差在8％左右，图3(b)为本申请电量预测方法中考虑可逆热预测电量的精度偏差表，横坐标为时间(s)，纵坐标为剩余电量(SOC)偏差百分比(％)，在本实施例中的所述电量预测方法预测电池剩余电量的误差降低至3％左右。
在本实施例中，在计算获得所述电池的散热功率中，不断地对所述电池的散热系数进行修正，从而不断地对预测温度(如所述第二温度)进行修正，提高了所述散热功率的准确度，进一步地降低了预测所述电池温度的误差，从而进一步降低了预测所述电池剩余电量的误差。例如，图6(a)为现有技术中未修正散热系数的在环境温度从40℃变到-10℃时预测电量的精度偏差表，横坐标为时间(s)，纵坐标为剩余电量(SOC)偏差百分比(％)，在环境温度从40℃变到-10℃的环境工况下，通过现有技术(如利用TI算法预测电池剩余电量的方法)与本实施例中电量预测方法进行对比，现有的TI算法预测得到电池剩余电量的误差在7％左右，图6(b)为本申请电量预测方法中修正散热系数的在环境温度从40℃变到-10℃时预测电量的精度偏差表，横坐标为时间(s)，纵坐标为剩余电量(SOC)偏差百分比(％)，在本实施例中的所述电量预测方法预测电池剩余电量的误差降低至3％以内。
其中一种可能的实现方式中，所述第二端电压由第二内阻、第二开路电压和所述电流 计算获得，其中，所述第二内阻基于所述第二荷电状态、所述第二温度和所述电流在所述第二关系表中查询获得，所述第二开路电压基于所述第二荷电状态在所述第三关系表中查询获得。
其中一种可能的实现方式中，图7为本申请电量预测方法中一个实施例的等效电路模型图，基于该等效电路，所述第二端电压可以由公式U(SOC 2)＝OCV(SOC 2)+IR CC(SOC 2,T 2,I)计算获得，其中，U(SOC 2)为第二端电压，OCV(SOC 2)为第二开路电压，I为电流，R CC(SOC 2,T 2,I)为第二内阻，SOC 2为第二荷电状态，T 2为第二温度。
其中一种可能的实现方式中，所述第二荷电状态由所述预设时间段、所述第一荷电状态、所述电流、所述电池的容量计算获得。
其中一种可能的实现方式中，所述第二荷电状态由公式
Figure PCTCN2020133314-appb-000023
计算获得，其中，SOC 2为第二荷电状态，SOC 1为第一荷电状态，cap为所述电池的容量，Δt为预设时间段，I为电流。
其中一种可能的实现方式中，所述预设时间段由电池的容量、所述电流和荷电状态间隔确定。通常情况下，所述预设时间段若过大，则会影响预测所述电池温度的精度，若所述预设时间段过小，则会增加处理器的计算负担。因此，在本实施例中，所述预设时间段随电流的变化而变化。
其中一种可能的实现方式中，所述预设时间段由公式
Figure PCTCN2020133314-appb-000024
计算获得，其中，cap为所述电池的容量，ΔSOC为所述电池的荷电状态间隔，I为电流。
S104、检测所述第二端电压是否小于或等于预设电压。
其中一种可能的实现方式中，所述预设电压为所述电池的放电截止电压U 0，所述预设电压基于电池的材料体系确定。所述放电截止电压表示所述电子设备的最低电量保护机制设定的电压。
其中一种可能的实现方式中，所述电池选自：材料体系为钴酸锂体系和所述预设电压在3.0V至3.4V范围内，材料体系为三元材料体系和所述预设电压在2.8V至3.2V范围内，材料体系为磷酸铁锂体系和所述预设电压在2.5V至2.9V范围内中的其中一种。当然，熟知本领域的技术人员可知，所述电池的材料体系还可以包括其他种类的材料体系，在此不受限制。
S105、若所述第二端电压小于或等于所述预设电压，基于所述第一荷电状态和所述第二荷电状态，获得剩余电量，若所述第二端电压大于等于所述预设电压，令所述第一荷电状态等于所述第二荷电状态，所述第一温度等于所述第二温度，重复所述步骤S102至所 述步骤S105。
也就是说，当所述第二端电压大于等于所述预设电压时，令T 1＝T 2，SOC 1＝SOC 2，计算下一个预设时间段后的所述电池的温度、荷电状态以及端电压，然后再次判断所述端电压是否小于或等于所述预设电压，若大于，继续重复所述步骤S102至所述步骤S105，直至下一个预设时间段后的所述电池的端电压小于或等于所述预设电压时，获得所述电池的剩余电量。
其中一种可能的实现方式中，所述剩余电量由公式
Figure PCTCN2020133314-appb-000025
计算获得，其中，RM为剩余电量，SOC 1为第一荷电状态，SOC 2为第二荷电状态。
在本实施例中，如果预测所述电池的内阻(如所述第一内阻和所述第二内阻)不准确，即使预测所述电池温度可以达到百分百的准确度，也会导致预测所述电池的剩余电量存在较大误差。在现有技术中，只考虑了电池内阻随温度和电荷状态的变化关系，并未考虑电池内阻随放电电流的变化关系。然而，特别在低温下，放电电流对电池内阻的影响明显较大，因此，在本实施例中，所述第二关系表包括荷电状态、温度、电流和内阻之间的映射关系，考虑了不同放电电流对电池内阻的影响，以提高预测所述电池内阻的准确度，降低预测所述电池剩余电量的误差。
其中一种可能的实现方式中，所述第二关系表中的荷电状态、温度、电流和内阻之间的映射关系满足公式：
Figure PCTCN2020133314-appb-000026
其中，R cc(SOC,T,I)为内阻，SOC为荷电状态，T为温度，I为电流，U(SOC,T)为端电压，OCV(SOC,T)为开路电压。
进一步地，本实施例还提供了电池内阻的测试方法，包括：
S401、将电流预设为0.1c放电倍率，即电流I＝0.1*cap[1/h]。
所述电流还可以被预设为0.2c或0.5c等放电倍率。
S402、在预设环境温度区间内分别测试获得电池内阻。
所述预设环境温度区间可以包括-20℃至55℃。在所述步骤S402中，当在低温(如0℃以下)时，预设测试温度间隔为3℃，即每间隔3℃测试获得所述电池内阻，当在中低温(如0至15℃)时，预设测试温度间隔为5℃，即每间隔5℃测试获得所述电池内阻，当在中高温(如15至55℃)时，预设测试温度间隔为10℃，即每间隔10℃测试获得所述电池内阻。
可以理解的是，基于所述测试方法，可以建立所述第二关系表中的荷电状态、温度、电流和内阻之间的映射关系。
在所述测试方法中，还包括：将所述电池裸露于高低温箱中，利用强对流环境，对所 述电池进行散热。需要指出的是，所述测试方法不仅适用于所述电池在放电过程中测试获得所述电池内阻，也适用于所述电池在充电过程中测试获得所述电池内阻。
其中一种可能的实现方式中，所述第二关系表还包括预设倍率修正系数，所述内阻由荷电状态、温度、电流和所述预设倍率修正系数确定。
具体地，在温度较高(如大于等于25℃)时，由于电池材料活性较大，电化学反应较易进行，放电电流对电池内阻的影响较小，可以近似认为电池的伏安特性曲线(U-I曲线)为一条直线(斜率随温度的变化而变化)。但是，当温度较低(如低于25℃)时，由于材料的动力学性能较差，不仅温度对电池内阻的影响较大，而且放电电流(或放电倍率)对电池内阻的影响也较大。
因此，本实施例中，通过所述预设倍率修正系数a对所述内阻进行修正(修正后的值＝修正前的值/a)。所述预设倍率修正系数基于测试温度和放电倍率确定。
如图8(a)为本申请电量预测方法中在-15℃环境下未采用预设倍率修正系数对内阻进行修正的预测内阻与测试值的比较，纵坐标为内阻(单位为欧姆)，横坐标为放电深度(即DOD，其与剩余电量SOC相反)，0.1C-test表示在0.1c放电倍率下实测内阻曲线，0.1C-predic表示在0.1c放电倍率下预测内阻曲线，0.2C-test表示在0.2c放电倍率下实测内阻曲线，0.2C-predic表示在0.2c放电倍率下预测内阻曲线，0.5C-test表示在0.5c放电倍率下实测内阻曲线，0.5C-predic表示在0.5c放电倍率下预测内阻曲线。
图8(b)为本申请电量预测方法中在-15℃环境下采用预设倍率修正系数对内阻进行修正的预测内阻与测试值的比较，纵坐标为内阻(单位为欧姆)，横坐标为放电深度(即DOD，其与剩余电量SOC相反)，0.1C-DC-test表示在0.1c放电倍率下实测内阻曲线，0.1C-DC-predic表示在0.1c放电倍率下预测内阻曲线，0.2C-DC-test表示在0.2c放电倍率下实测内阻曲线，0.2C-DC-predic表示在0.2c放电倍率下预测内阻曲线，0.5C-DC-test表示在0.5c放电倍率下实测内阻曲线，0.5C-DC-predic表示在0.5c放电倍率下预测内阻曲线。
从图8(a)和图8(b)中可以看出，本实施例电量预测方法采用预设倍率修正系数预测内阻的准确性和可靠性更高。
进一步地，所述测试方法分别在不同放电倍率(如0.1c、0.2c或0.5c等)的放电电流下，分别测试获得在不同测试温度下的电池内阻，对测试结果进行分析处理，确定所述预设倍率修正系数。例如，图9为本申请电量预测方法中一个实施例的预设倍率修正系数表，在放电倍率为0.2c时，对应于不同的测试温度如-12℃、-11.5℃、-7.5℃、-6℃、-3℃、1.5 ℃、4℃、6℃、9℃，所述预设倍率修正系数分别为1.45、1.45、1.4、1.3、1.15、1、1、1、1。在放电倍率为0.5c时，对应于不同的测试温度如-12℃、-11.5℃、-7.5℃、-6℃、-3℃、1.5℃、4℃、6℃、9℃，所述预设倍率修正系数分别为1.45、1.45、1.45、1.45、1.45、1.45、1.4、1.3、1.15。
可以理解的是，所述放电倍率可以包括其他倍率的放电倍率，所述预设倍率修正系数并不限于本实施例中提供的值。对于其他倍率的放电倍率，所述预设倍率修正系数可以通过分段线性插值算法确定，若所述放电倍率大于0.5c或低于0.2c，则由线性外推得到对应的预设倍率修正系数。同样地，若测试温度超出一定的温度区间，则可以采用线性插值并外推的方式确定。需要指出的是，对于不同材料体系的电池，所述预设倍率修正系数可以不同或相同，均可以采用上述的测试方法对不同材料体系的电池进行测试，以确定对应不同材料体系电池的预设倍率修正系数，在此不受限制。
可以看出的是，相对于现有技术，在本实施例中提供的所述第二关系表提高了预测电池内阻的准确度，降低了预测电池剩余电量的误差。例如，图10(a)为现有技术中在-20℃环境下预测电量的精度偏差表，横坐标为时间(s)，纵坐标为剩余电量(SOC)偏差百分比(％)，在-20℃的环境下，通过现有技术(如利用TI算法预测电池剩余电量的方法)与本实施例中电量预测方法进行对比，现有的TI算法预测得到电池剩余电量的误差在13％左右，图10(b)为本申请电量预测方法中未采用预设倍率修正系数对内阻进行修正的在-20℃环境下预测电量的精度偏差表，横坐标为时间(s)，纵坐标为剩余电量(SOC)偏差百分比(％)，本实施例中的所述电量预测方法预测电池剩余电量的误差降低至7％左右。另外，由于本实施例中，所述第二关系表中还包括所述预设倍率修正系数对预测电池内阻进行修正，进一步提高了预测电池内阻的准确度，降低了预测电池剩余电量的误差，例如，图10(c)为本申请电量预测方法中采用预设倍率修正系数对内阻进行修正的在-20℃环境下预测电量的精度偏差表，横坐标为时间(s)，纵坐标为剩余电量(SOC)偏差百分比(％)，在-20℃环境下，本实施例中的所述电量预测方法预测电池剩余电量的误差进一步地降低至3％左右。
可以理解的是，上述实施例中的部分或全部步骤骤或操作仅是示例，本申请实施例还可以执行其它操作或者各种操作的变形。此外，各个步骤可以按照上述实施例呈现的不同的顺序来执行，并且有可能并非要执行上述实施例中的全部操作。
第二方面，如图11为本申请电量预测装置的结构示意图，本申请提供了一种电量预测装置，包括：第一数据获取模块10，用于获取第一数据，所述第一数据包括第一荷电状 态、第一温度和电流；熵热系数获得模块20，用于基于所述第一荷电状态在预设第一关系表中进行查询，获得第一熵热系数，其中，所述第一关系表包括荷电状态与熵热系数之间的映射关系；计算模块30，用于基于预设时间段、所述第一数据、所述第一熵热系数、预设第二关系表和预设第三关系表，获得第二数据，其中，所述第二数据包括所述预设时间段后的第二温度、第二荷电状态和第二端电压，所述第二关系表包括荷电状态、温度、电流和内阻之间的映射关系，所述第三关系表包括荷电状态和开路电压之间的映射关系；电压检测模块40，用于检测所述第二端电压是否小于或等于预设电压；电量获得模块50，用于若所述第二端电压小于或等于所述预设电压，基于所述第一荷电状态和所述第二荷电状态，获得剩余电量，若所述第二端电压大于所述预设电压，令所述第一荷电状态等于所述第二荷电状态，所述第一温度等于所述第二温度，并由所述熵热系数获得模块、所述计算模块、所述检测模块和所述循环判断模块进行循环处理。
其中一种可能的实现方式中，所述第二关系表中的荷电状态、温度、电流和内阻之间的映射关系满足公式：
Figure PCTCN2020133314-appb-000027
其中，R cc(SOC,T,I)为内阻，SOC为荷电状态，T为温度，I为电流，U(SOC,T)为端电压，OCV(SOC,T)为开路电压。
其中一种可能的实现方式中，所述第二关系表还包括预设倍率，所述内阻由荷电状态、温度、电流和所述预设倍率确定。
其中一种可能的实现方式中，所述第二数据还包括第一产热功率，其中，所述第一产热功率由可逆热、所述电流和第一内阻计算获得，所述可逆热由所述第一数据和所述第一熵热系数确定，所述第一内阻基于所述第一荷电状态、所述第一温度、所述电流在所述第二关系表中查询获得。
其中一种可能的实现方式中，所述第二温度由所述预设时间段、所述第一产热功率、第一散热功率、所述第一温度、电池的比热容和质量计算获得，其中，所述第一散热功率由所述第一温度、预设环境温度、所述电池的散热系数和表面积计算获得。
其中一种可能的实现方式中，所述第一产热功率由公式
Figure PCTCN2020133314-appb-000028
计算获得，其中，Pin为产热功率，I为电流、Rcc为内阻，T为温度，
Figure PCTCN2020133314-appb-000029
为熵热系数，OCV为开路电压，
Figure PCTCN2020133314-appb-000030
为可逆热。
其中一种可能的实现方式中，所述第二温度由公式
Figure PCTCN2020133314-appb-000031
计算获得，其中，T 2为第二温度，T 1为第一温度，P in为产热功率，P out为散热功率，Δt为预设时间段，c为所述电池的比热容，m为所述电池的质量。
其中一种可能的实现方式中，所述第一散热功率由公式P out＝hS(T-T en)计算获得，其中，P out为散热功率，h为所述电池的散热系数，S为所述电池的表面积，T为温度，T en为预设环境温度。
其中一种可能的实现方式中，所述第二端电压由第二内阻、第二开路电压和所述电流计算获得，其中，所述第二内阻基于所述第二荷电状态、所述第二温度和所述电流在所述第二关系表中查询获得，所述第二开路电压基于所述第二荷电状态在所述第三关系表中查询获得。
其中一种可能的实现方式中，所述第二端电压由公式U(SOC 2)＝OCV(SOC 2)+IR CC(SOC 2,T 2,I)计算获得，其中，U(SOC 2)为第二端电压，OCV(SOC 2)为第二开路电压，I为电流，R CC(SOC 2,T 2,I)为第二内阻，SOC 2为第二荷电状态，T 2为第二温度。
其中一种可能的实现方式中，所述第二荷电状态由所述预设时间段、所述第一荷电状态、所述电流、电池的容量计算获得。
其中一种可能的实现方式中，所述第二荷电状态由公式
Figure PCTCN2020133314-appb-000032
计算获得，其中，SOC 2为第二荷电状态，SOC 1为第一荷电状态，cap为所述电池的容量，Δt为预设时间段，I为电流。
其中一种可能的实现方式中，所述预设时间段由电池的容量、所述电流和荷电状态间隔确定。
其中一种可能的实现方式中，所述预设时间段由公式
Figure PCTCN2020133314-appb-000033
计算获得，其中，cap为所述电池的容量，ΔSOC为所述电池的荷电状态间隔，I为电流。
其中一种可能的实现方式中，所述第一数据获取模块包括：
采集模块，用于获取第一端电压和电流；
电流检测模块，用于检测所述电流是否小于或等于预设电流；
第一荷电状态确定模块，用于若所述电流小于或等于所述预设电流，则基于第一开路电压在所述第三关系表中进行查询，获得所述第一荷电状态，其中，所述第一开路电压由所述第一端电压确定。
其中一种可能的实现方式中，所述装置还包括：
第三数据获取模块，用于获取第三数据，所述第三数据包括第三荷电状态、第三温度和电流；
第四温度获得模块，用于基于所述第三数据和预设放电值，获得第四温度；
放电模块，用于基于所述预设放电值，从所述第三荷电状态放电至第四荷电状态；
温度检测模块，用于检测所述第四荷电状态下的第五温度；
差值计算模块，用于获得所述第四温度和所述第五温度之间的差值；
散热系数确定模块，用于若所述差值大于预设阈值，则基于所述第四温度、所述第五温度、预设散热初值和预设修正值，确定所述电池的散热系数。
其中一种可能的实现方式中，所述散热系数由公式h＝h 0+(T sim-T test)*b计算获得，其中，h为散热系数，h 0为预设散热初值，T sim为所述第四温度，T test为所述第五温度，b为预设修正值。
其中一种可能的实现方式中，所述预设阈值小于或等于2℃。
其中一种可能的实现方式中，所述预设修正值在0.3至0.7范围内。
其中一种可能的实现方式中，所述预设放电值为5％至10％放电百分比。
其中一种可能的实现方式中，所述预设电压基于电池的材料体系确定。
其中一种可能的实现方式中，所述电池选自：材料体系为钴酸锂体系和所述预设电压在3.0V至3.4V范围内，材料体系为三元材料体系和所述预设电压在2.8V至3.2V范围内，材料体系为磷酸铁锂体系和所述预设电压在2.5V至2.9V范围内中的其中一种。
其中一种可能的实现方式中，所述剩余电量由公式
Figure PCTCN2020133314-appb-000034
计算获得，其中，RM为剩余电量，SOC 1为第一荷电状态，SOC 2为第二荷电状态。
图11所示实施例提供的电量预测装置可用于执行本申请图1所示方法实施例的技术方案，其实现原理和技术效果可以进一步参考方法实施例中的相关描述。
应当理解的是，所述电路预测装置可以对应于图12所示的电子设备900。其中，熵热系数获得模块20、计算模块30、电压检测模块40以及电量获得模块50及其包括的子模块的功能可以由图12所示的电子设备900中的处理器910实现，第一数据获取模块10及其包括的子模块的功能可以由图12所示的电子设备900中的传感器实现。
应理解以上图11所示的电量预测装置的各个模块的划分仅仅是一种逻辑功能的划分，实际实现时可以全部或部分集成到一个物理实体上，也可以物理上分开。且这些模块可以全部以软件通过处理元件调用的形式实现；也可以全部以硬件的形式实现；还可以部分模块以软件通过处理元件调用的形式实现，部分模块通过硬件的形式实现。例如，检测模块可以为单独设立的处理元件，也可以集成在电子设备的某一个芯片中实现。其它模块的实现与之类似。此外这些模块全部或部分可以集成在一起，也可以独立实现。在实现过程中，上述方法的各步骤或以上各个模块可以通过处理器元件中的硬件的集成逻辑电路或者软件形式的指令完成。
例如，以上这些模块可以是被配置成实施以上方法的一个或多个集成电路，例如：一个或多个特定集成电路(Application Specific Integrated Circuit；以下简称：ASIC)，或，一个或多个微处理器(Digital Singnal Processor；以下简称：DSP)，或，一个或者多个现场可编程门阵列(Field Programmable Gate Array；以下简称：FPGA)等。再如，这些模块可以集成在一起，以片上系统(System-On-a-Chip；以下简称：SOC)的形式实现。
图12为本申请电子设备一个实施例的结构示意图，如图12所示，上述电子设备可以包括：显示屏；一个或多个处理器；存储器；多个应用程序；以及一个或多个计算机程序。
其中，上述显示屏可以包括车载计算机(移动数据中心Mobile Data Center)的显示屏；上述电子设备可以为移动终端(手机)，智慧屏，无人机，智能网联车(Intelligent Connected Vehicle；以下简称：ICV)，智能(汽)车(smart/intelligent car)或车载设备等设备。
其中上述一个或多个计算机程序被存储在上述存储器中，上述一个或多个计算机程序包括指令，当上述指令被上述设备执行时，使得上述设备执行以下步骤：A、获取第一数据，所述第一数据包括第一荷电状态、第一温度和电流；B、基于所述第一荷电状态在预设第一关系表中进行查询，获得第一熵热系数，其中，所述第一关系表包括荷电状态与熵热系数之间的映射关系；C、基于预设时间段、所述第一数据、所述第一熵热系数、预设第二关系表和预设第三关系表，获得第二数据，其中，所述第二数据包括所述预设时间段后的第二温度、第二荷电状态和第二端电压，所述第二关系表包括荷电状态、温度、电流和内阻之间的映射关系，所述第三关系表包括荷电状态和开路电压之间的映射关系；D、检测所述第二端电压是否小于或等于预设电压；E、若所述第二端电压小于或等于所述预设电压，基于所述第一荷电状态和所述第二荷电状态，获得剩余电量，若所述第二端电压大于所述预设电压，令所述第一荷电状态等于所述第二荷电状态，所述第一温度等于所述第二温度，重复所述步骤B-E。
其中一种可能的实现方式中，所述第二关系表中的荷电状态、温度、电流和内阻之间的映射关系满足公式：
Figure PCTCN2020133314-appb-000035
其中，R cc(SOC,T,I)为内阻，SOC为荷电状态，T为温度，I为电流，U(SOC,T)为端电压，OCV(SOC,T)为开路电压。
其中一种可能的实现方式中，所述第二关系表还包括预设倍率修正系数，所述内阻由荷电状态、温度、电流和所述预设倍率修正系数确定。
其中一种可能的实现方式中，所述第二数据还包括第一产热功率，其中，所述第一产热功率由可逆热、所述电流和第一内阻计算获得，所述可逆热由所述第一数据和所述第一 熵热系数确定，所述第一内阻基于所述第一荷电状态、所述第一温度、所述电流在所述第二关系表中查询获得。
其中一种可能的实现方式中，所述第二温度由所述预设时间段、所述第一产热功率、第一散热功率、所述第一温度、电池的比热容和质量计算获得，其中，所述第一散热功率由所述第一温度、预设环境温度、所述电池的散热系数和表面积计算获得。
其中一种可能的实现方式中，所述第一产热功率由公式
Figure PCTCN2020133314-appb-000036
计算获得，其中，Pin为产热功率，I为电流、Rcc为内阻，T为温度，
Figure PCTCN2020133314-appb-000037
为熵热系数，OCV为开路电压，
Figure PCTCN2020133314-appb-000038
为可逆热。
其中一种可能的实现方式中，所述第二温度由公式
Figure PCTCN2020133314-appb-000039
计算获得，其中，T 2为第二温度，T 1为第一温度，P in为产热功率，P out为散热功率，Δt为预设时间段，c为所述电池的比热容，m为所述电池的质量。
其中一种可能的实现方式中，所述第一散热功率由公式P out＝hS(T-T en)计算获得，其中，P out为散热功率，h为所述电池的散热系数，S为所述电池的表面积，T为温度，T en为预设环境温度。
其中一种可能的实现方式中，所述第二端电压由第二内阻、第二开路电压和所述电流计算获得，其中，所述第二内阻基于所述第二荷电状态、所述第二温度和所述电流在所述第二关系表中查询获得，所述第二开路电压基于所述第二荷电状态在所述第三关系表中查询获得。
其中一种可能的实现方式中，所述第二端电压由公式U(SOC 2)＝OCV(SOC 2)+IR CC(SOC 2,T 2,I)计算获得，其中，U(SOC 2)为第二端电压，OCV(SOC 2)为第二开路电压，I为电流，R CC(SOC 2,T 2,I)为第二内阻，SOC 2为第二荷电状态，T 2为第二温度。
其中一种可能的实现方式中，所述第二荷电状态由所述预设时间段、所述第一荷电状态、所述电流、电池的容量计算获得。
其中一种可能的实现方式中，所述第二荷电状态由公式
Figure PCTCN2020133314-appb-000040
计算获得，其中，SOC 2为第二荷电状态，SOC 1为第一荷电状态，cap为所述电池的容量，Δt为预设时间段，I为电流。
其中一种可能的实现方式中，所述预设时间段由电池的容量、所述电流和荷电状态间隔确定。
其中一种可能的实现方式中，所述预设时间段由公式
Figure PCTCN2020133314-appb-000041
计算获得，其中， cap为所述电池的容量，ΔSOC为所述电池的荷电状态间隔，I为电流。
其中一种可能的实现方式中，当上述指令被上述设备执行时，使得上述设备执行所述步骤A、获取所述第一数据时，所述设备还执行以下步骤：A1、获取第一端电压和电流；A2、检测所述电流是否小于或等于预设电流；A3、若所述电流小于或等于所述预设电流，则基于第一开路电压在所述第三关系表中进行查询，获得所述第一荷电状态，其中，所述第一开路电压由所述第一端电压确定。
其中一种可能的实现方式中，当上述指令被上述设备执行时，使得上述设备还执行以下步骤：F1、获取第三数据，所述第三数据包括第三荷电状态、第三温度和电流；F2、基于所述第三数据和预设放电值，获得第四温度；F3、基于所述预设放电值，从所述第三荷电状态放电至第四荷电状态；F4、检测所述第四荷电状态下的第五温度；F5、获得所述第四温度和所述第五温度之间的差值；F6、若所述差值大于预设阈值，则基于所述第四温度、所述第五温度、预设散热初值和预设修正值，确定所述电池的散热系数。
其中一种可能的实现方式中，所述散热系数由公式h＝h 0+(T sim-T test)*b计算获得，其中，h为散热系数，h 0为预设散热初值，T sim为所述第四温度，T test为所述第五温度，b为预设修正值。
其中一种可能的实现方式中，所述预设阈值小于或等于2℃。
其中一种可能的实现方式中，所述预设修正值在0.3至0.7范围内。
其中一种可能的实现方式中，所述预设放电值为5％至10％放电百分比。
其中一种可能的实现方式中，所述预设电压基于电池的材料体系确定。
其中一种可能的实现方式中，所述电池选自：材料体系为钴酸锂体系和所述预设电压在3.0V至3.4V范围内，材料体系为三元材料体系和所述预设电压在2.8V至3.2V范围内，材料体系为磷酸铁锂体系和所述预设电压在2.5V至2.9V范围内中的其中一种。
其中一种可能的实现方式中，所述剩余电量由公式
Figure PCTCN2020133314-appb-000042
计算获得，其中，RM为剩余电量，SOC 1为第一荷电状态，SOC 2为第二荷电状态。
图12所示的电子设备可以是终端设备也可以是内置于上述终端设备的电路设备。该设备可以用于执行本申请图1所示实施例提供的方法中的功能/步骤。
如图12所示，电子设备900包括处理器910和收发器920。可选地，该电子设备900还可以包括存储器930。其中，处理器910、收发器920和存储器930之间可以通过内部连接通路互相通信，传递控制和/或数据信号，该存储器930用于存储计算机程序，该处理器910用于从该存储器930中调用并运行该计算机程序。
上述存储器930可以是只读存储器(read-only memory，ROM)、可存储静态信息和指令的其它类型的静态存储设备、随机存取存储器(random access memory，RAM)或可存储信息和指令的其它类型的动态存储设备，也可以是电可擦可编程只读存储器(electrically erasable programmable read-only memory，EEPROM)、只读光盘(compact disc read-only memory，CD-ROM)或其他光盘存储、光碟存储(包括压缩光碟、激光碟、光碟、数字通用光碟、蓝光光碟等)、磁盘存储介质或者其它磁存储设备，或者还可以是能够用于携带或存储具有指令或数据结构形式的期望的程序代码并能够由计算机存取的任何其它介质等。
可选地，电子设备900还可以包括天线940，用于将收发器920输出的无线信号发送出去。
上述处理器910可以和存储器930可以合成一个处理装置，更常见的是彼此独立的部件，处理器910用于执行存储器930中存储的程序代码来实现上述功能。具体实现时，该存储器930也可以集成在处理器910中，或者，独立于处理器910。
除此之外，为了使得电子设备900的功能更加完善，该电子设备900还可以包括输入单元960、显示单元970、音频电路980、摄像头990和传感器901等中的一个或多个，所述音频电路还可以包括扬声器982、麦克风984等。其中，显示单元970可以包括显示屏。
可选地，上述电子设备900还可以包括电源950，用于给终端设备中的各种器件或电路提供电源。
应理解，图12所示的电子设备900能够实现本申请图1所示实施例提供的方法的各个过程。电子设备900中的各个模块的操作和/或功能，分别为了实现上述方法实施例中的相应流程。具体可参见本申请图1所示方法实施例中的描述，为避免重复，此处适当省略详细描述。
应理解，图12所示的电子设备900中的处理器910可以是片上系统SOC，该处理器910中可以包括中央处理器(Central Processing Unit；以下简称：CPU)，还可以进一步包括其他类型的处理器，例如：图像处理器(Graphics Processing Unit；以下简称：GPU)等。
总之，处理器910内部的各部分处理器或处理单元可以共同配合实现之前的方法流程，且各部分处理器或处理单元相应的软件程序可存储在存储器930中。
本申请还提供一种电子设备，所述设备包括存储介质和中央处理器，所述存储介质可以是非易失性存储介质，所述存储介质中存储有计算机可执行程序，所述中央处理器与所述非易失性存储介质连接，并执行所述计算机可执行程序以实现本申请图1所示实 施例提供的方法。
以上各实施例中，涉及的处理器可以例如包括CPU、DSP、微控制器或数字信号处理器，还可包括GPU、嵌入式神经网络处理器(Neural-network Process Units；以下简称：NPU)和图像信号处理器(Image Signal Processing；以下简称：ISP)，该处理器还可包括必要的硬件加速器或逻辑处理硬件电路，如ASIC，或一个或多个用于控制本申请技术方案程序执行的集成电路等。此外，处理器可以具有操作一个或多个软件程序的功能，软件程序可以存储在存储介质中。
本申请实施例还提供一种计算机可读存储介质，该计算机可读存储介质中存储有计算机程序，当其在计算机上运行时，使得计算机执行本申请第一方面实施例提供的方法。
本申请实施例还提供一种计算机程序产品，该计算机程序产品包括计算机程序，当其在计算机上运行时，使得计算机执行本申请第一方面实施例提供的方法。
本申请实施例中，“至少一个”是指一个或者多个，“多个”是指两个或两个以上。“和/或”，描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示单独存在A、同时存在A和B、单独存在B的情况。其中A，B可以是单数或者复数。字符“/”一般表示前后关联对象是一种“或”的关系。“以下至少一项”及其类似表达，是指的这些项中的任意组合，包括单项或复数项的任意组合。例如，a，b和c中的至少一项可以表示：a，b，c，a和b，a和c，b和c或a和b和c，其中a，b，c可以是单个，也可以是多个。
本领域普通技术人员可以意识到，本文中公开的实施例中描述的各单元及算法步骤，能够以电子硬件、计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统、装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在本申请所提供的几个实施例中，任一功能如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以 以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(Read-Only Memory；以下简称：ROM)、随机存取存储器(Random Access Memory；以下简称：RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，仅为本申请的具体实施方式，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。本申请的保护范围应以所述权利要求的保护范围为准。

Claims (25)

  1. 一种电量预测方法，其特征在于，包括：
    A、获取第一数据，所述第一数据包括第一荷电状态、第一温度和电流；
    B、基于所述第一荷电状态在预设第一关系表中进行查询，获得第一熵热系数，其中，所述第一关系表包括荷电状态与熵热系数之间的映射关系；
    C、基于预设时间段、所述第一数据、所述第一熵热系数、预设第二关系表和预设第三关系表，获得第二数据，其中，所述第二数据包括所述预设时间段后的第二温度、第二荷电状态和第二端电压，所述第二关系表包括荷电状态、温度、电流和内阻之间的映射关系，所述第三关系表包括荷电状态和开路电压之间的映射关系；
    D、检测所述第二端电压是否小于或等于预设电压；
    E、若所述第二端电压小于或等于所述预设电压，基于所述第一荷电状态和所述第二荷电状态，获得剩余电量，若所述第二端电压大于所述预设电压，令所述第一荷电状态等于所述第二荷电状态，所述第一温度等于所述第二温度，重复所述步骤B-E。
  2. 根据权利要求1所述的方法，其特征在于，所述第二关系表中的荷电状态、温度、电流和内阻之间的映射关系满足公式：
    Figure PCTCN2020133314-appb-100001
    其中，R cc(SOC,T,I)为内阻，SOC为荷电状态，T为温度，I为电流，U(SOC,T)为端电压，OCV(SOC,T)为开路电压。
  3. 根据权利要求1所述的方法，其特征在于，所述第二关系表还包括预设倍率修正系数，所述内阻由荷电状态、温度、电流和所述预设倍率修正系数确定。
  4. 根据权利要求1所述的方法，其特征在于，所述第二数据还包括第一产热功率，其中，所述第一产热功率由可逆热、所述电流和第一内阻计算获得，所述可逆热由所述第一数据和所述第一熵热系数确定，所述第一内阻基于所述第一荷电状态、所述第一温度、所述电流在所述第二关系表中查询获得。
  5. 根据权利要求4所述的方法，其特征在于，所述第二温度由所述预设时间段、所述第一产热功率、第一散热功率、所述第一温度、电池的比热容和质量计算获得，其中，所述第一散热功率由所述第一温度、预设环境温度、所述电池的散热系数和表面积计算获得。
  6. 根据权利要求5所述的方法，其特征在于，所述第一产热功率由公式
    Figure PCTCN2020133314-appb-100002
    计算获得，
    其中，Pin为产热功率，I为电流，Rcc为内阻，T为温度，
    Figure PCTCN2020133314-appb-100003
    为熵热系数，OCV为开路电压，
    Figure PCTCN2020133314-appb-100004
    为可逆热。
  7. 根据权利要求5所述的方法，其特征在于，所述第二温度由公式
    Figure PCTCN2020133314-appb-100005
    计算获得，
    其中，T 2为第二温度，T 1为第一温度，P in为产热功率，P out为散热功率，Δt为预设时间段，c为所述电池的比热容，m为所述电池的质量。
  8. 根据权利要求5所述的方法，其特征在于，所述第一散热功率由公式
    P out＝hS(T-T en)计算获得，
    其中，P out为散热功率，h为所述电池的散热系数，S为所述电池的表面积，T为温度，T en为预设环境温度。
  9. 根据权利要求1所述的方法，其特征在于，所述第二端电压由第二内阻、第二开路电压和所述电流计算获得，其中，所述第二内阻基于所述第二荷电状态、所述第二温度和所述电流在所述第二关系表中查询获得，所述第二开路电压基于所述第二荷电状态在所述第三关系表中查询获得。
  10. 根据权利要求9所述的方法，其特征在于，所述第二端电压由公式
    U(SOC 2)＝OCV(SOC 2)+IR CC(SOC 2,T 2,I)计算获得，
    其中，U(SOC 2)为第二端电压，OCV(SOC 2)为第二开路电压，I为电流，R CC(SOC 2,T 2,I)为第二内阻，SOC 2为第二荷电状态，T 2为第二温度。
  11. 根据权利要求1所述的方法，其特征在于，所述第二荷电状态由所述预设时间段、所述第一荷电状态、所述电流、电池的容量计算获得。
  12. 根据权利要求11所述的方法，其特征在于，所述第二荷电状态由公式
    Figure PCTCN2020133314-appb-100006
    计算获得，
    其中，SOC 2为第二荷电状态，SOC 1为第一荷电状态，cap为所述电池的容量，Δt为预设时间段，I为电流。
  13. 根据权利要求1所述的方法，其特征在于，所述预设时间段由电池的容量、所述电流和荷电状态间隔确定。
  14. 根据权利要求13所述的方法，其特征在于，所述预设时间段由公式
    Figure PCTCN2020133314-appb-100007
    计算获得，
    其中，cap为所述电池的容量，ΔSOC为所述电池的荷电状态间隔，I为电流。
  15. 根据权利要求1所述的方法，其特征在于，所述步骤A、获取所述第一数据，包括：
    A1、获取第一端电压和电流；
    A2、检测所述电流是否小于或等于预设电流；
    A3、若所述电流小于或等于所述预设电流，则基于第一开路电压在所述第三关系表中进行查询，获得所述第一荷电状态，其中，所述第一开路电压由所述第一端电压确定。
  16. 根据权利要求1-15任一项所述的方法，其特征在于，所述方法还包括：
    F1、获取第三数据，所述第三数据包括第三荷电状态、第三温度和电流；
    F2、基于所述第三数据和预设放电值，获得第四温度；
    F3、基于所述预设放电值，从所述第三荷电状态放电至第四荷电状态；
    F4、检测所述第四荷电状态下的第五温度；
    F5、获得所述第四温度和所述第五温度之间的差值；
    F6、若所述差值大于预设阈值，则基于所述第四温度、所述第五温度、预设散热初值和预设修正值，确定所述电池的散热系数。
  17. 根据权利要求16所述的方法，其特征在于，所述散热系数由公式
    h＝h 0+(T sim-T test)*b计算获得，
    其中，h为散热系数，h 0为预设散热初值，T sim为所述第四温度，T test为所述第五温度，b为预设修正值。
  18. 根据权利要求17所述的方法，其特征在于，所述预设阈值小于或等于2℃。
  19. 根据权利要求17所述的方法，其特征在于，所述预设修正值在0.3至0.7范围内。
  20. 根据权利要求17所述的方法，其特征在于，所述预设放电值为5％至10％放电百分比。
  21. 根据权利要求1-15任一项所述的方法，其特征在于，所述预设电压基于电池的材料体系确定。
  22. 根据权利要求21所述的方法，其特征在于，所述电池选自：材料体系为钴酸锂体系和所述预设电压在3.0V至3.4V范围内，材料体系为三元材料体系和所述预设电压在2.8V至3.2V范围内，材料体系为磷酸铁锂体系和所述预设电压在2.5V至2.9V范围内中的其中一种。
  23. 根据权利要求1至15任一项所述的方法，所述剩余电量由公式
    Figure PCTCN2020133314-appb-100008
    计算获得，
    其中，RM为剩余电量，SOC 1为第一荷电状态，SOC 2为第二荷电状态。
  24. 一种电子设备，其特征在于，包括：
    显示屏；一个或多个处理器；存储器；多个应用程序；以及一个或多个计算机程序，其中所述一个或多个计算机程序被存储在所述存储器中，所述一个或多个计算机程序包括指令，当所述指令被所述设备执行时，使得所述设备执行如权利要求1-23中任一项所述的方法。
  25. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质中存储有计算机程序，当其在计算机上运行时，使得计算机执行如权利要求1-23任一项所述的方法。
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