CN116953520A - Method for predicting state of charge, energy storage device, and computer-readable storage medium - Google Patents

Method for predicting state of charge, energy storage device, and computer-readable storage medium Download PDF

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Publication number
CN116953520A
CN116953520A CN202310709450.7A CN202310709450A CN116953520A CN 116953520 A CN116953520 A CN 116953520A CN 202310709450 A CN202310709450 A CN 202310709450A CN 116953520 A CN116953520 A CN 116953520A
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China
Prior art keywords
charge
battery pack
state
voltage
target
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Inventor
许柏皋
陈熙
王雷
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Ecoflow Technology Ltd
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Ecoflow Technology Ltd
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Priority to CN202310709450.7A priority Critical patent/CN116953520A/en
Publication of CN116953520A publication Critical patent/CN116953520A/en
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    • 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]
    • 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
    • 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
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
    • 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]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G01R31/387—Determining ampere-hour charge capacity or SoC
    • G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
    • 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]
    • G01R31/389—Measuring internal impedance, internal conductance or related variables
    • 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]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Secondary Cells (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The application discloses a state of charge prediction method, energy storage equipment and a readable storage medium. The prediction method comprises the following steps: after entering a charging terminal stage, acquiring a first battery pack voltage of a target battery pack at the current moment; calculating a first state of charge according to the first battery pack voltage and the first prediction model; obtaining the highest battery pack voltage detected after the target battery pack enters a charging end stage, and calculating a second charge state according to the highest battery pack voltage and a second prediction model; and determining the target state of charge of the target battery pack at the current moment according to the first state of charge and the second state of charge. According to the prediction method, after the target battery pack enters the charging end stage, the target state of charge is determined according to the first state of charge calculated by the first battery pack voltage and the first prediction model and the second state of charge calculated by the highest battery pack voltage and the second prediction model, so that the accuracy of predicting the state of charge of the battery pack can be effectively improved.

Description

Method for predicting state of charge, energy storage device, and computer-readable storage medium
Technical Field
The present application relates to the field of battery technologies, and in particular, to a method for predicting a state of charge, an energy storage device, and a computer readable storage medium.
Background
Currently, the energy storage device needs to display the State of Charge (SOC) of the battery pack in real time, and usually predicts the SOC of the battery pack by using an ampere-hour integration method. The ampere-hour integration method is easy to cause the problem of accumulated error, so that the SOC displayed by the battery pack at the end stage of charging is jumped. In order to avoid jump of the SOC displayed by the battery pack, the displayed SOC needs to be corrected, and in the related art, generally, the battery pack is firstly judged to be in which charging mode, linear regression is performed on the voltage in the standard charging mode, and interpolation is performed on the voltage obtained by the linear regression to obtain the displayed SOC. The existing charging mode is complicated, so that the accuracy of predicting the SOC of the battery pack is low.
Therefore, how to improve the accuracy of predicting the state of charge of the battery pack after entering the end-of-charge stage is a major issue.
Disclosure of Invention
The application provides a state of charge prediction method, energy storage equipment and a computer readable storage medium, which solve the problem of lower accuracy of predicting the state of charge of a battery pack caused by linear regression and interpolation of voltage in a standard charging mode in the related technology.
In a first aspect, the present application provides a method of predicting a state of charge, the method comprising: after entering a charging terminal stage, acquiring a first battery pack voltage of a target battery pack at the current moment; calculating a first state of charge according to the first battery pack voltage and a first prediction model; obtaining the highest battery pack voltage detected after the target battery pack enters the charging terminal stage, and calculating a second charge state according to the highest battery pack voltage and a second prediction model; and determining the target state of charge of the target battery pack at the current moment according to the first state of charge and the second state of charge.
According to the method, after the target battery pack enters the charging end stage, the target state of charge is determined according to the first state of charge calculated by the first battery pack voltage and the first prediction model and the second state of charge calculated by the highest battery pack voltage and the second prediction model, wherein the problem that when the real state of charge of the battery pack is predicted by adopting an ampere-hour integration method in the related art, the predicted real state of charge has errors due to current sampling errors, and jump occurs in the state of charge displayed in the charging end stage can be solved.
In addition, when the first state of charge is predicted based on the first battery pack voltage, the first state of charge may also fluctuate due to certain fluctuations in the first battery pack voltage during different charging phases. Therefore, the method of the application predicts the first charge state according to the first battery pack voltage, predicts the second charge state according to the highest battery pack voltage, combines the first charge state and the second charge state to determine the target charge state, uses the second charge state as a pocket bottom, reduces the fluctuation of the target charge state, solves the problem that the related technology needs to carry out linear regression and interpolation on the voltage in the standard charge mode, and the accuracy of predicting the charge state of the battery pack is lower due to the fluctuation of the battery pack voltage.
In a second aspect, the present application also provides an energy storage device comprising a memory, a processor, and a battery pack;
the memory is used for storing a computer program;
the processor is configured to execute the computer program and implement the method for predicting the state of charge as described above when the computer program is executed.
In a third aspect, the present application also provides a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to implement a method of predicting a state of charge as described above.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic structural diagram of an energy storage device according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a state of charge prediction method provided by an embodiment of the present application;
FIG. 3 is a graph of voltage provided by an embodiment of the present application;
FIG. 4 is a graph of a state of charge provided by an embodiment of the present application;
FIG. 5 is a graph of another state of charge provided by an embodiment of the present application;
FIG. 6 is a graph of another state of charge provided by an embodiment of the present application;
fig. 7 is a graph of a battery pack voltage provided by an embodiment of the present application;
FIG. 8 is a graph of a charging current provided by an embodiment of the present application;
FIG. 9 is a schematic flow chart of a sub-step of calculating a first state of charge provided by an embodiment of the present application;
FIG. 10 is a schematic flow chart of a sub-step of constructing a polarization voltage prediction model provided by an embodiment of the present application;
FIG. 11 is a schematic flow chart of sub-steps for determining target hyper-parameters provided by an embodiment of the application;
FIG. 12 is a schematic flow chart of a sub-step of testing a battery pack for a charge test provided by an embodiment of the present application;
FIG. 13 is a graph of voltage at an end charge stage according to an embodiment of the present application;
FIG. 14 is a graph of voltage at another end charge stage provided by an embodiment of the present application;
FIG. 15 is a graph of voltage at another end charge stage provided by an embodiment of the present application;
FIG. 16 is a graph of voltage at another end charge stage provided by an embodiment of the present application;
FIG. 17 is a graph of voltage at another end charge stage provided by an embodiment of the present application;
FIG. 18 is a schematic flow chart of a sub-step of calculating a second state of charge provided by an embodiment of the present application;
fig. 19 is a schematic flow chart of a substep of displaying state of charge provided by an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations.
It is to be understood that the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should also be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Embodiments of the present application provide a state of charge prediction method, an energy storage device, and a computer readable storage medium. The method for predicting the state of charge can be applied to energy storage equipment, and after a target battery pack enters a charging end stage, the target state of charge is determined according to a first state of charge calculated by a first battery pack voltage and a first prediction model and a second state of charge calculated by a highest battery pack voltage and a second prediction model, wherein the problem that when the related technology predicts the real state of charge of the battery pack by adopting an ampere-hour integration method, the predicted real state of charge has errors due to current sampling errors, and jump occurs in the state of charge displayed in the charging end stage can be solved.
In addition, when the first state of charge is predicted based on the first battery pack voltage, the first state of charge may also fluctuate due to certain fluctuations in the first battery pack voltage during different charging phases. Therefore, the method of the application predicts the first charge state according to the first battery pack voltage, predicts the second charge state according to the highest battery pack voltage, combines the first charge state and the second charge state to determine the target charge state, uses the second charge state as a pocket bottom, reduces the fluctuation of the target charge state, solves the problem that the related technology needs to carry out linear regression and interpolation on the voltage in the standard charge mode, and the accuracy of predicting the charge state of the battery pack is lower due to the fluctuation of the battery pack voltage.
The energy storage device may be a mobile energy storage device, a home energy storage device, or a vehicle-mounted energy storage device, for example. Wherein the energy storage device may be provided with at least one battery pack.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an energy storage device 10 according to an embodiment of the application. The energy storage device 10 may include a processor 1001, a memory 1002, and a battery pack 1003, wherein the processor 1001, the processor 1002, and the battery pack 1003 may be connected by a bus, which may be any suitable bus such as an integrated circuit (Inter-integrated Circuit, I2C) bus.
Wherein the memory 1002 may store an operating system and computer programs. The computer program comprises program instructions that, when executed, cause the processor 1001 to perform the state of charge prediction method described in any of the embodiments.
Wherein the processor 1001 is configured to provide computing and control capabilities to support the operation of the overall energy storage device 10. The battery pack 1003 may include a battery cell and a battery management system (Battery Management System, BMS). The BMS system is used to collect battery parameters of the battery pack 1003, such as a charging voltage, a charging current, a temperature value, a state of charge, and the like.
The processor 1001 may be a central processing unit (Central Processing Unit, CPU) and may also be a general purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (application specific integrated circuit, ASIC), a Field-programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The general purpose processor may be a microprocessor, or it may be any conventional processor or the like.
In one embodiment, the processor 1001 is configured to execute a computer program stored in the memory 1002, so as to implement the following steps:
after entering a charging terminal stage, acquiring a first battery pack voltage of a target battery pack at the current moment; calculating a first state of charge according to the first battery pack voltage and the first prediction model; obtaining the highest battery pack voltage detected after the target battery pack enters a charging end stage, and calculating a second charge state according to the highest battery pack voltage and a second prediction model; and determining the target state of charge of the target battery pack at the current moment according to the first state of charge and the second state of charge.
In one embodiment, the first predictive model is a polarization voltage predictive model; the processor 1001, when implementing calculating the first state of charge according to the first battery pack voltage and the first prediction model, is configured to implement:
obtaining the actually measured charging current of a target battery pack at the current moment; acquiring an actual temperature value of a target battery pack at the current moment; determining a target super-parameter of a polarization voltage prediction model according to the actually measured charging current and the actual temperature value; inputting the target super-parameters, the first battery pack voltage and the actually measured charging current into a polarized voltage prediction model for calculation to obtain the residual charging time required by the target battery pack to be charged to a full-charge state; and determining the first charge state according to the residual charge time, the actually measured charge current and the rated charge capacity of the target battery pack.
In one embodiment, the processor 1001 is further configured to, prior to implementing calculating the first state of charge from the first battery pack voltage and the first predictive model, implement:
constructing a first voltage observation equation corresponding to the target battery pack at the current moment; constructing a second voltage observation equation corresponding to the target battery pack when the target battery pack is charged to a full-charge state; acquiring a first polarization voltage updating equation and a second polarization voltage updating equation of a target battery pack, wherein the first polarization voltage updating equation is an updating equation corresponding to electrochemical polarization voltage, and the second polarization voltage updating equation is an updating equation corresponding to concentration polarization voltage; and generating a polarization voltage prediction model according to the first voltage observation equation, the second voltage observation equation, the first polarization voltage update equation and the second polarization voltage update equation.
In one embodiment, the processor 1001, when implementing the first voltage observation equation corresponding to the current time of construction of the target battery pack, is configured to implement:
acquiring a preset open-circuit voltage; determining the ohmic pressure drop of the target battery pack at the current moment according to the actually measured charging current and the actual temperature value; and inputting the first battery pack voltage, the ohmic drop and the open circuit voltage into an initial voltage observation equation to obtain a first voltage observation equation.
In one embodiment, the processor 1001, when implementing determining the target hyper-parameters of the polarization voltage prediction model based on the measured charging current and the actual temperature value, is configured to implement:
acquiring rated charge capacity of a target battery pack; determining the actual charging rate of the target battery pack according to the actually measured charging current and the rated charge capacity; inquiring a preset super-parameter database, and determining super-parameters corresponding to the actual charging multiplying power and the actual temperature value as target super-parameters, wherein the super-parameter database comprises super-parameters corresponding to different charging multiplying powers and different temperature values.
In one embodiment, the processor 1001 is further configured to implement:
determining a plurality of test charging parameters, wherein different test charging parameters comprise different test charging multiplying powers, test charge states and test temperature values; performing charging test on the test battery pack based on the charging parameters, and determining the super parameters of the polarized voltage state equation under the test charging parameters according to the test results; and correlating the test charging parameters with the corresponding super parameters and adding the test charging parameters to a super parameter database.
In one embodiment, the processor 1001, when implementing calculating the second state of charge from the highest battery pack voltage and the second predictive model, is configured to implement:
Acquiring the voltage of a second battery pack when the test battery pack is in the first test charge state; acquiring a third battery pack voltage of the test battery pack in the second test state of charge; determining a mapping relation between the voltage and the state of charge according to the first test state of charge, the second battery pack voltage, the second test state of charge and the third battery pack voltage; the second state of charge is determined based on the highest battery pack voltage and the mapping.
In one embodiment, the processor 1001 is further configured to, after implementing the determining the target state of charge of the target battery pack at the current time according to the first state of charge and the second state of charge, implement:
acquiring a current display charge state of a battery pack; and determining a target following multiplying power according to a difference value between the target state of charge and the display state of charge, controlling the display state of charge to follow the change of the target state of charge according to the target following multiplying power, and enabling the target following multiplying power and the difference value to form a positive correlation.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict. Referring to fig. 2, fig. 2 is a schematic flowchart of a method for predicting a state of charge according to an embodiment of the present application. As shown in fig. 2, the method for predicting the state of charge includes steps S101 to S104.
Step S101, after entering a charging end stage, a first battery pack voltage of a target battery pack at a current time is obtained.
It should be noted that, the method for predicting the state of charge in the embodiment of the present application may be applied to a charging scenario of a battery pack. For example, after the battery pack enters the end-of-charge phase, the target state of charge of the battery pack at the current time is predicted. The charging end stage refers to that the state of charge enters a preset first state of charge range in the charging process of the battery pack. The preset first state of charge range may be set according to practical situations, for example, the first state of charge range may be 80% -95%.
In some embodiments, after detecting that the target battery pack enters the end-of-charge phase, a first battery pack voltage of the target battery pack at the current time is obtained.
The target battery pack refers to a battery pack in the energy storage device. The first pack voltage refers to the measured charge voltage of the target pack.
In an exemplary embodiment, when the state of charge of the target battery pack is detected to enter a preset first state of charge range, it is determined that the target battery pack enters a charging end stage. For example, if the first state of charge range is 80% -95%, when the state of charge of the target battery pack is detected to reach 80%, the target battery pack is determined to enter the charging end stage. Wherein the state of charge of the target battery pack may be read by the BMS system.
For example, when the first battery pack voltage of the target battery pack at the current time is acquired, the measured charging voltage of the target battery pack recorded by the BMS system at the current time may be determined as the first battery pack voltage. Wherein the first battery pack voltage may be represented as U t,k 。
In the above embodiment, after the target battery pack enters the charging end stage, the first battery pack voltage of the target battery pack at the current time is obtained, and then the first state of charge of the target battery pack at the current time may be calculated according to the first battery pack voltage and the first prediction model.
Step S102, calculating a first state of charge according to the first battery pack voltage and the first prediction model.
It should be noted that the first preset model may be a polarization voltage prediction model, where the polarization voltage prediction model is used to calculate a remaining charging time required for the target battery pack to be charged to a full-power state at the current moment. After the remaining charge time is calculated, a first state of charge may be calculated based on the remaining charge time and the measured charge current and rated charge capacity of the target battery pack.
Illustratively, the polarization voltage prediction model may be constructed based on a second-order RC (Resistor-capacitor) model, or may be constructed based on a first-order RC model. In the embodiment of the present application, a polarization voltage prediction model constructed based on a second-order RC model will be taken as an example, to describe how to calculate the first state of charge according to the first battery pack voltage and the first prediction model.
In some embodiments, the first battery pack voltage may be input into a polarization voltage prediction model to calculate a remaining charge time required for the target battery pack to be charged to a full state at the current time, and then the first state of charge may be calculated from the remaining charge time. Wherein the first state of charge may be expressed as SOC 1 。
In other embodiments, the first preset model may be other types of prediction models, such as polynomial models, trigonometric models, etc., which are not limited by the present application.
And step S103, obtaining the highest battery pack voltage detected after the target battery pack enters the charging end stage, and calculating the second charge state according to the highest battery pack voltage and the second prediction model.
In some embodiments, the highest battery pack voltage detected after the target battery pack enters the end-of-charge phase may be obtained and the second state of charge calculated based on the highest battery pack voltage and the second predictive model.
Exemplary, if the target battery pack enters the end-of-charge phase at time k 0 At the current moment k 1 Then the time k can be obtained 0 To the current time k 1 The highest battery pack voltage within. After the highest battery pack voltage is obtained, a second state of charge may be calculated based on the highest battery pack voltage and a second predictive model.
It should be noted that the second prediction model may be a model including a mapping relationship between a voltage and a state of charge. In the embodiment of the application, the first or second linear fitting can be performed on the voltages of a plurality of battery packs and the test charge states after the test battery packs enter the charge end stage in advance, so as to obtain the second prediction model containing the mapping relation between the voltages and the charge states. In other embodiments, the mapping relationship between the voltage and the state of charge may be fitted by other types of prediction models, such as a polarization voltage prediction model, a polynomial model, a trigonometric function model, and the like, which is not limited in the present application.
For example, the highest battery pack voltage may be substituted into the second prediction model, and the state of charge corresponding to the highest battery pack voltage may be the second state of charge based on the mapping relationship between the voltage and the state of charge. Wherein the second state of charge may be represented as SOC 2 。
Step S104, determining the target state of charge of the target battery pack at the current moment according to the first state of charge and the second state of charge.
After the first battery pack voltage of the target battery pack at the current moment is obtained and the second state of charge is calculated according to the highest battery pack voltage and the second prediction model, the target state of charge of the target battery pack at the current moment can be determined according to the first state of charge and the second state of charge.
For example, the larger of the first state of charge and the second state of charge may be determined as the target state of charge of the target battery pack at the current time. For example, at the current time k 1 At the time of the first state of charge SOC 1 Greater than a second state of charge SOC 2 The first state of charge SOC may be set 1 Determining that the target battery pack is at the current moment k 1 Is set to the target state of charge of (a). Also for example, at the next time k 2 At the time of the first state of charge SOC 1 Less than the second state of charge SOC 2 The second state of charge SOC may be set 2 Determining that the target battery pack is at time k 2 Is set to the target state of charge of (a).
Referring to fig. 3, fig. 3 is a voltage graph according to an embodiment of the application. As shown in fig. 3, the abscissa indicates time, the ordinate indicates the pack voltage of the target battery pack, the voltage curve 1 indicates the actual voltage of the target battery pack, the voltage curve 2 indicates the voltage curve of the highest pack voltage, and the voltage curve 3 indicates the first pack voltage of the target battery pack. After the first pack voltage and the highest pack voltage of the target pack are obtained, a first state of charge may be calculated from the first pack voltage and a second state of charge may be calculated from the highest pack voltage.
In the above embodiment, after the target battery pack enters the charging end stage, the target state of charge is determined according to the first state of charge calculated by the first battery pack voltage and the first prediction model and the second state of charge calculated by the highest battery pack voltage and the second prediction model, where the state of charge is predicted according to the battery pack voltage, so that the problem that when the real state of charge of the battery pack is predicted by adopting the ampere-hour integration method in the related art, the predicted real state of charge has an error due to current sampling error, and the state of charge displayed in the charging end stage jumps can be solved.
According to the embodiment, the target state of charge is determined by combining the first state of charge and the second state of charge, and the first state of charge is obtained according to the first battery pack voltage, and the second state of charge is obtained according to the highest battery pack voltage, so that the target state of charge in different stages is determined according to the first battery pack voltage and the highest battery pack voltage, the problem that in the related art, linear regression and interpolation are required to be carried out on the voltage in the standard charging mode, and the accuracy of predicting the state of charge of the battery pack is lower due to voltage fluctuation is solved.
Referring to fig. 4, fig. 4 is a graph of a state of charge according to an embodiment of the present application. As shown in fig. 4, the abscissa represents time and the ordinate represents the state of charge of the target battery pack. Curve 4 represents a first state of charge of the target battery pack and curve 5 represents a second state of charge of the target battery pack.
Referring to fig. 5, fig. 5 is a graph illustrating another state of charge according to an embodiment of the present application. As shown in fig. 5, the abscissa represents time and the ordinate represents the state of charge of the target battery pack. The curve 6 represents the target state of charge of the target battery pack, and it should be noted that the curve 6 can be obtained from the larger of the curves 4 and 5 in fig. 4.
Referring to fig. 6, fig. 6 is a graph illustrating another state of charge according to an embodiment of the present application. As shown in fig. 6, the abscissa represents time and the ordinate represents the state of charge of the target battery pack. Curve 6 represents the target state of charge of the target battery pack, and curve 7 represents the state of charge of the target battery pack predicted based on the ampere-hour integration method. Compared with a time integration method, the embodiment of the application can better represent the part with the suddenly increased charge state at the charge end stage and can reflect the real charge state of the target battery pack by carrying out combined prediction on the charge state according to the first prediction model and the second prediction model.
Referring to fig. 7, fig. 7 is a graph of a battery pack voltage according to an embodiment of the application. As shown in fig. 7, the abscissa represents time and the ordinate represents the battery pack voltage of the target battery pack. During charging, the battery pack voltage of the target battery pack gradually increases, but during the end charging stage, the battery pack voltage gradually decreases.
Referring to fig. 8, fig. 8 is a graph of a charging current according to an embodiment of the application. As shown in fig. 8, the abscissa represents time and the ordinate represents the charging current of the target battery pack. During charging, the charging current of the target battery pack gradually increases, but during the end charging stage, the charging current gradually decreases.
According to the embodiment, the target state of charge of the target battery pack at the current moment is determined according to the first state of charge and the second state of charge, so that the problem that the predicted actual state of charge of the battery pack has errors due to current sampling errors when the prior art adopts an ampere-hour integration method to predict the actual state of charge of the battery pack, so that jump occurs in the state of charge displayed at the charging end stage is solved, and the problem that the voltage in a standard charging mode is required to be subjected to linear regression and interpolation in the prior art, so that the accuracy of predicting the state of charge of the battery pack is lower is solved, and the accuracy of predicting the state of charge of the battery pack is effectively improved.
Referring to fig. 9, fig. 9 is a schematic flowchart of a sub-step of calculating a first state of charge according to an embodiment of the present application, and step S102 of calculating the first state of charge according to a first battery pack voltage and a first prediction model may include the following steps S201 to S205.
Step S201, obtaining an actually measured charging current of the target battery pack at the current moment.
For example, the measured charging current of the target battery pack at the current time recorded by the BMS system may be read. Wherein the measured charging current may be represented as I k 。
Step S202, obtaining an actual temperature value of a target battery pack at the current moment.
It should be noted that, since the target hyper-parameter of the polarization voltage prediction model is related to the actual temperature value of the target battery pack, different actual temperature values correspond to different hyper-parameters, so that the actual temperature value of the target battery pack at the current moment needs to be obtained.
For example, the actual temperature value of the target battery pack at the current time recorded by the BMS system may be read. Wherein the actual temperature value may be denoted T.
And step S203, determining a target hyper-parameter of the polarization voltage prediction model according to the actually measured charging current and the actual temperature value.
In the embodiment of the application, the polarization voltage prediction model is as follows:
Wherein R is 0 ,R 1 ,R 2 ,C 1 ,C 2 For target super-parameters, R 0,k ,R 1,k ,R 2,k ,C 1,k ,C 2,k For the target super parameter of the current moment k, R 0,k+1 The target super-parameters are the target super-parameters when the charging is stopped; t_charging is the remaining charging time; OCV (optical clear video) k For the open circuit voltage at current time k, OCV k+1 Open circuit voltage at charge cutoff; u (U) t,k+1 Is the charge cutoff voltage. U (U) 1,k For the electrochemical polarization voltage at the current instant k, U 2,k The concentration polarization voltage at the current time k. Wherein R is 0 For ohmic internal resistance, the charge current I can be measured k And the actual temperature value T. It should be noted that, when the polarization voltage prediction model is constructed based on the first-order RC model, the corresponding target superparameter includes R 0 ,R 1 ,C 1 。
In some embodiments, after obtaining the measured charging current and the actual temperature value of the target battery pack at the current time, the target super-parameter of the polarization voltage prediction model may be determined according to the measured charging current and the actual temperature value.
It should be noted that, because of different actually measured charging currents and different actual temperature values, different target super parameters R are corresponding to 0,k ,R 1,k ,R 2,k ,C 1,k ,C 2,k ,R 0,k+1 To improve the accuracy of predicting the state of charge of a target battery packThe determination of the target hyper-parameters of the polarization voltage prediction model is therefore required to be determined according to the measured charging current and the actual temperature value of the target battery pack at the current moment.
For example, the target hyperparameter R of the polarization voltage prediction model can be determined based on a preset hyperparameter database according to the measured charging current and the actual temperature value 0,k ,R 1,k ,R 2,k ,C 1,k ,C 2,k ,R 0,k+1 . The super-parameter database comprises super-parameters corresponding to different charge states, charge multiplying powers and temperature values. It should be noted that, in the embodiment of the present application, the hyper parameters in the hyper parameter database are obtained by performing a charging test on the test battery pack in advance.
According to the embodiment, the target super-parameter of the polarization voltage prediction model is determined according to the actually measured charging current and the actual temperature value, so that the target super-parameter corresponding to the actually measured charging current and the actual temperature value of the target battery pack at the current moment can be obtained, the accuracy of the target super-parameter is improved, and the accuracy of predicting the charge state of the target battery pack can be improved.
And S204, inputting the target super-parameters, the first battery pack voltage and the actually measured charging current into a polarization voltage prediction model for calculation, and obtaining the residual charging time required by the target battery pack to be charged to a full-charge state.
After determining the target superparameter of the polarization voltage prediction model, the target superparameter, the first battery pack voltage, the initial battery pack voltage and the actually measured charging current may be input into the polarization voltage prediction model for calculation, so as to obtain the remaining charging time required by the target battery pack to be charged to the full state.
Exemplary, the target superparameter R may be 0,k ,R 1,k ,R 2,k ,C 1,k ,C 2,k ,R 0,k+1 First battery pack voltage U t,k And the actual charging current I k And (5) inputting a polarization voltage prediction model for calculation to obtain the residual charging time t_charging required by the target battery pack to be charged to the full-charge state. The specific calculation process is not described herein.
Step S205, determining a first charge state according to the remaining charge time, the actually measured charge current and the rated charge capacity of the target battery pack.
For example, the rated charge capacity of the target battery pack recorded by the BMS system may be read. Wherein the rated charge capacity can be expressed as Q 0 。
In some embodiments, the first state of charge may be calculated based on the remaining charge time, the measured charge current, and the rated charge capacity of the target battery pack.
The first state of charge SOC may be calculated, for example, by 1 。
SOC 1 ＝1-t_charging*I k /Q 0
For example, the remaining charging time t_charging, the measured charging current I may be k Rated charge capacity Q 0 Substituting the first SOC into the above calculation to obtain the first SOC 1 。
In the above embodiment, the first state of charge of the target battery pack may be determined by calculating from the remaining charge time, the measured charge current, and the rated charge capacity of the target battery pack.
It should be noted that, in the embodiment of the present application, before calculating the first state of charge according to the first battery pack voltage and the first prediction model, a polarization voltage prediction model is also required to be constructed. The following will explain in detail how the polarization voltage prediction model is constructed.
Referring to fig. 10, fig. 10 is a schematic flowchart of a sub-step of constructing a polarization voltage prediction model according to an embodiment of the present application, which may include the following steps S301 to S304.
Step 301, a first voltage observation equation corresponding to the current time of the target battery pack is constructed.
In the embodiment of the present application, a first voltage observation equation corresponding to the current time of the target battery pack and a second voltage observation equation corresponding to the target battery pack when the target battery pack is charged to the full state may be respectively constructed. The following will describe in detail how the voltage observation equation is constructed.
In some embodiments, constructing a first voltage observation equation corresponding to the target battery pack at the current time may include: acquiring a preset open-circuit voltage; determining the ohmic pressure drop of the target battery pack at the current moment according to the actually measured charging current and the actual temperature value; and inputting the first battery pack voltage, the ohmic drop and the open circuit voltage into an initial voltage observation equation to obtain a first voltage observation equation.
For example, the open circuit voltage of the target battery pack recorded by the BMS system may be read, and the open circuit voltage may be expressed as OCV k . The open circuit voltage refers to a terminal voltage of the target battery pack in an open circuit state. The open circuit voltage is a fixed value, i.e., the open circuit voltage of the target battery pack at different times is the same.
For example, in determining the ohmic drop of the target battery pack at the present time, the charging current I may be measured k And inquiring a preset ohm internal resistance meter by the actual temperature value T to obtain the ohm internal resistance R of the target battery pack at the current moment k 0,k The method comprises the steps of carrying out a first treatment on the surface of the Then, the actual charging current I k And ohmic internal resistance R 0,k Multiplying to obtain ohmic voltage drop I of the target battery pack at current time k k *R 0,k 。
The preset ohm internal resistance meter comprises ohm internal resistances corresponding to different charging currents and different temperature values.
In an embodiment of the present application, the initial voltage observation equation may be expressed as:
OCV+I*R 0 +U 1 +U 2 ＝U
wherein OCV is open circuit voltage, U 1 For electrochemical polarization voltage, U 2 The concentration polarization voltage is the charging current of the battery pack, I is the charging voltage of the battery pack, and U is the charging current of the battery pack.
For example, the first battery pack voltage U of the target battery pack at the current time k t,k Ohmic pressure drop I k *R 0,k Open circuit voltage OCV k And inputting the initial voltage observation equation to obtain a first voltage observation equation. Wherein the first voltage observation equation is as followsThe following is shown:
OCV k +I k *R 0,k +U 1,k +U 2,k ＝U t,k 。
and step S302, constructing a second voltage observation equation corresponding to the target battery pack when the target battery pack is charged to a full-charge state.
For example, a second voltage observation equation corresponding to the target battery pack charged to the full state may be constructed. Wherein the second voltage observation equation is as follows:
OCV k+1 +I k *R 0,k+1 +U 1,k+1 +U 2,k+1 ＝U t,k+1
wherein R is 0,k+1 Ohmic internal resistance when the target battery is fully charged, U 1,k+1 Electrochemical polarization voltage at full state of target battery, U 2,k+1 The concentration polarization voltage when the target battery is fully charged.
It should be noted that, the process of constructing the second voltage observation equation is similar to the process of constructing the first voltage observation equation, and will not be described herein.
Step S303, a first polarization voltage update equation and a second polarization voltage update equation of the target battery pack are obtained, wherein the first polarization voltage update equation is an update equation corresponding to electrochemical polarization voltage, and the second polarization voltage update equation is an update equation corresponding to concentration polarization voltage.
For example, a first polarization voltage update equation of the target battery pack stored in the memory may be obtained, where the first polarization voltage update equation is an update equation corresponding to the electrochemical polarization voltage. The first polarization voltage update equation for the target battery pack is as follows:
U 1,k+1 ＝U 1,k *exp(-t_charging/(R 1,k *C 1,k ))+R 1,k *I k *exp(1-t_charging/(R 1,k *C 1,k ))。
For example, a second polarization voltage update equation of the target battery pack stored in the memory may be obtained, where the second polarization voltage update equation is an update equation corresponding to the concentration polarization voltage. The second polarization voltage update equation for the target battery pack is as follows:
U 2,k+1 ＝U 2,k *exp(-t_charging/(R 2,k ×C 2,k ))+R 2,k *I k *exp(1-t_charging/(R 2,k *C 2,k ))。
step S304, a polarization voltage prediction model is generated according to the first voltage observation equation, the second voltage observation equation, the first polarization voltage update equation and the second polarization voltage update equation.
For example, the first voltage observation equation, the second voltage observation equation, the first polarization voltage update equation, and the second polarization voltage update equation may be combined to generate the polarization voltage prediction model. The specific generation process is not described herein. The generated polarization voltage prediction model is as follows:
in the above embodiment, the polarization voltage prediction model may be generated by acquiring the first voltage observation equation, the second voltage observation equation, the first polarization voltage update equation, and the second polarization voltage update equation.
In the embodiment of the application, besides constructing the polarization voltage prediction model based on the second-order RC model, the polarization voltage prediction model can also be constructed based on the first-order RC model. The polarization voltage prediction model constructed based on the first-order RC model is as follows:
It should be noted that, the polarization voltage prediction model constructed based on the first-order RC model has fewer super parameters and stronger robustness.
Referring to fig. 11, fig. 11 is a schematic flowchart of a sub-step of determining a target superparameter according to an embodiment of the present application, and determining the target superparameter of the polarization voltage prediction model in step S203 may include the following steps S401 to S403.
Step S401, obtaining the rated charge capacity of the target battery pack.
Exemplary, the rated charge capacity Q recorded by the BMS system can be read 0 。
Step S402, determining the actual charging rate of the target battery pack according to the actually measured charging current and the rated charge capacity.
For example, the measured charging current I k And rated charge capacity Q 0 And dividing to obtain the actual measurement charging rate of the target battery pack.
It will be appreciated that the measured charging current is proportional to the measured charging rate when the rated charge capacity is the same. For example, rated charge capacity Q 0 When the target battery pack of 100Ah was charged with the measured charging current of 20A, the corresponding charging rate was 0.2C. Also for example, rated charge capacity Q 0 When a target battery pack of 100Ah is charged with a measured charging current of 50A, the corresponding charging rate is 0.5C.
Step S403, inquiring a preset super-parameter database, and determining super-parameters corresponding to the actual charging rate and the actual temperature value as target super-parameters, wherein the super-parameter database comprises super-parameters corresponding to different charging rates and different temperature values.
For example, since different charging rates and different temperature values correspond to different target hyper-parameters, after the actual charging rate and the actual temperature value of the target battery pack are obtained, a preset hyper-parameter database may be queried according to the actual charging rate and the actual temperature value of the target battery pack, and the hyper-parameters corresponding to the actual charging rate and the actual temperature value may be determined as the target hyper-parameters.
For example, the super-parameter database may be queried according to the actual charging rate and the actual temperature value of the target battery pack, and the super-parameter R corresponding to the actual charging rate and the actual temperature value may be determined 1 Is a target superparameter.
According to the embodiment, the super-parameters corresponding to the actual charging multiplying power and the actual temperature value of the target battery pack can be determined to be the target super-parameters by inquiring the preset super-parameter database, and the accuracy of the target super-parameters of the target battery pack is improved.
The super parameters in the super parameter database are obtained by performing a charging test on the test battery pack in advance, and the charging test on the test battery pack will be described in detail below.
Referring to fig. 12, fig. 12 is a schematic flowchart of a sub-step of testing a battery pack for charging test according to an embodiment of the present application, which may include the following steps S501 to S503.
Step S501, determining a plurality of test charging parameters, where different test charging parameters include different test charging rates, test states of charge and test temperature values.
For example, a plurality of test charging parameters may be determined prior to performing a charging test on the test battery pack. The test charging parameters may include different test charging rates, different test states of charge, and different test temperature values, among others.
For example, the test state of charge may be 80%, 83%, 88%, 90%, 92%, 95%, etc.; the test charge rate may be 0.25C, 0.5C, 1C, etc.; the test temperature value may be 85 ℃, 90 ℃, 95 ℃, etc. Wherein, different test charging multiplying powers, different test charge states and different test temperature values can be combined to obtain different test charging parameters.
And step S502, carrying out charging test on the test battery pack based on the charging parameters, and determining the super parameters of the polarized voltage state equation under the test charging parameters according to the test result.
The test battery pack may be the target battery pack described above, or may be another battery pack, and is not limited thereto.
For example, after determining a plurality of test charging parameters, a charging test may be performed on the test battery pack based on each test charging parameter, and a super parameter of the polarization voltage state equation under the test charging parameters may be determined according to the test result.
For example, the test battery pack may be subjected to a charging test based on the test charging parameters having a test charging rate of 0.25C, a test temperature value of 90℃, and a test state of charge of 80%, 83%, 88%, 90%, 92%, 95%, respectively.
In some embodiments, performing a charging test on the test battery pack based on the charging parameter, and determining a super parameter of the polarization voltage state equation under the test charging parameter according to the test result may include: in the process of carrying out charging test on the test battery pack based on the test charging parameters, recording battery pack voltage, charging current and residual charging time of the test battery pack at each sampling point; and inputting the battery pack voltage, the charging current and the residual charging time of each sampling point into a polarized voltage state equation for performing superparameter calculation to obtain the superparameter of the polarized voltage state equation under the test charging parameters.
The number of sampling points can be determined according to the number of the test charge states. The remaining charge time can be estimated based on the charge capacity of the test battery pack at the sampling point, the charge current, and the rated charge capacity.
For example, the hyper-parameter calculation may be based on a least squares formula or other linear fitting algorithm. The specific hyper-parameter calculation process is not described in detail herein.
Referring to fig. 13, fig. 13 is a voltage graph at an end charging stage according to an embodiment of the application. Fig. 13 is a schematic diagram showing a test battery pack charged from a state of charge of 0.95 to a full state, the abscissa indicates time, and the ordinate indicates battery pack voltage of the test battery pack, and the measured charging current is 5A. The voltage curve 1 represents the actual charging voltage curve of the target battery pack, and the voltage curve 4 represents the voltage curve of the end charging stage calibrated using the second-order RC model. In order to secure the fitting effect, the state of charge of the end point of the charge end stage may be set to 0.985 regardless of the coordinate point at which the voltage abruptly changes in the charge end stage. The voltage curve 4 corresponds to a group of RC super-parameters, and coordinate points on the voltage curve 4 can be substituted into the polarization voltage prediction model to perform super-parameter solution, so as to obtain corresponding super-parameters.
Referring to fig. 14, fig. 14 is a voltage chart of another end charging stage according to an embodiment of the application. Fig. 14 is a schematic diagram showing a test battery pack charged from a state of charge of 0.8 to a full state, the abscissa indicates time, and the ordinate indicates battery pack voltage of the test battery pack, and the measured charging current is 20A. The voltage curve 1 represents the actual charging voltage curve of the target battery pack, and the voltage curve 8 represents the voltage curve of the end charging stage calibrated using the second-order RC model. The voltage curve 8 corresponds to a group of RC super-parameters, and coordinate points on the voltage curve 8 can be substituted into the polarization voltage prediction model to perform super-parameter solution, so as to obtain corresponding super-parameters.
Referring to fig. 15, fig. 15 is a voltage chart of another end charging stage according to an embodiment of the application. Fig. 15 is a schematic diagram showing a test battery pack charged from a state of charge of 0.8 to a full state, the abscissa indicates time, and the ordinate indicates battery pack voltage of the test battery pack, and the measured charging current is 20A. The voltage curve 1 represents the actual charging voltage curve of the target battery pack, and the voltage curve 8 represents the voltage curve of the end charging stage calibrated using the first-order RC model. The voltage curve 8 corresponds to a group of RC super-parameters, and coordinate points on the voltage curve 8 can be substituted into the polarization voltage prediction model to perform super-parameter solution, so as to obtain corresponding super-parameters.
Referring to fig. 16, fig. 16 is a voltage chart of another end charging stage according to an embodiment of the application. Fig. 16 is a schematic diagram showing the test battery pack charged from the state of charge 0.88 to the full state, the abscissa indicates time, and the ordinate indicates the battery pack voltage of the test battery pack, and the measured charging current is 20A. The voltage curve 1 represents the actual charging voltage curve of the target battery pack, and the voltage curve 8 represents the voltage curve of the end charging stage calibrated using the first-order RC model. The voltage curve 8 corresponds to a group of RC super-parameters, and coordinate points on the voltage curve 8 can be substituted into the polarization voltage prediction model to perform super-parameter solution, so as to obtain corresponding super-parameters.
Referring to fig. 17, fig. 17 is a voltage graph of another end charging stage according to an embodiment of the application. Fig. 17 is a schematic diagram showing the test battery pack charged from the state of charge 0.93 to the full state, the abscissa indicates time, and the ordinate indicates the battery pack voltage of the test battery pack, and the measured charging current is 5A. The voltage curve 1 represents the actual charging voltage curve of the target battery pack, and the voltage curve 8 represents the voltage curve of the end charging stage calibrated using the first-order RC model. The voltage curve 8 corresponds to a group of RC super-parameters, and coordinate points on the voltage curve 8 can be substituted into the polarization voltage prediction model to perform super-parameter solution, so as to obtain corresponding super-parameters.
Exemplary, the superparameter of the polarization voltage state equation at the test charging parameters is shown in table 1.
TABLE 1
R0 R1*C1 R1 Multiplying power (C) Temperature (. Degree. C.)
6.611325394 99993.89 750.8078 0.25 90
5.615661822 99998.97 914.5345 0.5 90
5.219678166 92829.26 1111.785 1 90
7.022725922 99994.72 1768.144 0.25 95
5.762994642 99999.57 2306.339 0.5 95
5.126428161 83644.52 2786.179 1 95
It should be noted that table 1 is a hyperparameter of a polarization voltage prediction model constructed based on a first-order RC model. Table 1 includes hyper-parameters R0, R1, and C1 corresponding to different charge rates and temperature values.
Step S503, the test charging parameters are correlated with the corresponding super parameters and added to the super parameter database.
After determining the hyper-parameters of the polarization voltage state equation under the test charging parameters according to the test results, the test charging parameters and the corresponding hyper-parameters may be correlated and added to the hyper-parameter database.
According to the embodiment, the test battery pack is subjected to the charging test based on the test charging parameters, and the super parameters of the polarized voltage state equation under the test charging parameters are determined according to the test results, so that the super parameters corresponding to different test charging parameters can be obtained.
Referring to fig. 18, fig. 18 is a schematic flowchart of a sub-step of calculating the second state of charge according to an embodiment of the present application, and the step S103 of calculating the second state of charge according to the highest battery pack voltage and the second prediction model may include the following steps S601 to S604.
Step S601, obtaining a second battery pack voltage of the test battery pack in the first test state of charge.
It should be noted that, in the embodiment of the present application, before calculating the second state of charge, the mapping relationship between the voltage and the state of charge may be established according to the state of charge of the battery pack at the beginning end of the charging end stage and the voltage of the battery pack, and the state of charge of the terminal end of the charging end stage and the voltage of the battery pack.
In some embodiments, a second battery pack voltage of the test battery pack at the first test state of charge may be obtained. The first test state of charge refers to a state of charge of the test battery pack at a start end of a charging end stage.
For example, the second battery pack voltage of the test battery pack at the first test state of charge recorded by the BMS system may be read. The first test state of charge may be denoted as soc_start; the second battery pack voltage may be denoted as u_start.
Step S602, obtaining a third battery pack voltage of the test battery pack in the second test state of charge.
In some embodiments, a third battery pack voltage may be obtained for the test battery pack at the second test state of charge. The second test state of charge refers to the state of charge of the end of the test battery pack at the end-of-charge stage.
For example, a third battery pack voltage of the test battery pack at the second test state of charge recorded by the BMS system may be read. The second test state of charge may be denoted as SOC end; the third battery pack voltage may be denoted as U end.
Step S603, determining a mapping relationship between the voltage and the state of charge according to the first test state of charge, the second battery pack voltage, the second test state of charge, and the third battery pack voltage.
After the second battery pack voltage of the test battery pack in the first test state of charge and the third battery pack voltage of the test battery pack in the second test state of charge are obtained, the mapping relation between the voltage and the state of charge can be determined according to the first test state of charge, the second battery pack voltage, the second test state of charge and the third battery pack voltage.
For example, the first test state of charge soc_start, the second battery pack voltage u_start, the second test state of charge soc_end, and the third battery pack voltage u_end may be linearly fitted based on a linear fitting formula to obtain a mapping relationship between the voltage and the state of charge.
For example, the linear fit formula is as follows:
SOC＝a*U+b
wherein U represents the battery pack voltage; a and b represent parameters.
For example, the first test state of charge soc_start, the second battery pack voltage u_start, the second test state of charge soc_end, and the third battery pack voltage u_end may be substituted into the above linear fitting formula, and the values of the parameters a and b may be calculated, so as to obtain the mapping relationship between the voltage and the state of charge.
Step S604, determining the second state of charge based on the highest battery pack voltage and the mapping relation.
For example, when determining the second state of charge based on the highest battery pack voltage and the mapping relationship, the highest battery pack voltage may be substituted into the above linear fitting equation soc=a×u+b to calculate, so as to obtain the second state of charge. The specific calculation process is not described herein.
According to the embodiment, the mapping relation between the voltage and the state of charge can be obtained by performing linear fitting according to the first test state of charge, the second battery pack voltage, the second test state of charge and the third battery pack voltage, and the second state of charge corresponding to the highest battery pack voltage can be determined based on the mapping relation between the voltage and the state of charge.
In the embodiment of the application, after the target state of charge of the target battery pack at the current moment is determined, the display state of charge can be adjusted according to the target state of charge. The following will explain in detail how the display state of charge is adjusted.
Referring to fig. 19, fig. 19 is a schematic flowchart of a sub-step of displaying a state of charge according to an embodiment of the present application, which may include the following steps S701 and S702.
Step S701, acquiring a display charge state of a current display of the battery pack.
It should be noted that, displaying the state of charge refers to displaying the state of charge on a display screen of the energy storage device.
For example, a display state of charge currently displayed by the target battery pack may be obtained. For example, the actual state of charge of the target battery pack recorded by the BMS system may be read as the display state of charge.
And step S702, determining a target following multiplying power according to a difference value between the target state of charge and the display state of charge, and controlling the display state of charge to follow the change of the target state of charge according to the target following multiplying power, wherein the target following multiplying power and the difference value form a positive correlation.
It will be appreciated that in order to avoid display state of charge jumps, an adaptive follow-up factor is required to control the display state of charge to vary with the change in target state of charge.
In some embodiments, a target following rate is determined according to a difference between the target state of charge and the display state of charge, and the display state of charge is controlled to follow the change of the target state of charge according to the target following rate, wherein the target following rate and the difference are in positive correlation.
For example, when the difference between the target state of charge and the display state of charge is large, controlling the display state of charge to follow the change of the target state of charge according to the first following rate; and when the difference between the target charge state and the display charge state is smaller, controlling the display charge state to follow the change of the target charge state according to the second following multiplying power. Wherein the first following multiplying power is larger than the second following multiplying power.
Such as: the amount of change in the display state of charge for a single run period may be multiplied by the following magnification, plus the display state of charge before the change, to obtain the display state of charge after the change.
According to the embodiment, the target following multiplying power is determined according to the difference between the target state of charge and the display state of charge, and the display state of charge is controlled to follow the change of the target state of charge according to the target following multiplying power, so that the display state of charge of the target battery pack can reflect the real state of charge of the target battery pack, and jump of the display state of charge can be avoided.
The embodiment of the application also provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and the computer program includes program instructions, and a processor executes the program instructions to implement any of the methods for predicting the state of charge provided by the embodiment of the application.
For example, the program is loaded by a processor, and the following steps may be performed:
after entering a charging terminal stage, acquiring a first battery pack voltage of a target battery pack at the current moment; calculating a first state of charge according to the first battery pack voltage and the first prediction model; obtaining the highest battery pack voltage detected after the target battery pack enters a charging end stage, and calculating a second charge state according to the highest battery pack voltage and a second prediction model; and determining the target state of charge of the target battery pack at the current moment according to the first state of charge and the second state of charge.
The computer readable storage medium may be an internal storage unit of the energy storage device of the foregoing embodiment, for example, a hard disk or a memory of the energy storage device. The computer readable storage medium may also be an external storage device of the energy storage device, such as a plug-in hard disk, a Smart Media Card (SMC), a secure digital Card (Secure Digital Card, SD Card), a Flash memory Card (Flash Card) or the like, which are provided on the energy storage device.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, a program required for at least one function, and the like; the storage data area may store data created according to each program, and the like.
The present application is not limited to the above embodiments, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the present application, and these modifications and substitutions are intended to be included in the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.

Claims (10)

1. A method of predicting state of charge, the method comprising:
after entering a charging terminal stage, acquiring a first battery pack voltage of a target battery pack at the current moment;
calculating a first state of charge according to the first battery pack voltage and a first prediction model;
obtaining the highest battery pack voltage detected after the target battery pack enters the charging terminal stage, and calculating a second charge state according to the highest battery pack voltage and a second prediction model;
and determining the target state of charge of the target battery pack at the current moment according to the first state of charge and the second state of charge.
2. The method of claim 1, wherein the first prediction model is a polarization voltage prediction model;
The calculating a first state of charge according to the first battery pack voltage and a first predictive model includes:
obtaining the actually measured charging current of the target battery pack at the current moment;
acquiring an actual temperature value of the target battery pack at the current moment;
determining a target hyper-parameter of the polarization voltage prediction model according to the actually measured charging current and the actual temperature value;
inputting the target super-parameters, the first battery pack voltage and the actually measured charging current into the polarization voltage prediction model for calculation to obtain the residual charging time required by the target battery pack to be charged to a full-charge state;
and determining the first charge state according to the residual charge time, the actually measured charge current and the rated charge capacity of the target battery pack.
3. The method of claim 2, further comprising, prior to said calculating a first state of charge from said first battery pack voltage and a first predictive model:
constructing a first voltage observation equation corresponding to the target battery pack at the current moment;
constructing a second voltage observation equation corresponding to the target battery pack when the target battery pack is charged to a full-charge state;
Acquiring a first polarization voltage updating equation and a second polarization voltage updating equation of the target battery pack, wherein the first polarization voltage updating equation is an updating equation corresponding to electrochemical polarization voltage, and the second polarization voltage updating equation is an updating equation corresponding to concentration polarization voltage;
and generating the polarization voltage prediction model according to the first voltage observation equation, the second voltage observation equation, the first polarization voltage update equation and the second polarization voltage update equation.
4. A method of predicting a state of charge as set forth in claim 3, wherein said constructing a first voltage observation equation corresponding to the target battery pack at the current time comprises:
acquiring a preset open-circuit voltage;
determining the ohmic pressure drop of the target battery pack at the current moment according to the actually measured charging current and the actual temperature value;
and inputting the first battery pack voltage, the ohmic voltage drop and the open-circuit voltage into an initial voltage observation equation to obtain the first voltage observation equation.
5. The method of claim 2, wherein determining the target hyper-parameters of the polarization voltage prediction model based on the measured charging current and the actual temperature value comprises:
Acquiring rated charge capacity of the target battery pack;
determining the actual charging rate of the target battery pack according to the actually measured charging current and the rated charge capacity;
and inquiring a preset super-parameter database, and determining the super-parameters corresponding to the actual charging multiplying power and the actual temperature value as the target super-parameters, wherein the super-parameter database comprises super-parameters corresponding to different charging multiplying powers and temperature values.
6. The method of predicting state of charge of claim 5, further comprising:
determining a plurality of test charging parameters, wherein different test charging parameters comprise different test charging multiplying powers, test charge states and test temperature values;
performing charging test on the test battery pack based on the charging parameters, and determining the super parameters of the polarized voltage state equation under the test charging parameters according to test results;
and correlating the test charging parameters with corresponding super parameters and adding the correlated test charging parameters to the super parameter database.
7. The method of claim 1, wherein calculating a second state of charge from the highest battery pack voltage and a second prediction model comprises:
Acquiring the voltage of a second battery pack when the test battery pack is in the first test charge state;
acquiring a third battery pack voltage of the test battery pack in a second test charge state;
determining a mapping relationship between voltage and state of charge according to the first test state of charge, the second battery pack voltage, the second test state of charge and the third battery pack voltage;
and determining the second state of charge based on the highest battery pack voltage and the mapping relationship.
8. The method according to any one of claims 1-7, wherein the determining the target state of charge of the target battery pack at the current time based on the first state of charge and the second state of charge further comprises:
acquiring a current display charge state of the battery pack;
and determining a target following multiplying power according to a difference value between the target state of charge and the display state of charge, controlling the display state of charge to follow the change of the target state of charge according to the target following multiplying power, wherein the target following multiplying power and the difference value form a positive correlation.
9. An energy storage device comprising a memory, a processor, and a battery pack;
The memory is used for storing a computer program;
the processor being configured to execute the computer program and to implement the state of charge prediction method according to any one of claims 1 to 8 when the computer program is executed.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, causes the processor to implement the state of charge prediction method according to any one of claims 1 to 8.
CN202310709450.7A 2023-06-14 2023-06-14 Method for predicting state of charge, energy storage device, and computer-readable storage medium Pending CN116953520A (en)

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