CN117310521B - Method, system, equipment and storage medium for calibrating charging state of lithium ion battery - Google Patents
Method, system, equipment and storage medium for calibrating charging state of lithium ion battery Download PDFInfo
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- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 title claims abstract description 153
- 229910001416 lithium ion Inorganic materials 0.000 title claims abstract description 153
- 238000000034 method Methods 0.000 title claims abstract description 65
- 239000011159 matrix material Substances 0.000 claims abstract description 129
- 238000007599 discharging Methods 0.000 claims abstract description 14
- 230000010287 polarization Effects 0.000 claims description 38
- 239000013598 vector Substances 0.000 claims description 32
- 238000000354 decomposition reaction Methods 0.000 claims description 31
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- 230000032683 aging Effects 0.000 description 4
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- 238000009825 accumulation Methods 0.000 description 3
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- 102100029469 WD repeat and HMG-box DNA-binding protein 1 Human genes 0.000 description 2
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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]
- G01R31/382—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
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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
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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]
- G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Abstract
The method comprises the steps of acquiring current values of the lithium ion battery during charging, acquiring a current state field, acquiring an open-circuit voltage value of the lithium ion battery during discharging, further predicting the current state and the voltage state to obtain a battery state predicted value and a battery state calibrated value, comparing the battery state predicted value and the battery state calibrated value to obtain an electric quantity deviation matrix, and calibrating the state of charge of the lithium ion battery according to the electric quantity deviation matrix.
Description
Technical Field
The present application relates to the technical field of lithium ion batteries, and more particularly, to a method, a system, a device, and a storage medium for calibrating a charging state of a lithium ion battery.
Background
The lithium ion battery is a secondary battery (rechargeable battery) which mainly relies on lithium ions to move between an anode and a cathode to work, has the characteristics of high voltage, light weight, high energy density and the like, can be charged rapidly, has quite rapid development in modern society, and has been applied to various fields of society, and the charging state of the lithium ion battery refers to the ratio of the charging quantity of the battery to the capacity of the lithium ion battery, so that the charging state of the lithium ion battery is monitored and controlled, and the stability of the lithium ion battery can be ensured.
In general, the state of charge of a lithium ion battery is biased due to environmental influences, and the state of charge of the lithium ion battery needs to be calibrated, and currently, common methods for calibrating the state of charge of a lithium ion battery include a voltage method and a coulomb counting method, wherein the voltage method measures the state of charge according to a battery discharge curve measured in advance, but the voltage method causes errors in measurement of the state of charge along with aging of the lithium ion battery, and the coulomb counting method needs to continuously measure from the state of charge of 0, and small errors generated in the measurement process are accumulated, so that the calibration errors of the state of charge of the battery are large due to the conventional lithium ion battery calibration schemes.
Disclosure of Invention
The application provides a method, a system, equipment and a storage medium for calibrating the charging state of a lithium ion battery, which are used for solving the problem of larger calibration error of the charging state of the lithium ion battery.
In order to solve the technical problems, the application adopts the following scheme:
in a first aspect, the present application provides a method for calibrating a state of charge of a lithium ion battery, specifically including the following steps:
collecting current values of a lithium ion battery during charging, determining a current charging state domain, determining a current prediction point set according to the current charging state domain, performing current charging state prediction on each current prediction point in the current prediction point set to obtain a plurality of current prediction candidate values, determining a battery charging state prediction value through the plurality of current prediction candidate values, and determining a prediction deviation matrix according to the plurality of current prediction candidate values and the battery charging state prediction value;
collecting an open-circuit voltage value when the lithium ion battery is discharged, and determining a current state domain according to the open-circuit voltage value when the lithium ion battery is discharged;
determining a voltage prediction point set according to the prediction deviation matrix, predicting the voltage charge state of each voltage prediction point in the voltage prediction point set through the current state domain to obtain a plurality of voltage prediction candidate values, determining a battery charge state calibration value by the plurality of voltage prediction candidate values, and determining a calibration deviation value according to the battery charge state calibration value;
And determining an electric quantity deviation matrix according to the battery charging state predicted value and the battery charging state calibration value, and calibrating the battery charging state by the calibration deviation value and the electric quantity deviation matrix.
In some embodiments, collecting a current value when the lithium ion battery is charged, and determining the current charging state domain may specifically include:
according to the curve of the current value relative to time change of the current value when the lithium ion battery is charged;
and (3) variably integrating the curve of the current value relative to time variation to obtain a current charging state domain.
In some embodiments, determining a set of current prediction points from the current state of charge domain may specifically include:
determining a state of charge offset matrix according to the current state of charge domain;
square root decomposition is carried out on the charge state offset matrix to obtain a prediction decomposition matrix;
and determining a current prediction point set according to the prediction decomposition matrix and the charging state value corresponding to the current charging state domain at the current moment.
In some embodiments, collecting the open circuit voltage value when the lithium ion battery discharges may specifically include:
discharging the fully charged lithium ion battery, stopping discharging after the electric quantity is reduced by a fixed amount, measuring the open-circuit voltage value of the lithium ion battery after the lithium ion battery is kept stand for a fixed time, then continuously discharging the lithium ion battery, and repeating the steps to obtain a plurality of open-circuit voltage values until the electric quantity of the lithium ion battery is discharged.
In some embodiments, determining the current state domain according to the open circuit voltage value when the lithium ion battery is discharged may specifically include:
and performing curve fitting according to each open-circuit voltage value, and taking a curve obtained after curve fitting as a current state domain.
In some embodiments, determining the set of voltage prediction points from the prediction bias matrix may specifically include:
performing square root decomposition on the prediction deviation matrix to obtain a calibration decomposition matrix;
and determining a voltage prediction point set according to the calibration decomposition matrix and the voltage charge state value at the current calibration moment.
In some embodiments, calibrating the battery state of charge from the calibration bias values and the charge bias matrix may specifically include:
determining battery state of charge predictionsAnd a power deviation matrix>；
Determining battery state of charge calibration valuesAnd a calibration offset +.>；
According to the electric quantity biasDifference matrixAnd the calibration deviation value->Determining a calibration weight matrix->；
According to the calibration weight matrixThe battery state of charge calibration value +.>And said battery state of charge prediction value +.>Determining a calibrated battery state of charge value, wherein the calibrated battery state of charge value is determined according to the following formula:
Wherein,for the calibrated battery state of charge value, +.>Is the battery state of charge prediction value, +.>Is a transpose of the calibration weight matrix, < >>The battery state of charge calibration value.
In a second aspect, the present application provides a state of charge calibration system for a lithium ion battery, comprising:
the prediction deviation matrix determining module is used for collecting current values when the lithium ion battery is charged, determining a current charging state domain, determining a current prediction point set according to the current charging state domain, performing current charging state prediction on each current prediction point in the current prediction point set to obtain a plurality of current prediction candidate values, determining a battery charging state prediction value according to the plurality of current prediction candidate values and the battery charging state prediction value, and determining a prediction deviation matrix according to the plurality of current prediction candidate values;
the current state domain determining module is used for collecting an open-circuit voltage value when the lithium ion battery is discharged and determining a current state domain according to the open-circuit voltage value when the lithium ion battery is discharged;
the calibration deviation value determining module is used for determining a voltage prediction point set according to the prediction deviation matrix, predicting the voltage charge state of each voltage prediction point in the voltage prediction point set through the current state domain to obtain a plurality of voltage prediction candidate values, determining a battery charge state calibration value according to the plurality of voltage prediction candidate values, and determining a calibration deviation value according to the battery charge state calibration value;
And the battery charging state calibration module is used for determining an electric quantity deviation matrix according to the battery charging state predicted value and the battery charging state calibration value, and further calibrating the battery charging state by the calibration deviation value and the electric quantity deviation matrix.
In a third aspect, the present application provides a computer device comprising a memory storing code and a processor configured to obtain the code and perform the above-described method of calibrating the state of charge of a lithium ion battery.
In a fourth aspect, the present application provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the above-described method for calibrating a state of charge of a lithium ion battery.
The technical scheme provided by the embodiment of the application has the following beneficial effects:
the application provides a method, a system, equipment and a storage medium for calibrating the charging state of a lithium ion battery, wherein the charging state of the lithium ion battery is measured through the total amount of current passing through the lithium ion battery during charging, the history characteristic of the lithium ion battery during charging can be collected, the charging state detection error of the lithium ion battery caused by aging is eliminated, a plurality of current prediction candidate values are obtained by predicting the charging state of the battery measured through the current, and the accurate current charging state is determined through the plurality of current prediction candidate values; the method has the advantages that the instantaneous characteristics of the lithium ion battery can be acquired through the charging state of the lithium ion battery measured by the voltage characteristics, error accumulation in the process of measuring the charging state by a current method can be avoided, a plurality of candidate values are obtained through prediction of the battery charging state measured by the current, and the accurate voltage charging state is determined through the plurality of candidate values; after the charging states of the lithium ion battery are measured through voltage and current means and are predicted to obtain accurate charging states, the charging states predicted by the two methods are compared, the charging states predicted by the two methods are combined after a weight matrix is constructed, errors when the charging states of the lithium ion battery are measured through voltage or current single means are reduced, the charging states of the lithium ion battery can be obtained more accurately, and therefore calibration errors of the charging states of the lithium ion battery are reduced.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
FIG. 1 is an exemplary flow chart of a method of calibrating a state of charge of a lithium-ion battery according to some embodiments of the present application;
FIG. 2 is a schematic diagram of exemplary hardware and/or software of a lithium-ion battery state-of-charge calibration system shown in accordance with some embodiments of the present application;
fig. 3 is a schematic structural diagram of a computer device to which a method for calibrating a state of charge of a lithium ion battery is applied according to some embodiments of the present application.
Detailed Description
The method comprises the steps of acquiring a current value of a lithium ion battery during charging, acquiring a current state-of-charge field, acquiring an open-circuit voltage value of the lithium ion battery during discharging, acquiring the current state field, predicting a battery state-of-charge predicted value and a battery state-of-charge calibrated value through predicting the current state-of-charge and the voltage state-of-charge, acquiring an electric quantity deviation matrix through comparing the battery state-of-charge predicted value and the battery state-of-charge calibrated value, and calibrating the state of charge of the lithium ion battery according to the electric quantity deviation matrix.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
Referring to fig. 1, which is an exemplary flowchart of a method for calibrating a state of charge of a lithium-ion battery according to some embodiments of the present application, a method 100 for calibrating a state of charge of a lithium-ion battery mainly includes the steps of:
in step 101, current values of lithium ion battery charging are collected, a current charging state domain is determined, a current prediction point set is determined according to the current charging state domain, current charging state prediction is performed on each current prediction point in the current prediction point set, a plurality of current prediction candidate values are obtained, a battery charging state prediction value is determined according to the plurality of current prediction candidate values, and a prediction deviation matrix is determined according to the plurality of current prediction candidate values and the battery charging state prediction value.
In particular, the method may further include, before collecting the current value when the lithium ion battery is charged:
the internal resistance value of the lithium ion battery and the polarization resistance value, the polarization capacitance value and the polarization voltage value at the time of charging are measured, and in some embodiments, the internal resistance value of the lithium ion battery and the polarization resistance value, the polarization capacitance value and the polarization voltage value at the time of charging may be measured by a multimeter, for example, and in other embodiments, the internal resistance value of the lithium ion battery and the polarization resistance value, the polarization capacitance value and the polarization voltage value at the time of charging may be measured by other methods, which are not limited herein.
In some embodiments, the current value of the lithium ion battery during charging is collected, and the determination of the current charging state domain may specifically be implemented by the following steps, that is:
according to the curve of the current value relative to time change of the current value when the lithium ion battery is charged;
and (3) performing indefinite integration on the curve of the current value relative to time change to obtain a current charging state domain, namely taking the curve obtained by performing indefinite integration on the curve of the current value relative to time change as the current charging state domain.
In some embodiments, determining the current prediction point set according to the current charging state domain in the present application may be implemented by the following steps:
determining a state of charge offset matrix according to the current state of charge domain;
square root decomposition is carried out on the charge state offset matrix to obtain a prediction decomposition matrix;
and determining a current prediction point set according to the prediction decomposition matrix and the current charge state value at the current calibration moment.
Additionally, in some embodiments, determining a state of charge offset matrix from the current state of charge domain may be accomplished by:
determining a current calibration time；
Determining a current state of charge domainAt->Current charge state value corresponding to time +. >；
Acquiring a curve of polarization voltage value relative to time change during charging of lithium ion battery；
According to the current calibration timeThe current state of charge domain->At->Current charge state value corresponding to time +.>And the curve of the polarization voltage value with respect to the time change during charging of a lithium ion battery +.>Determining a state of charge offset matrix->Wherein the state of charge offset matrix +.>The determination can be made by the following formula:
wherein,is a time variable, +.>Is->Polarization voltage at time, ">Representing the transpose of the matrix>Indicating a desire to find the matrix.
It should be noted that, in some embodiments, the decomposition process for performing square root decomposition on the state of charge offset matrix to obtain the prediction decomposition matrix may be expressed according to the following formula:
wherein,for the +.>Line->Column element->For the +.>Line->Column element->For the +.>Line->Column element->For the +.>Line->Column element->For the +.>Line->The column elements, it should be noted that, since the dimension of the charge state offset matrix is 2×2, in the above formula for determining the calibration decomposition matrix, +. >The values of (2) are 1 and 2,/l>The values of (2) are 1 and 2.
In some embodiments, determining the current prediction point set according to the prediction decomposition matrix and the current charge state value at the current calibration time may be implemented by the following steps:
determining a current calibration time；
Determining a current state of charge domain；
Determining a predictive decomposition matrix；
According to the current calibration timeCurrent state of charge domain->And predictive decomposition matrix->Determining a set of current prediction points, wherein the set of current prediction points can be determined according to the following formula:
wherein,for the +.>Current prediction points->Is the polarization voltage value versus time curve, < >>For a preset predictive vector cluster, +.>Representation->Middle->A vector;
it should be noted that, in the present application, the preset prediction vector cluster may be determined by the dimension of the state of charge offset matrix, for example, the dimension of the state of charge offset matrix in the present application isThe predetermined predictive vector cluster is then composed of all two-dimensional vectors of 1,0 and-1, in other implementationsIn an example, other prediction vector clusters may be preset by other methods to determine the current prediction point set, which is not limited herein.
In some embodiments, the current state of charge prediction is performed on each current prediction point in the current prediction point set, so as to obtain a plurality of current prediction candidate values, which may specifically be implemented in the following manner, that is:
Determining a current calibration time；
Obtaining polarization resistance value of lithium ion battery during chargingPolarization capacitance value->、/>Polarization voltage value +.>And->Current value +.>；
According to the current calibration timePolarization resistance value +.>Polarization capacitance value +.>、/>Polarization voltage value +.>And->Current value +.>Determining a current prediction candidate, wherein the current prediction candidate may be determined by the following formula:
wherein,for the 9 th current prediction candidate, +.>Is->Current prediction candidates ∈>Is->Current prediction points->Is a natural constant->Is the current charge state domain +.>Since the number of the current prediction points is 8, the above formula for determining the voltage prediction candidate value is +.>Is of the value of (2)Starting from 1 to 8.
In some embodiments, determining the battery state of charge prediction value from the plurality of current prediction candidate values may be implemented in such a way that:
determining a current calibration time；
Obtaining charging time of lithium ion batteryPolarization voltage value +.>；
According to the current calibration timeAnd lithium ion battery charging->Polarization voltage value +. >Determining a battery state of charge prediction value, wherein the battery state of charge prediction value may be determined according to the following formula:
wherein,is the battery state of charge prediction value, +.>Is the predicted polarization voltage value of the lithium ion battery during charging,/->Is->Current prediction candidates ∈>For the 9 th current prediction candidate, +.>Representing the +.f in the preset predictor cluster>A vector; it should be noted that, 8 and 4 in the above formula for determining the predicted value of the battery state of charge are determined according to a preset predicted vector cluster, and the values are different in different application environments, and in other embodiments, other values may be preset for determining the predicted value of the battery state of charge, which is not limited herein.
In some embodiments, determining the prediction bias matrix from the battery state of charge predictions may be implemented by:
determining a current calibration time；
Determining battery state of charge predictions；
Determining a predicted polarization voltage value when charging a lithium ion battery；
According to the current calibration timePredicted value of battery state of charge->And predicted polarization voltage value +.>Determining a prediction bias matrix, wherein the prediction bias matrix may be determined according to the following formula:
Wherein,representing a prediction bias matrix, < >>Is->Current prediction candidates ∈>For the 9 th current prediction candidate, +.>Representing the transpose of the matrix>For determining intermediate variables of the prediction bias matrix, +.>Representation->Is used to determine the transposed matrix of (a),representing the +.f in the preset predictive vector cluster>The vectors, it should be noted, are +_in the above formula for determining the prediction bias matrix>The above-mentioned common equation for determining the prediction bias matrix is only one process quantity, and has no practical meaningThe values 8 and 4 in the formula are determined according to a preset prediction vector cluster, and are different in different application environments, and in other embodiments, other values may be preset to determine a prediction deviation matrix, which is not limited herein.
It should be noted that, the charging state of the lithium ion battery, that is, the number of charge loads, can be determined by the total amount of current passing through the lithium ion battery during charging, by collecting the historical characteristics of the lithium ion battery during charging, the method eliminates the charging state detection error caused by aging of the lithium ion battery, in this embodiment, the charging state of the battery measured by the current is predicted to obtain multiple candidate values, and the accurate charging state is determined by the multiple candidate values, thereby improving the measurement accuracy of the charging state of the lithium ion battery.
In step 102, an open-circuit voltage value of the lithium ion battery during discharging is collected, and a current state domain is determined according to the open-circuit voltage value of the lithium ion battery during discharging.
In some embodiments, the following methods may be specifically adopted to collect the open circuit voltage value when the lithium ion battery discharges, namely:
discharging the fully charged lithium ion battery, stopping discharging when the electric quantity is reduced by a fixed amount, measuring the open-circuit voltage value of the lithium ion battery at the moment after the lithium ion battery is kept stand for a fixed time, then continuously discharging the lithium ion battery, and repeating the steps to obtain a plurality of open-circuit voltage values until the electric quantity of the lithium ion battery is discharged.
In some embodiments, determining the current state domain according to the open circuit voltage value when the lithium ion battery discharges may specifically be implemented by the following steps:
and performing curve fitting according to the open-circuit voltage values, and taking a curve obtained after the curve fitting as a current state domain.
It should be noted that, in the present application, curve fitting the plurality of open circuit voltage values to obtain the current state domain may be performed by a polynomial fitting method in the prior art, and in other embodiments, curve fitting may also be performed by other methods, which is not limited herein.
In particular implementations, the current state domain may be expressed according to the following formula:
wherein,polynomial coefficients for polynomial fitting>For the coefficients according to the polynomial>Determined fitting function, +.>For the open-circuit voltage value of the lithium ion battery, < >>And the charge state value of the lithium ion battery.
In step 103, a voltage prediction point set is determined according to the prediction deviation matrix, each voltage prediction point in the voltage prediction point set is subjected to voltage charging state prediction through the current state domain, a plurality of voltage prediction candidate values are obtained, a battery charging state calibration value is determined according to the plurality of voltage prediction candidate values, and a calibration deviation value is determined according to the battery charging state calibration value.
In some embodiments, determining the set of voltage prediction points from the prediction bias matrix may be accomplished by:
performing square root decomposition on the prediction deviation matrix to obtain a calibration decomposition matrix;
and determining a voltage prediction point set according to the calibration decomposition matrix and the battery charge state prediction value.
In some embodiments, the calibration decomposition matrix may be determined according to the following equation:
wherein,is said predictive decomposition matrix, >For the state of charge offset matrix, +.>For the +.>Line->Column element->For the +.>Line->Column element->For the +.>Line->Column element->For the +.>Line->The elements of the column are arranged such that,for the +.>Line->The elements of the columns, it should be noted that, since the dimension of the prediction bias matrix is 2×2, in the above formula for determining the calibration decomposition matrix, +.>The value of (2) is 1 or 2,/or%>The value of (2) is 1 or 2.
In some embodiments, determining the set of voltage prediction points from the calibration decomposition matrix and the battery state of charge predictions may be accomplished by:
determining a current calibration time；
Determining a calibration decomposition matrix；
Determining battery state of charge predictions；
According to the current calibration timeThe calibration decomposition matrix->And said battery state of charge prediction value +.>Determining a set of voltage prediction points, wherein the set of voltage prediction points can be determined according to the following formula:
wherein,is->Voltage prediction points, ">Is the predicted polarization voltage value of the lithium ion battery during charging,/->Is the current calibration time, +. >For a preset predictive vector cluster, +.>Is the +.o in the preset predictive vector cluster>A vector;
it should be noted that, in the present application, the preset prediction vector cluster may be determined by the dimension of the state of charge offset matrix, for example, the dimension of the state of charge offset matrix in the present application isThe preset prediction vector cluster is composed of all two-dimensional vectors composed of 1,0 and-1, and in other embodiments, the current prediction point set may be determined by other methods preset by other prediction vector clusters, which is not limited herein.
In some embodiments, the predicting the voltage charging state of each voltage predicting point in the set of voltage predicting points may specifically be the following manner to obtain a plurality of voltage predicting candidate values, where:
determining a current calibration time；
Measuring internal resistance value of lithium ion battery；
Measuring the calibration time of a lithium ion batteryCurrent value +.>；
According to the calibration timeVoltage prediction Point set->Internal resistance value of lithium ion battery>And lithium ion battery at calibration time +.>Current value +.>Determining a voltage prediction candidate +.>Wherein the voltage prediction candidate may be determined according to the following formula:
wherein,is->A voltage prediction candidate value ∈ >For the 9 th voltage prediction candidate, +.>Polynomial coefficients for the current state domain,/->For the coefficients according to the polynomial>Determined fitting function, +.>Is->Is an inverse function of +.>Is->Voltage prediction points, ">Is the battery state of charge prediction value, +.>Is the predicted polarization voltage value when the lithium ion battery is charged.
In some embodiments, the battery state of charge calibration value may be determined according to the following equation:
wherein,for battery state of charge calibration value, +.>Is->A voltage prediction candidate value ∈>For the 9 th voltage prediction candidate, +.>Is the +.o in the preset predictive vector cluster>The vectors, it should be noted that, 8 and 4 in the above formula for determining the predicted value of the battery state of charge are determined according to a preset predicted vector cluster, and the values are different in different application environments, and in other embodiments, other values may be preset for determining the predicted value of the battery state of charge, which is not limited herein.
In some embodiments, the calibration offset may be determined in such a way that:
determining battery state of charge calibration values；
Calibrating a value according to the battery state of chargeDetermining a calibration offset +. >Wherein the calibration offset value may be determined according to the following formula:
wherein,is->A voltage prediction candidate value ∈>Is the +.o in the preset predictive vector cluster>The vectors, it is noted that +.>For determining the intermediate variable of the calibration offset, +.>Is->9>Is a value of (2).
It should be noted that, the above-mentioned state of charge of the lithium ion battery measured by voltage characteristics is a measurement method that only considers the transient characteristics of the lithium ion battery, and this method can avoid error accumulation in the process of measuring the state of charge by a current method.
In step 104, a power deviation matrix is determined according to the predicted value of the battery charging state and the calibration value of the battery charging state, and then the calibration deviation value and the power deviation matrix calibrate the battery charging state.
In some embodiments, the power deviation matrix may be implemented in the following manner, that is:
determining battery state of charge predictions ；
Determining battery state of charge calibration values；
According to the battery charge state predicted valueAnd said battery state of charge calibration value +.>Determining a power deviation matrix>Wherein the power deviation matrix may be determined according to the following formula:
wherein,for the power deviation matrix,/a>Is->Current prediction candidates ∈>Is the battery state of charge prediction value, +.>Is the predicted polarization voltage value of the lithium ion battery during charging,/->Is->A voltage prediction candidate value ∈>For said battery state of charge calibration value, < >>Is the +.>The vectors, it is noted that +.>For determining the intermediate variables of the power deviation matrix, +.>Is->9>It should be noted that, the constant 8 and the constant 4 in the above formula for determining the predicted value of the battery state of charge are preset according to the number of the candidate voltage values, and may be different in different application environments, and in other embodiments, other values may be preset to determine the predicted value of the battery state of charge, which is not limited herein.
In some embodiments, calibrating the battery state of charge according to the charge bias matrix and the calibration bias value may be accomplished by:
Calculating a calibration weight matrix from the power deviation matrix and the calibration deviation values, the calibration weight matrix being representable according to the following formula:
wherein,for the calibration weight matrix,/a>For the power deviation matrix,/a>And (c) correcting the deviation value.
Calibrating the battery charging state calibration value and the battery charging state prediction value according to the calibration weight matrix to obtain a calibrated battery charging state, wherein the calibration can be realized according to the following formula:
wherein,for the value of the calibrated state of charge, +.>Is the battery state of charge prediction value, +.>Is the transpose of the calibration weights, +.>The battery state of charge calibration value.
It should be noted that, after the above steps measure the charging state of the lithium ion battery by two means of voltage and current and predict the charging state to obtain an accurate charging state, the charging states predicted by the two methods are compared in step 104, and the charging states predicted by the two methods are combined after the weight matrix is constructed, so that the error when the charging state of the lithium ion battery is measured by a single means of voltage or current is reduced, and the charging state of the lithium ion battery can be obtained more accurately, thereby realizing calibration of the charging state of the lithium ion battery.
Additionally, in some embodiments, reference is made to fig. 2, which is a schematic diagram of exemplary hardware and/or software of a state of charge calibration system for a lithium-ion battery, which may include: the prediction bias matrix determination module 201, the current state domain determination module 202, the calibration bias value determination module 203, and the battery state of charge calibration module 204 are described as follows:
the prediction deviation matrix determining module 201, where the prediction deviation matrix determining module 201 is mainly configured to collect current values when a lithium ion battery is charged, determine a current charging state domain, determine a current prediction point set according to the current charging state domain, perform current charging state prediction on each current prediction point in the current prediction point set, obtain a plurality of current prediction candidate values, determine a battery charging state prediction value according to the plurality of current prediction candidate values, and determine a prediction deviation matrix according to the plurality of current prediction candidate values and the battery charging state prediction value;
the current state domain determining module 202, where the current state domain determining module 202 is mainly configured to collect an open-circuit voltage value when the lithium ion battery is discharged, and determine a current state domain according to the open-circuit voltage value when the lithium ion battery is discharged;
The calibration deviation value determining module 203, where the calibration deviation value determining module 203 is configured to determine a voltage prediction point set according to the prediction deviation matrix, predict a voltage charging state of each voltage prediction point in the voltage prediction point set through the current state domain, obtain a plurality of voltage prediction candidate values, determine a battery charging state calibration value according to the plurality of voltage prediction candidate values, and determine a calibration deviation value according to the battery charging state calibration value;
the battery charging state calibration module 204, herein, the battery charging state calibration module 204 is mainly configured to determine an electric quantity deviation matrix according to the battery charging state predicted value and the battery charging state calibration value, and then calibrate the battery charging state by the calibration deviation value and the electric quantity deviation matrix.
In some embodiments, reference is made to fig. 3, which is a schematic structural diagram of a computer device employing a method for calibrating the state of charge of a lithium-ion battery according to some embodiments of the present application. The method of calibrating the state of charge of the lithium-ion battery in the above embodiment may be implemented by a computer device shown in fig. 3, which includes at least one processor 301, a communication bus 302, a memory 303, and at least one communication interface 304.
The processor 301 may be a general purpose central processing unit (central processing unit, CPU), application-specific integrated circuit (ASIC), or execution of one or more of the methods for controlling the state of charge calibration of the lithium-ion battery of the present application.
Communication bus 302 may include a path to transfer information between the above components.
The Memory 303 may be, but is not limited to, a read-only Memory (ROM) or other type of static storage device that can store static information and instructions, a random access Memory (random access Memory, RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only Memory (electrically erasable programmable read-only Memory, EEPROM), a compact disc (compact disc read-only Memory) or other optical disk storage, a compact disc storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory 303 may be stand alone and be coupled to the processor 301 via the communication bus 302. Memory 303 may also be integrated with processor 301.
The memory 303 is used for storing program codes for executing the embodiments of the present application, and the processor 301 controls the execution. The processor 301 is configured to execute program code stored in the memory 303. One or more software modules may be included in the program code. The method of calibrating the state of charge of the lithium-ion battery in the above embodiments may be implemented by one or more software modules in the program code in the processor 301 and the memory 303.
Communication interface 304, using any transceiver-like device for communicating with other devices or communication networks, such as ethernet, radio access network (radio access network, RAN), wireless local area network (wireless local area networks, WLAN), etc.
In a specific implementation, as an embodiment, a computer device may include a plurality of processors, where each of the processors may be a single-core (single-CPU) processor or may be a multi-core (multi-CPU) processor. A processor herein may refer to one or more devices, circuits, and/or processing cores for processing data (e.g., computer program instructions).
The computer device may be a general purpose computer device or a special purpose computer device. In particular implementations, the computer device may be a desktop, laptop, web server, palmtop (personal digital assistant, PDA), mobile handset, tablet, wireless terminal device, communication device, or embedded device. Embodiments of the present application are not limited in the type of computer device.
In addition, the application also discloses a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the method for calibrating the charging state of the lithium ion battery when being executed by a processor.
The computer-readable medium or machine-readable medium of the present application may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device that implements charge state calibration of a lithium ion battery, the computer-readable medium may be a machine-readable signal medium or machine-readable storage medium, and the computer-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory or flash memory, an optical fiber, a portable compact disc read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In summary, the method and the device measure the charging state of the lithium ion battery through the total amount of the current passing through the lithium ion battery during charging, can collect the history characteristic of the lithium ion battery during charging, eliminate the charging state detection error caused by aging of the lithium ion battery, predict the charging state of the battery measured through the current to obtain a plurality of candidate values, and determine the accurate current charging state through the plurality of candidate values; the method has the advantages that the instantaneous characteristics of the lithium ion battery can be acquired through the charging state of the lithium ion battery measured by the voltage characteristics, error accumulation in the process of measuring the charging state by a current method can be avoided, a plurality of candidate values are obtained through prediction of the battery charging state measured by the current, and the accurate voltage charging state is determined through the plurality of candidate values; after the charging states of the lithium ion battery are measured through voltage and current means and are predicted to obtain accurate charging states, the charging states predicted by the two methods are compared, the charging states predicted by the two methods are combined after a weight matrix is constructed, errors when the charging states of the lithium ion battery are measured through voltage or current single means are reduced, the charging states of the lithium ion battery can be obtained more accurately, and therefore calibration errors of the charging states of the lithium ion battery are reduced.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the invention. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (5)
1. The method for calibrating the charging state of the lithium ion battery is characterized by comprising the following steps of:
collecting current values of a lithium ion battery during charging, determining a current charging state domain, determining a current prediction point set according to the current charging state domain, performing current charging state prediction on each current prediction point in the current prediction point set to obtain a plurality of current prediction candidate values, determining a battery charging state prediction value through the plurality of current prediction candidate values, and determining a prediction deviation matrix according to the plurality of current prediction candidate values and the battery charging state prediction value;
Collecting an open-circuit voltage value when the lithium ion battery is discharged, and determining a current state domain according to the open-circuit voltage value when the lithium ion battery is discharged;
determining a voltage prediction point set according to the prediction deviation matrix, predicting the voltage charge state of each voltage prediction point in the voltage prediction point set through the current state domain to obtain a plurality of voltage prediction candidate values, determining a battery charge state calibration value by the plurality of voltage prediction candidate values, and determining a calibration deviation value according to the battery charge state calibration value;
determining an electric quantity deviation matrix according to the battery charging state predicted value and the battery charging state calibration value, and calibrating the battery charging state by the calibration deviation value and the electric quantity deviation matrix;
the method for determining the current charging state domain specifically comprises the following steps of:
according to the curve of the current value relative to time change of the current value when the lithium ion battery is charged;
variably integrating the curve of the current value with respect to time variation to obtain a current charging state domain;
wherein determining a current prediction point set according to the current state of charge domain specifically includes:
Determining a state of charge offset matrix according to the current state of charge domain;
square root decomposition is carried out on the charge state offset matrix to obtain a prediction decomposition matrix;
determining a current prediction point set according to the prediction decomposition matrix and a charging state value corresponding to the current charging state domain at the current moment;
the method for predicting the current charging state of each current prediction point in the current prediction point set, and obtaining a plurality of current prediction candidate values specifically comprises the following steps:
determining a current calibration time；
Obtaining polarization resistance value of lithium ion battery during chargingPolarization capacitance value->、/>Polarization voltage value +.>And->Current value +.>；
According to the current calibration timePolarization resistance value +.>Polarization capacitance value +.>、/>Polarization voltage value +.>And->Current value +.>Determining a current prediction candidate, wherein the current prediction candidate is determined by the following formula:
wherein,for the 9 th current prediction candidate, +.>Is->Current prediction candidates ∈>Is->Current prediction points->Is a natural constant->Is the current charge state domain +.>A current state of charge value corresponding to the moment;
wherein determining a battery state of charge prediction value according to the plurality of current prediction candidate values specifically includes:
Determining a current calibration time；
Obtaining charging time of lithium ion batteryPolarization voltage value +.>；
According to the current calibration timeAnd lithium ion battery charging->Polarization voltage value +.>Determining a battery state of charge prediction value, wherein the battery state of charge prediction value is determined according to the following formula:
wherein,is the state of charge of the batteryPredictive value->Is the predicted polarization voltage value when the lithium ion battery is charged,is->Current prediction candidates ∈>For the 9 th current prediction candidate, +.>Representing the +.f in the preset predictor cluster>A vector;
wherein determining a prediction bias matrix from the plurality of current prediction candidates and the battery state of charge prediction value specifically includes:
determining the current calibration time;
determining a battery state of charge prediction value;
determining a predicted polarization voltage value when the lithium ion battery is charged;
determining a prediction deviation matrix according to the current calibration time, the battery charging state predicted value and the predicted polarization voltage value when the lithium ion battery is charged, wherein the prediction deviation matrix is determined according to the following formula:
wherein,representing a prediction bias matrix, < >>Is->Current prediction candidates ∈ >For the 9 th current prediction candidate, +.>Representing the transpose of the matrix>For determining intermediate variables of the prediction bias matrix, +.>Representation->Transposed matrix of>Representing the +.f in the preset predictive vector cluster>Vector(s)>Is a process quantity, without actual meaning;
the method for determining the current state domain according to the open-circuit voltage value when the lithium ion battery discharges specifically comprises the following steps:
performing curve fitting according to each open-circuit voltage value, and taking a curve obtained after curve fitting as a current state domain;
the method for predicting the voltage charging state of each voltage prediction point in the voltage prediction point set to obtain a plurality of voltage prediction candidate values specifically comprises the following steps:
determination ofCurrent calibration time；
Measuring internal resistance value of lithium ion battery；
Measuring the calibration time of a lithium ion batteryCurrent value +.>；
According to the calibration timeVoltage prediction Point set->Internal resistance value of lithium ion battery>And lithium ion battery at calibration timeCurrent value +.>Determining a voltage prediction candidate +.>Wherein the voltage prediction candidate is determined according to the following formula:
wherein,is->A voltage prediction candidate value ∈>For the 9 th voltage prediction candidate, +.>Polynomial coefficients for the current state domain,/- >For the coefficients according to the polynomial>Determined fitting function, +.>Is thatIs an inverse function of +.>Is->Voltage prediction points, ">Is the battery state of charge prediction value, +.>The predicted polarization voltage value is the polarization voltage value when the lithium ion battery is charged;
wherein, the calibration deviation value is determined by the following method that:
determining battery state of charge calibration values；
Calibrating a value according to the battery state of chargeDetermining a calibration offset +.>Wherein the calibration offset is determined according to the following formula:
wherein,is->A voltage prediction candidate value ∈>Is the +.o in the preset predictive vector cluster>The vectors, it is noted that +.>For determining the intermediate variable of the calibration offset, +.>Is->9>Is a value of (2);
the method comprises the following steps of:
determining a current calibration time；
Determining a current state of charge domainAt->Current charge state value corresponding to time +.>；
Acquiring a curve of polarization voltage value relative to time change during charging of lithium ion battery；
According to the current calibration timeThe current state of charge domain->At->Current charge state value corresponding to time +.>And the curve of the polarization voltage value with respect to the time change during charging of a lithium ion battery +. >Determining a state of charge offset matrixWherein the state of charge offset matrix +.>Is determined by the following formula:
wherein,is a time variable, +.>Is->Polarization voltage at time, ">Representing the transpose of the matrix>Representing a desire to find the matrix;
the method for determining the voltage prediction point set according to the prediction deviation matrix specifically comprises the following steps:
performing square root decomposition on the prediction deviation matrix to obtain a calibration decomposition matrix;
determining a voltage prediction point set according to the calibration decomposition matrix and the voltage charge state value at the current calibration moment;
wherein a battery state of charge calibration value is determined from the plurality of voltage prediction candidates, the battery state of charge calibration value being determined by the following formula:
wherein,for battery state of charge calibration value, +.>Is->A voltage prediction candidate value ∈>For the 9 th voltage prediction candidate, +.>Is the +.o in the preset predictive vector cluster>A vector;
the determining the electric quantity deviation matrix according to the battery charging state predicted value and the battery charging state calibrated value specifically comprises:
determining battery state of charge predictions；
Determining battery state of charge calibration values；
According to the battery charge state predicted valueAnd said battery state of charge calibration value +. >Determining a power deviation matrix>WhereinThe electric quantity deviation matrix is determined according to the following formula:
wherein,for the power deviation matrix,/a>Is->Current prediction candidates ∈>Is the battery state of charge prediction value, +.>Is the predicted polarization voltage value of the lithium ion battery during charging,/->Is->A voltage prediction candidate value ∈>For said battery state of charge calibration value, < >>Is the +.>Vector(s)>For determining the intermediate variables of the power deviation matrix, +.>Is->9>Is a value of (2);
the calibrating the battery charging state by the calibration deviation value and the electric quantity deviation matrix specifically comprises the following steps:
determining battery state of charge predictionsAnd a power deviation matrix>；
Determining battery state of charge calibration valuesAnd a calibration offset +.>；
According to the electric quantity deviation matrixAnd the calibration deviation value->Determining a calibration weight matrix->；
According to the calibration weight matrixThe battery state of charge calibration value +.>And the battery state of charge prediction valueDetermining a calibrated battery state of charge value, wherein the calibrated battery state of charge value is determined according to the following formula:
wherein,for the calibrated battery state of charge value, +. >Is the battery state of charge prediction value, +.>Is a transpose of the calibration weight matrix, < >>The battery state of charge calibration value.
2. The method of claim 1, wherein the step of collecting the open circuit voltage value of the lithium ion battery when discharging comprises:
discharging the fully charged lithium ion battery, stopping discharging after the electric quantity is reduced by a fixed amount, measuring the open-circuit voltage value of the lithium ion battery after the lithium ion battery is kept stand for a fixed time, then continuously discharging the lithium ion battery, and repeating the steps to obtain a plurality of open-circuit voltage values until the electric quantity of the lithium ion battery is discharged.
3. A state of charge calibration system for a lithium ion battery calibrated using the method of claim 1, comprising:
the prediction deviation matrix determining module is used for collecting current values when the lithium ion battery is charged, determining a current charging state domain, determining a current prediction point set according to the current charging state domain, performing current charging state prediction on each current prediction point in the current prediction point set to obtain a plurality of current prediction candidate values, determining a battery charging state prediction value according to the plurality of current prediction candidate values and the battery charging state prediction value, and determining a prediction deviation matrix according to the plurality of current prediction candidate values;
The current state domain determining module is used for collecting an open-circuit voltage value when the lithium ion battery is discharged and determining a current state domain according to the open-circuit voltage value when the lithium ion battery is discharged;
the calibration deviation value determining module is used for determining a voltage prediction point set according to the prediction deviation matrix, predicting the voltage charge state of each voltage prediction point in the voltage prediction point set through the current state domain to obtain a plurality of voltage prediction candidate values, determining a battery charge state calibration value according to the plurality of voltage prediction candidate values, and determining a calibration deviation value according to the battery charge state calibration value;
and the battery charging state calibration module is used for determining an electric quantity deviation matrix according to the battery charging state predicted value and the battery charging state calibration value, and further calibrating the battery charging state by the calibration deviation value and the electric quantity deviation matrix.
4. A computer device comprising a memory storing code and a processor configured to obtain the code and to perform the method of calibrating the state of charge of a lithium-ion battery according to any of claims 1 or 2.
5. A computer readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the method of calibrating the state of charge of a lithium ion battery according to any of claims 1 or 2.
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