CN113484771A - Method for estimating wide-temperature full-life SOC and capacity of lithium ion battery - Google Patents

Method for estimating wide-temperature full-life SOC and capacity of lithium ion battery Download PDF

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CN113484771A
CN113484771A CN202110746424.2A CN202110746424A CN113484771A CN 113484771 A CN113484771 A CN 113484771A CN 202110746424 A CN202110746424 A CN 202110746424A CN 113484771 A CN113484771 A CN 113484771A
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soc
battery
model
migration
capacity
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高承志
申江卫
陈峥
沈世全
赵红茜
赵广达
马文赛
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Kunming University of Science and Technology
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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]
    • 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
    • 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]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health

Abstract

The invention relates to a method for estimating wide-temperature full-life SOC and capacity of a lithium ion battery, which specifically comprises the following steps: (1) obtaining corresponding technical parameters of the battery; (2) selecting a second-order RC equivalent circuit model as a basic battery model of the migration model; (3) obtaining internal parameter information of a battery model; (4) establishing a framework of a migration model; (5) carrying out online determination and SOC value determination on the migration factor of the migration model by using a risk minimization particle filter algorithm; (6) an estimate of the actual available capacity is made. The method is used for realizing the joint accurate estimation of the SOC and the capacity of the lithium ion battery. According to the method, the influence of temperature and aging on the battery is regarded as uncertain quantity, online migration of the initial migration model under different temperatures and aging states can be realized only by using the migration model established by a small amount of offline data and the data of the battery in the actual use process, and the offline experimental workload in the traditional aging battery model modeling process is greatly reduced.

Description

Method for estimating wide-temperature full-life SOC and capacity of lithium ion battery
Technical Field
The invention belongs to the technical field of lithium ion batteries, and particularly relates to a method for estimating wide-temperature full-life SOC and capacity of a lithium ion battery.
Background
The SOC is an index used for describing the state of the residual electric quantity of the power battery, and accurate SOC estimation is an important guarantee for guaranteeing the high-efficiency performance of the power battery; there are many methods for estimating the SOC state, including roughly three categories, that is, ampere-hour integration method, black box SOC estimation model, and state space model-based method. The ampere-hour integration method has a simple principle and a high calculation speed, but has the defect that the SOC value at the initial moment is generally unknown, so the error of the SOC estimation value is relatively large; black box models commonly used to estimate battery SOC include neural network models, fuzzy logic models, and support vector regression models, among others. These models have high dependency on the data amount and are therefore poor in terms of practicality. The method based on the space state model is also called a model-based method, the most common model is an equivalent circuit model, SOC estimation is carried out based on the model, the estimation accuracy depends heavily on the model accuracy, and the model accuracy is seriously influenced by temperature and battery aging, so that the SOC estimation accuracy is reduced. Therefore, the method takes the second-order RC equivalent circuit model as a basic model to construct a migration model, takes the influence of temperature and aging on model precision as uncertain quantity, and obtains an accurate SOC state value by carrying out linear migration on the migration factor in the migration model.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for estimating the wide-temperature full-life SOC and the capacity of a lithium ion battery to solve the problem of low SOC estimation precision caused by the fact that a battery model is greatly influenced by temperature and aging in the prior art.
In order to solve the technical problems, the technical scheme of the invention is as follows: the method for estimating the wide-temperature full-life SOC and the capacity of the lithium ion battery is characterized by comprising the following steps of: the method specifically comprises the following steps:
(1) randomly selecting the type and the type of the lithium ion battery to obtain corresponding technical parameters of the battery;
(2) selecting a second-order RC equivalent circuit model as a basic battery model of the migration model;
(3) performing working condition experiments on the selected lithium ion battery to obtain characteristic parameters of the battery, and performing parameter identification by utilizing an optimization method by utilizing the relationship between the open-circuit voltage (OCV) and the SOC to obtain internal parameter information of a battery model;
(4) establishing a relation curve between the parameter information obtained by parameter identification in the step (3) and the SOC by utilizing a polynomial fitting method, and establishing a frame of a migration model;
(5) carrying out online determination and SOC value determination on the migration factor of the migration model by using a risk minimization particle filter algorithm;
(6) and (5) estimating the actual available capacity by carrying out capacity back-pushing on the accurate SOC value obtained in the step (5) by an ampere-hour integration method.
Further, the technical parameters corresponding to the battery obtained in the step (1) include a rated capacity, a rated voltage, a charging mode, an allowable charging temperature and a discharging temperature.
Further, the selected second-order RC equivalent circuit model in the step (2) is used as a basic battery model of the migration model, wherein a specific model equation formula of the second-order RC equivalent circuit model is as follows:
Figure BDA0003143071350000021
in the formula R0Indicating the ohmic internal resistance, R, of the interior of the cell1、R2Denotes the polarization resistance, C1、C2Represents polarization capacitance, OCVtIndicating the open circuit voltage, V, of the battery at time ttRepresenting terminal voltage, U, of the battery at time t0、U1、U2Each represents R0、R1C1、R2C2Voltage across, focv(SOCt) Is a functional expression of the open circuit voltage OCV with respect to the SOC.
Further, the step (3) of performing a working condition experiment on the selected battery to obtain characteristic parameters of the battery, establishing a relationship between an open-circuit voltage OCV and an SOC by using an open-circuit voltage method, and performing parameter identification by using an optimization method to obtain internal parameter information of the battery specifically includes the following steps:
(31) carrying out a standard capacity test experiment on the selected battery at a specific temperature to obtain the standard capacity of the battery;
(32) performing a working condition experiment at a specific temperature to obtain characteristic parameters of the battery, wherein the characteristic parameters of the battery comprise the voltage, the current and the temperature of the battery, and a relation between open-circuit voltage SOC is established;
(33) and (4) performing parameter identification on the characteristic parameters obtained in the step (32) by an optimal method to obtain parameter information inside the battery model, wherein the parameter information comprises electrochemical polarization capacitance, concentration difference polarization capacitance, electrochemical polarization voltage and concentration difference polarization voltage.
Further, the specific temperature in the steps (31) and (32) is a high temperature, a low temperature, or a normal temperature, wherein the high temperature, the low temperature, or the normal temperature is estimated corresponding to the SOC and the capacity of the battery in a state of the high temperature, the low temperature, or the normal temperature selected as needed.
Further, the optimal method in the step (33) includes a recursive least square method, a particle swarm algorithm and a genetic algorithm.
Further, the establishing of the framework of the migration model in the step (4) specifically includes the following steps:
(41) and (3) constructing a relation curve between the parameter information obtained by parameter identification in the step (3) and the SOC by utilizing a polynomial fitting method, wherein a specific fitting formula is as follows:
Figure BDA0003143071350000031
in the formula of alphaiIs the fitting coefficient i 1,2,3.. 6, SOCt SOCtThe SOC value at the time t is obtained, and f is an SOC and battery model parameter curve;
(42) establishing a framework of a migration model based on an SOC and battery internal parameter curve, wherein a specific expression is as follows:
Figure BDA0003143071350000041
wherein X is ═ X1,x2,x3,…,x14]Is a migration factor matrix of the model, x1SOCt+x2SOC in this formula for corrected SOC valuet SOCtThe method is obtained by an ampere-hour integration method, an inaccurate SOC value of the battery, which is influenced by temperature and aging, is not considered, and f is a relation curve of the SOC and a battery model parameter.
Further, the online determination and SOC value determination of the migration factor of the migration model by using the risk minimization particle filter algorithm in the step (5) specifically includes the following steps:
(51) converting the migration matrix X into [ X ]1,x2,x3,…,x14]As system state variables, where [ x ]1,x2,x3,…,x14For the migration factor to be determined, the battery terminal voltage is used as the observed quantity of the system, and the load current I in the battery working processtAnd inaccurate state of charge SOCtAs input to the system, a system discrete state equation is established:
Figure BDA0003143071350000042
in the formula sigmai 2Measuring the variance, ω, of the noise for the systemiIs the system noise, and N is the number of particles;
(52) by a priori probability p (x)t) Sampling:
Figure BDA0003143071350000043
wherein δ (·) is a dirichlet function, and i is an index of the particle population;
(53) calculating a risk minimization parameter:
Figure BDA0003143071350000051
wherein
Figure BDA0003143071350000052
Is a hypothetical system state xkFunction of the true value of p2Is a continuous strictly convex function, pi2In order to be a risk parameter,
Figure BDA0003143071350000053
minimizing an estimate for the risk; since the SOC is used as an important parameter to be estimated in the migration model process, it is used as a risk minimization parameter in the risk minimization particle filter algorithm:
Figure BDA0003143071350000054
in the formula (I), the compound is shown in the specification,
Figure BDA0003143071350000055
by migration factor
Figure BDA0003143071350000056
And
Figure BDA0003143071350000057
corrected preliminary SOC estimation values, i.e.
Figure BDA0003143071350000058
Figure BDA0003143071350000059
Minimizing an estimate for the risk;
Figure BDA00031430713500000510
Figure BDA00031430713500000511
in the formula of omegat jIs the particle weight;
(55) resampling:
Figure BDA00031430713500000512
in the formula EfftFor particle efficiency, when EfftWhen the value is less than 85% of the resampling threshold value set in the text, the following resampling process is carried out on the particles, wherein n is the number of the particles:
3) regenerating the sample Qm(m＝1,2,3...N)。
4) The resampled particles should satisfy the following equation:
Figure BDA0003143071350000061
the resampled particle weight is
Figure BDA0003143071350000062
(56) SOC estimation:
Figure BDA0003143071350000063
in the formula
Figure BDA0003143071350000064
The actual SOC value obtained by carrying out linear migration on the migration factor;
(57) estimating the model terminal voltage:
Figure BDA0003143071350000065
further, in the step (6), the actual available capacity is estimated by performing capacity back-pushing on the actual SOC value estimated in the step (5) through an ampere-hour integration method; the specific implementation method comprises the following steps:
Figure BDA0003143071350000066
in the formula Qcurr,tIs the current integral value of the battery starting to discharge from the fully charged state until time t,
Figure BDA0003143071350000067
is an estimate of the SOC at time t,
Figure BDA0003143071350000068
is the actual available capacity value for the kth cycle.
Compared with the prior art, the invention has the beneficial effects that:
(1) the invention discloses a migration model-based method for estimating the wide-temperature full-life SOC and the capacity of a lithium ion battery, which is used for realizing the combined accurate estimation of the SOC and the capacity of the lithium ion battery. According to the method, the influence of temperature and aging on the battery is regarded as uncertain quantity, online migration of the initial migration model under different temperatures and aging states can be realized only by using the migration model established by a small amount of offline data and the data of the battery in the actual use process, and the offline experimental workload in the traditional aging battery model modeling process is greatly reduced.
(2) The method realizes real-time online migration of the migration factors in the migration model by using the risk minimization particle filter algorithm, and effectively solves the problem that the estimation performance of the algorithm is greatly reduced due to the fact that the weights of most particles are degenerated after several generations of particle iteration in the actual use process of the common particle filter algorithm.
(3) The method realizes the estimation of the available capacity by the accurate SOC value obtained by estimation in a capacity reverse-deducing mode, and has the advantages of simple calculation, easy realization and practical engineering application.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a method for estimating wide-temperature full-life SOC and capacity of a lithium ion battery according to the present invention.
FIG. 2 is a second order RC equivalent circuit model diagram of the present invention.
Detailed Description
In order that those skilled in the art will better understand the disclosure, the invention will be further described with reference to the accompanying drawings and specific embodiments.
The invention provides a method for estimating the wide-temperature full-life SOC and the capacity of a lithium ion battery, a specific flow chart of which is shown in figure 1, and the method specifically comprises the following steps:
(1) randomly selecting the type and type of the lithium ion battery, and obtaining corresponding technical parameters of the battery, wherein the technical parameters comprise rated capacity, rated voltage, a charging mode, allowable charging temperature and discharging temperature;
(2) selecting a second-order RC equivalent circuit model as a basic battery model of the migration model, wherein the equation formula of a specific model of the second-order RC equivalent circuit model is as follows:
Figure BDA0003143071350000081
in the formula R0Indicating the ohmic internal resistance, R, of the interior of the cell1、R2Denotes the polarization resistance, C1、C2Represents polarization capacitance, OCVtIndicating the open circuit voltage, V, of the battery at time ttRepresenting terminal voltage, U, of the battery at time t0、U1、U2Each represents R0、R1C1、R2C2Voltage across, focv(SOCt) A second-order RC equivalent circuit model diagram is shown in FIG. 2, which is a function expression of the open-circuit voltage OCV with respect to the SOC;
(3) the method comprises the following steps of performing working condition experiments on a selected lithium ion battery to obtain characteristic parameters of the battery, establishing a relation between open-circuit voltage (OCV) and System On Chip (SOC), and performing parameter identification by using an optimization method to obtain internal parameter information of a battery model, wherein the working condition experiments specifically comprise the following steps:
(31) carrying out a standard capacity test experiment on the selected battery at a specific temperature to obtain the standard capacity of the battery;
(32) performing a working condition experiment at a specific temperature to obtain characteristic parameters of the battery, wherein the characteristic parameters of the battery comprise the voltage, the current and the temperature of the battery, and establishing a relation between open-circuit voltage SOC-OCV;
(33) performing parameter identification on the characteristic parameters obtained in the step (32) by an optimal method to obtain parameter information inside the battery model, wherein the parameter information comprises electrochemical polarization capacitance, concentration difference polarization capacitance, electrochemical polarization resistance and concentration difference polarization resistance;
the specific temperature in the steps (31) and (32) is high temperature or low temperature or normal temperature, wherein the high temperature or the low temperature or the normal temperature is the SOC and the capacity of the battery in a high temperature or low temperature or normal temperature state selected correspondingly according to needs to be estimated; and the optimization method in the step (33) comprises a recursive least square method, a particle swarm algorithm and a genetic algorithm.
(4) Establishing a relation curve between the parameter information obtained by parameter identification in the step (3) and the SOC by utilizing a polynomial fitting method, and establishing a frame of a migration model, wherein the method specifically comprises the following steps:
(41) and (3) constructing a relation curve between the parameter information obtained by parameter identification in the step (3) and the SOC by utilizing a polynomial fitting method, wherein a specific fitting formula is as follows:
Figure BDA0003143071350000091
in the formula of alphaiIs the fitting coefficient i 1,2,3.. 6, SOCtThe SOC value at the time t is obtained, and f is an SOC and battery model parameter curve;
(42) establishing a framework of a migration model based on an SOC and battery internal parameter curve, wherein a specific expression is as follows:
Figure BDA0003143071350000092
wherein X is ═ X1,x2,x3,…,x14]Is a migration factor matrix of the model, x1SOCt+x2SOC in this formula for corrected SOC valuet SOCtThe method is obtained by an ampere-hour integration method, an inaccurate SOC value influenced by temperature and aging on the battery is not considered, and f is a relation curve of the SOC and a battery model parameter;
(5) the method comprises the following steps of utilizing a risk minimization particle filter algorithm to carry out online determination and SOC value determination on a migration factor of a migration model, and specifically comprising the following steps:
(51) converting the migration matrix X into [ X ]1,x2,x3,…,x14]As system state variables, where [ x ]1,x2,x3,…,x14For the migration factor to be determined, the battery terminal voltage is used as the observed quantity of the system, and the load current I in the battery working processtAnd inaccurate state of charge SOCtAs input to the system, a system discrete state equation is established:
Figure BDA0003143071350000101
in the formula sigmai 2Measuring the variance, ω, of the noise for the systemiIs the system noise, and N is the number of particles;
(52) by a priori probability p (x)t) Sampling:
Figure BDA0003143071350000102
wherein δ (·) is a dirichlet function, and i is an index of the particle population;
(53) calculating a risk minimization parameter:
Figure BDA0003143071350000103
wherein
Figure BDA0003143071350000104
Is a hypothetical system state xkFunction of the true value of p2Is a continuous strictly convex function, pi2In order to be a risk parameter,
Figure BDA0003143071350000105
minimizing an estimate for the risk; since the SOC is used as an important parameter to be estimated in the migration model process, it is used as a risk minimization parameter in the risk minimization particle filter algorithm:
Figure BDA0003143071350000106
in the formula (I), the compound is shown in the specification,
Figure BDA0003143071350000107
by a migration factor x1 iAnd x2 iCorrected preliminary SOC estimation values, i.e.
Figure BDA0003143071350000108
Figure BDA0003143071350000109
Minimizing an estimate for the risk;
(54) calculating the weight and normalization of the particles:
Figure BDA0003143071350000111
in the formula
Figure BDA0003143071350000112
Is the particle weight;
(55) resampling:
Figure BDA0003143071350000113
in the formula EfftFor particle efficiency, when EfftWhen the value is less than 85% of the resampling threshold value set in the text, the following resampling process is carried out on the particles, wherein n is the number of the particles:
5) regenerating the sample Qm(m＝1,2,3...N)。
6) The resampled particles should satisfy the following equation:
Figure BDA0003143071350000114
the resampled particle weight is
Figure BDA0003143071350000115
(56) SOC estimation:
Figure BDA0003143071350000116
in the formula
Figure BDA0003143071350000117
The actual SOC value obtained by carrying out linear migration on the migration factor;
(57) estimating the model terminal voltage:
Figure BDA0003143071350000118
(6) and (5) estimating the actual available capacity of the accurate SOC value obtained in the step (5) in a capacity backward-pushing mode by an ampere-hour integration method, wherein the specific implementation method is as follows:
Figure BDA0003143071350000121
in the formula, Qcurr,tIs the current integral value of the battery starting to discharge from the fully charged state until time t,
Figure BDA0003143071350000122
is an estimate of the SOC at time t,
Figure BDA0003143071350000123
is the actual available capacity value for the kth cycle.
The specific embodiment of the invention according to the steps of the method for estimating the wide-temperature full-life SOC and the capacity of the lithium ion battery is as follows:
according to the method, the model and the type of the lithium ion battery are selected according to the step (1), in order to verify the insensitivity of the method to temperature and aging, two lithium ion battery data sets are specially selected, one is a data set of a ternary lithium battery with the capacity of 4Ah, and the type of battery is subjected to a temperature working condition experiment to verify the insensitivity of the method to temperature. The other is a data set of a lithium iron phosphate battery with the capacity of 2.55Ah, and the battery is subjected to an aging experiment to verify the insensitivity of the method to aging. Specific information of the two types of batteries is shown in tables 1 and 2:
TABLE 1
Figure BDA0003143071350000124
TABLE 2
Figure BDA0003143071350000125
According to the scheme of the invention, in the step (2), a second-order RC equivalent circuit model is selected as a basic model of a migration model, the selected battery is subjected to a method for estimating the wide-temperature full-life SOC and the capacity of the lithium ion battery, in the step (3), working condition experiments of HPPC and UDDS are carried out at a specific temperature, wherein a ternary lithium ion battery is selected to be subjected to working condition tests at different temperatures, and a lithium iron phosphate battery is subjected to aging working condition tests at a normal temperature state to obtain characteristic parameters of the selected battery, and the relation between SOC and OCV is established. And performing parameter identification on the characteristic parameters through an optimization method, wherein the parameter identification is performed by using a recursive least square method as the optimization method to obtain parameter information of the battery model.
Constructing a relation curve between parameter information obtained by parameter identification and the SOC by using a polynomial fitting method according to the step (4), establishing a frame of a migration model,
in the example, a ternary lithium ion battery is selected to perform an HPPC working condition experiment (SOH is 100%) at a normal temperature of 20 ℃, and a migration frame is established in sequence according to the steps (3) and (4). And obtaining actual internal parameters of the battery under different temperature states by carrying out linear migration on the migration factors in the migration model framework. And (3) carrying out the steps (3) and (4) by selecting and utilizing HPPC working condition data (normal temperature 20 ℃) of the lithium iron phosphate battery under the state of SOH (state of 100%), and establishing a migration model frame, and carrying out linear migration on migration factors in the migration model frame to obtain actual battery internal parameters under different aging states.
And (5) carrying out online determination and SOC value determination on the migration factor of the migration model by using a risk minimization particle filter algorithm.
To verify the validity of the SOC estimation of the migration model under different temperatures and aging conditions of the battery, the performance of the proposed method is evaluated using the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE) and the maximum absolute error (MAX), and the calculation formula is as follows:
Figure BDA0003143071350000131
Figure BDA0003143071350000132
Figure BDA0003143071350000133
where m represents the length of the test data, t represents the serial number of the test data, SOCkWhich represents the actual SOC-value of the battery,
Figure BDA0003143071350000134
representing the SOC estimation value obtained by linear migration through the migration model.
The results of verifying SOC estimation by UDDS working condition experimental data under different temperature conditions are shown in Table 3:
TABLE 3
Figure BDA0003143071350000141
The results of verifying the SOC estimation by UDDS working condition experimental data under different aging conditions are shown in table 4:
TABLE 4
Figure BDA0003143071350000142
Estimating the capacity of the accurate SOC value estimated in the step (5) by an ampere-hour integration capacity back-pushing method according to the step (6), and performing capacity back-pushing by using 4 groups of SOC estimation results under different aging states obtained in the table 4, wherein the obtained capacity estimation results under different aging conditions are shown in the table 5:
TABLE 5
Figure BDA0003143071350000143
Since the battery capacity estimation normally considers only the influence of battery aging, the present embodiment does not describe the result of the capacity estimation obtained using table 3.
The example shows that the invention can carry out accurate SOC state estimation on the lithium ion battery under different temperatures and different aging states, and the obtained SOC deduced capacity has higher accuracy, thus proving the effectiveness of the invention.
The above description is only a preferred embodiment of the present invention, and does not limit the present invention in any way. It will be understood by those skilled in the art that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (9)

1. A method for estimating wide-temperature full-life SOC and capacity of a lithium ion battery is characterized by comprising the following steps: the method specifically comprises the following steps:
(1) randomly selecting the type and the type of the lithium ion battery to obtain corresponding technical parameters of the battery;
(2) selecting a second-order RC equivalent circuit model as a basic battery model of the migration model;
(3) performing working condition experiments on the selected lithium ion battery to obtain characteristic parameters of the battery, establishing a relation between open-circuit voltage (OCV) and System On Chip (SOC), and performing parameter identification by using an optimization method to obtain internal parameter information of a battery model;
(4) establishing a relation curve between the parameter information obtained by parameter identification in the step (3) and the SOC by utilizing a polynomial fitting method, and establishing a frame of a migration model;
(5) carrying out online determination and SOC value determination on the migration factor of the migration model by using a risk minimization particle filter algorithm;
(6) and (5) estimating the actual available capacity by carrying out capacity back-pushing on the accurate SOC value obtained in the step (5) by an ampere-hour integration method.
2. The method of claim 1, wherein the wide temperature full life SOC and capacity estimation method comprises: the technical parameters corresponding to the battery obtained in the step (1) comprise rated capacity, rated voltage, charging mode, allowable charging temperature and allowable discharging temperature.
3. The method of claim 1, wherein the wide temperature full life SOC and capacity estimation method comprises: selecting a second-order RC equivalent circuit model as a basic battery model of the migration model in the step (2), wherein a specific model equation of the second-order RC equivalent circuit model is as follows:
Figure FDA0003143071340000011
in the formula R0Indicating the ohmic internal resistance, R, of the interior of the cell1、R2Denotes the polarization resistance, C1、C2Represents polarization capacitance, OCVtIndicating the open circuit voltage, V, of the battery at time ttRepresenting terminal voltage, U, of the battery at time t0、U1、U2Each represents R0、R1C1、R2C2Voltage across, focv(SOCt) Is a functional expression of the open circuit voltage OCV with respect to the SOC.
4. The method according to claim 1, wherein the step (3) of performing a working condition experiment on the selected battery to obtain characteristic parameters of the battery, establishing a relationship between an open-circuit voltage (OCV) and the SOC, and performing parameter identification by using an optimization method to obtain internal parameter information of the battery specifically comprises the following steps:
(31) carrying out a standard capacity test experiment on the selected battery at a specific temperature to obtain the standard capacity of the battery;
(32) performing a working condition experiment at a specific temperature to obtain characteristic parameters of the battery, wherein the characteristic parameters of the battery comprise the voltage, the current and the temperature of the battery, and a relation between the open-circuit voltage SOC and the OCV is established;
(33) and (4) performing parameter identification on the characteristic parameters obtained in the step (32) through an optimal method to obtain parameter information inside the battery model, wherein the parameter information comprises ohmic resistance, electrochemical polarization capacitance, concentration difference polarization capacitance, electrochemical polarization resistance and concentration difference polarization resistance.
5. The method for estimating the wide-temperature full-life SOC and capacity of the lithium ion battery according to claim 4, wherein the specific temperature in the steps (31) and (32) is a high temperature or a low temperature or a normal temperature, wherein the high temperature or the low temperature or the normal temperature is estimated according to the SOC and the capacity of the battery in a state of the high temperature or the low temperature or the normal temperature selected according to requirements.
6. The method for wide temperature and full life SOC and capacity estimation of lithium ion battery as claimed in claim 4, wherein the optimal method in step (33) comprises recursive least squares, particle swarm algorithm and genetic algorithm.
7. The method for estimating the wide-temperature full-life SOC and the capacity of the lithium ion battery according to claim 1, wherein the step (4) of establishing the framework of the migration model specifically comprises the following steps:
(41) and (3) constructing a relation curve between the parameter information obtained by parameter identification in the step (3) and the SOC by utilizing a polynomial fitting method, wherein a specific fitting formula is as follows:
Figure FDA0003143071340000031
in the formula of alphaiTo fit toCoefficient i 1,2,3.. 6, SOCtThe SOC value at the time t is obtained, and f is an SOC and battery model parameter curve;
(42) establishing a framework of a migration model based on an SOC and battery internal parameter curve, wherein a specific expression is as follows:
Figure FDA0003143071340000032
wherein X is ═ X1,x2,x3,…,x14]Is a migration factor matrix of the model, x1SOCt+x2SOC in this formula for corrected SOC valuetThe method is obtained by an ampere-hour integration method, an inaccurate SOC value of the battery, which is influenced by temperature and aging, is not considered, and f is a relation curve of the SOC and a battery model parameter.
8. The method for estimating wide-temperature full-life SOC and capacity of a lithium ion battery according to claim 1, wherein the online determination of the migration factor and SOC value of the migration model by using the risk-minimizing particle filter algorithm in the step (5) specifically comprises the following steps:
(51) converting the migration matrix X into [ X ]1,x2,x3,…,x14]As a system state variable, wherein
Figure FDA00031430713400000410
For the migration factor to be determined, the battery terminal voltage is used as the observed quantity of the system, and the load current I in the battery working processtAnd inaccurate state of charge SOCtAs input to the system, a system discrete state equation is established:
Figure FDA0003143071340000041
in the formula sigmai 2Measuring the variance, ω, of the noise for the systemiIs the system noise, and N is the number of particles;
(52) by a priori probability p (x)t) Sampling:
Figure FDA0003143071340000042
wherein δ (·) is a dirichlet function, and i is an index of the particle population;
(53) calculating a risk minimization parameter:
Figure FDA0003143071340000043
wherein
Figure FDA0003143071340000044
Is a hypothetical system state xkFunction of the true value of p2Is a continuous strictly convex function, pi2In order to be a risk parameter,
Figure FDA0003143071340000045
minimizing an estimate for the risk; since the SOC is used as an important parameter to be estimated in the migration model process, it is used as a risk minimization parameter in the risk minimization particle filter algorithm:
Figure FDA0003143071340000046
in the formula (I), the compound is shown in the specification,
Figure FDA0003143071340000047
by a migration factor x1 iAnd x2 iCorrected preliminary SOC estimation values, i.e.
Figure FDA0003143071340000048
Figure FDA0003143071340000049
Minimizing an estimate for the risk;
(54) calculating the weight and normalization of the particles:
Figure FDA0003143071340000051
in the formula
Figure FDA0003143071340000052
Is the particle weight;
(55) resampling:
Figure FDA0003143071340000053
in the formula EfftFor particle efficiency, when EfftWhen the value is less than 85% of the resampling threshold value set in the text, the following resampling process is carried out on the particles, wherein n is the number of the particles:
1) regenerating the sample Qm(m＝1,2,3...N)。
2) The resampled particles should satisfy the following equation:
Figure FDA0003143071340000054
the resampled particle weight is
Figure FDA0003143071340000055
(56) SOC estimation:
Figure FDA0003143071340000056
in the formula
Figure FDA0003143071340000057
Is a passing pairCarrying out linear migration on the migration factor to obtain a real SOC value;
(57) estimating the model terminal voltage:
Figure FDA0003143071340000058
。
9. the method for estimating the wide-temperature full-life SOC and the capacity of the lithium ion battery according to claim 1, wherein in the step (6), the actual available capacity of the actual SOC value estimated in the step (5) is estimated by capacity back-pushing through an ampere-hour integration method; the specific implementation method comprises the following steps:
Figure FDA0003143071340000061
in the formula, Qcurr,tIs the current integral value of the battery starting to discharge from the fully charged state until time t,
Figure FDA0003143071340000062
is an estimate of the SOC at time t,
Figure FDA0003143071340000063
is the actual available capacity value for the kth cycle.
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