CN111308373A - Identification method of Thevenin model parameter of battery and application thereof - Google Patents

Identification method of Thevenin model parameter of battery and application thereof Download PDF

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CN111308373A
CN111308373A CN202010267442.8A CN202010267442A CN111308373A CN 111308373 A CN111308373 A CN 111308373A CN 202010267442 A CN202010267442 A CN 202010267442A CN 111308373 A CN111308373 A CN 111308373A
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battery
parameters
model
thevenin
thevenin model
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王璐
那娜
王正君
薛晓萌
刘强
于洋
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Zaozhuang Vocational College
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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]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G01R31/387—Determining ampere-hour charge capacity or SoC
    • G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—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]
    • G01R31/389—Measuring internal impedance, internal conductance or related variables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery

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Abstract

The method is suitable for estimating the battery model parameters of a single battery or/and the whole battery pack, is also suitable for identifying the model parameters of the single battery in series connection and/or parallel connection in the battery pack, establishes the combination of methods such as the Thevenin model, discretization, transformation, identification algorithm and the like, realizes the method for quickly, timely and accurately estimating the model parameters of the battery, is simple, practical and efficient, and further provides a basis for estimating various states of the battery, such as SOC (state of charge), SOP (state of power) and SOH (state of health).

Description

Identification method of Thevenin model parameter of battery and application thereof
Technical Field
The invention belongs to the technical field of batteries, and relates to an equivalent circuit model of a battery and parameter identification of the model, in particular to a method for identifying Thevenin model parameters of the battery and application thereof.
Background
The appearance of batteries has promoted the development of all industries, such as mobile phones, computers, wearable devices, electric tools, and the like. Although the battery can improve the flexibility of the equipment and make the equipment smaller, dangerous accidents are easy to happen if the battery is not reasonably used. If the battery is overcharged or overdischarged, lithium precipitation occurs inside the battery, and lithium dendrite is caused, which not only accelerates the decay of the service life of the battery, but also causes thermal runaway of the battery, and then safety accidents such as explosion occur.
Therefore, if the battery is to be kept within a safe and reasonable operation range, the battery must be monitored and the state parameters must be estimated quickly, accurately and effectively in real time. The method is mainly used for improving estimation accuracy, the existing commonly used lithium battery equivalent circuit model comprises a Rint model, an RC model, a Thevenin model, a PNGV model and the like, wherein the Thevenin model has the advantages of clear physical significance, easiness in execution of model parameter identification experiments and the like, and is widely applied to mathematical modeling of the battery.
Disclosure of Invention
In order to solve the above problems, the present invention provides a method for estimating Thevenin model parameters of a battery efficiently, quickly, in real time, and accurately, thereby providing a basis for estimating various states of the battery, including but not limited to SOC (state of charge), SOP (state of power), and SOH (state of health).
The technical scheme of the application is as follows:
a method for identifying Thevenin model parameters of a battery is characterized by comprising the following steps:
(1) establishing a Thevenin model and setting the current to be positive during charging, wherein the Thevenin model is represented by the following formula:
Figure BDA0002441167490000021
wherein U is terminal voltage of battery in Thevenin model, UpFor the polarization voltage, RI for the ohmic drop and UoIs an open circuit voltage;
(2) the first formula can be represented as a second formula after discretization and transformation:
Figure BDA0002441167490000022
further obtaining:
Figure BDA0002441167490000023
wherein: t issIs the sampling time;
(3) estimating the parameters to be estimated by using the order of ykU (k), parameters to be estimated
Figure BDA0002441167490000024
Estimating theta by adopting an identification algorithm, and establishing a state equation as follows:
Figure BDA0002441167490000025
equation two can be written as follows:
Figure BDA0002441167490000026
wherein: ε (k) is noise.
Further, the recognition algorithm includes, but is not limited to, Recursive Least Squares (RLS), recursive least squares with forgetting factor(s).
The beneficial effect of this application does:
the method for estimating the model parameters of the battery quickly, in real time and accurately is simple, practical and efficient, and further provides a basis for estimating various states of the battery, such as SOC (state of charge), SOP (state of power), SOH (state of health) and the like.
Drawings
FIG. 1 is a schematic diagram of the thevenin equivalent circuit model of the present invention;
FIG. 2 is a flow chart of Thevenin parameter identification according to the present invention;
FIG. 3 is a schematic diagram of a series connection of cells according to the present invention;
FIG. 4 is a schematic diagram of series-parallel connection of batteries in accordance with the present invention;
FIG. 5 is a schematic diagram of the parallel connection of batteries in the present invention;
FIG. 6 is a table of HPPC test data for the series cells of FIG. 3 in accordance with the present invention.
Detailed Description
The present application will be further described with reference to the accompanying drawings and detailed description, wherein the drawings required for describing the embodiments of the present invention or the technical solutions in the prior art are briefly described below, so as to more clearly illustrate the embodiments or the technical solutions in the prior art, and it is obvious that the drawings in the description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without inventive efforts.
A set of HPPC (hybrid pulse Power spectroscopy) test experiments were performed on three lithium iron phosphate battery packs connected in series, and voltage and current data of 60 seconds were collected, as shown in fig. 6.
The identification of Thevenin model parameters of the three-section series lithium iron phosphate battery pack by RLS is exemplarily shown below.
1) Sampling the voltage and the current of the battery for L times (L is more than or equal to 2);
2) determining theta and covariance matrices PNInitial value of (d):
let theta0＝[0 0 0 0 0 0]T，P0＝σ2I, I is the identity matrix, σ2≥106；
3) Computing a gain matrix Gi+1：
Figure BDA0002441167490000031
4) Calculate an estimate of θ:
Figure BDA0002441167490000032
5) covariance matrix Pi+1：
Figure BDA0002441167490000033
6) Obtaining parameters of the Thevenin model by using a formula III;
7) and repeating the steps 3) to 5) until the L times of sampling are finished.
Obtaining the identification result of the Thevenin model, wherein the identification result under the HPPC test system is as follows:
ohmic internal resistance/m omega Polarization internal resistance/m omega Polarization capacitance/F Time constant/s Open circuit voltage/V Battery with a battery cell
2.925 0.210 3912.264 0.823 9.612 Entire battery pack
1.073 0.063 13014.803 0.814 3.149 Monomer 1
1.073 0.077 10155.834 0.783 3.310 Monomer 2
0.755 0.094 8042.997 0.783 3.314 Monomer 3
The Thevenin model is simulated by using the parameters obtained by identification, the average error of the simulated value of the whole battery pack relative to the real value is 0.205V, the mean square error is 0.211V, the average error of the simulated value relative to the real value is 0.013V and the mean square error is 0.071V for the single battery, and the simulation method is applied to estimation analysis of SOC, SOP and SOH according to the obtained parameters.
The algorithm can not only realize identification of Thevenin model parameters, but also has high accuracy of the identified model parameters.
The method is suitable for estimating the model parameters of the single battery or/and the whole battery pack, and is also suitable for identifying the model parameters of the single battery in series connection and/or parallel connection in the battery pack, and the figures 3-5 are only schematic diagrams of certain battery connection and do not represent all connection modes.
Finally, it should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.

Claims (3)

1. A method for identifying Thevenin model parameters of a battery is characterized by comprising the following steps:
(1) establishing a Thevenin model and setting the current to be positive during charging, wherein the Thevenin model is represented by the following formula:
Figure FDA0002441167480000011
wherein U is terminal voltage of battery in Thevenin model, UpFor the polarization voltage, RI for the ohmic drop and UoIs an open circuit voltage;
(2) the first formula can be represented as a second formula after discretization and transformation:
Figure FDA0002441167480000012
further obtaining:
Figure FDA0002441167480000013
wherein: t issIs the sampling time;
(3) estimating the parameters to be estimated by using the order of ykU (k), parameters to be estimated
Figure FDA0002441167480000014
Estimating theta by adopting an identification algorithm, and establishing a state equation as follows:
Figure FDA0002441167480000015
equation two can be written as follows:
Figure FDA0002441167480000016
wherein: ε (k) is noise;
(4) calculating the model parameters according to the formula:
1) sampling the voltage and the current of the battery for L times (L is more than or equal to 2);
2) determining theta and covariance matrices PNInitial value of (d):
let theta0＝[0 0 0 0 0 0]T，P0＝σ2I, I is the identity matrix, σ2≥106；
3) Computing a gain matrix Gi+1：
Figure FDA0002441167480000021
6) Calculate an estimate of θ:
Figure FDA0002441167480000022
5) covariance matrix Pi+1：
Figure FDA0002441167480000023
6) And solving the parameters of the Thevenin model by using a formula III.
2. The method as claimed in claim 1, wherein the identification algorithm includes but is not limited to Recursive Least Squares (RLS) algorithm, recursive least squares with forgetting factor (RLS) algorithm.
3. Use of the method for identifying parameters of the Thevenin model of a battery according to any one of claims 1 or 2, wherein the obtained parameters are used in the estimation and analysis of SOC, SOP and SOH.
CN202010267442.8A 2020-04-07 2020-04-07 Identification method of Thevenin model parameter of battery and application thereof Pending CN111308373A (en)

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Application publication date: 20200619