CN110596604B - Lithium battery SOC estimation method based on ampere-hour integration method - Google Patents

Lithium battery SOC estimation method based on ampere-hour integration method Download PDF

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CN110596604B
CN110596604B CN201910918620.6A CN201910918620A CN110596604B CN 110596604 B CN110596604 B CN 110596604B CN 201910918620 A CN201910918620 A CN 201910918620A CN 110596604 B CN110596604 B CN 110596604B
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battery
soc
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魏千翔
刘春波
张诗军
顾志东
冯建伟
王联智
王鹏
胡微
王信科
谢敏
王保强
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Hainan Digital Power Grid Research Institute of China Southern Power Grid Co Ltd
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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
    • G01R31/3828—Arrangements for monitoring battery or accumulator variables, e.g. SoC using current integration
    • G01R31/3832—Arrangements for monitoring battery or accumulator variables, e.g. SoC using current integration without measurement of battery voltage
    • G01R31/3833—Arrangements for monitoring battery or accumulator variables, e.g. SoC using current integration without measurement of battery voltage using analog integrators, e.g. coulomb-meters

Abstract

The invention provides a lithium battery SOC estimation method based on an ampere-hour integration method, which adopts the ampere-hour integration method to estimate the SOC state of a lithium battery, wherein, the charge-discharge initial state, the battery capacity and the coulomb efficiency used in the ampere-hour integration method are corrected by obtaining a plurality of groups of initial data of the same parameter through a plurality of ways, averaging the initial data to obtain the corrected data, wherein the initial data acquisition comprises a part acquired by adopting a neural network, the neural network is formed by training corresponding data of a lithium battery in a long-term use process, because each parameter in the ampere-hour integration method is corrected, the finally obtained error of the SOC state of the lithium battery is small, therefore, the using state of the battery can be correctly judged, and the lithium battery can be conveniently maintained or replaced subsequently.

Description

Lithium battery SOC estimation method based on ampere-hour integration method
Technical Field
The invention relates to the technical field of battery management, in particular to a lithium battery SOC estimation method based on an ampere-hour integral method.
Background
In an unmanned aerial vehicle flight system, the state of charge (SOC) of a power battery is an important parameter of the battery state and is used for directly reflecting the residual electric quantity of the battery, the SOC of the battery is also an important basis for an unmanned aerial vehicle control system to formulate an optimal energy management strategy, the SOC value of the power battery is accurately estimated, and the method has important research significance for prolonging the service life of the battery, improving the safety and reliability of the battery and improving the performance of the battery.
The SOC of the battery is influenced by various factors, the SOC cannot be directly measured through a sensor, the SOC must be obtained by measuring physical quantities such as battery voltage, working current and temperature and adopting a certain mathematical model and algorithm for estimation, the current common methods comprise an open-circuit voltage method, an ampere-hour integration method, a neural network method and a Kalman filtering method, the ampere-hour integration method is widely applied due to the advantages of low cost, convenience in measurement and the like, and the expression of the ampere-hour integration method is as follows:
Figure BDA0002216856420000011
from this, the battery SOC and the initial charge/discharge state SOC are knownOThe total capacity C of the battery and the coulomb efficiency η of the battery are related, and the parameters used by the ampere-hour integration method at present have certain errors, so that the measurement of the SOC of the battery is not accurate enough, and the use state of the battery cannot be judged correctly.
Disclosure of Invention
Therefore, the invention provides a lithium battery SOC estimation method based on an ampere-hour integration method, which adopts the ampere-hour integration method to estimate the SOC state of the lithium battery, wherein each parameter in the ampere-hour integration method is corrected, so that the finally obtained SOC state error of the battery is small, and the use state of the battery can be correctly judged.
The technical scheme of the invention is realized as follows:
a lithium battery SOC estimation method based on an ampere-hour integration method comprises the following steps:
step S1, reading the SOC of the battery stored in the database after the previous use as a first charge-discharge initial state; obtaining environmental temperature information and pressure information as input of a trained first neural network to obtain a second charge-discharge initial state and a third charge-discharge initial state, and calculating an average value of the first charge-discharge initial state, the second charge-discharge initial state and the third charge-discharge initial state to obtain a charge-discharge initial state correction value SOCO；
Step S2, obtaining the SOH value of the battery state of health and obtaining a first battery capacity according to the SOH value; acquiring the service life of the battery as the input of the trained second neural network to obtain the capacity of a second battery; acquiring full charge time and discharge time, and acquiring third battery capacity and fourth battery capacity according to the full charge time and the discharge time; calculating the average value of the first battery capacity, the second battery capacity, the third battery capacity and the fourth battery capacity to obtain a battery capacity correction value C;
step S3, obtaining the discharging electric quantity and the charging electric quantity of the battery, and obtaining a first coulomb efficiency; acquiring environment temperature information as input of a third neural network to obtain second coulombic efficiency, and calculating an average value of the first coulombic efficiency and the second coulombic efficiency to obtain a coulombic efficiency correction value eta;
step S4, obtaining battery charging and discharging current I;
step S5, using ampere-hour integration method according to SOCOC, eta, and I obtain the SOC state of the battery.
Preferably, the method further comprises the following steps:
step S6 is to store the SOC value at the time of stopping the discharge of the battery in the database as the first charge/discharge initial state at the time of next calculation of the SOC state of the battery.
Preferably, the step S2 of obtaining the SOH value of the battery, and the specific step of obtaining the first battery capacity by using the SOH value of the battery, includes:
step S21, the SOH value of the battery state of health is obtained by using the internal resistance of the battery, and the SOH value obtaining formula is as follows:
Figure BDA0002216856420000021
wherein R isOThe internal resistance R of the lithium battery at the end of the service lifenThe internal resistance of the lithium battery when leaving the factory is shown, and R is the internal resistance measured in the use process of the battery;
step S22, first battery capacity C1＝SOH*CNIn which C isNThe rated capacity of the battery.
Preferably, the specific steps of obtaining the fully charged time period and the fully discharged time period in step S2, and obtaining the third battery capacity and the fourth battery capacity according to the fully charged time period and the fully discharged time period include: obtaining a third battery capacity and a fourth battery capacity using the following formulas:
Figure BDA0002216856420000031
wherein C is3Is the third battery capacity, T3For the duration of the full charge collected, CNFor rated capacity of battery, TN3For a nominal full charge duration, C4Is the fourth battery capacity, T4For the duration of the discharge time, T, of the acquisitionN4The rated discharge time is set.
Preferably, the expression of the first coulombic efficiency in step S3 is:
Figure BDA0002216856420000032
wherein QdisFor discharging the battery, QchaThe battery is charged.
Preferably, in step S4, a current sensor is used to collect the battery charging/discharging current I.
Preferably, the expression of the ampere-hour integration method in step S5 is:
Figure BDA0002216856420000033
preferably, the first neural network, the second neural network and the third neural network are trained by data stored in the long-term use process of various types of lithium batteries.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a lithium battery SOC estimation method based on an ampere-hour integral method, which adopts a traditional ampere-hour integral method to estimate the SOC state of a lithium battery, corrects parameters used in the ampere-hour integral method, including a charge-discharge initial state, battery capacity and coulombic efficiency, and adopts at least two groups of initial data to obtain a correction value for each parameter, and the initial data also comprises a part obtained by combining a neural network with data in the long-term use process of the lithium battery, so that each parameter of the ampere-hour integral method can be corrected, the difference of the SOC state of the lithium battery caused by the error of each parameter is reduced, the finally obtained SOC state error of the battery is smaller, and the use state of the battery can be correctly judged.
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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 description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only preferred embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a flowchart of an embodiment of a lithium battery SOC estimation method based on an ampere-hour integration method according to the present invention.
Detailed Description
For a better understanding of the technical content of the present invention, a specific embodiment is provided below, and the present invention is further described with reference to the accompanying drawings.
Referring to fig. 1, the method for estimating the SOC of the lithium battery based on the ampere-hour integration provided by the invention comprises the following steps:
step S1, reading the SOC of the battery stored in the database after the previous use as a first charge-discharge initial state; obtaining environmental temperature information and pressure information as input of a trained first neural network to obtain a second charge-discharge initial state and a third charge-discharge initial state, and calculating an average value of the first charge-discharge initial state, the second charge-discharge initial state and the third charge-discharge initial state to obtain a charge-discharge initial state correction value SOCO；
Step S2, obtaining the SOH value of the battery state of health and obtaining a first battery capacity according to the SOH value; acquiring the service life of the battery as the input of the trained second neural network to obtain the capacity of a second battery; acquiring full charge time and discharge time, and acquiring third battery capacity and fourth battery capacity according to the full charge time and the discharge time; calculating the average value of the first battery capacity, the second battery capacity, the third battery capacity and the fourth battery capacity to obtain a battery capacity correction value C;
step S3, obtaining the discharging electric quantity and the charging electric quantity of the battery, and obtaining a first coulomb efficiency; acquiring environment temperature information as input of a third neural network to obtain second coulombic efficiency, and calculating an average value of the first coulombic efficiency and the second coulombic efficiency to obtain a coulombic efficiency correction value eta;
step S4, obtaining battery charging and discharging current I;
detecting and storing the measured battery charging and discharging current I in real time through a current sensor;
step S5, using ampere-hour integration method according to SOCOObtaining the SOC state of the battery by C, eta and I;
step S6 is to store the SOC value at the time of stopping the discharge of the battery in the database as the first charge/discharge initial state at the time of next calculation of the SOC state of the battery.
The lithium battery SOC estimation method based on the ampere-hour integration method estimates the SOC state of the lithium battery by adopting the ampere-hour integration method, wherein a plurality of parameters used in the ampere-hour integration method are corrected, so that the finally obtained SOC state of the lithium battery has small error, the service state of the battery can be judged, and the subsequent maintenance or replacement is convenient.
The initial charge-discharge state is first read from the data base to obtain the SOC state after the last use as the first initial charge-discharge stateO1Then, environment temperature information and pressure information are obtained and input into a first neural network, and the first neural network processes the environment temperature information to obtain a second charge-discharge initial state SOCO2The first neural network processes the pressure information to obtainThird initial state SOCO3Taking SOCO1、SOCO2、SOCO3The average value of the charge and discharge initial state correction value SOC can be obtainedONamely:
Figure BDA0002216856420000051
wherein the SOCO1Can be directly read from the database, and the SOCO2、SOCO3Then the first neural network is needed to process the result, for the SOCO2、SOCO3In other words, the acquired environmental temperature information and the acquired pressure information are a plurality of groups, the first neural network processes the acquired plurality of groups of environmental temperature information, and then the acquired data are averaged to obtain a second charge-discharge initial state SOCO2Similarly, the first neural network also processes the collected multiple groups of pressure information and averages the results to obtain a third charge-discharge initial state SOCO3In this embodiment, the first neural network is trained before use, and the training data is the charge and discharge initial states of different types of lithium batteries at different temperatures and different pressures during long-time use, so that the second charge and discharge initial state and the third charge and discharge initial state can be obtained through the first neural network after obtaining the ambient temperature information and the pressure information, and the charge and discharge initial state correction value SOC is obtainedOIs SOCO1、SOCO2And SOCO3So that the charge-discharge initial-state correction value SOC obtained in combination with the previous battery SOC value and the charge-discharge initial state obtained from the neural networkOThe error of (2) is small.
For the battery capacity, after acquiring a plurality of sets of initial data, the average value is obtained, wherein the initial data comprises the first battery capacity C1Second battery capacity C2Third battery capacity C3Fourth battery capacity C4Wherein a first battery capacity C is obtained1The method comprises the following specific steps:
step S21, the SOH value of the battery state of health is obtained by using the internal resistance of the battery, and the SOH value obtaining formula is as follows:
Figure BDA0002216856420000061
wherein R isOThe internal resistance R of the lithium battery at the end of the service lifenThe internal resistance of the lithium battery when leaving the factory is shown, and R is the internal resistance measured in the use process of the battery;
step S22, first battery capacity C1＝SOH*CNIn which C isNThe rated capacity of the battery.
For the second battery capacity C2In other words, the data is obtained by processing the second neural network, the input data of the second neural network is the service life of the lithium battery, and the output data is the second battery capacity C2The second neural network is trained before use, and the training data is the battery capacity corresponding to different use durations of the lithium battery in the long-term use process.
For third battery capacity C3And a fourth battery capacity C4In other words, the third battery capacity and the fourth battery capacity are obtained using the following equations:
Figure BDA0002216856420000062
wherein C is3Is the third battery capacity, T3For the duration of the full charge collected, CNFor rated capacity of battery, TN3For a nominal full charge duration, C4Is the fourth battery capacity, T4For the duration of the discharge time, T, of the acquisitionN4The rated discharge time is set.
Third battery capacity C3And a fourth battery capacity C4The calculation basis is that the discharge time length and the full charge time length of the batteries with different capacities are different in the daily use process, but the rated capacity, the rated full charge time length and the rated discharge time length of each battery are fixed and unchanged when each battery leaves a factory, and the discharge time length, the rated full charge time length and the rated full charge time length are fixed and are along with the capacity of the batteryThe variation of the third battery capacity C is in a linear rule, and the ratio of the rated full charge time (or the rated discharge time) to the rated battery capacity is equal to the ratio of the collected full charge time (or the collected discharge time), so that the third battery capacity C can be calculated according to the equation relation3And a fourth battery capacity C4。
Obtaining a first battery capacity C1Second battery capacity C2Third battery capacity C3Fourth battery capacity C4After, battery capacity correction value
Figure BDA0002216856420000071
Thereby allowing an error in the capacity of the battery to be reduced.
For the acquisition of the coulombic efficiency, firstly, a first coulombic efficiency eta is obtained according to the discharge electric quantity and the charge electric quantity of the battery1Wherein
Figure BDA0002216856420000072
In the formula, QdisFor discharging the battery, QchaCharging the battery and obtaining a second coulombic efficiency eta2Second coulombic efficiency η2The obtained mode is obtained by processing the third neural network, the third neural network is trained before use, the trained data is the coulomb efficiency of the lithium battery at different environmental temperatures in the long-time use process, and therefore, the measured environmental temperature is input into the third neural network and is output after being processed by the third neural network to obtain the second coulomb efficiency eta2And the coulomb efficiency correction value eta is expressed as
Figure BDA0002216856420000073
Therefore, the correction of the coulombic efficiency can be realized, and the error of the coulombic efficiency is smaller.
Preferably, the expression of the ampere-hour integration method in step S5 is:
Figure BDA0002216856420000074
obtaining the correction value SOC at the initial state of charging and dischargingOAfter the battery capacity correction value C, the coulomb efficiency correction value eta and the battery charging and discharging current I are carried into an expression of an ampere-hour integration method, the SOC state of the battery can be obtained, after the battery is in the end use state, the SOC state of the battery in the end state is stored into a database to be used as a first charging and discharging initial state of the next estimation battery SOC state, in the above formula, the integration represents the charging and discharging current I of the battery at the time [0, t [ ]]The discharge current I is positive and the charge current I is negative.
The invention relates to a lithium battery SOC estimation method based on an ampere-hour integration method, which is used for correcting a charge-discharge initial state correction value SOCOThe battery capacity correction value C and the coulomb efficiency correction value eta are corrected, initial data of the parameters are acquired through various ways, then the average value of multiple groups of initial data is obtained, so that errors generated by the parameters are reduced, and finally when the SOC state of the battery is estimated by using an ampere-hour integration method, the errors of the SOC state of the battery obtained finally are small, so that the use state of the battery can be judged correctly, and the subsequent maintenance or replacement of the lithium battery is facilitated.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (7)

1. A lithium battery SOC estimation method based on an ampere-hour integration method is characterized by comprising the following steps:
step S1, reading the SOC of the battery stored in the database after the previous use as a first charge-discharge initial state; obtaining environment temperature information and pressure information as the input of the trained first neural network to obtain a second charge-discharge initial state and a third charge-discharge initial state, and solving a first charge-discharge initial stateObtaining a charge-discharge initial state correction value SOC from the average value of the initial state, the second charge-discharge initial state and the third charge-discharge initial stateO；
Step S2, obtaining the SOH value of the battery state of health and obtaining a first battery capacity according to the SOH value; acquiring the service life of the battery as the input of the trained second neural network to obtain the capacity of a second battery; acquiring full charge time and discharge time, and acquiring third battery capacity and fourth battery capacity according to the full charge time and the discharge time; calculating the average value of the first battery capacity, the second battery capacity, the third battery capacity and the fourth battery capacity to obtain a battery capacity correction value C;
step S3, obtaining the discharging electric quantity and the charging electric quantity of the battery, and obtaining a first coulomb efficiency; acquiring environment temperature information as input of a third neural network to obtain second coulombic efficiency, and calculating an average value of the first coulombic efficiency and the second coulombic efficiency to obtain a coulombic efficiency correction value eta;
step S4, obtaining battery charging and discharging current I;
step S5, using ampere-hour integration method according to SOCOObtaining the SOC state of the battery by C, eta and I;
the specific steps of obtaining the full charge duration and the discharge duration in step S2, and obtaining the third battery capacity and the fourth battery capacity accordingly include: obtaining a third battery capacity and a fourth battery capacity using the following formulas:
Figure FDA0003432791400000011
wherein C is3Is the third battery capacity, T3For the duration of the full charge collected, CNFor rated capacity of battery, TN3For a nominal full charge duration, C4Is the fourth battery capacity, T4For the duration of the discharge time, T, of the acquisitionN4The rated discharge time is set.
2. The lithium battery SOC estimation method based on the ampere-hour integration method as claimed in claim 1, further comprising the steps of:
step S6 is to store the SOC value at the time of stopping the discharge of the battery in the database as the first charge/discharge initial state at the time of next calculation of the SOC state of the battery.
3. The method for estimating the SOC of the lithium battery based on the ampere-hour integration method as claimed in claim 1, wherein the step S2 of obtaining the SOH value of the battery state of health and obtaining the first battery capacity by the method comprises the following specific steps:
step S21, the SOH value of the battery state of health is obtained by using the internal resistance of the battery, and the SOH value obtaining formula is as follows:
Figure FDA0003432791400000021
wherein R isOThe internal resistance R of the lithium battery at the end of the service lifenThe internal resistance of the lithium battery when leaving the factory is shown, and R is the internal resistance measured in the use process of the battery;
step S22, first battery capacity C1＝SOH*CNIn which C isNThe rated capacity of the battery.
4. The method for estimating the SOC of the lithium battery based on the ampere-hour integration method as claimed in claim 1, wherein the expression of the first coulombic efficiency in the step S3 is as follows:
Figure FDA0003432791400000022
wherein QdisFor discharging the battery, QchaThe battery is charged.
5. The method for estimating the SOC of the lithium battery based on the ampere-hour integration method as claimed in claim 1, wherein a current sensor is used to collect the charging and discharging current I of the battery in step S4.
6. The method for estimating the SOC of the lithium battery based on the ampere-hour integration method as claimed in claim 1, wherein the expression of the ampere-hour integration method in the step S5 is as follows:
Figure FDA0003432791400000023
7. the lithium battery SOC estimation method based on the ampere-hour integration method as claimed in claim 1, wherein the first neural network, the second neural network and the third neural network are trained by data stored in a long-term use process of various types of lithium batteries.
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