CN106646252B - Method for predicting service life of lead-acid battery - Google Patents

Method for predicting service life of lead-acid battery Download PDF

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Publication number
CN106646252B
CN106646252B CN201611095170.8A CN201611095170A CN106646252B CN 106646252 B CN106646252 B CN 106646252B CN 201611095170 A CN201611095170 A CN 201611095170A CN 106646252 B CN106646252 B CN 106646252B
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battery
failure
function
lead
coefficient
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CN106646252A (en
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王莉
杨永辉
张军阳
王芮琳
杜彦强
邵峰
薛诗萌
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State Grid Corp of China SGCC
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Dalian Optoelectronics Communication Development Co ltd
State Grid Corp of China SGCC
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • HELECTRICITY
    • H01BASIC ELECTRIC ELEMENTS
    • H01MPROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00Secondary cells; Manufacture thereof
    • H01M10/06Lead-acid accumulators
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10Energy storage using batteries

Abstract

The invention discloses a method for predicting the application life of a lead-acid battery, which comprises the following steps: acquiring the battery discharge electric quantity value to be predicted in a work project of the lead-acid battery in different environments, and calculating the attenuation ratio of the current battery capacity; calculating the failure coefficient of the lead-acid battery to be predicted in practical application according to the attenuation ratio, and further establishing a failure average rate model of the battery with the failure coefficient: setting the self-learning battery state of charge to be equal to the initial quantity, and when the battery failure coefficient is set to be constant, obtaining a least square vector machine LS-SVM decision function according to a least square vector machine and a structure risk minimization principle: wherein, K (x)i,xj) Solving a regression prediction function by adopting internal operation for a kernel function which is an inverse function of an exponential function, and substituting the experimental numerical value into the formula to obtain a value a and a value b corresponding to a plurality of batteries to be detected; and substituting the values a and b after the error optimization into a battery residual life prediction model: resulting in a battery operating life.

Description

Method for predicting service life of lead-acid battery
Technical Field
The invention relates to a method and a system for detecting the application dynamic service life of a lead-acid storage battery. To patent classification number, G01 measurement; test G01R measures an electrical variable; measuring a magnetic variable; a testing device for G01R31/00 electric property; a detection device of an electrical fault; electrical test apparatus characterised by the test being performed being not provided elsewhere; G01R31/36 is used in instruments for testing the electrical condition of a battery or cells, such as for testing the life or state of charge.
Background
At present, various lead-acid storage batteries are widely applied to life and production and become an indispensable part of the current society. In the process of using the storage battery, the actual application life of the storage battery is often required to be known, so that a device capable of simply predicting the application life of the storage battery is required, and in an actual environment, the active parameters of the storage battery are periodically collected and processed to predict the residual application life.
In the traditional monitoring of the lead-acid battery, only a measuring instrument can be used for testing the voltage, the current, the internal resistance, the temperature and the capacity of the lead-acid battery, and the residual operation cycle life of the battery in the service environment cannot be predicted through monitoring parameters in real time. The running lead-acid battery can be replaced only when an accident occurs, or the battery can be replaced blindly, so that the battery can be replaced passively, and potential safety hazards are brought to life and production. If the operation life under the actual environment can be preliminarily predicted according to the failure mechanism of the storage battery, the standby lead-acid battery is treated in a planned way, and the method has great significance for economy, society and resource saving.
Disclosure of Invention
The invention provides a method for predicting the service life of a lead-acid battery, which is developed aiming at the problems and comprises the following steps:
acquiring the battery discharge electric quantity value to be predicted in a work project of the lead-acid battery in different environments, and calculating the attenuation ratio of the current battery capacity;
calculating the failure coefficient of the lead-acid battery to be predicted in practical application according to the attenuation ratio, and further establishing a failure average rate model of the battery with the failure coefficient:
setting the self-learning battery state of charge to be equal to the initial quantity, and setting the self-learning battery state of charge to be equal to the initial quantity according to a least square vector machine according to a structural risk minimization principle when a battery failure coefficient is constantObtaining a least square vector machine LS-SVM decision function:
wherein, K (x)i,xj) Solving a regression prediction function by adopting internal operation for a kernel function which is an inverse function of an exponential function, and substituting the experimental numerical value into the formula to obtain a value a and a value b corresponding to a plurality of batteries to be detected;
-substituting the error-optimized a, b values into a battery residual life prediction model:
(t) was obtained as (0.798+ ln)Δc).t2+ΔT93.7;
And (3) wherein t represents the operation period of the storage battery, the general trend is reduced, the experimental numerical value is substituted into the number of the solving detection periods, and the inverse function is solved to obtain the operation life of the storage battery.
The battery failure rate is the speed of the decrease of the battery failure coefficient in the battery operation period; using a bell-shaped function y-ax2+ b, where the value of a is varied and Y represents the remaining life cycle of the battery; because a is that the changed parameter is not a fixed value, a vector machine function is adopted to solve a, and meanwhile, the derivative of the whole bell-shaped function is solved to obtain the failure rate in a period.
In a preferred embodiment, the failure coefficient calculation is performed according to the following formula to obtain the i-th point failure coefficient βmi
Wherein, C actual discharge capacity, ambient T temperature; t1i is the first discharge temperature, and the failure coefficient of the m-th discharge is calculated as:
β=(βm1m2+…+βmN)/N
n is the number of standard points taken on the cell discharge curve.
In a preferred embodiment, the step of establishing the average failure rate model of the battery with the failure coefficient further comprises the following steps:
comprehensively optimizing and disposing test data by using a bell-shaped function and a vector machine, wherein the optimization method comprises the following steps: the battery failure rate is the rate at which the battery failure coefficient drops during the battery operating cycle;
the model is a bell-shaped function y ═ ax2+ b (wherein the value a is changed), Y represents the remaining life cycle of the battery, and since the parameter a is changed and not a fixed value, a is solved by adopting a vector machine function, and meanwhile, the derivative of the whole bell-shaped function is obtained, namely the failure rate in one cycle;
establishing a storage battery actual application life prediction model under different use environments comprises the following steps: the power supply frequency of the battery, the magnitude of load current, the stability of the power supply current and the environmental temperature of the machine room;
calculating the battery failure rate based on the battery failure rate and the active vulcanization model instead of the battery vulcanization mechanism, wherein the established predicted life model expression is as follows:
where Δ β is the failure coefficient change, the period of the Δ T coefficient change,
whereinThe main factor affecting the activity of the battery to cause vulcanization and ultimately leading to the reduction of the service life of the application, a.b is the property coefficient affecting the frequency of the battery and the inherent property of the battery active under different environmental use conditions.
Furthermore, under the condition of not considering physical inherent factors such as battery active substances, design materials and the like, the relationship of the factors influencing the reduction of the battery activity is shown in the following expression of the relationship among power supply time, current, temperature and capacity:
is a factor of the reaction depth between the active and the medium,a factor for supplying energy to participate in chemical reactions, a being the active motor capacity of the battery, whereinThe expression is as follows:
I10standard current; t standard time; kTA temperature correction coefficient; ki=It/I10A current correction factor;where t is the real-time period;while maleIn the formula TtWhen greater than 10, set I10.t10Is C10,It.ttAnd Ct, because of the environmental factors of the actual machine room, the self-learning battery state of charge is equal to the initial quantity, and the relational expression of the application life and the battery residual capacity temperature is shown as follows.
Due to the adoption of the technical scheme, the battery pack service life detection method disclosed by the invention can continuously optimize the battery discharge function according to the historical discharge state of the battery, and finally can be used as a method for evaluating the current battery service life, so that the detection of the battery service life can be completed remotely, and the method has the advantages of high precision, simplicity and convenience in operation and the like which are not possessed by the existing algorithm.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a graph of the mean rate of failure model of the present invention
FIG. 2 is a schematic diagram illustrating comparison of calculation results according to an embodiment of the present invention
FIG. 3 is a flow chart of the algorithm of the present invention
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the following describes the technical solutions of the embodiments of the present invention clearly and completely with reference to the accompanying drawings in the embodiments of the present invention:
example, as shown in fig. 1-3, it is assumed in this example that 1 lead-acid battery operating performance removes the effects of physical damage factors; the lead-acid battery 2 is supposed to be charged in time after the operation and discharge are finished so as to be free from the influence of vulcanization; a400 AH two-class valve-regulated lead-acid battery monomer is selected as a research sample.
The method comprises the following steps: analyzing factors influencing the reduction of the running performance of the lead-acid battery and the relation between the performance reduction and the change of each running parameter, and determining parameters influencing the performance reduction of the lead-acid battery;
the performance parameters of the lead-acid battery in actual operation change along with the service time, the reason for causing the activity reduction of the battery is objective factors of manufacturing process and design,
for example in the manufacture of batteries
The cleaning degree of the active matter of the polar plate,
The quality of the lead plaster,
The strength of the grid material,
An isolating material,
The material of the shell body,
Medium, and efficiency of hydrogen-oxygen combination.
The decline in performance of lead acid batteries during use refers to the mechanism of change in the rated capacity of the battery in operation, either up or down, but the general trend is irreversible.
The reasons for the reduction of the service life of the lead-acid battery are as follows:
1. the normal working temperature of the environment temperature in the actual operation of the lead-acid battery is 25 ℃, the activity of the battery is reduced below zero, and the physical damage of the battery is serious when the temperature is higher than 35 ℃;
2. the current of the lead-acid battery is too large in the charging and discharging processes, so that the reaction efficiency of active substances of a battery plate is reduced, and the battery with insufficient capacity fails;
3. the lead-acid battery discharges deeply in the discharging process, and the deep discharging causes irreversible vulcanization of active substances of a battery pole plate, so that the failure of the battery is accelerated;
the prediction of the battery application life refers to that the capacity discharged each time is gradually attenuated along with the increase of the service time of the battery under the condition that the battery is used in different application environments, the battery capacity attenuation ratio is calculated, and the failure coefficient of the lead-acid battery is obtained and is used as an important parameter and basis in the prediction of the battery application life.
Step two: designing an accelerated lead-acid storage battery application life test, and periodically adopting different parameters of reference points related to capacity attenuation ratio to obtain test data;
in order to accelerate the life test, a class II valve-regulated lead-acid battery is used as a research sample, the nominal capacity is 400AH, the working temperature is 25 ℃, and the working charge-discharge current is C10The rated capacity discharge cutoff voltage of the battery is 1.800V.
The test conditions are carried out according to actual use, the secondary lead acid battery supplies power for the three types of commercial power communication machine room equipment, the actual use environment supplies power for 4 times per month, 80% of the actual capacity is provided each time, and the cut-off voltage is 1.800V. I.e., discharge testing was performed monthly and data was recorded, with a test current of 30A. The same group was selected and 4 monomers with different deep activity were used, the frequency of collection was monthly and 5 data were recorded, and the test data are shown in table 1.
TABLE 1 lead-acid Battery operational Life test raw data
Step three: and calculating the practical application failure coefficient of the lead-acid battery, and establishing a failure average rate model.
The method is characterized in that the service life of the storage battery is ended under actual conditions, namely the residual rated capacity of the storage battery is lower than a fixed numerical value, the reduction of the use capacity in different periods is closely related to current, temperature and depth, but the average failure rate of the lead-acid battery is close to the same in different periods of the same environment, so that the establishment of a storage battery average failure rate model is an important premise for predicting the actual application life of the battery.
The failure coefficient β of the ith point is calculated according to the following formulami
Wherein, C actual discharge capacity, ambient T temperature; t1i is the first discharge temperature. The m-th discharge failure coefficient is calculated to be
β=(βm1m2+…+βmN)/N (2)
The average rate of failure model of the storage battery calculated according to the formula is shown in the figure I, wherein N is the number of standard points of a battery discharge curve.
Step four: and comprehensively optimizing and disposing the test data by using a bell-shaped function and a vector machine under the condition of calculating the failure rate of the battery.
The optimization method comprises the following steps: the battery failure rate is the rate at which the battery failure coefficient drops during the battery operating cycle.
The model is a bell-shaped function y ═ ax2+ b (wherein the value of a is changed), Y represents the remaining life cycle of the battery, and since the parameter that a is changed is not a fixed value, a vector machine function is adopted to solve a, and meanwhile, the derivative of the whole bell-shaped function is obtained, and the failure rate in one cycle is obtained.
Establishing storage battery actual application life prediction models in different use environments; the method mainly comprises the following steps of battery power supply frequency, load current, power supply current stability and machine room environment temperature.
Calculating the failure rate of the battery based on the battery vulcanization mechanism, and establishing a predicted service life model expression as follows:
answering: the conversion relation between the battery failure rate and the failure coefficient is as follows:
where Δ β is the failure coefficient change, the period of the Δ T coefficient change,
whereinInfluence on the activity of the battery leading to vulcanization and ultimately to a reduction in the service life of the applicationA.b is the inherent property coefficient of the battery active matter and the frequency of the battery under different environmental use conditions;
under the condition that physical inherent factors such as battery active substances, design materials and the like are not considered, the relationship of the battery activity reducing factors is influenced, and the relationship among power supply time, current, temperature and capacity is as follows:
is a factor of the reaction depth between the active and the medium,a factor for supplying energy to participate in chemical reactions, a being the active motor capacity of the battery, whereinThe expression is as follows:
I10standard current; t standard time; kTA temperature correction coefficient; ki=It/I10A current correction factor;
t real time period
When T is in the formulatWhen greater than 10, set I10.t10Is C10,It.ttCt, the air conditioning regulation cannot be negative due to the environmental factors of the actual machine room, if the temperature is negativeThe activity of the battery is limited under zero, the performance is particularly poor, so that the self-learning battery charge state is equal to the initial quantity, and the relational expression of the application life and the residual capacity temperature of the battery is as follows:
when the battery failure coefficient is constant, according to the structure risk minimization principle and the least square vector machine theory:
the core of the optimization is the battery failure rate omega;
wherein ξ is an error variable, | ω! y2Controlling the complexity of the model, C being a penalty factor of constant, b being a deviation, xiA battery state of charge decay variable.
The Lagrange function of least square support vector machine model conversion represents that:
α thereini(i ═ 1, 2.. times, l) is a lagrange multiplier;
the four variables ω, b, ξ, a are separately subjected to partial derivation by an optimization condition, namely, a lagrange function, so as to obtain:
the following can be obtained:
order toThe optimization problem is then transformed to solve the following system of linear equations:
wherein α ═ (α)12,…,α1)T,y=(y1,y2,…,y1)T
B and a are calculated by a least square method, and the decision function of the LS-SVM is obtained as follows:
due to K (x)i,xj) And solving the regression prediction function by adopting internal operation for the inverse function of the kernel function which is an exponential function.
Substituting the experimental values into the vector machine formula yields the following values in table 2:
battery numbering a value b value
#1 0.975 95.2
#2 0.971 96.1
#3 0.982 95.5
#4 0.953 87.8
And performing error optimization according to the data obtained in the table 2, and substituting the data into a life prediction mathematical model formula 8:
f(t)=(0.798+lnΔc).t2+ΔT93.7 (17)
wherein t represents the operating period of the storage battery, since the service life of the operating battery is dynamically changed but the general trend is reduced, the number of detection periods is solved by substituting the experimental condition numerical value into the formula 17, and the inverse function is solved to obtain:
battery numbering Operating life (period)
#1 24.5
#2 24
#3 25
#4 23.5
The method for predicting the residual operation life of the battery is to calculate the failure rate of the battery according to a battery life model formula in the last detection period of the battery and predict the residual application period number.
Calculation of prediction error
The result predicted by any method has a certain difference with the actual value, and the difference between the predicted service life of the storage battery and the actual service life of the storage battery is the prediction error. The prediction error should reflect the accuracy of the prediction result, and the error value is inversely related to the accuracy. There are of course many different indicators of the calculation of the prediction error, and one is given below to evaluate the error predicted herein: relative Error Relative Percentage Error, RPE
Wherein Q isiIs the actual measured value, fiIs a predicted value.
Prediction result and error
The predicted values and the measured values are visually expressed as shown in fig. 2, and it can be seen from the figure that the predicted values obtained by using the principle of the least squares support vector machine and the measured values obtained by experiments have high consistency. The fitness function is calculated by the formula as:
according to the calculation results, the errors of the prediction result and the actual measurement result of the model do not exceed 10%, and the calculation result of the fitness is 0.0865, which also shows that the least square support vector machine can obtain a very accurate result in the application of the battery life prediction.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.

Claims (5)

1. A method for predicting the service life of a lead-acid battery is characterized by comprising the following steps:
acquiring the battery discharge electric quantity value to be predicted in a work project of the lead-acid battery in different environments, and calculating the attenuation ratio of the current battery capacity;
calculating the failure coefficient of the lead-acid battery to be predicted in practical application according to the attenuation ratio, and further establishing a failure average rate model of the battery with the failure coefficient:
setting the self-learning battery state of charge to be equal to the initial quantity, and setting the self-learning battery state of charge to be equal to the initial quantity according to a least square vector machine according to a structural risk minimization principle when a battery failure coefficient is constantObtaining a least square vector machine LS-SVM decision function:
wherein, K (x, x)i) Solving a regression prediction function by adopting internal operation for a kernel function which is an inverse function of an exponential function, and substituting the experimental numerical value into the formula to obtain a value a and a value b corresponding to a plurality of batteries to be detected; a. b is the inherent attribute coefficient of the battery active matter and influencing the battery frequency under different environmental use conditions;
-substituting the error-optimized a, b values into a battery residual capacity prediction model:
(t) (0.798+ ln Δ c) × t is obtained2+ΔT93.7
And (3) wherein t represents the operation period of the storage battery, the general trend is reduced, the experimental numerical value is substituted into the number of the solving detection periods, and the inverse function is solved to obtain the operation life of the storage battery.
2. The method of claim 1, wherein the battery failure rate is the rate at which the battery failure coefficient decreases during the battery operating cycle; using a bell-shaped function y-ax2+ b, where the value of a is varied and y represents the remaining life cycle of the battery; because a is that the changed parameter is not a fixed value, a vector machine function is adopted to solve a, and meanwhile, the derivative of the whole bell-shaped function is solved to obtain the failure rate in a period.
3. The method for predicting the service life of the lead-acid battery according to claim 1, wherein the failure coefficient calculation is performed according to the following formula to obtain the i-th point failure coefficient βmi
Wherein, C actual discharge capacity, ambient T temperature; t1i is the first discharge temperature, and the failure coefficient of the m-th discharge is calculated as:
β=(βm1m2+…+βmN)/N
n is the number of standard points taken on the cell discharge curve.
4. The method for predicting the service life of the lead-acid battery according to claim 1, further characterized by further establishing an average failure rate model of the battery with the failure coefficient as follows:
comprehensively optimizing and disposing test data by using a bell-shaped function and a vector machine, wherein the optimization method comprises the following steps: the battery failure rate is the rate at which the battery failure coefficient drops during the battery operating cycle;
the model is a bell-shaped function y ═ ax2+ b, wherein the value a is changed, y represents the remaining life cycle of the battery, and since the parameter a is changed and not a fixed value, a is solved by adopting a vector machine function, and meanwhile, the derivative of the whole bell-shaped function is obtained, namely the failure rate in one cycle;
establishing a storage battery actual application life prediction model under different use environments comprises the following steps: the power supply frequency of the battery, the magnitude of load current, the stability of the power supply current and the environmental temperature of the machine room;
calculating the battery failure rate based on the battery failure rate and the active vulcanization model instead of the battery vulcanization mechanism, wherein the established predicted life model expression is as follows:
where Δ β is the failure coefficient change, the period of the Δ T coefficient change,
whereinThe main factors influencing the activity of the storage battery to cause vulcanization and finally causing the reduction of the service life of the application are a and b, and the a and b are the property coefficients influencing the frequency of the battery and inherent properties of battery active matters under different environmental use conditions.
5. The method for predicting the service life of the lead-acid battery according to claim 4, wherein the relationship between factors affecting the reduction of the battery activity is not considered in consideration of physical inherent factors such as battery active substances and design materials, and the relationship between the power supply time, the current, the temperature and the capacity is expressed as follows:
is a factor of the reaction depth between the active and the medium,a factor for supplying energy to participate in chemical reactions, a being the active motor capacity of the battery, whereinThe expression is as follows:
I10standard current; t standard time; kTA temperature correction coefficient; ki=It/I10A current correction factor;where t is the real-time period;when T is in the formulatWhen greater than 10, set I10*t10Is C10,It*ttCt, the self-learning battery state of charge is equal to the initial quantity due to the practical use environment factors of the machine room, and the relational expression of the application life and the battery residual capacity temperature is
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