CN111308351B - Low-temperature environment power battery SOC estimation method, storage medium and equipment - Google Patents

Low-temperature environment power battery SOC estimation method, storage medium and equipment Download PDF

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CN111308351B
CN111308351B CN201910993581.6A CN201910993581A CN111308351B CN 111308351 B CN111308351 B CN 111308351B CN 201910993581 A CN201910993581 A CN 201910993581A CN 111308351 B CN111308351 B CN 111308351B
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CN111308351A (en
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李玉芳
徐国放
赵红伟
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Nanjing University of Aeronautics and Astronautics
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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
    • G01R31/005—Testing of electric installations on transport means
    • G01R31/006—Testing of electric installations on transport means on road vehicles, e.g. automobiles or trucks
    • 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

Abstract

The invention discloses a low-temperature environment power battery estimation method, a storage medium and equipment, wherein the method comprises the following steps: establishing a power battery equivalent circuit model, and determining a circuit model state equation and an observation equation; establishing a table taking the temperature and the discharge rate as item columns and the actual total capacity of the battery in the current state as a data column; identifying the open-circuit voltage of the battery and the parameters of the circuit model under different temperature conditions; and establishing an extended Kalman filtering model, substituting the identified open-circuit voltage and the parameters of the circuit model, and estimating the SOC of the battery. The invention considers the influence of low temperature environment on battery parameters, and provides an SOC calculation method based on multi-parameter identification; the influence of a plurality of influence factors on the SOC of the battery is considered, the effective identification of the power battery parameters in the low-temperature environment is realized, and the estimation accuracy of the SOC of the power battery in different temperature intervals is improved.

Description

Low-temperature environment power battery SOC estimation method, storage medium and equipment
Technical Field
The invention relates to the field of power battery management systems, in particular to a low-temperature environment power battery SOC (State of Charge) estimation method based on multi-parameter identification, a storage medium and equipment.
Background
The SOC is a key state parameter of the power battery and an important control parameter of the electric automobile, but the accurate estimation of the SOC in the actual use process is not easy due to the complex electrochemical reaction process, the difficulty in online measurement of internal parameters and the like. At present, many methods for estimating the SOC of the power battery at home and abroad mainly comprise: a current integration method, a discharge experiment method, an open-circuit voltage method, a Kalman filtering method, a neural network method and the like. At present, the calculation methods are generally unified calculation models under normal temperature conditions or wide temperature conditions, and no SOC calculation method specifically aiming at low temperature conditions exists. However, since the low-temperature environment has a significant influence on the battery parameters, the characteristics of the power battery at low temperature are greatly changed, and the SOC calculation model in a uniform form or model parameters has a large calculation error in the low-temperature environment.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a low-temperature environment power battery SOC estimation method, a storage medium and equipment aiming at a low-temperature environment and based on parameter identification, so as to improve the estimation accuracy of the SOC of a power battery in different temperature intervals.
In a first aspect, the invention provides a low-temperature environment power battery SOC estimation method based on parameter identification, which comprises the following steps: step 1, establishing a power battery equivalent circuit model, and determining a circuit model state equation and an observation equation; step 2, establishing a table taking the temperature and the discharge rate as item columns and the actual total capacity of the battery in the current state as a data column; step 3, identifying the open-circuit voltage of the battery and the parameters of the circuit model under different temperature conditions; and 4, establishing an extended Kalman filtering model, substituting the open-circuit voltage and the parameters of the circuit model obtained by identification in the step 3, and estimating the SOC of the battery.
Preferably, the temperature is in the range of [ -35 ℃, 5 ℃ ], and the discharge rate variation is in the range of [0.1C, 1C ].
Preferably, the step 3 of identifying the open-circuit voltages of the batteries at different temperatures specifically includes:
s311, charging the battery to a charge cut-off voltage at different temperature nodes by using a nominal multiplying power, standing to enable the battery to reach thermal balance, and measuring an open-circuit voltage value of the battery, wherein the open-circuit voltage value is recorded as an open-circuit voltage value when the SOC is equal to 100%; s312, performing constant-current discharge on the battery at each temperature node at a nominal discharge rate until the released electric quantity is 10% of the actual discharge total capacity or the battery voltage reaches a discharge cut-off voltage, and standing for 6 hours to measure the open-circuit voltage value of the battery; s313, repeatedly executing S32 at different discharge rates, and finally finishing the discharge of the battery when the battery voltage reaches a discharge cut-off voltage; s314, establishing corresponding curves of the battery open-circuit voltage and the SOC at different temperature nodes; s315, fitting equation
Figure BDA0002239050870000011
Wherein U isOCIs the open circuit voltage, xi, of the battery1、ξi、ξnIs the coefficient of a fitting formula, where i ∈ [3, n-1 ]]，n>＝4。
Preferably, the nominal multiplying power and the nominal discharge rate are both 0.1C.
Preferably, the identifying the parameters of the circuit model in step 3 specifically includes:
s321, the following functions are adopted to identify the battery parameters to be identified: internal resistance R of battery0Battery polarization resistance RpPolarization of battery
Capacitor CpThe rational approximation is carried out, and the method,
Figure BDA0002239050870000021
Figure BDA0002239050870000022
Figure BDA0002239050870000023
wherein k is0-k1415 model parameters identified by a genetic algorithm;
s322, establishing the SOC and the actually measured terminal voltage U of the battery under different temperature nodeslThe corresponding curve of (a);
s323, calculating voltage and actually measured terminal voltage U by using modellAnd when the error in the whole identification process meets the preset precision, the acquisition process of the battery parameters is finished.
Preferably, the specific process of step 4 is as follows: establishing an extended Kalman filtering model for a circuit model state equation and an observation equation, wherein initial conditions comprise an initial charge state of a battery, initial voltages at two ends of a polarization capacitor of the circuit model and an initial error covariance matrix; the extended Kalman filter firstly obtains a predicted value at the moment k according to a filtering result at the moment k-1, then obtains an error covariance matrix predicted value at the moment k, then obtains a Kalman gain matrix at the moment k, carries out filtering processing on a predicted state vector according to an observation value to obtain a state vector filtering value, and then calculates the error covariance matrix filtering value.
In a second aspect, the present invention provides a storage medium, which includes a program stored in the storage medium, and when the program runs, the apparatus where the storage medium is located is controlled to execute the method for estimating SOC of a low-temperature environment power battery based on multi-parameter identification according to any one of the above technical solutions.
In a third aspect, the invention provides a low-temperature environment power battery SOC estimation device based on multi-parameter identification, which includes a processor, where the processor is configured to run a program, and the program runs to execute the low-temperature environment power battery SOC estimation method based on multi-parameter identification in any one of the above technical solutions.
The invention has the beneficial effects that: considering the influence of a low-temperature environment on battery parameters, providing an SOC calculation method based on multi-parameter identification; the influence of a plurality of influence factors on the SOC of the battery is considered, the effective identification of the power battery parameters in the low-temperature environment is realized, and the estimation accuracy of the SOC of the power battery in different temperature intervals is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings 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 flowchart of a method for estimating SOC of a power battery in a low temperature environment based on parameter identification according to an embodiment of the present invention;
fig. 2 is a device setup diagram for parameter identification in the embodiment of fig. 1.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The present embodiment describes a method for estimating the SOC of a power battery in a low-temperature environment based on parameter identification,
step 1, establishing a power battery equivalent circuit model, in this embodiment, a Thevenin model, and a state equation and an observation equation of the model are as follows:
Figure BDA0002239050870000031
Figure BDA0002239050870000032
wherein, UP,kRepresenting the voltage across the polarization resistance and polarization capacitance of the Thevenin model, TsTo sample time, RPIs a polarization resistance, CPIs a polarization capacitance, ηkFor cell discharge efficiency, CNIs the actual total capacity, omega, of the battery in the current state1,k-1、ω2,k-1As system noise, Ul,kFor the experimentally determined voltage at the electrical time, R0Is the internal resistance of the battery, vkTo observe noise, UocIs the battery open circuit voltage.
Next, the actual total capacity C of the battery in the equivalent circuit model of the power battery in the current state is measuredNOpen circuit voltage UocParameter and battery internal resistance R0Battery polarization resistance RPBattery polarization capacitor CPAnd (5) performing identification.
And 2, establishing a table taking the temperature and the discharge rate as item columns and the actual total capacity of the battery in the current state as a data column.
Considering low temperature environment and small discharge rate discharge, the temperature variation range is firstly set in the range of [ -35 ℃, 5 ℃ and the discharge rate variation range is set in the range of [0.1C, 1C ]. Under a low-temperature environment, the actual total capacity of the battery changes remarkably relative to the rated total capacity, and the battery mainly discharges at a small rate in the actual working process.
Recording 0.1C at 5 ℃ as a nominal condition, and recording the capacity of the battery which is completely discharged after the battery is completely charged under the nominal condition as a nominal capacity; the capacity obtained by completely discharging the battery under the non-nominal condition was recorded as the non-nominal capacity. Respectively discharging the battery which is fully charged under the current temperature discharge rate under the conditions that the temperature is 5 ℃, 15 ℃, 25 ℃ and 35 ℃ and the discharge rate is 0.1C, 0.2C, 0.3C, 0.4C, 0.5C, 0.6C, 0.7C, 0.8C, 0.9C and 1C, and recording the actual total discharge capacity from discharging to the discharge cut-off voltage as QI,T. The standing time is 5-2 h, -5-4 h, -15-6 h, -25-8 h and-35-10 h.
As shown in the following table, the actual total capacity C of the battery in the current stateNIt can be obtained by looking up the table of the temperature and the discharge rate of the current state.
TABLE 1 Table of actual total capacity of battery at different temperatures and discharge rates
Figure BDA0002239050870000041
And 3, identifying the open-circuit voltage of the battery and the parameters of the circuit model under different temperature conditions.
Firstly, parameter identification of open circuit voltage
Open circuit voltage fitting equation
Figure BDA0002239050870000042
Wherein U isOCIs the open circuit voltage, xi, of the battery1、ξi、ξnIs the coefficient of a fitting formula, where i ∈ [3, n-1 ]]，n>4. In this embodiment, it is considered that the relationship curves of the open-circuit voltage and the state of charge of the battery are different at different temperatures, and therefore the relationship between the open-circuit voltage and the state of charge of the battery is identified at different temperatures.
a. The battery was charged at a nominal rate of 0.1C at a temperature of 5 ℃, -15 ℃, -25 ℃, -35 ℃ respectively, until the charge cut-off voltage was reached, and after the battery was allowed to stand to reach thermal equilibrium, it usually took 6 hours, the terminal voltage value of the battery was measured, and this value was recorded as the open circuit voltage value at which SOC was 100%.
b. The battery was subjected to constant current discharge operation at a discharge rate of 0.1C at a temperature of 5 deg.c, -15 deg.c, -25 deg.c, -35 deg.c. According to the table Q established in step 2I,TThe cutoff discharge condition is that the discharge capacity is QI,TOr the cell voltage reaches the discharge cut-off voltage, and after the cell is allowed to stand to reach thermal equilibrium, it usually takes 6 hours to test the cell terminal voltage value.
c. The battery voltage drops to the cut-off discharge voltage, at which point the battery charge is discharged.
d. Corresponding curves of the OCV and the SOC of the battery are respectively established under the conditions that the temperature is 5 ℃, 15 ℃, 25 ℃ and 35 ℃.
e. According to fitting equation Uoc＝ξ3·SOC5+ξ4·SOC4+ξ5·SOC3+ξ6·SOC2+ξ7·SOC+ξ8Obtaining fitting formula coefficients xi at the temperatures of 5 ℃, 15 ℃, 25 ℃ and 35 ℃ respectively3、ξ4、ξ5、ξ6、ξ7、ξ8. And then establishing an equation of open-circuit voltage, temperature and state of charge by a fitting formula at different temperatures:
Figure BDA0002239050870000051
obtaining the coefficient xi of the OCV-SOC fitting formula1、ξ2、ξ3、ξ4、ξ5、ξ6、ξ7、ξ8。
② power battery parameter namely battery internal resistance R0Battery polarization resistance RPBattery polarization capacitor CPIs identified by
Considering the influence of the SOC and the temperature of the battery on the battery parameters, the following functions are selected to carry out rational approximation on the battery parameters, k0-k14Are 15 model parameters to be identified:
Figure BDA0002239050870000052
Figure BDA0002239050870000053
Figure BDA0002239050870000054
in particular, a genetic algorithm is used for k0-k14The identification is carried out, and the SOC and the actually measured terminal voltage U of the battery under different temperature conditions can be obtained in the step 1lThe corresponding curve of (1) identifying the model terminal voltage generated by current excitation and the actual test terminal voltage U of the batterylComparing, when the model calculates the voltage ULAnd the actual test voltage value UlWhen the error between the whole identification process meets the setting precision (the minimum error value of the battery is set to be 0.01V), the acquisition process of the battery parameters is finished. The process is as follows:
a. changing k to [ k ]0,k1,k2,…,k14]Defined as a chromosome in solution space, any one parameter kiCalled as gene, the specific value of each parameter is taken as gene individual, and the value range of the parameter is the search space of the problem:
[k0min,k1min,k2min,…,k14min]-[k0max,k1max,k2max,…,k14max]。
b. establishing a fitness function: f ═ Ul-UL|1+|Ul-UL|2+|Ul-UL|3+…+|Ul-UL|nWherein U islFor actually testing the voltage value, ULThe voltage value is calculated for the model and n is the number of sampling points. Smaller f-numbers indicate higher model accuracy.
c. Selecting individuals by a method combining sorting selection and elite reservation, and according to the adaptive value of a population, firstly selecting the optimal individual to be directly reserved asIn the next generation, other individuals are ranked in descending order of fitness value and then selected by the assigned probability using roulette. The selection probability for each individual is:
Figure BDA0002239050870000055
wherein i is the ranking of the individual in the population, P is the selection probability of the individual with the ranking of i, P is the selection probability of the worst individual obtained by a proportional selection method, and N is the number of the individuals in the population.
d. And carrying out intersection operation by adopting an arithmetic intersection operator.
Figure BDA0002239050870000061
In the formula (I), the compound is shown in the specification,
Figure BDA0002239050870000062
a is a coefficient randomly generated in the interval (0, 1),
Figure BDA0002239050870000063
the filial generation individuals after the crossing.
e. In order to increase the diversity of the population, the population is subjected to mutation operation.
Figure BDA0002239050870000064
Figure BDA0002239050870000065
In the formula (I), the compound is shown in the specification,
Figure BDA0002239050870000066
is the original value of the change point,
Figure BDA0002239050870000067
is a new value of the change point, ki min、ki maxIs kiThe values are upper and lower limits, and alpha and beta are random numbers between 0 and 1.
f. A group k0-k14The difference between the model output voltage value and the actual test voltage value is minimized by the value of (A), and the difference is substituted into a formula to obtain the battery R0，RPAnd CPThe value of (c).
And 4, establishing an extended Kalman filtering model, substituting the open-circuit voltage and the parameters of the circuit model obtained by identification in the step 3, and estimating the SOC of the battery.
Estimating the SOC of the battery by an extended Kalman filtering method, wherein the process is as follows:
updating state estimation time:
Figure BDA0002239050870000068
error covariance time update:
Figure BDA0002239050870000069
kalman gain matrix:
Figure BDA00022390508700000610
estimation of observed variables: u shapel,k＝Uoc,k+R0,kIk+UP,k。
State estimation measurement update:
Figure BDA00022390508700000611
error covariance measurement update: pk/k＝(E-KkCk)Pk/k-1。
Wherein the content of the first and second substances,
Figure BDA00022390508700000612
Dk＝[R0]，
Figure BDA00022390508700000613
in order to predict the value of the state being estimated,
Figure BDA00022390508700000614
for filtered values of the estimated state, Pk/kFor filtering error covariance matrix, Pk/k-1Prediction error covariance matrix, QkIs a process excitation noise covariance matrix, ΓkAs an interference matrix, KkIs a Kalman gain matrix, ykAs an observed value, E is an identity matrix.
The initial conditions of the filter equation include the initial state of charge of the battery, the initial voltage across the polarization capacitor of the circuit model, and the initial error covariance matrix. The initial state of charge of the battery is obtained by measuring the open-circuit voltage after the battery is placed still and then through an equation of the open-circuit voltage, the temperature and the SOC, the initial voltage at two ends of the polarization capacitor is zero, an appropriate value needs to be selected according to engineering practice by the initial error covariance matrix, and the convergence speed can be accelerated by the appropriate value. The extended Kalman filter firstly according to the filtering result at the k-1 moment
Figure BDA0002239050870000071
Obtaining the predicted value of k time
Figure BDA0002239050870000072
Then, the predicted value P of the error covariance matrix at the moment k is obtainedk/k-1Then obtaining a K moment Kalman gain matrix KkAnd then obtaining the observation quantity predicted value ykPredicted state vector from observation value pairs
Figure BDA0002239050870000073
And carrying out filtering processing to obtain a state vector filtering value, and then calculating an error covariance matrix filtering value.
As shown in FIG. 2, the device building diagram of the parameter identification test is shown, and the device comprises an industrial personal computer, a charging and discharging test platform and a parameter identification temperature control box. The industrial personal computer is used for controlling the temperature setting of the parameter identification temperature control box and charging and discharging of the battery through the charging and discharging test platform, the parameter identification temperature control box changes the ambient temperature of the battery after receiving control information, and the charging and discharging test platform charges and discharges the battery in the parameter identification temperature control box according to the control information. And then the parameter identification temperature control box transmits the temperature information and the battery voltage and current signals back to the industrial personal computer so as to monitor the battery state.
The invention also provides a storage medium, which comprises a program stored in the storage medium, and when the program runs, the device where the storage medium is located is controlled to execute the low-temperature environment power battery SOC estimation method based on multi-parameter identification in any one of the technical schemes.
The invention also provides a low-temperature environment power battery SOC estimation device based on multi-parameter identification, which comprises a processor, wherein the processor is used for running a program, and the program is used for executing the low-temperature environment power battery SOC estimation method based on multi-parameter identification in any one of the technical schemes during running.
The technical means disclosed in the invention scheme are not limited to the technical means disclosed in the above embodiments, but also include the technical scheme formed by any combination of the above technical features.

Claims (5)

1. The low-temperature environment power battery SOC estimation method is characterized by comprising the following steps of:
step 1, establishing a power battery equivalent circuit model, and determining a circuit model state equation and an observation equation;
step 2, establishing a table taking the temperature and the discharge rate as item columns and the actual total capacity of the battery in the current state as a data column;
step 3, identifying the open-circuit voltage of the battery and the parameters of the circuit model under different temperature conditions;
step 4, establishing an extended Kalman filtering model, substituting the open-circuit voltage and the parameters of the circuit model obtained by identification in the step 3, and estimating the SOC of the battery;
the step 3 of identifying the open-circuit voltages of the batteries at different temperatures specifically comprises:
s311, charging the battery to a charge cut-off voltage at different temperature nodes by using a nominal multiplying power, standing to enable the battery to reach thermal balance, and measuring an open-circuit voltage value of the battery, wherein the open-circuit voltage value is recorded as an open-circuit voltage value when the SOC is equal to 100%;
s312, performing constant-current discharge on the battery at the nominal discharge rate at each temperature node until the released electric quantity is 10% of the actual total discharge capacity or the battery voltage reaches the discharge cut-off voltage, and standing to reach the thermal balance of the battery and then measuring the open-circuit voltage value of the battery;
s313, repeatedly executing S32 at different discharge rates, and finally finishing the discharge of the battery when the battery voltage reaches a discharge cut-off voltage;
s314, establishing corresponding curves of the battery open-circuit voltage and the SOC at different temperature nodes;
s315, fitting equation
Figure FDA0003080524300000011
Wherein U isOCIs the open circuit voltage, xi, of the battery1、ξi、ξnIs the coefficient of a fitting formula, where i ∈ [3, n-1 ]]，n>＝4；
The identifying of the parameters of the circuit model specifically comprises:
s321, the following functions are adopted to identify the battery parameters to be identified: internal resistance R of battery0Battery polarization resistance RpBattery polarization capacitor CpThe rational approximation is carried out, and the method,
Figure FDA0003080524300000012
Figure FDA0003080524300000013
Figure FDA0003080524300000014
wherein k is0-k1415 model parameters identified by a genetic algorithm;
s322, establishing the SOC and the actually measured terminal voltage U of the battery under different temperature nodeslCorresponding curve of；
S323, calculating voltage U by using modelLAnd the measured terminal voltage UlWhen the error in the whole identification process meets the preset precision, the battery parameter obtaining process is finished;
the nominal multiplying power and the nominal discharge rate are both 0.1C.
2. The low-temperature environment power battery SOC estimation method according to claim 1, wherein the temperature is in a range of [ -35 ℃, 5 ℃ C ], and the discharge rate variation range is [0.1C, 1C ].
3. The method for estimating the SOC of the power battery in the low-temperature environment according to claim 1, wherein the specific process of the step 4 is as follows:
establishing an extended Kalman filtering model for a circuit model state equation and an observation equation, wherein initial conditions comprise an initial charge state of a battery, initial voltages at two ends of a polarization capacitor of the circuit model and an initial error covariance matrix;
the extended Kalman filter firstly obtains a predicted value at the moment k according to a filtering result at the moment k-1, then obtains an error covariance matrix predicted value at the moment k, then obtains a Kalman gain matrix at the moment k, carries out filtering processing on a predicted state vector according to an observation value to obtain a state vector filtering value, and then calculates the error covariance matrix filtering value.
4. A storage medium, characterized by: the low-temperature environment power battery SOC estimation method comprises a program stored in the storage medium, and when the program runs, the device where the storage medium is located is controlled to execute the low-temperature environment power battery SOC estimation method according to any one of claims 1 to 3.
5. The utility model provides a low temperature environment power battery SOC estimation equipment based on multi-parameter discernment which characterized in that: the low-temperature environment power battery SOC estimation method comprises a processor, wherein the processor is used for running a program, and the program is used for executing the low-temperature environment power battery SOC estimation method according to any one of claims 1-3.
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