CN102788957B - Estimating method of charge state of power battery - Google Patents

Estimating method of charge state of power battery Download PDF

Info

Publication number
CN102788957B
CN102788957B CN201110142292.9A CN201110142292A CN102788957B CN 102788957 B CN102788957 B CN 102788957B CN 201110142292 A CN201110142292 A CN 201110142292A CN 102788957 B CN102788957 B CN 102788957B
Authority
CN
China
Prior art keywords
battery
soc
state
last
time
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Fee Related
Application number
CN201110142292.9A
Other languages
Chinese (zh)
Other versions
CN102788957A (en
Inventor
张育华
刘锦娟
刘贤兴
孙金虎
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hengchi Science & Technology Co Ltd Zhenjiang
Original Assignee
Hengchi Science & Technology Co Ltd Zhenjiang
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hengchi Science & Technology Co Ltd Zhenjiang filed Critical Hengchi Science & Technology Co Ltd Zhenjiang
Priority to CN201110142292.9A priority Critical patent/CN102788957B/en
Publication of CN102788957A publication Critical patent/CN102788957A/en
Application granted granted Critical
Publication of CN102788957B publication Critical patent/CN102788957B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Landscapes

  • Secondary Cells (AREA)

Abstract

The invention relates to an estimating method of the charge state of a power battery. The estimating method is characterized by including the following steps: estimating system on chip (SOC) initial value of the battery according to last charge-discharge state of the battery and SOC value and standing time of the battery since last-time use comprehensively, calculating battery inner resistance and polarization voltage of the battery, collecting working current and voltage at two ends of the battery in real time, estimating SOC of the battery according to the SOC comprehensive estimation algorithm based on an expansion Kalman filtering method and an ampere-hour metering method, storing the SOC of the battery and the corresponding working current and voltage at two ends of the battery, storing current time, battery SOC and battery charge-discharge state before the system is closed down and closing the system down. The estimating method has the advantages of being small in SOC initial value, simple in algorithm, small in metering quantity and high in accuracy.

Description

A kind of power battery charged state evaluation method
Technical field
The present invention relates to a kind of evaluation method of battery charge state, be specifically related to a kind of state-of-charge evaluation method of Prospect of EVS Powered with Batteries.
Background technology
The state-of-charge (SOC-State Of Charge) of battery is one of important parameter of reflection battery performance, and what it was electric battery administers and maintains provides important evidence.For electric motor car driver, the SOC of battery just, as the fuel contents gauge of general-utility car, only has the SOC that has known that accurately battery is current, just can judge the continual mileage of automobile.But due to the nonlinearity of battery, affect the many factors of battery SOC, the SOC of estimating battery is more than calculating the many of oil mass complexity.
In theory, the SOC of battery is defined as the current battery dump energy Q of electric battery cwith electric battery rated capacity Q 0ratio, can explain by following formula: when battery Full Charge Capacity, definition SOC is 1; When battery discharge is complete, definition SOC is 0.
At present, the method for estimating battery SOC has both at home and abroad: internal resistance method, open-circuit voltage method, Ah counting method, Kalman filtering method, neural network etc.Wherein, internal resistance method, according to the funtcional relationship between the internal resistance of cell and battery SOC, is calculated the SOC of battery by detecting the internal resistance of cell, but it is more difficult accurately to detect the internal resistance of cell, and practicality is not strong; Open-circuit voltage method is determined the SOC of battery according to the funtcional relationship between the open-circuit voltage of battery and battery SOC, but the method need to leave standstill for a long time to battery, is generally used for laboratory or battery maintenance stage, cannot on real vehicle, be applied; Ah counting method draws inflow electric weight battery or that battery is emitted by the charging and discharging currents of battery being carried out to integration, Ah counting method algorithm is simple, easily realizes, and is the algorithm of a kind of estimating battery SOC of relatively commonly using, but also there are some problems in Ah counting method, comprising:
(1) cannot calculate the initial SOC of battery;
(2) there is cumulative errors, higher to the accuracy requirement of current detecting system;
Kalman filtering is a kind of Recursive Linear minimum variance estimate algorithm, utilizes the value in a upper moment and the parameter value of measurement in real time to estimate in real time.When Kalman filter is applied to battery SOC estimation, battery is represented as a linear discrete system, and battery SOC is a state variable of system.Kalman filtering algorithm is insensitive to the error of initial SOC, and the error of current detecting is had to correcting action.But when the SOC of application card Thalmann filter estimating battery, need an accurate battery model, and Kalman filter calculated amount is large, requires higher to the computing power of master controller.
Method of estimation (the application number: 200710064294.4) of the nickel-hydrogen power battery charged state based on standard battery model of Tsing-Hua University, use the SOC of Kalman filtering algorithm estimating battery, arithmetic accuracy is high, but the estimating algorithm of SOC initial value is single, do not consider the time of repose of battery.Whole algorithm complexity, calculated amount is larger.
A kind of method for estimating charge state of power cell (application number: 200610167393) of BYD company, its outstanding feature is the temperature that considered battery, discharges and recharges the factors such as number of times, charge-discharge magnification, battery actual capacity, also different to these parameters of different batteries, need to do a large amount of experiments and determine these parameters, algorithm practicality is not strong, and the algorithm of the initial SOC of estimating battery is also single open-circuit voltage method, and error is larger.
Method (the application number: 200680016312.5) of the SOC of the battery of the estimation hybrid electric vehicle of LG Chemical Ltd., use the SOC of open-circuit voltage method and Ah counting method estimating battery, there is cumulative errors in this algorithm, requires higher to current detection accuracy.
The comprehensive inquiry to the Patent data retrieving, finds that the subject matter of current various battery charge state methods of estimation is that the initial SOC algorithm for estimating of battery is single, and error is larger; But whole algorithm or ensured precision too complex, or algorithm is simple but cannot ensure precision, there is no the algorithm of a kind of integration algorithm complexity and arithmetic accuracy.
Summary of the invention
The object of this invention is to provide a kind of state-of-charge evaluation method of electrokinetic cell, the comprehensive estimate method of having used open-circuit voltage method, EKF method and Ah counting method to combine, to improve the precision of algorithm, shortcut calculation, is more applied algorithm in practice.
Shown in Fig. 1, be the battery model of selecting in the present invention, wherein E (t) is ideal voltage source, represents the open-circuit voltage of battery, and at definite temperature, the SOC of it and battery has fixing funtcional relationship.R is that battery ohmic internal resistance, the Up polarizing voltage that is battery, I (t) working current that is battery (when charging for just, when electric discharge for bearing), V (t) are battery both end voltage.
According to the circuit structure of this battery model, build the battery status spatial model of discrete form as shown in formula (1), the sampling instant point that k is battery management system, Ts is two time intervals between sampled point:
SOC k = SOC k - 1 - 1 Q · I k - 1 · Ts V k = E k - R · I k + Up k Formula (1)
Wherein, the theoretical capacity that Q is electric battery, in the time of batteries charging, Q is the rated capacity of electric battery; In the time of battery power discharge, Q is the battery theoretical capacity calculating according to Peukert equation; SOC kfor the SOC value of battery of this sampling instant, SOC k-1for the SOC value of battery of a upper sampling instant; I k-1for the current value of a upper sampling instant, I kfor the current value of this sampling instant; V kfor the battery terminal voltage sampled value of this sampling instant; E kfor the open-circuit voltage of this sampling instant battery; Up kfor the polarizing voltage of this sampling instant battery.
According to the discrete state spatial model of battery, set up Kalman filtering algorithm:
X k / k - 1 = X k - 1 / k - Q - 1 Q · I k - 1 · Ts Y k = F ( X k / k ) | X k / k = X kk / k - 1 + R · I k + Up k C k = d ( F ( X k / k ) ) d ( X k / k ) | X k / k = X k / k - 1 P k / k - 1 = P k - 1 / k - 1 + D X k = P k / k - 1 · C k [ C k 2 · P k / k - 1 + W ] X k / k = X k / k - 1 + K k · ( V k - Y k ) P k / k = P k / k - 1 - C k · C k · P k / k - 1 k = 1,2,3 , · · · · · · Formula (2)
Wherein, the sampling instant point that k is battery management system; X k/k-1for the predicted value of this sampling instant of battery SOC; X k/kfor battery SOC is at the estimated value of this sampling instant; Y kthe battery both end voltage of calculating by battery model for this sampling instant; I k-1for the current value of a upper sampling instant, I kfor the current value of this sampling instant; R is the internal resistance of cell; Up kfor the polarizing voltage of this sampling instant battery; C kfor observing matrix, P k/k-1for the predicted value mean square deviation of SOC; P k/kfor the estimated value mean square deviation of SOC; D is system noise variance matrix; K kfor systematic error gain; W is systematic survey noise variance matrix; V kfor system is at the cell voltage of this sampling instant collection; F (X k/k) representing the function of battery open circuit voltage E (k) and the SOC relation of battery, expression formula is as shown in formula (3):
E k = F ( X k / k ) = a 0 · X k / k n + a 1 · X k / k n - 1 + a 2 · X k / k n - 2 + · · · + a n - 1 · X k / k + a n Formula (3)
Wherein, E kfor the open-circuit voltage of battery, X k/kfor the SOC of battery, a 0, a 1, a 2..., a nfor multinomial coefficient, n is natural number, the number of times of representative polynomial.Formula (3) draws data by experiment in advance, and draws a by MATLAB data fitting instrument 0, a 1, a 2..., a nvalue with n.
As shown in Figure 2, the invention is characterized in, comprise successively following steps:
Step (1):
In the start moment of battery management system, master controller carries out initialization to following parameter:
1) the theoretical capacity Q of calculating battery;
2) systematic survey noise variance matrix W k, W kfor the measuring error of battery management system voltage detection module;
3) system noise variance matrix D, is taken as 1;
4) the sampling interval Ts of battery management system;
5) system prediction error covariance matrix initial value P 0/0;
Step (2):
Calculate the initial SOC of battery according to the process flow diagram shown in Fig. 3:
Step (2.1) is measured the open-circuit voltage of battery;
Step (2.2) reads the data in the memory block of depositing the last unused time in Flash, and calculates the initial SOC of battery;
Step (2.2.1) is if the data in Flash are 0xFFFFFF, the initial SOC of battery while calculating this start by battery open circuit voltage being applied to formula (4).
Y=p 0x n+ p 1x n-1+ ... + p n-1x+p nformula (4)
Wherein, the SOC that y is battery, the open-circuit voltage that x is battery, p 0p nfor multinomial coefficient, n is natural number, the number of times of representative polynomial.Formula (4) draws data by experiment in advance, and draws p by MATLAB data fitting instrument 0p nand the value of n.
Step (2.2.2), if the data in Flash are not 0xFFFFFF, read the time of this on time and last shutdown, and the difference that juice is calculated is between the two Δ T;
Step (2.2.2.1) is if Δ T >=6 hour, read in Flash district the data in the charging and discharging state ex_state of battery while representing last shutdown, the initial SOC that the charging and discharging state of battery open circuit voltage and last battery is applied to related function calculating battery, wherein said related function represents by formula (5):
y = p 0 · x n + p 1 · x n - 1 + · · · + p n - 1 · x + p n ex _ state = 1 y = q 0 · x n + q 1 · x n - 1 + · · · + q n - 1 · x + q n ex _ state = 2 Formula (5)
Wherein, the state-of-charge that y is battery, the open-circuit voltage that x is battery, p 0p nand q 0q nfor multinomial coefficient, n is natural number, and ex_state=1 represents that last battery is in charged state, and ex_state=2 represents that last battery is in discharge condition.Formula (5) draws data by experiment in advance, and draws p by MATLAB data fitting instrument 0p n, q 0q nand the value of n.
Step (2.2.2.2) is if 4 hours≤Δ T < 6 hours, data when reading in Flash district the charging and discharging state-ex_state of battery while representing last shutdown and representing last shutdown in the state-of-charge-ex_soc of battery, and battery open circuit voltage and the charging and discharging state of battery last time being applied to the battery SOC recalculating when formula (5) draws this start, the battery SOC calculating during using ex_soc and start is got the initial SOC of average battery when this is started shooting.
Step (2.2.2.3) if Δ T < 4 hours, the initial SOC of battery when the state-of-charge-ex_soc of battery starts shooting as this while reading last shutdown.
Step (3):
Control battery and carry out work, detect the start working battery terminal voltage of moment of working current, the battery of battery, calculate the internal resistance of cell according to formula (6):
R = | V - E I | Formula (6)
Wherein, R is the internal resistance of cell, and V is the start working battery terminal voltage of moment of battery, the open-circuit voltage that E is battery, the working current that I is battery.
Step (4):
Step (4.1) is for k=1, and 2,3 ..., 100 sampling instant point, loops following operation:
Step (4.1.1) battery management system gathers battery both end voltage V kworking current I with battery k.
The initial value of step (4.1.2) using initial battery SOC as expanded Kalman filtration algorithm, according to formula (2), carries out Kalman filtering algorithm.
Step (4.1.3) is every 5s, and the battery SOC that step (4.1.2) is calculated and corresponding cell voltage, electric current, charging and discharging state deposit Flash memory module in, complete calculating and the recording process of a SOC value.
Step (4.2) is for ensuing k=101, and 102,103 ... sampling instant point, loop following operation:
Step (4.2.1) battery management system gathers battery both end voltage V kworking current I with battery k.
The initial value of step (4.2.2) using the SOC value of last sampling instant of expanded Kalman filtration algorithm as Ah counting method, according to formula (7), carries out Ah counting method, the SOC of estimating battery.Wherein, formula (7) is explained by following formula:
SOC k = SOC k - 1 - I k - 1 &CenterDot; Ts Q Formula (7)
Wherein, SOC kfor the battery SOC of this sampling instant point, SOC k-1for the battery SOC of a upper sampling instant point, I k-1for the electric current of a upper sampling instant point, Ts is sampling time interval, the theoretical capacity that Q is battery, and in the time that battery charges, the rated capacity that Q is battery, in the time of battery discharge, Q is the theoretical capacity calculating according to Peukert equation.
Step (4.2.3) is every 5s, and the battery SOC that step (4.2.2) is calculated and corresponding cell voltage, electric current, charging and discharging state deposit Flash memory module in, complete calculating and the recording process of a SOC value.
Step (5):
When system provides after off signal, the SOC of current time, battery, battery charging and discharging state are stored in to Flash memory block, then shutdown system.
According to above-mentioned steps, the initial SOC value of first accurate estimating battery, then according to battery current, the information of voltage of the initial SOC of battery and Real-time Collection, the SOC value of real-time estimating battery, and the voltage of battery, electric current, SOC, charging and discharging state are stored in to Flash memory module, so that in the time of subsequent examination battery performance, can be with the data in Flash memory module as a reference.
Advantage of the present invention:
1. accurate battery SOC initial value estimating algorithm.When the initial SOC of estimating battery, considered charging and discharging state when battery is last to be used, the last rear SOC of battery and the time of repose of battery of using, arithmetic accuracy is high.
2. battery SOC real-time estimation algorithm has been taked the comprehensive estimate algorithm based on EKF method and Ah counting method, and precision is high, calculated amount is little, and the computing power of master controller is required not quite, is easy to realize.
3. while using the SOC of EKF method estimating battery, according to the difference of the charging and discharging state of battery, algorithm also has corresponding change, and estimation result is more accurate.
Brief description of the drawings
The circuit structure of Fig. 1 battery model
In Fig. 2 the present invention, calculate the algorithm flow chart of battery SOC
In Fig. 3 the present invention, calculate the algorithm flow chart of the initial SOC of battery
Its open-circuit voltage and SOC relation curve when the charging of Fig. 4 6AH Ni-MH battery
Its open-circuit voltage and SOC relation curve when the electric discharge of Fig. 5 6AH Ni-MH battery
Specific implementation method
Implement concrete steps of the present invention and can be divided into following four steps:
Step (1): battery set charge/discharge experiment;
Step (2): experimental data is processed, drawn Peukert constant and battery open circuit voltage and SOC relation curve and the mathematic(al) representation of Ni-MH battery;
Step (3): build complete SOC estimating algorithm;
Step (4): algorithm application is in battery management system.
Be composed in series by 6 joint cells with one below, rated capacity is 6AH, and the Ni-MH battery that nominal voltage is 7.2V is example, specifically introduce implementation process of the present invention.
Step (1) is carried out battery set charge/discharge experiment.
1) constant current charge-discharge experiment: respectively battery is discharged with the discharge-rate of 0.2C, 1C, 2C, 5C, draw discharge data as shown in table 1.
Discharge data under the different discharge-rates of table 1
Discharge current/A Electric weight/the mA.H emitting Discharge time/min
1.1012(0.2C) 5570.6 303.4
5.506(1C) 5469.2 59.5
11.0119(2C) 5353.4 29.2
27.5205(5C) 5274.6 11.5
2) combination discharges and recharges experiment:
First to battery with low discharging current, sampling battery two ends voltage, after battery both end voltage is lower than 6V, think that the electric weight of battery all gives out light, now the SOC of battery is 0, stops electric discharge, leave standstill 6 hours, it is 0 o'clock corresponding open-circuit voltage that the voltage at measurement battery two ends is SOC; Battery, with 0.1C (0.6A) current charges, after 0.5 hour, is stopped to charging, the voltage at sampling battery two ends, sampling interval is 1s, after 30 data points of sampling, stops sampling; Battery standing 6 hours, corresponding open-circuit voltage when measuring the voltage at battery two ends and being SOC and being 5%; Continue battery with the current charges of 0.1C 0.5 hour, the voltage at sampling battery two ends, sampling interval is 1s, after 30 data points of sample, stops sampling; Then leave standstill 6 hours, corresponding battery open circuit voltage when obtaining SOC and being 10%; Using such method, obtains respectively the battery open circuit voltage that SOC is 15%, 20%, 30%, 40%, 50%, 60%, 70%, 75%, 80%, 85%, 90%, 95%, 100% correspondence.Meanwhile, according to the voltage difference before and after leaving standstill, can obtain the size of the polarizing voltage that the different SOC points of battery are corresponding.
Battery is full of after electricity, with same method to battery the multiplying power discharging with 0.1C, draw SOC and the battery open circuit voltage relation data of when electric discharge battery.
As shown in table 2ly discharge and recharge the data that draw of experiment for this combination.
Table 2 combination discharges and recharges experimental data
The processing of step (2) experimental data
1) calculating of Peukert constant
In the time of battery discharge, there is Peukert equation:
I nt=K formula (8)
Wherein, n and K are constants, and for same Battery pack, these two constants are identical.According to the data in table 1, choose arbitrarily two groups of data, can calculate Peukert constant.Through calculating, the value of Peukert constant is:
n=1.0185,K=5.6052。So the theoretical capacity of battery during taking the electric discharge of different discharge current is Q=I 1-n* K, that is: Q=I -0.0185* 5.6052.
2) obtaining of battery open circuit voltage and SOC relation function
Based on the data in table 2, simulate the relation curve of battery open circuit voltage and SOC with MATLAB data fitting instrument cftool.Through trial repeatedly, find with 4 order polynomial matchings matching open-circuit voltage and SOC relation curve more accurately, battery open circuit voltage and SOC relation curve while being illustrated in figure 4 the charging simulating, battery open circuit voltage and SOC relation curve when Fig. 5 is electric discharge.Wherein, when charging battery open circuit voltage and SOC relation function expression formula as described in formula (9); When electric discharge, battery open circuit voltage and SOC relation function expression formula are as described in formula (10).
E charge=-1.9632S 4+ 6.9125S 3-7.953S 2+ 3.8715S+7.4085 formula (9)
E discharge=-0.7394S 4+ 4.3622S 3-5.8873S 2+ 3.2522S+7.312 formula (10)
According to same method, we can draw the mathematic(al) representation that represents battery SOC and battery open circuit voltage funtcional relationship.
Formula (4) can specifically be expressed as so:
Y=1.289*x 3-28.041*x 2+ 202.900*x-488.319 formula (11)
Formula (5) can specifically be expressed as:
y = 1.289 * x 3 - 28.041 * x 2 + 202.900 * x - 488.319 ex _ state = 1 y = - 2.200 * x 3 + 52.390 * x 2 - 409.500 * x + 1063.2 ex _ state = 2 Formula (12)
Step (3) builds complete battery SOC estimating algorithm
The estimated value computing formula of the measurement amount in Kalman filtering algorithm is:
Y k = F ( X k / k ) | X k / k = X k / k - 1 + R &CenterDot; I k + Up ( k ) Formula (13)
Wherein, F (X k/k) expression formula can obtain by formula (9) and formula (10):
F (X k/k) c=-1.9632X k/k 4+ 6.9125X k/k 3-7.953X k/k 2+ 3.8715X k/k+ 7.4085 formula (14)
F (X k/k) d=-0.7394X k/k 4+ 4.3622X k/k 3-5.8873X k/k 2+ 3.2522X k/k+ 7.312 formula (15)
When battery is in the time charging, F (X k/k) express by formula (14); When battery is in the time discharging, F (X k/k) express by formula (15).
The polarizing voltage that Up (k) is battery, is made into table by experimental data, in the time calculating, by tabling look-up and the method for linear interpolation calculates the value of Up (k).
Kalman filter observing matrix C kexpression formula can calculate by formula (9) and (10):
C k=-7.853X k/k 3+ 20.738X k/k 2-15.906X k/k+ 3.872 formula (16)
C k=-2.958X k/k 3+ 13.087X k/k 2-11.775X k/k+ 3.252 formula (17)
When battery is in the time charging, C kexpression formula by formula (16) express; When battery is in the time discharging, C kexpression formula by formula (17) express.
Step (4) algorithm application is in battery management system
For the present invention, the electric current I of battery management system Real-time Collection battery k, battery both end voltage V k, main control MCU is according to SOC estimating algorithm estimating battery SOC of the present invention and store and show.
SOC estimating algorithm in the present invention has been considered charging and discharging state that battery is current and the time of repose of previous battery charging and discharging state and battery in the time of the initial SOC of estimating battery simultaneously, and estimation result is more accurate.In the time of real-time estimation battery SOC, take the comprehensive estimate algorithm based on EKF method and Ah counting method, arithmetic accuracy is high, and calculated amount is less, and the computing power of master controller is required not quite, is easy to realize.
The electric battery that the present invention is composed in series taking the Ni-MH battery of 6 joint 6AH is example, has set forth concrete implementation method, the invention is not restricted to set forth battery types and design parameter.

Claims (1)

1. a power battery charged state evaluation method, is characterized in that, contains successively following steps:
Step (1) is measured the open-circuit voltage of battery;
Whether for the first time step (2) judges system operation, whether for the first time determination methods is: judge system operation by the data of depositing in the Flash memory block of last unused time, if data are 0xFFFFFF, system is operation for the first time, if data are not 0xFFFFFF, system is not to move for the first time, and its concrete steps are divided into:
Step (2.1) is if system is to move for the first time, the initial SOC of battery while calculating this start by battery open circuit voltage being applied to related function, wherein, SOC is the abbreviation of English State Of Charge, the state-of-charge that represents electric battery, described related function is expressed by following formula:
Wherein, the SOC that y is battery, the open-circuit voltage that x is battery, for multinomial coefficient, n is natural number, the number of times of representative polynomial, and this formula draws data by experiment in advance and draws by MATLAB data fitting instrument value with n;
Step (2.2), if system is not to move for the first time, reads the time of this on time and last shutdown, and the difference of calculating is between the two ;
Step (2.2.1) if hour, read in Flash district the data in the charging and discharging state ex_state of battery while representing last shutdown, the initial SOC that the charging and discharging state of battery open circuit voltage and last battery is applied to related function calculating battery, wherein said related function represents by following formula:
Wherein, the state-of-charge that y is battery, the open-circuit voltage that x is battery, and for multinomial coefficient, n is natural number, the number of times of representative polynomial, ex_state=1 represents that last battery is in charged state, ex_state=2 represents that last battery is in discharge condition, and this formula draws data by experiment in advance, and by MATLAB data fitting instrument draw n and and value;
Step (2.2.2) if data when reading in Flash district the charging and discharging state-ex_state of battery while representing last shutdown and representing last shutdown in the state-of-charge-ex_soc of battery, and battery open circuit voltage and the charging and discharging state of battery last time are applied to the battery SOC recalculating when correlation formula draws this start, the battery SOC that ex_soc and when start are calculated is got the initial SOC of average battery when this is started shooting, and wherein related correlation formula is expressed by following formula:
Wherein, the state-of-charge that y is battery, the open-circuit voltage that x is battery, and for multinomial coefficient, n is natural number, the number of times of representative polynomial, ex_state=1 represents that last battery is in charged state, ex_state=2 represents that last battery is in discharge condition, and this formula draws data by experiment in advance, and by MATLAB data fitting instrument draw n and and value;
Step (2.2.3) if hour, the initial SOC of battery when the state-of-charge-ex_soc of battery starts shooting as this while reading last shutdown;
Step (3) is controlled battery and is carried out work, detects the start working battery terminal voltage of moment of working current, the battery of battery, calculates the internal resistance of cell according to following formula:
Wherein, R is the internal resistance of cell, and V is the start working battery terminal voltage of moment of battery, the open-circuit voltage that E is battery, the working current that I is battery;
Step (4) is according to the data of the SOC of battery and characterizing battery polarizing voltage and battery SOC relation, and by the polarizing voltage of interpolation calculation battery, wherein, the data of the SOC relation of characterizing battery polarizing voltage and battery are obtained by experiment in advance;
The initial value of step (5) using initial battery SOC as expanded Kalman filtration algorithm, according to EKF method, calculates the state-of-charge of one-shot battery every 1s, and records the execution number of times kalman_count of expanded Kalman filtration algorithm;
If kalman_count>100, the battery charge state last expanded Kalman filtration algorithm being drawn, as the initial value of Ah counting method, according to Ah counting method, calculates the state-of-charge of one-shot battery every 1s;
Step (6) is every 5s, and the battery SOC that step (5) is calculated and corresponding cell voltage, electric current, charging and discharging state deposit Flash memory module in, complete calculating and the recording process of a SOC value, and so circulation, until system closedown;
Step (7) system provides after off signal, the SOC of current time, battery, battery charging and discharging state is stored in to Flash memory block, shutdown system after completing.
CN201110142292.9A 2011-05-20 2011-05-20 Estimating method of charge state of power battery Expired - Fee Related CN102788957B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201110142292.9A CN102788957B (en) 2011-05-20 2011-05-20 Estimating method of charge state of power battery

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201110142292.9A CN102788957B (en) 2011-05-20 2011-05-20 Estimating method of charge state of power battery

Publications (2)

Publication Number Publication Date
CN102788957A CN102788957A (en) 2012-11-21
CN102788957B true CN102788957B (en) 2014-11-12

Family

ID=47154412

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201110142292.9A Expired - Fee Related CN102788957B (en) 2011-05-20 2011-05-20 Estimating method of charge state of power battery

Country Status (1)

Country Link
CN (1) CN102788957B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109085505A (en) * 2018-07-25 2018-12-25 深圳华中科技大学研究院 A kind of power battery charging and discharging state evaluation method

Families Citing this family (37)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103018679A (en) * 2012-12-10 2013-04-03 中国科学院广州能源研究所 Estimation method of initial state of charge (SOC0) of lead-acid cell
JP6209821B2 (en) * 2013-01-16 2017-10-11 日産自動車株式会社 Idle stop vehicle
CN103135065A (en) * 2013-01-25 2013-06-05 文创太阳能(福建)科技有限公司 Iron phosphate lithium battery electric quantity detecting method based on feature points
CN103235266B (en) * 2013-03-29 2017-03-08 重庆长安汽车股份有限公司 The charging state estimation method and a charging state estimation device of electrokinetic cell
CN103344917B (en) * 2013-06-13 2015-08-12 北京交通大学 A kind of lithium battery cycle life method for rapidly testing
CN103616647B (en) * 2013-12-09 2016-03-02 天津大学 A kind of estimation method of battery dump energy for cell management system of electric automobile
CN103728567B (en) * 2013-12-31 2016-06-08 电子科技大学 A kind of charge state estimation method based on optimized initial value
CN104833856A (en) * 2014-02-12 2015-08-12 鸿富锦精密工业(深圳)有限公司 Method and device for estimating internal resistance of battery
CN103901354B (en) * 2014-04-23 2016-08-17 武汉市欧力普能源与自动化技术有限公司 A kind of electric automobile vehicle-mounted electrokinetic cell SOC Forecasting Methodology
CN104122504B (en) * 2014-08-11 2016-10-05 电子科技大学 A kind of SOC estimation method of battery
CN104502849A (en) * 2014-12-12 2015-04-08 国家电网公司 Online and real-time measuring method for surplus capacity of transformer substation valve control type sealed lead-acid storage battery
WO2016145621A1 (en) * 2015-03-18 2016-09-22 华为技术有限公司 Electrical power estimating method and terminal
CN105068006A (en) * 2015-06-24 2015-11-18 汪建立 Fast learning method based on combination of coulomb state of charge (SOC) and voltage SOC
CN105093128A (en) * 2015-08-31 2015-11-25 山东智洋电气股份有限公司 Storage battery state of charge (SOC) estimation method based on extended Kalman filtering (EKF)
CN105301513B (en) * 2015-12-03 2018-07-17 北京航空航天大学 A kind of lithium battery capacity accurately measures method
CN106896273A (en) * 2015-12-18 2017-06-27 北汽福田汽车股份有限公司 The internal resistance detection method of battery cell, detection means and the vehicle with it
CN105652207A (en) * 2015-12-31 2016-06-08 浙江华丰电动工具有限公司 Electric quantity monitoring device and method for power type lithium battery
CN106093517A (en) * 2016-05-30 2016-11-09 广西大学 Lithium ion battery open circuit voltage curve approximating method based on Hermite's interpolation method
CN106405423B (en) * 2016-06-30 2019-09-13 南京金邦动力科技有限公司 Battery cell monitoring method and battery monitor system
CN106291375A (en) * 2016-07-28 2017-01-04 河南许继仪表有限公司 A kind of SOC estimation method based on cell degradation and device
CN106093793A (en) * 2016-07-28 2016-11-09 河南许继仪表有限公司 A kind of SOC estimation method based on battery discharge multiplying power and device
CN107037366B (en) * 2016-12-02 2018-03-30 江苏富威能源有限公司 A kind of electric rail car lithium ion battery control system
CN106970328B (en) * 2017-01-17 2020-05-08 深圳市沛城电子科技有限公司 SOC estimation method and device
CN108511814A (en) * 2018-01-24 2018-09-07 上海广为美线电源电器有限公司 Intelligent battery management system with learning functionality
CN108445422B (en) * 2018-06-08 2020-09-25 江苏大学 Battery state of charge estimation method based on polarization voltage recovery characteristics
CN110888074B (en) * 2018-08-15 2022-02-01 上海汽车集团股份有限公司 Voltage determination method and device for SOC initial value calculation
JP7172690B2 (en) * 2019-02-12 2022-11-16 トヨタ自動車株式会社 BATTERY SYSTEM AND SECONDARY BATTERY SOC ESTIMATION METHOD
CN111693869B (en) * 2019-03-13 2022-06-03 上海汽车集团股份有限公司 Battery SOC correction method and device based on cell depolarization time
CN110244236B (en) * 2019-05-16 2021-08-13 深圳猛犸电动科技有限公司 Lithium ion battery pack SOC estimation method and device and terminal equipment
CN110133510B (en) * 2019-05-30 2021-08-13 陕西科技大学 SOC hybrid estimation method for lithium ion battery
CN110231567A (en) * 2019-07-16 2019-09-13 奇瑞新能源汽车股份有限公司 A kind of electric car SOC estimating algorithm
CN110531276B (en) * 2019-09-05 2022-04-26 江苏智蓝电源科技有限公司 Battery condition detection method and device
CN110940927A (en) * 2019-11-19 2020-03-31 国网河南省电力公司电力科学研究院 Retired battery classification method and system
CN111289911A (en) * 2020-04-03 2020-06-16 深圳天邦达新能源技术有限公司 SOC estimation method and device based on battery and electronic equipment
CN115248386B (en) * 2021-04-28 2024-09-03 宁德新能源科技有限公司 State of charge prediction method, electric quantity prediction method and electronic equipment
CN113341330B (en) * 2021-05-25 2022-11-08 西南大学 Lithium-sulfur power battery SOC estimation method based on OCV correction and Kalman filtering algorithm
CN114264966A (en) * 2021-12-06 2022-04-01 阳光储能技术有限公司 Method and device for evaluating state of charge of battery, terminal device and medium

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6534954B1 (en) * 2002-01-10 2003-03-18 Compact Power Inc. Method and apparatus for a battery state of charge estimator
CN1601295A (en) * 2004-10-25 2005-03-30 清华大学 Estimation for accumulator loading state of electric vehicle and carrying out method thereof
JP2006025538A (en) * 2004-07-08 2006-01-26 Toyota Motor Corp State-of-charge estimating method of secondary battery, recording medium for recording program for making computer execute state-of-charge estimating method and battery control system
JP2007010664A (en) * 2005-06-30 2007-01-18 Lg Chem Ltd Method of estimating residual capacity of battery, and battery control system by the same
CN101022178A (en) * 2007-03-09 2007-08-22 清华大学 Method for estimating nickel-hydrogen power battery charged state based on standard battery model
JP2007292778A (en) * 1998-06-02 2007-11-08 Toyota Motor Corp Method for estimating state of battery charge
CN101320079A (en) * 2008-06-25 2008-12-10 哈尔滨工业大学 Computing method for battery electric quantity state
CN101430366A (en) * 2008-12-12 2009-05-13 苏州金百合电子科技有限公司 Battery charge state detection method
CN101598769A (en) * 2009-06-29 2009-12-09 杭州电子科技大学 A kind of estimation method of battery dump energy based on sampling point Kalman filtering
CN101604005A (en) * 2009-06-29 2009-12-16 杭州电子科技大学 A kind of estimation method of battery dump energy based on combined sampling point Kalman filtering
CN101629992A (en) * 2009-05-27 2010-01-20 重庆大学 Method for estimating residual capacity of iron-lithium phosphate power cell
CN101966820A (en) * 2010-08-26 2011-02-09 清华大学 On-line monitoring method for self-adaptively correcting lithium ion battery state-of-charge

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TW510977B (en) * 2000-03-13 2002-11-21 Nippon Telegraph & Telephone Capacity estimation method, degradation estimation method and degradation estimation apparatus for lithium-ion cells, and lithium-ion batteries

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007292778A (en) * 1998-06-02 2007-11-08 Toyota Motor Corp Method for estimating state of battery charge
US6534954B1 (en) * 2002-01-10 2003-03-18 Compact Power Inc. Method and apparatus for a battery state of charge estimator
JP2006025538A (en) * 2004-07-08 2006-01-26 Toyota Motor Corp State-of-charge estimating method of secondary battery, recording medium for recording program for making computer execute state-of-charge estimating method and battery control system
CN1601295A (en) * 2004-10-25 2005-03-30 清华大学 Estimation for accumulator loading state of electric vehicle and carrying out method thereof
JP2007010664A (en) * 2005-06-30 2007-01-18 Lg Chem Ltd Method of estimating residual capacity of battery, and battery control system by the same
CN101022178A (en) * 2007-03-09 2007-08-22 清华大学 Method for estimating nickel-hydrogen power battery charged state based on standard battery model
CN101320079A (en) * 2008-06-25 2008-12-10 哈尔滨工业大学 Computing method for battery electric quantity state
CN101430366A (en) * 2008-12-12 2009-05-13 苏州金百合电子科技有限公司 Battery charge state detection method
CN101629992A (en) * 2009-05-27 2010-01-20 重庆大学 Method for estimating residual capacity of iron-lithium phosphate power cell
CN101598769A (en) * 2009-06-29 2009-12-09 杭州电子科技大学 A kind of estimation method of battery dump energy based on sampling point Kalman filtering
CN101604005A (en) * 2009-06-29 2009-12-16 杭州电子科技大学 A kind of estimation method of battery dump energy based on combined sampling point Kalman filtering
CN101966820A (en) * 2010-08-26 2011-02-09 清华大学 On-line monitoring method for self-adaptively correcting lithium ion battery state-of-charge

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
吴红斌等.蓄电池荷电状态预测的改进新算法.《电子测量与仪器学报》.2010,第24卷(第11期),第993-998页. *
蓄电池荷电状态预测的改进新算法;吴红斌等;《电子测量与仪器学报》;20101130;第24卷(第11期);第993-998页 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109085505A (en) * 2018-07-25 2018-12-25 深圳华中科技大学研究院 A kind of power battery charging and discharging state evaluation method

Also Published As

Publication number Publication date
CN102788957A (en) 2012-11-21

Similar Documents

Publication Publication Date Title
CN102788957B (en) Estimating method of charge state of power battery
CN108375739B (en) State of charge estimation method and state of charge estimation system for lithium battery of electric vehicle
CN104678316B (en) Charge states of lithium ion battery evaluation method and device
CN103323781B (en) Power battery pack on-line parameter detection system and SOC method of estimation
CN104977537B (en) The determination method of battery SOC and the battery management system for using this method
CN102680795B (en) Real-time on-line estimation method for internal resistance of secondary battery
CN103744026A (en) Storage battery state of charge estimation method based on self-adaptive unscented Kalman filtering
Gao et al. Battery state of charge online estimation based on particle filter
CN105548896A (en) Power-cell SOC online closed-loop estimation method based on N-2RC model
CN103744028A (en) UKF-based storage battery SOC (state of charge) estimation method
CN106249171A (en) A kind of electrokinetic cell system identification for the wide sampling interval and method for estimating state
CN109557477A (en) A kind of battery system health status evaluation method
CN103529398A (en) Online lithium ion battery SOC (state of charge) estimation method based on extended Kalman filter
CN106772104B (en) A kind of power battery SOC value evaluation method
CN102540081B (en) Method for determining charge state of vehicle-mounted storage battery
CN101813754A (en) State estimating method for automobile start illumination type lead-acid storage battery
CN102756661A (en) Determination method and device for state of charge of vehicular battery
CN102435950A (en) Method and apparatus for estimating battery capacity of a battery
CN102331314A (en) Dynamic estimation of cell core temperature by simple external measurements
CN102407784A (en) Method and apparatus for estimating soc of battery
CN109358293A (en) Lithium ion battery SOC estimation method based on IPF
CN103507644A (en) Mileage device and method and controller for electric vehicle
CN103259055A (en) Electric vehicle battery pack OCV-SOC curve correction circuit convenient to operate, and method thereof
CN103314303A (en) Method for ascertaining the open circuit voltage of a battery, battery with a module for ascertaining the open circuit voltage and a motor vehicle having a corresponding battery
CN113125967B (en) Lithium battery SOE calculation method based on temperature rise prediction

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
DD01 Delivery of document by public notice

Addressee: Zhang Jinhua

Document name: Notification of Passing Examination on Formalities

C14 Grant of patent or utility model
GR01 Patent grant
C56 Change in the name or address of the patentee
CP02 Change in the address of a patent holder

Address after: 212009 Zhenjiang city of Jiangsu province science and Technology Park pan Zong Lu 38-4 Dingmao

Patentee after: Hengchi Science & Technology Co., Ltd., Zhenjiang

Address before: 212009 room 668, No. 12, 201, Jiangsu, Zhenjiang, China

Patentee before: Hengchi Science & Technology Co., Ltd., Zhenjiang

CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20141112

Termination date: 20200520

CF01 Termination of patent right due to non-payment of annual fee