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

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:

$\left\{\begin{array}{c}{\mathrm{SOC}}_k={\mathrm{SOC}}_{k-1}-\frac{1}{Q}\·I_{k-1}\·\mathrm{Ts}\\ V_k=E_k-R\·I_k+{\mathrm{Up}}_k\end{array}\right.$ 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:

$\left\{\begin{array}{c}{X}_{k/k-1}=X_{k-1/k-Q}-\frac{1}{Q}\·I_{k-1}\·\mathrm{Ts}\\ Y_k=F\left(X_{k/k}\right){|}_{X_{k/k=X_{\mathrm{kk}/k-1}}}+R\·I_k+{\mathrm{Up}}_k\\ C_k=\frac{d\left(F\left(X_{k/k}\right)\right)}{d\left(X_{k/k}\right)}{|}_{X_{k/k}=X_{k/k-1}}\\ P_{k/k-1}=P_{k-1/k-1}+D\\ X_k=P_{k/k-1}\·C_k[C_k^2\·P_{k/k-1}+W]\\ X_{k/k}=X_{k/k-1}+K_k\·(V_k-Y_k)\\ P_{k/k}=P_{k/k-1}-C_k\·C_k\·P_{k/k-1}\\ k=\mathrm{1,2,3},\·\·\·\·\·\·\end{array}\right.$ 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):

$E_k=F\left(X_{k/k}\right)=a_0\·X_{k/k}^n+a_1\·X_{k/k}^{n-1}+a_2\·X_{k/k}^{n-2}+\·\·\·+a_{n-1}\·X_{k/k}+a_n$ 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):

$\left\{\begin{array}{cc}y=p_0\·x^n+p_1\·x^{n-1}+\·\·\·+p_{n-1}\·x+p_n& \mathrm{ex}\_\mathrm{state}=1\\ y=q_0\·x^n+q_1\·x^{n-1}+\·\·\·+q_{n-1}\·x+q_n& \mathrm{ex}\_\mathrm{state}=2\end{array}\right.$ 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):

$R=\left|\frac{V-E}{I}\right|$ 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:

${\mathrm{SOC}}_k={\mathrm{SOC}}_{k-1}-\frac{I_{k-1}\&CenterDot;\mathrm{Ts}}{Q}$ 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:

$\left\{\begin{array}{cc}y=1.289*x^3-28.041*x^2+202.900*x-488.319& \mathrm{ex}\_\mathrm{state}=1\\ y=-2.200*x^3+52.390*x^2-409.500*x+1063.2& \mathrm{ex}\_\mathrm{state}=2\end{array}\right.$ Formula (12)

Step (3) builds complete battery SOC estimating algorithm

The estimated value computing formula of the measurement amount in Kalman filtering algorithm is:

$Y_k=F\left(X_{k/k}\right){|}_{X_{k/k}=X_{k/k-1}+R\&CenterDot;I_k+\mathrm{Up}\left(k\right)}$ 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.