CN110109019A - A kind of SOC estimation method of the hybrid power lithium battery based on EKF algorithm - Google Patents
A kind of SOC estimation method of the hybrid power lithium battery based on EKF algorithm Download PDFInfo
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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
- G01R31/005—Testing of electric installations on transport means
- G01R31/006—Testing of electric installations on transport means on road vehicles, e.g. automobiles or trucks
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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
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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]
- G01R31/385—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 kind of SOC estimation methods of hybrid power lithium battery based on EKF algorithm, comprising the following steps: the Order RC equivalent-circuit model for establishing hybrid power lithium battery obtains Equivalent circuit equations；Offline parameter identification is carried out, the data about battery behavior are obtained by HPPC test, the corresponding relationship curve of SOC and open-circuit voltage OCV is fitted and identifies R0、R1And R2Resistance value and C1And C2Capacitance；According to Equivalent circuit equations and R0、R1And R2Resistance value and C1And C2Capacitance, establish the discrete state equations of lithium battery；In conjunction with the expression formula of the end voltage indicated in the form of voltage in Equivalent circuit equations, state-transition matrix, system control matrix and systematic observation matrix are obtained；It is estimated in conjunction with SOC of the EKF algorithm to battery.This method carries out fitting of a polynomial to the parameter that off-line identification goes out, and avoids, in conjunction with expanded Kalman filtration algorithm, improving the SOC estimation precision of lithium battery using fixed offline parameter.
Description
Technical field
The invention belongs to the power battery field of New-energy electric vehicle, specifically a kind of mixing based on EKF algorithm is dynamic
The SOC estimation method of power lithium battery.
Background technique
Countries in the world and government increasingly pay attention to environmental protection at present, promote the development of new-energy automobile.New energy
The advantages of automobile, is to pollute less, can support the sustainable development of human society.Three comparisons are crucial in new-energy automobile
Technology is motor respectively, automatically controlled and battery.
Battery is the power device of new-energy automobile, and battery management system (Battery Management System,
BMS) be battery brain, battery can be controlled, prevent overcharge, the behaviors such as over-discharge, improve the service life of battery, enhance
The service efficiency of battery.So BMS is the indispensable set of system of new-energy automobile.
Core the most is exactly state-of-charge (State Of Charge, SOC) to battery in BMS, i.e., remaining capacity into
Row estimation.The estimation to SOC is all regarded as core technology by each vehicle enterprise at present.SOC is equivalent to the oil meter of orthodox car, Neng Gouxian
The battery electric quantity state for showing electric vehicle allows driver according to circumstances to charge and drive.Since the electricity of battery cannot lead to
It crosses instrument to measure, so accurately estimating SOC is that exploitation BMS has to solve the problems, such as.There are four types of methods at present
It is verified the method it is estimated that SOC, is current integration method, open circuit voltage method, Kalman filtering algorithm and mind respectively
Through network algorithm.Currently on the market substantially using first two method, latter two algorithm is due to the computing capability to chip
It is required that high, substantially it is also present in theoretical research stage at present, but a large amount of result of study is it has been proved that latter two method
Estimation precision is higher than first two.The estimation precision of mature BMS is all largely the essence 5% or more, 5% currently on the market
It is not enough to the utilization rate of battery, also defective to the management of charging and discharging of battery although degree is able to use, so to estimation
The research of battery SOC also needs to deepen continuously.
Extended Kalman filter (EKF) algorithm is a kind of extension of Kalman filtering algorithm, can to nonlinear system into
Row estimation, and battery is exactly a kind of nonlinear system, so the algorithm can be used to estimate the SOC of battery.
Wang Jie uses off-line identification in document " the dynamic lithium battery SOC based on Extended Kalman filter estimates research "
Method regards a constant as the time response τ=R*C come is picked out.A kind of actually battery nonlinear system that is complexity
System, time response τ=R*C can change with the variation of electricity.
Summary of the invention
In view of the deficiencies of the prior art, the technical issues of present invention intends to solve is to provide a kind of mixing based on EKF algorithm
The SOC estimation method of dynamic lithium battery.
The technical solution that the present invention solves the technical problem is to provide a kind of hybrid power lithium electricity based on EKF algorithm
The SOC estimation method in pond, it is characterised in that method includes the following steps:
Step 1, the Order RC equivalent-circuit model for establishing hybrid power lithium battery, obtain Order RC equivalent-circuit model
Equivalent circuit equations；
Step 2 carries out offline parameter identification, obtains the data parameters about battery behavior by HPPC test, fits
The corresponding relationship curve of SOC and open-circuit voltage OCV；By by zero input response end voltageWith the end voltage expression in Equivalent circuit equationsSimultaneous enables u0=u identifies R0、R1And R2
Resistance value and C1And C2Capacitance；
The R that step 3, the Equivalent circuit equations obtained according to step 1 and step 2 obtain0、R1And R2Resistance value and C1
And C2Capacitance, establish the discrete state equations of lithium battery；In conjunction with the end indicated in the form of voltage in Equivalent circuit equations
The expression formula of voltage obtains state-transition matrix, system control matrix and systematic observation matrix；
Step 4, the discrete state equations of the lithium battery obtained according to step 3, state-transition matrix, system control matrix and
Systematic observation matrix combination EKF algorithm estimates the SOC of battery.
Compared with prior art, the beneficial effects of the invention are that:
(1) this method is estimated using offline identification method, it is possible to reduce calculation amount, the less high chip of processing capacity
Also it is able to satisfy requirement, the usage amount of chip can also be reduced, reducing the cost of BMS, (cost of chip will account in BMS
70% or so of entire BMS).
(2) this method carries out fitting of a polynomial to the parameter that off-line identification goes out, and avoids reducing using fixed offline parameter
Error when estimation improves the SOC estimation precision of hybrid power lithium battery.And in conjunction with expanded Kalman filtration algorithm, the calculation
Method can accurately estimate the SOC of hybrid power lithium battery.Integrated application can greatly improve the SOC estimation essence of lithium battery
Degree.
(3) power battery model parameter can be updated and be optimized according to the intention of developer, avoid it is complicated and
Cumbersome proving operation.
Detailed description of the invention
Fig. 1 is the Order RC equivalent-circuit model of hybrid power lithium battery of the present invention；
Fig. 2 is HPPC operating condition of test figure of the present invention；
Fig. 3 is the comparison diagram of hybrid power lithium battery SOC estimated value and true value that the present invention obtains；
Fig. 4 is the Error Graph for the hybrid power lithium battery SOC estimated value that the present invention obtains；
Specific embodiment
Specific embodiments of the present invention are given below.Specific embodiment is only used for that present invention be described in more detail, unlimited
The protection scope of the claim of this application processed.
The present invention provides a kind of SOC estimation method (abbreviation method) of hybrid power lithium battery based on EKF algorithm,
Be characterized in that method includes the following steps:
Step 1, the Order RC equivalent-circuit model (as shown in Figure 1) for establishing hybrid power lithium battery, obtain Order RC etc.
The Equivalent circuit equations for imitating circuit model, as shown in formula (1):
Formula 1) in, u indicates end voltage, Voc(SOC) indicate that open-circuit voltage, i (t) indicate the electric current changed over time, τ1=
R1C1, τ2=R2C2；R0Indicate the internal resistance of cell, R1Indicate activation polarization resistance, R2Indicate concentration difference polarization resistance, C1Indicate electricity
Chemical polarization capacitor, C2Indicate concentration difference polarization capacity, V1Indicate polarizing voltage, V2Indicate concentration polarization voltage；
Step 2 carries out offline parameter identification, is obtained by HPPC test (hybrid power pulse testing) about battery behavior
Data parameters, fit OCV-SOC curve (obtaining the corresponding relationship of SOC Yu open-circuit voltage OCV)；By the way that zero input is rung
Voltage should be heldWith the end voltmeter in Equivalent circuit equations
Up to formulaSimultaneous enables u0=u identifies R0、
R1And R2Resistance value and C1And C2Capacitance；
Step 3, the Equivalent circuit equations obtained according to step 1 and step 2 obtain R0、R1And R2Resistance value and C1And C2
Capacitance, establish the discrete state equations of lithium battery；In conjunction with the end voltage indicated in the form of voltage in Equivalent circuit equations
Expression formula, obtain state-transition matrix, system control matrix and systematic observation matrix；
yk=Voc(soc,k)-R0i(k)-V1(k)-V2(k) (3)
Formula 2) be lithium battery discrete state equations, formula 3) be the end voltage that indicates in the form of voltage in Equivalent circuit equations
Expression formula；Formula 3) in yk=u, Voc(soc, k)=Voc(soc), R0I (k)=i (t) R0,
By formula 2) and formula 3) simultaneous, it obtains:
D=[R0] (4)
Formula 4) in, A indicates that state-transition matrix, B indicate that system controls matrix, and D and H indicate systematic observation matrix；Δt
Indicate the unit time, η indicates efficiency for charge-discharge, C0Indicate battery capacity；
Step 4, the discrete state equations of the lithium battery obtained according to step 3, state-transition matrix, system control matrix and
Systematic observation matrix combination EKF (Extended Kalman filter) algorithm estimates the SOC of battery；
Before starting EKF algorithm, due to original state matrix x=[Voc(soc) V1 V2]TMiddle polarizing voltage is very small, therefore
V1And V2Initial value is 0；The initial value of initial covariance matrix P is selected as unit matrix；
(1) start EKF algorithm, first to state matrix x before iterationk-1, covariance matrix P before iterationk-1At the beginning of noise Q mono-
Initial value, then bring into Kalman's time update equation and obtain current state matrix xkWith current covariance matrix PkValue；
(2) x for again obtaining step 1)kAnd PkValue is brought into Kalman state renewal equation, and state matrix after iteration is obtained
xk+1With covariance matrix P after iterationk+1Value；
(3) x for again obtaining step 2)k+1And Pk+1Value is brought into again replaces x respectively in Kalman's time update equationk-1
And Pk-1, obtain new xk；Do not stop loop iteration and updates xk, the SOC estimated value of each sampled point is obtained, until battery discharge stops
Only, the SOC estimation of entire operating condition is obtained.
Kalman's time update equation includes state equation xk=Axk-1+BIk-1With covariance equation Pk=APk-1AT+
Q；The Kalman state renewal equation includes kalman gain equation Kk=PkHT(HPkHT+D)-1, state renewal equation xk+1=
xk+Kk(yk-Hxk) and covariance renewal equation Pk+1=(1-KkH)Pk；Wherein, Ik-1Indicate electric current；1 indicates unit matrix；KkTable
Show gain matrix；ATIndicate the transposed matrix of A；
Step 4) is specifically:
Before starting EKF algorithm, due to original state matrix x=[Voc(soc) V1 V2]TMiddle polarizing voltage is very small, therefore
V1And V2Initial value is 0；The initial value of initial covariance matrix P is selected as unit matrix；
(1) start EKF algorithm, first to state matrix x before iterationk-1, covariance matrix P before iterationk-1At the beginning of noise Q mono-
Initial value, then bring the state equation x in Kalman's time update equation intok=Axk-1+BIk-1In obtain xkValue, when bringing Kalman into
Between covariance equation P in renewal equationk=APk-1ATP is obtained in+QkValue；
(2) P for again obtaining step 1)kValue brings the kalman gain equation K of Kalman state renewal equation intok=PkHT
(HPkHT+D)-1In, obtain KkValue；The K that will be obtained againkThe x that value and step 1) obtainkValue brings Kalman state renewal equation into
State renewal equation xk+1=xk+Kk(yk-Hxk) in obtain xk+1Value, the K that will be obtainedkThe P that value and step 1) obtainkValue brings card into
The covariance renewal equation P of Germania state renewal equationk+1=(1-KkH)PkIn obtain Pk+1Value；
(3) x for again obtaining step 2)k+1And Pk+1Value is brought into again replaces x respectively in Kalman's time update equationk-1
And Pk-1, obtain new xk；Do not stop loop iteration and updates xk, the SOC estimated value of each sampled point is obtained, until battery discharge stops
Only, the SOC estimation of entire operating condition is obtained.
Embodiment 1
Battery uses the ternary lithium battery that capacity is 3.5V for 37Ah, nominal voltage in the present embodiment.
Step 1, the Order RC equivalent-circuit model (as shown in Figure 1) for establishing hybrid power lithium battery, obtain Order RC etc.
Imitate the Equivalent circuit equations of circuit model；
Step 2 carries out offline parameter identification, obtains the data parameters about battery behavior by HPPC test, fits
The corresponding relationship curve of SOC and open-circuit voltage OCV；By by zero input response end voltageWith the end voltage expression in Equivalent circuit equationsSimultaneous enables u0=u identifies R0、R1And R2
Resistance value and C1And C2Capacitance；
9 minor peaks charge and discharge (as shown in Figure 2) are carried out altogether in HPPC test, respectively 0.895SOC, 0.7965SOC,
0.6985SOC, 0.6005SOC, 0.5035SOC, 0.402SOC, 0.2965SOC, 0.199SOC and 0.101SOC.It is filled in peak value
In electric discharge, 4C electric discharge 10s is carried out, 40s is stood；3C charging 10s.In Fig. 2, by zero input response end voltageWith the end voltage expression in Equivalent circuit equationsSimultaneous enables u0=u can fill every time
R is identified in electric discharge0、R1、R2、C1And C2；Then each parameter 9 times discernable numerical value are carried out fitting of a polynomial.Quiet
The corresponding relationship of available SOC and OCV during setting.
Fitting of a polynomial is carried out by MATLAB are as follows:
R0Six order polynomials about SOC are
R0=(101soc6-329.6soc5+426.1soc4-281.4soc3+102.4soc2-20.28soc+2.917)/
1000；
R1Six order polynomials about SOC are
R1=(79.64soc6-254.9soc5+314soc4-189.3soc3+59.25soc2-9.295soc+0.9976)/
1000；
R2Six order polynomials about SOC are
R2=(245.1soc6-767.4soc5+957soc4-607.5soc3+208soc2-37.17soc+3.82)/1000；
C1Quartic polynomial about SOC is
C1=(317.3soc4-544.1soc3+241.3soc2-8.874soc+23.88)*1000；
C2Cubic polynomial about SOC is
C2=(0.893soc3-1.58soc2-0.997soc+0.98051)*1000；
The quintic algebra curve of OCV and SOC is
Voc(soc)=1.849soc5-7.167soc4+9.617soc3-4.943soc2+1.379soc+3.455；
Step 3, the Equivalent circuit equations obtained according to step 1 and step 2 obtain R0、R1And R2Resistance value and C1And C2
Capacitance, establish the discrete state equations of lithium battery；In conjunction with the end voltage indicated in the form of voltage in Equivalent circuit equations
Expression formula, obtain state-transition matrix, system control matrix and systematic observation matrix；
The discrete state equations of lithium battery such as formula 2) shown in:
yk=Voc(soc,k)-R0i(k)-V1(k)-V2(k) (3)
Formula 3) be Equivalent circuit equations in indicate in the form of voltage end voltage expression formula；Formula 3) in yk=u, Voc
(soc, k)=Voc(soc), R0I (k)=i (t) R0,
By formula 2) and formula 3) simultaneous, it obtains:
D=[R0] (4)
Formula 4) in, A indicates that state-transition matrix, B indicate that system controls matrix, and D and H indicate systematic observation matrix；Δt
Indicate the unit time, η indicates efficiency for charge-discharge, C0Indicate battery capacity；
Step 4, the discrete state equations of the lithium battery obtained according to step 3, state-transition matrix, system control matrix and
Systematic observation matrix combination EKF algorithm estimates the SOC of battery；
Before starting EKF algorithm, due to original state matrix x=[Voc(soc) V1 V2]TMiddle polarizing voltage is very small, therefore
V1And V2Initial value is 0；The initial value of initial covariance matrix P is selected as unit matrix；
(1) start EKF algorithm, first to state matrix x before iterationk-1, covariance matrix P before iterationk-1At the beginning of noise Q mono-
Initial value, then bring into Kalman's time update equation and obtain current state matrix xkWith current covariance matrix Pk；
(2) x for again obtaining step 1)kAnd PkIt brings into Kalman state renewal equation, obtains state matrix after iteration
xk+1With covariance matrix P after iterationk+1；
(3) x for again obtaining step 2)k+1And Pk+1It brings into again and replaces x in Kalman's time update equation respectivelyk-1With
Pk-1, obtain new xk；Do not stop loop iteration and updates xk, the SOC estimated value of each sampled point (different moments) is obtained, until battery
Electric discharge stops, and obtains the SOC estimation of entire operating condition.
By this method predict hybrid power lithium battery SOC as shown in figure 3, error as shown in figure 4, by Fig. 3 and Fig. 4
The estimation error maximum of SOC be can be seen that 2.5% or so, well below 6% or so precision in the market.
The present invention does not address place and is suitable for the prior art.
Claims (5)
1. a kind of SOC estimation method of the hybrid power lithium battery based on EKF algorithm, it is characterised in that this method includes following step
It is rapid:
Step 1, the Order RC equivalent-circuit model for establishing hybrid power lithium battery, obtain the equivalent of Order RC equivalent-circuit model
Circuit equation；
Step 2 carries out offline parameter identification, obtains data parameters about battery behavior by HPPC test, fit SOC with
The corresponding relationship curve of open-circuit voltage OCV；By by zero input response end voltageWith the end voltage expression in Equivalent circuit equationsSimultaneous enables u0=u identifies R0、R1
And R2Resistance value and C1And C2Capacitance；
The R that step 3, the Equivalent circuit equations obtained according to step 1 and step 2 obtain0、R1And R2Resistance value and C1And C2's
Capacitance establishes the discrete state equations of lithium battery；In conjunction with the end voltage indicated in the form of voltage in Equivalent circuit equations
Expression formula obtains state-transition matrix, system control matrix and systematic observation matrix；
Discrete state equations, state-transition matrix, system control matrix and the system of step 4, the lithium battery obtained according to step 3
Observing matrix combination EKF algorithm estimates the SOC of battery.
2. the SOC estimation method of the hybrid power lithium battery according to claim 1 based on EKF algorithm, it is characterised in that
The Equivalent circuit equations of Order RC equivalent-circuit model, such as formula 1) shown in:
Formula 1) in, u indicates end voltage, Voc(SOC) indicate that open-circuit voltage, i (t) indicate the electric current changed over time, τ1=R1C1,
τ2=R2C2；R0Indicate the internal resistance of cell, R1Indicate activation polarization resistance, R2Indicate concentration difference polarization resistance, C1Indicate electrochemistry
Polarization capacity, C2Indicate concentration difference polarization capacity, V1Indicate polarizing voltage, V2Indicate concentration polarization voltage.
3. the SOC estimation method of the hybrid power lithium battery according to claim 1 based on EKF algorithm, it is characterised in that
Step 3 is specifically: the discrete state equations of lithium battery such as formula 2) shown in:
yk=Voc(soc,k)-R0i(k)-V1(k)-V2(k) (3)
Formula 3) be Equivalent circuit equations in indicate in the form of voltage end voltage expression formula；Formula 3) in yk=u, Voc(soc,k)
=Voc(soc), R0I (k)=i (t) R0,
By formula 2) and formula 3) simultaneous, it obtains:
D=[R0] (4)
Formula 4) in, A indicates that state-transition matrix, B indicate that system controls matrix, and D and H indicate systematic observation matrix；Δ t is indicated
Unit time, η indicate efficiency for charge-discharge, C0Indicate battery capacity.
4. the SOC estimation method of the hybrid power lithium battery according to claim 1 based on EKF algorithm, it is characterised in that
Step 4 is specifically: before starting EKF algorithm, due to original state matrix x=[Voc(soc) V1 V2]TMiddle polarizing voltage is very small,
Therefore V1And V2Initial value is 0；The initial value of initial covariance matrix P is selected as unit matrix；
(1) start EKF algorithm, first to state matrix x before iterationk-1, covariance matrix P before iterationk-1It is initial with noise Q mono-
Value, then bring into Kalman's time update equation and obtain current state matrix xkWith current covariance matrix PkValue；
(2) x for again obtaining step 1)kAnd PkValue is brought into Kalman state renewal equation, and state matrix x after iteration is obtainedk+1
With covariance matrix P after iterationk+1Value；
(3) x for again obtaining step 2)k+1And Pk+1Value is brought into again replaces x respectively in Kalman's time update equationk-1With
Pk-1, obtain new xk；Do not stop loop iteration and updates xk, until battery discharge stops, the SOC for obtaining entire operating condition is estimated.
5. the SOC estimation method of the hybrid power lithium battery according to claim 4 based on EKF algorithm, it is characterised in that
Kalman's time update equation includes state equation xk=Axk-1+BIk-1With covariance equation Pk=APk-1AT+Q；The card
Germania state renewal equation includes kalman gain equation Kk=PkHT(HPkHT+D)-1, state renewal equation xk+1=xk+Kk(yk-
Hxk) and covariance renewal equation Pk+1=(1-KkH)Pk；Wherein, Ik-1Indicate electric current；1 indicates unit matrix；KkIndicate gain square
Battle array；ATIndicate the transposed matrix of A；
Before starting EKF algorithm, due to original state matrix x=[Voc(soc) V1 V2]TMiddle polarizing voltage is very small, therefore V1With
V2Initial value is 0；The initial value of initial covariance matrix P is selected as unit matrix；
(1) start EKF algorithm, first to state matrix x before iterationk-1, covariance matrix P before iterationk-1It is initial with noise Q mono-
Value, then bring the state equation x in Kalman's time update equation intok=Axk-1+BIk-1In obtain xkValue, brings Kalman's time into
Covariance equation P in renewal equationk=APk-1ATP is obtained in+QkValue；
(2) P for again obtaining step 1)kValue brings the kalman gain equation K of Kalman state renewal equation intok=PkHT(HPkHT
+D)-1In, obtain KkValue；The K that will be obtained againkThe x that value and step 1) obtainkValue brings the state of Kalman state renewal equation into more
New EQUATION xk+1=xk+Kk(yk-Hxk) in obtain xk+1Value, the K that will be obtainedkThe P that value and step 1) obtainkValue brings Kalman's shape into
The covariance renewal equation P of state renewal equationk+1=(1-KkH)PkIn obtain Pk+1Value；
(3) x for again obtaining step 2)k+1And Pk+1Value is brought into again replaces x respectively in Kalman's time update equationk-1With
Pk-1, obtain new xk；Do not stop loop iteration and updates xk, obtain the SOC estimated value of each sampled point, until battery discharge stop,
Obtain the SOC estimation of entire operating condition.
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| CN110515011A (en) * | 2019-09-16 | 2019-11-29 | 华南农业大学 | A kind of Accurate Estimation Method of lithium-ion-power cell SOC |
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