WO2018161486A1  Method and system for estimating soc of power battery on the basis of dynamic parameters  Google Patents
Method and system for estimating soc of power battery on the basis of dynamic parameters Download PDFInfo
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 WO2018161486A1 WO2018161486A1 PCT/CN2017/092130 CN2017092130W WO2018161486A1 WO 2018161486 A1 WO2018161486 A1 WO 2018161486A1 CN 2017092130 W CN2017092130 W CN 2017092130W WO 2018161486 A1 WO2018161486 A1 WO 2018161486A1
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 battery
 soc
 equivalent circuit
 order
 ocv
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 238000004364 calculation methods Methods 0.000 claims description 13
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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]
 G01R31/389—Measuring internal impedance, internal conductance or related variables

 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 lookup tables
Abstract
Description
The invention relates to the field of electric vehicle and energy storage battery management systems, and particularly to a power battery SOC estimation method and system based on dynamic parameters.
At present, domestic and international methods for estimating the State of Charge (SOC) of power batteries include: internal resistance method, amperetime integration method, open circuit voltage method, Kalman filter method, observer method, particle filter method and neural network. law. Among them, the internal resistance method is based on the relationship between the internal resistance of the battery and the SOC, and the battery SOC is calculated by detecting the internal resistance of the battery internal resistance. However, the online and accurate measurement of the internal resistance of the battery is difficult, which limits the actual engineering of the method. Application in . Although the principle of Ampere integration is simple and easy to implement, it cannot eliminate the initial error of SOC and the accumulated error caused by inaccurate current measurement. The open circuit voltage method calculates the battery SOC based on the correspondence between the open circuit voltage (OCV) and the SOC. It is necessary to fully rest the battery before measuring the OCV, and thus is not suitable for online estimation of the SOC. Both the Kalman filter method and the observer method can well correct the initial error of the battery SOC and have good antinoise ability. However, they have very high requirements on model accuracy. Particle filtering method, the convergence time is too long. The neural network method requires a large number of training samples. In practical applications, it is impossible to obtain sample data covering all actual working conditions, so the accuracy will be affected to some extent, and the calculation method is difficult to implement in hardware. The power battery is a complex nonlinear power system, and the battery model parameters are obviously affected by many factors such as temperature, battery selfdischarge, and aging.
In the actual application, the existing battery SOC estimation method has some inconveniences and defects to varying degrees, so further improvement is necessary.
Summary of the invention
It is an object of the present invention to solve the problem of the accuracy and speed of SOC estimation of existing batteries.
To achieve the above object, in a first aspect, the present invention provides a method and system for estimating a SOC of a power battery based on dynamic parameters, the method comprising the steps of: performing a dischargestation test on a battery to obtain an OCV of the battery at different temperatures. SOC characteristic curve, fitting the relational expression of OCVSOC; performing a pulse dischargestation experiment of constant current on the battery, recording the voltage response during the period, and identifying the second order of the battery by offline method according to the obtained voltage response curve The initial value of the parameters of the RC equivalent circuit model; the recursive least squares method RRFLS with forgetting factor is used to identify the dynamic parameters of the secondorder RC equivalent circuit model; the EKF algorithm is used to estimate the battery SOC online.
Preferably, the secondorder RC equivalent circuit model of the battery is mainly composed of a first resistor (R _{0} ), a second resistor (R _{1} ), a third resistor (R _{2} ), a first capacitor (C _{1} ), and a second capacitor ( C _{2} ) constitutes.
Preferably, the value of the forgetting factor is from 0.95 to 0.98.
In a second aspect, the present invention provides a power battery SOC estimation system based on dynamic parameters, the system comprising: a first calculation module for performing a dischargestation experiment on a battery to obtain an OCVSOC of the battery at different temperatures The characteristic curve is fitted to the relational expression of OCVSOC; the second calculation module is used for performing a constant current pulse dischargestation experiment on the battery, and the voltage response during recording is identified by an offline method according to the obtained voltage response curve. The parameter initial value of the secondorder RC equivalent circuit model of the battery; the third calculation module is used for dynamic parameter identification of the secondorder RC equivalent circuit model by using the recursive least squares method RRFLS with forgetting factor; fourth calculation module Used to estimate the battery SOC online using the EKF algorithm.
The invention overcomes the phenomenon that the initial value of the SOC is inaccurate and accumulated error in the integration time method, adapts to the dynamic change of the battery characteristic, the battery model has high precision, the convergence speed is fast, stable and reliable, and the accuracy of the SOC online estimation is improved, and the invention can be widely used. Used in the field of electric vehicles and energy storage battery management systems.
1 is a schematic flow chart of a method for estimating a SOC of a power battery based on dynamic parameters according to an embodiment of the present invention;
2 is a schematic structural diagram of a secondorder RC equivalent circuit model of a battery.
The technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. It is a partial embodiment of the invention, and not all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts are within the scope of the present invention.
In order to facilitate the understanding of the embodiments of the present invention, the embodiments of the present invention are not to be construed as limiting.
1 is a schematic flow chart of a method for estimating a SOC of a power battery based on dynamic parameters according to an embodiment of the present invention. As shown in Figure 1, the method includes the following steps:
Step 1. Perform a dischargestation test on the battery to obtain the OCVSOC characteristic curve of the battery at different temperatures, and fit the relationship expression of OCVSOC.
Step 2: Perform a pulsed dischargestation experiment on the battery with constant current, record the voltage response during the period, and identify the initial value of the parameter of the secondorder RC equivalent circuit model of the battery by offline method according to the obtained voltage response curve.
In a preferred solution, the secondorder RC equivalent circuit model of the battery is shown in FIG. 2. The secondorder RC equivalent circuit model of the battery is mainly composed of a first resistor R _{0} , a second resistor R _{1} , and a third resistor R _{2} . The first capacitor C _{1} and the second capacitor C _{2 are} formed; wherein U _{oc} represents an open circuit voltage (OCV) of the battery; U _{1} is a terminal voltage of the battery; R _{0} is an ohmic internal resistance of the battery; R _{1} , R _{2} They are the electrochemical polarization and concentration difference polarization resistance during charge and discharge of the battery respectively; C _{1} and C _{2} are the transient capacitance effect, electrochemical polarization and concentration difference polarization capacitance during charge and discharge of the battery, respectively; U _{1} U _{2} is the voltage value through the capacitors C _{1} and C _{2} respectively; U is the battery terminal voltage; I is the battery terminal current.
Step 3: Using the recursive least squares method RRFLS with forgetting factor, the secondorder RC equivalent circuit model is parameterized. Preferably, the value of the forgetting factor is from 0.95 to 0.98.
Specifically, from the Kirchhoff's law and the Labrador transform, the state equation in the frequency domain of the secondorder RC equivalent circuit model is:
Let the time constant τ _{1} = R _{1} C _{1} , τ _{2} = R _{2} C _{2} ;
Then the above formula can be reduced to:
τ _{1} τ _{2} U _{oc} s ^{2} +(τ _{1} +τ _{2} )U _{oc} s+U _{oc} =τ _{1} τ _{2} IR _{0} s ^{2} +IsR _{1} τ _{2} +R _{2} τ _{1} +R _{0} (τ _{1} + τ _{2} )+I(R _{1} +R _{2} +R _{0} )+τ _{1} τ _{2} Us ^{2} +(τ _{1} +τ _{2} )Us+U;
Let a = τ _{1} τ _{2} , b = τ _{1} + τ _{2} , c = R _{1} + R _{2} + R _{0} , d = R _{1} τ _{2} + R _{2} τ _{1} + R _{0} (τ _{1} + τ _{2} )
Then the above formula can be simplified as:
aU _{oc} s ^{2} +bU _{oc} s+U _{oc} =aR _{0} Is ^{2} +dIs+cI+aUs ^{2} +bUs+U;
The above formula is discretized, where T is the sampling time, and the finishing is available:
U _{oc} (k)U=k _{1} U(k1)U _{oc} (k1)+k _{2} U(k2)U _{oc} (k2)+k _{3} I( k)+k _{4} I(k1)+k _{5} I(k2)
among them,
In the formula, you can substitute the recursive least squares identification method, the current time θ =  k _{1} k _{2} k _{3} k _{4} k _{5}  ^{T} value, and then according to the following formula:
R _{0} = k _{5} /k _{2}
R _{1} =(τ _{1} c+τ _{2} R _{i} d)/(τ _{1} τ _{2} )
R _{2} =cR _{1} R _{i}
C _{1} =τ _{1} /R _{1}
C _{2} =τ _{2} /R _{2}
The secondorder RC equivalent circuit model parameters R _{0} , R _{1} , R _{2} , C _{1} , and C _{2 are} calculated to realize dynamic identification of model parameters.
Step 4: The EKF algorithm is used to estimate the battery SOC online. The EKF algorithm is called Extended Kalman Filter, which is an extended Kalman filter, a highefficiency recursive filter (autoregressive filter).
Specifically, according to the selected secondorder RC equivalent circuit model, the state equation and the measurement equation of the battery are obtained as follows:
Discrete model after discretization of the state equation:
Let the state variable in the battery model be x=[x _{1} x _{2} x _{3} ]=[U _{oc} U _{1} U _{2} ] ^{T} , the system input u is the operating current I of the lithium ion battery, and the discharge is positive, the system output y is Lithiumion battery operating voltage U, sampling time is T.
The discrete state space model of a lithium ion battery is:
among them
D _{k} =R _{0} (k)
Algorithm system parameter state quantity initialization
x _{0} =[SOC(0) 0 0] ^{T}
Running extended Kalman filter algorithm
Prediction module:
(1) Status prediction:
(2) State prediction error covariance matrix:
Error correction module:
(1) Kalman gain:
among them,
(2) State estimation:
(3) State estimation mismatched variance matrix:
P _{k} =(IG _{k} C _{k} )P _{kk1}
Where P _{k} is the covariance; G _{k} is the Kalman gain; Q _{k1} is the process noise error; and R _{k1} is the observed noise error.
Step 5: From the SOC estimation value, according to the OCVSOC characteristic curve obtained in step one, the open circuit voltage value U _{oc} at time k is obtained, and the RRFLS algorithm is used to obtain θ=k _{1} k _{2} k _{3} k _{4} at time k. k _{5}  ^{T} value, then calculate the model parameter values R _{0} , R _{1} , R _{2} , C _{1} , C _{2} at time k;
Step 6: Update the parameter values Ak, Bk, Ck, and Dk of the state equation in the EFK algorithm in real time, and then run the extended Kalman filter algorithm to obtain the SOC estimation value at time k+1, and then return to step 4.
The two steps of calculating the updated model parameters and estimating the SOC by step six are performed, and each obtained SOC and time model parameter values R _{0} , R _{1} , R _{2} , C _{1} , and C _{2 are} substituted into the discrete state space equation. The new predicted value, calculated by continuous prediction and modified recursive method, can recurively obtain the realtime parameter value of the lithium battery model and the current SOC estimation value, so that the final SOC and model parameter values R _{0} , R _{1} The R _{2} , C _{1} , and C _{2} filtering results are constantly approaching the actual situation of the battery.
Correspondingly, an embodiment of the present invention provides a power battery SOC estimation system based on dynamic parameters, and the system includes:
The first calculation module is configured to perform a dischargestation experiment on the battery, obtain an OCVSOC characteristic curve of the battery at different temperatures, and fit a relationship expression of OCVSOC;
The second calculation module is used for performing a constant current pulse dischargestation experiment on the battery, and the voltage response during recording, according to the obtained voltage response curve, the parameter initial value of the secondorder RC equivalent circuit model of the battery is identified by an offline method. ;
The third calculation module is configured to perform dynamic parameter identification on the secondorder RC equivalent circuit model by using a recursive least squares method RRFLS with a forgetting factor;
The fourth calculation module is configured to perform online estimation of the battery SOC by using the EKF algorithm.
The embodiment of the invention overcomes the phenomenon that the initial value of the SOC is inaccurate and the cumulative error in the integration time method, adapts to the dynamic change of the battery characteristics, the battery model has high precision, the convergence speed is fast, stable and reliable, and the accuracy of the SOC online estimation is improved. Can be widely used in the field of electric vehicles and energy storage battery management systems.
The present invention has been described in detail above, and the present invention is further described in conjunction with the specific embodiments. It is pointed out that the description of the above embodiments is not intended to be limiting but merely to assist in understanding the present invention. The core idea is that those skilled in the art, without departing from the principles of the invention, any modifications of the invention and equivalents to the invention are also within the scope of the invention.
Claims (4)
 A method for estimating a SOC of a power battery based on dynamic parameters, comprising the steps of:Conducting a dischargestation experiment on the battery, obtaining the OCVSOC characteristic curve of the battery at different temperatures, and fitting the relationship expression of OCVSOC;The battery is subjected to a constant current pulse dischargestation test, the voltage response during recording, and the initial value of the parameter of the secondorder RC equivalent circuit model of the battery is identified by an offline method according to the obtained voltage response curve;The dynamic parameter identification of the secondorder RC equivalent circuit model is carried out by using the recursive least squares method RRFLS with forgetting factor.The SOC of the battery is estimated online using the EKF algorithm.
 The method according to claim 1, wherein the secondorder RC equivalent circuit model of the battery is mainly composed of a first resistor (R _{0} ), a second resistor (R _{1} ), and a third resistor (R _{2} ). A capacitor (C _{1} ) and a second capacitor (C _{2} ) are formed.
 The method according to claim 1, wherein the value of the forgetting factor is 0.95 to 0.98.
 A dynamic battery based SOC estimation system based on dynamic parameters, comprising:The first calculation module is configured to perform a dischargestation experiment on the battery, obtain an OCVSOC characteristic curve of the battery at different temperatures, and fit a relationship expression of OCVSOC;The second calculation module is used for performing a constant current pulse dischargestation experiment on the battery, and the voltage response during recording, according to the obtained voltage response curve, the parameter initial value of the secondorder RC equivalent circuit model of the battery is identified by an offline method. ;The third calculation module is configured to perform dynamic parameter identification on the secondorder RC equivalent circuit model by using a recursive least squares method RRFLS with a forgetting factor;The fourth calculation module utilizes the EKF algorithm to estimate the battery SOC online.
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