CN106646253A - On-line identification method for intrinsic parameters of battery - Google Patents
On-line identification method for intrinsic parameters of battery Download PDFInfo
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- CN106646253A CN106646253A CN201611115540.XA CN201611115540A CN106646253A CN 106646253 A CN106646253 A CN 106646253A CN 201611115540 A CN201611115540 A CN 201611115540A CN 106646253 A CN106646253 A CN 106646253A
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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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Abstract
The invention relates to an on-line identification method for intrinsic parameters of a battery, which is characterized in that a linear neural network is constructed by using input and output data of the battery on the basis of a standard equivalent first-order RC model, current and voltage data in a composite pulse test is taken as a training set, and parameter identification is performed on an equivalent circuit model by using a neural network parameter identification method. The on-line identification method identifies each parameter of a power storage battery online conveniently and efficiently, provides model parameters for battery power estimation, and is conducive to improving control effects of a battery management system, giving full play to the performance of the storage battery and prolonging the cycle life of the storage battery.
Description
Technical field
The present invention relates to the technical field of battery detecting, more particularly to a kind of side of on-line identification inside battery parameter
Method.
Background technology
Increasingly mature with electric vehicle engineering, electric automobile gradually comes into the life of people.However, motor, battery
With the development that automatically controlled this three big problem governs electric automobile, wherein, battery is important " bottleneck " of Development of EV.It is electronic
Automobile power cell group has that electricity is unbalanced in charge and discharge process, in order to estimate the residue electricity of battery in real time
Amount, needs the on-line identification for realizing battery model parameter.Distinguished using least-squares parameter more than the identification of Model Parameters of current battery
Know and Kalman filtering identification, these methods are used for the parameter identification of time-invariant system, for the not true of battery this time-varying
Determine nonlinear system, these traditional parameter identification methods often reduce the adaptivity of systematic parameter, cause self-adaptive PID
Controller cannot produce accurately response to system, so that system is absorbed in the repetition adjustment of a new round or because unstable and straight
Connect collapse.The present invention, using battery inputoutput data, constructs linear neural on the basis of standard equivalent circuit single order RC models
Network, using the electric current and voltage data in composite pulse test as training set, calculates inside battery parameter.
The content of the invention
It is an object of the invention to overcome the deficiencies in the prior art, there is provided one kind is convenient, fast, be conducive to raising cell tube
The on-line identification inside battery parameter of the service life of reason system control accuracy, the ability for playing battery and prolongation battery
Method.
For achieving the above object, technical scheme provided by the present invention is：It includes following steps：
1) with electric current as input variable, voltage is output variable, and ohmic internal resistance, polarization resistance and polarization capacity are middle anaplasia
Amount, builds battery equivalent circuit model；
2) battery equivalent circuit model is converted into the battery differential equation, derivation system transmission function, using Laplce
Become transmission function discretization of changing commanders, acquisition facilitates Neural Networks Solution system difference equation；
3) composite pulse power test is carried out to battery using battery testing system, detects and record the electric current and electricity of battery
Pressure signal；
4) off-line training is carried out to the battery current voltage data for gathering using neutral net, reaches and meet desired control
After precision, the initial value that the neural network weight that off-line training is obtained learns as Neural Network Online；
5) by the weights of steepest descent method on-line tuning neutral net, on-line study adjustment is carried out to system so that god
The output of Jing networks close to actual value, so as to obtain ohmic internal resistance, polarization resistance and the polarization capacity of battery.
Further, the composite pulse power test, using systems soft ware charging and discharging currents size and time are arranged, and are
System program carries out discharge and recharge, the current signal of real time record battery and the electricity under this input current to battery according to the work step of setting
The output voltage signal in pond.
Further, the battery equivalent circuit model adopts single order RC models, by an ideal voltage source, an electric capacity
Constitute with two resistance, two resistance represent respectively the ohmic internal resistance R of battery0With polarization resistance Rp, electric capacity represents the polarization of battery
Electric capacity Cp, wherein, polarization capacity CpWith polarization resistance RpParallel connection, then with Ohmic resistance R0And ideal voltage source composition series electrical
Road；The circuit model terminal voltage is Ut, the voltage on ideal voltage source is expressed as cell emf Uoc, input current is I, polarization
Voltage is Up；
Further, the neutral net is the linear neural network of a two-layer, and the input layer of neutral net has three
Input neuron, respectively I (k), I (k-1) and Urc(k-1), respectively composite pulse test in current time electric current, it is previous
The electric current at moment and previous moment inside battery pressure drop, output layer has a neuron, is the internal pressure drops at battery current time
Urc(k).Connection weight between output layer and input layer is respectively D1、D2And D3。
Further, described detailed parameter identification step is as follows：
1)) based on single order RC models, the dynamic differential equation of battery, U are derivedoc=Ut-IR0-Up, Up=I/Cp-Up/
(RpCp)；
2)) transmission function represents the ratio of output voltage and input current, and inside battery pressure drop is Urc, pushed away by the differential equation
Leading to obtain, Urc=Ut-Uoc=IR0+Up,Urc+τp Using Laplace transform to described
Differential equation carries out equivalent transformation, and the transmission function is represented by
3)) using Euler method by unknown parameter s discretizations, willSubstitute into the ssystem transfer function, Jing abbreviations
Arrangement can be obtained, and battery system discretization model is Urc(k)=D1I(k)+D2I(k-1)+D3Urc(k-1)；
4)) composite pulse test is carried out to battery using battery testing system, gathers the electric current and voltage data of battery,
And data are preserved as the training set of next step parameter identification；
5)) neutral net is built, and using multigroup historical data U for collectingrc(k)、UrcAnd I (k), I (k-1) (k-1)
Off-line training is carried out to neutral net, by the weights D for becoming the steepest descent method of learning rate to adjust neutral net1、D2With
D3, until meeting desired performance indications.Then the weights D for being obtained with off-line training1、D2And D3, as the initial of on-line study
Value；
6) weights of neutral net) are adjusted by the steepest descent method of steepest change learning rate, neutral net is carried out
On-line study is adjusted so that the battery current estimate of neutral net output is close to actual current value I (k) of battery, so as to
Obtain the weights D of time-varying1、D2And D3.According to battery system discretization model, the weights of neutral net can be by ohm of battery
Resistance Ro, polarization resistance RpAnd polarization capacity CpRepresent, by the weights D of time-varying1、D2、D3And battery equivalent circuit model time constant,
The battery testing system sampling time can anti-solution obtain the ohmic internal resistance R of batteryo, polarization resistance RpAnd polarization capacity CpIdentifier,
Respectively：
Compared with prior art, this programme is on the basis of standard equivalent circuit single order RC models, using battery input and output
Data, construct linear neural network, using the electric current and voltage data in composite pulse test as training set, quickly and easily
The each parameter of on-line identification power accumulator, is conducive to improving battery management system control accuracy, plays the ability of battery and prolong
The service life of long battery.
Description of the drawings
Fig. 1 is the modeling method figure of the present invention；
Fig. 2 is the Mathematical Modeling figure of power lithium-ion battery in the embodiment of the present invention.
Specific embodiment
With reference to specific embodiment, the invention will be further described：
Referring to shown in accompanying drawing 1 to 2, the present embodiment is on-line identification power lithium-ion battery inner parameter.
The first step, sets up accurate battery Mathematical Modeling, shown in accompanying drawing 2, there is energy-storage travelling wave tube C in figurep, lithium-ion electric
The model order in pond can be identified as single order RC models, and the model is made up of an ideal voltage source, an electric capacity and two resistance,
Two resistance represent respectively the ohmic internal resistance R of battery0With polarization resistance Rp, electric capacity represents the polarization capacity C of batteryp, wherein, pole
Change electric capacity CpWith polarization resistance RpParallel connection, then with Ohmic resistance R0And ideal voltage source composition series circuit；The circuit model end
Voltage is Ut, the voltage on ideal voltage source is expressed as cell emf Uoc, input current is I, and polarizing voltage is Up, modeling
Emphasis is determination R0、Rp、Cp、UocValue, due to electromotive force u in figureocVary less during whole battery use, so this
In set UocFor constant, then, it is thus necessary to determine that actually only have R0、Rp、CpThree parameters.
Second step, battery equivalent circuit model is converted into the battery differential equation, derivation system transmission function, due to nerve net
Network identification model is discrete Mathematical Modeling, and ssystem transfer function is continuous function, need to adopt Laplace transform by unknown ginseng
Number s discretizations, willSsystem transfer function is substituted into, single order RC equivalent-circuit models are converted into discretization model, obtained
The Neural Networks Solution system difference equation, identification model is facilitated to be, Urc(k)=D1I(k)+D2I(k-1)+D3Urc(k-1),
Wherein, D1、D2And D3The unknown parameter of type, is exactly weights that neutral net need to be recognized.R0=d0’,R0=D1,
3rd step, using battery testing system composite pulse power test is carried out to battery, and systems soft ware arranges discharge and recharge
Size of current and time, system program carries out discharge and recharge, the current signal of real time record battery to battery according to the work step of setting
And under this input current battery output voltage signal.
4th step, off-line training is carried out with neutral net to the battery current voltage data for gathering, and is reached and is met what is required
After control accuracy, the initial value that the neural network weight that off-line training is obtained learns as Neural Network Online.The nerve
Network is the linear neural network of a two-layer, and the input layer of neutral net has three input neurons, respectively I (k), I (k-
And U 1)rc(k-1), respectively composite pulse test in current time electric current, in the electric current and previous moment battery of previous moment
Portion's pressure drop, output layer has a neuron, is internal pressure drops U at battery current timerc(k).Between output layer and input layer
Connection weight is respectively D1、D2And D3。
5th step, by the weights of steepest descent method on-line tuning neutral net, to system on-line study adjustment is carried out, and is made
The output of neutral net is obtained close to actual value, so as to obtain polarization resistance R of batteryp, polarization capacity CpAnd ohmic internal resistance R0。
Described detailed parameter identification step is as follows：
(1) based on single order RC models, the dynamic differential equation of battery, U are derivedoc=Ut-IR0-Up, Up=I/Cp-Up/
(RpCp)；
(2) transmission function represents the ratio of output voltage and input current, and inside battery pressure drop is urc, pushed away by the differential equation
Leading to obtain, Urc=Ut-Uoc=IR0+Up,Urc+τp Laplace transform is carried out to the formula,
The transmission function is
(3) Euler method, will by unknown parameter s discretizationsSsystem transfer function is substituted into, battery discretization model is
Urc(k)=D1I(k)+D2I(k-1)+D3Urc(k-1)；
(4) composite pulse test is carried out to battery using battery testing system, gathers the electric current and voltage data of battery,
And data are preserved as the training set of next step parameter identification；
(5) neutral net is built, and using data U of multigroup historyrc(k)、UrcAnd I (k), I (k-1) are to nerve (k-1)
Network carries out off-line training, by the weights D for becoming the steepest descent method of learning rate to adjust neutral net1、D2And D3, when reaching
To after the performance indications for meeting requirement, the weights D that off-line training is obtained1、D2And D3As the initial value of on-line study；
(6) by the weights of steepest descent method on-line tuning neutral net, on-line study adjustment is carried out to system so that god
Jing networks output battery current estimate close to battery actual current value I (k), so as to obtain the weights D of time-varying1、D2With
D3, and then obtain the ohmic internal resistance R of batteryo, polarization resistance RpAnd polarization capacity CpIdentifier, respectively
On the basis of standard equivalent circuit single order RC models, using battery inputoutput data, construction is linear for the present embodiment
Neutral net, the electric current and voltage data in being tested using composite pulse stores as training set, quickly and easily on-line identification power
The each parameter of battery, is conducive to improving battery management system control effect, plays the performance of battery and extend the circulation of battery
Life-span.
The examples of implementation of the above are only the preferred embodiments of the invention, not limit the enforcement model of the present invention with this
Enclose, therefore the change that all shapes according to the present invention, principle are made, all should cover within the scope of the present invention.
Claims (3)
1. a kind of method of on-line identification inside battery parameter, it is characterised in that：Comprise the following steps：
1) with electric current as input variable, voltage is output variable, and ohmic internal resistance, polarization resistance and polarization capacity are intermediate variable,
Build battery equivalent circuit model；
2) battery equivalent circuit model is converted into the battery differential equation, derivation system transmission function, using Laplace transform
Transmission function discretization, acquisition are facilitated into the system difference equation of Neural Networks Solution；
3) composite pulse power test is carried out to battery using battery testing system, detects and record the electric current and voltage letter of battery
Number；
4) off-line training is carried out to the battery current that collects and voltage data using neutral net, reaches and meet permissible accuracy
Afterwards, the initial value that the neural network weight for off-line training being obtained learns as Neural Network Online；
5) by the weights of steepest descent method on-line tuning neutral net, on-line study adjustment is carried out to system so that nerve net
The output of network close to actual value, so as to obtain ohmic internal resistance, polarization resistance and the polarization capacity of battery.
2. the method for a kind of on-line identification inside battery parameter according to claim 1, it is characterised in that：The composite vein
Power test is rushed, charging and discharging currents size and time are set using systems soft ware, system program is according to the work step of setting to battery
Carry out discharge and recharge, the current signal of real time record battery and under this input current battery output voltage signal.
3. the method for a kind of on-line identification inside battery parameter according to claim 1, it is characterised in that：Described battery etc.
Effect circuit model adopts single order RC models, is made up of an ideal voltage source, an electric capacity and two resistance, two resistance difference
Represent the ohmic internal resistance R of battery0With polarization resistance Rp, electric capacity represents the polarization capacity C of batteryp, wherein, polarization capacity CpAnd pole
Change resistance RpParallel connection, then with Ohmic resistance R0And ideal voltage source composition series circuit；The circuit model terminal voltage is Ut, reason
Think that the voltage on voltage source is expressed as cell emf Uoc, input current is I, and polarizing voltage is Up。
The neutral net is the linear neural network of a two-layer, and the input layer of neutral net has three input neurons, point
Wei not I (k), I (k-1) and Urc(k-1), respectively composite pulse test in current time electric current, the electric current of previous moment and
Previous moment inside battery pressure drop, output layer has a neuron, is internal pressure drops U at battery current timerc(k).Output layer
Connection weight and input layer between is respectively D1、D2And D3。
Described detailed parameter identification step is as follows：
1)) based on single order RC models, the dynamic differential equation of battery, U are derivedoc=Ut-IR0-Up, Up=I/Cp-Up/
(RpCp)；
2)) transmission function represents the ratio of output voltage and input current, and inside battery pressure drop is Urc, being derived by the differential equation can
, Urc=Ut-Uoc=IR0+Up,Using Laplace transform to described
Differential equation carries out equivalent transformation, and the transmission function is represented by
3)) using Euler method by unknown parameter s discretizations, willThe ssystem transfer function is substituted into, Jing abbreviations are arranged
Can obtain, battery system discretization model is Urc(k)=D1I(k)+D2I(k-1)+D3Urc(k-1)；
4)) composite pulse test is carried out to battery using battery testing system, gathers the electric current and voltage data of battery, and protected
Training set of the deposit data as next step parameter identification；
5)) neutral net is built, and using multigroup historical data U for collectingrc(k)、UrcAnd I (k), I (k-1) are to god (k-1)
Jing networks carry out off-line training, by the weights D for becoming the steepest descent method of learning rate to adjust neutral net1、D2And D3, directly
To the performance indications for meeting requirement.Then the weights D for being obtained with off-line training1、D2And D3, as the initial value of on-line study；
6) weights of neutral net) are adjusted by the steepest descent method of steepest change learning rate, neutral net is carried out online
Study adjustment so that the battery current estimate of neutral net output is close to actual current value I (k) of battery, so as to obtain
The weights D of time-varying1、D2And D3.According to battery system discretization model, the weights of neutral net can be by the ohmic internal resistance R of batteryo、
Polarization resistance RpAnd polarization capacity CpRepresent, by the weights D of time-varying1、D2、D3And battery equivalent circuit model time constant, battery
The detecting system sampling time can anti-solution obtain the ohmic internal resistance R of batteryo, polarization resistance RpAnd polarization capacity CpIdentifier, respectively
For：
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| CN107817451A (en) * | 2017-11-24 | 2018-03-20 | 北京机械设备研究所 | Discrimination method, system and the storage medium of electrokinetic cell model on-line parameter |
| CN108983108A (en) * | 2018-08-10 | 2018-12-11 | 山东大学 | A kind of power battery pack peak power estimation method |
| CN109143092A (en) * | 2017-06-19 | 2019-01-04 | 宁德时代新能源科技股份有限公司 | Method and device for generating cell model and acquiring cell voltage and battery management system |
| CN109786878A (en) * | 2019-03-25 | 2019-05-21 | 哈尔滨理工大学 | A kind of electric automobile power battery charging/method for heating and controlling |
| CN109950661A (en) * | 2019-03-25 | 2019-06-28 | 清华大学 | A kind of device and method that the inside and outside of power battery pack is heated simultaneously |
| CN109950660A (en) * | 2019-03-25 | 2019-06-28 | 清华大学 | The method that ternary lithium-ion-power cell is preheated using itself energy storage excitation |
| CN110554329A (en) * | 2019-10-17 | 2019-12-10 | 东软睿驰汽车技术（沈阳）有限公司 | battery internal resistance measuring method and device |
| CN110709716A (en) * | 2018-02-07 | 2020-01-17 | 株式会社Lg化学 | Method for estimating parameters of equivalent circuit model of battery and battery management system |
| CN110794319A (en) * | 2019-11-12 | 2020-02-14 | 河南工学院 | Method and device for predicting parameters of lithium battery impedance model and readable storage medium |
| CN110888057A (en) * | 2019-11-27 | 2020-03-17 | 上海交通大学 | Power lithium ion battery electrochemical parameter identification method and system |
| CN110907835A (en) * | 2019-12-10 | 2020-03-24 | 北京理工大学 | Battery model parameter identification and SOC estimation method with noise immunity characteristic |
| CN113884904A (en) * | 2021-11-01 | 2022-01-04 | 国网湖南省电力有限公司 | Equivalent circuit model of super capacitor battery and parameter identification method thereof |
| CN113960482A (en) * | 2021-09-03 | 2022-01-21 | 西南科技大学 | Lithium battery state of charge intelligent prediction method based on improved wolf particle filtering |
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| CN107037375B (en) * | 2017-05-23 | 2020-07-24 | 北京新能源汽车股份有限公司 | Method and device for measuring direct current internal resistance of battery |
| CN107037375A (en) * | 2017-05-23 | 2017-08-11 | 北京新能源汽车股份有限公司 | Battery DC internal resistance measurement method and device |
| CN109143092A (en) * | 2017-06-19 | 2019-01-04 | 宁德时代新能源科技股份有限公司 | Method and device for generating cell model and acquiring cell voltage and battery management system |
| CN109143092B (en) * | 2017-06-19 | 2021-01-12 | 宁德时代新能源科技股份有限公司 | Method and device for generating cell model and acquiring cell voltage and battery management system |
| CN107817451A (en) * | 2017-11-24 | 2018-03-20 | 北京机械设备研究所 | Discrimination method, system and the storage medium of electrokinetic cell model on-line parameter |
| CN110709716B (en) * | 2018-02-07 | 2021-12-28 | 株式会社Lg化学 | Method for estimating parameters of equivalent circuit model of battery and battery management system |
| CN110709716A (en) * | 2018-02-07 | 2020-01-17 | 株式会社Lg化学 | Method for estimating parameters of equivalent circuit model of battery and battery management system |
| US11125823B2 (en) | 2018-02-07 | 2021-09-21 | Lg Chem, Ltd. | Method for estimating parameter of equivalent circuit model for battery, and battery management system |
| CN108983108A (en) * | 2018-08-10 | 2018-12-11 | 山东大学 | A kind of power battery pack peak power estimation method |
| CN108983108B (en) * | 2018-08-10 | 2021-04-02 | 山东大学 | Power battery pack peak power estimation method |
| CN109950660A (en) * | 2019-03-25 | 2019-06-28 | 清华大学 | The method that ternary lithium-ion-power cell is preheated using itself energy storage excitation |
| CN109950661B (en) * | 2019-03-25 | 2022-04-12 | 清华大学 | Device and method for simultaneously heating inside and outside of power battery pack |
| CN109950661A (en) * | 2019-03-25 | 2019-06-28 | 清华大学 | A kind of device and method that the inside and outside of power battery pack is heated simultaneously |
| CN109786878B (en) * | 2019-03-25 | 2022-01-21 | 哈尔滨理工大学 | Charging/heating control method for power battery of electric automobile |
| CN109950660B (en) * | 2019-03-25 | 2022-01-18 | 清华大学 | Method for preheating ternary lithium ion power battery by utilizing self energy storage excitation |
| CN109786878A (en) * | 2019-03-25 | 2019-05-21 | 哈尔滨理工大学 | A kind of electric automobile power battery charging/method for heating and controlling |
| CN110554329A (en) * | 2019-10-17 | 2019-12-10 | 东软睿驰汽车技术（沈阳）有限公司 | battery internal resistance measuring method and device |
| CN110794319A (en) * | 2019-11-12 | 2020-02-14 | 河南工学院 | Method and device for predicting parameters of lithium battery impedance model and readable storage medium |
| CN110888057A (en) * | 2019-11-27 | 2020-03-17 | 上海交通大学 | Power lithium ion battery electrochemical parameter identification method and system |
| CN110888057B (en) * | 2019-11-27 | 2020-11-17 | 上海交通大学 | Power lithium ion battery electrochemical parameter identification method and system |
| CN110907835B (en) * | 2019-12-10 | 2020-12-11 | 北京理工大学 | Battery model parameter identification and SOC estimation method with noise immunity characteristic |
| CN110907835A (en) * | 2019-12-10 | 2020-03-24 | 北京理工大学 | Battery model parameter identification and SOC estimation method with noise immunity characteristic |
| CN113960482A (en) * | 2021-09-03 | 2022-01-21 | 西南科技大学 | Lithium battery state of charge intelligent prediction method based on improved wolf particle filtering |
| CN113884904A (en) * | 2021-11-01 | 2022-01-04 | 国网湖南省电力有限公司 | Equivalent circuit model of super capacitor battery and parameter identification method thereof |
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