CN106154168B - The method for estimating charge state of power cell of data-driven - Google Patents
The method for estimating charge state of power cell of data-driven Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 85
- 238000012549 training Methods 0.000 claims abstract description 14
- 239000011159 matrix material Substances 0.000 claims description 25
- 238000005070 sampling Methods 0.000 claims description 15
- 230000006870 function Effects 0.000 claims description 12
- 238000007476 Maximum Likelihood Methods 0.000 claims description 6
- 241001269238 Data Species 0.000 claims description 3
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 230000005611 electricity Effects 0.000 description 6
- 238000012937 correction Methods 0.000 description 5
- 238000013528 artificial neural network Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 238000007599 discharging Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 239000005955 Ferric phosphate Substances 0.000 description 1
- 229910052493 LiFePO4 Inorganic materials 0.000 description 1
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 239000003990 capacitor Substances 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 238000005183 dynamical system Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 229940032958 ferric phosphate Drugs 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- WBJZTOZJJYAKHQ-UHFFFAOYSA-K iron(3+) phosphate Chemical compound [Fe+3].[O-]P([O-])([O-])=O WBJZTOZJJYAKHQ-UHFFFAOYSA-K 0.000 description 1
- 229910000399 iron(III) phosphate Inorganic materials 0.000 description 1
- 229910052744 lithium Inorganic materials 0.000 description 1
- 229910001416 lithium ion Inorganic materials 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 238000011056 performance test Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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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]
- G01R31/3644—Constructional arrangements
- G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
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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
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Abstract
The present invention relates to a kind of method for estimating charge state of power cell of data-driven, comprising steps of (1), off-line training, obtain the Gaussian process model of state-of-charge SOC value of the battery under setting state；(2), On-line Estimation, under battery actual motion state, acquire the data such as end voltage, operating current and the temperature at each moment, state-of-charge SOC value is estimated according to the state-of-charge SOC Gaussian process model that off-line training obtains, and calculates the mean value and variance yields of the state-of-charge SOC value of estimation；Then according to the state-of-charge SOC value of variance yields amendment estimation.Compared with prior art, the beneficial effects of the invention are as follows the power battery SOC estimation methods of a kind of combination lot of experimental data and dynamic model, the mass data obtained in laboratory can be efficiently used, the uncertainty that can be considered system model in actual moving process again, acquire data, the mean value and error of dynamic estimation SOC value, to obtain a high-precision, steady power battery SOC estimation method.
Description
Technical field
The present invention relates to the method for estimating charge state of power cell of data-driven.
Background technique
Its state-of-charge estimation method of for example widely used lithium ion battery of existing power battery is broadly divided into three classes:
The first kind is electric quantity accumulation method, also known as ampere-hour method, estimates electricity by electricity of the battery when being charged and discharged
The state-of-charge (state of charge, SOC) in pond, and SOC is modified according to battery temperature and discharge rate etc., this side
Method is simple, and algorithm is easier to realize, but existing main problem has: (1) parameter being related to is more, if parameter measurement is inaccurate,
Easily cause error；(2) cell degradation and cycle-index are not compensated；(3) by current measurement precision and correction factor etc. because
Element is affected；(4) initial SOC value of given accuracy is needed, this is difficult to provide in practical applications.
Second class is that voltage measurement method passes through according to the relationship between the open-circuit voltage of battery and the depth of discharge of battery
The open-circuit voltage of battery is measured to estimate SOC value, this method is simple, however wants to obtain accurate SOC value in practical applications,
Battery must be stood for a long time, just can determine that SOC value after the voltage is settled, electric current is big ups and downs in actual work,
Therefore less for practical applications such as electric cars, but the criterion as battery charging and discharging cut-off.
Third class is based on non-linear modeling methods such as neural network, fuzzy neural network and Gaussian processes in battery-end electricity
Nonlinear model is established between the input parameter such as pressure, temperature, electric current and SOC value of battery output, according to many experiments curve sum number
It is trained according to system.The main problem of such method is that not account for battery operation be a dynamic process, and is
There are biggish uncertainties for system operation, cannot estimate the uncertainty degree and correction model of dynamical system.
4th class is the method based on battery Type Equivalent Circuit Model, using current source, resistance and capacitor to LiFePO4
Charge-discharge circuit carries out mathematical modeling, and offline or on-line identification model parameter regards battery SOC as such as internal resistance and capacitance
The one-component of internal system state vector, using Extended Kalman filter (extended Kalman filter, EKF) or nothing
The methods of mark Kalman filtering (unscented Kalman filter, UKF) comes dynamic estimation SOC value and new breath, main
Problem is: (1) state equation is generally expressed as linear model by such method, and observational equation is expressed as nonlinear equation, so
And SOC value is influenced by battery pack temperature, charging and discharging currents and using non-linear factors such as times, linear modelling cannot reflect
Its actual physical process；(2) side that the SOC value in observational equation and the non-linear relation of open-circuit voltage are approached with fitting of a polynomial
The relationship of method, end voltage and open-circuit voltage that when work is surveyed is determined further according to equivalent circuit, therefore also relies on system
Modeling accuracy and parameter identification precision.
Summary of the invention
An object of the present invention is in order to overcome the shortcomings in the prior art, to provide a kind of high-precision, steady power
Battery SOC estimation method.
In order to achieve the above object, being achieved through the following technical solutions:
The method for estimating charge state of power cell of data-driven, which is characterized in that comprising steps of
(1) off-line training obtains the Gaussian process model of state-of-charge SOC value of the battery under setting state；
(2) On-line Estimation under battery actual motion state, acquires end voltage, operating current and the temperature at each moment etc.
Data estimate state-of-charge SOC value according to the state-of-charge SOC Gaussian process model that off-line training obtains, and calculate estimation
The mean value and variance yields of state-of-charge SOC value；Then according to the state-of-charge SOC value of variance yields amendment estimation.
Preferably, the state that sets is battery multiplying power or environment temperature, the offline estimating step include (1.a),
Acquire the end voltage v of each sampling instant t of battery at a temperature of different multiplying, varying environmentt, operating current itAnd temperature value
ct, and SOC value estimation is carried out, obtain SOCt, and all data are standardized, so that meeting all of Gaussian Profile
State-of-charge SOC value mean value is 0.
Preferably, described to estimate that specific step is as follows offline:
If the input of system are as follows: ut=[it；ct], wherein itFor the current value of t moment sampling, ctFor the temperature of t moment sampling
Angle value, utFor the input vector of t moment, it is made of the current value of the sampling of t moment and temperature value；
The state variable of system are as follows:
xt=SOCt
Wherein SOCtThe state-of-charge SOC estimation for the t moment demarcated when to estimate offline, xtThe state of expression system becomes
Amount, just by the state-of-charge SOC for the t moment demarcatedtIt constitutes；
The observational variable of system are as follows:
yt=vt
Wherein vtFor the battery terminal voltage value of t moment sampling, ytThe as observational variable of t moment, the electricity sampled by t moment
Pond terminal voltage value vtIt constitutes；
(1.b) is according to the data study SOC dynamic Gaussian process acquired in real time:
The data at k moment constitute matrix before wherein collecting:
I-th row of matrix constitutes a data vector
If the SOC value at k moment obeys Gaussian process, i.e.,
WhereinIndicate that mean value is 0 vector, covariance matrix KgGaussian Profile, covariance matrix KgIt is every
A element is set as:
WhereinRepresenting matrixThe i-th row m column element, parameter θg=(wg1,wg2,wg3,τg0,αg0,αg1,
σg0) it is model parameter to be learned, δijFor delta operator；N sections of continuous k time datas are chosen, the maximum likelihood science of law is utilized
Practise model parameter θg, objective function are as follows:
Its subscript n indicates the n-th segment data, and optimizing this objective function using gradient method can be obtained by model parameter θgEstimate
Evaluation；
Similarly, learn the dynamic Gaussian process of observation model:
Wherein a matrix is constituted according to the data of t moment and the data of preceding k-1:
If the end voltage observation at k moment obeys Gaussian process, i.e.,
Wherein covariance matrix KhEach element be set as:
WhereinRepresenting matrixThe i-th row m column element, matrix parameter θh=(wh1,wh2,wh3,τh0,αh0,
αh1,σh0) it is model parameter to be learned；According to corresponding N sections continuous k time data, maximum likelihood method learning model is utilized
Parameter θh, objective function are as follows:
Equally optimizing this objective function using gradient method can be obtained by model parameter θgEstimated value.
Preferably, the On-line Estimation the following steps are included:
Area update when (2.a) state estimation:
First according to the estimated value of previous moment SOC valueGenerate three Sigma points:
HereinCorresponding predicted value is generated according to trained dynamic model Gaussian process parameter:
Herein
According toThe core of compositionThe first row；
Therefore prior estimate of the available SOC value in t moment:
Wherein
Area update when (2.b) variance
Using the prior estimate of SOC value, the prior estimate of variance is obtained:
Wherein
(2.c) generates the corresponding output estimation value of Sigma point according to trained observation model Gaussian process parameter:
Herein
According toThe core of compositionThe first row.Its output estimation value are as follows:
(2.d) gain estimation
It calculates
Obtain yield value:
The estimated value and estimate of variance of state variable after (2.e) is filtered:
The as estimated value of t moment SOC.
Compared with prior art, the beneficial effects of the invention are as follows the power of a kind of combination lot of experimental data and dynamic model
Battery SOC estimation method can efficiently use the mass data obtained in laboratory and consider system in actual moving process
Unite model, acquire the uncertainties of data, the mean value and error of dynamic estimation SOC value, to obtain high-precision, steady
Power battery SOC estimation method.
Detailed description of the invention
Fig. 1 is the flow chart that the invention patent is implemented.
Specific embodiment
The present invention is described in detail with reference to the accompanying drawings and embodiments:
As shown in Figure 1, the method for estimating charge state of power cell of data-driven, which is characterized in that comprising steps of
(1) off-line training, process are as follows:
(1.a) measures battery in different multiplying, difference using test equipments such as battery general performance test and insulating boxs
At a temperature of each sampling instant t end voltage vt, operating current itWith temperature value ct, and SOC value estimation is carried out, obtain SOCt；Institute
There are data to be standardized, so that obeying the Gaussian Profile that mean value is 0.
If the input of system are as follows: ut=[it；ct], wherein itFor the current value of t moment sampling, ctFor the temperature of t moment sampling
Angle value, utFor the input vector of t moment, it is made of the current value of the sampling of t moment and temperature value.
The state variable of system are as follows: xt=SOCt, wherein SOCtThe state-of-charge SOC for the t moment demarcated when to estimate offline
Estimated value, xtThe state variable of expression system, just by the state-of-charge SOC for the t moment demarcatedtIt constitutes.
The observational variable of system are as follows: yt=vt
Wherein vtFor the battery terminal voltage value of t moment sampling, ytThe as observational variable of t moment, the electricity sampled by t moment
Pond terminal voltage value vtIt constitutes.
(1.b) is according to the data study SOC dynamic Gaussian process acquired in real time:
The data at k moment constitute matrix before wherein collecting:
I-th row of matrix constitutes a data vector
If the SOC value at k moment obeys Gaussian process, i.e.,
Wherein covariance matrix KgEach element be set as:
WhereinRepresenting matrixThe i-th row m column element, parameter θg=(wg1,wg2,wg3,τg0,αg0,αg1,
σg0) it is model parameter to be learned, δijFor delta operator.N sections of continuous k time datas are chosen, the maximum likelihood science of law is utilized
Practise model parameter θg, objective function are as follows:
Its subscript n indicates the n-th segment data, and optimizing this objective function using gradient method can be obtained by model parameter θgEstimate
Evaluation.
Similarly, learn the dynamic Gaussian process of observation model:
Wherein a matrix is constituted according to the data of t moment and the data of preceding k-1:
If the end voltage observation at k moment obeys Gaussian process, i.e.,
Wherein covariance matrix KhEach element be set as:
WhereinRepresenting matrixThe i-th row m column element, parameter θh=(wh1,wh2,wh3,τh0,αh0,αh1,
σh0) it is model parameter to be learned.According to corresponding N sections continuous k time data, maximum likelihood method learning model parameter is utilized
θh, objective function are as follows:
Equally optimizing this objective function using gradient method can be obtained by model parameter θgEstimated value.
(2) On-line Estimation
It is stored in data cell from choosing representational M sections of continuous data in training data first, according to work electricity
Flow valuve and temperature value choose initial value of the immediate one group of data as On-line Estimation link Gaussian process core.SettingVariance yields is determined according to training dataInitial value.It is worth emphasizing that training data has carried out
Standardization, to obtain actual result, the mean value and variance for needing to be used according to the result and standardization of estimation are carried out
Amendment is restored.Next On-line Estimation is carried out using UKF, process is as follows:
Area update when (2.a) state estimation
First according to the estimated value of previous moment SOC valueGenerate three Sigma points:
HereinCorresponding predicted value is generated according to trained dynamic model Gaussian process parameter:
Herein
According toThe core of compositionThe first row.
Therefore prior estimate of the available SOC value in t moment:
Wherein
Area update when (2.b) variance
Using the prior estimate of SOC value, the prior estimate of variance is obtained:
Wherein
(2.c) generates the corresponding output estimation value of Sigma point according to trained observation model Gaussian process parameter:
Herein
According toThe core of compositionThe first row.Its output estimation value are as follows:
(2.d) gain estimation
It calculates
It obtains:
The estimated value and estimate of variance of state variable after (2.e) is filtered:
ThereforeThe as estimated value of t moment SOC.
Since this method is the dynamic of a kind of method of data-driven, the method for being not based on circuit model, therefore battery SOC
The dynamic process of state process and observation data is all modeled by Gaussian process, and the training of Gaussian process model is then that basis is gone through
History data obtain.SOC value estimation method based on data-driven methods such as neural networks is only established between input and output
One fixed Nonlinear Mapping relationship can not carry out dynamic corrections, and side of the present invention according to the observation at a upper moment
Method can carry out dynamic corrections due to establishing a dynamic model according to system operation data, therefore have preferably dynamic
Adaptability.
It for the validity for ensuring the method for the present invention, needs to collect lot of experimental data and carries out model training, and tested
Verifying, and then model parameter is adjusted, obtain optimal Gaussian process model.
There are two advantages by the present invention: (1) reducing the dependence to SOC estimation initial set value.Traditional ampere-hour method needs to mark
Determine the initial value of SOC, is generally realized by deep discharge.It, can be according to historical data since the method is data-driven
Carry out SOC value according to a preliminary estimate, simultaneously because estimation procedure is a dynamic process, can dynamic corrections adjust SOC estimation, thus
Even if can also obtain more accurate SOC estimation in the case where SOC initial value gives inaccurate；(2) evaluated error is small.Pass through
Test to ferric phosphate lithium cell laboratory operating condition and the simulation test according to electric automobile work condition operation, this method
SOC estimation absolute value error is less than 3% in most cases, and the SOC of ampere-hour method estimation absolute value error is generally 5%.
Embodiment in the present invention is only used for that the present invention will be described, and is not construed as limiting the scope of claims limitation,
Other substantially equivalent substitutions that those skilled in that art are contemplated that, are within the scope of the invention.
Claims (2)
1. the method for estimating charge state of power cell of data-driven, which is characterized in that comprising steps of
(1) off-line training obtains the Gaussian process model of state-of-charge SOC value of the battery under setting state；The setting shape
State is battery multiplying power or environment temperature, and the off-line training step includes (1.a), acquisition battery in different multiplying, varying environment
At a temperature of each sampling instant t end voltage vt, operating current itWith temperature value ct, and SOC value estimation is carried out, obtain SOCt,
And all data are standardized, so that all state-of-charge SOC value mean values for meeting Gaussian Profile are 0；
Specific step is as follows for the off-line training:
If the input of system are as follows:
ut=[it；ct]
Wherein itFor the current value of t moment sampling, ctFor the temperature value of t moment sampling, utFor the input vector of t moment, when by t
The current value and temperature value of the sampling at quarter are constituted；
The state variable of system are as follows:
xt=SOCt
Wherein SOCtThe state-of-charge SOC estimation for the t moment demarcated when to estimate offline, xtThe state variable of expression system, just
State-of-charge SOC by the t moment demarcatedtIt constitutes；
The observational variable of system are as follows:
yt=vt
Wherein vtFor t moment sampling battery terminal voltage value,yT is the observational variable of t moment, the battery-end sampled by t moment
Voltage value vtIt constitutes；
(1.b) is according to the data study SOC dynamic Gaussian process acquired in real time:
The data at k moment constitute matrix before wherein collecting:
I-th row of matrix constitutes a data vectorIf the SOC value at k moment obeys Gaussian process, i.e.,
WhereinIndicate that mean value is 0 vector, covariance matrix KgGaussian Profile, covariance matrix KgEach member
Element is set as:
WhereinRepresenting matrixThe i-th row m column element, parameter θg=(wg1,wg2,wg3,τg0,αg0,αg1,σg0) be
Model parameter to be learned, δijFor delta operator；N sections of continuous k time datas are chosen, maximum likelihood method learning model is utilized
Parameter θg, objective function are as follows:
Its subscript n indicates the n-th segment data, and optimizing this objective function using gradient method can be obtained by model parameter θgEstimated value；
Similarly, learn the dynamic Gaussian process of observation model:
Wherein a matrix is constituted according to the data of t moment and the data of preceding k-1:
If the end voltage observation at k moment obeys Gaussian process, i.e.,
Wherein covariance matrix KhEach element be set as:
WhereinRepresenting matrixThe i-th row m column element, matrix parameter θh=(wh1,wh2,wh3,τh0,αh0,αh1,σh0)
For model parameter to be learned；According to corresponding N sections continuous k time data, maximum likelihood method learning model parameter θ is utilizedh,
Its objective function are as follows:
Equally optimizing this objective function using gradient method can be obtained by model parameter θgEstimated value.
(2) On-line Estimation under battery actual motion state, acquires end voltage, operating current and the temperature data at each moment, root
State-of-charge SOC value is estimated according to the state-of-charge SOC Gaussian process model that off-line training obtains, and calculates the state-of-charge of estimation
The mean value and variance yields of SOC value；Then according to the state-of-charge SOC value of variance yields amendment estimation.
2. the method for estimating charge state of power cell of data-driven according to claim 1, which is characterized in that it is described
Line estimation the following steps are included:
Area update when (2.a) state estimation:
First according to the estimated value of previous moment SOC valueGenerate three Sigma points:
HereinCorresponding predicted value is generated according to trained dynamic model Gaussian process parameter:
Herein
According toThe core of compositionThe first row；
Therefore prior estimate of the available SOC value in t moment:
Wherein
Area update when (2.b) variance
Using the prior estimate of SOC value, the prior estimate of variance is obtained:
Wherein
(2.c) generates the corresponding output estimation value of Sigma point according to trained observation model Gaussian process parameter:
Herein
According toThe core of compositionThe first row.Its output estimation value are as follows:
(2.d) gain estimation
It calculates
Obtain yield value:
The estimated value and estimate of variance of state variable after (2.e) is filtered:
The as estimated value of t moment SOC.
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| CN108318823B (en) * | 2017-12-28 | 2020-06-02 | 上海交通大学 | Lithium battery state of charge estimation method based on noise tracking |
| CN108732508B (en) * | 2018-05-23 | 2020-10-09 | 北京航空航天大学 | A real-time estimation method of lithium-ion battery capacity |
| JP7106362B2 (en) * | 2018-06-15 | 2022-07-26 | 大和製罐株式会社 | Storage battery charge/discharge curve estimation device and charge/discharge curve estimation method |
| JP7082603B2 (en) * | 2019-12-25 | 2022-06-08 | 本田技研工業株式会社 | Machine learning device, machine learning method, charge rate estimation device, and charge rate estimation system |
| CN113466723B (en) * | 2020-03-31 | 2022-09-09 | 比亚迪股份有限公司 | Method and apparatus for determining battery state of charge, battery management system |
| CN113933725B (en) * | 2021-09-08 | 2023-09-12 | 深圳大学 | Method for determining state of charge of power battery based on data driving |
| CN115113069B (en) * | 2021-12-24 | 2024-12-27 | 长城汽车股份有限公司 | Battery SOC estimation method and related device |
| CN115290980B (en) * | 2022-07-04 | 2025-05-30 | 岚图汽车科技有限公司 | A method and system for online monitoring of internal resistance of electric vehicle power battery |
| CN115015786B (en) * | 2022-07-21 | 2025-02-11 | 上海洛轲智能科技有限公司 | Power battery charge state determination method, device and electronic equipment |
| CN115079012B (en) * | 2022-07-22 | 2022-11-29 | 羿动新能源科技有限公司 | Characteristic data extraction system and method for big data prediction battery SOC |
| CN118665269B (en) * | 2024-07-17 | 2025-09-16 | 重庆君歌电子科技有限公司 | New energy automobile battery management system based on battery state matching alarm scheme |
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