CN106777786A - A kind of lithium ion battery SOC estimation method - Google Patents
A kind of lithium ion battery SOC estimation method Download PDFInfo
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- CN106777786A CN106777786A CN201710021905.0A CN201710021905A CN106777786A CN 106777786 A CN106777786 A CN 106777786A CN 201710021905 A CN201710021905 A CN 201710021905A CN 106777786 A CN106777786 A CN 106777786A
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- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/043—Architecture, e.g. interconnection topology based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS]
Abstract
The invention discloses a kind of lithium ion battery SOC estimation method, by the analysis of experimental data to lithium ion battery rebound voltage under high/low temperature, the lithium ion battery residual capacity forecast model of Adaptive Neuro-fuzzy Inference is set up；It is determined that rebound voltage and environment temperature are the input of lithium ion battery residual capacity forecast model, lithium ion battery residual capacity is output；On MATLAB platforms, the lithium ion battery residual capacity forecast model of Adaptive Neuro-fuzzy Inference is trained by with experimental data, is verified；Gained model is used for the residual capacity prediction of different battery packs and is verified.The present invention is adapted to, by the model insertion to current existing other battery management systems, accurately to estimate the residual capacity of battery, prevent from overcharging, cross and put, and improves battery, reduces user cost, and it has huge economic benefit and social benefit.
Description
Technical field
The present invention relates to a kind of lithium ion battery SOC estimation method, more particularly to one kind can accurately estimate lithium-ion electric
The method of the residual capacity in pond.
Background technology
Lithium ion battery obtains popularity in various fields and uses, because the complexity of course of reaction is, it is necessary to perfect
Battery management system, wherein the accurately estimation to SOC is most important.SOC in lithium ion battery applications accurately estimates
Problem, vast researcher is done a lot of work.Effectively increase the security and energy utilization efficiency during use.State
Inside and outside research institution and colleges and universities expand substantial amounts of research and experiment for the SOC estimations of lithium ion battery.The weight of battery management
It is exactly Residual capacity prediction to want link, and Residual capacity prediction to be always industry compare one of stubborn problem, become at present
One of bottleneck of new energy popularization and application.
Because SOC is inside battery state parameter, it is impossible to obtained by direct measurement, only by measuring voltage x current etc.
Relevant parameter estimated indirectly, it is existing such as：Ampere-hour method, open circuit voltage method, Kalman filtering method, neural network etc..Due to
Influenceed by factors such as charging and discharging currents, voltage, temperature, internal resistance, declines, unified standard is there is no in SOC estimation problems
Method.The above method cuts both ways.
1) ampere-hour method
Ampere-hour method is also called current integration method, is to carry out time integral to electric current to obtain the electricity that battery has been released, so
The method for obtaining SOC divided by total capacity with dump energy afterwards.Ampere-hour method is obtained because calculating process is simple and easy to apply in engineering
Extensive use.But the method depends on the total capacity of SOC, and this value can become with factors such as temperature and degree of agings
Change, thus produce error.
2) open circuit voltage method
Open circuit voltage method is the SOC-OCV curves obtained according to experiment, and inquiring about curve chart after measurement open-circuit voltage obtains
The method of SOC.Found Rahimi-Eichi H of university et al. and use variable coefficient piecewise linearity in North Carolina in 2014
Approximatioss, characterizes battery OCV-SOC unintentional nonlinearity relations；Truchot C of Hawaii, America university in 2014 et al.
The SOC estimations of battery are realized based on OCV=f (SOC) immediate reasoning；Xing YJ of City University of Hong Kong in 2014 et al. are logical
Structure OCV-SOC- thermometers are crossed, realizes considering the SOC estimations of temperature influence.Zhang CP of Beijing Jiaotong University in 2015 etc.
People realizes SOC estimations based on SOC-OCV relations with reference to KF, and precision is 3%.But, open-circuit voltage needs to stand for a long time
To obtain, it is impossible to meet the demand of on-line checking.
3) Kalman filtering method
Kalman filtering method is applied than wide in SOC estimations at present, is built by System Discrimination and parameter Estimation
Mould, can preserve good precision in estimation process, solve the problems, such as initial value error correction, and have very strong suppression to noise
Effect, can make the optimal estimation in minimum variance meaning, it is adaptable to which current fluctuation is more violent to the state of dynamical system
Occasion.The Gregory L.Plett in the U.S. teach and were devoted to Kalman filtering and its expansion algorithm is estimated in SOC earliest in 2006
Application study in terms of calculation, by using lithium ion battery as complete dynamical system, using SOC as system state variables, profit
Updated with observation variable value, constantly correct SOC value.Based on the battery equivalent model set up, can be realized using Kalman filtering
SOC estimates that the influence of decrease polarity effect improves estimation precision.But the method is relatively strong, it is necessary to build to battery model dependence
Founding high-precision model could accurately estimate SOC, while design matrix computing, amount of calculation is larger.
4) neural network
It is the method with looking for the development of artificial intelligence (AI) this new branch of science to get up, the method relies on a large amount of samples
Notebook data training obtains preferable estimation precision.It is advantageous that being independent of mathematical models, only it is given only study and trains
Complicated, uncertain, nonlinear system modeling and process problem can be just solved, but it is defeated in design neural network model and input
When going out parameter, there is relative technical difficulty, it is considered to which inconsiderate or setting is improper, many estimation results influences are larger.
Although being estimated residual capacity thering is precedent before based on ANFIS, such as：
Min, Qi Jianjia more than University of Electronic Science and Technology, Xu Wenjin of Xiamen University etc., are to set up plumbic acid around internal resistance detection to store
The ANFIS models of battery, its input is：The internal resistance of cell, discharge voltage, temperature, discharge current.Because the internal resistance of cell is smaller, survey
The accuracy requirement of amount is very high, and excessive |input paramete can reduce the engineering of system.
The research of Central China University of Science and Technology king happiness, is detected by the electrochemical properties and conventional SOC of analyzing lead-acid accumulator
Method, it is determined that the key parameters of influence SOC, and propose a kind of battery SOC based on adaptive neural network-fuzzy inference system
Identification Method.Residual capacity prediction is carried out using the density of electrolyte of lead-acid accumulator.Joined using homemade silver/silver sulfate
Than electrode positive and negative electrode potential is obtained in battery charging and discharging experiment.Using BP networks, radial basis function network (RBF) and adaptive
Answer Neural-fuzzy system (ANFIS) carries out parameter identification to density of electrolyte.
Applicant's early stage in terms of residual capacity research is done a lot of work.Applicant in 2011 begins one's study battery rebound
Voltage estimate residual capacity, and applied for national patent, deliver series of articles.But due to the relation of time, for temperature
The problem brought in estimation problem, the experiment of correlation is not carried out also.
In sum, in lithium ion battery SOC estimation researchs, without comprehensively and effectively solution, based on battery etc.
The multiple parameters evaluation method that effect model purchase is built turns into development trend at present, but must be sought between precision and reduction amount of calculation improving
Optimal balance point is sought, to continue to optimize and improve evaluation method.
The content of the invention
It is an object of the invention to provide a kind of method of accurate estimation lithium ion battery SOC.By setting up SOC with rebound electricity
The neural network model of relation, on the basis of experimental study, is trained and verifies, finally to the model between pressure, temperature
Reach the online accurate estimation of SOC.
Technical scheme is as follows：
A kind of lithium ion battery SOC estimation method, it is characterised in that：By the voltage that knock-oned to lithium ion battery under high/low temperature
Analysis of experimental data, set up the lithium ion battery residual capacity forecast model of Adaptive Neuro-fuzzy Inference；It is determined that returning
It is the input of lithium ion battery residual capacity forecast model to jump voltage and environment temperature, and lithium ion battery residual capacity is output；
On MATLAB platforms, the lithium ion battery residual capacity of Adaptive Neuro-fuzzy Inference is predicted by with experimental data
Model is trained, verifies；Gained model is used for the residual capacity prediction of different battery packs and is verified.
Further, methods described comprises the following steps：
Step 1：Determine that the input quantity of the lithium ion battery residual capacity forecast model to be set up in step 2 is rebound electricity
Pressure, environment temperature, are output as lithium ion battery residual capacity；
Step 2：Based on MATLAB softwares, according to experimental data, lithium ion battery residual capacity forecast model is set up；
Step 3：Based on MATLAB softwares, realistically displayed is carried out under Simulink environment, to verify that model is reliably applicable.
Further, in step 1, determine that the process of input quantity comprises the following steps：
Step 1-1：From rebound voltage, environment temperature and discharge-rate as the lithium-ion electric to be set up in step 2
The pre-selection input quantity of pond residual capacity forecast model；
Step 1-2：By the experiment influence of analysis rebound voltage, environment temperature and discharge-rate to residual capacity respectively；
Step 1-3：Determine that the input quantity of the lithium ion battery residual capacity forecast model to be set up in step 2 is rebound
Voltage, environment temperature.
Further, in step 1-1, big difference test is carried out to temperature, under high temperature, lithium ion battery can be released more
Capacity, therefore the influence of temperature is must take into consideration when residual capacity is estimated；It is continuous with discharge-rate in discharge-rate experiment
Increase, the residual capacity of battery pack is constantly reduced under same depth of discharge, but discharge-rate is affected by a load, in reality
Its mean difference is little in the battery operation of border, therefore does not consider the influence of discharge-rate when residual capacity is estimated.
Further, step 3 comprises the following steps：
Step 3-1：The lithium ion battery residual capacity forecast model set up with step 2 carries out the lithium on each same day experimental day
The SOC value estimation of ion battery；
Step 3-2：The SOC value that model is exported is compared with actual experiment SOC value；
Step 3-3：SOC model predication values are small with actual soc-value deviation, the lithium ion battery residual capacity prediction set up
Model is reliably applicable.
Beneficial effects of the present invention are as follows：
The present invention is adapted in the model insertion to current existing other battery management systems, accurately to estimate battery
Residual capacity, prevents from overcharging, crosses and put, and improves battery, reduces user cost, and it has huge economic benefit and society
Can benefit.
Brief description of the drawings
Fig. 1 is the network structure of model system；
Fig. 2 system construction drawings；
Fig. 3 components of system as directed rule views；
The 20th experimental day battery SOC predictive simulation figure of Fig. 4；
The SOC predicted values of Fig. 5 part Experiments day and desired value figure.
Specific embodiment
The present invention is described in further detail below in conjunction with the accompanying drawings.
(1) basic ideas of design
By the analysis of experimental data of the voltage that knock-ons to lithium ion battery under high/low temperature, establish that adaptive neural network is fuzzy to be pushed away
The lithium ion battery residual capacity forecast model of reason system (ANFIS).It is the defeated of forecasting system that rebound voltage and temperature is determined
Enter, residual capacity is output.On MATLAB platforms, ANFIS models are trained by with substantial amounts of experimental data, school
Test, this model is used for the residual capacity prediction of different battery packs and is verified.Demonstrate the reliability and applicability of the model.
(2) experimental analysis
1) influence to Residual capacity prediction of temperature
Influence of the temperature to battery capacity be it is well known that with the extension in battery applications field, lithium ion battery
Operating ambient temperature difference is huge, changeable.Under low temperature condition, electrochemical reaction speed reduction, lithium ion battery is equal
Under the conditions of residual capacity decline；Temperature is raised can then accelerate chemical reaction velocity, cause and discharge under equal conditions more appearances
Amount.Environment temperature open-circuit voltage OCV has a certain impact, and temperature is lower, and equivalent capability OCV is lower.And the voltage that knock-ons is out
A part for road voltage, therefore, under identical rebound voltage, low temperature can be released than more SOC under the condition of high temperature.
In addition, estimation of the charge-discharge magnification to residual capacity also has a certain impact.When battery is with different multiplying power dischargings
When, the released capacity of battery is different, and discharge current is bigger, and the capacity that can be released is fewer；Discharge current is smaller, relatively
The capacity of releasing is more.Therefore the change of discharge-rate brings certain difficulty to the SOC predictions of secondary cell.And frequently big times
Rate is discharged or is charged, and all will bring a certain degree of damage to secondary cell.
2) description of test and analysis
We conducted the corresponding experiment of lithium ion battery SOC estimations.Experiment is right to study with two groups of Li-ion batteries piles
As, it is in series with three sheet lithium ion batteries per Battery pack group, cell model INCMP58145155N-I, rated voltage
It is 3.7V, rated capacity 10Ah.Specifically experimentation is：Two Battery pack groups are first carried out the depth from 10%-70% daily
Electric discharge, record rebound voltage, and shelves 2 hours, after after voltage recovery, then carries out 0.2C and discharges completely.Whenever depth of discharge sets
The experimental period put terminates, and changes discharge-rate and repeats to test.And two Battery packs are put respectively in high temperature and low temperature environment
Under, the influence with observing environment temperature to battery remaining power.Experiment uses battery comprehensive parameters ATE, model
It is BTS-M300A/12V.
Detection explanation to residual capacity：We carry out residual capacity detection using real surplus capacity calculation methods.Example
As depth of discharge 50% (after releasing 5Ah with regard to 10Ah batteries, is stood 2 hours, discharged with 0.2C again, measure releasing capacity)
I. due to the minor variations of temperature, the influence for residual capacity is simultaneously little, therefore we are especially carried out to temperature
Big difference test.Table 1 is the actual releasing value of residual capacity of the temperature difference more than 10 DEG C.Find out from experimental data, under high temperature,
More capacity (excluding the influence to cell damage) can be released.Therefore the shadow of temperature is must take into consideration when residual capacity is estimated
Ring.
Influence of the temperature of table 1 to residual capacity
| Temperature (DEG C) | Residual capacity (Ah) |
| 10 | 4.242778 |
| 27 | 4.370833 |
| 37 | 4.451944 |
Ii. the analysis of discharge-rate
As described above, the size of discharge-rate will produce influence to the capacity of battery.In order to observe discharge-rate pair
The influence of lithium ion battery residual capacity, is provided with the experiment under different discharge-rates under same experimental conditions in experiment, wherein
1# battery section experimental datas are as shown in table 2.
Residual capacity under the different discharge-rate difference depth of discharges of table 2
From Table 2, it can be seen that being continuously increased with discharge-rate, the remaining appearance of battery pack under same depth of discharge
Amount is reduced constantly.It can be seen that, discharge-rate affects the capacity of battery really.But, discharge-rate is affected by a load,
Its average value basic difference is little in actual battery operation.
In sum, the input quantity in following modeling has determined as：Rebound voltage, environment temperature.
(3) setting up neural network model carries out SOC estimations
General fuzzy inference system needs substantial amounts of expertise knowledge to be used as fuzzy rule base, its subjective colo(u)r ratio
Relatively strong, for a research object not understood completely also, this is undoubtedly a big fatal defect.And neutral net is to ring
Border change adapt to have extremely strong self-learning capability, in terms of modeling have black box mode of learning the characteristics of.Other neutral net
Not only clear data can be processed, fuzzy message can also be processed.Using this ability, it is possible to achieve fuzzy rule is pushed away with fuzzy
Reason function, the function of even realizing whole fuzzy controls.In fuzzy system, the two kinds of typical generation being typically used
Table：Mamdani types, Sugeno types.The combination of both fuzzy reasoning and neutral net compensate for respective deficiency well, utilize
Neural network learning generate fuzzy inference system required for objective rule base, and possess adaptive ability, ANFIS is very
It is suitably applied in the modeling of the complication system for also failing to grasp.Fuzzy inference system (Fuzzy in model of the present invention
Inference System, FIS) to be set up using adaptive neural network fuzzy system, the system is according to according to a large amount of reliable experiments
Data set up FIS so that the fuzzy system of structure no longer manually induction and conclusion, relatively more objective.
1) the step of Fuzzy Neural Networks
1. in command window (command window) inputs：fx>>anfisedit；Open anfis editor:
Untitled (anfis editing machines)；
2. " import data " is clicked at interface；Open the file to be selected；
3. the cxcel files chosen, selection which part (n rows m row) are opened；" Matrix " is selected above interface；Point
Hit " import selection " button；
4. in workspace windows on the right side of main interface, the partial document data is renamed；Repeat more than work respectively according to
Secondary input data a1, b1, c1；
5. in anfis editor:In untitled (anfis editing machines), training, testing are selected respectively,
" worksp " is selected in cheching, from, data are input into respectively；
6. " Grid partition " is selected, clicks on " Generate FIS " button；" in Number of MFS, selection
Membership function level ", the selection membership function type in " MF type ", such as：gaussmf；
7. in anfis editor:Visible #of in " Anfis into " column of untitled (anfis editing machines) right sides
input 2、#of output 1；
8. right-hand button " structure " in editing machine is clicked on, that is, establishes ANFIS models.
The step of according to above in connection with Fuzzy Neural Networks, according to experimental data, establish based on rebound voltage with
The SOC forecast models of temperature.
2) emulation and verification
MATLAB provides ANFIS tool box functions and graphical edit tool, can use order line and graphic user interface
(GUI) two methods construction FIS.Concrete operation method is as follows：
(1) in MATLAB (R2013a), there is provided the graphical interface editing device of Adaptive Neuro-fuzzy Inference,
It is that may bring up that " anfiseditor " is keyed in main window.Herein using experimental data as the training of model system, checksum test
Sample.The network structure of the model system set up is as shown in figure 1, mode input is rebound voltage and temperature.System architecture Fig. 2
Shown, the part rule view of system is as shown in Figure 3.Final system model is trained to by experimental data.
After system model is established, realistically displayed is carried out under Simulink environment, to verify that model is reliably applicable.Will
In 20th the rebound voltage and temperature value input model of experimental day, the simulation result for obtaining is as shown in Figure 4.As can be known from Fig. 5,
The SOC value of the battery is 0.6093.It is 0.6071 that inquiry same day experimental data obtains SOC value, and it is only 0.0022 to be differed with 0.6093.
The SOC of the lithium ion battery on each same day experimental day is first carried out with model with above method, then model is exported
SOC value is compared with actual experiment SOC value, and both comparings of part Experiment day are as shown in Figure 5.As can be seen from the figure
SOC model predication values and the basic very little of actual soc-value deviation.It can be seen that, the lithium ion battery SOC forecast models set up herein can
By being applicable.
Presently preferred embodiments of the present invention is the foregoing is only, is not intended to limit the invention.It is all in essence of the invention
Any modification, equivalent and improvement made within god and principle etc., should be included within the scope of the present invention.
Claims (5)
1. a kind of lithium ion battery SOC estimation method, it is characterised in that：By the voltage that knock-oned to lithium ion battery under high/low temperature
Analysis of experimental data, sets up the lithium ion battery residual capacity forecast model of Adaptive Neuro-fuzzy Inference；It is determined that rebound
Voltage and environment temperature are the input of lithium ion battery residual capacity forecast model, and lithium ion battery residual capacity is output；
On MATLAB platforms, mould is predicted to the lithium ion battery residual capacity of Adaptive Neuro-fuzzy Inference by with experimental data
Type is trained, verifies；Gained model is used for the residual capacity prediction of different battery packs and is verified.
2. a kind of lithium ion battery SOC estimation method according to claim 1, it is characterised in that：Comprise the following steps：
Step 1：Determine the input quantity of the lithium ion battery residual capacity forecast model to be set up in step 2 for rebound voltage,
Environment temperature, is output as lithium ion battery residual capacity；
Step 2：Based on MATLAB softwares, according to experimental data, lithium ion battery residual capacity forecast model is set up；
Step 3：Based on MATLAB softwares, realistically displayed is carried out under Simulink environment, to verify that model is reliably applicable.
3. a kind of lithium ion battery SOC estimation method according to claim 1, it is characterised in that：In step 1, determine defeated
The process for entering amount comprises the following steps：
Step 1-1：It is surplus as the lithium ion battery to be set up in step 2 from rebound voltage, environment temperature and discharge-rate
The pre-selection input quantity of covolume amount forecast model；
Step 1-2：By the experiment influence of analysis rebound voltage, environment temperature and discharge-rate to residual capacity respectively；
Step 1-3：Determine that the input quantity of the lithium ion battery residual capacity forecast model to be set up in step 2 is rebound electricity
Pressure, environment temperature.
4. a kind of lithium ion battery SOC estimation method according to claim 3, it is characterised in that：In step 1-1, to temperature
Degree carries out big difference test, and under high temperature, lithium ion battery can release more capacity, therefore necessary when residual capacity is estimated
Consider the influence of temperature；In discharge-rate experiment, with being continuously increased for discharge-rate, the battery pack under same depth of discharge
Residual capacity is constantly reduced, but discharge-rate is affected by a load, and its mean difference is little in actual battery operation,
Therefore the influence of discharge-rate is not considered when residual capacity is estimated.
5. a kind of lithium ion battery SOC estimation method according to claim 1, it is characterised in that：Step 3 includes following step
Suddenly：
Step 3-1：The lithium ion battery residual capacity forecast model set up with step 2 carries out the lithium ion on each same day experimental day
The SOC value estimation of battery；
Step 3-2：The SOC value that model is exported is compared with actual experiment SOC value；
Step 3-3：SOC model predication values are small with actual soc-value deviation, the lithium ion battery residual capacity forecast model set up
It is reliable to be applicable.
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| CN110188376A (en) * | 2019-04-12 | 2019-08-30 | 汉腾汽车有限公司 | A kind of power battery for hybrid electric vehicle initial quantity of electricity algorithm |
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