CN103336248B - Based on the cycle life of lithium ion battery Forecasting Methodology of cell degradation state model - Google Patents
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Abstract
Based on the cycle life of lithium ion battery Forecasting Methodology of cell degradation state model, the present invention relates to the cycle life of lithium ion battery Forecasting Methodology based on cell degradation state model.The problem of modeling difficulty is there is in existing cycle life of lithium ion battery forecasting process in it in order to solve.The step comprised based on the cycle life of lithium ion battery Forecasting Methodology of cell degradation state model is: step one, collection battery detection data, and carries out pre-service to these data; Step 2, obtain cell degradation state model according to the training of cell degradation state model, step 3, obtain cell degradation state model according to step 2 cycle life of lithium ion battery is predicted, obtain cycle life of lithium ion battery value, the cycle life of lithium ion battery realized based on cell degradation state model is predicted.The present invention is applicable to field of batteries.
Description
Technical field
The present invention relates to a kind of cell degradation state modeling method, be specifically related to the cycle life of lithium ion battery Forecasting Methodology based on cell degradation state model.
Background technology
Although lithium ion battery is a kind of stored energy and conversion equipment, it is not infinitely to use, and namely its service life cycle is limited, this is because the performance of battery can decline gradually along with the use of battery.In order to better set up the Life Prediction Model of lithium ion battery, first analyze performance degradation process and the mechanism thereof of lithium ion battery at this.
Lithium ion battery is a kind of rechargeable battery, it mainly rely on lithium ion between a positive electrode and a negative electrode movement carry out work, the chemomotive force of whole battery comes from the difference of its two electrode chemical potential.Convert electrical energy into chemical energy during charge in batteries to store in the battery, during electric discharge, then chemical energy is converted to electric energy for load.Due to the reversibility of two kinds of energy conversion, it is unlimited for seeming the cyclic process of discharge and recharge, actually this is not so, this is because in the cyclic process of discharge and recharge, some irreversible processes can be there are in inside battery, cause the change of internal driving, output current etc., cause the decay of battery capacity, thus have impact on the service life cycle of battery.
Lithium ion battery is in cycle charge discharge electric process, and some irreversible chemical reaction processes can occur inside battery, cause the loss of the Li+ that electrode " embeds/deviates from ", thus internal battery impedance is improved, and directly translate into the decline of battery open circuit voltage.
According to the multinomial research of NASA (NASA) with multiple laboratories of american energy office subordinate, concrete influence factor can be summarized as follows:
A, due to inside battery electric current by time be subject to the impact of internal battery impedance, thus produce certain heat at inside battery.
B, galvanochemistry cause impedance kinetically, as ion must overcome the obstruction of electrode and electrolytic solution intersection.
C, ion in the electrolytic solution from an electrode movement to the process of another electrode occur to transform the impedance that brings.
Except the decline of open-circuit voltage, the impedance variation that the non-reversible reaction of inside battery causes also will cause the reduction of battery discharge rates, thus cause the decay of battery capacity.The discharge rate of battery is larger, and electric current is larger, now polarizes more serious, and when ion diffuse speed does not reach required speed, the capacity of battery can reduce.Fig. 2 gives typical battery discharge curve, and the impact that different discharge rate is degenerated on battery capacity.Every bar curve all with one discharge rate value corresponding (assuming that temperature conditions is constant).
As can see from Figure 2, the capacity of battery reduces gradually along with the increase in battery charging and discharging cycle, and discharge rate is larger simultaneously, and battery capacity reduces faster.
From elaboration above, the non-reversible reaction of inside battery causes battery impedance to increase, and the change of this impedance is the major parameter of reflection cell degradation state.Utilize resistive impedance spectrometry to record internal resistance of cell impedance and comprise charge transfer resistance RCT, Warburg impedance RW and bath resistance RE, wherein the impact of Warburg impedance RW on cell degradation process is insignificant, therefore can ignore.The experimental data that the PCoE research centre of NASA is a large amount of by analysis finds to have the linear dependence of height between battery capacity and internal driving, as shown in Figure 3.
From analyzing above, battery capacity will be degenerated gradually along with the ageing process of battery, namely the battery capacity after each charge and discharge cycles can decline gradually, thus does not reach rated capacity, and the degeneration of battery capacity therefore can be utilized as the main in circulating battery serviceable life.
Current lithium ion battery life deterioration process Problems existing has been carried:
(1) historical data is few: can be seen by lithium ion battery degraded data feature, circulating battery serviceable life token state---battery capacity has a small amount of historical data, along with charge and discharge process carries out, obtain data gradually, therefore be difficult to obtain a large amount of historical data in forecasting process, so Cycle life prediction is the problem that an a small amount of historical data of basis carries out predicting, particularly in actual condition, a large amount of historical datas cannot be provided to carry out modeling training, this needs prediction algorithm to be suitable for a small amount of historical data (this content is also verified on 811 current existing data bases).
(2) model difficulty is set up: known by cell degradation process analysis procedure analysis, life deterioration process due to lithium ion battery is because the electrochemical reaction of inside battery complexity causes, be subject to extraneous factor as the impact of temperature, load etc. simultaneously, make that the physical model of its degenerative process is very complicated, the difficult analysis of failure mechanism, the rule followed that simultaneously between the remaining life of battery and influence factor, neither one is fixed, be therefore difficult to one accurately mathematical model represent the life deterioration process of this battery clearly.
(3) uncertain: the problem being directed to life prediction, only provide predicting the outcome of a single-point, quantity of information is few, and be unfavorable for that decision maker makes the maintenance decision based on life prediction result, because the credibility that single-point predicts the outcome is low, reference value is little.
Summary of the invention
The problem of modeling difficulty is there is in existing cycle life of lithium ion battery forecasting process in the present invention in order to solve.Thus the cycle life of lithium ion battery Forecasting Methodology proposed based on cell degradation state model.
Based on the cycle life of lithium ion battery Forecasting Methodology of cell degradation state model, it comprises the steps:
Step one, collection battery detection data, and pre-service is carried out to these data;
Step 2, obtain cell degradation state model according to the training of cell degradation state model,
Step 3, obtain cell degradation state model predict cycle life of lithium ion battery according to step 2, obtain cycle life of lithium ion battery value, the cycle life of lithium ion battery realized based on cell degradation state model is predicted.
Collection battery detection data described in step one, and pretreated detailed process is carried out to these data be:
Step one by one, gather battery detection data, described Monitoring Data comprises monitoring time, sparking voltage, electric current and battery capacity;
Step one two, according to battery detection data, carry out pressure drop sequences discharge time such as data prediction acquisition.
The detailed process of pressure drop sequences discharge time such as the acquisition described in step one two is:
Step one 21, selected constant-current discharge pattern, extract the Monitoring Data that each cycle constant-current discharge pattern is corresponding;
Step one two or two, the scope of pressure drop sparking voltage is set etc.;
Step one two or three, to calculate the pressure drop such as each poor for discharge time, and acquisition waits pressure drop sequence x discharge time (n).
The detailed process obtaining cell degradation state model according to the training of cell degradation state model described in step 2 is:
Carry out ESN training: pressure drop sequence x discharge time (n) will be waited as input data, battery capacity y (n) as training set, carry out ESN and train the cell degradation state model obtained based on ESN, adopt the method for cross validation to obtain and obtain deposit pond scale N, spectral radius sr, input block yardstick (Input Scaling respectively, and input block displacement (InputShift IS), IF) optimal value, and the output weights adopting the quadratic programming equation training ESN with Monotone constraint.
Cycle life of lithium ion battery Forecasting Methodology based on cell degradation state model of the present invention adopts ESN algorithm realization to wait pressure drop sequence discharge time to set up the model of capacity of lithium ion battery degeneration by employing, and the efficiency and applicability of the method is demonstrated by NASA battery and 3ICP10 battery, reach the simple object of modeling in cycle life of lithium ion battery forecasting process.
Accompanying drawing explanation
Fig. 1 is the cycle life of lithium ion battery Forecasting Methodology process flow diagram based on cell degradation state model of the present invention;
Fig. 2 represents typical battery discharge curve figure;
Fig. 3 represents the correlation curve figure between capacity and impedance parameter;
Fig. 4 represents pressure drop sequence chart discharge time such as NASA lithium ion battery;
Fig. 5 represents NASA capacity of lithium ion battery figure;
Fig. 6 represents the degeneration modeling checking curve map based on the NASA lithium ion battery of ESN; In figure
represent the actual value of battery capacity,
represent the capacity of lithium ion battery estimated value calculated based on ESN degradation model;
Fig. 7 represents NASA lithium ion battery degeneration modeling error curve map;
Fig. 8 represents modeling effect curve figure during 30% training data, and in figure, U represents real battery capacity degenerated curve, and P represents the battery capacity degenerated curve that employing waits pressure drop sequence discharge time to bring the degradation model based on ESN into estimate;
Fig. 9 represents error curve diagram during 30% training data;
Figure 10 represents modeling effect curve figure during 50% training data;
Figure 11 represents error curve diagram during 50% training data;
Figure 12 represents modeling effect curve figure during 70% training data;
Figure 13 represents error curve diagram during 70% training data;
Embodiment
Embodiment one, composition graphs 1 illustrate present embodiment, and the cycle life of lithium ion battery Forecasting Methodology based on cell degradation state model described in present embodiment comprises the steps:
Step one, collection battery detection data, and pre-service is carried out to these data;
Step 2, obtain cell degradation state model according to the training of cell degradation state model,
Step 3, obtain cell degradation state model predict cycle life of lithium ion battery according to step 2, obtain cycle life of lithium ion battery value, the cycle life of lithium ion battery realized based on cell degradation state model is predicted.
The difference of the cycle life of lithium ion battery Forecasting Methodology based on cell degradation state model described in embodiment two, present embodiment and embodiment one is, collection battery detection data described in step one, and pretreated detailed process is carried out to these data be:
Step one by one, gather battery detection data, described Monitoring Data comprises monitoring time, sparking voltage, electric current and battery capacity;
Step one two, according to battery detection data, carry out pressure drop sequences discharge time such as data prediction acquisition.
Comparatively large by the degree of association between pressure drop sequence discharge time such as grade described in the known present embodiment of grey correlation analysis and battery capacity sequence, namely can with waiting pressure drop characterizing battery discharge time capacity.
It is larger that the lithium ion battery public data collection adopting NASA AMES PCoE research centre Idaho National Laboratory of associating USDOE to provide proves to wait the degree of association between pressure drop sequence discharge time and battery capacity sequence further.Experiment is charged to battery under room temperature (25 DEG C), discharges and impedance measurement.NASA provides 3 groups of 3 group data sets obtained under different experimental conditions.First group is 25 to No. 28 batteries, and second group is 5,6 of 25 to No. 44 batteries and the 3rd group, No. 7 and No. 18 batteries.The data of battery provide with the form of Array for structural body, comprise experiment model (charging, impedance and electric discharge), environment temperature, monitoring time and Monitoring Data structure, the specifying information of experiment has detailed introduction on website (http://ti.arc.nasa.gov/project/prognostic-data-repository).
Select No. 18 batteries as typical sample, realize lithium ion battery indirect degradation prediction experiment (experimentation of other batteries is identical).According to experiment flow, obtaining the voltage of lithium ion battery discharge process in the discharge mode of battery, select the common electric voltage scope of each discharge process, is 4V-3.6V, and then calculating waits pressure drop discharge time.Pressure drop sequences discharge time such as the NASA battery obtained and capability value corresponding to each cycle are as shown in Figure 4 and Figure 5.It is directly perceived that from figure, we just can to find out etc. that the degenerated curve of pressure drop discharge time and battery remaining power is closely similar.
The concrete calculation procedure of grey correlation analysis is as follows:
A, determine analyze ordered series of numbers:
Battery capacity is as reference sequence, and pressure drop sequences discharge time such as battery are as comparative sequences.If reference sequence (also known as auxiliary sequence) is Y={y (k) | k=1,2, Λ, n}; Relatively ordered series of numbers (also known as subsequence) X
i={ x
i(k) | k=1,2, Λ, n}, i=1,2, Λ, m.N is ordered series of numbers length, and m is the number comparing ordered series of numbers.
B, compute associations coefficient:
Y (k) and x
ik the correlation coefficient of () is:
Wherein, ρ ∈ (0, ∞), is called resolution ratio.ρ is less, and resolving power is larger, and the interval of general ρ is (0,1), and concrete value can depend on the circumstances.When ρ≤0.5463, resolving power is best, usually gets ρ=0.5.
C, compute associations degree
Because correlation coefficient compares ordered series of numbers and the reference sequence correlation degree value at each moment (each point namely in curve), so more than one of its number, and information is too disperseed to be not easy to carry out globality and is compared.Therefore be necessary that being concentrated by the correlation coefficient in each moment (each point namely in curve) is a value, namely asks its mean value, represent as comparing the quantity of correlation degree between ordered series of numbers and reference sequence, degree of association ri formula is as follows:
Calculate the similarity degree between pressure drop sequence discharge time and residual capacity such as battery according to the step of grey correlation analysis, select battery remaining power as with reference to sequence, pressure drop sequences discharge time such as battery are as comparative sequences.Select ρ=0.5463, obtain degree of association r=0.8154, the scope of the degree of association is (0,1), more represents that association is larger, so can prove that battery etc. has very high similarity between pressure drop sequence discharge time and residual capacity close to 1.
So by the analysis of NASA battery data, the degree of association between equal-pressure-difference time series and battery capacity sequence is comparatively large, namely can with waiting pressure drop characterizing battery discharge time capacity.
The difference of the cycle life of lithium ion battery Forecasting Methodology based on cell degradation state model described in embodiment three, present embodiment and embodiment two is, the detailed process of pressure drop sequences discharge time such as the acquisition described in step one two is:
Step one 21, selected constant-current discharge pattern, extract the Monitoring Data that each cycle constant-current discharge pattern is corresponding;
Step one two or two, the scope of pressure drop sparking voltage is set etc.;
Step one two or three, to calculate the pressure drop such as each poor for discharge time, and acquisition waits pressure drop sequence x discharge time (n).
The difference of the cycle life of lithium ion battery Forecasting Methodology based on cell degradation state model described in embodiment four, present embodiment and embodiment one is, the detailed process obtaining cell degradation state model according to the training of cell degradation state model described in step 2 is:
Carry out ESN training: pressure drop sequence x discharge time (n) will be waited as input data, battery capacity y (n) as training set, carry out ESN and train the cell degradation state model obtained based on ESN, adopt the method for cross validation to obtain and obtain deposit pond scale N, spectral radius sr, input block yardstick (Input Scaling respectively, and input block displacement (InputShift IS), IF) optimal value, and the output weights adopting the quadratic programming equation training ESN with Monotone constraint.Thus make battery capacity estimation value
and the error sum of squares between actual value y (n) is minimum.
Embodiment five, present embodiment adopt based on the cycle life of lithium ion battery Forecasting Methodology of cell degradation state model to the modeling of NASA lithium ion battery degenerate state, and modeling process is:
1, input and output data set is prepared.Extract battery capacity y (n) and wait pressure drop sequence x discharge time (n).Pressure drop sequences discharge time such as battery are as list entries, and residual capacity corresponding is with it as output sequence.No. 18 battery datas have 132 groups of discharge data
wherein front 66 groups of data are as training dataset, and rear 66 groups of data are as test data set.
2, training dataset is adopted
eSN model training is carried out in input, obtains deposit pond scale N, spectral radius sr, input block yardstick (Input Scaling, IS) and input block displacement (Input Shift, IF), thus obtains the degradation model based on ESN.
3, by test data set etc. pressure drop sequence discharge time
input degradation model, estimating battery capability value
4, model evaluation.By estimated value
with truly
contrast, the accuracy of analytical model.
Model error: use root-mean-square error (Root Mean Squared Error, RMSE) as the evaluation index of approximation capability, as shown in Equation (3):
2) overall fit effect: adopt R
2the overall fit effect of evaluation function, as shown in Equation (4), when the fitting effect of model non-constant time, the quadratic sum of the error of model output valve and actual value can be greater than the error sum of squares of the average of model output valve and actual value, i.e. R
2negative value may be there is.
Degeneration modeling result:
The degradation model result obtained according to above-mentioned modeling process and model-evaluation index as described in Table 1, table 1 represents degradation model result and model-evaluation index.
Table 1
Degradation model is verified:
4 parameters obtaining lithium ion battery degenerate state model based on ESN as shown in table 1, from the degradation model obtaining lithium ion battery, and by calculating the evaluation index of training process model.Below the accuracy of degeneration modeling is verified, pressure drop sequence discharge time will be waited
bring cell degradation state model into, by the capability value of model assessment battery, and this estimated value and actual value are analyzed, thus the accuracy of verification model, its checking is as shown in Figure 6.
The actual value for battery capacity that red dotted line represents.Fig. 7 is the capacity of lithium ion battery estimated value that modeling obtains and the graph of errors recorded between capacity actual value.
Root-mean-square error between calculated capacity estimated value and actual value and R
2result is as shown in table 2.Table 2 represents the degradation model checking evaluation index based on ESN.
Table 2
In sum, employing waits pressure drop sequence discharge time can the capacity of characterizing battery, and by the degeneration modeling achieving battery of ESN algorithm, Fig. 6 demonstrates the accuracy of cell degradation state model, and model error is between-0.04 ~ 0.12 as can be seen from Figure 7.The overall fit effect of the root-mean-square error provided from table 2 and model also can show the validity of degenerate state modeling method.
Embodiment six, present embodiment adopt based on the cycle life of lithium ion battery Forecasting Methodology of cell degradation state model to the modeling of 3ICP10 cell degradation state, and described 3ICP10 battery data length is 10402, and modeling process is:
The first step, waits pressure drop sequence discharge time and battery capacity interpolation, and builds corresponding data set.
Second step, chooses training set and to go forward side by side row degradation modeling.When carrying out degeneration modeling, the training set of different length is adopted to carry out model training to verify the adaptability of put forward the methods.Adopt 30%, 50% and 70% of total data respectively as training data, remaining data are as test data.
3rd step, by training the evaluation index of 4 parameters and degradation model and the model obtaining ESN as shown in table 3.Table 3 represents model parameter and evaluation index
Table 3
4th step, modelling verification, modelling verification effect is as shown in Fig. 8 to 13.
Known from Fig. 8 to Figure 13, modeling curve can follow the tracks of real degradation in capacity curve on the whole.Right figure gives the graph of errors between modeling capacity and true capacity.Table 4 represents that table 5 represents errors table between modeling capacity and true value based on the setting of MONESN residual capacity Prediction Parameters and model evaluation.
Table 4
Table 5
Table 5(continues)
From Fig. 8 to Figure 11 and table 4 and table 5, wait pressure drop sequence discharge time to may be used for characterizing the degenerate state of lithium ion battery, and adopt ESN algorithm realization lithium ion battery degeneration modeling, the validity of experiment show the method and adaptability.And increasing along with training data, the degradation in capacity information that can obtain from training data is more, and modeling effect is better, and the capacity calculated by model is more close to actual value.
Claims (3)
1., based on the cycle life of lithium ion battery Forecasting Methodology of cell degradation state model, it is characterized in that: it comprises the steps:
Step one, collection battery detection data, and pre-service is carried out to these data;
Step 2, obtain cell degradation state model according to the training of cell degradation state model,
Step 3, obtain cell degradation state model predict cycle life of lithium ion battery according to step 2, obtain cycle life of lithium ion battery value, the cycle life of lithium ion battery realized based on cell degradation state model is predicted;
Collection battery detection data described in step one, and pretreated detailed process is carried out to these data be:
Step one by one, gather battery detection data, described Monitoring Data comprises monitoring time, sparking voltage, electric current and battery capacity;
Step one two, according to battery detection data, carry out pressure drop sequences discharge time such as data prediction acquisition.
2. the cycle life of lithium ion battery Forecasting Methodology based on cell degradation state model according to claim 1, is characterized in that: the detailed process of pressure drop sequences discharge time such as the acquisition described in step one two is:
Step one 21, selected constant-current discharge pattern, extract the Monitoring Data that each cycle constant-current discharge pattern is corresponding;
Step one two or two, the scope of pressure drop sparking voltage is set etc.;
Step one two or three, to calculate the pressure drop such as each poor for discharge time, and acquisition waits pressure drop sequence x discharge time (n).
3. the cycle life of lithium ion battery Forecasting Methodology based on cell degradation state model according to claim 1, is characterized in that: the detailed process obtaining cell degradation state model according to the training of cell degradation state model described in step 2 is:
Carry out ESN training: pressure drop sequence x discharge time (n) will be waited as input data, battery capacity y (n) as training set, carry out ESN and train the cell degradation state model obtained based on ESN, adopt the method for cross validation to obtain the optimal value of deposit pond scale, spectral radius, input block yardstick and input block displacement respectively, and adopt the output weights of the quadratic programming equation training ESN with Monotone constraint.
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