CN103336248A - Battery degradation state model-based lithium ion battery cycle life prediction method - Google Patents

Battery degradation state model-based lithium ion battery cycle life prediction method Download PDF

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CN103336248A
CN103336248A CN2013103172823A CN201310317282A CN103336248A CN 103336248 A CN103336248 A CN 103336248A CN 2013103172823 A CN2013103172823 A CN 2013103172823A CN 201310317282 A CN201310317282 A CN 201310317282A CN 103336248 A CN103336248 A CN 103336248A
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lithium ion
degradation state
battery
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ion battery
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CN103336248B (en
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彭宇
刘大同
周建宝
王红
彭喜元
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Harbin Institute of Technology
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Abstract

The invention relates to a battery degradation state model-based lithium ion battery cycle life prediction method, aiming at solving the problem of difficulty in modeling existing in the conventional lithium ion battery cycle life prediction process. The battery degradation state model-based lithium ion battery cycle life prediction method comprises the following steps: 1, acquiring battery monitoring data and preprocessing the data; 2, obtaining a battery degradation state model according to battery degradation state model training; and 3, predicting the lithium ion battery cycle life according to the battery degradation state model obtained by the step 2 to obtain a lithium ion battery cycle life value so as to realize battery degradation state model-based lithium ion battery cycle life prediction. The battery degradation state model-based lithium ion battery cycle life prediction method is suitable for the field of batteries.

Description

Lithium ion battery cycle life Forecasting Methodology based on the cell degradation state model
Technical field
The present invention relates to a kind of cell degradation state modeling method, be specifically related to the lithium ion battery cycle life Forecasting Methodology based on the cell degradation state model.
Background technology
Though lithium ion battery is a kind of energy storage and conversion equipment, it is not infinitely to use, and namely its life-span that recycles is limited, and this is because the performance of battery can descend gradually along with the use of battery.In order better to set up the life prediction model of lithium ion battery, at first analyze performance degradation process and the mechanism thereof of lithium ion battery at this.
Lithium ion battery is a kind of rechargeable battery, and it mainly relies on lithium ion to move work between positive pole and negative pole, and the chemomotive force of entire cell comes from the difference of its two electrode chemical potential.Convert electrical energy into chemical energy during charge in batteries and be stored in the battery, then chemical energy is converted to electric energy during discharge and uses for load.Because the reversibility of two kinds of energy conversion, as if the cyclic process that discharges and recharges be unlimited, actually this is not so, this is because in the cyclic process that discharges and recharges, some irreversible processes can take place in inside battery, cause the variation of internal driving, output current etc., cause the decay of battery capacity, thereby influenced the life-span that recycles of battery.
Lithium ion battery is in the cycle charge discharge electric process, and some irreversible chemical reaction processes can take place inside battery, causes the loss of the Li+ that " embeds/deviate from " on the electrode, thereby internal battery impedance is improved, and directly translates into the decline of battery open circuit voltage.
According to the multinomial research of NASA (NASA) with a plurality of laboratories of the subordinate of american energy office, concrete influence factor can be summarized as follows:
A, since the electric current of inside battery by the time be subjected to the influence of internal battery impedance, thereby produce certain heat at inside battery.
B, galvanochemistry cause the impedance on the dynamics, must overcome the obstruction of electrode and electrolytic solution intersection as ion.
C, ion take place to transform the impedance that brings from an electrode movement to the process of another electrode in electrolytic solution.
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 speed, thereby cause the decay of battery capacity.The discharge rate of battery is more big, and electric current is more big, and this moment, polarization was more serious, and when ions diffusion speed did not reach needed speed, the capacity of battery can reduce.Fig. 2 has provided typical battery discharge curve, and different discharge rate is to the influence of battery capacity degeneration.Every curve all is worth corresponding (the supposition temperature conditions is constant) with a discharge rate.
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 more big simultaneously, and battery capacity reduces more soon.
By the elaboration of front as can be known, the non-reversible reaction of inside battery causes battery impedance to increase, and the variation of this impedance is the major parameter of reflection cell degradation state.Utilize the 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 influence of the cell degradation process of Warburg impedance RW is insignificant, so can ignore.The PCoE research centre of NASA great deal of experiment data by analysis finds to have the linear dependence of height between battery capacity and the internal driving, as shown in Figure 3.
By above analysis as can be known, battery capacity is along with the ageing process of battery will be degenerated gradually, be that battery capacity after each charge and discharge cycles can descend gradually, thereby do not reach rated capacity, therefore can utilize the degeneration of battery capacity to recycle the main sign in life-span as battery.
The problem of lithium ion battery life-span degenerative process existence has been carried at present:
(1) historical data is few: can be seen by lithium ion battery degraded data characteristics, battery recycles that the sign amount in life-span---battery capacity has a small amount of historical data, along with charge and discharge process carries out, obtain data gradually, therefore in forecasting process, be difficult to obtain a large amount of historical datas, so the cycle life prediction is a problem that a small amount of historical data of basis is predicted, particularly in actual condition, can't provide a large amount of historical datas to carry out the modeling training, this needs prediction algorithm to be suitable for a small amount of historical data (this content is also verified on 811 present existing data bases).
(2) model is difficult sets up: by the cell degradation process analysis procedure analysis as can be known, because the life-span degenerative process of lithium ion battery is because the electrochemical reaction of inside battery complexity causes, be subjected to the influence of extraneous factor such as temperature, load etc. simultaneously, make the physical model of its degenerative process very complicated, the difficult analysis of failure mechanism, the fixing rule of following of neither one between the remaining life of battery and the influence factor simultaneously, therefore be difficult to one accurately mathematical model represent the life-span degenerative process of this battery clearly.
(3) uncertainty: be directed to the problem of life prediction, only provide predicting the outcome of a single-point, quantity of information is few, is unfavorable for that the decision maker makes the maintenance decision based on the life prediction result, and credible low because single-point predicts the outcome, reference value is little.
Summary of the invention
The present invention is in order to solve the problem that has the modeling difficulty in the existing lithium ion battery cycle life forecasting process.Thereby the lithium ion battery cycle life Forecasting Methodology based on the cell degradation state model has been proposed.
Based on the lithium ion battery cycle life Forecasting Methodology of cell degradation state model, it comprises the steps:
Step 1, gather the battery detection data, and these data are carried out pre-service;
Step 2, obtain the cell degradation state model according to the training of cell degradation state model,
Step 3, obtain the cell degradation state model according to step 2 lithium ion battery cycle life is predicted, obtain lithium ion battery cycle life value, realize the lithium ion battery cycle life prediction based on the cell degradation state model.
The described collection battery detection of step 1 data, and these data are carried out pretreated detailed process be:
Step one by one, gather the battery detection data, described Monitoring Data comprises monitoring time, sparking voltage, electric current and battery capacity;
Step 1 two, according to the battery detection data, carry out pressure drop sequences discharge time such as data pre-service acquisition.
The detailed process of pressure drop sequences discharge time such as step 1 two described acquisitions is:
Step 121, selected constant-current discharge pattern are extracted weekly the Monitoring Data of phase constant-current discharge pattern correspondence;
Step 1 two or two, the scope of pressure drop sparking voltage is set etc.;
Step 1 two or three, to calculate pressure drop such as each poor discharge time, pressure drop sequence discharge time x (n) such as acquisition.
The described detailed process according to cell degradation state model training acquisition cell degradation state model of step 2 is:
Carry out the ESN training: will wait pressure drop sequence discharge time x (n) as importing data, battery capacity y (n) as training set, carry out ESN and train the cell degradation state model that obtains based on ESN, the method of employing cross validation is obtained and is obtained deposit pond scale N, spectral radius sr, input block yardstick (Input Scaling respectively, IS) and input block displacement (Input Shift, IF) optimal value, and adopt the output weights that have the dull quadratic programming equation training ESN that retrains.
Lithium ion battery cycle life Forecasting Methodology based on the cell degradation state model of the present invention adopts the ESN algorithm to realize waiting pressure drop sequence discharge time to set up the model that capacity of lithium ion battery is degenerated by employing, and verified by NASA battery and 3ICP10 battery and validity and the applicability of this method to have reached the simple purpose of modeling in the lithium ion battery cycle life forecasting process.
Description of drawings
Fig. 1 is the lithium ion battery cycle life Forecasting Methodology process flow diagram based on the cell degradation state model of the present invention;
Fig. 2 represents typical battery discharge curve map;
Fig. 3 represents the correlativity curve map between capacity and the 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; Among the figure The actual value of expression battery capacity, The capacity of lithium ion battery estimated value that expression calculates based on the ESN degradation model;
Fig. 7 represents NASA lithium ion battery degeneration modeling error curve map;
Modeling effect curve figure when Fig. 8 represents 30% training data, U represents real battery capacity degenerated curve among the figure, P such as represents to adopt bring battery capacity degenerated curve based on the degradation model estimation of ESN at pressure drop sequence discharge time;
Error curve diagram when Fig. 9 represents 30% training data;
Modeling effect curve figure when Figure 10 represents 50% training data;
Error curve diagram when Figure 11 represents 50% training data;
Modeling effect curve figure when Figure 12 represents 70% training data;
Error curve diagram when Figure 13 represents 70% training data;
Embodiment
Embodiment one, specify present embodiment in conjunction with Fig. 1, the described lithium ion battery cycle life Forecasting Methodology based on the cell degradation state model of present embodiment comprises the steps:
Step 1, gather the battery detection data, and these data are carried out pre-service;
Step 2, obtain the cell degradation state model according to the training of cell degradation state model,
Step 3, obtain the cell degradation state model according to step 2 lithium ion battery cycle life is predicted, obtain lithium ion battery cycle life value, realize the lithium ion battery cycle life prediction based on the cell degradation state model.
The difference of the described lithium ion battery cycle life Forecasting Methodology based on the cell degradation state model of embodiment two, present embodiment and embodiment one is, the described collection battery detection of step 1 data, and these data are carried out pretreated detailed process be:
Step one by one, gather the battery detection data, described Monitoring Data comprises monitoring time, sparking voltage, electric current and battery capacity;
Step 1 two, according to the battery detection data, carry out pressure drop sequences discharge time such as data pre-service acquisition.
Described pressure drop sequence discharge time and the degree of association between the battery capacity sequence of waiting of present embodiment is bigger as can be known by grey correlation analysis, namely can be with capacity such as characterizing battery discharge time such as pressure drop such as grade.
The degree of association between pressure drop sequences discharge time such as the lithium ion battery public data collection that adopts NASA AMES PCoE research centre Idaho National Laboratory of associating USDOE to provide further proves and the battery capacity sequence is bigger.Experiment be under room temperature (25 ℃) to battery charge, discharge and impedance measurement.NASA provides 3 groups of 3 group data sets that obtain under different experimental conditions.First group is 25 to No. 28 batteries, and second group is 5,6, No. 7 and No. 18 batteries of 25 to No. 44 batteries and the 3rd group.The data of battery are that the form with the structure array provides, comprise experiment model (charging, impedance and discharge), environment temperature, monitoring time and Monitoring Data structure, (http://ti.arc.nasa.gov/project/prognostic-data-repository) has detailed introduction to the specifying information of experiment in the website.
Select No. 18 batteries as typical sample, realize the lithium ion battery prognostic experiment (experimentation of other batteries is identical) of degenerating indirectly.According to experiment flow, in the discharge mode of battery, obtain the voltage of lithium ion battery discharge process, select the common electric voltage scope of each discharge process, be 4V-3.6V, then discharge time such as pressure drop such as calculatings grade.Pressure drop sequences discharge time such as the NASA battery that obtains and phase capacity corresponding value is as shown in Figure 4 and Figure 5 weekly.Intuitively we just wait the degenerated curve of pressure drop discharge time and battery remaining power closely similar as can be seen from figure.
The concrete calculation procedure of grey correlation analysis is as follows:
A, definite ordered series of numbers of analyzing:
Battery capacity is as the reference sequence, and pressure drop sequences discharge time such as battery are sequence as a comparison.If reference sequence (claiming auxiliary sequence again) is Y={y (k) | k=1,2, Λ, n}; Compare ordered series of numbers (claiming subsequence again) 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 for comparing the number of ordered series of numbers.
B, compute associations coefficient:
Y (k) and x i(k) correlation coefficient is:
ξ i ( k ) = min i min k | y ( k ) - x i ( k ) | + ρ max i max k | y ( k ) - x i ( k ) | | y ( k ) - x i ( k ) | + ρ max i max k | y ( k ) - x i ( k ) | - - - ( 1 )
Wherein, and ρ ∈ (0, ∞), be called resolution ratio.ρ is more little, and resolving power is more big, 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, gets ρ=0.5 usually.
C, compute associations degree
Because correlation coefficient is comparison ordered series of numbers and reference sequence in each correlation degree value of (being the each point in the curve) constantly, thus more than one of its number, and information too disperses to be not easy to carry out globality relatively.Therefore being necessary the correlation coefficient in each moment (being the each point in the curve) is concentrated is a value, namely asks its mean value, the quantitaes of correlation degree between ordered series of numbers and reference sequence as a comparison, and degree of association ri formula is as follows:
r i = 1 n Σ k = 1 n ξ i ( k ) , k = 1,2 , . . . , n - - - ( 2 )
Calculate the similarity degree between pressure drop sequence discharge time such as battery and the residual capacity according to the step of grey correlation analysis, select battery remaining power as the reference sequence, pressure drop sequences discharge time such as battery are sequence as a comparison.Select ρ=0.5463, obtain degree of association r=0.8154, the scope of the degree of association be (0,1), more represents that near 1 association is more big, so can prove between pressure drop sequence discharge time such as battery and the residual capacity that very high similarity is arranged.
So, by the analysis of NASA battery data, wait the degree of association between pressure reduction time series and the battery capacity sequence bigger, namely can be with waiting pressure drop characterizing battery discharge time capacity.
The difference of the described lithium ion battery cycle life Forecasting Methodology based on the cell degradation state model of embodiment three, present embodiment and embodiment two is that the detailed process of pressure drop sequences discharge time such as step 1 two described acquisitions is:
Step 121, selected constant-current discharge pattern are extracted weekly the Monitoring Data of phase constant-current discharge pattern correspondence;
Step 1 two or two, the scope of pressure drop sparking voltage is set etc.;
Step 1 two or three, to calculate pressure drop such as each poor discharge time, pressure drop sequence discharge time x (n) such as acquisition.
The difference of the described lithium ion battery cycle life Forecasting Methodology based on the cell degradation state model of embodiment four, present embodiment and embodiment one is, the described detailed process that obtains the cell degradation state model according to the training of cell degradation state model of step 2 is:
Carry out the ESN training: will wait pressure drop sequence discharge time x (n) as importing data, battery capacity y (n) as training set, carry out ESN and train the cell degradation state model that obtains based on ESN, the method of employing cross validation is obtained and is obtained deposit pond scale N, spectral radius sr, input block yardstick (Input Scaling respectively, IS) and input block displacement (Input Shift, IF) optimal value, and adopt the output weights that have the dull quadratic programming equation training ESN that retrains.Thereby make the battery capacity estimation value And the error sum of squares minimum between the actual value y (n).
Embodiment five, present embodiment adopt lithium ion battery cycle life Forecasting Methodology based on the cell degradation state model to the modeling of NASA lithium ion battery degenerate state, and modeling process is:
1, prepares the input and output data set.Extract battery capacity y (n) and etc. pressure drop sequence discharge time x (n).Pressure drop sequences discharge time such as battery are as list entries, and Dui Ying residual capacity is as output sequence with it.No. 18 battery data has 132 groups of discharge data Wherein preceding 66 groups of data are as training dataset, and the 66 groups of data in back are as test data set.
2, adopt training dataset The ESN model training is carried out in input, obtain laying in pond scale N, spectral radius sr, input block yardstick (Input Scaling, IS) and the input block displacement (Input Shift IF), thereby obtains degradation model based on ESN.
3, with test data set etc. pressure drop sequence discharge time The input degradation model, the estimating battery capability value { y ~ ( n ) } n = 67 n = 132 .
4, model evaluation.With estimated value With truly Compare the accuracy of analytical model.
Model error: the use root-mean-square error (Root Mean Squared Error, RMSE) as the evaluation index of approaching performance, as shown in Equation (3):
RMSE = Σ i = 1 n ( y ( x i ) - y ~ ( x i ) ) 2 n - - - ( 3 )
2) overall fit effect: adopt R 2The overall fit effect of evaluation function, as shown in Equation (4), in the time of the non-constant of fitting effect of model, 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 appear.
R 2 = 1 - Σ i = 1 n ( y ( x i ) - y ~ ( x i ) ) 2 Σ i = 1 n ( y ( x i ) - y ‾ ( x i ) ) 2 - - - ( 4 )
The degeneration modeling result:
As described in Table 1 according to degradation model result and model evaluation index that above-mentioned modeling process obtains, table 1 expression degradation model result and model evaluation index.
Table 1
The degradation model checking:
4 parameters that obtained based on the lithium ion battery degenerate state model of ESN as shown in table 1, from having obtained the degradation model of lithium ion battery, and by calculating the evaluation index of training process model.Below the accuracy of degeneration modeling is verified, will be waited pressure drop sequence discharge time Bring the cell degradation state model into, by the capability value of model assessment battery, and this estimated value and actual value be analyzed, thus the accuracy of verification model, and it is verified as shown in Figure 6.
What the dotted line of redness was represented is the actual value of battery capacity.The capacity of lithium ion battery estimated value that Fig. 7 obtains for modeling and record graph of errors between the capacity actual value.
Root-mean-square error between calculated capacity estimated value and the actual value and R 2The result is as shown in table 2.Table 2 expression is based on the degradation model checking evaluation index of ESN.
Table 2
In sum, employing waits the capacity that pressure drop sequence discharge time can characterizing battery, and the realization by the ESN algorithm degeneration modeling of battery, Fig. 6 has verified the accuracy of cell degradation state model, model error is between-0.04~0.12 as can be seen from Figure 7.The root-mean-square error that provides from table 2 and the overall fit effect of model also can show the validity of degenerate state modeling method.
Embodiment six, present embodiment adopt lithium ion battery cycle life Forecasting Methodology based on the 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 makes up corresponding data set.
In second step, choose the training set row degradation modeling of going forward side by side.When degenerating modeling, adopt the training set of different length to carry out model training with the adaptability of checking proposition method.Adopt respectively total data 30%, 50% and 70% as training data, remaining data are as test data.
In the 3rd step, the evaluation index that obtains 4 parameters of ESN and degradation model and model by training is as shown in table 3.Table 3 representation model parameter and evaluation index
Table 3
The 4th step, modelling verification, the modelling verification effect is shown in Fig. 8 to 13.
As can be known, the modeling curve can be followed the tracks of real degradation in capacity curve on the whole from Fig. 8 to Figure 13.Right figure has provided the graph of errors between modeling capacity and the true capacity.Table 4 expression arranges and model evaluation based on MONESN residual capacity Prediction Parameters, errors table between table 5 expression modeling capacity and the true value.
Table 4
Table 5
Table 5(is continuous)
From Fig. 8 to Figure 11 and table 4 and table 5 as can be known, wait pressure drop sequence discharge time can be used for characterizing the degenerate state of lithium ion battery, and adopt the ESN algorithm to realize lithium ion battery degeneration modeling, experiment show validity and the adaptability of this method.And along with increasing of training data, the degradation in capacity information that can access from training data is just more many, and the modeling effect is more good, and the capacity that calculates by model is more near actual value.

Claims (4)

1. based on the lithium ion battery cycle life Forecasting Methodology of cell degradation state model, it is characterized in that: it comprises the steps:
Step 1, gather the battery detection data, and these data are carried out pre-service;
Step 2, obtain the cell degradation state model according to the training of cell degradation state model,
Step 3, obtain the cell degradation state model according to step 2 lithium ion battery cycle life is predicted, obtain lithium ion battery cycle life value, realize the lithium ion battery cycle life prediction based on the cell degradation state model.
2. the lithium ion battery cycle life Forecasting Methodology based on the cell degradation state model according to claim 1 is characterized in that: the described collection battery detection of step 1 data, and these data are carried out pretreated detailed process be:
Step one by one, gather the battery detection data, described Monitoring Data comprises monitoring time, sparking voltage, electric current and battery capacity;
Step 1 two, according to the battery detection data, carry out pressure drop sequences discharge time such as data pre-service acquisition.
3. the lithium ion battery cycle life Forecasting Methodology based on the cell degradation state model according to claim 2, it is characterized in that: the detailed process of pressure drop sequences discharge time such as step 1 two described acquisitions is:
Step 121, selected constant-current discharge pattern are extracted weekly the Monitoring Data of phase constant-current discharge pattern correspondence;
Step 1 two or two, the scope of pressure drop sparking voltage is set etc.;
Step 1 two or three, to calculate pressure drop such as each poor discharge time, pressure drop sequence discharge time x (n) such as acquisition.
4. the lithium ion battery cycle life Forecasting Methodology based on the cell degradation state model according to claim 1 is characterized in that: the described detailed process that obtains the cell degradation state model according to the training of cell degradation state model of step 2 is:
Carry out the ESN training: will wait pressure drop sequence discharge time x (n) as importing data, battery capacity y (n) as training set, carry out ESN and train the cell degradation state model that obtains based on ESN, adopt the method for cross validation to obtain the optimal value of obtaining 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 that has dull constraint.
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