CN108804764A - A kind of aging of lithium battery trend forecasting method based on extreme learning machine - Google Patents

A kind of aging of lithium battery trend forecasting method based on extreme learning machine Download PDF

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CN108804764A
CN108804764A CN201810427737.XA CN201810427737A CN108804764A CN 108804764 A CN108804764 A CN 108804764A CN 201810427737 A CN201810427737 A CN 201810427737A CN 108804764 A CN108804764 A CN 108804764A
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刘震
梅文娟
程玉华
余宜璐
黄建国
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University of Electronic Science and Technology of China
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Abstract

The invention discloses a kind of aging of lithium battery trend forecasting method based on extreme learning machine, original time series Accurate Model of this method limit of utilization learning machine to the lithium battery charging voltage of acquisition, using Volterra series models as the input layer of extreme learning machine model, simultaneously, to improve the accuracy of cell degradation data model, the hidden layer neuron with higher precision of prediction is generated by genetic algorithm in the structure forecast model stage, aging of lithium battery trend is predicted by the prediction model of lithium battery, the results show, this method has good estimated performance, precision is high.

Description

A kind of aging of lithium battery trend forecasting method based on extreme learning machine
Technical field
The invention belongs to battery life predicting technical fields, more specifically, are related to a kind of based on extreme learning machine Aging of lithium battery trend forecasting method.
Background technology
With the gradually popularization of the new energy vehicles such as electric vehicle, lithium ion battery is as main " green energy resource " It has received widespread attention.Lithium battery due to it is light-weight, adaptable, environmentally protective the advantages that, become the weight in new energy traffic Want the energy.However lithium battery is in use, with the increase of charge and discharge number, the capacity of battery can constantly decay, and work as drop When as low as below some threshold value, battery is up to end of life.Therefore, during the use of battery, to cell degradation data Trend prediction is carried out, its health status is understood in due course, knows life information in advance, to be effectively prevented from since the energy content of battery declines The safety accident for moving back initiation, is of great significance.
Currently, common battery life predicting method is divided into two classes：The physics of failure (PhysicsofFailure, PoF) mould Type and data-driven method (DataDriven).Failure physical model needs largely about lithium ion battery material characteristic and failure The information of mechanism, but sensor technology common at present is difficult to measure the electrochemical reaction of inside battery.Data-driven method is logical The training that monitoring cell degradation data carry out prediction model is crossed, the implicit information between input and output is excavated, to not The decline for carrying out energy carries out trend prediction.Compared with physical failure modelling, data-driven method need not specifically study failure Mechanism is realized simply, therefore has obtained universal application.But current data-driven fado is agreed with now using analytic modell analytical model There is sample data, since cell performance degradation rule is complicated, non-linear relation is apparent, the analytic modell analytical model found by such methods And realistic model can have certain deviation, and the precision of life prediction result is caused to reduce.
Invention content
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of aging of lithium battery based on extreme learning machine Trend forecasting method, this method limit of utilization learning machine accurately build the original time series of the lithium battery charging voltage of acquisition Mould, using Volterra series models as the input layer of extreme learning machine model, meanwhile, to improve cell degradation data model Accuracy generates the hidden layer neuron with higher precision of prediction by genetic algorithm in the structure forecast model stage, passes through The prediction model of lithium battery predicts that aging of lithium battery trend, the results show, this method have good estimated performance, precision It is high.
For achieving the above object, a kind of aging of lithium battery trend forecasting method based on extreme learning machine of the present invention, Specifically include following steps：
(1) original time series of lithium battery charging voltage are acquired；
(2) phase space reconfiguration is carried out to the original time series of acquisition to correspond to minimum entropy rate using optimal entropy rate method Delay time and Embedded dimensions as optimal phase space model of delay time and Embedded dimensions, obtain phase space model；
(3) according to phase space Construction of A Model Volterra series models, composition sequence is obtained, and using composition sequence as defeated Enter sequence, the list entries is divided into learning data set, correction data set and test data set；
(4) it using the learning data set as the input layer of extreme learning machine model, is selected according to maximum relation degree principle Each optimal neuron, and each optimal neuron is added to the hidden layer of the extreme learning machine model, construct initial hidden layer；
(5) the extreme learning machine model established by initial hidden layer using the correction data set re -training is based on Cp Criterion determines the neuron number of final hidden layer, to obtain initial predicted model；
(6) learning data set and correction data set are combined as to the input layer of the initial predicted model, passed through Training determines the hidden layer of the initial predicted model again, obtains final prediction model；
(7) test data set is input to the final prediction model and carries out prediction aging of lithium battery trend.
Advantageous effect of the present invention：Terseness, the height of the accuracy and extreme learning machine of present invention combination Volterra models The characteristics of robustness, this method limit of utilization learning machine method is to time series Accurate Model, with Volterra series models Input layer of the composition sequence as extreme learning machine model, while having by genetic algorithm generation in the structure forecast model stage The hidden layer neuron of higher precision of prediction further increases the precision of sequence prediction.The simulation experiment result show set forth herein Method on precision of prediction more existing several typical prediction techniques have higher promotion, and tectonic model is more succinct, Accurately, efficiently.
In order to better illustrate technical solution of the present invention, following improvement can be also done：
Further, further include using the minimum angle Return Law in the Volterra series models of construction in the step (3) Parameter screened.
Advantageous effect using above-mentioned further scheme is：So that the cell degradation Data Tendency Forecast Based model established is more Accurately.
Further, the step (4) specifically includes：
(41) weights and deviation for waiting for each neuron in scavenger of hidden layer are generated at random；
(42) using the learning data set as the input layer of extreme learning machine model, calculating waits for each neuron in scavenger Output valve, and calculate the degree of correlation of current predictive residual error corresponding with real output value；
(43) it selects and is added as optimal neuron, and by optimal neuron with each maximum neuron of the output valve degree of correlation In current hidden layer, and update the output layer weights and desired output of extreme learning machine model；
(44) according to maximum relation degree principle, the weights and deviation of each neuron in scavenger are waited for using genetic algorithm update, It selects with the maximum each neuron of the current desired output degree of correlation as optimal neuron, and institute is added in each optimal neuron The hidden layer for stating extreme learning machine model constructs initial hidden layer.
Advantageous effect using above-mentioned further scheme is：Select the neuron for generating optimal hidden layer.
Further, the C of the step (5)pCriterion is：
Wherein, i is i-th of neuron, RSSPFor assessment prediction model for the precision of prediction of correction data set, definition For：
Wherein, j is the number of data intensive data, tjFor the voltage actual value at jth moment, yjLearn for the jth moment limit The output valve of model；
MSEfullIt is precision of prediction value of the learning data set under the initial predicted model comprising whole neurons, l is school The parameter number of correction data；
By minimum CpThe corresponding extreme learning machine model of value is as initial predicted model.
Advantageous effect using above-mentioned further scheme is：Generate the hidden layer with minimum neuron.
Description of the drawings
Fig. 1 is the prediction technique flow chart of the present invention；
Fig. 2 is the effect that the present invention predicts four groups of lithium battery charging voltage datas of acquisition using prediction technique Figure.
Specific implementation mode
The specific implementation mode of the present invention is described below in conjunction with the accompanying drawings, preferably so as to those skilled in the art Understand the present invention.Requiring particular attention is that in the following description, when known function and the detailed description of design perhaps When can desalinate the main contents of the present invention, these descriptions will be ignored herein.
As shown in Figure 1, the present invention is low to service life of lithium battery prediction result precision in the prior art in order to solve the problems, such as, carry A kind of aging of lithium battery trend forecasting method based on extreme learning machine is gone out, specific steps include：
(1) the original time series X={ x of lithium battery charging voltage are acquired1,x2,...,xN, wherein xiIndicated for the i-th moment Lithium battery charging voltage numerical value；
(2) to the original time series X={ x of acquisition1,x2,...,xNPhase space reconfiguration is carried out, utilize optimal entropy rate side Method calculates delay time T and Embedded dimensions m under optimum state, and using delay time T with Embedded dimensions m as optimal mutually empty Between model delay time ToptWith Embedded dimensions mopt, obtain phase space model Xs；The entropy rate of the phase space tectonic model can determine Justice is：
Wherein, H (x, m, τ) is micro- entropy of present construction phase space sequence,<H(x,m,τ)>For the average value of micro- entropy, ρjFor Euclidean distance between j-th of delay vector and nearest neighbor point, n=N- (m-1) τ, and CeFor Euler's constant, value is 0.5772.When entropy rate reaches minimum, corresponding phase space model is optimal solution, and according to its corresponding parameter, construction is mutually empty Between model be：
Xs={ xt,xt-τ,xt-2τ,...,xt-(m-1)τ}
Wherein, XsFor the phase space model established according to delay time T and sequence dimension m, xt-kτFor original time series The voltage value at t-k τ moment, k=0,1,2 ... (m-1),.
(3) according to phase space model, Volterra series models are constructed, this method is using Second-Order Volterra series Model：
Wherein, Xv,iFor Second-Order Volterra series model, xi-kτIt is initiation sequence in the voltage value at i-k τ moment, k=0, 1,2 ... (m-1), xi-pτxi-qτFor the quadratic term of Volterra series, p=0,1,2 ... (m-1), q=, 1,2 ... (m- 1)。
It, can be to the parameter in model into traveling to make the cell degradation Data Tendency Forecast Based model established more accurate The screening of one step.This method screens the parameter in the Volterra series models of construction using the minimum angle Return Law, will For composition sequence after screening as list entries, list entries is divided into learning data set, correction data set and test by the present invention Three parts of data set.
(4) it using the learning data set as the input layer of extreme learning machine model, is selected according to maximum relation degree principle Each optimal neuron, and each optimal neuron is added to the hidden layer of the extreme learning machine model, initial hidden layer is constructed, It specifically includes：
(41) generate hidden layer at random waits for the weight w of each neuron and deviation b in scavenger；
(42) using the learning data set as the input layer of extreme learning machine model, calculating waits for each neuron in scavenger Output valve, and the degree of correlation of current predictive residual error corresponding with real output value is calculated, process is：
For waiting for i-th of neuron in scavenger, according to learning data set, the output valve h of the neuron is calculatedi, and Calculate the degree of correlation of current predictive residual error corresponding with real output value：
ci=(ttest-y)Thi
Wherein, y is the predicted value of the extreme learning machine model of construction, ttestFor the real output value of data set, ciIt is i-th The degree of correlation of a hidden layer neuron output valve and real output value.
(43) it selects and is added as optimal neuron, and by optimal neuron with each highest neuron of the output valve degree of correlation The hidden layer currently established, and update the output layer weights and desired output of extreme learning machine model
Wherein, H is the output valve of current hidden layer, H+For the pseudoinverse of H, hoptFor the output valve of optimal neuron, hT optFor The transposition of optimal neuron output, I are unit battle array, ttestFor the real output value of data set；
To reduce the greedy degree of algorithm, avoids omitting the higher neuron of performance, the output of existing model be done as follows Update：
Wherein, y is the output valve of existing model,For desired output, γ is the weighted value for reducing greedy degree, aiIt is the degree of correlation for waiting for i-th of neuron and current residue in scavenger of hidden layer, γ and aiCalculating it is as follows：
Wherein, cmaxFor the maximum relation degree for waiting for each neuron output value and desired output in scavenger of hidden layer, ciFor The degree of correlation for waiting for i-th of neuron output value and desired output in scavenger of hidden layer, aiIt is that hidden layer waits for i-th in scavenger The degree of correlation of a neuron and current residue, hi,normThe normalization output for waiting for scavenger neuron for i-th.
Meanwhile by the output valve h of optimal neuronoptIt is added in the output H of hidden layer.
(44) according to maximum relation degree principle, the weight w and deviation of each neuron in scavenger are waited for using genetic algorithm optimization B is selected with the maximum each neuron of the current desired output degree of correlation as optimal neuron, and each optimal neuron is added The hidden layer of the extreme learning machine model, constructs initial hidden layer.
(5) the extreme learning machine model established by initial hidden layer using the correction data set re -training is based on Cp Criterion determines the neuron number of final hidden layer, to obtain initial predicted model；
Detailed process is：
Optimal hidden layer is chosen based on Cp criterion, for including the hidden layer of preceding i neuron, Cp rule definitions are such as Under：
Wherein, i is i-th of neuron, RSSPFor assessment prediction model for the precision of prediction of correction data set, definition For：
Wherein, j is the number of data intensive data, tjFor the voltage actual value of moment j, yjFor corresponding moment limit study The output valve of model.
MSEfullIt is precision of prediction value of the learning data set under the initial predicted model comprising whole neurons, l is school The parameter number of correction data；
By minimum CpThe corresponding extreme learning machine model of value is as initial predicted model.
(6) learning data set and correction data set are combined as to the input layer of the initial predicted model, passed through Training determines the hidden layer of the initial predicted model again, obtains final prediction model；
β=(HTH)-1HTttrain
Wherein, β is the weights of output layer, and H is the output valve of hidden layer, ttrainFor the real output value of data set.
(7) test data set is input to final prediction model and carries out prediction aging of lithium battery trend.Embodiment 1
Technique effect to illustrate the invention, using a specific application example (aging data of lithium battery) to this hair It is bright to carry out implementation verification.Experiment uses the aging data of certain lithium battery, by 4 groups of lithium batteries under 1.5A constant current source power supply environment It charges, measures the charging voltage of lithium battery, until the voltage of battery reaches 4.2V.Multigroup charging voltage data is acquired to be used for Prediction data.In test, 2/3 measurement data is used as to the training data of model, and 1/3 data are used as test number According to.To weigh and comparing test performance, LevenbergMarquart algorithms, Bayesian regression algorithm, ratio conjugation ladder are utilized Degree, ELM, CSELM and the method for the present invention predict voltage data.
Table 1 is 4 groups of lithium battery charging voltage datas acquiring respectively under existing prediction technique and prediction technique of the present invention Operation result and model parameter.
Table 1
As can be seen from Table 1, prediction technique of the invention has precision of prediction high compared to existing prediction technique, and model is advised The small feature of mould.
(a) in Fig. 2, (b), (c), (d) figure are respectively the effect that 4 groups of lithium battery charging voltage datas are predicted in table 1 Figure.As can be seen from Figure, the prediction model that this method is established can accurately predict actual battery aging curve.Emulation is real Test the result shows that, method proposed in this paper more existing several typical prediction techniques on precision of prediction have higher promotion, And tectonic model is more succinct, accurately, efficiently.
Although the illustrative specific implementation mode of the present invention is described above, in order to the technology of the art Personnel understand the present invention, it should be apparent that the present invention is not limited to the range of specific implementation mode, to the common skill of the art For art personnel, if various change the attached claims limit and determine the spirit and scope of the present invention in, these Variation is it will be apparent that all utilize the innovation and creation of present inventive concept in the row of protection.

Claims (4)

1. a kind of aging of lithium battery trend forecasting method based on extreme learning machine, which is characterized in that include the following steps：
(1) original time series of lithium battery charging voltage are acquired；
(2) phase space reconfiguration is carried out to the original time series of acquisition, using optimal entropy rate method, prolonged minimum entropy rate is corresponding The slow delay time and Embedded dimensions of time and Embedded dimensions as optimal phase space model, obtain phase space model；
(3) according to phase space Construction of A Model Volterra series models, composition sequence is obtained, and using composition sequence as input sequence Row, the list entries are divided into learning data set, correction data set and test data set；
(4) it using the learning data set as the input layer of extreme learning machine model, is selected respectively most according to maximum relation degree principle Excellent neuron, and each optimal neuron is added to the hidden layer of the extreme learning machine model, construct initial hidden layer；
(5) the extreme learning machine model established by initial hidden layer using the correction data set re -training is based on CpCriterion, The neuron number for determining final hidden layer, to obtain initial predicted model；
(6) learning data set and correction data set are combined as to the input layer of the initial predicted model, by again Training determines the hidden layer of the initial predicted model, obtains final prediction model；
(7) test data set is input to the final prediction model and carries out prediction aging of lithium battery trend.
2. the aging of lithium battery trend forecasting method according to claim 1 based on extreme learning machine, which is characterized in that institute State in step (3) further includes being screened to the parameter in the Volterra series models of construction using the minimum angle Return Law.
3. the aging of lithium battery trend forecasting method according to claim 1 based on extreme learning machine, which is characterized in that institute Step (4) is stated to specifically include：
(41) weights and deviation for waiting for each neuron in scavenger of hidden layer are generated at random；
(42) using the learning data set as the input layer of extreme learning machine model, the output for waiting for each neuron in scavenger is calculated Value, and calculate the degree of correlation of current predictive residual error corresponding with real output value；
(43) it selects with each maximum neuron of the output valve degree of correlation as optimal neuron, and optimal neuron is added currently Hidden layer in, and update the output layer weights and desired output of extreme learning machine model；
(44) according to maximum relation degree principle, the weights and deviation of each neuron in scavenger are waited for using genetic algorithm update, are selected With the maximum each neuron of the current desired output degree of correlation as optimal neuron, and the pole is added in each optimal neuron The hidden layer for limiting learning machine model, constructs initial hidden layer.
4. the aging of lithium battery trend forecasting method according to claim 1 based on extreme learning machine, which is characterized in that institute State the C of step (5)pCriterion is：
Wherein, i is i-th of neuron, RSSPFor assessment prediction model for the precision of prediction of correction data set, it is defined as：
Wherein, j is the number of data intensive data, tjThe voltage actual value for being j for number, yjFor the output of limit learning model Value；
MSEfullIt is precision of prediction value of the learning data set under the initial predicted model comprising whole neurons, l is correction data Parameter number；
By minimum CpThe corresponding extreme learning machine model of value is as initial predicted model.
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CN110333462A (en) * 2019-08-08 2019-10-15 首都师范大学 A kind of lithium ion battery life-span prediction method under random discharge environment based on DGWO-ELM
CN110333462B (en) * 2019-08-08 2021-04-30 首都师范大学 DGWO-ELM-based lithium ion battery life prediction method in random discharge environment
CN111950205A (en) * 2020-08-25 2020-11-17 哈尔滨理工大学 Lithium battery SOH prediction method based on FWA optimization extreme learning machine
CN114528772A (en) * 2022-04-20 2022-05-24 深圳市森树强电子科技有限公司 Charger charging prediction method in electromechanical converter control system
CN114528772B (en) * 2022-04-20 2022-07-01 深圳市森树强电子科技有限公司 Charger charging prediction method in electromechanical converter control system
CN114859231A (en) * 2022-04-27 2022-08-05 电子科技大学 Method for predicting remaining life of battery based on wiener process and extreme learning machine
CN114859231B (en) * 2022-04-27 2023-06-09 电子科技大学 Battery remaining life prediction method based on wiener process and extreme learning machine

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Application publication date: 20181113