CN110161425B - Method for predicting remaining service life based on lithium battery degradation stage division - Google Patents
Method for predicting remaining service life based on lithium battery degradation stage division Download PDFInfo
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Abstract
The invention discloses a method for predicting the remaining service life based on lithium battery degradation stage division, which comprises the following steps: establishing a capacity prediction model according to the relationship between the health indexes of the training set and the capacity value of the battery; predicting the capacity value of the test set; dividing the capacity values of the training set and the testing set into 3 stages, and establishing a classification model; establishing an RUL prediction model according to the health indexes and the corresponding residual service life in the third stage; collecting health indexes of a to-be-predicted residual service life sample, and acquiring a predicted capacity value; inputting the predicted capacity value into a classification model to obtain a sample class; and if the current working life is in the third stage, inputting the health index of the sample into the RUL prediction model to obtain the predicted value of the remaining service life. The method accurately predicts the remaining service life of the battery in the third stage according to the classification model and the RUL prediction model, and only predicts the remaining service life of the battery in the third stage, so that the calculation amount is small.
Description
Technical Field
The invention belongs to the field of prediction of the remaining service life of a lithium battery, and particularly relates to a method for predicting the remaining service life based on lithium battery degradation stage division.
Background
The lithium battery has the advantages of high energy density, low self-discharge rate, wide working temperature range, high charging and discharging speed and the like, and is widely applied to the fields of traffic power supplies, electric energy storage supplies, mobile communication power supplies and the like. However, in the recycling process of the lithium battery, the performance of the lithium battery can be degraded due to the internal physical structure of the battery, the external environmental conditions, the improper use mode and the like, for example, the capacity value can be reduced along with the increase of the number of use cycles, so that the establishment of a degradation model of the battery and the prediction of the residual service life of the battery have important significance.
During the use of the battery, due to complex environmental changes or irregular operating conditions, the degradation of the battery may exhibit non-monotonicity, such as slow degradation stage, accelerated degradation stage, or imminent failure stage. If the service life of the data in the slow degradation stage is predicted, the service life is not necessary to be too large, so that if the degradation stage of the battery is divided, the data in the imminent failure stage is selected to predict the residual service life, the prediction cost can be saved, the better prediction precision can be achieved, and the method has important significance in practical engineering application.
The conventional lithium battery residual life prediction method is model-based and data-driven. The model-based prediction method needs to satisfy a prerequisite that the model of the predicted object can be represented and known by a mathematical model, and then the failure mechanism of the predicted object is analyzed according to the known mathematical model. The method generally describes the equipment degradation process based on random processes such as a Wiener process and a Gamma process, but in practical application, it is difficult to establish an accurate mathematical model for different complex systems, so that the most common residual life prediction method at present is a data-driven method. The main points of the method are as follows: regression model-based methods such as PLS (Partial Least Square); an autoregressive model (AR); artificial Neural Networks (ANN); a support vector machine (SVR); correlation vector machines (RVMs), etc. The method does not need to acquire a mathematical model of equipment degradation, and can realize accurate life prediction only through data measured in the process.
At present, a method for dividing the degradation process of a lithium battery into stages comprises the degradation rate based on health factors and the equidistant division of full-period samples in the degradation process. However, when the degradation rates of the selected health factors are not obviously different in the degradation process, the partitioning method based on the degradation rates of the health factors is failed; the equal-interval classification method based on the full-period samples is still slightly insufficient in classification accuracy. The accurate division of the degradation stage directly influences the precision of the residual life prediction, so that the method for researching the division of the degradation stage has important significance.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a method for predicting the remaining service life based on the degradation stage division of a lithium battery, and aims to solve the problem of low prediction precision caused by the fact that the degradation stage division is not carried out in the existing lithium battery remaining life prediction.
In order to achieve the above object, the present invention provides a method for predicting remaining service life based on lithium battery degradation stage division, comprising the steps of:
(1) establishing a capacity prediction model according to the relation between the health index of the training set with known data and the battery capacity value;
(2) extracting health indexes in a test set with unknown capacity values as input of a capacity prediction model, and predicting the capacity values of the test set;
(3) dividing the capacity values of the training set and the testing set into 3 stages according to the capacity attenuation trend, and attaching class labels to the data of the 3 stages to establish a classification model;
(4) establishing an RUL prediction model according to the health indexes and the corresponding residual service life in the third stage;
(5) acquiring health indexes of the remaining service life to be predicted, and acquiring a predicted capacity value;
(6) and inputting the predicted capacity value into a classification model to judge whether the battery is in the 3 rd stage of the battery degradation process, if so, inputting the health index into an RUL prediction model to obtain a predicted value of the remaining service life, and otherwise, outputting the prediction range of the remaining service life.
Preferably, the health index of the lithium ion battery training set in the step (1) comprises discharge voltage from v1To v2Time difference of interval, discharge time from t1To t2A value of temperature change within the interval;
preferably, in step (1), the health index of the training set is used as an input of a Support Vector Regression (SVR) method, the capacity of the battery in the training set is output, a capacity prediction model is built, and a kernel function of the support vector regression method adopts a radial basis kernel function (RBF).
Preferably, the basis for dividing the battery capacity value into 3 stages according to the capacity fading trend in the step (3) is as follows:
if it is
The battery capacity is in the first phase;
if it is
The battery capacity is in the second stage;
if it is
The battery capacity is in the third stage;
wherein cpt represents a battery capacity value; cptmaxRepresents the maximum capacity value of the battery; cptminRepresenting the minimum capacity value of the battery.
Labeling the battery capacity value of the first stage with a label of '1'; labeling the battery capacity value of the second stage with a label of '2'; labeling the battery capacity value of the third stage with a label of '3';
preferably, the remaining useful life is;
wherein N isiDenotes the number of i-th discharge cycles, NEOLRepresenting the discharge cycle times of the lithium battery at the time of degradation to failure; RUL is the remaining useful life.
Preferably, the health index of the third stage is used as the input of a Support Vector Regression (SVR) method, the remaining service life is output, and an RUL prediction model is established; the kernel function of the support vector regression method adopts a radial basis kernel function (RBF).
Preferably, the health indicators of the training set include: the time difference of the discharge voltage from 4.2V to 3.7V and the temperature change value of the discharge time from 1000s to 2000 s.
Through the technical scheme, compared with the prior art, the invention has the following beneficial effects:
(1) the capacity values of a training set and a testing set are divided into 3 stages according to the capacity attenuation trend, the three stages correspond to the three stages of the lithium battery degradation process, category labels are attached to the three stages to establish a classification model, and an RUL prediction model is established based on the lithium battery in the third stage; the residual service life of the battery can be accurately predicted according to the classification model and the RUL prediction model.
(2) When the service life of the battery is predicted, firstly, a health index prediction capacity value is collected, whether the battery is in a third degradation stage or not is judged according to the prediction capacity value, if the battery is in the third degradation stage, a prediction value of the remaining service life is obtained through an RUL prediction model, otherwise, only a prediction range of the remaining service life is given, and compared with the prior art that the battery degradation stages are not divided, the method disclosed by the invention is small in calculation amount and easy to realize.
Drawings
FIG. 1 is a flow chart of a method for predicting remaining service life based on lithium battery degradation staging provided by the present invention;
FIG. 2(a) is a schematic diagram of the change in discharge voltage over time for several cycles provided by the present invention;
FIG. 2(b) is a schematic diagram of the discharge temperature over time for several cycles provided by the present invention;
FIG. 3(a) is a first health indicator extracted from a discharge voltage curve provided by the present invention;
FIG. 3(b) is a second health indicator extracted from the discharge temperature curve provided by the present invention;
FIG. 4(a) is a schematic diagram of a predicted capacity value of a training set provided by the present invention;
FIG. 4(b) is a schematic diagram of a capacity prediction value of a test set provided by the present invention;
FIG. 5(a) is a staged category label partitioned according to health index projected capacity values provided by the present invention;
FIG. 5(b) is a diagram of the classification effect of the training set obtained by the support vector classification method for the prediction capacity value provided by the present invention;
FIG. 5(c) is a diagram of the classification effect of the test set obtained by the support vector classification method for the predicted capacity value provided by the present invention;
FIG. 6(a) is a schematic diagram of a RUL prediction model established based on the third stage health indicator of FIG. 5(a) according to the present invention;
FIG. 6(b) is a diagram illustrating the effect of the third stage health indicator verification RUL prediction model provided by the present invention based on FIG. 5 (c).
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides a method for predicting the remaining service life based on lithium battery degradation stage division, which comprises the following steps:
(1) establishing a capacity prediction model according to the relation between the health index of the training set with known data and the battery capacity value;
(2) extracting health indexes in a test set with unknown capacity values as input of a capacity prediction model, and predicting the capacity values of the test set;
(3) dividing the capacity values of the training set and the testing set into 3 stages according to the capacity attenuation trend, and attaching class labels to the data of the 3 stages to establish a classification model;
(4) establishing an RUL prediction model according to the health indexes and the corresponding residual service life in the third stage;
(5) acquiring health indexes of the remaining service life to be predicted, and acquiring a predicted capacity value;
(6) and inputting the predicted capacity value into a classification model, judging whether the predicted capacity value is in the 3 rd stage, if so, inputting the health index into an RUL prediction model, and acquiring a predicted value of the residual service life, otherwise, outputting a prediction range of the residual service life.
Preferably, the health index of the training set in the step (1) comprises a discharge voltage from v1To v2Time difference of interval, discharge time from t1To t2A value of temperature change within the interval; the set formed by the health indexes is X belongs to RI×JWherein, I is a discharge cycle training sample, J is the number of health indexes acquired by each discharge cycle training sample, and J is 2;
defining v of discharge voltage according to actual demand1And v2And discharge time t1And t2；
In the invention, the health indexes of the training set comprise a time difference value of a discharge voltage interval from 4.2v to 3.7v, and the calculation formula is as follows: t is ti_DtD_EVI＝tU_3.7-tU_4.2I is 1,2,3 … n; the temperature variation value in the interval of the discharge time from 1000s to 2000s is calculated by the formula Ti_DTD_EtI＝Tt_max-Tt_minI is 1,2,3 … n; wherein, tU_3.7The time when the discharge voltage was 3.7 v; t is tU_4.2The time when the discharge voltage was 4.2 v; t is ti_DtD_EVIThe time difference of the discharge voltage from 4.2v to 3.7v interval; t ist_maxThe discharge time is 2000 s; t ist_minThe discharge time is 1000 s; t isi_DTD_EtIIs the temperature variation value in the interval of the discharge time from 1000s to 2000 s; i denotes the number of cycles.
Preferably, the method for establishing the capacity prediction model in step (1) is as follows: and taking the health index of the training set as the input of the support vector regression method, taking the capacity of the battery in the training set as the output of the support vector regression method, and training to obtain a capacity prediction model.
Preferably, the basis for dividing the battery capacity value into 3 stages according to the capacity fading trend in the step (3) is as follows:
if it is
The battery capacity is in the first phase;
if it is
The battery capacity is in the second stage;
if it is
The battery capacity is in the third stage;
wherein cpt represents a battery capacity value; cptmaxRepresents the maximum capacity value of the battery; cptminRepresenting the minimum capacity value of the battery.
Preferably, the remaining service life in step (4) is:
wherein N isiDenotes the number of i-th discharge cycles, NEOLRepresenting the discharge cycle times of the lithium battery at the time of degradation to failure; RUL is the remaining useful life.
Preferably, the method for establishing the RUL prediction model in step (4) is as follows:
and taking the health index of the third stage as the input of the support vector regression method, taking the residual service life of the third stage as the output of the support vector regression method, and training to obtain the RUL prediction model.
Preferably, the kernel function of the support vector regression method employs a radial basis kernel function.
Preferably, the battery capacity value of the first stage is labeled with "1"; labeling the battery capacity value of the second stage with a label of '2'; labeling the battery capacity value of the third stage with a label of '3';
in order to more intuitively and simply understand the method for predicting the remaining service life based on the lithium battery degradation stage division, the lithium battery with data disclosure is provided for verification and explanation.
The lithium batteries disclosed by the data are divided into 9 groups, each group comprises 3 or 4 batteries, the experimental conditions among the groups are different, and the main difference is represented by experimental temperature and discharge current; the experimental conditions of the same group of batteries are also not much the same, and are mainly shown in the difference of discharge cut-off voltage, and the specific conditions are shown in table 1.
TABLE 1
Taking the first group of cells as an example, the cells underwent three different stages under accelerated life degradation experiments: charge cycle, discharge cycle and impedance check; in the process of charging circulation, firstly, constant current (the current is 1.5A) mode charging is kept until the voltage reaches the preset upper limit voltage of 4.2v, and then the charging power is switched into a constant voltage mode until the current is lower than the lower limit threshold value of 20 mA; discharging with a constant current (current 2A) during a discharge cycle until the voltage reaches a preset lower voltage limit, i.e. the preset lower voltage limits of the #5, #6, #7 and #18 cells are 2.7v, 2.5v, 2.2v and 2.5v respectively;
data collected during the cyclic charging process include: measuring voltage, measuring current, temperature and charger current, charger voltage and time; the data collected during the cyclic discharge process include: measured voltage, measured current, temperature, load current, load voltage, time and capacity as shown in table 2.
TABLE 2
The verification adopts a first group of #5 batteries, selected data are data of two variables of measured voltage and temperature acquired in a cyclic discharge process, see fig. 2(a) and 2(b), two health indexes are extracted from the two groups of data of the measured voltage and the temperature according to a calculation formula of the health indexes, the extraction result is shown in fig. 3(a) and 3(b), the first health index, namely a time difference value of a discharge voltage interval from 4.2v to 3.7v, monotonically decreases along with the cycle number, the second health index, namely a temperature change value of a discharge time interval from 1000s to 2000s, monotonically increases along with the cycle number, and the two health indexes monotonically change along with the discharge cycle number, so that the degradation trend of the batteries can be expressed.
The #5 battery is discharged for 168 discharge cycles, the first 70% of data (118) are taken as a training set, and the rest 50 cycles are taken as a test set; and (3) taking the two health indexes of the training set as the input of Support Vector Regression (SVR), taking the collected capacity value as the output of the SVR, and establishing a capacity prediction model. The capacity prediction fitting result of the training set is shown in fig. 4(a), and as can be seen from fig. 4(a), the actual data and the prediction data output by the capacity prediction model are consistent, and the similarity between the actual data and the prediction data is 99.4%; inputting the health indexes of the test set into a capacity prediction model obtained by the training set to obtain capacity prediction data of the test set; and simultaneously performing the following 50 times of discharging processes to obtain actual data of the capacity of the test set, wherein the pair of the predicted data and the actual data of the capacity of the test set is shown in fig. 4(b), and as can be seen from fig. 4(b), the change trends of the predicted data and the actual data are consistent, the capacity values obtained by the predicted data and the actual data are close, and the similarity between the capacity values is 96.57%.
Fig. 5(a) is a classification model established by dividing the capacity prediction data shown in fig. 4(a) and 4(b) into 3 stages according to the capacity fading trend, and taking 70% of all data out of the data in a disorderly sequence; fig. 5(b) is a result of dividing the actual data and the predicted data of the training set into 3 stages, and it can be seen from fig. 5(b) that the classification accuracy is 97.5%; fig. 5(c) is a result of dividing the actual data and the predicted data of the test set into 3 stages, and it can be seen from fig. 5(c) that the classification accuracy is 96%; the classification accuracy obtained from fig. 5(b) and 5(c) has a good effect.
Dividing the degradation stages of the lithium battery and attaching class labels according to the predicted capacity values, wherein data in the third stage represents the stage with serious degradation, and RUL prediction is urgently needed, so that only data in the third stage is selected to establish an RUL prediction model in the RUL prediction stage of the lithium battery, and FIG. 6(a) is a training result of third-stage data modeling in a training set; taking the data of the third stage in fig. 5(c) as a verification set of the RUL prediction model, the result is shown in fig. 6(b), and as can be seen from fig. 6(a) and fig. 6(b), the prediction data obtained by the RUL prediction model established according to the data of the third stage in the training set is consistent with the actual remaining service life data of the third stage in the training set, and the similarity is 95.4%; the prediction data obtained by the RUL prediction model established according to the data of the third stage of the test set is consistent with the actual residual service life data of the third stage of the test set, and the similarity is 96.5%; from the similarity, it can be seen that the method for predicting remaining service life of the present invention is effective.
In practical application, if the capacity value of the lithium battery is not in the third stage, the residual service life does not need to be predicted, and only the residual service life range is required to be provided to be more than 25%.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. A method for predicting the remaining service life based on the lithium battery degradation stage division is characterized by comprising the following steps:
(1) establishing a capacity prediction model according to the relation between the health index of the training set with known data and the battery capacity value;
(2) extracting health indexes in a test set with unknown capacity values as input of a capacity prediction model, and predicting the capacity values of the test set;
(3) dividing the capacity values of the training set and the testing set into 3 stages according to the capacity attenuation trend, and attaching class labels to the data of the 3 stages to establish a classification model;
(4) establishing an RUL prediction model according to the health indexes and the corresponding residual service life in the third stage;
(5) acquiring health indexes of the remaining service life to be predicted, and acquiring a predicted capacity value;
(6) and inputting the predicted capacity value into a classification model, judging whether the predicted capacity value is in the 3 rd stage, if so, inputting the health index into an RUL prediction model, and acquiring a predicted value of the residual service life, otherwise, outputting a prediction range of the residual service life.
2. The prediction method of claim 1, wherein the health indicators of the training set in step (1) comprise discharge voltages selected from the group consisting of v1To v2Time difference of interval, discharge time from t1To t2The value of the temperature change within the interval.
3. The prediction method according to claim 1 or 2, wherein the capacity prediction model in step (1) is established by: and taking the health index of the training set as the input of the support vector regression method, taking the capacity of the battery in the training set as the output of the support vector regression method, and training to obtain a capacity prediction model.
4. The prediction method according to claim 3, wherein the step (3) of dividing the battery capacity value into 3 stages according to the capacity fade tendency is based on:
if it is
The battery capacity is in the first phase;
if it is
The battery capacity is in the second stage;
if it is
The battery capacity is in the third stage;
wherein cpt represents a battery capacity value; cptmaxRepresents the maximum capacity value of the battery; cptminRepresenting the minimum capacity value of the battery.
5. The prediction method according to claim 1 or 4, wherein the remaining service life in step (4) is:
wherein N isiDenotes the number of i-th discharge cycles, NEOLRepresenting the discharge cycle times of the lithium battery at the time of degradation to failure; RUL is the remaining useful life.
6. The prediction method of claim 5, wherein the step (4) of building the RUL prediction model comprises:
and taking the health index of the third stage as the input of the support vector regression method, taking the residual service life of the third stage as the output of the support vector regression method, and training to obtain the RUL prediction model.
7. The prediction method of claim 3, wherein the kernel function of the support vector regression method employs a radial basis kernel function.
8. The prediction method of claim 2, wherein the health indicators of the training set comprise: the time difference of the discharge voltage from 4.2V to 3.7V and the temperature change value of the discharge time from 1000s to 2000 s.
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