CN110095731B - Method for directly predicting residual life of lithium ion battery applied to long-life space - Google Patents
Method for directly predicting residual life of lithium ion battery applied to long-life space Download PDFInfo
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Abstract
A direct residual life prediction method applied to a long-life space lithium ion battery relates to the technical field of lithium ion battery life prediction methods. The method aims to solve the problem that the traditional method for predicting the residual life based on the degradation track modeling is difficult to be applied to the application scenes with long prediction level and slow degradation for the spatial lithium ion battery with the cycle life of 5-8 years. Acquiring battery capacity data of each lithium ion battery in each period to construct a data set, and acquiring the residual life of each battery in different periods according to the set maximum value of the life of each battery and the set period number; inputting a data set as training data, taking the residual life of the battery as output data, and substituting the input data and the output data into a correlation vector machine model to obtain a trained capacity sequence and residual life mapping model; and inputting the capacity of the battery to be predicted in each period into the mapping model to obtain an estimated value of the residual life of the battery to be predicted. For predicting the remaining life of the lithium ion battery.
Description
Technical Field
The invention relates to a method for directly predicting the residual life of a lithium ion battery applied to a long-life space. Belonging to the technical field of lithium ion battery service life prediction methods.
Background
Lithium ion batteries have become third-generation space energy storage batteries and are widely applied to various spacecrafts. With the on-orbit operation of a spacecraft, the lithium ion battery is subjected to charge and discharge cycles continuously, and a series of irreversible electrochemical reactions occur inside the lithium ion battery to cause the performance of the battery to degrade. The method is used for accurately predicting the residual service life of the lithium ion battery, is one of the prerequisites for ensuring the safe and stable operation of the spacecraft, and is also a precondition for realizing flexible and autonomous task planning of a spacecraft cluster. However, the traditional lithium ion battery life prediction method focuses on the modeling problem of the performance degradation trend of the lithium ion battery, namely, the performance degradation track of the lithium ion battery is predicted firstly, and then the residual life of the lithium ion battery is deduced based on a failure threshold value specified in advance. However, with the continuous development of battery materials and processes and the continuous improvement of the requirement on the service life of a spacecraft, the service life of the conventional space lithium ion battery can reach 5-8 years and about 48000 orbit periods. For the problem of life prediction, the long-term prediction scene directly causes the mismatch of the traditional life prediction model based on the degradation characteristic prediction, and an accurate and stable prediction result is difficult to obtain.
Disclosure of Invention
The method aims to solve the problem that the traditional method for predicting the residual life based on the degradation track modeling is difficult to be applied to the application scenes with long prediction level and slow degradation for the spatial lithium ion battery with the cycle life of 5-8 years. A direct method for predicting remaining life for a long-life space lithium ion battery is now provided.
The method for directly predicting the residual life of the lithium ion battery applied to the long-life space comprises the following steps:
step one, collecting battery capacity data of each lithium ion battery in each service cycle, constructing a data set,
obtaining the residual life of each battery in different cycles according to the set maximum value of the life of each battery and the cycle number;
taking the data set in the step one as input data of training data, taking the residual life of the battery in a corresponding period as output data of the training data, and bringing the input data and the output data into a relevant vector machine model for training to obtain a trained capacity sequence and residual life mapping model;
step three, the capacity of the battery to be predicted in each period is used as input data of training data and is input into the mapping model in the step two, and output data of the mapping model is obtained and is used as an estimated value of the residual life of the battery to be predicted;
and step four, filtering the estimated value of the residual life by adopting a Kalman filtering method to obtain the estimated result of the residual life of the battery to be predicted.
The invention has the beneficial effects that:
the method is a direct method for predicting the residual life of the battery. According to the method, the prediction result of the remaining life of the battery can be obtained by taking the capacity sequence as input. Meanwhile, aiming at the influence of the nonlinear degradation of the battery capacity and the battery capacity measurement noise on the prediction result during the prediction of the residual life of the lithium ion battery, the method uses a RVM (remote Vector Machine) model to map the battery capacity space to the linear residual life space, so that the nonlinear degradation influence is reduced, and simultaneously, the Kalman Filter algorithm (KF) algorithm is combined to Filter the battery residual life prediction result, so that the influence of the measurement noise of the battery capacity is reduced, and the high-precision prediction result is obtained. The method is verified on 4 groups of batteries, and has good prediction effect on different batteries. The maximum error of the prediction result of the residual life of the battery is less than 72 cycles, and the prediction accuracy of the prediction result is gradually improved along with the increase of the cycles. The accuracy of the predicted result is higher than that of the traditional method.
Drawings
Fig. 1 is a flowchart of a method for directly predicting remaining life of a lithium ion battery applied to a long-life space according to a first embodiment;
fig. 2 is a graph of the remaining life of a lithium ion battery obtained by the method of the present application.
Detailed Description
The first embodiment is as follows: specifically, the present embodiment is described with reference to fig. 1 and fig. 2, and the method for directly predicting the remaining life of a lithium ion battery applied to a long-life space in the present embodiment includes the following steps:
step one, collecting battery capacity data of each lithium ion battery in each service cycle, constructing a data set,
obtaining the residual life of each battery in different cycles according to the set maximum value of the life of each battery and the cycle number;
taking the data set in the step one as input data of training data, taking the residual life of the battery in a corresponding period as output data of the training data, and bringing the input data and the output data into a relevant vector machine model for training to obtain a trained capacity sequence and residual life mapping model;
step three, the capacity of the battery to be predicted in each period is used as input data of training data and is input into the mapping model in the step two, and output data of the mapping model is obtained and is used as an estimated value of the residual life of the battery to be predicted;
and step four, filtering the estimated value of the residual life by adopting a Kalman filtering method to obtain the estimated result of the residual life of the battery to be predicted.
In the embodiment, the direct prediction model between the degradation characteristic and the residual life of the lithium ion battery is established, the direct mapping between the capacity sequence and the residual life is realized by adopting a Relevance Vector Machine (RVM) algorithm, the estimation result is used as an observation value, the result of a data driving model is fused with a physical model by a Kalman Filter (KF) algorithm, the influence of measurement noise on the prediction result is reduced, and the optimal estimation of the residual life of the lithium ion battery is realized.
Example 1:
the results of predicting the remaining life of the three batteries are shown in the following table, and specific evaluation indexes thereof are shown in table 1.
Table 1:
step one, testing by adopting a battery data set of the university of Maryland, wherein CX2-26, CX2-37 and CX2-38 battery data are contained in the battery data set, and the data of the battery capacity of each battery in each period is constructed into a data set which is marked as Capi(k) Wherein k is the number of cycles, i is the number of batteries; setting the maximum value of the lifetime of the ith battery
Calculating the residual life RUL of the battery in the k-th period according to the following formula (1) for the corresponding period number when the battery capacity is degraded to 80% of the rated capacityi(k)；
Step two, constructing a training set [ xtrain, ytrain ] of the RVM model by using the data of two batteries CX2-37 and CX2-38 as training data:
wherein
Substituting a training set [ xtrain, ytrain ] into the RVM model, wherein xtrain is a training input, and ytrain is a training output; training the RVM, the RVM equation can be written as the following formula (2):
wherein, xtrainiColumn i data representing xtrain, σ bandwidth, RVM (·) the output of RVM model, ω ═ ω (ω ·)0,...ωn)TIs a weight vector;
for ω ═ ω (ω) in the RVM model0,...ωn)TTraining parameters;
step three, constructing the battery capacity data of the predicted battery CX2-26 as follows:
xtest＝{Captest(k-n+1),Captest(k-n+2),...,Captest(k)}；
wherein k is the number of cycles and k belongs to Z, and n is less than or equal to k.
Xtest is substituted into the trained RVM model as shown in equation (3) below.
Wherein the content of the first and second substances,
outputting a result for the RVM model;
step four, filtering the prediction result through a Kalman filtering method: first, the initialization covariance P (k-1) and
setting covariance as the variance of residual life of the I monomers in the training set, and initial state value as the mean value of residual life of the I monomers;
the state transition equation is established as follows:
wherein the content of the first and second substances,
is the state quantity of the battery RUL at the kth cycle, and ω (k) is the system noise at the kth cycle;
predictive estimation covariance matrix p (k):
P(k)＝P(k-1)+Q(k) (6)
wherein Q (k) is the system noise variance of the kth period;
calculating the kalman filter gain kg (k):
wherein, R (k) is the measurement noise variance of the k period;
calculating a final predicted value
The second embodiment is as follows: in this embodiment, the method for directly predicting remaining life of a lithium ion battery applied to a long-life space according to the first embodiment is further described, in this embodiment, in the first step, a data set is recorded as: capi(k) Wherein k is the number of cycles, i is the number of batteries, and the number of the batteries comprises data of one battery;
residual service life RUL of each battery under different periodsi(k) Comprises the following steps:
in the formula (I), the compound is shown in the specification,
the ith maximum battery life.
In the present embodiment, it is preferred that,
is the number of cycles corresponding to the battery capacity degrading to 80% of the rated capacity.
The third concrete implementation mode: in the present embodiment, the method for directly predicting remaining life of a lithium ion battery applied to a long-life space according to the first embodiment is further described, in the second embodiment, the input data xtrain in the training data is:
in the formula (I), the compound is shown in the specification,
the output data ytrain in the training data is:
the fourth concrete implementation mode: in this embodiment, the input data and the output data are introduced into a correlation vector machine model for training, and a mapping model of a trained capacity sequence and a trained remaining life is obtained as follows:
in the formula, xtrainiColumn i data representing xtrain, σ represents the gaussian kernel width, RVM (·) represents the output value of the correlation vector machine model, and ω ═ ω (ω ·)0,...ωn)TIs a weight vector.
In this embodiment, the training set [ xtrain, ytrain]The training is carried out by substituting into equation 4. For ω ═ ω (ω) in the RVM model0,...ωn)TThe parameters are trained.
The fifth concrete implementation mode: in this embodiment, the method for directly predicting the remaining life of a lithium ion battery applied to a long-life space is further described in the first embodiment, and in the third step, the capacity Cap of the battery to be measured in each period is usedtestInput data xtest as training data is:
xtest＝{Captest(k-n+1),Captest(k-n+2),...,Captest(k) the formula 5 is described in the following,
wherein k belongs to Z, and n is less than or equal to k.
The sixth specific implementation mode: in this embodiment, the method for directly predicting the remaining life of a lithium ion battery applied to a long-life space described in the fifth embodiment is further described, in the third step of the present embodiment, a process of obtaining an estimated value of the remaining life of the battery to be predicted is as follows:
bringing xtest into a trained correlation vector machine model:
in the formula (I), the compound is shown in the specification,
and outputting the result for the correlation vector machine model.
The seventh embodiment: in this embodiment, the method for directly predicting the remaining life of a lithium ion battery applied to a long-life space according to the first embodiment is further described, in the fourth embodiment, the specific process of obtaining the remaining life of the battery to be predicted is as follows:
step four, setting the covariance and the average life of the residual lives of the monomers in the current training set as the initialized covariance and the initialized residual life estimated value of filtering;
step two, obtaining the covariance of the residual life of the monomer to be predicted in the next period by using the covariance in the step one, and obtaining the estimated value of the residual life of the monomer to be predicted in the next period by using the initialized estimated value of the residual life in the step one;
step four, acquiring Kalman filtering gain according to the covariance of the residual life of the monomer to be predicted in the next period;
and fourthly, acquiring the residual life of the battery to be predicted by adopting a Kalman filtering method according to the estimated value and the covariance of the residual life of the battery to be predicted, the estimated value of the monomer residual life in the last period, the initialized residual life estimated value and Kalman filtering gain.
The specific implementation mode is eight: in this embodiment, the method for directly predicting the remaining life of a lithium ion battery applied to a long-life space described in the seventh embodiment is further described, in the fourth embodiment, the estimated value of the remaining life to be predicted in the next cycle is obtained as follows:
establishing a state transition equation according to the initialized covariance of the residual lives of the I monomers in the current training set:
in the formula (I), the compound is shown in the specification,
the state quantity of the battery RUL for the k-th cycle,
is the state quantity of battery RUL in the k-1 th period, and W (k) is the system noise in the k-th period;
the covariance of the residual lifetime of the kth monomer was obtained as:
p (k) ═ P (k-1) + q (k) formula 9,
wherein Q (k) is the system noise variance of the k period, P (k) is the covariance of the residual life of the monomer of the k period, R (k) is the measurement noise variance of the k period, and P (k-1) is the covariance of the residual life of the monomer of the k-1 period.
In this embodiment, the covariance is the variance of the remaining lives of the l monomers in the training set, and the initial state value is the mean value of the remaining lives of the l monomers.
The specific implementation method nine: in this embodiment, the method for directly predicting the remaining life of a lithium ion battery applied to a long-life space in the specific embodiment eight is further described, in the present embodiment, in the step four and the step three, the kalman filter gain kg (k) obtained is:
where R (k) is the measurement noise variance of the k-th period.
The detailed implementation mode is ten:in this embodiment, the method for directly predicting the remaining life of a lithium ion battery applied to a long-life space described in the ninth embodiment is further described, in this embodiment, in the fourth step, the remaining life of the battery to be predicted is obtained
Comprises the following steps:
Claims (9)
1. the method for directly predicting the residual life of the lithium ion battery applied to the long-life space is characterized by comprising the following steps of:
step one, collecting battery capacity data of each lithium ion battery in each service cycle, constructing a data set,
obtaining the residual life of each battery in different cycles according to the set maximum value of the life of each battery and the cycle number;
taking the data set in the step one as input data of training data, taking the residual life of the battery in a corresponding period as output data of the training data, and bringing the input data and the output data into a relevant vector machine model for training to obtain a trained capacity sequence and residual life mapping model;
step three, the capacity of the battery to be predicted in each period is used as input data of training data and is input into the mapping model in the step two, and output data of the mapping model is obtained and is used as an estimated value of the residual life of the battery to be predicted;
filtering the residual life estimation value by adopting a Kalman filtering method to obtain a residual life estimation result of the battery to be predicted;
in step one, the data set is recorded as: capi(k) Wherein k is the number of cycles, i is the number of batteries, and the number of the batteries comprises data of one battery;
residual service life RUL of each battery under different periodsi(k) Comprises the following steps:
in the formula (I), the compound is shown in the specification,
the ith maximum battery life.
2. The method for directly predicting the remaining life of the lithium ion battery in the long-life space according to claim 1, wherein in the second step, the input data xtrain in the training data is:
in the formula (I), the compound is shown in the specification,
the output data ytrain in the training data is:
3. the method for directly predicting the remaining life of a lithium ion battery in a long-life space according to claim 1, wherein the input data and the output data are substituted into a correlation vector machine model for training, and the obtained trained mapping model of the capacity sequence and the remaining life is as follows:
in the formula, xtrainiColumn i data, σ Table, representing xtrainRvm (g) represents the output value of the correlation vector machine model, ω ═ ω (ω ═ ω), which represents the width of the gaussian kernel function0,Kωn)TIs a weight vector.
4. The method for directly predicting the remaining life of a lithium ion battery in a long-life space according to claim 1, wherein the step three is to measure the capacity Cap of the battery to be measured in each cycletestInput data xtest as training data is:
xtest＝{Captest(k-n+1),Captest(k-n+2),...,Captest(k) the formula 5 is described in the following,
wherein k belongs to Z, and n is less than or equal to k.
5. The method for directly predicting the remaining life of a lithium ion battery in a long-life space according to claim 4, wherein the step three is to obtain the estimated value of the remaining life of the battery to be predicted by:
bringing xtest into a trained correlation vector machine model:
in the formula (I), the compound is shown in the specification,
and outputting the result for the correlation vector machine model.
6. The method for directly predicting the remaining life of a lithium ion battery in a long-life space according to claim 1, wherein in the fourth step, the specific process of obtaining the remaining life of the battery to be predicted is as follows:
step four, setting the covariance and the average life of the residual lives of the monomers in the current training set as the initialized covariance and the initialized residual life estimated value of filtering;
step two, obtaining the covariance of the residual life of the monomer to be predicted in the next period by using the covariance in the step one, and obtaining the estimated value of the residual life of the monomer to be predicted in the next period by using the initialized estimated value of the residual life in the step one;
step four, acquiring Kalman filtering gain according to the covariance of the residual life of the monomer to be predicted in the next period;
and fourthly, acquiring the residual life of the battery to be predicted by adopting a Kalman filtering method according to the estimated value and the covariance of the residual life of the battery to be predicted, the estimated value of the monomer residual life in the last period, the initialized residual life estimated value and Kalman filtering gain.
7. The method for directly predicting the remaining life of a lithium ion battery in a long-life space according to claim 6, wherein in the step four, the estimated value of the remaining life to be predicted in the next cycle is obtained as follows:
establishing a state transition equation according to the initialized covariance of the residual lives of the I monomers in the current training set:
in the formula (I), the compound is shown in the specification,
the state quantity of the battery RUL for the k-th cycle,
is the state quantity of battery RUL in the k-1 th period, and W (k) is the system noise in the k-th period;
the covariance of the remaining lifetime of the monomer for the kth cycle was obtained as:
p (k) ═ P (k-1) + q (k) formula 9,
wherein Q (k) is the system noise variance of the k period, P (k) is the covariance of the residual life of the monomer of the k period, R (k) is the measurement noise variance of the k period, and P (k-1) is the covariance of the residual life of the monomer of the k-1 period.
8. The method for directly predicting the remaining life of the lithium ion battery in the long-life space according to claim 7, wherein in the fourth step and the third step, kalman filter gain kg (k) is obtained as follows:
where R (k) is the measurement noise variance of the k-th period.
9. The method for directly predicting the remaining life of a lithium ion battery in a long-life space according to claim 8, wherein in the fourth step, the remaining life of the battery to be predicted is obtained
Comprises the following steps:
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| CN111880099B (en) * | 2020-06-30 | 2023-03-24 | 国网浙江省电力有限公司电力科学研究院 | Method and system for predicting service life of battery monomer in energy storage power station |
| CN111856298A (en) * | 2020-07-23 | 2020-10-30 | 上海空间电源研究所 | On-orbit residual capacity prediction method for lithium ion storage battery for spacecraft |
| CN112114268A (en) * | 2020-08-26 | 2020-12-22 | 昆山贝思泰智能科技有限公司 | Batch battery core external characteristic fault prediction method based on embedded AI algorithm |
| CN113064091B (en) * | 2021-03-10 | 2023-02-10 | 北京理工新源信息科技有限公司 | Method and system for predicting remaining life of battery |
| CN114216558B (en) * | 2022-02-24 | 2022-06-14 | 西安因联信息科技有限公司 | Method and system for predicting remaining life of battery of wireless vibration sensor |
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