CN115047350B - Digital-analog linkage based lithium ion battery remaining service life prediction method - Google Patents

Digital-analog linkage based lithium ion battery remaining service life prediction method Download PDF

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CN115047350B
CN115047350B CN202210729394.9A CN202210729394A CN115047350B CN 115047350 B CN115047350 B CN 115047350B CN 202210729394 A CN202210729394 A CN 202210729394A CN 115047350 B CN115047350 B CN 115047350B
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CN115047350A (en
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张九思
李翔
罗浩
田纪伦
李明磊
蒋宇辰
尹珅
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Harbin Institute of Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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Abstract

The invention discloses a method for predicting the remaining service life of a lithium ion battery based on digital-analog linkage, and relates to a method for predicting the remaining service life of the lithium ion battery. The invention aims to solve the problems that a model and data-driven residual service life prediction method is difficult to combine, the traditional data-driven method is difficult to measure the uncertainty of the residual service life, and the importance degree of different moments in time window data is difficult to reflect. The process is as follows: step one, building a bidirectional gating cycle unit network model based on a time attention mechanism; the process is as follows: the model sequentially comprises three parts of an attention mechanism network, a bidirectional gate control circulation unit network and a full connection layer; step two, training a bidirectional gating cycle unit network model based on a time attention mechanism; step three, constructing a battery degradation model based on particle filtering; and fourthly, predicting the residual service life on line. The method is suitable for the field of battery service life prediction.

Description

Digital-analog linkage based lithium ion battery remaining service life prediction method
Technical Field
The invention relates to the interdisciplinary field of deep learning, state space theory and battery remaining service life prediction, in particular to a lithium ion battery remaining service life prediction method.
Background
With the rapid development of new energy technologies, lithium ion batteries are widely used due to their advantages of high energy density, wide working temperature range, long cycle life, portability, and the like. However, during the cycle of charging and discharging of the lithium ion battery, the performance of the battery is gradually degraded due to factors such as the formation of an internal passivation film, the decomposition of an electrolyte, and the irreversible dissolution of an electrode active material, which greatly reduces the reliability and safety of electric devices. Therefore, how to accurately predict the Remaining service life (RUL) of the lithium ion battery in time is of great significance to the energy supply system.
The remaining useful life of a lithium-ion battery refers to the number of charge and discharge cycles that the state of health of the battery can undergo before degrading to a point where the device will not continue to operate or reach (failure threshold) under cyclic charge and discharge conditions. The existing lithium ion battery remaining service life prediction methods can be generally divided into two types: model-based methods and data-driven methods. The model-based method comprises an equivalent circuit model, an electrochemical model and a state space modeling method. However, due to the complexity of the electrochemical reactions inside the cell, there are significant limitations to accurate electrochemical modeling. Correspondingly, the state space modeling method, such as Kalman Filter (KF), extended Kalman Filter (EKF), and Particle Filter (PF), can realize effective remaining service life prediction in the framework of a probability model. Unlike the model-based remaining life method, the data-driven method does not need to consider the internal chemical reactions of the battery and the cause of failure, and (3) only mining the degradation rule of the battery from historical data, and further realizing the prediction of the residual service life of the battery. Common data-driven methods include machine learning methods such as Random Forest Regression (RFR), support vector machine regression (SVR), extreme gradient boosting (XGBoost), and deep learning methods such as a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), and a variant Long-Term Memory Network (LSTM).
The method for predicting the remaining service life of the lithium ion battery based on deep learning, such as a long-time memory network, a gating cycle unit and the like, which are rising in the recent days, achieves the purpose of prediction by combining data into a time window form. Although these methods can learn the dependency of the battery capacity data on the time level through the gating structure, it is difficult to reflect the degree of importance of different times in the time window data, and there is no description of the uncertainty of the remaining service life of the lithium ion battery in the degradation process. Meanwhile, when the iterative prediction of the battery capacity is carried out by using a time window mode in the data-driven deep learning method, the feedback and correction of data are lacked. Furthermore, when only a model-based method is used to predict the battery capacity, only trend extrapolation can be performed on the basis of a set prediction starting point, and model parameters are not updated in the process.
Disclosure of Invention
The invention aims to solve the problems that a model and data driving-based residual service life prediction method is difficult to combine, the traditional data driving method is difficult to measure the uncertainty of the residual service life and the importance degree of different moments in time window data is difficult to reflect, and provides a digital-analog linkage-based lithium ion battery residual service life prediction method.
A method for predicting the remaining service life of a lithium ion battery based on digital-analog linkage comprises the following specific processes:
step one, building a bidirectional gating cycle unit network model based on a time attention mechanism; the specific process is as follows:
the bidirectional gating circulation unit network model based on the time attention mechanism sequentially comprises an attention mechanism network, a bidirectional gating circulation unit network and a full connection layer;
step two, training a bidirectional gating cycle unit network model based on a time attention mechanism; the specific process is as follows:
taking 80% of historical data of the battery as a training data set training model, and taking the rest 20% of the historical data as a verification data set to test the prediction effect of the model;
inputting a training data set into the bidirectional gating cycle unit network model which is built in the step one and is based on the time attention mechanism, and constructing a mapping relation between input past battery capacity data and input future capacity data;
selecting and storing a bidirectional gating cycle unit network model with the best prediction effect on the verification data set based on a time attention mechanism for online battery capacity prediction;
step three, constructing a battery degradation model based on particle filtering;
and fourthly, predicting the residual service life on line.
The invention has the beneficial effects that:
the invention aims to solve the problem that a model-based residual service life prediction method and a data-driven residual service life prediction method are difficult to combine, and provides a digital-analog linkage battery residual service life prediction method based on a bidirectional gate control cycle unit (PF-TAM-BiGRU) of a particle filter-time attention machine system.
(1) Compared with the traditional data-driven residual service life prediction method, the time attention mechanism layer can consider the importance degree of different moments in time window data and allocate different weights to each time step in the time window data.
(2) In order to combine the advantages of a model-based method and a data driving method, the digital-analog linkage method combines a particle filter algorithm and a time attention mechanism-bidirectional gate control circulation unit network, and enables the two methods to be mutually corrected in the prediction process.
(3) According to the invention, by introducing a model-based particle filtering algorithm into the data-driven residual service life method, the uncertainty of the residual service life can be well described in the battery degradation process according to the distribution of particles.
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FIG. 1 is a flow chart of the operation of the present invention;
FIG. 2 is a schematic diagram of the time attention mechanism of the present invention;
FIG. 3 is a schematic diagram of the structure of a gated loop unit of the present invention;
FIG. 4 is a diagram of a deep neural network architecture for a time attention mechanism bi-directional gated loop unit, including details of portions of the network; wherein BiGRU represents a bidirectional gating circulation unit, and FC represents a full connection layer;
FIG. 5a is a graph showing the predicted and true values of battery capacity for B0005 battery at 60 th duty cycle according to the method for predicting remaining useful life of the present invention;
FIG. 5B is a graph showing the predicted and actual values of the remaining service life of the B0006 battery under the 60 th duty cycle according to the method for predicting remaining service life of the present invention;
FIG. 5c is a graph showing the predicted and true values of battery capacity at 60 th duty cycle for the remaining useful life prediction method of the present invention for a B0007 battery;
fig. 5d is a graph showing the predicted and true values of the battery capacity at the 60 th duty cycle for the B0018 battery in the method for predicting remaining useful life of the present invention.
Detailed Description
The first embodiment is as follows: the method for predicting the remaining service life of the lithium ion battery based on digital-analog linkage comprises the following specific processes:
step one, building a bidirectional gating cycle unit network model based on a time attention mechanism; the specific process is as follows:
the bidirectional gating circulation unit network model based on the time attention mechanism sequentially comprises an attention mechanism network, a bidirectional gating circulation unit network and a full connection layer;
the main functions are as follows:
attention to time network: an attention mechanism is adopted to give greater weight to important moments in an important time window;
bidirectional gated cyclic cell network: extracting the implicit time dependence in the time sequence data to obtain high-dimensional characteristics;
full connection of a single layer: mapping of the high-dimensional features to the remaining service life is achieved.
Step two, training a bidirectional gating cycle unit network model based on a time attention mechanism; the specific process is as follows:
training the model by taking 80% of historical data of the battery as a training data set, and taking the rest 20% of the historical data as a verification data set to check the prediction effect of the model;
inputting a training data set into the bidirectional gating cycle unit network model which is built in the step one and is based on the time attention mechanism, and constructing a mapping relation between input past battery capacity data and input future capacity data;
selecting and storing a bidirectional gating cycle unit network model with the best prediction effect on the verification data set based on a time attention mechanism for online battery capacity prediction;
step three, constructing a battery degradation model based on particle filtering;
a dynamic degradation model based on particle filtering is constructed to describe the degradation process of the battery through online data of the battery capacity, and the battery degradation model is constructed through the steps of particle initialization, importance sampling, weight updating and normalization and state variable updating, so that the battery degradation model is used for online residual service life prediction.
And fourthly, predicting the residual service life on line.
The particle filter algorithm and a bidirectional gating circulation unit of a time attention mechanism are combined, so that the two methods are mutually corrected in the prediction process. Specifically, starting from a set residual service life prediction starting point, updating parameters of a particle filter model by taking a predicted value of the residual service life of a bidirectional gating circulation unit based on a time attention mechanism as a posterior value of the particle filter, and inputting a predicted value of the battery capacity of the particle filter model into a time window for predicting the future battery capacity;
the above process is repeated until the predicted battery capacity is less than the battery threshold, and the number of battery cycles in the middle is the remaining service life of the battery.
Wherein, the first step to the second step belong to an off-line network training stage, and the third step to the fourth step belong to an on-line network prediction stage.
Evaluating the residual service life prediction effect:
and measuring the residual service life prediction effect of the lithium ion battery based on digital-analog linkage by adopting an Absolute Error (AE).
The second embodiment is as follows: the first embodiment is different from the first embodiment in that a bidirectional gating cycle unit network model based on a time attention mechanism is built in the first step;
the bidirectional gating circulation unit network model based on the time attention mechanism sequentially comprises an attention mechanism network, a bidirectional gating circulation unit network and a full connection layer;
the specific process is as follows:
in deep learning, the more network model parameters are, the larger the amount of stored information is, the stronger the ability of approximating a nonlinear relationship is, but the problem of information overload is brought along. Attention mechanisms stem from the complex cognitive functions essential to humans, and can focus limited computing resources on information critical to the current task, while reducing attention to garbage. Therefore, the attention mechanism is introduced into the prediction problem of the lithium ion battery capacity, on the premise that the prior knowledge of battery degradation is not needed, the importance degree of the capacities of different working cycles in a time window to the battery capacity of the future cycle is determined by utilizing the attention neural network layer, a larger weight is given to an important moment, a smaller weight is given to a minor moment, and the purpose of improving the battery capacity prediction effect is achieved.
The time window data input to the attention mechanism network may be expressed as:
Q＝[q 1 ,q 2 ,…,q j ,…,q L ]
wherein L is the length of the time window; q. q.s j Is the battery capacity at the jth moment;
note that the mechanism is schematically shown in fig. 2. The core of the attention mechanism proposed by the invention lies in the construction of the battery capacity q at the jth moment in the time window j And its importance s j The mapping relationship between can be expressed asAs shown in formula (1):
Figure BDA0003712408280000051
wherein s is j Is the importance degree of the jth moment, e is the natural logarithm, T is the transposition operation of the matrix, sigma is the sigmoid activation function, W j And b j Weights and offsets representing respective time instants;
the weights of the attention mechanism network can be written as W s ＝[W 1 ,…,W j ,…,W L ]，b s ＝[b 1 ,…,b j ,…,b L ]；
The importance degree is normalized by a softmax function, and the expression is shown as (2):
Figure BDA0003712408280000052
wherein alpha is j Representing the degree of importance of the respective moment;
on the basis, the output of the attention mechanism network can be obtained
Figure BDA0003712408280000053
The expression is shown in formula (3):
Figure BDA0003712408280000054
wherein the content of the first and second substances,
Figure BDA0003712408280000055
the output of the network layer is controlled for attention at the jth moment;
after the attention mechanism network has assigned weights to the battery capacities at different times in the time window, it will assign weights to the battery capacities at the different times in the time window
Figure BDA0003712408280000056
Input to a bidirectional gated cyclic cell network (bidirection)nal Gated recovery Unit, biGRU);
the specific process of the bidirectional gating circulation unit network is as follows:
a gated cyclic unit is an improved recurrent neural network. A typical gated cyclic unit consists of an update gate, which is used to control the extent to which the current state utilizes past time information, and a reset gate, which controls the extent to which new input information is combined with past memory. The schematic diagram of the gating cycle unit is shown in fig. 3, and the calculation formula of the gating cycle unit is shown as formula (4) -formula (7):
Figure BDA0003712408280000057
Figure BDA0003712408280000061
Figure BDA0003712408280000062
Figure BDA0003712408280000063
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003712408280000064
representing the input vector of the cell at time t, h t And h t-1 Representing the outputs of the network elements of the unit at time t and time t-1, respectively, z t 、r t 、c t Respectively representing the outputs of the refresh gate, reset gate and memory cell, W z 、W r 、W c Connection matrix, U, representing the input information of the refresh gate, reset gate and memory cell, respectively z And b z Respectively representing the weight and offset vector, U, of the update gate r And b r Respectively representing the weight and offset vector, U, of the reset gate c And b c Respectively represent the weights of the memory cellsA bias vector, σ denotes a sigmoid activation function, tanh denotes a hyperbolic tangent function, and->
Figure BDA0003712408280000065
Representing a dot product operation;
in order to utilize the reverse battery capacity time sequence, the invention adopts a bidirectional GRU network, adopts circulation layers in the forward direction and the reverse direction to respectively obtain the states of a hidden layer, and then obtains the output of the hidden layer through splicing. On this basis, the input of the forward GRU is
Figure BDA0003712408280000066
Through forward operation (contents of formulas (4) to (7)), a forward output sequence of the hidden layer is obtained as shown in formula (8):
Figure BDA0003712408280000067
wherein the content of the first and second substances,
Figure BDA0003712408280000068
representing the mapping relation of forward GRU units;
accordingly, the inputs to the GRU are
Figure BDA0003712408280000069
The input sequence is opposite to the forward GRU, and the reverse output sequence of the hidden layer is obtained through reverse operation (contents of formulas (4) to (7)) and is shown in a formula (9):
Figure BDA00037124082800000610
wherein the content of the first and second substances,
Figure BDA00037124082800000611
representing the mapping relation of backward GRU units;
thus, the hidden layer output at the current moment is obtained as shown in the formula (10):
Figure BDA00037124082800000612
the forward and reverse battery capacity sequence information is effectively combined in the splicing mode, the use efficiency of the information is increased, and therefore the battery capacity prediction effect of the traditional unidirectional GRU is improved.
Inputting the output of a Bidirectional Gated recirculation Unit (BiGRU) network layer into a full connection layer;
the specific process of the full connection layer is as follows:
after obtaining the output of the hidden layer, the TAM-BiGRU network will complete the conversion from the hidden layer to the future battery capacity through the full connection layer, i.e. the formula (11)
Figure BDA00037124082800000613
The mapping relationship of (1):
Figure BDA0003712408280000071
wherein
Figure BDA0003712408280000072
A battery capacity value at the L +1 th moment predicted by a bidirectional gate-controlled cyclic unit network model (TAM-BiGRU) based on a time attention mechanism, wherein eta (eta) represents an entire all-connected layer mapping function, xi (eta)) represents an activation function of an all-connected layer, and W (eta) (. Eta.)) represents an activation function of the all-connected layer 1 And b 1 Weight matrix and offset vector, W, representing the 1 st fully-connected layer, respectively u And b u Respectively representing the weight matrix and the offset vector of the u-th fully-connected layer. />
Other steps and parameters are the same as those in the first embodiment.
The third concrete implementation mode: the second step is to train a bidirectional gating cycle unit network model based on a time attention mechanism; the specific process is as follows:
training the model by taking 80% of historical data of the battery as a training data set, and taking the rest 20% of the historical data as a verification data set to check the prediction effect of the model;
inputting a training data set into the bidirectional gating cycle unit network model which is built in the step one and is based on the time attention mechanism, and constructing a mapping relation between input past battery capacity data and input future capacity data;
selecting and storing a bidirectional gating cycle unit network model with the best prediction effect on the verification data set based on a time attention mechanism for online battery capacity prediction;
the specific process is as follows:
to minimize the difference between the input vector and the output vector, the network parameters of the two-way gated-round cell network model (TAM-BiGRU) based on the time attention mechanism will be parametrically updated by the mean square error loss function shown in equation (12):
Figure BDA0003712408280000073
where T' is the number of training data samples, a is the sample number, and W and b are the set of weight matrix and offset vector W = { W =, respectively s ,W z ,W r ,W c ,U z ,U r ,U c ,W 1 ,…,W u }，b＝{b s ,b z ,b r ,b c ,b 1 ,…,b u }；q a Is the actual battery capacity of the a-th sample,
Figure BDA0003712408280000074
for the predicted battery capacity of the a-th sample, ->
Figure BDA0003712408280000075
The square operation is two norms;
the loss function of the network model training is a mean square error loss function, the optimization algorithm is an Adam optimization algorithm, the learning rate is 0.001, and the network model training process is carried out in the hardware environment of 1 GPU (GTX 1660Ti display card).
Other steps and parameters are the same as those in the first or second embodiment.
The fourth concrete implementation mode: the present embodiment is different from one of the first to third embodiments in that a battery degradation model based on particle filtering is constructed in the third step;
a dynamic degradation model based on particle filtering is constructed to describe the degradation process of the battery through online data of the battery capacity, and the battery degradation model is constructed through the steps of particle initialization, importance sampling, weight updating and normalization and state variable updating, so that the battery degradation model is used for online residual service life prediction.
The specific process is as follows:
compared with a Kalman filtering algorithm and an extended Kalman filtering algorithm, the particle filtering algorithm has more remarkable advantages in processing the parameter identification problem of the nonlinear non-Gaussian system. The particle filter based battery degradation model may be expressed in the form of a state space in the form shown in equation (13):
Figure BDA0003712408280000081
wherein x is k For the state variable at the kth duty cycle (previously offline, which is equivalent to online, k is the duty cycle number from start of commissioning to failed online), x k ＝[a k ,b k ,c k ,d k ] T ，a k For the first component of the state variable at the kth duty cycle (first term of the dual-exponential function in equation (14))
Figure BDA0003712408280000082
Coefficient of (b), b k For the second component of the state variable in the kth operating cycle (first term of the double exponential function in equation (14))>
Figure BDA0003712408280000083
Index of (c)), c) k Is a state variable at the kth work cycleIs determined (in equation (14)) in the second term of the double exponential function>
Figure BDA0003712408280000084
Coefficient of (d), d k For the fourth component of the state variable in the kth operating cycle (second term of the double exponential function in equation (14))>
Figure BDA0003712408280000085
Index of (d); f (x) k ) Is the state variable at the kth duty cycle, f (x) k )＝x k ；u k ＝[u a ,u b ,u c ,u d ] T For the noise term of the state transition equation, u a Is the noise term, u, of the first component of the state variable at the k-th duty cycle b As a noise term, u, of the second component of the state variable at the k-th duty cycle c Noise term, u, being the third component of the state variable at the k-th duty cycle d As a noise term of the fourth component of the state variable at the k-th duty cycle, v k To measure the noise term, v k ∈R 1×1 1 × 1 is a matrix with a length and width of 1,
Figure BDA0003712408280000086
q k battery capacity of kth duty cycle, g (x) k ) Is a measurement equation;
wherein the content of the first and second substances,
Figure BDA0003712408280000087
to measure the variance of the noise, N represents a normal distribution, meaning obeying a certain distribution;
accordingly, the measurement equation can be written in the form of equation (14)
Figure BDA0003712408280000088
According to an initial state variable x 0 By a predetermined mean value of u 0 Variance is
Figure BDA0003712408280000089
Normal distribution p (x) 0 ) Can produce a set of particles->
Figure BDA00037124082800000810
N p Is the number of particles and the initial weight value of each particle is
Figure BDA0003712408280000091
The essence of the particle filtering algorithm is that Bayes filtering and Monte Carlo algorithms are fused, and the posterior probability density function of the particle set is solved through state variable updating and measurement updating. Specifically, for the kth battery operation process, the prior probability density function p (x) k |q 1:k-1 ) Can be expressed in the form shown in equation (15):
p(x k |q 1:k-1 )＝∫p(x k |x k-1 )p(x k-1 |q 1:k-1 )dx k-1 (15)
wherein q is 1:k-1 ＝[q 1 ,q 2 ,…,q k-1 ]Data representing the battery capacity from the initial state to the k-1 th working cycle, p (x) k |q 1:k-1 ) Is x k At q is 1:k-1 Probability density function under the condition, p (x) k |x k-1 ) Is x k At x k-1 A probability density function under the condition;
after the k-th time battery capacity q is obtained k Then, according to bayesian filtering, a probability density function of the state variables under the posterior condition can be obtained, as shown in equation (16):
Figure BDA0003712408280000092
wherein, p (x) k |q 1:k ) Is x k At q is 1:k Probability density function under the condition, p (q) k |x k ) Is q k At x k The probability density function under the conditions of the condition,p(x k |q 1:k-1 ) Is x k At q 1:k-1 Probability density function under the condition, p (q) k |q 1:k-1 ) Is q k At q 1:k-1 Probability density function under the condition, p (x) k |q 1:k-1 ) Is x k At q 1:k-1 A conditional probability density function;
considering the difficulty of the integration operation in equation (16), it is necessary to convert the calculation of the posterior probability density function into the summation of particles by generating a large number of random particles in the manner of equation (17), as shown in equation (17):
Figure BDA0003712408280000093
where delta (.) represents the dirichlet function,
Figure BDA0003712408280000094
the weight represented by the ith particle in the k working process after normalization; />
Figure BDA0003712408280000095
A state variable representing the ith particle;
to solve
Figure BDA0003712408280000096
The weight of the particle may be updated first, as shown in equation (18):
Figure BDA0003712408280000097
wherein the content of the first and second substances,
Figure BDA0003712408280000101
is q k Is at>
Figure BDA0003712408280000102
Probability density function under conditions->
Figure BDA0003712408280000103
Is->
Figure BDA0003712408280000104
In or on>
Figure BDA0003712408280000105
Probability density function under conditions->
Figure BDA0003712408280000106
Is->
Figure BDA0003712408280000107
Is at>
Figure BDA0003712408280000108
Probability density function under conditions->
Figure BDA0003712408280000109
For all state variables for the ith particle from the initial state to the (k-1) th working cycle, a decision is made as to whether the change is positive or negative>
Figure BDA00037124082800001010
For the weight of the ith particle at k-1 working cycles, based on the number of the cells in the sample>
Figure BDA00037124082800001011
The weight of the ith particle under k work cycles;
Figure BDA00037124082800001012
through weight normalization of the particles, a normalized particle weight expression can be obtained as shown in equation (19):
Figure BDA00037124082800001013
to remove the low-weight particles, the iteration continues with the retention of the high-weight particlesIn the invention, the particle filtering is carried out in a random resampling mode to obtain
Figure BDA00037124082800001014
On the basis of the state variable, the state variable under the posterior probability density can be obtained
Figure BDA00037124082800001015
And measuring a variable->
Figure BDA00037124082800001016
As shown in equations (20) to (21):
Figure BDA00037124082800001017
Figure BDA00037124082800001018
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00037124082800001019
for status variables in a posterior condition>
Figure BDA00037124082800001020
Is the cell capacity under posterior conditions>
Figure BDA00037124082800001021
Is the operation result of the posterior state variable through the measurement equation.
Other steps and parameters are the same as those in one of the first to third embodiments.
The fifth concrete implementation mode: the difference between the present embodiment and one of the first to the fourth embodiments is that the remaining service life is predicted online in the fourth step; the specific process is as follows:
assuming that the kth working process of the lithium ion battery is the predicted initial prediction point, the dual prediction point in the form of formula (13) can be obtained by constructing a particle filter-based dualSpecifically, the model parameters of the dual-exponential model are updated according to a particle filtering algorithm by using online battery capacity data of the lithium ion battery from the 1 st working process to the kth working process, and the specific process is shown as a formula (14) - (21). Then, after the k work process, the bidirectional gating circulation unit network of the time attention mechanism will pass through the battery capacity of the k +1 work process in the form of a time window
Figure BDA00037124082800001022
The prediction can be expressed in the form shown in equation (24):
Figure BDA0003712408280000111
/>
wherein, g TAM-BiGRU Mapping of bi-directional gated cyclic units, q, representing a temporal attention mechanism k Represents the battery capacity of the kth work cycle;
on the basis of the formula (24), the predicted value of the battery capacity of the bidirectional gating cycle unit of the time attention mechanism is used as the posterior value of the particle filter double-exponential degradation model at the current moment, so as to guide the updating of the parameters of the particle filter model, and the updated value under the posterior condition of the battery capacity can be obtained through the formulas (17) - (21)
Figure BDA0003712408280000112
This updated mapping relationship can be expressed as shown in equation (25):
Figure BDA0003712408280000113
wherein, g PF Representing the mapping relation of the particle filter algorithm, and then, updating the particle filter
Figure BDA0003712408280000114
Adding to the time window data, deleting the first data in the time window accordingly, and continuingPerforming the above process until the predicted value of the battery capacity reaches the failure threshold value; the number of battery cycles in between is the remaining useful life of the battery.
Suppose the v th (v)>L) Battery Capacity prediction value obtained by iterative prediction
Figure BDA0003712408280000115
For the first time below the failure threshold of the battery, the entire online prediction process can be combined into the form shown in expression (26):
Figure BDA0003712408280000116
wherein, g TAM-BiGRU Mapping of bidirectional gated cyclic units, g, representing a temporal attention mechanism PF Representing the mapping of the particle filter algorithm, q k Represents the battery capacity of the kth work cycle;
the number of cycles v is a predicted value of the remaining service life of the battery during the kth operation.
Other steps and parameters are the same as in one of the first to fourth embodiments.
The sixth specific implementation mode is as follows: the difference between the embodiment and one of the first to fifth embodiments is that the bidirectional gated cyclic unit network model based on the time attention mechanism sequentially comprises three parts of an attention mechanism network, a 3-layer bidirectional gated cyclic unit network and a 2-layer fully-connected layer;
the number of neurons of the 3-layer bidirectional gating circulation unit network is 128;
wherein the number of layer 1 fully-linked layer neurons is 64;
the number of layer 2 full connectivity layer neurons was 128.
The structure of the deep neural network of the time attention mechanism bidirectional gating cycle unit of the invention is shown in FIG. 4.
Other steps and parameters are the same as those in one of the first to fifth embodiments.
The following examples were used to demonstrate the beneficial effects of the present invention:
the first embodiment is as follows:
the invention provides a residual service life prediction method based on digital-analog linkage, which is provided by lithium ion battery data verification provided by NASA Ames prediction center. The data set contains data generated from 4 lithium ion cells (numbered B0005, B0006, B0007, and B0018) rated for 2Ah operating at room temperature. The 4 batteries include two modes of operation, a discharge mode and a charge mode. For the discharge mode, 4 cells will be discharged under a constant current condition of 2A, and the discharge will be stopped until the voltage of each cell drops to a certain threshold. For the charge mode, 4 cells will be charged under a constant current condition of 1.5A until the voltage of the cells rises to 4.2V. Then, the charging was continued again at a constant voltage until the charging current dropped to 20mA. After each charge and discharge cycle is finished, the lithium ion battery obtains internal parameters of the battery in a mode of impedance test, and therefore the battery capacity of the battery at the end of the corresponding cycle is obtained. The cell capacity fade curves for these 4 cells are shown in fig. 3. The related literature indicates that when the capacity of the lithium ion battery is reduced to 70% -80% of the initial capacity, the performance of the battery is difficult to achieve the performance of normal operation. Considering that the B0007 battery did not reach 70% of the initial capacity, that is, 1.4Ah, the present invention sets the degradation threshold of 4 batteries to 1.45Ah for the convenience of study.
Step 1, constructing a bidirectional gating cycle unit network based on a time attention mechanism: the bidirectional gated cycle unit network of the time attention mechanism comprises three parts of an attention mechanism sub-network, a bidirectional gated cycle unit sub-network and a full connection layer sub-network. Thus, a bidirectional gated cyclic cell network based on a temporal attention mechanism can be constructed according to the structure shown in FIG. 4.
Step 2, training a bidirectional gating circulation unit network based on a time attention mechanism: and (3) inputting the historical data of the battery into the neural network built in the step (1) as a training data set of the neural network, and constructing a mapping relation between the input past battery capacity data and the future capacity data. And randomly dividing 80% of the whole data set to serve as a training data set training model, and using the rest 20% of the whole data set as a verification data set to check the prediction effect of the model. The model with the best prediction effect on the verification data set is selected and stored for final prediction. In this embodiment, when one battery is used as test data, the capacity degradation data of the other three batteries is used as a training data training model. For example, when B0005 batteries are used as test data, B0006, B0007 and B0018 batteries are used as training data.
And 3, constructing a battery degradation model based on particle filtering: a dynamic degradation model based on particle filtering is constructed to describe the degradation process of the battery through online data of the battery capacity, and the battery degradation model is constructed through the steps of particle initialization, importance sampling, weight updating and normalization and state variable updating. Assuming that the k-th work cycle is taken as a prediction starting point, a battery degradation model based on particle filtering is constructed from online data from an initial state to the k-th work cycle for the remaining service life prediction of the k-th process as the prediction starting point.
And 4, predicting the remaining service life on line: and combining the particle filter algorithm with the TAM-BiGRU, and mutually correcting the two methods in the prediction process. Specifically, from the set remaining service life prediction start point, the particle filter model parameters are updated using the predicted value of the remaining service life of the TAM-BiGRU as the posterior value of the particle filter, and the predicted value of the battery capacity of the particle filter model is input into the time window for predicting the future battery capacity. The above process is repeated until the predicted battery capacity is less than the battery threshold, and the number of battery cycles that are passed in the middle is the remaining service life of the battery. Finally, each particle is respectively substituted into the model to obtain a confidence interval of the remaining service life, so that the uncertainty of the remaining service life of the battery in the degradation process is well described. Taking the 60 th cycle of each battery as an example, fig. 5a, 5b, 5c, 5d respectively show the true and predicted values of the remaining service life of 4 batteries on the NASA battery data set by the proposed method. It can be seen that the predicted value and the true value of the residual service life in the method provided by the invention are very close, so that the method provided by the invention has a good prediction effect. Table 1 shows the confidence interval of the residual service life prediction of 95%, and it can be seen that the true values of the residual service life basically fall within the confidence interval, so that it can be seen that the method provided by the present invention can well describe the uncertainty of the battery in the degradation process.
TABLE 1 confidence intervals for prediction of 95% of remaining useful life
Figure BDA0003712408280000131
And 5, evaluating the prediction effect of the residual service life: the prediction effect of the residual service life of the lithium ion battery based on digital-analog linkage is measured by adopting Absolute Error (AE), and the specific result is summarized as shown in Table 2.
TABLE 2 Absolute error of remaining life prediction
Figure BDA0003712408280000132
The present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof, and it is therefore intended that all such changes and modifications be considered as within the spirit and scope of the appended claims.

Claims (5)

1. A method for predicting the remaining service life of a lithium ion battery based on digital-analog linkage is characterized by comprising the following steps: the method comprises the following specific processes:
step one, building a bidirectional gating cycle unit network model based on a time attention mechanism; the specific process is as follows:
the bidirectional gating circulation unit network model based on the time attention mechanism sequentially comprises an attention mechanism network, a bidirectional gating circulation unit network and a full connection layer;
step two, training a bidirectional gating cycle unit network model based on a time attention mechanism; the specific process is as follows:
training the model by taking 80% of historical data of the battery as a training data set, and taking the rest 20% of the historical data as a verification data set to check the prediction effect of the model;
inputting a training data set into the bidirectional gating cycle unit network model which is built in the step one and is based on the time attention mechanism, and constructing a mapping relation between input past battery capacity data and input future capacity data;
selecting and storing a bidirectional gating cycle unit network model with the best prediction effect on the verification data set based on a time attention mechanism for online battery capacity prediction;
step three, constructing a battery degradation model based on particle filtering;
step four, predicting the residual service life on line;
building a bidirectional gating cycle unit network model based on a time attention mechanism in the first step;
the bidirectional gating circulation unit network model based on the time attention mechanism sequentially comprises an attention mechanism network, a bidirectional gating circulation unit network and a full connection layer;
the specific process is as follows:
the time window data input to the attention mechanism network may be expressed as:
Q＝[q 1 ,q 2 ,…,q j ,…,q L ]
wherein L is the length of the time window; q. q.s j Is the battery capacity at the jth moment;
constructing battery capacity q at jth moment in time window j And its importance s j The mapping relationship between the two can be expressed as shown in formula (1):
Figure FDA0004117387690000011
wherein s is j Is the importance degree of the jth moment, e is the natural logarithm, T is the transposition operation of the matrix, sigma is the sigmoid activation function, W j And b j Weights and offsets representing respective time instants;
the weights of the attention mechanism network can be written as W s ＝[W 1 ,…,W j ,…,W L ]，b s ＝[b 1 ,…,b j ,…,b L ]；
The importance degree is normalized by a softmax function, and the expression is shown as (2):
Figure FDA0004117387690000021
wherein alpha is j Representing the degree of importance of the respective moment;
on the basis, the output of the attention mechanism network can be obtained
Figure FDA0004117387690000022
The expression is shown in formula (3):
Figure FDA0004117387690000023
wherein the content of the first and second substances,
Figure FDA0004117387690000024
the output of the network layer is controlled for attention at the jth moment;
will be provided with
Figure FDA0004117387690000025
Inputting the data into a bidirectional gating circulation unit network; />
The specific process of the bidirectional gating circulation unit network is as follows:
the calculation formula of the gating cycle unit is shown as the formula (4) to the formula (7):
Figure FDA0004117387690000026
Figure FDA0004117387690000027
Figure FDA0004117387690000028
Figure FDA0004117387690000029
wherein the content of the first and second substances,
Figure FDA00041173876900000216
representing the input vector of the cell at time t, h t And h t-1 Representing the outputs of the network elements of the unit at time t and time t-1, respectively, z t 、r t 、c t Respectively representing the outputs of the refresh gate, reset gate and memory cell, W z 、W r 、W c Connection matrix, U, representing the input information of the refresh gate, reset gate and memory cell, respectively z And b z Respectively representing the weight and offset vector, U, of the update gate r And b r Respectively representing the weight and offset vector, U, of the reset gate c And b c Respectively representing the weight and the offset vector of the memory unit, sigma representing a sigmoid activation function, tanh representing a hyperbolic tangent function, and/or->
Figure FDA00041173876900000210
Representing a dot product operation;
with a bidirectional GRU network, the input to the forward GRU is
Figure FDA00041173876900000211
Obtaining forward output of the hidden layerThe sequence is shown as formula (8):
Figure FDA00041173876900000212
wherein the content of the first and second substances,
Figure FDA00041173876900000213
representing the mapping relation of forward GRU units;
accordingly, the inputs to the GRU are
Figure FDA00041173876900000214
Obtaining the reverse output sequence of the hidden layer is shown as formula (9):
Figure FDA00041173876900000215
wherein the content of the first and second substances,
Figure FDA0004117387690000031
representing the mapping relation of backward GRU units;
the hidden layer output at the current moment is obtained as shown in formula (10):
Figure FDA0004117387690000032
inputting the output of the network layer of the bidirectional gating circulation unit into a full connection layer;
the specific process of the full connection layer is as follows:
equation (11) accomplishes the transformation from hidden layer to future battery capacity
Figure FDA0004117387690000033
The mapping relationship of (1):
Figure FDA0004117387690000034
wherein
Figure FDA0004117387690000035
The battery capacity value at the L +1 th moment predicted by the bidirectional gating circulation unit network model based on the time attention mechanism is represented by eta (eta)) in a mapping function of the whole full-connection layer, xi (eta)) in an activation function of the full-connection layer, and W 1 And b 1 Weight matrix and offset vector, W, representing the 1 st fully-connected layer, respectively u And b u Respectively representing the weight matrix and the offset vector of the u full-link layer.
2. The method for predicting the remaining service life of the lithium ion battery based on digital-analog linkage according to claim 1, wherein the method comprises the following steps: training a bidirectional gating cycle unit network model based on a time attention mechanism in the second step; the specific process is as follows:
taking 80% of historical data of the battery as a training data set training model, and taking the rest 20% of the historical data as a verification data set to test the prediction effect of the model;
inputting the training data set into the bidirectional gating cycle unit network model which is built in the first step and is based on the time attention mechanism, and building a mapping relation between input past battery capacity data and input future capacity data;
selecting and storing a bidirectional gating cycle unit network model with the best prediction effect on the verification data set based on a time attention mechanism for online battery capacity prediction;
the specific process is as follows:
the network parameters of the bidirectional gated cyclic unit network model based on the temporal attention mechanism will be updated parametrically by the mean square error loss function shown in equation (12):
Figure FDA0004117387690000036
wherein T' is a training data sampleA is the number of samples, W and b are the set of weight matrix and offset vector W = { W =, respectively s ,W z ,W r ,W c ,U z ,U r ,U c ,W 1 ,…,W u }，b＝{b s ,b z ,b r ,b c ,b 1 ,…,b u }；q a Is the actual battery capacity of the a-th sample,
Figure FDA0004117387690000037
for the predicted battery capacity of the a-th sample, ->
Figure FDA0004117387690000038
The square operation of two norms is carried out;
the loss function of the network model training is a mean square error loss function, the optimization algorithm is an Adam optimization algorithm, the learning rate is 0.001, and the network model training process is carried out in the hardware environment of 1 GPU.
3. The method for predicting the remaining service life of the lithium ion battery based on digital-analog linkage according to claim 2, wherein the method comprises the following steps: in the third step, a battery degradation model based on particle filtering is constructed;
the specific process is as follows:
the particle filter based battery degradation model may be expressed in the form of a state space in the form shown in equation (13):
Figure FDA0004117387690000041
wherein x is k Is a state variable at the kth duty cycle, x k ＝[a k ,b k ,c k ,d k ] T ，a k Is the first component of the state variable at the kth duty cycle, b k As a second component of the state variable at the k-th duty cycle, c k Is the third component of the state variable at the k-th duty cycle, d k Is the fourth component of the state variable at the kth duty cycle; f (x) k ) Is the state variable at the kth duty cycle, f (x) k )＝x k ；u k ＝[u a ,u b ,u c ,u d ] T For the noise term of the state transition equation, u a Is the noise term, u, of the first component of the state variable at the k-th duty cycle b As a noise term, u, of the second component of the state variable at the k-th duty cycle c Noise term, u, being the third component of the state variable at the k-th duty cycle d As a noise term of the fourth component of the state variable at the k-th duty cycle, v k In order to measure the noise term(s),
Figure FDA0004117387690000042
q k battery capacity, g (x) for the k-th duty cycle k ) Is a measurement equation;
wherein the content of the first and second substances,
Figure FDA0004117387690000043
to measure the variance of the noise, N represents a normal distribution, the representation obeys a certain distribution;
accordingly, the measurement equation can be written in the form of equation (14)
Figure FDA0004117387690000044
According to an initial state variable x 0 By a predetermined mean value of u 0 Variance is
Figure FDA0004117387690000045
Normal distribution p (x) 0 ) Can produce a set of particles->
Figure FDA0004117387690000046
N p Is the number of particles and the initialization weight value of each particle is->
Figure FDA0004117387690000047
For the kth battery operation, the prior probability density function p (x) k |q 1:k-1 ) Can be expressed in the form shown in equation (15):
p(x k |q 1:k-1 )＝∫p(x k |x k-1 )p(x k-1 |q 1:k-1 )dx k-1 (15)
wherein q is 1:k-1 ＝[q 1 ,q 2 ,…,q k-1 ]Representing the battery capacity data from the initial state to the k-1 th working cycle, p (x) k |q 1:k-1 ) Is x k At q 1:k-1 Probability density function under the condition, p (x) k |x k-1 ) Is x k At x k-1 A probability density function under the condition;
after the k-th battery capacity q is obtained k Then, according to bayesian filtering, a probability density function of the state variables under the posterior condition can be obtained, as shown in equation (16):
Figure FDA0004117387690000051
wherein, p (x) k |q 1:k ) Is x k At q 1:k Probability density function under the condition, p (q) k |x k ) Is q k At x k Probability density function under the condition, p (x) k |q 1:k-1 ) Is x k At q 1:k-1 Probability density function under the condition, p (q) k |q 1:k-1 ) Is q k At q 1:k-1 Probability density function under the condition, p (x) k |q 1:k-1 ) Is x k At q 1:k-1 A probability density function under the condition;
the calculation of the posterior probability density function is converted to a summation of particles, as shown in equation (17):
Figure FDA0004117387690000052
where delta (.) represents the dirichlet function,
Figure FDA0004117387690000053
the weight represented by the ith particle in the k working process after normalization; />
Figure FDA0004117387690000054
A state variable representing the ith particle;
to solve
Figure FDA0004117387690000055
The weight of the particle may be updated first, as shown in equation (18):
Figure FDA0004117387690000056
wherein the content of the first and second substances,
Figure FDA0004117387690000057
is q k In or on>
Figure FDA0004117387690000058
Probability density function under conditions->
Figure FDA0004117387690000059
Is->
Figure FDA00041173876900000510
Is at>
Figure FDA00041173876900000511
Probability density function under conditions->
Figure FDA00041173876900000512
Is->
Figure FDA00041173876900000513
Is at>
Figure FDA00041173876900000514
Probability density function under conditions->
Figure FDA00041173876900000515
For all state variables for the ith particle from the initial state to the (k-1) th working cycle, a decision is made as to whether the change is positive or negative>
Figure FDA00041173876900000516
For the weight of the ith particle at k-1 working cycles, based on the number of the cells in the sample>
Figure FDA00041173876900000517
The weight of the ith particle under k work cycles;
Figure FDA00041173876900000518
through weight normalization of the particles, a normalized particle weight expression can be obtained as shown in equation (19):
Figure FDA0004117387690000061
the particle filtering is carried out by adopting a random resampling mode to obtain
Figure FDA0004117387690000062
The state variable under the posterior probability density can be obtained
Figure FDA0004117387690000063
And measuring a variable->
Figure FDA0004117387690000064
As shown in equations (20) to (21): />
Figure FDA0004117387690000065
Figure FDA0004117387690000066
Wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0004117387690000067
for a state variable under posterior conditions, <' >>
Figure FDA0004117387690000068
Is the cell capacity under posterior conditions>
Figure FDA0004117387690000069
Is the operation result of the posterior state variable through the measurement equation.
4. The method for predicting the remaining service life of the lithium ion battery based on digital-analog linkage according to claim 3, wherein the method comprises the following steps: predicting the remaining service life on line in the fourth step; the specific process is as follows:
suppose the v th (v)>L) Battery Capacity prediction value obtained by iterative prediction
Figure FDA00041173876900000610
For the first time below the failure threshold of the battery, the entire online prediction process can be put together in the form shown by expression (26):
Figure FDA00041173876900000611
wherein, g TAM-BiGRU Mapping relation of bidirectional gating circulation unit for representing time attention mechanism，g PF Representing the mapping of the particle filter algorithm, q k Represents the battery capacity of the kth work cycle;
the number of cycles v is a predicted value of the remaining service life of the battery during the kth operation.
5. The method for predicting the remaining service life of the lithium ion battery based on digital-analog linkage according to claim 4, wherein the method comprises the following steps: the bidirectional gating circulation unit network model based on the time attention mechanism sequentially comprises three parts of an attention mechanism network, a 3-layer bidirectional gating circulation unit network and a 2-layer full-connection layer;
the number of neurons of the 3-layer bidirectional gating circulation unit network is 128;
wherein the number of layer 1 fully-linked layer neurons is 64;
the number of layer 2 full connectivity layer neurons was 128.
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111103544A (en) * 2019-12-26 2020-05-05 江苏大学 Lithium ion battery remaining service life prediction method based on long-time and short-time memory LSTM and particle filter PF
CN112784798A (en) * 2021-02-01 2021-05-11 东南大学 Multi-modal emotion recognition method based on feature-time attention mechanism
CN113094822A (en) * 2021-03-12 2021-07-09 华中科技大学 Method and system for predicting residual life of mechanical equipment
CN113204921A (en) * 2021-05-13 2021-08-03 哈尔滨工业大学 Method and system for predicting remaining service life of airplane turbofan engine
CN113971489A (en) * 2021-10-25 2022-01-25 哈尔滨工业大学 Method and system for predicting remaining service life based on hybrid neural network
CN114186500A (en) * 2022-02-16 2022-03-15 华中科技大学 Marine bearing residual life prediction method based on transfer learning and multiple time windows
CN114266278A (en) * 2021-12-29 2022-04-01 合肥工业大学 Dual-attention-network-based method for predicting residual service life of equipment

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104459560B (en) * 2014-12-29 2017-04-05 合肥工业大学 Lithium battery method for predicting residual useful life based on wavelet de-noising and Method Using Relevance Vector Machine
US10209314B2 (en) * 2016-11-21 2019-02-19 Battelle Energy Alliance, Llc Systems and methods for estimation and prediction of battery health and performance

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111103544A (en) * 2019-12-26 2020-05-05 江苏大学 Lithium ion battery remaining service life prediction method based on long-time and short-time memory LSTM and particle filter PF
CN112784798A (en) * 2021-02-01 2021-05-11 东南大学 Multi-modal emotion recognition method based on feature-time attention mechanism
CN113094822A (en) * 2021-03-12 2021-07-09 华中科技大学 Method and system for predicting residual life of mechanical equipment
CN113204921A (en) * 2021-05-13 2021-08-03 哈尔滨工业大学 Method and system for predicting remaining service life of airplane turbofan engine
CN113971489A (en) * 2021-10-25 2022-01-25 哈尔滨工业大学 Method and system for predicting remaining service life based on hybrid neural network
CN114266278A (en) * 2021-12-29 2022-04-01 合肥工业大学 Dual-attention-network-based method for predicting residual service life of equipment
CN114186500A (en) * 2022-02-16 2022-03-15 华中科技大学 Marine bearing residual life prediction method based on transfer learning and multiple time windows

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