CN115879378A - Training method and device for expansion force prediction model of battery cell - Google Patents
Training method and device for expansion force prediction model of battery cell Download PDFInfo
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Abstract
The embodiment of the application provides a training method and a device of an expansion force prediction model of an electric core, a method for predicting the expansion force of the electric core, computer equipment and a storage medium, wherein the training method of the expansion force prediction model of the electric core comprises the steps of obtaining the expansion force of the electric core at different moments and actual values of temperature, voltage and current corresponding to the expansion force, generating a sample data set, dividing the sample data set into a training set and a verification set, training a model built based on a neural network by taking the training set as model input, verifying the trained model by taking the verification set as model input, obtaining the expansion force prediction model of the electric core, predicting the future expansion force by adopting multi-input single output, namely combining a plurality of inputs of voltage, current, temperature and expansion force, improving the accuracy of the model, considering the time sequence among the expansion force data (namely the influence of the current state on the subsequent state change), and predicting the expansion force change after a long time.
Description
Technical Field
The present disclosure relates to the field of battery production technologies, and in particular, to a method and an apparatus for training a swelling force prediction model of a battery cell, a method for predicting a swelling force of a battery cell, a computer device, and a storage medium.
Background
With the development of technology, batteries have become one of the important energy sources for human beings, provide continuous and stable working electric energy for devices in various fields, and gradually become an indispensable product in the development of human science and technology, wherein lithium batteries are widely applied due to high energy density and long cycle life. However, a large amount of heat is generated in the use process of the battery, and particularly, the heat generated during the operation of a large-sized battery pack in a closed environment is considerable, so that the performance of the whole battery pack is influenced, and dangerous accidents such as fire and explosion of the battery pack can be caused even by the heat accumulated for a long time.
At present, the fire and explosion of the battery have adverse effects on the society. In order to play a role in predicting an alarm, the expansion force of the battery core can be tested, but the current expansion force prediction technology is still not mature, so that the phenomena of delay in early warning, insensitive response and the like exist.
Therefore, a new method for predicting the swelling force is needed to solve the above problems.
Disclosure of Invention
In order to solve one or more technical problems in the prior art, embodiments of the present application provide a training method and apparatus for a swelling force prediction model of a battery cell, a swelling force prediction method of a battery cell, a computer device, and a storage medium, so as to solve the problems of early warning delay, insensitive response, and the like caused by an immature battery cell swelling force prediction technology in the prior art.
In order to achieve the above object, the technical solution adopted by the present application to solve the technical problem is:
in a first aspect, the present application provides a method for training an expansion force prediction model of a battery cell, where the method includes:
the method comprises the steps of obtaining the expansion force of a battery cell at different moments and actual values of temperature, voltage and current corresponding to the expansion force, and generating a sample data set;
dividing the sample data set into a training set and a verification set;
and training a model built based on a neural network by taking the training set as model input, and verifying the trained model by taking the verification set as model input to obtain an expansibility prediction model of the battery cell.
In a specific embodiment, before dividing the sample data set into a training set and a verification set, the method further includes:
and respectively carrying out normalization processing on the actual values of the expansion force, the temperature, the voltage and the current in the sample data set.
In a specific embodiment, the calculation formula of the normalization process is:
wherein, F is one of the actual values of the expansion force, the temperature, the voltage and the current, fmin is the minimum value of the value range of the expansion force, the temperature, the voltage or the current, and Fmax is the maximum value of the value range of the expansion force, the temperature, the voltage or the current.
In a specific embodiment, the training of the model built based on the neural network with the training set as the model input includes:
step one, inputting the normalized data at the first moment in the training set into a model built based on a neural network to obtain a predicted value of the expansion force at the second moment;
calculating to obtain a loss value according to the predicted value and the actual value of the expansion force at the second moment;
and step three, repeating the steps from the first step to the second step until the loss value is smaller than a preset threshold value or the iteration times exceed a preset iteration time upper limit.
In a specific embodiment, the calculation formula for calculating the loss value according to the predicted value and the actual value of the expansion force is as follows:
where N is the number of data samples brought in,
is a predictive value of the expansion force output by the model at the moment i>
The actual value of the expansion force at time i.
In a specific embodiment, the neural network comprises a LSTM.
In a second aspect, the present application further provides a training apparatus for an expansion force prediction model of a battery cell, where the apparatus includes:
the data acquisition module is used for acquiring the expansion force of the battery cell at different moments and actual values of temperature, voltage and current corresponding to the expansion force, and generating a sample data set;
the data processing module is used for dividing the sample data set into a training set and a verification set;
and the model acquisition module is used for training the model built based on the neural network by taking the training set as model input, verifying the trained model by taking the verification set as model input, and acquiring the expansibility test model of the battery cell.
In a third aspect, a method for predicting an expansion force of a battery cell is further provided, where the method includes:
acquiring a current expansion force corresponding to the current moment of the battery core to be predicted and a temperature, a voltage and a current corresponding to the current expansion force;
normalizing the current expansion force and the temperature, the voltage and the current corresponding to the current expansion force to obtain a normalization result;
and inputting the normalization result into the expansion force prediction model of the battery cell obtained according to the training method of the expansion force prediction model of the battery cell, and obtaining the predicted value of the expansion force of the battery cell to be predicted at the moment to be predicted.
In a fourth aspect, a computer device is further provided, which includes a memory and a processor, where the memory stores a computer program executable on the processor, and when the computer program is executed by the processor, the computer device implements a training method for an expansion force prediction model of a battery cell.
In a fifth aspect, a computer-readable storage medium is further provided, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed, the computer program implements a method for training an expansion force prediction model of a battery cell.
The technical scheme provided by the embodiment of the application has the following beneficial effects:
the training method of the expansion force prediction model of the battery cell comprises the steps of obtaining the expansion force of the battery cell at different moments and actual values of temperature, voltage and current corresponding to the expansion force, generating a sample data set, dividing the sample data set into a training set and a verification set, training the model built based on a neural network by taking the training set as model input, verifying the trained model by taking the verification set as model input, obtaining the expansion force prediction model of the battery cell, predicting the future expansion force by adopting multiple input and single output, namely combining multiple inputs of voltage, current, temperature and expansion force, improving the accuracy of the model, considering the time sequence among the expansion force data (namely the influence of the current state on the change of the subsequent state), and predicting the change of the expansion force after a long time;
further, according to the training method and device for the expansion force prediction model of the battery cell, the expansion force prediction method of the battery cell, the computer device and the storage medium provided by the embodiment of the application, in the model training process, the loss value is calculated by adopting a cost function (RMSE) to perform repeated iteration until the loss value is smaller than a preset loss value or the iteration number exceeds the maximum iteration number upper limit, so that the accuracy of the model obtained by training is higher.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a training method of an expansion force prediction model of a battery cell according to an embodiment of the present application;
FIG. 2 is a block diagram of an LSTM unit provided in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a training device of an expansion force prediction model of a battery cell provided in an embodiment of the present application;
fig. 4 is a flowchart of a method for predicting an expansion force of a battery cell according to an embodiment of the present application;
fig. 5 is a mechanism diagram of a computer device provided in an embodiment of the present application.
Detailed Description
In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
As described in the background art, the expansion prediction technology of the battery cell in the prior art is not mature, which results in the phenomena of delay in early warning, insensitive response and the like, and therefore, the expansion prediction technology of the battery cell in the prior art cannot meet the accurate prediction of the expansion of the battery cell, and cannot make an accurate early warning.
In order to solve one or more of the problems, the present application creatively provides a new training method for an expansion force prediction model of a battery cell, in the method, in the training process of the expansion force prediction model of the battery cell, on one hand, different from the existing model input and output mode, multiple inputs and single outputs are adopted, that is, multiple inputs of voltage, current, temperature and expansion force are combined to predict future expansion force, so as to improve the accuracy of the model, and on the other hand, a cost function (RMSE) is adopted to calculate a loss value to perform repeated iteration until the loss value is smaller than a preset loss value or the iteration number exceeds the maximum iteration number upper limit, so as to further improve the accuracy of the model obtained by training.
The embodiments of the present application will be described in detail below with reference to the drawings and various embodiments.
Example one
In order to implement the solution of the present application, an embodiment of the present application provides a training method for an expansion force prediction model of a battery cell, and as shown in fig. 1, the method includes the following steps:
s110: and acquiring the expansion force of the battery cell at different moments and actual values of temperature, voltage and current corresponding to the expansion force, and generating a sample data set.
Specifically, in order to improve the accuracy of the prediction result of the model, the model is different from the input and output mode adopted by the model in the prior art in that the model trained in the implementation of the present application adopts a multi-input and single-output mode, and the factors such as voltage, current and temperature which affect the expansion force of the battery cell and the expansion force at the preorder moment are all used as the input of the model to predict the expansion force at a certain future moment, so that on one hand, the accuracy of the prediction result of the model is improved, and on the other hand, the time sequence among the expansion force data is considered, that is, the influence of the current state on the subsequent state change is considered, so that the expansion force change after a long time can be predicted.
Based on the above reasons, in the embodiment of the application, before model training, the cell expansion force of the target cell at each time and the corresponding actual values of voltage, current and temperature are collected, and a large data set generated after the four types of data are in one-to-one correspondence is used as a sample data set. It should be noted that, in the embodiment of the present application, there is no specific limitation on the target battery cell, and any battery cell that may generate an expansion force may be used as the target battery cell in the present application, and preferably, the target battery cell is the same type or the same type of battery cell as a subsequent battery cell to be predicted, where the expansion force needs to be predicted.
S120: the sample data set is divided into a training set and a verification set.
Specifically, in the model training process, the model needs to be trained by using sample data, and the model after training needs to be verified by using the sample data to find the optimal model, so that after the sample data set is generated, the sample data set can be divided into two parts, namely a training set and a verification set, according to a certain proportion. It should be noted that, in the embodiment of the present application, no specific limitation is made on the division ratio, and a user may set according to actual training requirements without violating the inventive concept of the present application, for example, 70% of the sample data set is divided into a training set, and the remaining 30% is divided into a verification set.
S130: and training a model built based on a neural network by taking the training set as model input, and verifying the trained model by taking the verification set as model input to obtain an expansive force prediction model of the battery cell.
Specifically, after training data are prepared, a neural network model is built at first, preferably, the neural network comprises an LSTM, the neural network model built in advance is trained by using data in a training set, after the training is completed, the model obtained by the training is verified by using data in a verification set, and an optimal model is obtained to serve as the expansion force prediction model of the battery cell in the application.
As a preferred implementation manner, in the embodiment of the present application, before dividing the sample data set into a training set and a verification set, the method further includes:
and respectively carrying out normalization processing on the actual values of the expansion force, the temperature, the voltage and the current in the sample data set.
Specifically, before the sample data set is divided into the training set and the verification set, normalization processing may be performed on the data in the sample data set to remove dimensions, that is, to remove non-uniformity among data units, so as to remove influence of the units on numerical values, and enable all variables to participate in subsequent processing on the same level.
As a preferred implementation manner, in the embodiment of the present application, a calculation formula of the normalization processing is:
wherein,
the expansion force is one of actual values of expansion force, temperature, voltage and current, fmin is the minimum value of a value range of the expansion force, the temperature, the voltage or the current, and Fmax is the maximum value of the value range of the expansion force, the temperature, the voltage or the current.
Specifically, all data in the sample data set need to be normalized, taking the normalization of the expansion force as an example,
and in the same way, the normalization processing of the temperature, the voltage or the current can refer to the calculation mode of the expansion force.
As a preferred implementation manner, in this embodiment of the present application, the training the model built based on the neural network with the training set as the model input includes:
step one, inputting the normalized data at the first moment in the training set into a model built based on a neural network to obtain a predicted value of the expansion force at the second moment;
calculating to obtain a loss value according to the predicted value and the actual value of the expansion force at the second moment;
and step three, repeating the steps from the first step to the second step until the loss value is smaller than a preset threshold value or the iteration times exceed a preset iteration time upper limit.
Specifically, taking the neural network employed as LSTM for example, it generally includes an input layer, a hidden layer, and an output layer. After the data is normalized, the data in the training set is sequentially input into a pre-built neural network model, wherein X is 1 (T 1 ,V 1 ,C 1 ,F 1 )、 X 2 (T 2 ,V 2 ,C 2 ,F 2 ) …X n (T n ,V n ,C n ,F n ) The method sequentially shows 1 st and 2 nd moments \8230, the temperature, voltage, current and expansion force corresponding to the nth moment, y1 and y2 \8230, and yn sequentially shows predicted values of the expansion force at the next moment predicted according to the 1 st and 2 nd moments \8230andthe temperature, voltage, current and expansion force corresponding to the nth moment, wherein the predicted values of the expansion force at the next moment correspond to actual values of the expansion force at a certain moment in a training set. And then calculating the loss value of the predicted expansion force at the next moment and the actual value of the expansion force corresponding to the moment, and repeating iteration until the loss value is smaller than a preset threshold value or the iteration number exceeds a preset iteration number upper limit. And updating the parameters of the LSTM unit to the parameters under the optimal loss value to serve as an expansion force prediction model of the battery cell.
Wherein, the calculation formula of the loss value calculated according to the predicted value and the actual value of the expansion force is as follows:
where N is the number of data samples brought in,
for the prediction of the expansion force output by the model at point i, in conjunction with the evaluation of the actual value of the expansion force>
The actual value of the expansion force at time i.
Referring to fig. 2, as a preferred implementation, in the embodiment of the present application, the LSTM model includes a general forgetting gate, an input gate, and an output gate, where Xt represents an input of the network at the current time, ht-1 represents an output value of the LSTM unit at the previous time, and Ct-1 represents a state of the unit at the previous time.
Specifically, the forgetting gate is represented as:
the forgetting gate determines how much data information can be retained by the cell state Ct-1 at the previous time to the current time Ct,
middle sigma refers to Sigmoid activation function, <' > or>
A weight matrix for a forgetting gate, ->
Is a bias term.
Has a numerical value range of [0, 1%]A 0 indicates that the information is discarded and a 1 indicates that the information is completely retained.
Wherein the sigmoid function formula is as follows:
wherein,
is expressed as->
。
The input gates are represented as:
the input gate determines how much data information of the input Xt of the network is retained in the cell state at the present moment
. The input gate is divided into two parts, one part for finding the state to be reserved in the input Xt and for ^ ing the section in the column>
And &>
Descriptionthe other part is to update>
The state of (c). Wherein it is present>
Is the weight matrix of the input gate, < > is>
Is the bias term.
The output gate is represented as
Output door control unit status
How much to output to the current output value of LSTM. Wherein +>
Is the weight matrix of the input gate, < > is>
Is the bias term.
Wherein, the formula of the tanh function in the output gate is as follows:
wherein in the formula
Represents->
。
In summary, the LSTM model in the present application adopts multiple inputs and single outputs, so that after a certain amount of data is trained, the model accuracy is high, and meanwhile, because the time sequence between the expansion force data (i.e. the influence of the current state on the subsequent state change) is considered, the expansion force change after a long time can be predicted.
Example two
Corresponding to the first embodiment, the present application further provides a training device for a model for predicting an expansion force of an electrical core, where in this embodiment, the same or similar contents as those in the first embodiment may be referred to the above description, and are not repeated in the following. Referring to fig. 3, the apparatus includes:
the data acquisition module is used for acquiring the expansion force of the battery cell at different moments and actual values of temperature, voltage and current corresponding to the expansion force, and generating a sample data set;
the data processing module is used for dividing the sample data set into a training set and a verification set;
and the model acquisition module is used for training the model built based on the neural network by taking the training set as model input, verifying the trained model by taking the verification set as model input, and acquiring the expansibility test model of the battery cell.
EXAMPLE III
Corresponding to the first and second embodiments, the present application further provides a method for predicting an expansion force of an electrical core, where in this embodiment, the same or similar contents as those in the first or second embodiment may be referred to the above description, and are not repeated in the following. Referring to fig. 4, the method includes the steps of:
s210: acquiring a current expansion force corresponding to the current moment of the battery core to be predicted and a temperature, a voltage and a current corresponding to the current expansion force;
s220: normalizing the current expansion force and the temperature, voltage and current corresponding to the current expansion force to obtain a normalization result;
s230: and inputting the normalization result into the expansion force prediction model of the battery cell obtained according to the training method of the expansion force prediction model of the battery cell, and obtaining the predicted value of the expansion force of the battery cell to be predicted at the moment to be predicted.
Example four
Corresponding to the first to third embodiments, the present application further provides a computer device, including: the device comprises a processor and a memory, wherein the memory is stored with a computer program which can run on the processor, and when the computer program is executed by the processor, the training method of the expansion force prediction model of the battery cell provided by any one of the above embodiments is executed.
FIG. 5 illustrates, among other things, a computer device 1500 that can include, among other things, a processor 1510, a video display adapter 1511, a disk drive 1512, an input/output interface 1513, a network interface 1514, and a memory 1520. The processor 1510, video display adapter 1511, disk drive 1512, input/output interface 1513, network interface 1514, and memory 1520 may be communicatively coupled via a communication bus 1530.
The processor 1510 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solution provided by the present invention.
The Memory 1520 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, or the like. The memory 1520 may store an operating system 1521 for controlling the operation of the electronic device, a Basic Input Output System (BIOS) 1522 for controlling low-level operations of the electronic device. In addition, a web browser 1523, a data storage management system 1524, and a device identification information processing system 1525 and the like can also be stored. The device identification information processing system 1525 may be an application program that implements the operations of the foregoing steps in the embodiment of the present invention. In summary, when the technical solution provided by the present invention is implemented by software or firmware, the relevant program codes are stored in the memory 1520 and called for execution by the processor 1510.
The input/output interface 1513 is used for connecting an input/output module to realize information input and output. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The network interface 1514 is used to connect a communication module (not shown) to enable the communication interaction of the present device with other devices. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, bluetooth and the like).
The bus includes a path that transfers information between the various components of the device, such as the processor 1510, the video display adapter 1511, the disk drive 1512, the input/output interface 1513, the network interface 1514, and the memory 1520.
In addition, the electronic device may further obtain information of specific pickup conditions from the virtual resource object pickup condition information database for performing condition judgment, and the like.
It should be noted that although the above devices only show the processor 1510, the video display adapter 1511, the disk drive 1512, the input/output interface 1513, the network interface 1514, the memory 1520, the bus, etc., in the implementation, the device may also include other components necessary for normal operation. Furthermore, it will be understood by those skilled in the art that the apparatus described above may also include only the components necessary to implement the inventive arrangements, and need not include all of the components shown in the figures.
EXAMPLE five
Corresponding to the first to third embodiments, embodiments of the present application further provide a computer-readable storage medium, where in this embodiment, the same or similar contents as those in the first to third embodiments may refer to the above description, and are not repeated herein.
The computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring the expansion force of the battery cell at different moments and actual values of temperature, voltage and current corresponding to the expansion force, and generating a sample data set;
dividing the sample data set into a training set and a verification set;
and training a model built based on a neural network by taking the training set as model input, and verifying the trained model by taking the verification set as model input to obtain an expansibility prediction model of the battery cell.
In some embodiments, in the embodiments of the present application, when the computer program is executed by the processor, steps corresponding to the method in the first embodiment may also be implemented, which may refer to the detailed description in the first embodiment, and are not repeated herein.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
All the embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, the system or system embodiments, which are substantially similar to the method embodiments, are described in a relatively simple manner, and reference may be made to some descriptions of the method embodiments for relevant points. The above-described system and system embodiments are only illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The technical solutions provided by the present invention are described in detail above, and the principles and embodiments of the present invention are explained herein by using specific examples, which are merely used to help understanding the method and the core ideas of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A training method of an expansion force prediction model of a battery cell is characterized by comprising the following steps:
acquiring the expansion force of the battery cell at different moments and actual values of temperature, voltage and current corresponding to the expansion force, and generating a sample data set;
dividing the sample data set into a training set and a verification set;
and training a model built based on a neural network by taking the training set as model input, and verifying the trained model by taking the verification set as model input to obtain an expansibility prediction model of the battery cell.
2. The training method of the expansion force prediction model of the electric core according to claim 1, wherein the sample data set is divided into a training set and a validation set, and the method further comprises:
and respectively carrying out normalization processing on the actual values of the expansion force, the temperature, the voltage and the current in the sample data set.
3. The method for training the expansion force prediction model of the battery cell according to claim 2, wherein the calculation formula of the normalization process is as follows:
wherein,
the expansion force is one of actual values of expansion force, temperature, voltage and current, fmin is the minimum value of a value range of the expansion force, the temperature, the voltage or the current, and Fmax is the maximum value of the value range of the expansion force, the temperature, the voltage or the current.
4. The method for training the expansion force prediction model of the electric core according to any one of claims 1 to 3, wherein the training of the model built based on the neural network with the training set as the model input comprises:
step one, inputting the normalized data at the first moment in the training set into a model built based on a neural network to obtain a predicted value of the expansion force at the second moment;
calculating to obtain a loss value according to the predicted value and the actual value of the expansion force at the second moment;
and step three, repeating the steps from the first step to the second step until the loss value is smaller than a preset threshold value or the iteration times exceed a preset iteration time upper limit.
5. The method for training the expansion force prediction model of the battery core according to claim 4, wherein the calculation formula for calculating the loss value according to the predicted value and the actual value of the expansion force is as follows:
where N is the number of data samples brought in,
is a predictive value of the expansion force output by the model at the moment i>
The actual value of the expansion force at time i.
6. The training method for the expansion force prediction model of the battery cell according to any one of claims 1 to 3, wherein the neural network comprises LSTM.
7. A training device for an expansion force prediction model of a battery cell is characterized by comprising:
the data acquisition module is used for acquiring the expansion force of the battery cell at different moments and actual values of temperature, voltage and current corresponding to the expansion force, and generating a sample data set;
the data processing module is used for dividing the sample data set into a training set and a verification set;
and the model acquisition module is used for training the model built based on the neural network by taking the training set as model input, verifying the trained model by taking the verification set as model input, and acquiring the expansibility test model of the battery cell.
8. A method for predicting an expansion force of a battery cell, the method comprising:
acquiring a current expansion force corresponding to the current moment of the battery core to be predicted and a temperature, a voltage and a current corresponding to the current expansion force;
normalizing the current expansion force and the temperature, voltage and current corresponding to the current expansion force to obtain a normalization result;
inputting the normalization result into the expansion force prediction model of the battery cell obtained by the method according to any one of claims 1 to 6, and obtaining a predicted value of the expansion force of the battery cell to be predicted at the time to be predicted.
9. A computer device comprising a memory and a processor, the memory having stored thereon a computer program executable on the processor, the computer program, when executed by the processor, implementing the method of training a predictive model of the expansion force of a cell of any of claims 1 to 6.
10. A computer-readable storage medium, in which a computer program is stored, and when the computer program is executed, the method for training the expansion force prediction model of the battery cell according to any one of claims 1 to 6 is implemented.
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