CN116840692A - Method, device, equipment, medium and program for estimating composite electrode cell aging - Google Patents
Method, device, equipment, medium and program for estimating composite electrode cell aging Download PDFInfo
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- 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
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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]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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Abstract
The application discloses a method, a device, equipment, a medium and a program for estimating the aging of a composite electrode battery, wherein the method comprises the following steps: receiving the temperature, the SOC and the OCV of the composite electrode battery; based on the temperature, the SOC, the OCV and the aging degree estimation model, calculating to obtain the aging degree of the composite electrode battery; the aging degree estimation model is preset and is configured to be obtained based on training of a plurality of groups of data samples; wherein each set of data samples includes an independent variable including a temperature, a SOC, and an OCV of the battery, and a dependent variable including a degree of aging of the battery.
Description
Technical Field
The application belongs to the field of battery state estimation, and particularly relates to a method, a device, equipment, a medium and a program for estimating the aging of a composite electrode battery.
Background
The composite electrode battery refers to a battery using a composite electrode material, wherein the composite electrode material is composed of two or more materials, and each material has unique properties, so that the advantages of various materials can be fully utilized, and the composite electrode battery has the characteristics of high energy density, high power density, long service life, high safety and the like.
As an electrochemical system, the aging degree of the battery is increased continuously along with the increase of the cyclic charge and discharge times in the use process, the available capacity of the battery is reduced, and serious safety accidents can be caused by incorrectly using the battery. Therefore, the method has important practical significance for accurately estimating the aging state of the composite electrode battery. The existing method for estimating the aging state of the composite electrode battery regards the SOC-OCV characteristic of the battery as a constant value, and estimates the aging state of the composite electrode battery based on a data driving method or a model-based method.
Disclosure of Invention
The embodiment of the application provides a method, a device, equipment, a medium and a program for estimating the aging degree of a composite electrode battery, which can improve the accuracy of estimating the aging degree of the composite electrode battery.
In a first aspect, an embodiment of the present application provides a method for estimating aging of a composite electrode battery, where a relationship between a state of charge SOC and an open circuit voltage OCV of the composite electrode battery varies with a number of cycles of the battery. The method comprises the following steps: and receiving the temperature, the SOC and the OCV of the composite electrode battery, and calculating the aging degree of the composite electrode battery based on the temperature, the SOC, the OCV and the aging degree estimation model. The aging degree estimation model is preset and is configured to be trained based on a plurality of sets of data samples, each set of data samples including an independent variable including a temperature, an SOC, and an OCV of the battery, and a dependent variable including a degree of aging of the battery.
According to the composite electrode battery aging estimation method, the SOC-OCV characteristics of the composite electrode battery are regarded as variables, the temperature, the SOC and the OCV of the battery are regarded as independent variables, the aging degree of the battery is regarded as the dependent variables, the aging program estimation model is obtained through training, the accurate aging degree of the composite electrode battery can be determined based on the temperature, the SOC, the OCV and the aging degree estimation model of the battery, and the accuracy of the aging degree estimation of the composite electrode battery is improved.
As one possible implementation manner, the training method of the aging degree estimation model includes: and acquiring a plurality of groups of data samples, dividing the plurality of groups of data samples into a training set and a testing set, wherein the training set is used for indicating the data samples for training the model, and the testing set is used for indicating the data samples for determining the generalization error of the model after training. And establishing an initial model, and setting parameters of the initial model, wherein independent variables of the initial model comprise the temperature, the SOC and the OCV of the battery, and the dependent variables of the initial model comprise the aging degree of the battery. And setting a loss function of the initial model, wherein the loss function is used for reflecting the aging degree error of the model. Based on the training set and the test set, training the initial model to obtain an aging degree estimation model, wherein the aging degree error of the aging degree estimation model is smaller than a preset error threshold.
By the method, the aging degree estimation model which takes the temperature, the SOC and the OCV of the battery as inputs and takes the aging degree of the battery as output can be obtained through training, and the error of the aging degree estimation model is smaller than a preset error threshold value.
As one possible implementation, acquiring multiple sets of data samples includes: and selecting a battery cell sample, wherein the model of the battery cell sample is the same as that of the battery cell of the composite electrode battery, testing the standard cycle life of the battery cell sample, obtaining the aging parameters of the whole life cycle of the battery cell sample, and obtaining a plurality of groups of data samples based on the aging parameters. The aging parameters are configured as a function of temperature, SOC, OCV, and degree of aging.
Through the method, multiple groups of data samples corresponding to the battery cell model of the composite electrode battery can be obtained, model training is carried out based on the multiple groups of data samples, the aging degree estimation model obtained through final training can be guaranteed to be capable of estimating the aging degree of the composite electrode battery with the battery cell model, in addition, the data samples are constructed through the mode of carrying out standard cycle life test on the battery cell samples, the effectiveness of the data samples is guaranteed, and the accuracy of the aging degree estimation model obtained through training is further improved.
As one possible implementation, the initial model includes a symbolic regression model or a neural network model.
The symbolic regression model is a model based on a symbolic regression method, fully plays the advantages that the machine learning method does not depend on the model and is high in precision, takes the symbolic regression model as an initial model, enables the finally obtained aging estimation model to be a dominant mathematical formula meeting the error precision requirement, and can be used for calculating an accurate solution of the battery aging degree. The neural network model is composed of a plurality of neurons, has large-scale parallel, distributed storage and processing, self-organization, self-adaption and self-learning capabilities, is particularly suitable for processing the problem of inaccurate and fuzzy information processing which needs to consider a plurality of factors and conditions at the same time, can fully approximate a complex nonlinear relation, takes the neural network model as an initial model, and can improve the accuracy of a finally obtained aging estimation model.
As one possible implementation, setting parameters of the initial model includes: and setting various parameters of the symbolic regression model based on the genetic algorithm regressive, or setting various parameters of the neural network model based on the genetic algorithm regressive.
By the mode, the regression device based on the genetic algorithm can quickly and efficiently complete setting of model parameters.
As a possible implementation manner, after training the initial model based on the training set and the test set, the method further includes: the aging degree estimation model is deployed to a battery management system.
Based on the temperature, the SOC, the OCV and the aging degree estimation model, the aging degree of the composite electrode battery is calculated, and the method comprises the following steps: in the battery management system, the aging degree of the composite electrode battery is calculated based on the temperature, the SOC, the OCV, and the aging degree estimation model.
By the method, the aging degree estimation model is deployed in the battery management system BMS, so that the aging degree of the composite electrode battery can be directly estimated in the BMS, data transmission is reduced, and estimation efficiency is improved.
In a second aspect, an embodiment of the present application provides a composite electrode battery aging estimation apparatus, in which a relationship between a state of charge SOC and an open circuit voltage OCV of a composite electrode battery varies with the number of cycles of the battery. The device comprises: the receiving module is used for receiving the temperature, the SOC and the OCV of the composite electrode battery, and the aging degree calculating module is used for calculating the aging degree of the composite electrode battery based on the temperature, the SOC, the OCV and the aging degree estimation model. The aging degree estimation model is preset and is configured to be trained based on a plurality of sets of data samples, each set of data samples including an independent variable including a temperature, an SOC, and an OCV of the battery, and a dependent variable including a degree of aging of the battery.
As a possible implementation manner, the apparatus further includes: the model training module, the model training module includes: the data sample acquisition submodule is used for acquiring a plurality of groups of data samples, the sample dividing submodule is used for dividing the plurality of groups of data samples into a training set and a test set, the training set is used for indicating the data samples for training the model, and the test set is used for indicating the data samples for determining the generalization error of the model after training. And the initial model building sub-module is used for building an initial model, wherein independent variables of the initial model comprise the temperature, the SOC and the OCV of the battery, and the dependent variables of the initial model comprise the aging degree of the battery. And the parameter setting sub-module is used for setting parameters of the initial model. The loss function setting submodule is used for setting a loss function of the initial model, and the loss function is used for reflecting the aging degree error. The training sub-module is used for training the initial model based on the training set and the test set to obtain an aging degree estimation model, and the aging degree error of the aging degree estimation model is smaller than a preset error threshold.
As a possible implementation manner, the data sample acquiring sub-module is specifically configured to: and selecting a battery cell sample, wherein the model of the battery cell sample is the same as that of the battery cell of the composite electrode battery, testing the standard cycle life of the battery cell sample, obtaining the aging parameters of the whole life cycle of the battery cell sample, and obtaining a plurality of groups of data samples based on the aging parameters. The aging parameters are configured as a function of temperature, SOC, OCV, and degree of aging.
As one possible implementation, the initial model includes a symbolic regression model or a neural network model.
As a possible implementation manner, the parameter setting sub-module is specifically configured to: and setting various parameters of the symbolic regression model based on the genetic algorithm regressive, or setting various parameters of the neural network model based on the genetic algorithm regressive.
As a possible implementation manner, the apparatus further includes: the deployment module is used for training the initial model based on the training set and the test set, and deploying the aging degree estimation model on the battery management system after the aging degree estimation model is obtained. Correspondingly, the aging degree calculating module is specifically configured to: in the battery management system, the aging degree of the composite electrode battery is calculated based on the temperature, the SOC, the OCV, and the aging degree estimation model.
In a third aspect, an embodiment of the present application provides an electronic device, including: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements the method for estimating the aging of the composite electrode battery as in the first aspect.
In a fourth aspect, embodiments of the present application provide a computer storage medium having stored thereon computer program instructions which, when executed by a processor, implement a method for estimating the degradation of a composite electrode cell as in the first aspect.
In a fifth aspect, embodiments of the present application provide a computer program product, instructions in which, when executed by a processor of an electronic device, cause the electronic device to perform a method of estimating the aging of a composite electrode battery as in the first aspect.
According to the method, the device, the equipment, the medium and the program for estimating the aging of the composite electrode battery, the SOC-OCV characteristic of the composite electrode battery is taken as a variable, the temperature, the SOC and the OCV of the battery are taken as independent variables, the aging degree of the battery is taken as a dependent variable, an aging program estimation model is obtained through training, the accurate aging degree of the composite electrode battery can be determined based on the temperature, the SOC, the OCV and the aging program estimation model of the battery, and the accuracy of the aging degree estimation of the composite electrode battery is improved.
Drawings
In order to more clearly describe the technical solution of the embodiments of the present application, the following will briefly describe the drawings that are required to be used in the embodiments of the present application.
Fig. 1 is a schematic flow chart of a method for estimating aging of a composite electrode battery according to an embodiment of the present application;
FIG. 2 is a flow chart of a training method of an aging degree estimation model according to an embodiment of the present application;
FIG. 3 is a schematic diagram of training an aging degree estimation model according to an embodiment of the present application;
FIG. 4 is a schematic diagram of another aging estimation model training provided by an embodiment of the present application;
fig. 5 is a schematic structural diagram of a device for estimating aging of a composite electrode battery according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail below with reference to the accompanying drawings and the detailed embodiments. It should be understood that the particular embodiments described herein are meant to be illustrative of the application only and not limiting.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
In order to facilitate understanding of embodiments of the present application, terms involved in the embodiments of the present application will be explained first.
The electrode of the battery comprises a positive electrode and a negative electrode, and the composite electrode battery refers to a battery with the positive electrode and/or the negative electrode made of composite electrode materials. For example, a battery in which the positive electrode includes two or more positive electrode active materials, and/or in which the negative electrode includes two or more negative electrode active materials is a composite electrode battery. Wherein the positive electrode active material includes but is not limited to Lithium Cobalt Oxide (LCO), lithium Manganate (LMO), lithium iron phosphate (LFP), ternary materials (nickel cobalt lithium manganate: NCM), and nickel cobalt lithium aluminate (NCA)), and the negative electrode active material includes but is not limited to graphite, silicon, hard carbon, soft carbon, and the like. The battery core of the composite electrode battery can be a sodium ion battery core, a lithium ion battery core and other secondary battery cores.
SOC, collectively referred to as State of Charge, i.e., battery State of Charge, is the ratio of the remaining capacity of a battery after a period of use or long-term rest to the capacity of its fully charged State.
OCV, collectively open circuit voltage, i.e., the open circuit voltage of the cell, refers to the potential difference between the positive and negative electrodes of the cell when the cell is in a non-operating state.
The BMS, the generic term Battery Management System, i.e., a battery management system, can monitor the state of the battery and manage the charge and discharge of the battery. In some implementations of the application, the BMS may be embodied in the form of a printed circuit board on which electronic components, including a Microcontroller (MCU), charge switches, discharge switches, resistors, capacitors, etc., may be disposed.
In order to solve the problem of inaccurate battery aging degree estimation in the prior art, the embodiment of the application provides a novel composite electrode battery aging estimation method, device, equipment, medium and program, which are used for performing aging estimation on a composite electrode battery.
The method for estimating the aging of the composite electrode battery provided by the embodiment of the application is first described below.
Fig. 1 is a schematic flow chart of a method for estimating aging of a composite electrode battery according to an embodiment of the application. As shown in fig. 1, the method may include the following steps S110-S120.
S110, receiving the temperature, the SOC and the OCV of the composite electrode battery.
The temperature may be the temperature of the composite electrode battery itself or the temperature of the environment where the composite electrode battery is located.
The inventors of the present application have found that the characteristic of the relationship of SOC-OCV in a composite electrode battery varies with the number of battery cycles, which is linearly related to the degree of aging. Based on this, the present embodiment relates the SOC-OCV relation characteristic of the composite electrode battery to the aging estimation of the composite electrode battery, and in addition, the SOC-OCV relation characteristic is also affected by temperature, so in order to ensure the accuracy of the battery aging estimation result, the OCV, SOC, temperature of the composite electrode battery are received to determine the aging degree of the battery according to the temperature, SOC, and OCV of the composite electrode battery.
In practical application, the data such as OCV, SOC and temperature of the composite electrode battery can be acquired through the related acquisition device. The harvesting device may include a sensor and an Analog Front End (AFE) circuit disposed on the BMS.
As one example, the OCV, SOC, and temperature of the composite electrode battery may be collected in real time through the AFE on the BMS during use of the composite electrode battery.
S120, calculating the aging degree of the composite electrode battery based on the temperature, the SOC, the OCV and the aging degree estimation model.
The aging degree estimation model may be preset in the BMS and is configured to be trained based on a plurality of sets of data samples, each set of data samples including an independent variable including a temperature, an SOC, and an OCV of the battery, and a dependent variable including a degree of aging of the battery.
As an example, before the aging estimation is performed on the composite electrode battery, multiple sets of data samples may be acquired first, and model training may be performed based on the multiple sets of data samples, so as to obtain an aging degree estimation model with the temperature, SOC, and OCV of the battery as inputs and the aging degree of the battery as output. Based on the above, when the aging estimation is performed on the composite electrode battery, the temperature, the SOC and the OCV of the composite electrode battery are received, the received temperature, SOC and OCV are input into the aging degree estimation model, and the aging degree output by the aging degree estimation model is further obtained, the aging degree corresponds to the input temperature, SOC and OCV, and the aging degree output by the aging degree estimation model is used as the aging degree of the composite electrode battery, that is, the aging estimation result.
According to the composite electrode battery aging estimation method, the SOC-OCV characteristics of the composite electrode battery are regarded as variables, the temperature, the SOC and the OCV of the battery are regarded as independent variables, the aging degree of the battery is regarded as dependent variables, an aging program estimation model is obtained through training, the accurate aging degree of the composite electrode battery can be determined based on the temperature, the SOC, the OCV and the aging degree estimation model of the battery, and the accuracy of the aging degree estimation of the composite electrode battery is improved.
In some embodiments, as shown in fig. 2, the training method of the aging degree estimation model may specifically include the following steps:
s210, acquiring a plurality of groups of data samples.
In order to ensure that the aging degree estimation model obtained by final training can perform aging estimation on the composite electrode battery in S110 (hereinafter referred to as "current composite electrode battery" for convenience of description), multiple groups of data samples corresponding to the cell type of the current composite electrode battery can be obtained.
As described above, each set of data samples includes an independent variable including the temperature, SOC, and OCV of the battery and a dependent variable including the degree of aging of the battery, wherein the value of the independent variable corresponds to the value of the dependent variable, i.e., the degree of aging in a set of data samples, at the temperature, SOC, and OCV of the battery in the set of data samples.
S220, dividing the multiple groups of data samples into a training set and a testing set.
The training set is used for indicating data samples for training the model, and the testing set is used for indicating data samples for determining generalization errors of the model after training. The training set and the test set have no coincident data. It will be appreciated that after the aging degree estimation model is trained through the training set, the accuracy of the aging degree estimation model may be verified using the test set.
As an example, a division ratio, which is a ratio of the data amount of the training set to the data amount of the test set, may be preset, and the plurality of sets of sample data may be divided into the training set and the test set based on the division ratio. The dividing ratio may be set according to actual situations. For example, the division ratio may be set to 7:3, so assuming that 1000 sets of data samples are total, 700 sets of data samples are used as training sets and the remaining 300 sets of data samples are used as test sets based on the division ratio.
As another example, multiple sets of data samples may be preprocessed to obtain a test set. For example, 100 sets of data samples are used as training sets, and the 100 sets of data samples are preprocessed, for example, by interpolation or random noise, to process the 100 sets of data to obtain test sets.
S230, establishing an initial model and setting parameters of the initial model.
The independent variables of the initial model comprise the temperature, the SOC and the OCV of the battery, and the dependent variables of the initial model comprise the aging degree of the battery.
S240, setting a loss function of the initial model.
Wherein the loss function is used to reflect the aging degree error of the model.
S250, training an initial model based on the training set and the test set to obtain an aging degree estimation model.
The aging degree error of the aging degree estimation model is smaller than a preset error threshold, wherein the error threshold can be set according to the actual model precision requirement.
As an example, based on the training set and the test set, the initial model may be trained using a supervised training method to obtain an aging degree estimation model. During training, aiming at a data sample in a training set, inputting an independent variable value in the data sample into a model to obtain a prediction result which is output by the model and corresponds to sample data, determining an aging degree error of the trained model based on the dependent variable value, the prediction result and a loss function in the data sample, adjusting parameters in the model under the condition that the aging degree error is greater than or equal to an error threshold value, continuing training the model after parameter adjustment based on the training set until the aging degree error of the model after training is less than the error threshold value, and taking the model obtained by the last training as an aging degree estimation model.
By the method, the aging degree estimation model which takes the temperature, the SOC and the OCV of the battery as inputs and takes the aging degree of the battery as output can be obtained through training, and the error of the aging degree estimation model is smaller than a preset error threshold value.
In some embodiments, in the step S210, a plurality of sets of data samples corresponding to the current composite electrode battery may be obtained based on a battery aging experiment, and specifically the method may include the following steps:
selecting a battery cell sample, wherein the model of the battery cell sample is the same as the model of the battery cell of the current composite electrode battery;
testing the standard cycle life of the battery cell sample to obtain the aging parameter of the whole life cycle of the battery cell sample;
based on the aging parameters, a plurality of sets of data samples are acquired.
Wherein the aging parameters are configured as a function of temperature, SOC, OCV, and degree of aging.
As an example, by utilizing the characteristic that the SOC-OCV characteristic of the composite electrode battery changes along with the change of the cycle number, a standard cycle life test is developed on a battery cell sample in a laboratory, and the aging parameters of the battery cell sample in the full life cycle are obtained, wherein the aging parameters comprise the SOC and OCV data of the battery cell sample at different temperatures and different cycle numbers. When testing, a plurality of test temperatures can be selected according to actual requirements, and for each test temperature, a standard cycle life test is conducted on the battery cell sample at the test temperature, and ageing parameters of the battery cell sample at a plurality of different cycle numbers are measured. For example, according to the characteristic that the temperature of the battery is usually at-20 ℃ to 45 ℃, it is possible to select-20 ℃, -10 ℃, 0 ℃, 25 ℃, 45 ℃ as the test temperature, and then develop a standard cycle life test for the electrical core sample at-20 ℃, -10 ℃, 0 ℃, 25 ℃, 45 ℃ in order.
The cycle number under the standard condition of the laboratory is linearly related to the aging degree of the battery, so that a mapping relation between the cycle number and the aging degree can be obtained, the aging degree corresponding to each cycle number can be determined based on the mapping relation, the functional relation among the temperature, the aging degree, the OCV and the SOC can be further established, the aging parameter corresponding to the battery cell model can be obtained, and a plurality of groups of data samples can be constructed according to independent variables and dependent variables based on the aging parameter.
In the present embodiment, the aging parameter may represent a functional relationship among temperature, battery aging degree, OCV, and SOC by means of a table, a function, or an image, or the like.
As one example, the aging parameters may represent a functional relationship between temperature, battery aging, OCV, and SOC by a table as shown in table 1 below:
table 1:
| battery cell model | Temperature (. Degree. C.) | OCV(V) | SOC(％) | Degree of aging (%) |
| … | … | … | … | … |
In table 1, each row corresponds to a respective set of experimentally derived aging parameters, except for the first row, i.e., the header row.
Based on the aging parameters shown in table 1, a set of data samples can be constructed for each row of aging parameters. Specifically, for each row, the values corresponding to the temperature, OCV and SOC in the row are taken as a set of self-variable values, the values corresponding to the aging degree in the row are taken as dependent variable values, and the set of self-variable values and dependent variable values are formed into a set of data samples.
As an example, taking an experiment-based n sets of aging parameters as an example, n sets of independent variables and n dependent variables may be obtained based on the n sets of aging parameters, respectively, where the n sets of independent variables and the n dependent variables are respectively as follows:
independent variable:
dependent variables:
y= [ degree of aging 1, degree of aging 2, …, degree of aging n ]
The "temperature 1", "OCV1" and "SOC1" form a set of self-variable values, which together form a set of data samples with the dependent variable value of "aging degree 1", and the "temperature 2", "OCV2" and "SOC2" form a set of self-variable values, which together form a set of data samples with the dependent variable value of "aging degree 2", and so on, so that n sets of data samples can be obtained.
By the method, multiple groups of data samples corresponding to the battery cell model of the current composite electrode battery can be obtained, model training is carried out based on the multiple groups of data samples, and the aging degree estimation model obtained through final training can be ensured to estimate the aging degree of the current composite electrode battery. In addition, the data sample is constructed by carrying out standard cycle life test on the electric core sample, so that the effectiveness of the data sample is ensured, and the accuracy of the aging degree estimation model obtained by training is further improved.
In some embodiments, to further ensure validity of the data samples, the aging parameters may be pre-processed before the multiple sets of data samples are obtained based on the aging parameters, and then the multiple sets of data samples are obtained based on the pre-processed aging parameters.
The data preprocessing of the aging parameters can comprise data cleaning, and the data cleaning can supplement the exact values in the aging parameters to be complete, eliminate noise data and identify or delete outliers and solve inconsistencies.
By preprocessing the aging parameters, the validity of the data samples can be further improved.
In some embodiments, a symbolic regression model or a neural network model may be used as the initial model when the initial model is established.
Taking a symbolic regression model as an initial model as an example, when model training is carried out, establishing the symbolic regression model, setting various parameters of a model algorithm, training the established regression model based on data samples in a training set as shown in fig. 3, obtaining a mathematical mapping relation between an independent variable [ x1, x2, x3] and a dependent variable y meeting error requirements after model training is finished, and outputting the relation in the form of the following formula, wherein the following formula is the trained aging degree estimation model.
y＝f(x1,x2,x3)
The symbolic regression model is a model based on a symbolic regression algorithm, the symbolic regression algorithm (Symbolic Regression, SR for short) fully plays the advantages that a machine learning method does not depend on the model and is high in precision, the symbolic regression model is used as an initial model, the finally obtained aging estimation model is a dominant mathematical formula meeting the error precision requirement, and the model can be used for calculating an accurate solution of the battery aging degree.
As another example, taking a neural network model as an initial model, when model training is performed, the neural network model is built, various parameters of a model algorithm are set, as shown in fig. 4, the neural network model is trained by using data samples in a training set, after model training is completed, a neural network model meeting error requirements is obtained, and the neural network model is used as an aging degree estimation model.
It should be understood that the neural network model shown in fig. 4 is only an example, and is not limited to the neural network model used in the embodiment of the present application, and the neural network model used in the embodiment of the present application may include one or more hidden layers.
The neural network model consists of a plurality of neurons, has large-scale parallel, distributed storage and processing, self-organization, self-adaption and self-learning capabilities, is particularly suitable for processing the problem of inaccurate and fuzzy information processing which needs to consider a plurality of factors and conditions at the same time, can fully approximate a complex nonlinear relation, takes the neural network model as an initial model, and can improve the accuracy of a finally obtained aging estimation model.
In some embodiments, the parameters of the initial model may be set based on regression of genetic algorithm, specifically including:
under the condition that the initial model is a symbolic regression model, setting various parameters of the symbolic regression model based on a genetic algorithm regressor.
And setting various parameters of the neural network model based on a genetic algorithm regressor under the condition that the initial model is the neural network model.
Wherein, setting each parameter of the model refers to initializing each parameter in the model.
The model training process can be understood as a parameter optimizing process, and parameters enabling model errors to meet preset error conditions are found through model training. Therefore, before model training, each parameter of the model is initialized first to perform parameter adjustment based on the initial value of the parameter in the training process.
It will be appreciated that the parameters that need to be set for different models are different, for example, the parameters that need to be set for a symbolic regression model may include population, population size, method of population execution, etc. Parameters that the neural network model needs to set may include the center point and width vector of the hidden layer, i.e., the weights of the output layer, the threshold, etc.
By the mode, the regression device based on the genetic algorithm can quickly and efficiently complete setting of model parameters.
In some embodiments, after training the initial model based on the training set and the test set, the following steps may be further performed:
the aging degree estimation model is deployed to a battery management system.
Accordingly, the step S120 calculates the aging degree of the composite electrode battery based on the temperature, the SOC, the OCV, and the aging degree estimation model, and may include:
in the battery management system, the aging degree of the composite electrode battery is calculated based on the temperature, the SOC, the OCV, and the aging degree estimation model.
The battery management system BMS is generally used to monitor the state of the battery, and thus it generally receives data of the temperature, SOC, OCV, etc. of the battery, based on which an aging degree estimation model is deployed in the BMS so that the aging degree of the battery can be estimated directly based on the received temperature, SOC, and OCV of the battery in the BMS.
By the method, the aging degree of the composite electrode battery is directly estimated in the BMS, so that data transmission is reduced, and estimation efficiency is improved.
Based on the method for estimating the aging of the composite electrode battery provided by the embodiment, correspondingly, the application also provides a specific implementation mode of the device for estimating the aging of the composite electrode battery. Please refer to the following examples.
Referring to fig. 5, the device for estimating aging of a composite electrode battery provided by the embodiment of the application comprises the following modules: a receiving module 501 and an aging degree calculating module 502.
A receiving module 501 for receiving the temperature, SOC, and OCV of the composite electrode battery;
the aging degree calculation module 502 is configured to calculate the aging degree of the composite electrode battery based on the temperature, the SOC, the OCV, and the aging degree estimation model;
the aging degree estimation model is preset and is configured to be trained based on a plurality of sets of data samples, wherein each set of data samples comprises an independent variable and a dependent variable, the independent variable comprises the temperature, the SOC and the OCV of the battery, and the dependent variable comprises the aging degree of the battery.
According to the composite electrode battery aging estimation device, the SOC-OCV characteristic of the composite electrode battery is regarded as a variable, the temperature, the SOC and the OCV of the battery are regarded as independent variables, the aging degree of the battery is regarded as a dependent variable, an aging program estimation model is obtained through training, the accurate aging degree of the composite electrode battery can be determined based on the temperature, the SOC, the OCV and the aging program estimation model of the battery, and the accuracy of the aging degree estimation of the composite electrode battery is improved.
In some embodiments, the apparatus may further comprise: the model training module, the model training module includes:
The data sample acquisition sub-module is used for acquiring a plurality of groups of data samples;
the sample dividing sub-module is used for dividing a plurality of groups of data samples into a training set and a test set, wherein the training set is used for indicating the data samples for training the model, and the test set is used for indicating the data samples for determining the generalization error of the model after training;
the initial model building sub-module is used for building an initial model, independent variables of the initial model comprise the temperature, the SOC and the OCV of the battery, and the dependent variables of the initial model comprise the aging degree of the battery;
the parameter setting sub-module is used for setting parameters of the initial model;
the loss function setting submodule is used for setting a loss function of the initial model, and the loss function is used for reflecting the aging degree error;
the training sub-module is used for training the initial model based on the training set and the test set to obtain an aging degree estimation model; the aging degree error of the aging degree estimation model is smaller than a preset error threshold value.
In some embodiments, the data sample acquisition sub-module is specifically configured to:
selecting a battery cell sample, wherein the model of the battery cell sample is the same as that of the composite electrode battery; testing the standard cycle life of the battery cell sample to obtain the aging parameter of the whole life cycle of the battery cell sample; acquiring a plurality of groups of data samples based on the aging parameters; wherein the aging parameters are configured as a function of temperature, SOC, OCV, and degree of aging.
In some embodiments, the initial model includes a symbolic regression model or a neural network model.
In some embodiments, the parameter setting sub-module is specifically configured to:
setting each parameter of a symbolic regression model based on a genetic algorithm regressor; or setting various parameters of the neural network model based on a genetic algorithm regressor.
In some embodiments, the apparatus further comprises: a deployment module for:
training an initial model based on a training set and a test set, and deploying the aging degree estimation model in a battery management system after obtaining the aging degree estimation model;
correspondingly, the aging degree calculating module 502 is specifically configured to: in the battery management system, the aging degree of the composite electrode battery is calculated based on the temperature, the SOC, the OCV, and the aging degree estimation model.
The device for estimating the aging of the composite electrode battery provided by the embodiment of the application can realize each process realized by any method embodiment, and is not repeated here for avoiding repetition.
Fig. 6 shows a schematic hardware structure of an electronic device according to an embodiment of the present application.
The electronic device may include a processor 601 and a memory 602 storing computer program instructions.
In particular, the processor 601 may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured as one or more integrated circuits that implement embodiments of the present application.
Memory 602 may include mass storage for data or instructions. By way of example, and not limitation, memory 602 may include a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, magnetic tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of the above. The memory 602 may include removable or non-removable (or fixed) media, where appropriate. Memory 602 may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory 602 is a non-volatile solid state memory. The memory 602 may include Read Only Memory (ROM), random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory 602 includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and which, when executed (e.g., by one or more processors), perform the operations described by any of the composite electrode cell aging estimation methods of the above embodiments.
The processor 601 implements any of the methods of composite electrode cell aging estimation in the above embodiments by reading and executing computer program instructions stored in the memory 602.
In one example, the electronic device may also include a communication interface 603 and a bus 610. As shown in fig. 6, the processor 601, the memory 602, and the communication interface 603 are connected to each other through a bus 610 and perform communication with each other.
The communication interface 603 is mainly used for implementing communication between each module, apparatus, unit and/or device in the embodiment of the present application.
Bus 610 includes hardware, software, or both, coupling components of the online data flow billing device to each other. By way of example, and not limitation, the buses may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a micro channel architecture (MCa) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus, or a combination of two or more of the above. Bus 610 may include one or more buses, where appropriate. Although embodiments of the application have been described and illustrated with respect to a particular bus, the application contemplates any suitable bus or interconnect.
In addition, in combination with the method for estimating aging of a composite electrode battery in the above embodiment, the embodiment of the application may be implemented by providing a computer storage medium. The computer storage medium has stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the methods of composite electrode cell aging estimation described in the embodiments above.
It should be understood that the application is not limited to the particular arrangements and instrumentality described above and shown in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between steps, after appreciating the spirit of the present application.
The functional blocks shown in the above-described structural block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this disclosure describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, or may be performed in a different order from the order in the embodiments, or several steps may be performed simultaneously.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In the foregoing, only the specific embodiments of the present application are described, and it will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein. It should be understood that the scope of the present application is not limited thereto, and any equivalent modifications or substitutions can be easily made by those skilled in the art within the technical scope of the present application, and they should be included in the scope of the present application.
Claims (10)
1. A method for estimating the degradation of a composite electrode cell, comprising:
receiving the temperature, the SOC and the OCV of the composite electrode battery;
calculating the aging degree of the composite electrode battery based on the temperature, the SOC, the OCV and the aging degree estimation model;
the aging degree estimation model is preset and is configured to be obtained based on training of a plurality of groups of data samples;
wherein each set of the data samples includes an independent variable including a temperature, an SOC, and an OCV of the battery and a dependent variable including a degree of aging of the battery.
2. The method of claim 1, wherein the training method of the aging degree estimation model comprises:
acquiring a plurality of groups of data samples;
dividing a plurality of groups of data samples into a training set and a testing set, wherein the training set is used for indicating the data samples for training the model, and the testing set is used for indicating the data samples for determining the generalization error of the model after training;
establishing an initial model, and setting parameters of the initial model, wherein independent variables of the initial model comprise the temperature, the SOC and the OCV of the battery, and the dependent variables of the initial model comprise the aging degree of the battery;
setting a loss function of the initial model, wherein the loss function is used for reflecting an aging degree error of the model;
training the initial model based on the training set and the testing set to obtain an aging degree estimation model;
and the aging degree error of the aging degree estimation model is smaller than a preset error threshold value.
3. The method of claim 2, wherein the acquiring a plurality of sets of data samples comprises:
selecting a battery cell sample, wherein the model of the battery cell sample is the same as the model of the battery cell of the composite electrode battery;
Testing the standard cycle life of the battery cell sample to obtain the aging parameter of the whole life cycle of the battery cell sample;
acquiring a plurality of groups of data samples based on the aging parameters;
wherein the aging parameters are configured as a function of temperature, SOC, OCV, and degree of aging.
4. A method according to claim 2 or 3, wherein the initial model comprises a symbolic regression model or a neural network model.
5. The method of claim 4, wherein setting parameters of the initial model comprises:
setting each parameter of the symbolic regression model based on a genetic algorithm regressor; or alternatively, the process may be performed,
and setting various parameters of the neural network model based on a genetic algorithm regressor.
6. A method according to claim 2 or 3, wherein after training the initial model based on the training set and the test set to obtain an aging degree estimation model, the method further comprises:
deploying the aging degree estimation model in a battery management system;
the calculating, based on the temperature, the SOC, the OCV, and the aging degree estimation model, the aging degree of the composite electrode battery includes:
In the battery management system, the degree of aging of the composite electrode battery is calculated based on the temperature, the SOC, the OCV, and the degree of aging estimation model.
7. A method for estimating the degradation of a composite electrode cell, comprising:
a receiving module for receiving the temperature, the SOC and the OCV of the composite electrode battery;
the aging degree calculation module is used for calculating the aging degree of the composite electrode battery based on the temperature, the SOC, the OCV and the aging degree estimation model;
the aging degree estimation model is preset and is configured to be obtained based on training of a plurality of groups of data samples;
wherein each set of the data samples includes an independent variable including a temperature, an SOC, and an OCV of the battery and a dependent variable including a degree of aging of the battery.
8. An electronic device, the device comprising: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements a method for estimating the degradation of a composite electrode cell according to any one of claims 1 to 6.
9. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon computer program instructions, which when executed by a processor, implement the method of composite electrode cell aging estimation according to any one of claims 1-6.
10. A computer program product, characterized in that instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the composite electrode cell aging estimation method according to any one of claims 1-6.
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