CN117233630B - Method and device for predicting service life of lithium ion battery and computer equipment - Google Patents
Method and device for predicting service life of lithium ion battery and computer equipment Download PDFInfo
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Abstract
The application relates to a service life prediction method and device of a lithium ion battery and computer equipment. The method comprises the following steps: acquiring target charge and discharge data of a target battery; the target charge-discharge data are charge-discharge data when the battery state of health value of the target battery is larger than a preset threshold value; and inputting mechanism parameters corresponding to the target charge and discharge data into a prediction model to obtain the service life of the target battery. The method can improve the accuracy of the prediction result of predicting the service life of the lithium ion battery.
Description
Technical Field
The present disclosure relates to the field of lithium ion batteries, and in particular, to a method and an apparatus for predicting a service life of a lithium ion battery, and a computer device.
Background
The lithium ion battery is widely applied to the fields of portable electronic equipment, electric vehicles, energy storage systems and the like, but as the service time of the lithium ion battery increases, the capacity of the lithium ion battery gradually decays, so that the battery is used, and the life prediction of the lithium ion battery has important significance for evaluating the performance of the battery, guiding the design of the battery, optimizing the management of the battery, finding potential problems and reducing the development cost and time.
Generally, a machine learning model is trained by collecting data of the full life cycle of the lithium ion battery, and the trained machine learning model is utilized to predict the battery life of the lithium ion battery.
However, the conventional technology has a problem in that a prediction result for predicting the battery life of the lithium ion battery is inaccurate.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, an apparatus, and a computer device for predicting the service life of a lithium ion battery that can improve the accuracy of a prediction result for predicting the service life of the lithium ion battery.
In a first aspect, the present application provides a method for predicting a service life of a lithium ion battery, including:
acquiring target charge and discharge data of a target battery; the target charge-discharge data are charge-discharge data when the battery state of health value of the target battery is larger than a preset threshold value;
and inputting mechanism parameters corresponding to the target charge and discharge data into a prediction model to obtain the service life of the target battery.
In one embodiment, the prediction model includes a linear prediction branch and a nonlinear prediction branch, and the inputting the mechanism parameter corresponding to the target charge and discharge data into the prediction model to obtain the service life of the target battery includes:
And respectively inputting mechanism parameters corresponding to the target charge and discharge data into the linear prediction branch and the nonlinear prediction branch for prediction to obtain a service life prediction curve of the target battery, and obtaining the service life of the target battery according to the service life prediction curve.
In one embodiment, the inputting the mechanism parameters corresponding to the target charge and discharge data into the linear prediction branch and the nonlinear prediction branch to predict, to obtain a service life prediction curve of the target battery, and obtaining the service life of the target battery according to the service life prediction curve includes:
processing the target charge and discharge data to obtain a target aging factor of the target battery;
inputting the target aging factors into a machine learning model for analysis, and determining mechanism parameters corresponding to the target charge and discharge data;
and respectively inputting the mechanism parameters into the linear prediction branch and the nonlinear prediction branch for prediction to obtain a service life prediction curve of the target battery, and obtaining the service life of the target battery according to the service life prediction curve.
In one embodiment, the processing the target charge and discharge data to obtain the target aging factor of the target battery includes:
and processing the target charge and discharge data according to the type of the mechanism parameter to be solved to obtain a target aging factor matched with the type of the mechanism parameter.
In one embodiment, the method further comprises:
acquiring sample charge and discharge data of a sample battery; the sample charge and discharge data is charge and discharge data of the whole life cycle of the sample battery;
and training an initial machine learning model by using the sample charge and discharge data to obtain the machine learning model.
In one embodiment, the training the initial machine learning model by using the sample charge-discharge data to obtain the machine learning model includes:
processing the sample charge and discharge data to obtain a sample aging factor;
and training an initial machine learning model by using the sample aging factors to obtain the machine learning model.
In a second aspect, the present application further provides a service life prediction apparatus of a lithium ion battery, including:
the first acquisition module is used for acquiring target charge and discharge data of a target battery; the target charge-discharge data are charge-discharge data when the battery state of health value of the target battery is larger than a preset threshold value;
And the second acquisition module is used for inputting the mechanism parameters corresponding to the target charge and discharge data into a prediction model to obtain the service life of the target battery.
In a third aspect, the present application also provides a computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring target charge and discharge data of a target battery; the target charge-discharge data are charge-discharge data when the battery state of health value of the target battery is larger than a preset threshold value;
and inputting mechanism parameters corresponding to the target charge and discharge data into a prediction model to obtain the service life of the target battery.
In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring target charge and discharge data of a target battery; the target charge-discharge data are charge-discharge data when the battery state of health value of the target battery is larger than a preset threshold value;
and inputting mechanism parameters corresponding to the target charge and discharge data into a prediction model to obtain the service life of the target battery.
In a fifth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of:
acquiring target charge and discharge data of a target battery; the target charge-discharge data are charge-discharge data when the battery state of health value of the target battery is larger than a preset threshold value;
and inputting mechanism parameters corresponding to the target charge and discharge data into a prediction model to obtain the service life of the target battery.
The service life prediction method, the device and the computer equipment of the lithium ion battery acquire target charge and discharge data of a target battery; the target charge-discharge data are charge-discharge data when the battery state of health value of the target battery is larger than a preset threshold value; and inputting mechanism parameters corresponding to the target charge and discharge data into a prediction model to obtain the service life of the target battery. The mechanism parameters are introduced into the prediction model, so that the stability and the accuracy of the prediction model are improved, the service life of the target battery is acquired according to the charge and discharge data when the battery state of health value of the target battery is larger than the preset threshold value, and the charge and discharge data of all the battery periods are not required to be acquired, so that the service life of the target battery is acquired more accurately by using less data.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for a person having ordinary skill in the art.
FIG. 1 is an application environment diagram of a method for predicting the lifetime of a lithium ion battery in one embodiment;
FIG. 2 is a flow chart of a method for predicting the lifetime of a lithium ion battery according to an embodiment;
FIG. 3 is a life curve of a battery in one embodiment;
FIG. 4 is a flow chart of a method for predicting the lifetime of a lithium ion battery according to another embodiment;
FIG. 5 is a schematic diagram of the relationship between the mechanism parameter and the aging factor in one embodiment;
FIG. 6 is a flow chart of a method for predicting the lifetime of a lithium ion battery according to another embodiment;
FIG. 7 is a flow chart of a method for predicting the lifetime of a lithium ion battery according to another embodiment;
FIG. 8 is a flow chart of a method for predicting the lifetime of a lithium ion battery in another embodiment;
FIG. 9 is a block diagram of a life prediction apparatus of a lithium ion battery in one embodiment;
fig. 10 is a block diagram illustrating a life predicting apparatus of a lithium ion battery according to another embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description and claims of the present application and in the description of the figures above are intended to cover non-exclusive inclusions.
In the description of the embodiments of the present application, the technical terms "first," "second," etc. are used merely to distinguish between different objects and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated, a particular order or a primary or secondary relationship. In the description of the embodiments of the present application, the meaning of "plurality" is two or more unless explicitly defined otherwise.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In the prior art, models such as an experience model, a mechanism model, a machine learning model and the like are generally used for predicting the service life of the lithium ion battery. The empirical model is established based on statistical analysis and empirical rules, the service life attenuation conditions of all batteries cannot be accurately predicted, the parameter in the empirical function is poor in interpretability, and relatively accurate values can be fitted only by long-term observation data, so that the empirical model cannot realize early prediction of the batteries; the mechanism model is a model for realizing prediction based on internal physical and chemical processes of the battery, and can explain the reason of battery attenuation while predicting the service life of the battery, and the mechanism model describes the battery life attenuation by establishing a mathematical equation and a physical model based on the understanding of internal reaction, material properties and structural characteristics of the battery, but the mechanism model needs a large amount of internal parameters and material characteristic parameters of the battery, and has high calculation complexity and long calculation time; the data-driven machine learning model has higher accuracy, however, the machine learning model has poor interpretability, stability and generalization capability, and unreasonable prediction results may occur.
The service life prediction method of the lithium ion battery provided by the embodiment of the application can be applied to an application environment shown in fig. 1. The computer device may be a terminal, and an internal structure diagram thereof may be as shown in fig. 1. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program when executed by a processor implements a method for predicting the lifetime of a lithium ion battery. The display unit of the computer device is used for forming a visual picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 1 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, as shown in fig. 2, a method for predicting service life of a lithium ion battery is provided, and the method is applied to the terminal in fig. 1 for illustration, and includes:
s201, acquiring target charge and discharge data of a target battery; the target charge-discharge data is charge-discharge data when the battery state of health value of the target battery is larger than a preset threshold value.
The target charge and discharge data may include charge and discharge time, voltage, current, temperature, internal resistance, etc. of the target battery for a plurality of cycles; the battery state of health value SOH uses the capacity decay and the direct current internal resistance spectrum as indicators of the state of battery health for characterizing the current battery capacity of the target battery.
In this embodiment, the collecting device collects target charge and discharge data of the target battery, for example, the collecting device may be a charge and discharge cabinet, a battery management system BMS, or the like, where the target charge and discharge data is charge and discharge data when the SOH value of the target battery is greater than a preset threshold, so that the terminal predicts the battery attenuation condition of the full life cycle of the target battery according to the charge and discharge data when the SOH value of the target battery is greater than the preset threshold, thereby obtaining the service life of the target battery.
S202, inputting mechanism parameters corresponding to the target charge and discharge data into a prediction model to obtain the service life of the target battery.
In this embodiment, the mechanism parameters may include a plurality of parameters, and the mechanism parameters corresponding to each charge and discharge data are different, and the corresponding mechanism parameters are determined according to the target charge and discharge data, and then the mechanism parameters are input into the prediction model, so as to obtain the service life of the target battery. Optionally, the prediction model may output a lifetime of the target battery; or outputting the attenuation condition of the target battery by the prediction model, so as to determine the service life of the target battery according to the attenuation condition of the target battery.
Optionally, the target charge and discharge data can be calculated according to a preset calculation rule, so as to obtain mechanism parameters corresponding to each target charge and discharge data; or determining the mechanism parameter corresponding to the target charge and discharge data in a pre-established database according to the corresponding relation between the preset charge and discharge data and the mechanism parameter.
In the service life prediction method of the lithium ion battery, target charge and discharge data of a target battery are obtained; the target charge-discharge data are charge-discharge data when the battery state of health value of the target battery is larger than a preset threshold value; and inputting mechanism parameters corresponding to the target charge and discharge data into a prediction model to obtain the service life of the target battery. The mechanism parameters are introduced into the prediction model, so that the stability and the accuracy of the prediction model are improved, the service life of the target battery is acquired according to the charge and discharge data when the battery state of health value of the target battery is larger than the preset threshold value, and the charge and discharge data of all the battery periods are not required to be acquired, so that the service life of the target battery is acquired more accurately by using less data.
In an embodiment, an implementation manner of the foregoing S202 is provided, where the prediction model includes a linear prediction branch and a nonlinear prediction branch, and the foregoing "inputting the mechanism parameter corresponding to the target charge and discharge data to the prediction model to obtain a service life prediction curve of the target battery, and obtaining the service life of the target battery according to the service life prediction curve" includes: and respectively inputting mechanism parameters corresponding to the target charge and discharge data into the linear prediction branch and the nonlinear prediction branch for prediction to obtain the service life of the target battery.
In this embodiment, the prediction model may be as shown in equation 1:
(1)
Where Q represents the current state of health of the target battery, x is the cycle period,，/>or (E)>，a、b、c、x 0 As the mechanism parameters, the mechanism parameters corresponding to different charge and discharge data are different,linear prediction branch of the prediction model, +.>And is a nonlinear prediction branch of the prediction model.
In this embodiment, as shown in fig. 3, the mechanism parameters corresponding to the target charge and discharge data are respectively input to the linear prediction branch and the nonlinear prediction branch for prediction, so that a service life prediction curve of the battery health condition of the target battery in the full life cycle can be obtained, and the service life of the target battery is determined according to the prediction curve and the current cycle of the target battery. When the cycle period of the target battery is less than x 0 When the target battery is in a linear aging state, determining the service life of the target battery through a linear prediction branch in a prediction model; when the cycle period of the target battery is greater than x 0 When the target battery is in an accelerated aging state, the target battery is controlled by a nonlinear branch in a prediction modelThe path determines the life of the target battery. Alternatively, the cycle period may be a charge-discharge period of the battery, and each time the battery completes complete charge and discharge, the cycle is one period; or the cycle period may be a total aging calendar of the battery, the aging calendar being related to the number of cycles of the battery, the depth of charge and discharge, and the length of rest of the battery.
Alternatively, a least squares method may be used to determine the corresponding mechanism parameters for the target charge and discharge data, and the best functional match for the data is found by minimizing the square error between the predicted and actual values, such that the sum of squares of the distances of all data points to the line or curve is minimized. The specific implementation process is as follows: assuming that the model function is, where y is the target variable, x is the argument, p is the parameter vector of the model, p= (a, b, c, x) 0 ) So that p satisfies formula 2:
(2)
Specifically, the process of determining p by the least squares method is:
1. the parameter vector p is randomly initialized.
2. Computing jacobian matrix and residual vector: for each data point, the difference between the model predicted value and the actual value, i.e., the residual, is calculated, and the jacobian matrix of the model function with respect to the parameter is calculated. Jacobian J is an m x n matrix, where m is the number of data points and n is the number of parameters.Residual vector->。
3. Solving the update direction: and solving a linear least square problem by utilizing the jacobian matrix and the residual vector, and finding the updating direction of the parameter vector p. Assuming Δp is the update direction of the parameter vector, it can be obtained by solving the following normal equation:thereby updating the ginsengNumber vector->。
4. Checking convergence conditions: if the norm of the residual vector is less than a certain threshold or a preset maximum number of iterations is reached, the iteration is stopped. Otherwise, returning to the step 2.
In this embodiment, the prediction model includes a linear prediction branch and a nonlinear prediction branch, which are more matched with the aging rule of the battery, so that the battery health condition of the battery in different aging states can be predicted, thereby improving the accuracy of predicting the service life of the target battery.
In one embodiment, as shown in fig. 4, the above-mentioned "inputting the mechanism parameters corresponding to the target charge and discharge data into the linear prediction branch and the nonlinear prediction branch to perform prediction, to obtain a service life prediction curve of the target battery, and obtaining the service life of the target battery according to the service life prediction curve" includes:
S301, processing the target charge and discharge data to obtain a target aging factor of the target battery.
In this embodiment, the target charge-discharge data is processed, so as to extract numerical information capable of reflecting the aging characteristics of the target battery, for example, information such as the maximum value, the minimum value, the difference value, the average value, the variance and the like of the charge-discharge voltage, and the transformation trend of the information as the cycle proceeds; and taking the processed target charge and discharge data as a target aging factor according to the attenuation trend and variance of the capacity, the extreme point and variation trend of the power-voltage curve, the corresponding n-step distance and other statistical indexes.
Optionally, the target charge and discharge data may be processed according to the type of the mechanism parameter to be solved to obtain a target aging factor matched with the type of the mechanism parameter, and the aging factor with high correlation with the parameter to be predicted in the subtask may be selected according to the characteristics of the parameter in the subtask. Illustratively, the pearson correlation coefficient may be used to determine the correlation between each aging factor and the mechanism parameter; alternatively, a predetermined model may be used to determine the correlation between each aging factor and the mechanism parameter.
Alternatively, data before 100 weeks of the complete charge-discharge cycle of the target battery may be taken as target charge-discharge data, and the target charge-discharge data may be processed.
S302, inputting the target aging factors into a machine learning model for analysis, and determining mechanism parameters corresponding to the target charge and discharge data.
Alternatively, in this embodiment, as shown in fig. 5, the target aging factor is input into a machine learning model trained in advance for analysis, and the machine learning model outputs the mechanism parameter corresponding to the aging factor.
Alternatively, the machine learning model may be a logistic regression model, a gaussian process regression model, a random forest model, etc., and the logistic regression model may be used to determine, for example, the parameter a and the parameter x of the mechanism parameters 0 The parameter b of the mechanism parameters may be determined using a gaussian regression model and the parameter c of the mechanism parameters may be determined using a random forest model.
Alternatively, the range of each mechanism parameter may be obtained through a machine learning model, and then the value of the mechanism parameter is determined in the range of each mechanism parameter.
S303, inputting the mechanism parameters into a linear prediction branch and a nonlinear prediction branch for prediction, obtaining a service life prediction curve of the target battery, and obtaining the service life of the target battery according to the service life prediction curve.
In this embodiment, the mechanism parameters corresponding to the target charge and discharge data are respectively input to the linear prediction branch and the nonlinear prediction branch for prediction, so that a prediction curve of the battery health condition of the whole life cycle of the target battery can be obtained, and the service life of the target battery is determined according to the prediction curve and the current cycle of the target battery.
In this embodiment, the target aging factor is determined through the target charge-discharge data, so that the mechanism parameter is determined according to the target aging factor and the machine learning model, the accuracy of the approximated obtained mechanism parameter is higher, and the mechanism parameter corresponding to the target charge-discharge data is input into the linear prediction branch and the nonlinear prediction branch for prediction, so that the accuracy of the service life of the obtained target battery is higher.
In one embodiment, there is further provided a method for predicting service life of a lithium ion battery, as shown in fig. 6, that is, the page access method in the embodiment of fig. 4 further includes the steps of:
s304, sample charge and discharge data of a sample battery are obtained; the sample charge and discharge data is the charge and discharge data of the whole life cycle of the sample battery.
The sample charge and discharge data may include charge and discharge time, voltage, current, temperature, internal resistance, etc. of the sample battery for a plurality of cycles; the battery state of health value SOH uses the capacity fade or the direct current internal resistance as an indicator of the state of battery health for characterizing the current battery capacity of the target battery.
In this embodiment, the collection device collects or historic data to obtain sample charge and discharge data of the sample battery, for example, the collection device may be a charge and discharge cabinet, a battery management system BMS, etc., where the sample charge and discharge data is charge and discharge data of a full life cycle of the sample battery.
S305, training the initial machine learning model by using sample charge and discharge data to obtain the machine learning model.
In this embodiment, the initial learning model is trained using the sample charging data, and optionally, training of the initial machine learning model may be completed when a preset number of training times is reached, or training of the initial learning model may be completed when a reward value of the initial machine learning model is greater than a preset threshold. The initial machine learning model that completes the training is determined as a machine learning model.
In this embodiment, the initial machine learning model is trained based on the sample charge and discharge data of the full life cycle of the sample battery, so as to obtain the machine learning model, thereby obtaining the mechanism parameters by using the machine learning model, and improving the accuracy of the mechanism parameters.
In one embodiment, an implementation manner of S305 is provided, as shown in fig. 7, where "training the initial machine learning model with sample charge and discharge data to obtain the machine learning model" includes:
S401, sample charge and discharge data are processed, and sample aging factors are obtained.
In this embodiment, the sample charge and discharge data is processed, so as to extract numerical information capable of reflecting the aging characteristics of the sample battery, for example, information such as the maximum value, the minimum value, the difference value, the average value, the variance and the like of the charge and discharge voltage, and the transformation trend of the information along with the progress of the cycle; and taking the processed sample charge and discharge data as a sample aging factor according to the attenuation trend and variance of the capacity, the extreme point and variation trend of the power-voltage curve, the corresponding n-step distance and other statistical indexes.
S402, training an initial machine learning model by using the sample aging factors to obtain the machine learning model.
In this embodiment, the initial learning model is trained by using the sample aging factor, and optionally, training of the initial machine learning model may be completed when a preset training number is reached, or training of the initial learning model may be completed when a reward value of the initial machine learning model is greater than a preset threshold. The initial machine learning model that completes the training is determined as a machine learning model.
In this embodiment, the sample charge and discharge data is processed to obtain the sample aging factor, and the initial machine learning model is trained based on the sample aging factor, so that the accuracy of calculating the mechanism parameters by the machine learning model is further improved.
One embodiment of the present disclosure is described below in connection with a specific lithium ion battery life prediction scenario, as shown in fig. 8, the method comprising the steps of:
s1, acquiring sample charge and discharge data of a sample battery; the sample charge and discharge data is charge and discharge data of the whole life cycle of the sample battery; processing the sample charge and discharge data to obtain a sample aging factor; training the initial machine learning model by using the sample aging factors to obtain the machine learning model.
S2, acquiring target charge and discharge data of a target battery; the target charge-discharge data are charge-discharge data when the battery state of health value of the target battery is larger than a preset threshold value;
and S3, processing the target charge and discharge data according to the type of the mechanism parameter to be solved, and obtaining a target aging factor matched with the type of the mechanism parameter.
S4, inputting the target aging factors into a machine learning model for analysis, and determining mechanism parameters corresponding to the target charge and discharge data;
s5, inputting the mechanism parameters into the linear prediction branch and the nonlinear prediction branch for prediction, obtaining a service life prediction curve of the target battery, and obtaining the service life of the target battery according to the service life prediction curve.
In the service life prediction method of the lithium ion battery, target charge and discharge data of a target battery are obtained; the target charge-discharge data are charge-discharge data when the battery state of health value of the target battery is larger than a preset threshold value; and inputting mechanism parameters corresponding to the target charge and discharge data into a prediction model to obtain the service life of the target battery. The mechanism parameters are introduced into the prediction model, so that the stability and the accuracy of the prediction model are improved, the service life of the target battery is acquired according to the charge and discharge data when the battery state of health value of the target battery is larger than the preset threshold value, and the charge and discharge data of all the battery periods are not required to be acquired, so that the service life of the target battery is acquired more accurately by using less data.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a service life prediction device of the lithium ion battery for realizing the service life prediction method of the lithium ion battery. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the service life prediction device for one or more lithium ion batteries provided below may be referred to the limitation of the service life prediction method for a lithium ion battery hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 9, there is provided a life prediction apparatus of a lithium ion battery, comprising: a first acquisition module 10 and a second acquisition module 11, wherein:
a first acquisition module 10 for acquiring target charge and discharge data of a target battery; the target charge-discharge data are charge-discharge data when the battery state of health value of the target battery is larger than a preset threshold value;
the second obtaining module 11 is configured to input the mechanism parameter corresponding to the target charge and discharge data to the prediction model, so as to obtain the service life of the target battery.
The service life prediction device for a lithium ion battery provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar and will not be described herein.
In one embodiment, as shown in fig. 10, the second acquisition module 11 includes: and the obtaining unit 111 is configured to input the mechanism parameters corresponding to the target charge and discharge data to the linear prediction branch and the nonlinear prediction branch, respectively, to predict, obtain a service life prediction curve of the target battery, and obtain the service life of the target battery according to the service life prediction curve.
The service life prediction device for a lithium ion battery provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar and will not be described herein.
In one embodiment, the obtaining unit 111 is configured to process the target charge/discharge data to obtain a target aging factor of the target battery; inputting the target aging factors into a machine learning model for analysis, and determining mechanism parameters corresponding to the target charge and discharge data; and respectively inputting the mechanism parameters into a linear prediction branch and a nonlinear prediction branch for prediction to obtain a service life prediction curve of the target battery, and obtaining the service life of the target battery according to the service life prediction curve.
The service life prediction device for a lithium ion battery provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar and will not be described herein.
In one embodiment, the obtaining unit 111 is configured to process the target charge and discharge data according to the type of the mechanism parameter to be solved, so as to obtain a target aging factor matched with the type of the mechanism parameter.
The service life prediction device for a lithium ion battery provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar and will not be described herein.
In one embodiment, the acquiring unit 111 is configured to acquire sample charge and discharge data of a sample battery; the sample charge and discharge data is charge and discharge data of the whole life cycle of the sample battery; and training the initial machine learning model by using the sample charge and discharge data to obtain the machine learning model.
The service life prediction device for a lithium ion battery provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar and will not be described herein.
In one embodiment, the obtaining unit 111 is configured to process the sample charge and discharge data to obtain a sample aging factor; training the initial machine learning model by using the sample aging factors to obtain the machine learning model.
The service life prediction device for a lithium ion battery provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar and will not be described herein.
The modules in the lithium ion battery service life prediction device can be all or partially realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
acquiring target charge and discharge data of a target battery; the target charge-discharge data are charge-discharge data when the battery state of health value of the target battery is larger than a preset threshold value;
and inputting mechanism parameters corresponding to the target charge and discharge data into a prediction model to obtain the service life of the target battery.
The computer device provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar, and are not described herein again.
In one embodiment, the processor when executing the computer program further performs the steps of:
and respectively inputting mechanism parameters corresponding to the target charge and discharge data into the linear prediction branch and the nonlinear prediction branch for prediction to obtain a service life prediction curve of the target battery, and obtaining the service life of the target battery according to the service life prediction curve.
The computer device provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar, and are not described herein again.
In one embodiment, the processor when executing the computer program further performs the steps of:
processing the target charge and discharge data to obtain a target aging factor of the target battery;
inputting the target aging factors into a machine learning model for analysis, and determining mechanism parameters corresponding to the target charge and discharge data;
and respectively inputting the mechanism parameters into a linear prediction branch and a nonlinear prediction branch for prediction to obtain a service life prediction curve of the target battery, and obtaining the service life of the target battery according to the service life prediction curve.
The computer device provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar, and are not described herein again.
In one embodiment, the processor when executing the computer program further performs the steps of:
and processing the target charge and discharge data according to the type of the mechanism parameter to be solved to obtain a target aging factor matched with the type of the mechanism parameter.
The computer device provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar, and are not described herein again.
In one embodiment, the processor when executing the computer program further performs the steps of:
acquiring sample charge and discharge data of a sample battery; the sample charge and discharge data is charge and discharge data of the whole life cycle of the sample battery;
and training the initial machine learning model by using the sample charge and discharge data to obtain the machine learning model.
The computer device provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar, and are not described herein again.
In one embodiment, the processor when executing the computer program further performs the steps of:
processing the sample charge and discharge data to obtain a sample aging factor;
training the initial machine learning model by using the sample aging factors to obtain the machine learning model.
The computer device provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar, and are not described herein again.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring target charge and discharge data of a target battery; the target charge-discharge data are charge-discharge data when the battery state of health value of the target battery is larger than a preset threshold value;
And inputting mechanism parameters corresponding to the target charge and discharge data into a prediction model to obtain the service life of the target battery.
The computer readable storage medium provided in this embodiment may perform the above method embodiments, and its implementation principle and technical effects are similar, and will not be described herein.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and respectively inputting mechanism parameters corresponding to the target charge and discharge data into the linear prediction branch and the nonlinear prediction branch for prediction to obtain a service life prediction curve of the target battery, and obtaining the service life of the target battery according to the service life prediction curve.
The computer readable storage medium provided in this embodiment may perform the above method embodiments, and its implementation principle and technical effects are similar, and will not be described herein.
In one embodiment, the computer program when executed by the processor further performs the steps of:
processing the target charge and discharge data to obtain a target aging factor of the target battery;
inputting the target aging factors into a machine learning model for analysis, and determining mechanism parameters corresponding to the target charge and discharge data;
and respectively inputting the mechanism parameters into a linear prediction branch and a nonlinear prediction branch for prediction to obtain a service life prediction curve of the target battery, and obtaining the service life of the target battery according to the service life prediction curve.
The computer readable storage medium provided in this embodiment may perform the above method embodiments, and its implementation principle and technical effects are similar, and will not be described herein.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and processing the target charge and discharge data according to the type of the mechanism parameter to be solved to obtain a target aging factor matched with the type of the mechanism parameter.
The computer readable storage medium provided in this embodiment may perform the above method embodiments, and its implementation principle and technical effects are similar, and will not be described herein.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring sample charge and discharge data of a sample battery; the sample charge and discharge data is charge and discharge data of the whole life cycle of the sample battery;
and training the initial machine learning model by using the sample charge and discharge data to obtain the machine learning model.
The computer readable storage medium provided in this embodiment may perform the above method embodiments, and its implementation principle and technical effects are similar, and will not be described herein.
In one embodiment, the computer program when executed by the processor further performs the steps of:
Processing the sample charge and discharge data to obtain a sample aging factor;
training the initial machine learning model by using the sample aging factors to obtain the machine learning model.
The computer readable storage medium provided in this embodiment may perform the above method embodiments, and its implementation principle and technical effects are similar, and will not be described herein.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:
acquiring target charge and discharge data of a target battery; the target charge-discharge data are charge-discharge data when the battery state of health value of the target battery is larger than a preset threshold value;
and inputting mechanism parameters corresponding to the target charge and discharge data into a prediction model to obtain the service life of the target battery.
The computer program product provided in this embodiment may perform the above method embodiments, and its implementation principle and technical effects are similar, and will not be described herein.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and respectively inputting mechanism parameters corresponding to the target charge and discharge data into the linear prediction branch and the nonlinear prediction branch for prediction to obtain a service life prediction curve of the target battery, and obtaining the service life of the target battery according to the service life prediction curve.
The computer program product provided in this embodiment may perform the above method embodiments, and its implementation principle and technical effects are similar, and will not be described herein.
In one embodiment, the computer program when executed by the processor further performs the steps of:
processing the target charge and discharge data to obtain a target aging factor of the target battery;
inputting the target aging factors into a machine learning model for analysis, and determining mechanism parameters corresponding to the target charge and discharge data;
and respectively inputting the mechanism parameters into a linear prediction branch and a nonlinear prediction branch for prediction to obtain a service life prediction curve of the target battery, and obtaining the service life of the target battery according to the service life prediction curve.
The computer program product provided in this embodiment may perform the above method embodiments, and its implementation principle and technical effects are similar, and will not be described herein.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and processing the target charge and discharge data according to the type of the mechanism parameter to be solved to obtain a target aging factor matched with the type of the mechanism parameter.
The computer program product provided in this embodiment may perform the above method embodiments, and its implementation principle and technical effects are similar, and will not be described herein.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring sample charge and discharge data of a sample battery; the sample charge and discharge data is charge and discharge data of the whole life cycle of the sample battery;
and training the initial machine learning model by using the sample charge and discharge data to obtain the machine learning model.
The computer program product provided in this embodiment may perform the above method embodiments, and its implementation principle and technical effects are similar, and will not be described herein.
In one embodiment, the computer program when executed by the processor further performs the steps of:
processing the sample charge and discharge data to obtain a sample aging factor;
training the initial machine learning model by using the sample aging factors to obtain the machine learning model.
The computer program product provided in this embodiment may perform the above method embodiments, and its implementation principle and technical effects are similar, and will not be described herein.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A method for predicting the service life of a lithium ion battery, the method comprising:
acquiring target charge and discharge data of a target battery; the target charge-discharge data are charge-discharge data when the battery state of health value of the target battery is larger than a preset threshold value; the target charge-discharge data comprises charge-discharge time, voltage, current, temperature and internal resistance of the target battery for a plurality of cycles;
Charging and discharging data of the targetPerforming row processing to obtain a target aging factor of the target battery; inputting the target aging factors into a machine learning model for analysis, and determining mechanism parameters corresponding to the target charge and discharge data; the mechanism parameters are respectively input into a linear prediction branch and a nonlinear prediction branch for prediction to obtain a service life prediction curve of the target battery, and the service life of the target battery is obtained according to the service life prediction curve; wherein, the prediction model is:wherein Q (x) represents the health of the target battery, x is the cycle period,/->，/>Or (E)>，a、b、c、x 0 For the mechanism parameters, +_>A linear prediction branch of said prediction model, < >>And is a nonlinear prediction branch of the prediction model.
2. The method of claim 1, wherein processing the target charge and discharge data to obtain a target aging factor for the target battery comprises:
and processing the target charge and discharge data according to the type of the mechanism parameter to be solved to obtain a target aging factor matched with the type of the mechanism parameter.
3. The method according to claim 1, wherein the method further comprises:
Acquiring sample charge and discharge data of a sample battery; the sample charge and discharge data is charge and discharge data of the whole life cycle of the sample battery;
and training an initial machine learning model by using the sample charge and discharge data to obtain the machine learning model.
4. The method of claim 3, wherein training an initial machine learning model using the sample charge-discharge data results in the machine learning model, comprising:
processing the sample charge and discharge data to obtain a sample aging factor;
and training an initial machine learning model by using the sample aging factors to obtain the machine learning model.
5. The method of any one of claims 1-4, wherein the machine learning model is any one of a logistic regression model, a gaussian process regression model, a random forest model.
6. The method of any one of claims 1-4, wherein the target charge-discharge data is charge-discharge data for a first 100 weeks complete charge-discharge cycle of the target battery.
7. A life prediction device for a lithium ion battery, the device comprising:
The first acquisition module is used for acquiring target charge and discharge data of a target battery; the target charge-discharge data are charge-discharge data when the battery state of health value of the target battery is larger than a preset threshold value; the target charge-discharge data comprises charge-discharge time, voltage, current, temperature and internal resistance of the target battery for a plurality of cycles;
a second obtaining module, configured to process the target charge-discharge data to obtain target aging of the target batteryA factor; inputting the target aging factors into a machine learning model for analysis, and determining mechanism parameters corresponding to the target charge and discharge data; the mechanism parameters are respectively input into a linear prediction branch and a nonlinear prediction branch for prediction to obtain a service life prediction curve of the target battery, and the service life of the target battery is obtained according to the service life prediction curve; wherein, the prediction model is:wherein Q (x) represents the health of the target battery, x is the cycle period,/->，/>Or (E)>，a、b、c、x 0 For the mechanism parameters, +_>A linear prediction branch of said prediction model, < >>And is a nonlinear prediction branch of the prediction model.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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