CN114881316B - Lithium battery remaining life prediction method, system, terminal equipment and storage medium - Google Patents
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Abstract
The invention discloses a method, a system, terminal equipment and a storage medium for predicting the residual life of a lithium battery, wherein the method comprises the steps of collecting charge and discharge curves of two-phase reaction lithium batteries under different working conditions and different lives; extracting parameters based on a Nernst model from each collected charge and discharge curve; training a life prediction model according to the residual life of the lithium battery under different working conditions and the extracted parameters of the Nernst model; and predicting the residual life of the lithium battery to be predicted according to the trained life prediction model and a charge-discharge curve of the lithium battery to be predicted under a certain working condition, wherein the lithium battery to be predicted is a two-phase reaction lithium battery. According to the invention, parameters of the Nernst model are extracted through the collected charge-discharge curves, electrochemical model support can be provided for life prediction of the lithium battery, so that prediction accuracy is improved, and the whole training and prediction process is simpler.
Description
Technical Field
The invention relates to the field of lithium batteries, in particular to a method, a system, terminal equipment and a storage medium for predicting the residual life of a lithium battery.
Background
Under the global 'carbon neutralization' background, the heat is continuously raised when searching for clean energy which can replace petroleum energy. Solar energy, tidal energy, wind energy, water energy and the like are clean sustainable energy sources, but medium controllability of energy source generation is relatively weak. The lithium ion battery is a new generation secondary battery at present, has higher energy density and cycle life, is widely applied to the fields of mobile communication, digital science and technology, electric automobiles, energy storage and the like, and the requirements of the lithium ion battery and materials thereof are difficult to evaluate in the future, and the matched upstream and downstream industrial chains are huge in market, so that the research on the aspect of predicting the residual life of the lithium battery becomes a research hotspot.
Three main methods for predicting the remaining life of a lithium battery are: model method, data driven method and fusion method. The data driving method is the most widely used method at present, and the method does not need to consider the actual electrochemical reaction and failure mechanism inside the lithium battery, directly analyzes and mines hidden battery health state information and change rules thereof from battery performance test data and state monitoring data (such as voltage, current, temperature, impedance and the like) obtained by testing, and finally realizes RUL prediction of the lithium battery.
The main current method for predicting the residual life of the lithium battery based on data driving mainly comprises the following steps: an autoregressive time series model, an artificial neural network model, a support vector machine model, a Gaussian process regression model, a particle filter model and the like. These models, while having their own advantages and disadvantages in predicting the remaining life of a lithium battery, remain only data-driven, without support of electrochemical models, nor correspondence of electrochemical parameters in the actual process, which requires a trade-off and compromise between accuracy and complexity.
Disclosure of Invention
Aiming at the technical problems, the invention aims to solve the technical problems that the existing lithium battery residual life prediction method does not have electrochemical model support, so that the accuracy and the complexity are required to be compromised.
In order to achieve the above object, the present invention provides a lithium battery remaining life prediction method, comprising:
collecting charge and discharge curves of two-phase reaction lithium batteries under different working conditions and different service lives;
extracting parameters based on a Nernst model from each collected charge and discharge curve;
training a life prediction model according to the residual life of the lithium battery under different working conditions and the extracted parameters of the Nernst model;
and predicting the residual life of the lithium battery to be predicted according to the trained life prediction model and a charge-discharge curve of the lithium battery to be predicted under a certain working condition, wherein the lithium battery to be predicted is a two-phase reaction lithium battery.
In some embodiments, an initial mathematical form of the Nernst model is constructed:
optimizing an initial mathematical form of the Nernst model to obtain an optimized Nernst model, wherein the mathematical form of the optimized Nernst model is as follows: y=k 0 ln(l 0 )+k 0 ln(x)+k 1 (1-x) -1 ；
Fitting the collected charge and discharge curves according to the mathematical form of the optimized Nernst model to obtain the parameter k of the Nernst model corresponding to each charge and discharge curve 0 、k 1 And l 0 ；
Wherein y is the terminal voltage of the lithium battery; x is the SOC value of the lithium battery.
In some embodiments, the training life prediction model according to the remaining life of the lithium battery under different working conditions and the extracted parameters of the Nernst model specifically includes:
under different working conditions, the residual service life of the lithium battery is taken as a label, and k is taken as 0 、k 1 And l 0 The values are used as training characteristic values, and different artificial intelligent models are trained respectively;
and giving different weights to different trained artificial intelligent models for fusion to obtain a life prediction model of the lithium battery.
In some embodiments, predicting the remaining life of the lithium battery to be predicted according to the trained life prediction model and the charge-discharge curve of the lithium battery to be predicted under a certain working condition specifically includes:
collecting a charge-discharge curve of a lithium battery to be predicted under a certain working condition;
extracting parameters of a corresponding Nernst model according to a charge-discharge curve of the lithium battery to be predicted;
and inputting the extracted Nernst model parameters of the lithium battery to be predicted into a trained life prediction model, and outputting the residual life of the lithium battery to be predicted.
According to another aspect of the present invention, there is further provided a lithium battery remaining life prediction system including:
the data acquisition module is used for acquiring charge and discharge curves of the two-phase reaction lithium battery under different working conditions and different service lives;
the parameter extraction module is used for extracting parameters based on a Nernst model from each collected charge and discharge curve;
the model training module is used for training a life prediction model according to the residual life of the lithium battery under different working conditions and the extracted parameters of the Nernst model;
and the life prediction module is used for predicting the residual life of the lithium battery to be predicted according to the trained life prediction model and the charge-discharge curve of the lithium battery to be predicted under a certain working condition.
In some embodiments, the parameter extraction module comprises:
the model construction unit is used for constructing an initial mathematical form of the Nernst model and optimizing the initial mathematical form of the Nernst model;
the initial mathematical form of the constructed Nernst model is as follows:
the mathematical form of the optimized Nernst model is as follows:
y＝k 0 ln(l 0 )+k 0 ln(x)+k 1 (1-x) -1 ；
the data fitting unit is used for fitting the mathematical form of the optimized Nernst model according to the collected charge-discharge curves to obtain parameters of the Nernst model corresponding to each charge-discharge curvek 0 、k 1 And l 0 ；
Wherein y is the terminal voltage of the lithium battery; x is the SOC value of the lithium battery.
In some embodiments, the model training module comprises:
the model training unit is used for taking the residual service life of the lithium battery as a label and taking k as a label under different working conditions 0 、k 1 And l 0 The values are used as training characteristic values, and different artificial intelligent models are trained respectively;
and the model fusion unit is used for giving different weights to different trained artificial intelligent models to fuse so as to obtain a life prediction model of the lithium battery.
In some embodiments, the data acquisition module is further configured to acquire a charge-discharge curve of the lithium battery to be predicted under a certain working condition;
the parameter extraction module is also used for extracting parameters of a corresponding Nernst model according to a charge-discharge curve of the lithium battery to be predicted;
and the life prediction module is used for inputting the extracted Nernst model parameters of the lithium battery to be predicted into a trained life prediction model and outputting the residual life of the lithium battery to be predicted.
According to another aspect of the present invention, there is further provided a terminal device, including a processor, a memory, and a computer program stored in the memory and executable on the processor, where the processor is configured to execute the computer program stored in the memory, to implement the operations performed by the lithium battery remaining life prediction method according to any one of the foregoing embodiments.
According to another aspect of the present invention, there is further provided a storage medium having at least one instruction stored therein, the instruction being loaded and executed by a processor to implement the operation performed by the lithium battery remaining life prediction method according to any one of the above embodiments.
Compared with the prior art, the lithium battery remaining life prediction method, the lithium battery remaining life prediction system, the terminal equipment and the storage medium provided by the invention have the following beneficial effects:
1. according to the invention, the OCV-SOC curve of the two-phase reaction lithium battery is firstly collected, then a mathematical form with higher precision of the OCV-SOC curve of the two-phase reaction lithium battery is provided, namely, the mathematical form of a Nernst model is provided, the mathematical form has practical materialization significance, parameters of the Nernst model are extracted according to the OCV-SOC curve, and the relation between the parameters of the Nernst model and the residual life is constructed, so that the residual life of the lithium battery is predicted according to the parameters of the Nernst model, the input value of the predicted residual life is derived from the collected OCV-SOC curve, and the support of an electrochemical model is provided, so that the prediction precision can be improved, and the whole training and prediction process is simpler.
2. Under different working conditions and different service lives, the relationship between the OCV and the SOC can be used as input of electrode balance potential in the electrochemical model, so that the accuracy of the built electrochemical model is improved.
3. The application of the integrated learning and model fusion in the machine learning of the residual service life of the lithium battery can improve the model precision and further improve the prediction precision.
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The above features, technical features, advantages and implementation of the present invention will be further described in the following description of preferred embodiments with reference to the accompanying drawings in a clear and easily understood manner.
Fig. 1 is a flowchart of a method for predicting remaining life of a lithium battery according to the present invention.
Fig. 2 is a charge-discharge graph of a lithium iron phosphate battery;
FIG. 3 is a diagram showing Nernst model parameter extraction under different conditions;
FIG. 4 is a schematic diagram of integrated learning and model fusion;
fig. 5 is a block diagram schematically illustrating a structure of a lithium battery remaining life prediction system according to the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
For the sake of simplicity of the drawing, the parts relevant to the present invention are shown only schematically in the figures, which do not represent the actual structure thereof as a product. Additionally, in order to simplify the drawing for ease of understanding, components having the same structure or function in some of the drawings are shown schematically with only one of them, or only one of them is labeled. Herein, "a" means not only "only this one" but also "more than one" case.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
In addition, in the description of the present application, the terms "first," "second," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will explain the specific embodiments of the present invention with reference to the accompanying drawings. It is evident that the drawings in the following description are only examples of the invention, from which other drawings and other embodiments can be obtained by a person skilled in the art without inventive effort.
Referring to fig. 1, fig. 1 shows a schematic flowchart of a method for predicting remaining life of a lithium battery according to an embodiment of the present application, where the method includes:
s100, collecting charge and discharge curves of a two-phase reaction lithium battery under different working conditions and different service lives;
specifically, the two-phase reaction lithium battery refers to that in the reaction process, only two phases participate in the reaction of the positive electrode material, for example, a lithium iron phosphate battery, and the lithium storage reaction mechanism of the lithium iron phosphate battery is as follows:
LiFePO 4 →xLi+xFePO 4 +(1-x)LiFePO 4 ；
it can be seen from the equation that as the reaction proceeds, there are always two phases. Its nertus equation is e=Δrg/f= [ Δfg (FePO) 4 )-ΔfG(LiFePO 4 )]According to the Nernst equation of the lithium iron phosphate battery, the electrode potential of the lithium iron phosphate battery is irrelevant to x, is constant, and is represented by a long platform on an OCV-SOC curve, and the inflection point of the platform is caused by insufficient phase quantity at high and low contents. The charge-discharge curve of the lithium iron phosphate battery is shown in fig. 2.
For non-two-phase reacted lithium batteries, such as lithium cobaltate batteries, the lithium storage reaction mechanism of lithium cobaltate batteries is: liCoO 2 →xLi+Li {1-x} CoO 2 。
As can be seen from the equation, as the reaction proceeds, the composition of the right hand phase of the reaction is related to the phase. The energy tex equation for a lithium cobalt oxide battery is e=Δrg/f= [ Δfg (Li) {1-x} CoO 2 )-ΔfG(LiCoO 2 )]According to the Nernst equation of the lithium cobaltate battery, the electrode potential of the lithium cobaltate battery is related to x, and the electrode potential is represented by a continuous ascending curve on an OCV-SOC curve. More generally, it is believed that during the electrochemical reaction, there are several phases and thus several voltage plateaus.
The mathematical form of the Nernst model is:
the Nernst model is also provided with a platform in a curve form which is more similar to an OCV-SOC curve of the two-phase reaction lithium battery, so that the Nernst model is more suitable for the two-phase reaction lithium battery in theory, and the invention mainly adopts the Nernst model to predict the residual life of the two-phase reaction lithium battery.
In the step, the charge and discharge curves of the two-phase reaction lithium battery under different working conditions and different service lives are collected. Different working conditions refer to different charge-discharge multiplying powers and temperatures, and the charge-discharge curves of the same lithium battery are different when the charge-discharge multiplying powers of the same lithium battery are different, so that the charge-discharge curves of the same lithium battery under different charge-discharge multiplying powers and different temperatures need to be acquired when data are acquired.
The life (SOH, state of health) of a lithium battery refers to the remaining capacity of the battery/the standard capacity of the battery, i.e., the remaining life of the lithium battery. The invention aims to predict the residual life of a lithium battery, so that the life of the lithium battery is required to be reflected when the charge and discharge curves of the battery are acquired, and each charge and discharge curve has corresponding charge and discharge working conditions and life.
When the charge and discharge curves of the lithium battery are collected, the charge and discharge curves of one two-phase reaction lithium battery under different working conditions and different service lives can be collected to form a charge and discharge curve set, and the charge and discharge curves of a plurality of two-phase reaction lithium batteries under different working conditions and different service lives can be collected to form the charge and discharge curve set.
S200, extracting parameters based on a Nernst model from each collected charge and discharge curve;
specifically, the initial mathematical form of constructing the Nernst model is:
in order to save calculation force and improve accuracy, the initial mathematical form of the Nernst model is optimized to obtain:
y＝k 0 ln(l 0 )+k 0 ln(x)+k 1 (1-x) -1 ；
the x in the optimized Nernst model is the SOC value of the lithium battery, namely the abscissa, and the y is the terminal voltage of the lithium battery, namely the ordinate; k (k) 0 ln(l 0 ) Characterization of platform Voltage, k 0 、k 1 The lithium ion activity and the electrode number of the anode material and the cathode material are respectivelyWhat configuration, etc.
After constructing the mathematical form of the Nernst model, the acquired charge and discharge curves can be fitted according to the constructed mathematical form of the Nernst model to obtain the parameters k of the Nernst model corresponding to each charge and discharge curve 0 、k 1 And l 0 . The parameter extraction process of the Nernst model is shown in fig. 3.
Fitting each charge-discharge curve to extract the parameter k of Nernst model corresponding to each charge-discharge curve 0 、k 1 And l 0 Wherein, each charge-discharge curve can obtain a set of Nernst model parameters k 0 、k 1 And l 0 According to the collected multiple charge-discharge curves, multiple groups of data can be obtained, and each group of data comprises the following parameters: charge and discharge conditions, lithium battery remaining life, and Nernst model parameters.
S300, training a life prediction model according to the residual life of the lithium battery under different working conditions and the extracted parameters of the Nernst model;
specifically, after multiple sets of data are obtained according to the collected multiple charge-discharge curves, the life prediction model can be trained according to the multiple sets of data, and the corresponding relation between Nernst model parameters and the residual life is established. The life prediction model is a BP neural network model, a Random Forest model, an XGBoost decision tree model and the like, and the model training process of the life prediction model is the prior art and is not repeated here.
S400, predicting the residual life of the lithium battery to be predicted according to the trained life prediction model and a charge-discharge curve of the lithium battery to be predicted under a certain working condition, wherein the lithium battery to be predicted is a two-phase reaction lithium battery.
Specifically, when predicting the remaining life of the lithium battery through the trained life prediction model, the charge-discharge curve of the two-phase reaction lithium battery under a certain working condition can be collected first, and then the parameter k of the Nernst model in the charge-discharge curve is extracted 0 、k 1 And l 0 Finally according to parameter k of Nernst model 0 、k 1 And l 0 And the trained life prediction model is used for carrying out residual life prediction on the lithium battery to be predictedAnd (5) estimating.
According to the invention, parameters of the Nernst model are extracted through the collected charge-discharge curves, electrochemical model support can be provided for life prediction of the lithium battery, so that prediction accuracy is improved, and the whole training and prediction process is simpler.
According to the invention, the relationship of the OCV and the SOC under different working conditions and different service lives is used as the input of the electrode balance potential in the electrochemical model, so that the accuracy of the established electrochemical model can be improved, the characteristic value is provided for machine learning to learn the residual service life of the battery, and the prediction accuracy of the residual service life of the two-phase reaction lithium battery is improved.
In some embodiments, step S300 specifically includes training a life prediction model according to the remaining life of the lithium battery under different working conditions and the extracted parameters of the Nernst model:
s310, under different working conditions, taking the residual life of the lithium battery as a label and taking k as 0 、k 1 And l 0 The values are used as training characteristic values, and different artificial intelligent models are trained respectively;
s320, different weights are given to the trained different artificial intelligence models to fuse, and a life prediction model of the lithium battery is obtained.
Specifically, according to step S200, a plurality of sets of data may be obtained, where each set of data includes the following parameters: the method comprises the steps of inputting the remaining life of the lithium battery and the Nernst model parameters under different working conditions into different neural network models for training, for example, respectively inputting the remaining life of the lithium battery as a label and the Nernst model parameters as training feature values to construct the corresponding relation between the feature values and the remaining life when independently training in BP neural network, random Forest, XGBoost decision tree and other models.
As shown in fig. 4, after each of the different models is trained separately, the prediction results of the different models are fused together by using a model fusion method, so as to improve the accuracy of the models, that is, after the training is completed, a weight value is given to each model according to the prediction result and the label value of each model, so that the prediction result is closer to the label value (the actual remaining life of the lithium battery).
Assuming that the weight value of the BP neural network is 0.2,Random Forest, the weight value of the xgboost decision tree is 0.3, and the weight value of the xgboost decision tree is 0.5, the final predicted value is equal to 0.2×bp neural network predicted value+ 0.3*Random Forest network predicted value+0.5×xgboost decision tree predicted value.
In some embodiments, in step S400, predicting the remaining life of the lithium battery to be predicted according to the trained life prediction model and the charge-discharge curve of the lithium battery to be predicted under a certain working condition specifically includes:
s410, collecting a charge-discharge curve of a lithium battery to be predicted under a certain working condition;
s420, extracting parameters of a corresponding Nernst model according to a charge-discharge curve of the lithium battery to be predicted;
s430, inputting the extracted Nernst model parameters of the lithium battery to be predicted into a trained life prediction model, and outputting the residual life of the lithium battery to be predicted.
Specifically, if the life prediction model is a single neural network model, directly extracting Nernst model parameters k corresponding to the lithium battery to be predicted 0 、k 1 And l 0 And inputting the predicted residual life of the lithium battery to be predicted into the single neural network model, wherein the output value is the predicted residual life of the lithium battery to be predicted.
If the life prediction model is to fuse prediction results of different models together by adopting a model fusion method, extracting Nernst model parameters k corresponding to the lithium battery to be predicted 0 、k 1 And l 0 After that, nernst model parameter k 0 、k 1 And l 0 And respectively inputting the predicted values into trained BP neural network, random Forest and XGBoost decision trees, respectively outputting predicted values from the BP neural network, the Random Forest and the XGBoost decision trees, and then estimating the final residual life of the lithium battery to be predicted according to the weight value of each neural network model.
The present invention also provides an embodiment of a lithium battery remaining life prediction system, as shown in fig. 5, including:
the data acquisition module 10 is used for acquiring charge and discharge curves of the two-phase reaction lithium battery under different working conditions and different service lives;
the parameter extraction module 20 is used for extracting parameters based on a Nernst model for each collected charge and discharge curve;
the model training module 30 is configured to train a life prediction model according to the remaining life of the lithium battery under different working conditions and the extracted parameters of the Nernst model;
and the life prediction module 40 is used for predicting the remaining life of the lithium battery to be predicted according to the trained life prediction model and the charge-discharge curve of the lithium battery to be predicted under a certain working condition.
In some embodiments, the parameter extraction module 20 includes:
the model construction unit is used for constructing an initial mathematical form of the Nernst model and optimizing the initial mathematical form of the Nernst model;
the initial mathematical form of the constructed Nernst model is as follows:
the mathematical form of the optimized Nernst model is as follows:
y＝k 0 ln(l 0 )+k 0 ln(x)+k 1 (1-x) -1 ；
the data fitting unit is used for fitting the mathematical form of the optimized Nernst model according to the collected charge-discharge curves to obtain the parameter k of the Nernst model corresponding to each charge-discharge curve 0 、k 1 And l 0 ；
Wherein y is the terminal voltage of the lithium battery; x is the SOC value of the lithium battery.
In some embodiments, the model training module 30 includes:
the model training unit is used for taking the residual service life of the lithium battery as a label and taking k as a label under different working conditions 0 、k 1 And l 0 The values are used as training characteristic values, and different artificial intelligent models are trained respectively;
and the model fusion unit is used for giving different weights to different trained artificial intelligent models to fuse so as to obtain a life prediction model of the lithium battery.
In some embodiments, the data acquisition module 10 is further configured to acquire a charge-discharge curve of the lithium battery to be predicted under a certain working condition;
the parameter extraction module 20 is further configured to extract parameters of a corresponding Nernst model according to a charge-discharge curve of the lithium battery to be predicted;
the life prediction module 40 is configured to input the extracted Nernst model parameters of the lithium battery to be predicted into a trained life prediction model, and output the remaining life of the lithium battery to be predicted.
Specifically, the embodiment is an embodiment of a device corresponding to the embodiment of the method, and specific effects refer to the embodiment of the method, which is not described herein in detail.
It will be apparent to those skilled in the art that the above-described program modules are only illustrated in the division of the above-described program modules for convenience and brevity, and that in practical applications, the above-described functional allocation may be performed by different program modules, i.e., the internal structure of the apparatus is divided into different program units or modules, to perform all or part of the above-described functions. The program modules in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one processing unit, where the integrated units may be implemented in a form of hardware or in a form of a software program unit. In addition, the specific names of the program modules are also only for distinguishing from each other, and are not used to limit the protection scope of the present application.
An embodiment of the invention, a terminal device, including a processor, a memory, wherein the memory is used for storing a computer program; and the processor is used for executing the computer program stored in the memory to realize the lithium battery remaining life prediction method in the corresponding method embodiment.
The terminal equipment can be desktop computers, notebooks, palm computers, tablet computers, mobile phones, man-machine interaction screens and other equipment. The terminal device may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that the foregoing is merely an example of a terminal device and is not limiting of the terminal device and may include more or fewer components than shown, or may combine certain components, or different components, such as: the terminal device may also include input/output interfaces, display devices, network access devices, communication buses, communication interfaces, and the like. The communication interface and the communication bus may further comprise an input/output interface, wherein the processor, the memory, the input/output interface and the communication interface complete communication with each other through the communication bus. The memory stores a computer program, and the processor is configured to execute the computer program stored in the memory, so as to implement the method for predicting the remaining life of the lithium battery in the corresponding method embodiment.
The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may be an internal storage unit of the terminal device, for example: a hard disk or a memory of the terminal equipment. The memory may also be an external storage device of the terminal device, for example: a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) and the like, which are provided on the terminal device. Further, the memory may also include both an internal storage unit and an external storage device of the terminal device. The memory is used for storing the computer program and other programs and data required by the terminal device. The memory may also be used to temporarily store data that has been output or is to be output.
A communication bus is a circuit that connects the elements described and enables transmission between these elements. For example, the processor receives commands from other elements through the communication bus, decrypts the received commands, and performs calculations or data processing based on the decrypted commands. The memory may include program modules such as a kernel, middleware, application programming interfaces (Application Programming Interface, APIs), and applications. The program modules may be comprised of software, firmware, or hardware, or at least two of them. The input/output interface forwards commands or data entered by a user through the input/output interface (e.g., sensor, keyboard, touch screen). The communication interface connects the terminal device with other network devices, user devices, networks. For example, the communication interface may be connected to a network by wire or wirelessly to connect to external other network devices or user devices. The wireless communication may include at least one of: wireless fidelity (WiFi), bluetooth (BT), near field wireless communication technology (NFC), global Positioning System (GPS) and cellular communications, among others. The wired communication may include at least one of: universal Serial Bus (USB), high Definition Multimedia Interface (HDMI), asynchronous transfer standard interface (RS-232), and the like. The network may be a telecommunications network or a communication network. The communication network may be a computer network, the internet of things, a telephone network. The terminal device may be connected to the network through a communication interface, and protocols used by the terminal device to communicate with other network devices may be supported by at least one of an application, an Application Programming Interface (API), middleware, a kernel, and a communication interface.
In one embodiment of the present invention, a storage medium has at least one instruction stored therein, and the instruction is loaded and executed by a processor to implement the operations performed by the corresponding embodiments of the method for predicting remaining life of a lithium battery. For example, the storage medium may be read-only memory (ROM), random-access memory (RAM), compact disk read-only (CD-ROM), magnetic tape, floppy disk, optical data storage device, etc.
They may be implemented in program code that is executable by a computing device such that they may be stored in a memory device for execution by the computing device, or they may be separately fabricated into individual integrated circuit modules, or a plurality of modules or steps in them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and the parts of a certain embodiment that are not described or depicted in detail may be referred to in the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units may be stored in a storage medium if implemented in the form of software functional units and sold or used as stand-alone products. Based on this understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by sending instructions to related hardware by a computer program, where the computer program may be stored in a storage medium, and the computer program may implement the steps of each method embodiment described above when executed by a processor. Wherein the computer program may be in source code form, object code form, executable file or some intermediate form, etc. The storage medium may include: any entity or device capable of carrying the computer program, a recording medium, a USB flash disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that, the content contained in the storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example: in some jurisdictions, computer-readable storage media do not include electrical carrier signals and telecommunication signals, in accordance with legislation and patent practice.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
It should be noted that the above embodiments can be freely combined as needed. The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (4)
1. A method for predicting remaining life of a lithium battery, comprising:
collecting charge and discharge curves of two-phase reaction lithium batteries under different working conditions and different service lives;
extracting parameters based on a Nernst model from each collected charge and discharge curve;
training a life prediction model according to the residual life of the lithium battery under different working conditions and the extracted parameters of the Nernst model;
predicting the residual life of the lithium battery to be predicted according to a trained life prediction model and a charge-discharge curve of the lithium battery to be predicted under a certain working condition, wherein the lithium battery to be predicted is a two-phase reaction lithium battery;
the parameter extraction based on the Nernst model for each collected charge and discharge curve specifically comprises the following steps:
constructing an initial mathematical form of the Nernst model:
optimizing an initial mathematical form of the Nernst model to obtain an optimized Nernst model, wherein the mathematical form of the optimized Nernst model is as follows: y=k 0 ln(l 0 )+k 0 ln(x)+k 1 (1-x) -1 ；
Fitting the collected charge and discharge curves according to the mathematical form of the optimized Nernst model to obtain the parameter k of the Nernst model corresponding to each charge and discharge curve 0 、k 1 And l 0 ；
Wherein y is the terminal voltage of the lithium battery; x is the SOC value of the lithium battery:
the life prediction model is trained according to the residual life of the lithium battery under different working conditions and the extracted parameters of the Nernst model, and specifically comprises the following steps:
under different working conditions, the residual service life of the lithium battery is taken as a label, and k is taken as 0 、k 1 And l 0 The values are used as training characteristic values, and different artificial intelligent models are trained respectively;
different weights are given to different trained artificial intelligent models to be fused, so that a life prediction model of the lithium battery is obtained;
predicting the remaining life of the lithium battery to be predicted according to the trained life prediction model and a charge-discharge curve of the lithium battery to be predicted under a certain working condition specifically comprises:
collecting a charge-discharge curve of a lithium battery to be predicted under a certain working condition;
extracting parameters of a corresponding Nernst model according to a charge-discharge curve of the lithium battery to be predicted;
and inputting the extracted Nernst model parameters of the lithium battery to be predicted into a trained life prediction model, and outputting the residual life of the lithium battery to be predicted.
2. A lithium battery remaining life prediction system, comprising:
the data acquisition module is used for acquiring charge and discharge curves of the two-phase reaction lithium battery under different working conditions and different service lives;
the parameter extraction module is used for extracting parameters based on a Nernst model from each collected charge and discharge curve;
the model training module is used for training a life prediction model according to the residual life of the lithium battery under different working conditions and the extracted parameters of the Nernst model;
the life prediction module is used for predicting the residual life of the lithium battery to be predicted according to the trained life prediction model and a charge-discharge curve of the lithium battery to be predicted under a certain working condition;
the parameter extraction module comprises:
the model construction unit is used for constructing an initial mathematical form of the Nernst model and optimizing the initial mathematical form of the Nernst model;
the initial mathematical form of the constructed Nernst model is as follows:
the mathematical form of the optimized Nernst model is as follows:
y＝k 0 ln(l 0 )+k 0 ln(x)+k 1 (1-x) -1 ；
the data fitting unit is used for fitting the mathematical form of the optimized Nernst model according to the collected charge-discharge curves to obtain the parameter k of the Nernst model corresponding to each charge-discharge curve 0 、k 1 And l 0 ；
Wherein y is the terminal voltage of the lithium battery; x is the SOC value of the lithium battery;
the model training module comprises:
the model training unit is used for taking the residual service life of the lithium battery as a label and taking k as a label under different working conditions 0 、k 1 And l 0 The values are used as training characteristic values, and different artificial intelligent models are trained respectively;
the model fusion unit is used for giving different weights to different trained artificial intelligent models to fuse so as to obtain a life prediction model of the lithium battery;
the data acquisition module is also used for acquiring a charge-discharge curve of the lithium battery to be predicted under a certain working condition;
the parameter extraction module is also used for extracting parameters of a corresponding Nernst model according to a charge-discharge curve of the lithium battery to be predicted;
and the life prediction module is used for inputting the extracted Nernst model parameters of the lithium battery to be predicted into a trained life prediction model and outputting the residual life of the lithium battery to be predicted.
3. A terminal device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, the processor being configured to execute the computer program stored on the memory to perform the operations performed by the lithium battery remaining life prediction method of claim 1.
4. A storage medium having stored therein at least one instruction that is loaded and executed by a processor to perform the operations performed by the lithium battery remaining life prediction method of claim 1.
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| CN105954679A (en) * | 2016-04-29 | 2016-09-21 | 华中科技大学 | Lithium battery charge state online estimating method |
| CN111060835A (en) * | 2019-12-31 | 2020-04-24 | 湖州师范学院 | Method and system for predicting remaining life of lithium battery and readable storage medium |
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