Method and device for estimating residual cycle life of ternary lithium ion battery, storage medium and electronic equipment

Abstract

The embodiment of the invention provides a ternary lithium ion battery residual cycle life estimation method and device, a storage medium and electronic equipment, and relates to the technical field of battery life estimation, and the method comprises the steps: collecting aging parameters of a ternary lithium ion battery, and obtaining an aging data set; performing interpolation processing on the aging data set by using a cubic spline interpolation method to obtain an aging factor; according to the aging factor, a Gaussian regression model is adopted to establish a battery residual cycle life prediction model; and inputting real-time charging data of a to-be-tested battery into the battery residual cycle life prediction model, and estimating the residual cycle life of the battery. According to the method, the precision and anti-noise capability of data processing are improved, the change trend of the cycle life of the lithium battery can be dynamically updated and predicted, and reliable technical support is provided for a battery management system, so that the cycle life of the battery is effectively prolonged, and the operation safety is guaranteed.

Description

Technical Field

[0001] The present application relates to the technical field of battery life estimation, and in particular to a method, device, storage medium and electronic device for estimating the remaining cycle life of a ternary lithium-ion battery. Background Art

[0002] Ternary lithium-ion batteries, as energy storage batteries with high energy density, high discharge efficiency, and excellent cycle performance, have been widely used in new energy vehicles, mobile electronic devices, and household and commercial energy storage systems. However, during the long-term operation of electrical equipment, the performance of lithium batteries gradually degrades, affecting their service life and safety. Therefore, accurately estimating the remaining useful life (RUL) of ternary lithium-ion batteries is crucial for ensuring the reliability and safety of their applications. Meanwhile, the immaturity of RUL estimation technology in battery management systems (BMS) has led to serious safety risks during the use of lithium-ion batteries, which has restricted their application. Reliable BMS management of lithium-ion batteries relies on accurate RUL values. This means not only fully understanding the available capacity and cycle life of lithium-ion batteries, but also enabling timely replacement of lithium-ion batteries when they reach failure thresholds, thereby extending the service life of electrical equipment using lithium-ion batteries. Therefore, accurately estimating RUL values ​​is crucial for ensuring the performance, capacity, and safety of lithium-ion batteries.

[0003] Given the urgent need for RUL estimation, relevant research universities and institutions, as well as numerous colleges and research institutions, have invested in research in this field. For example, the University of Oxford, Stanford University, the University of Michigan, Shanghai Jiao Tong University, Sun Yat-sen University, Guangdong University of Technology, the U.S. National Energy Research Center, and NASA have all conducted extensive and in-depth research on RUL estimation. At the same time, many international and domestic academic journals, such as Energy, Renewable Energy, IEEE Transactions on Power Systems, and Power Electronics Technology, have established dedicated columns to showcase the latest research results in this field. Currently, methods for predicting the remaining cycle life of lithium-ion batteries can be mainly divided into three categories: mechanistic models, data-driven models, and hybrid models. Mechanistic models focus on analyzing the physical and chemical mechanisms within the battery to construct prediction models. However, due to the differences in the characteristics of various lithium-ion batteries, such methods often require separate modeling for different batteries, resulting in a large workload and low model versatility, making them less suitable for practical promotion. In contrast, the data-driven model constructs a capacity degradation model by analyzing the measurable data of the battery. Common methods include autoregressive models, neural networks, support vector machines, and Gaussian Process Regression (GPR). The advantage of this method is that it does not rely on complex physical and chemical expressions and can circumvent some limitations of the mechanism model; but its disadvantage is that it requires a large amount of historical data as support to ensure the high accuracy of the model. In view of the shortcomings of the first two methods, the research focus in recent years has gradually shifted to hybrid models that combine mechanism models and data-driven methods. The goal is to integrate the advantages of both and obtain higher prediction accuracy. At present, RUL estimation based on the internal resistance model is mostly used in practical applications, but this method still has large errors in accuracy and is affected by many factors.

[0004] Based on the above research status, exploring the RUL of lithium-ion batteries under standard cycle conditions is expected to achieve more accurate predictions. At the same time, considering that batteries experience both monotonic decay and local regeneration during aging, the estimation of remaining cycle life needs to take both factors into account to achieve effective life management in battery management systems. Future development trends are to build RUL models with long-term prediction and adaptive adjustment capabilities, and to optimize parameters using swarm intelligence optimization technology, thereby achieving an optimal balance between improving prediction accuracy and realistically simulating capacity degradation trends, and continuously refining and improving estimation methods. Summary of the Invention

[0005] The embodiments of the present application provide a method, device, storage medium and electronic device for estimating the remaining cycle life of a ternary lithium-ion battery, so as to provide efficient and reliable technical support for a battery management system.

[0006] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by practice of the present application.

[0007] According to a first aspect of an embodiment of the present application, a method for estimating the remaining cycle life of a ternary lithium-ion battery is provided, comprising:

[0008] Collect aging parameters of ternary lithium-ion batteries to obtain aging data sets;

[0009] interpolating the aging data set using a cubic spline interpolation method to obtain an aging factor;

[0010] According to the aging factor, a battery remaining cycle life prediction model is established using a Gaussian regression model;

[0011] The real-time charging data of the battery to be tested is input into the battery remaining cycle life prediction model to estimate the remaining cycle life of the battery.

[0012] In some embodiments of the present application, based on the above solution, collecting the aging parameters of the ternary lithium-ion battery to obtain the aging data set includes:

[0013] Conduct full-cycle life aging experiments on ternary lithium-ion batteries and collect key performance parameter data of the ternary lithium-ion batteries in the experiments;

[0014] The collected key performance parameter data are preliminarily cleaned, denoised, and normalized, and a charge incremental capacity curve is calculated to form the aging data set.

[0015] In some embodiments of the present application, based on the aforementioned solution, the key performance data include: capacity attenuation, internal resistance change, charge and discharge efficiency, temperature, charge and discharge rate, and discharge depth.

[0016] In some embodiments of the present application, based on the aforementioned solution, the interpolation processing of the aging dataset using a cubic spline interpolation method to obtain an aging factor includes:

[0017] Using a cubic spline interpolation method to smooth the aging data set and fit the battery performance change curve;

[0018] Identify key nodes and trend changes in the battery performance change curve, and extract aging factors that can truly reflect the battery aging characteristics.

[0019] In some embodiments of the present application, based on the above solution, in the process of smoothing the aging data set using the cubic spline interpolation method, a cubic polynomial is used to approximate the data points.

[0020] In some embodiments of the present application, based on the aforementioned solution, the battery remaining cycle life prediction model is established using a Gaussian regression model according to the aging factor, including:

[0021] Using the aging factor as a model input, training a Gaussian regression model;

[0022] During the model training process, a nonlinear kernel function is selected to fit the complex nonlinear relationship.

[0023] In some embodiments of the present application, based on the above solution, the method further includes:

[0024] Continuously obtain new aging parameters;

[0025] The battery remaining cycle life prediction model is continuously updated according to new aging parameters.

[0026] According to a second aspect of an embodiment of the present application, a device for estimating the remaining cycle life of a ternary lithium-ion battery is provided, comprising:

[0027] An acquisition unit, used for acquiring aging parameters of the ternary lithium-ion battery to obtain an aging data set;

[0028] a processing unit, configured to perform interpolation processing on the aging data set using a cubic spline interpolation method to obtain an aging factor;

[0029] An establishing unit, configured to establish a battery remaining cycle life prediction model using a Gaussian regression model according to the aging factor;

[0030] The estimation unit is used to input the real-time charging data of the battery to be tested into the battery remaining cycle life prediction model to estimate the remaining cycle life of the battery.

[0031] According to a third aspect of an embodiment of the present application, a computer-readable storage medium is provided, wherein the storage medium stores computer instructions. When the computer instructions are executed on a computer, the computer executes the method according to the first aspect.

[0032] According to a fourth aspect of the embodiments of the present application, there is provided an electronic device, including: a memory and a processor;

[0033] The memory is used to store computer instructions;

[0034] The processor is configured to call the computer instructions stored in the memory so that the electronic device executes the method according to the first aspect.

[0035] The technical solution of this application collects and preprocesses key performance parameters of the battery during the charge and discharge process in real time. It then uses cubic spline interpolation to interpolate the raw data and extract battery aging characteristics, thereby constructing a lithium battery aging factor. This is then combined with a Gaussian regression model to accurately predict the remaining cycle life of the lithium battery. This method not only improves data processing accuracy and noise immunity, but also dynamically updates and predicts trends in the cycle life of lithium batteries, providing reliable technical support for battery management systems, effectively extending battery cycle life and ensuring operational safety.

[0036] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings are incorporated into and constitute a part of the specification, illustrating embodiments consistent with the present application and, together with the specification, explaining the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can derive other drawings based on these drawings without inventive effort. In the drawings:

[0038] Figure 1 A schematic flow chart of a method for estimating the remaining cycle life of a ternary lithium-ion battery according to one embodiment of the present application is shown;

[0039] Figure 2 A schematic diagram showing the effect of using piecewise cubic spline interpolation in the 0.05V sub-interval for the charge incremental capacity curve according to one embodiment of the present application is shown;

[0040] Figure 3 A schematic diagram showing the effect of using piecewise cubic spline interpolation in the 0.005V sub-interval for the charge incremental capacity curve according to one embodiment of the present application is shown;

[0041] Figure 4 A schematic diagram showing the effect of using piecewise cubic spline interpolation in the 0.0005V sub-interval for the charge incremental capacity curve according to one embodiment of the present application is shown;

[0042] Figure 5 A schematic diagram of the attenuation trend of a ternary lithium battery according to an embodiment of the present application is shown;

[0043] Figure 6 A schematic diagram of the aging factor of a ternary lithium battery according to one embodiment of the present application is shown;

[0044] Figure 7 A schematic diagram showing the remaining cycle life prediction results of a ternary lithium-ion battery according to one embodiment of the present application is shown;

[0045] Figure 8 A block diagram of a device for estimating the remaining cycle life of a ternary lithium-ion battery according to one embodiment of the present application is shown;

[0046] Figure 9 A block diagram of an electronic device according to an embodiment of the present application is shown;

[0047] Figure 10 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0048] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0049] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner.In the following description, many specific details are provided so as to provide a full understanding of the embodiments of the present application. However, it will be appreciated by those skilled in the art that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps etc. can be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0050] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0051] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0052] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0053] The following will describe some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features therein may be combined with each other.

[0054] See also Figure 1 , shows a flow chart of a method for estimating the remaining cycle life of a ternary lithium-ion battery according to an embodiment of the present application.

[0055] like Figure 1 As shown, a method for estimating the remaining cycle life of a ternary lithium-ion battery is shown, which specifically includes steps S100 to S400.

[0056] refer to Figure 1 , step S100, collecting aging parameters of the ternary lithium-ion battery to obtain an aging data set.

[0057] In some feasible embodiments, based on the above solution, collecting the aging parameters of the ternary lithium-ion battery to obtain the aging data set includes:

[0058] Conduct full-cycle life aging experiments on ternary lithium-ion batteries and collect key performance parameter data of the ternary lithium-ion batteries in the experiments;

[0059] The collected key performance parameter data are preliminarily cleaned, denoised, and normalized, and a charge incremental capacity curve is calculated to form the aging data set.

[0060] It can be understood that the aging data set formed after cleaning, denoising, normalization and curve calculation has high quality and can ensure the reliability of subsequent data analysis.

[0061] In some feasible embodiments, based on the above solution, the key performance data include: capacity attenuation, internal resistance change, charge and discharge efficiency, temperature, charge and discharge rate and discharge depth.

[0062] Exemplarily, the process of collecting the aging dataset is as follows:

[0063] The sensors installed inside the battery pack or in the battery management unit continuously monitor and collect data such as capacity attenuation, internal resistance change, charge and discharge efficiency, temperature, charge and discharge rate, and depth of discharge of the ternary lithium-ion battery. The incremental capacity (IC) curve is then calculated based on formula (1):

[0064]

[0065] Wherein, dQ and dV represent the charged capacity and voltage change within a single sampling time, respectively; Q represents the charged quantity, V represents the voltage, and t represents the sampling time.

[0066] Continue to refer Figure 1 In step S200 , the aging data set is interpolated using a cubic spline interpolation method to obtain an aging factor.

[0067] In some feasible embodiments, based on the above solution, the interpolation processing of the aging dataset using a cubic spline interpolation method to obtain an aging factor includes:

[0068] Using a cubic spline interpolation method to smooth the aging data set and fit the battery performance change curve;

[0069] Identify key nodes and trend changes in the battery performance change curve, and extract aging factors that can truly reflect the battery aging characteristics.

[0070] It can be understood that in order to address the noise and discreteness problems in the original data, this embodiment uses the cubic spline interpolation method to process the data. The aging factor finally obtained by processing can reflect both the degradation of the internal materials of the battery and the impact of the external environment on the battery life.

[0071] For example, the specific process of obtaining the aging factor by interpolation is as follows:

[0072] (1) In view of the shortcomings of existing technologies at low sampling frequencies, this example uses a simple method called cubic spline interpolation to simulate samples to generate some new reliable data and achieve a certain degree of curve smoothing. Let y = f(x) at points x0, x1, ..., x n The corresponding values ​​are y0,y1,…,y n If the function S(x) satisfies the following conditions:

[0073] ①S(x i )=f(x i )=y i ,i=0,1,2,…,n.

[0074] ②S(x) is each subinterval [x i ,x i +1](i=0,1,2,…,n-1).

[0075] ③S(x) is continuously differentiable in the interval [a,b].

[0076] Then S(x) is the cubic spline interpolation function of function f(x), and the voltage range is generally 3.6V-4.2V. In this example, the optimal subinterval is 0.005V.

[0077] The charging incremental capacity curve uses piecewise cubic spline interpolation in different sub-intervals. Figures 2 to 4 shown.

[0078] (2) Select the aging characteristics after smoothing. Since the charge incremental capacity curve has two characteristic peaks and a characteristic valley between the two peaks, the three types of characteristics of the charge incremental capacity curve after smoothing in this example are:

[0079] ① Height aging factor: the highest height of the first characteristic peak and the second characteristic peak, and the lowest height of the characteristic valley.

[0080] ② Position aging factor: the voltage position of the highest height of the first characteristic peak and the second characteristic peak, and the voltage position of the lowest height of the characteristic valley.

[0081] ③ Area aging factor: The lowest height of the characteristic valley is used as the boundary, and the two area blocks on the left and right are used as features.

[0082] Lithium battery degradation trend is as follows Figure 5 As shown, the aging factor is Figure 6 shown.

[0083] (3) Aging correlation analysis and standardization: Pearson correlation analysis and initialization method are used for standardization. The results of Pearson analysis for the three types of features are as follows:

[0084] Table 1 Pearson analysis results

[0085]

[0086]

[0087] Continue to refer Figure 1 , step S300, based on the aging factor, a Gaussian regression model is used to establish a battery remaining cycle life prediction model.

[0088] In some feasible embodiments, based on the above solution, the battery remaining cycle life prediction model is established using a Gaussian regression model according to the aging factor, including:

[0089] Using the aging factor as a model input, training a Gaussian regression model;

[0090] During the model training process, a nonlinear kernel function is selected to fit the complex nonlinear relationship.

[0091] In some feasible embodiments, based on the above solution, in the process of smoothing the aging dataset using the cubic spline interpolation method, a cubic polynomial is used to approximate the data points.

[0092] For example, the model training process is as follows:

[0093] A prediction model based on Gaussian regression is constructed using aging factors. During model training, appropriate nonlinear kernel functions and hyperparameters are selected, and the model is fine-tuned using methods such as Bayesian optimization to ensure that the model can stably and accurately predict the remaining cycle life of the battery under various operating conditions.

[0094] A Gaussian process is a random process in which a finite number of random variables follow a Gaussian distribution. Gaussian process regression uses the Gaussian process prior to perform regression analysis on data. Due to the influence of noise, the output value y can be expressed in terms of the input value x and the noise ε as follows:

[0095] y=f(x)+ε; (2)

[0096] ε~N(0,σ 2 ) represents independent Gaussian noise. According to the nonlinear mapping relationship f(·) between the input matrix X and the output matrix Y learned from the training set, the GPR (Gaussian regression) model obtains the input matrix X of the new observation * , and predict its output matrix Y * The joint Gaussian distribution can be expressed as:

[0097]

[0098] Among them, K(X,X) is the covariance matrix of X; K(X,X * ) is X and X * The n×1 order covariance matrix between them, and K(X,X * )=K(X * ,X) T ; K(X * ,X * ) is X * According to the Bayesian posterior probability formula, we can get Y * The posterior distribution of , that is, the GPR model is:

[0099]

[0100] The covariance is a quadratic rational function with a constant mean, and the likelihood function uses a Gaussian likelihood. A Gaussian kernel function is further used for mapping. The Gaussian kernel function, also known as the radial basis function (RBF), is a commonly used kernel function widely used in GPR models. Its main function is to map data to a high-dimensional or even infinite-dimensional feature space through nonlinear transformation, making data that was originally nonlinearly inseparable in low-dimensional space linearly separable in high-dimensional space.

[0101]

[0102] In the formula, x and x' represent the input capacity decay data and aging characteristic factors, respectively; ||.|| represents the Euclidean distance between the two vectors. γ is a hyperparameter that controls the width of the kernel function and determines the rate at which the similarity between data points decays. Ultimately, the kernel function maps the predicted capacity to the number of remaining cycles, thereby predicting the RUL of the lithium battery.

[0103] Continue to refer Figure 1 In step S400 , the real-time charging data of the battery to be tested is input into the battery remaining cycle life prediction model to estimate the remaining cycle life of the battery.

[0104] In some feasible embodiments, based on the above solution, the method further includes:

[0105] Continuously obtain new aging parameters;

[0106] The battery remaining cycle life prediction model is continuously updated according to new aging parameters.

[0107] It is understandable that by setting up this real-time feedback update mechanism, real-time tracking and prediction of the battery aging status can be achieved.

[0108] The battery RUL is predicted using the method provided in the embodiment of the present application. The results are as follows: Figure 7 The prediction results are as follows:

[0109] Table 2 Prediction results

[0110]

[0111] In summary, this application proposes a new method for estimating the remaining cycle life of ternary lithium-ion batteries by combining cubic spline interpolation, aging factor extraction and Gaussian regression model. This method has been optimized in data preprocessing, feature extraction, model building and dynamic prediction, effectively solving the problems of insufficient prediction accuracy, poor real-time performance and limited applicability in the existing technology. Therefore, this application can not only improve the battery management system's ability to monitor the battery status, but also provide a scientific basis for battery maintenance and replacement, thus having important practical value in ensuring system safety and extending battery life.

[0112] The following describes an embodiment of the device of the present application, which can be used to implement a method for estimating the remaining cycle life of a ternary lithium-ion battery in the above embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method of the above application.

[0113] Reference Figure 8As shown, a device 800 for estimating the remaining cycle life of a ternary lithium-ion battery according to one embodiment of the present application includes:

[0114] The acquisition unit 801 is used to acquire aging parameters of the ternary lithium-ion battery to obtain an aging data set;

[0115] The processing unit 802 is configured to perform interpolation processing on the aging data set using a cubic spline interpolation method to obtain an aging factor;

[0116] An establishing unit 803 is configured to establish a battery remaining cycle life prediction model using a Gaussian regression model according to the aging factor;

[0117] The estimation unit 804 is configured to input the real-time charging data of the battery to be tested into the battery remaining cycle life prediction model to estimate the remaining cycle life of the battery.

[0118] like Figure 9 As shown, an embodiment of the present application also provides an electronic device 900, including a memory 910, a processor 920, and a computer program 911 stored in the memory 910 and executable on the processor. When the processor 920 executes the computer program 911, the steps of the above-mentioned method for estimating the remaining cycle life of a ternary lithium-ion battery are implemented.

[0119] Since the electronic device introduced in this embodiment is a device used to implement a ternary lithium-ion battery remaining cycle life estimation device in the embodiment of this application, based on the method introduced in the embodiment of this application, technical personnel in this field can understand the specific implementation of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of this application will not be introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of this application falls within the scope of protection to be protected by this application.

[0120] During the specific implementation process, when the computer program 911 is executed by the processor, any implementation method of the embodiments corresponding to the first aspect can be implemented.

[0121] Figure 10 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown.

[0122] It should be noted that Figure 10 The computer system 1000 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0123] like Figure 10As shown, the computer system 1000 includes a central processing unit (CPU) 1001, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1002 or the program loaded from the storage part 1008 into the random access memory (RAM) 1003, such as executing the method described in the above embodiment. Various programs and data required for system operation are also stored in the RAM 1003. The CPU 1001, ROM 1002 and RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0124] The following components are connected to the I / O interface 1005: an input section 1006 including a keyboard, a mouse, and the like; an output section 1007 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 1008 including a hard disk and the like; and a communication section 1009 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as needed. Removable media 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 1010 as needed, so that computer programs read therefrom can be installed into the storage section 1008 as needed.

[0125] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 1009, and / or installed from a removable medium 1011. When the computer program is executed by the central processing unit (CPU) 1001, the various functions defined in the system of the present application are executed.

[0126] It should be noted that the computer-readable medium shown in the embodiments of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0127] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Among them, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0128] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. In some cases, the names of these units do not constitute limitations on the units themselves.

[0129] As another aspect, the present application further provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method for estimating the remaining cycle life of a ternary lithium-ion battery described in the above embodiment.

[0130] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments, or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device implements the method for estimating the remaining cycle life of a ternary lithium-ion battery described in the above embodiments.

[0131] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the application, the features and functions of two or more modules or units described above can be concretized in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0132] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0133] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art that are not disclosed in this application. It should be understood that this application is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of this application is limited only by the appended claims.

Claims

1. A method for estimating the remaining cycle life of a ternary lithium-ion battery, characterized in that: include: Collect aging parameters of ternary lithium-ion batteries to obtain aging data sets; interpolating the aging data set using a cubic spline interpolation method to obtain an aging factor; According to the aging factor, a battery remaining cycle life prediction model is established using a Gaussian regression model; The real-time charging data of the battery to be tested is input into the battery remaining cycle life prediction model to estimate the remaining cycle life of the battery.

2. The method according to claim 1, characterized in that The collecting of aging parameters of the ternary lithium-ion battery to obtain an aging data set includes: Conduct full-cycle life aging experiments on ternary lithium-ion batteries and collect key performance parameter data of the ternary lithium-ion batteries in the experiments; The collected key performance parameter data are preliminarily cleaned, denoised, and normalized, and a charge incremental capacity curve is calculated to form the aging data set.

3. The method according to claim 2, characterized in that The key performance data include: capacity attenuation, internal resistance change, charge and discharge efficiency, temperature, charge and discharge rate and discharge depth.

4. The method according to claim 1, wherein The interpolation process of the aging data set using a cubic spline interpolation method to obtain an aging factor includes: Using a cubic spline interpolation method to smooth the aging data set and fit the battery performance change curve; Identify key nodes and trend changes in the battery performance change curve, and extract aging factors that can truly reflect the battery aging characteristics.

5. The method according to claim 4, characterized in that In the process of smoothing the aging data set using the cubic spline interpolation method, a cubic polynomial is used to approximate the data points.

6. The method according to any one of claims 1 to 5, characterized in that The method of establishing a battery remaining cycle life prediction model using a Gaussian regression model based on the aging factor includes: Using the aging factor as a model input, training a Gaussian regression model; During the model training process, a nonlinear kernel function is selected to fit the complex nonlinear relationship.

7. The method according to claim 1, characterized in that The method further comprises: Continuously obtain new aging parameters; The battery remaining cycle life prediction model is continuously updated according to new aging parameters.

8. A device for estimating the remaining cycle life of a ternary lithium-ion battery, characterized in that: include: An acquisition unit, used for acquiring aging parameters of the ternary lithium-ion battery to obtain an aging data set; a processing unit, configured to perform interpolation processing on the aging data set using a cubic spline interpolation method to obtain an aging factor; An establishing unit, configured to establish a battery remaining cycle life prediction model using a Gaussian regression model according to the aging factor; The estimation unit is used to input the real-time charging data of the battery to be tested into the battery remaining cycle life prediction model to estimate the remaining cycle life of the battery.

9. A computer-readable storage medium, characterized in that The storage medium stores computer instructions, which, when executed on a computer, enable the computer to execute the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: memory and processor; The memory is used to store computer instructions; The processor is configured to call the computer instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 7.

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