Method and device for determining failure data of target battery and electronic equipment

Abstract

The invention discloses a failure data determination method and device of a target battery and electronic equipment. The method comprises the following steps: acquiring a target battery; calling an aging model of a pseudo two-dimensional solid electrolyte interface corresponding to the target battery; setting model parameters of the aging model, the model parameters including charge-discharge cycle parameters; voltage parameters of a simulation battery are determined in the process that the aging model circularly charges and discharges according to the charge and discharge cycle parameters, and the simulation battery is a battery obtained by simulating the target battery; according to the voltage parameters, failure data of the target battery is determined, and the failure data comprises failure reasons. According to the invention, the technical problem that the failure reason of the battery is difficult to accurately determine when the battery fails in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of batteries, and in particular to a method, device and electronic equipment for determining failure data of a target battery. Background Art

[0002] The detection of the cycle life of batteries, such as lithium-ion batteries, is one of the difficulties that need to be overcome in the current lithium battery technology. At present, the capacity decay process of the battery during the cycle can be judged by the voltage change (SV) caused by the loss of active lithium and the voltage change (RV) caused by the increase of internal resistance for the cycle failure analysis of lithium-ion batteries. CN112382798A provides a method and system for judging the cycle failure of a battery. By calculating the SVC curve and the RVC curve, the change trend of the SVC curve and the RVC curve is observed. When the SVC curve and the RVC curve have obvious abnormal points, it is the starting time point of the battery cycle failure, and the charge and discharge test is terminated. CN115308630A provides a method for analyzing the decay of battery life. By calculating the median voltage, the internal resistance-voltage (RV) and the active lithium-voltage (SV) of the failed battery, it is judged whether the battery life decay is caused by RV or SV.

[0003] In the related art, the change of SV can usually tell the capacity change trend during the battery cycle. However, at present, this is only a qualitative judgment to observe which cycle circles the SV has a sudden change to identify the battery cycle failure, but the factors affecting the change of SV during the cycle have not been specifically analyzed. Another method is to calculate SV and RV and compare them to simply judge whether the battery failure is caused by the loss of active lithium or the increase of internal resistance. This is obviously a very rough judgment of the battery failure mode, and there is no in-depth research to further identify the cause of battery failure, which is of no substantial help in guiding the detection of battery life.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a method, device and electronic device for determining failure data of a target battery, so as to at least solve the technical problem in the related art that it is difficult to accurately determine the cause of failure of the battery when the battery fails.

[0006] According to one aspect of an embodiment of the present invention, there is provided a method for determining failure data of a target battery, comprising: acquiring a target battery; retrieving an aging model of a pseudo two-dimensional solid electrolyte interface corresponding to the target battery; setting model parameters of the aging model, wherein the model parameters include charge and discharge cycle parameters; determining voltage parameters of a simulated battery during cyclic charge and discharge of the aging model according to the charge and discharge cycle parameters, wherein the simulated battery is a battery obtained by simulating the target battery; determining failure data of the target battery based on the voltage parameters, wherein the failure data includes a cause of failure.

[0007] Optionally, determining the failure cause of the target battery based on the voltage parameters includes: constructing a voltage curve based on the voltage parameters, wherein the horizontal axis corresponding to the voltage curve is the number of cycles, and the vertical axis of the voltage curve is the voltage parameter; performing linear fitting on the voltage curve to obtain a fitting line segment; determining the line segment parameters of the fitting line segment; and determining the failure data of the target battery based on the line segment parameters.

[0008] Optionally, determining the failure cause of the target battery based on the line segment parameters includes: when the line segment parameters include a slope, determining a target control relationship, wherein the target control relationship includes a corresponding relationship between the slope and the failure cause, and the failure cause includes at least one of the following: battery ion consumption, porosity change, and electrical impedance change of a solid electrolyte interface membrane; from the target control relationship, determining the failure cause corresponding to the slope.

[0009] Optionally, determining the failure cause of the target battery based on the line segment parameters includes: when the line segment parameters include multiple slopes, determining the failure causes corresponding to the multiple slopes respectively, and determining the number of failure cycles corresponding to the multiple slopes respectively.

[0010] Optionally, before determining the target control relationship, it also includes: determining a target variable, wherein the target variable includes at least one of the following: battery ions, porosity, and electrical impedance of a solid electrolyte interface membrane; controlling the target variable to determine an influence coefficient of the target variable on a line segment slope; and determining the target control relationship based on the target variable and the influence coefficient.

[0011] Optionally, determining the voltage parameters of the simulated battery during the process of the aging model cycling according to the model parameters includes: determining the first median voltage of the target battery in the charging stage and the second median voltage of the discharge stage during each cycle of the aging model cycling according to the charge and discharge cycle parameters; determining the voltage parameters based on the first median voltage and the second median voltage corresponding to each cycle.

[0012] Optionally, determining the voltage parameters of the simulated battery during the cycle of the aging model according to the model parameters includes: when the charge and discharge cycle parameters include predetermined charging stage parameters and predetermined discharging stage parameters, and the predetermined charging stage parameters include first charging stage parameters, second charging stage parameters, and number of cycles, controlling the aging model to perform cyclic simulation according to the number of cycles, and in each cycle, charging the simulated battery according to the first charging stage parameters, and after reaching the first cutoff condition, charging according to the second charging stage parameters, and after reaching the second cutoff condition, discharging according to the predetermined discharging stage parameters, and completing a cycle when the third cutoff condition is reached, wherein the first charging stage parameters are parameters corresponding to the constant current charging to the cutoff voltage stage, the second charging stage parameters include parameters corresponding to the constant voltage charging to the cutoff current stage, and the predetermined discharge stage parameters include parameters corresponding to the constant current discharging to the predetermined threshold stage.

[0013] According to one aspect of an embodiment of the present invention, there is provided a device for determining failure data of a target battery, comprising: an acquisition module for acquiring a target battery; a call module for calling an aging model of a pseudo two-dimensional solid electrolyte interface corresponding to the target battery; a setting module for setting model parameters of the aging model, wherein the model parameters include charge and discharge cycle parameters; a first determination module for determining voltage parameters of a simulated battery during cyclic charge and discharge of the aging model according to the charge and discharge cycle parameters, wherein the simulated battery is a battery obtained by simulating the target battery; and a second determination module for determining failure data of the target battery based on the voltage parameters, wherein the failure data includes a cause of failure.

[0014] According to one aspect of an embodiment of the present invention, there is provided an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement any of the above-described methods for determining failure data of a target battery.

[0015] According to one aspect of an embodiment of the present invention, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute any of the above-mentioned methods for determining failure data of a target battery.

[0016] In an embodiment of the present invention, a target battery is obtained; an aging model of a pseudo two-dimensional solid electrolyte interface corresponding to the target battery is retrieved; model parameters of the aging model are set, wherein the model parameters include charge and discharge cycle parameters; the voltage parameters of the simulated battery are determined during the cyclic charge and discharge process according to the charge and discharge cycle parameters, wherein the simulated battery is a battery obtained by simulating the target battery; and failure data of the target battery is determined based on the voltage parameters, wherein the failure data includes the cause of failure. The voltage parameters of the simulated battery are simulated during the cyclic charge and discharge process according to the charge and discharge cycle parameters by the aging model, so as to accurately determine the cause of failure of the target battery through the voltage parameters, thereby solving the technical problem in the related art that it is difficult to accurately determine the cause of failure of the battery when the battery fails. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 is a flow chart of a method for determining failure data of a target battery according to an embodiment of the present invention;

[0019] Figure 2 is a first slope schematic diagram provided by an optional embodiment of the present invention;

[0020] Figure 3 is another slope schematic diagram provided by an optional embodiment of the present invention;

[0021] Figure 4 is another slope schematic diagram provided by an optional embodiment of the present invention;

[0022] Figure 5 is a schematic diagram of slope change during a charge and discharge test provided by an optional embodiment of the present invention;

[0023] Figure 6 4 is a structural block diagram of a device for determining failure data of a target battery according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the 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 creative work should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] Example 1

[0027] According to an embodiment of the present invention, an embodiment of a method for determining failure data of a target battery is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0028] Figure 1 FIG. 1 is a flow chart of a method for determining failure data of a target battery according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0029] Step S102, obtaining a target battery;

[0030] In step S102 provided in the present application, a target battery is obtained. The target battery is the specific battery that is concerned and studied during the battery research or monitoring process, and can be a battery of any type and size, such as a lithium-ion battery, a cylindrical battery of a ternary material / graphite system. The target battery is the object of experiment and analysis. If the target battery is a battery that has not yet failed, it can be used to predict when the target battery will fail and the cause of failure. If the target battery is a failed battery, it can be used to determine the cause of failure of the target battery.

[0031] Step S104, retrieving an aging model of a pseudo two-dimensional solid electrolyte interface corresponding to a target battery;

[0032] In step S104 provided in the present application, an aging model of a pseudo-two-dimensional solid electrolyte interface is designed, which is the P2D-SEI aging model. The aging model is a mathematical model that combines the formation and growth mechanisms of "pseudo-two-dimensional" (P2D) and "solid electrolyte interface" (SEI) films, and is used to predict and analyze the performance degradation (aging) of lithium-ion batteries during use. The P2D model focuses on the electrochemical process in the thickness direction of the battery electrode, while the SEI aging model focuses on the effect of the growth of the SEI film on performance parameters such as the battery internal resistance and capacity.

[0033] That is, in order to deeply analyze the aging process of the battery, it is necessary to call the pseudo two-dimensional solid electrolyte interface aging model (P2D-SEI aging model) that matches the target battery materials and parameters, so as to simulate the behavior of the battery under different conditions through the model, and thus understand the internal mechanism of battery aging.

[0034] Among them, "P2D" corresponds to "pseudo-two-dimensional", emphasizing that the model simplifies the electrochemical behavior of electrode materials into two dimensions, focusing on the thickness direction of the electrode; "SEI" corresponds to "solid electrolyte interface", which refers to the protective film formed on the surface of the negative electrode in lithium-ion batteries; "aging model" refers to a mathematical model used to simulate and predict the performance degradation of lithium-ion batteries during use. The entire model aims to predict the performance changes of batteries during the cycle process through theoretical analysis, especially the impact of the formation and growth of SEI film on battery aging.

[0035] In the field of battery research, detailed performance analysis and life prediction of target batteries often require the help of advanced mathematical models. As an important tool for studying the aging mechanism of lithium-ion batteries, the P2D-SEI aging model can simulate the electrochemical reactions inside the battery and the growth process of the SEI film based on information such as the material properties, geometric structure, and charge and discharge conditions of the battery. Through the model, key aging phenomena such as the increase in SEI film impedance, changes in negative electrode porosity, and loss of active lithium can be predicted, thereby evaluating the impact of these factors on performance parameters such as battery voltage and internal resistance.

[0036] Based on the specific parameters of the target battery, such as the chemical composition, thickness, porosity of the electrode material, the composition of the electrolyte, and the expected charge and discharge conditions, the corresponding P2D-SEI aging model is called or customized. The output results of the model, such as the trend of voltage, internal resistance, capacity, etc. with the number of charge and discharge cycles, can be used to guide the design optimization, quality control and use strategy formulation of the battery, which is of great significance for improving the performance of the battery and extending its service life. For example, if the model predicts that the increase in SEI film impedance is the main factor leading to the decline in battery performance, then in the battery design stage, the growth rate of the SEI film can be slowed down by optimizing the electrolyte formulation or electrode materials, thereby improving the cycle stability of the battery.

[0037] Step S106, setting model parameters of the aging model, wherein the model parameters include charge and discharge cycle parameters;

[0038] In step S106 provided in the present application, the model parameters of the aging model are involved. The model parameters of the P2D-SEI model are used to describe and define the physical and electrochemical properties of the battery. In the P2D-SEI model, in addition to the charge and discharge cycle parameters, the model parameters may also include the diffusion coefficient of the electrode material, the migration number of lithium ions, the growth rate of the SEI film, the geometric parameters of the electrode (such as thickness, porosity), the conductivity of the electrolyte, etc.

[0039] In this step, the charge and discharge cycle parameters are also involved. These are the parameters used in the aging model to describe the characteristics of the battery charge and discharge process, which may include charge and discharge current (C-rate), charge and discharge cut-off voltage, charge and discharge time, number of cycles, etc. These parameters directly affect the charge and discharge behavior of the battery and the growth process of the SEI film, and can accurately describe the aging process of the battery.

[0040] By setting the charge and discharge cycle parameters of the aging model, effective simulation can be performed when establishing and running the P2D-SEI aging model. For example, the selection of charge and discharge current (C-rate) will directly affect the electrochemical reaction rate inside the battery, thereby affecting the formation and growth of the SEI film; the setting of the charge and discharge cut-off voltage will affect the charge and discharge depth of the battery, and have a significant impact on the stability of the SEI film and the cycle life of the battery. Therefore, the cause of failure can be accurately determined.

[0041] Step S108, determining the voltage parameters of the simulated battery during the cyclic charging and discharging process according to the charging and discharging cycle parameters of the aging model, wherein the simulated battery is a battery obtained by simulating the target battery;

[0042] In step S108 provided in the present application, the voltage parameters of the simulated battery are involved. The voltage parameters are parameters related to the voltage changes of the simulated battery during the charge and discharge cycle using an aging model. These usually include the battery's charging voltage, discharge voltage, median voltage (Vc, Vd) and active lithium-voltage (SV), etc., which can be adaptively set according to actual applications and scenarios.

[0043] Among them, the simulated target battery is also involved. The simulated target battery is a virtual version of the target battery simulated by the aging model. It is created in the model based on the physical, chemical and electrochemical characteristics of the target battery, as well as the charge and discharge cycle parameters, to predict the battery aging behavior under specific cycle conditions.

[0044] Determine the aging model to simulate the voltage parameters of the battery during the cyclic charge and discharge process according to the charge and discharge cycle parameters, which means that after setting the charge and discharge current, voltage and other parameters in the P2D-SEI aging model, the model will simulate the charge and discharge behavior of the target battery under these parameters, and then predict the voltage change of the battery under different cycle times. This process includes simulating the voltage rise of the battery during charging, the voltage drop during discharging, and the derived active lithium-voltage (SV) characteristics of the battery. Model simulation can not only simulate the voltage behavior of the battery under specific charge and discharge conditions, but also further reveal the influence of microscopic processes such as SEI film growth, lithium ion diffusion, and electrode material changes on the macroscopic performance of the battery (such as capacity, internal resistance, and voltage).

[0045] For example, when the model determines the charge and discharge cycle parameters, the voltage parameters of the simulated battery will show how the battery voltage changes as the number of charge and discharge cycles increases. If the simulation results show that the active lithium-voltage (SV) of the battery decreases significantly with the number of cycles, this may indicate that the growth of the SEI film consumes a large amount of active lithium, resulting in battery capacity decay. Conversely, if the SV change is consistent with the trend of the battery's internal resistance change, it may be that the increase in the impedance of the SEI film affects the transmission of lithium ions, thereby affecting battery performance, thereby accurately determining the cause of battery failure.

[0046] Step S110, determining failure data of the target battery according to the voltage parameter, wherein the failure data includes a failure cause.

[0047] In step S110 provided in the present application, failure data is involved. Failure data refers to a data set when the battery performance deteriorates to the point where it cannot meet specific working requirements, including the failure cause of the battery, the number of charge and discharge cycles at the time of failure, the specific voltage drop value, etc. Analysis of failure data can help understand the specific mechanism of battery performance degradation and provide a basis for battery improvement and optimization.

[0048] Among them, the causes of failure are also involved. In lithium-ion batteries, the causes of performance degradation and ultimate failure may include loss of active lithium, excessive growth of SEI film, changes in electrode material structure, electrolyte decomposition, etc. Identifying specific failure causes will help take targeted measures to prevent or slow down battery aging.

[0049] Determining the failure data of the target battery based on the voltage parameters means that by analyzing the changes in the simulated or measured battery voltage parameters, the specific cause of battery failure can be diagnosed and determined. During the battery cycle, the changing trend of the voltage parameters can reflect the health of the battery. When these parameters are abnormal or reach the preset failure threshold, it can be determined that the battery is close to or has reached the failure state. By comparing and analyzing the relationship between the voltage parameters and the number of charge and discharge cycles, charge and discharge conditions, etc., the main causes of battery failure can be further determined, such as the increase in SEI film impedance, the loss of active lithium, or the decrease in porosity. In this way, quantitative indicators of battery performance degradation can be provided to help identify potential failure risks, so as to take corresponding preventive or remedial measures. Compared with post-disassembly analysis, this method does not require destructive testing of the battery, can achieve non-destructive monitoring of the battery, and has important practical value for real-time management and maintenance of the battery. In addition, by analyzing failure data, the design and manufacturing process of the battery can be optimized, and the stability and service life of the battery can be improved.

[0050] Through the above steps S102-S110, the target battery is obtained; the aging model of the pseudo two-dimensional solid electrolyte interface corresponding to the target battery is retrieved; the model parameters of the aging model are set, wherein the model parameters include charge and discharge cycle parameters; the voltage parameters of the simulated battery are determined in the process of cyclic charge and discharge according to the charge and discharge cycle parameters, wherein the simulated battery is a battery obtained by simulating the target battery; based on the voltage parameters, the failure data of the target battery is determined, wherein the failure data includes the failure cause. The voltage parameters of the battery are simulated in the process of cyclic charge and discharge according to the charge and discharge cycle parameters by the aging model, so as to accurately determine the failure cause of the target battery through the voltage parameters, thereby solving the technical problem in the related art that it is difficult to accurately determine the failure cause of the battery when the battery fails.

[0051] As an optional embodiment, the failure cause of the target battery is determined based on the voltage parameters, including: constructing a voltage curve based on the voltage parameters, wherein the horizontal axis corresponding to the voltage curve is the number of cycles, and the vertical axis of the voltage curve is the voltage parameter; performing linear fitting on the voltage curve to obtain a fitting line segment; determining the line segment parameters of the fitting line segment; and determining the failure data of the target battery based on the line segment parameters.

[0052] In this embodiment, a voltage curve is involved. The voltage curve is a graph used to visually display the trend of voltage parameters changing with the number of battery charge and discharge cycles. The horizontal axis of the voltage curve usually represents the number of cycles, and the vertical axis represents the voltage parameter.

[0053] Linear fitting is also involved, which is used to find a straight line (fitting line segment) to most closely describe a set of data points, thereby simplifying the complexity of the data and facilitating analysis and understanding. In this embodiment, linear fitting is applied to the voltage curve to obtain the average trend of the voltage parameter changing with the number of cycles.

[0054] Among them, the line segment parameters are after linear fitting. The parameters of the fitting line segment may include slope, intercept, etc. The slope can reflect the rate at which the voltage parameter changes with the number of cycles, and is a key indicator for judging the aging of the battery.

[0055] Through the steps in this embodiment, a voltage curve is constructed according to the voltage parameters, which involves collecting and drawing the voltage data of the battery at different cycle numbers to form a visual trend graph. Next, a linear fit is performed on the voltage curve to simplify the data, extract the average change trend, and obtain a fitting line segment describing the change of the voltage parameter with the number of cycles. Determining the line segment parameters (such as the slope) of the fitting line segment can quantitatively evaluate the rate of change of the voltage parameter with the number of charge and discharge cycles. Finally, based on these line segment parameters, the dominant factors of the battery performance degradation can be analyzed, thereby determining the failure data of the target battery, including detailed information on why and when it failed.

[0056] Through quantitative analysis, accurate diagnosis of battery aging and performance degradation can be achieved. Compared with relying only on a single data point or qualitative observation, linear fitting and line segment parameter analysis can provide dynamic trends of battery performance over time or cycle number, helping to identify the turning point and dominant cause of battery performance degradation.

[0057] As an optional embodiment, determining the failure cause of the target battery based on the line segment parameters includes: when the line segment parameters include a slope, determining a target control relationship, wherein the target control relationship includes a corresponding relationship between the slope and the failure cause, and the failure cause includes at least one of the following: battery ion consumption, porosity change, and electrical impedance change of the solid electrolyte interface membrane; from the target control relationship, determining the failure cause corresponding to the slope.

[0058] In this embodiment, a target comparison relationship is involved. The target captain relationship is a corresponding table or model based on theoretical analysis and experimental data, which defines the relationship between the slope and the failure cause. Through this comparison relationship, the slope obtained by linear fitting can be mapped to the specific failure cause, realizing accurate identification of the battery aging mechanism.

[0059] Through the steps in this embodiment, when the line segment parameters include the slope, the corresponding relationship between the slope and the failure cause in the target comparison relationship is determined, and the target comparison table is retrieved. The comparison table or model summarizes the slope characteristics under different aging mechanisms (such as battery ion consumption, porosity change, SEI membrane electrical impedance change). After the voltage curve of the battery is linearly fitted, the slope value obtained will be analyzed according to this comparison relationship to determine the dominant failure cause. For example, if the slope is negative and the absolute value is large, this may indicate that the increase in the electrical impedance of the SEI membrane is the main aging factor; conversely, if the slope changes little or is close to zero, then it may be a slight change in porosity or a slow consumption of battery ions. Therefore, based on the quantitative indicator of the slope, the main cause of battery aging can be accurately located and identified.

[0060] As an optional embodiment, the failure cause of the target battery is determined based on the line segment parameters, including: when the line segment parameters include multiple slopes, determining the failure causes corresponding to the multiple slopes respectively, and determining the number of failure cycles corresponding to the multiple slopes respectively.

[0061] In this embodiment, multiple slopes are involved, that is, when analyzing the battery voltage curve, if there are multiple stages in the battery aging process, the rate at which the voltage parameter changes with the number of cycles in each stage may be different, thereby forming a linear fitting curve with multiple slopes. Each slope represents the aging rate of the battery in a specific cycle stage.

[0062] The number of cycles to failure is also involved, which refers to the number of cycles at which the battery performance deteriorates to the point where it is insufficient to meet its intended function. Determining the number of cycles to failure can help identify the battery's service life and health status.

[0063] That is, through the steps in this embodiment, when the line segment parameters include multiple slopes, the failure causes corresponding to each slope are determined, which means that by piecewise linear fitting of the battery voltage curve, the different stages of the battery aging process can be identified, and the slope characteristics of each stage can be matched to specific failure causes. For example, the slope in the initial stage may reflect the influence of the stable growth of the SEI film, while the steeper slope in the later stage may indicate the rapid consumption of battery ions or a significant change in porosity. At the same time, determining the number of failure cycles corresponding to the multiple slopes is to link the slope change trend with the specific number of charge and discharge cycles, thereby identifying the turning point of battery performance degradation and predicting the remaining service life of the battery.

[0064] In this way, the battery aging process can be analyzed in detail, the dominant failure mechanisms at different stages can be identified, and a quantitative and segmented understanding of battery performance degradation can be provided. Compared with a single slope analysis, the determination of multiple slopes can more comprehensively reflect the health status and service life of the battery.

[0065] As an optional embodiment, before determining the target control relationship, it also includes: determining the target variable, wherein the target variable includes at least one of the following: battery ions, porosity, and electrical impedance of the solid electrolyte interface membrane; controlling the target variable to determine the influence coefficient of the target variable on the slope of the line segment; and determining the target control relationship based on the target variable and the influence coefficient.

[0066] In this embodiment, target variables are involved, that is, in battery research, target variables refer to physical quantities or electrochemical process parameters that can significantly affect battery performance, especially the trend of battery voltage parameters changing with the number of cycles. These variables include battery ions (usually lithium ions), porosity (the proportion of pore volume in electrode materials), electrical impedance of solid electrolyte interface membranes (conductivity characteristics of SEI membranes), etc.

[0067] The influence coefficient is also involved. In statistical analysis, the influence coefficient indicates the degree to which the change of one variable affects another variable (such as the slope of a line segment). In this scenario, the influence coefficient reveals the intensity of the influence of the change of the target variable (such as battery ions, porosity, and electrical impedance of the SEI membrane) on the rate of change of the battery voltage parameter with the number of cycles (i.e., the slope of the line segment).

[0068] Research testing is done by controlling the target variables, i.e. by precisely manipulating or changing the values ​​of the target variables to study their effects on battery aging and performance. This is usually achieved by changing the charge and discharge conditions, using specific battery materials, or adjusting parameters in the battery management system.

[0069] Through the steps in this embodiment, determining the target variable means first identifying the key physical quantities or electrochemical process parameters in the battery aging process, such as the electrical impedance, porosity and battery ion (lithium ion) concentration of the SEI film. Next, through experimental design or model simulation, these target variables are controlled to observe the impact of their changes on the slope of the battery voltage curve line segment. The determination of the influence coefficient is actually to quantify the contribution of each target variable to the line segment slope. This step requires in-depth statistical analysis of the data to separate and evaluate the impact of various factors. Finally, based on the target variable and the influence coefficient, the target control relationship is determined, that is, a relationship model is established, which can identify the dominant target variable that causes the slope change based on the characteristics of the line segment slope, thereby judging the specific cause of battery failure, and finally determining an effective and accurate corresponding relationship.

[0070] As an optional embodiment, determining the voltage parameters of the simulated battery during the cycle of the aging model according to the model parameters includes: determining the first median voltage of the target battery in the charging stage and the second median voltage of the discharge stage during each cycle of the aging model according to the charge and discharge cycle parameters; determining the voltage parameters based on the corresponding first median voltage and the corresponding second median voltage in each cycle.

[0071] In this embodiment, the first median voltage (median voltage in the charging stage) is involved, that is, during the battery charging process, the first median voltage refers to the middle value reached when the battery voltage rises from the lowest point to the highest point, which is usually determined by calculating the median of the voltage values ​​on the charging curve. It reflects half of the battery charging state, that is, the voltage value when the battery is charged from an empty state to a half-full state.

[0072] The second median voltage (median voltage in the discharge stage) is also involved. During the battery discharge process, the second median voltage refers to the middle value reached when the battery voltage drops from the highest point to the lowest point. It is determined by calculating the median of the voltage values ​​on the discharge curve, reflecting the voltage value of the battery when it is discharged from a half-full state to an empty state.

[0073] Then, a voltage parameter is determined, that is, the voltage parameter refers to a specific voltage value calculated from the first median voltage and the second median voltage, such as active lithium-voltage (SV), to describe the voltage behavior characteristics of the battery during the charge and discharge process.

[0074] Through the steps in this embodiment, the aging model is determined according to the charge and discharge cycle parameters. In each cycle, the target battery has the first median voltage in the charging stage and the second median voltage in the discharging stage, that is, the median voltage (first median voltage) in the charging stage and the median voltage (second median voltage) in the discharging stage need to be accurately measured or calculated in each charge and discharge cycle. These two voltage values ​​are quantitative indicators of the battery's charge and discharge state, through which the voltage change trend of the battery during the charge and discharge process can be monitored, and the health status and aging rate of the battery can be evaluated.

[0075] Determining the voltage parameter according to the first median voltage and the second median voltage corresponding to each cycle process is actually calculating a series of voltage parameters, such as active lithium voltage (SV), based on the two median voltages.

[0076] The benefit of this method is that it can provide a continuous and quantitative means to monitor and evaluate the aging process of the battery. By regularly measuring the median voltage during the charge and discharge phases, a voltage parameter change curve of the battery aging process can be constructed, providing an intuitive quantitative indicator for the decline of battery performance. These data can not only help predict the remaining life of the battery, but also identify the specific reasons for the performance degradation, such as the loss of active lithium, the increase of internal resistance or the thickening of the SEI film, thereby guiding the maintenance, optimization and design of the battery.

[0077] As an optional embodiment, determining the voltage parameters of the simulated battery during the cycle of the aging model according to the model parameters includes: when the charge and discharge cycle parameters include predetermined charging stage parameters, and predetermined discharge stage parameters, and the predetermined charging stage parameters include first charging stage parameters, second charging stage parameters, and the number of cycles, controlling the aging model to perform cycle simulation according to the number of cycles, and in each cycle, charging the simulated battery according to the first charging stage parameters, and after reaching the first cutoff condition, charging according to the second charging stage parameters, and after reaching the second cutoff condition, discharging according to the predetermined discharge stage parameters, and reaching the third cutoff condition to complete a cycle, wherein the first charging stage parameters are parameters corresponding to the constant current charging to the cutoff voltage stage, the second charging stage parameters include parameters corresponding to the constant voltage charging to the cutoff current stage, and the predetermined discharge stage parameters include parameters corresponding to the constant current discharge to the predetermined threshold stage.

[0078] In this embodiment, predetermined charging stage parameters are involved, which are predetermined parameters of the charging stage in the battery charge and discharge cycle, including initial constant current charging parameters (first charging stage parameters) and subsequent constant voltage charging parameters (second charging stage parameters), which are used to simulate different stages of the battery in the charging process.

[0079] Specifically, it includes the first charging stage parameter, which usually refers to the constant current charging mode adopted at the beginning of battery charging, that is, charging the battery with a constant current until the battery voltage reaches a predetermined cut-off voltage.

[0080] Specifically, it also includes the second charging stage parameters. After the battery voltage reaches the cut-off voltage of the first charging stage, it usually switches to a constant voltage charging mode, that is, keeping the voltage constant and the current gradually decreasing until the current reaches the predetermined cut-off current value. The parameters of this process are called the second charging stage parameters.

[0081] It also involves predetermined discharge phase parameters, that is, after the battery is fully charged, it is discharged according to predetermined discharge parameters until the battery voltage drops to a predetermined discharge cut-off voltage. The parameters of this discharge process are the predetermined discharge phase parameters.

[0082] Through the steps in this embodiment, it is explained how to perform cycle simulation according to the set charge and discharge cycle parameters, especially how to control the specific operation of the battery during the charging and discharging process to ensure the accuracy and reliability of the simulation. In each round of cycle simulation, first, the simulated battery is charged to the cut-off voltage at a constant current according to the first charging stage parameters, which simulates the initial stage of the battery in the actual charging process. When the battery voltage reaches the first cut-off condition (i.e., the cut-off voltage), it enters the second charging stage and continues to charge according to the second charging stage parameters (constant voltage charging) until the current drops to the second cut-off condition (i.e., the cut-off current). This step simulates the voltage platform stage during the battery charging process. Afterwards, discharge is performed according to the predetermined discharge stage parameters until the battery voltage drops to the third cut-off condition (discharge cut-off voltage), completing a complete charge and discharge cycle.

[0083] In this way, we can accurately simulate the various complex conditions in the actual battery charging and discharging process, including the dynamic changes of current and voltage, to ensure that the aging model can accurately reflect the performance degradation trend of the battery under a specific charging and discharging strategy. By carefully controlling the parameters of each stage of the charge and discharge cycle, we can better understand the electrochemical process inside the battery.

[0084] Based on the above embodiments and optional embodiments, an optional implementation is provided, which is described in detail below.

[0085] In an optional embodiment of the present invention, a method for determining failure data of a target battery is provided. The optional embodiment of the present invention separates the key factors affecting SV changes, and determines the dominant factors affecting SV changes by the slope changes during SV changes, so that the battery can be non-destructively and in real time qualitatively and quantitatively analyzed. In ternary lithium and graphite system batteries, it can combine the slope of SV changes and the factors affecting active lithium loss to analyze the specific factors affecting SV changes, helping to further solve battery cycle failures.

[0086] The main reason for the SV change caused by the loss of active lithium is SEI growth. SEI growth is mainly reflected in the changes in SEI film resistance and negative electrode porosity. Therefore, the P2D model is used to control the changes in film resistance and negative electrode porosity to isolate the specific factors affecting SV change:

[0087] 1. Keep the porosity constant and change the membrane resistance. The corresponding SV change is caused by the increase of membrane resistance during SEI growth.

[0088] 2. Keep the membrane resistance constant and change the porosity. The corresponding SV change is caused by the SEI product blocking the pores and causing the porosity to decrease.

[0089] 3. The membrane resistance and porosity are controlled to remain unchanged. At this time, the corresponding SV change is caused by the consumption of lithium ions by SEI growth.

[0090] Through the above scheme, the battery voltage of each cycle of the battery is obtained, and the median voltage Vc and Vd during the charging and discharging process are calculated.

[0091] The battery active lithium-voltage SV = 1 / 2 (Vc + Vd) is calculated based on the median voltage, and the obtained SV curve is linearly fitted to obtain the slope of the fitting line. The SV change can be associated with the SEI film resistance, porosity change and lithium ion consumption based on the positive and negative and size of the fitting slope, so as to explain which factor dominates the SV change.

[0092] Figure 2 is a first slope schematic diagram provided by an optional embodiment of the present invention, Figure 3 is another slope schematic diagram provided by an optional embodiment of the present invention, Figure 4 is another slope schematic diagram provided by an optional embodiment of the present invention, such as Figure 2-4 As shown, the straight line is the fitted straight line.

[0093] Figure 2 When the slope is negative, SV decreases as the membrane resistance increases. At this time, SV is greatly affected by the membrane impedance. Figure 3 In the figure, when the slope is smaller, SV is more affected by the porosity. Figure 4 When the slope increases further, SV is directly affected by the lithium ion consumption during SEI growth.

[0094] Specifically, the method for determining failure data of a target battery provided in an optional embodiment of the present invention includes the following steps:

[0095] S1, obtain the target battery;

[0096] For example, the battery selected is a ternary / graphite system cylindrical battery.

[0097] S2, calling the aging model of the pseudo two-dimensional solid electrolyte interface corresponding to the target battery;

[0098] For example, a P2D-SEI aging model is established based on the material parameters in cylindrical batteries to control the changes in membrane resistance and porosity in the model.

[0099] S3, setting model parameters of the aging model, wherein the model parameters include charge and discharge cycle parameters;

[0100] For example, the model parameters are set to cycle 700 times at a voltage of 2.75-4.25V. In the model, the charging condition is set to constant current charging to a cut-off voltage of 4.25V, and then constant voltage charging to a cut-off current of C / 20, and the discharge condition is set to constant current discharge to 2.75V.

[0101] S4, determining the voltage parameters of the simulated battery during the cyclic charging and discharging process according to the charging and discharging cycle parameters of the aging model, wherein the simulated battery is a battery obtained by simulating the target battery;

[0102] For example, the battery is charged and discharged in a measured manner. During the test, the charging condition is set to constant current charging to a cut-off voltage of 4.25V, and then constant voltage charging to a cut-off current of C / 20, and the discharge condition is set to constant current discharge to 2.75V. The median voltage of the charging curve is calculated, and then the SV of the battery is calculated.

[0103] S5, constructing a voltage curve according to the voltage parameter, wherein the horizontal axis corresponding to the voltage curve is the number of cycles, and the vertical axis of the voltage curve is the voltage parameter;

[0104] S6, performing linear fitting on the voltage curve to obtain a fitting line segment;

[0105] For example, the battery median voltage and SV during the cycle are calculated respectively, and the SV curve is linearly fitted to obtain the SV curve during the actual cycle of the cylindrical battery and obtain the fitting line segment.

[0106] S7, determining the line segment parameters of the fitting line segment;

[0107] For example, the slope of the SV curve during the cycle can be compared with the slope of the fitted straight line to analyze which factor dominates the SV in a specific cycle.

[0108] S8, determining the failure data of the target battery according to the line segment parameters.

[0109] Figure 5 Schematic diagram of slope variation during charge and discharge test provided by an optional embodiment of the present invention, such as Figure 5 As shown in the figure, in the early stage of the cycle, SV is mainly affected by the lithium ions consumed by SEI growth and the porosity. In the later cycles, the SV slope changes from positive to negative, which indicates that SV is mainly affected by membrane impedance in the later stage of the cycle. Therefore, the specific factors affecting SV can be determined during the cycle.

[0110] This optional implementation combines the P2D-SEI aging model to effectively separate the factors that affect SV changes, and uses the mechanism model to segment the three main factors that affect SV changes: membrane resistance, porosity, and lithium ion consumption. And by comparing the actual and mechanism model slopes, the reasons that affect SV changes during cycling are determined. The analysis method does not require post-disassembly analysis of batteries that have failed in cycles, nor does it require additional testing processes. It also plays a detailed mechanism analysis role in battery failure analysis.

[0111] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0112] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present invention.

[0113] Example 2

[0114] According to an embodiment of the present invention, a device for implementing the above-mentioned method for determining failure data of a target battery is also provided. Figure 6 is a structural block diagram of a device for determining failure data of a target battery according to an embodiment of the present invention. Figure 6 As shown, the device includes: an acquisition module 602, a call module 604, a setting module 606, a first determination module 608 and a second determination module 610. The device is described in detail below.

[0115] An acquisition module 602 is used to acquire a target battery; a call module 604 is connected to the acquisition module 602 and is used to call an aging model of a pseudo two-dimensional solid electrolyte interface corresponding to the target battery; a setting module 606 is connected to the call module 604 and is used to set model parameters of the aging model, wherein the model parameters include charge and discharge cycle parameters; a first determination module 608 is connected to the setting module 606 and is used to determine the voltage parameters of a simulated battery during the cyclic charge and discharge process of the aging model according to the charge and discharge cycle parameters, wherein the simulated battery is a battery obtained by simulating the target battery; a second determination module 610 is connected to the first determination module 608 and is used to determine the failure data of the target battery based on the voltage parameters, wherein the failure data includes the cause of failure.

[0116] It should be noted here that the above-mentioned acquisition module 602, call module 604, setting module 606, first determination module 608 and second determination module 610 correspond to steps S102 to S110 in the method for determining the failure data of the target battery, and the instances and application scenarios implemented by the multiple modules are the same as the corresponding steps, but are not limited to the contents disclosed in the above-mentioned embodiment 1.

[0117] Example 3

[0118] According to another aspect of an embodiment of the present invention, there is provided an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor, wherein the processor is configured to execute instructions to implement any of the above methods for determining failure data of a target battery.

[0119] Example 4

[0120] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device can execute any of the above-mentioned methods for determining failure data of a target battery.

[0121] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0122] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0123] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0124] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0125] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0126] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0127] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining failure data of a target battery, characterized in that: include: Get the target battery; Retrieving an aging model of a pseudo two-dimensional solid electrolyte interface corresponding to the target battery; Setting model parameters of the aging model, wherein the model parameters include charge-discharge cycle parameters; Determine the voltage parameters of the simulated battery during the cyclic charging and discharging process of the aging model according to the charge and discharge cycle parameters, wherein the simulated battery is a battery obtained by simulating the target battery; Determine failure data of the target battery based on the voltage parameter, wherein the failure data includes a failure cause.

2. The method according to claim 1, characterized in that Determining the failure cause of the target battery according to the voltage parameter includes: According to the voltage parameter, a voltage curve is constructed, wherein the horizontal axis corresponding to the voltage curve is the number of cycles, and the vertical axis of the voltage curve is the voltage parameter; Performing linear fitting on the voltage curve to obtain a fitting line segment; Determining line segment parameters of the fitted line segment; The failure data of the target battery is determined according to the line segment parameters.

3. The method according to claim 2, characterized in that Determining the failure cause of the target battery according to the line segment parameters includes: In the case where the line segment parameter includes a slope, determining a target control relationship, wherein the target control relationship includes a corresponding relationship between the slope and a failure cause, and the failure cause includes at least one of the following: battery ion consumption, porosity change, and electrical impedance change of a solid electrolyte interface membrane; From the target control relationship, a failure cause corresponding to the slope is determined.

4. The method according to claim 2, characterized in that: Determining the failure cause of the target battery according to the line segment parameters includes: In the case where the line segment parameters include multiple slopes, failure causes corresponding to the multiple slopes are determined, and the number of failure cycles corresponding to the multiple slopes are determined.

5. The method according to claim 3, characterized in that: Before determining the target control relationship, the method further includes: Determining a target variable, wherein the target variable includes at least one of the following: battery ions, porosity, and electrical impedance of a solid electrolyte interface membrane; Controlling the target variable and determining the influence coefficient of the target variable on the slope of the line segment; The target control relationship is determined based on the target variable and the influence coefficient.

6. The method according to claim 1, characterized in that Determining the voltage parameters of the simulated battery during the cycle of the aging model according to the model parameters includes: Determine a first median voltage of the target battery in a charging phase and a second median voltage of the target battery in a discharging phase during each cycle of the aging model according to the charge and discharge cycle parameters; The voltage parameter is determined according to the first median voltage and the second median voltage corresponding to each cycle.

7. The method according to any one of claims 1 to 6, characterized in that Determining the voltage parameters of the simulated battery during the cycle of the aging model according to the model parameters includes: In the case where the charge and discharge cycle parameters include predetermined charging stage parameters and predetermined discharging stage parameters, and the predetermined charging stage parameters include first charging stage parameters, second charging stage parameters, and number of cycles, the aging model is controlled to perform cycle simulation according to the number of cycles, and in each cycle, the simulation battery is charged according to the first charging stage parameters, and after reaching the first cut-off condition, it is charged according to the second charging stage parameters, and after reaching the second cut-off condition, it is discharged according to the predetermined discharging stage parameters, and one cycle is completed when the third cut-off condition is reached, wherein the first charging stage parameters are parameters corresponding to the constant current charging to the cut-off voltage stage, the second charging stage parameters include parameters corresponding to the constant voltage charging to the cut-off current stage, and the predetermined discharging stage parameters include parameters corresponding to the constant current discharging to the predetermined threshold stage.

8. A device for determining failure data of a target battery, characterized in that: include: An acquisition module, used for acquiring a target battery; A calling module, used to call an aging model of a pseudo two-dimensional solid electrolyte interface corresponding to the target battery; A setting module, used to set model parameters of the aging model, wherein the model parameters include charge and discharge cycle parameters; A first determination module is used to determine the voltage parameters of the simulated battery during the cyclic charging and discharging process of the aging model according to the charge and discharge cycle parameters, wherein the simulated battery is a battery obtained by simulating the target battery; The second determination module is used to determine the failure data of the target battery according to the voltage parameter, wherein the failure data includes a failure cause.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method for determining failure data of a target battery as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method for determining failure data of a target battery as claimed in any one of claims 1 to 7.

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