Battery aging mechanism identification method

Abstract

The invention belongs to the technical field of secondary batteries, and mainly relates to a battery aging mechanism identification method, which comprises the following steps of: firstly, judging whether other battery aging mechanisms exist in addition to the growth of a solid electrolyte interfacial film; meanwhile, peak valleys of an I C curve and a DV curve in the battery aging process are analyzed, and whether the aging mechanism of the battery is battery negative electrode active material loss or battery positive electrode active material loss is judged; and further, decomposing the electrochemical impedance spectrums before and after the aging of the battery through a relaxation time distribution technology, and comparing the electrochemical impedance value spectrum before the aging of the battery with the electrochemical impedance spectrum after the aging of the battery to obtain a change result of the electrochemical impedance value spectrum of the battery. Finally, the limitation of a relaxation time distribution technology of a differential voltage analysis method on battery capacity attenuation nondestructive analysis and internal resistance growth quantitative decomposition and quantification is overcome, so that the battery aging mechanism analysis is clearer.

Description

Technical Field

[0001] The invention belongs to the technical field of secondary batteries, and in particular relates to a method for identifying a battery aging mechanism. Background Art

[0002] During the use of existing batteries, capacity attenuation and internal resistance growth have become important issues restricting the development of the industry. This aging process not only affects the service life of the battery, but also directly affects the performance, safety and economy of the battery. Therefore, in-depth understanding of the aging mechanism of batteries (especially lithium batteries) and accurate and rapid identification of their aging causes have become technical difficulties that need to be solved in the industry.

[0003] At present, the analysis of battery aging mechanism mainly relies on two mainstream methods: DVA (differential voltage analysis) and DRT (relaxation time distribution technology). DVA technology is based on the differential processing of the battery capacity-voltage (Q-OCV) curve. By analyzing the differential voltage (DV) curve, the battery capacity decay can be evaluated. However, due to the sparse sampling points of the low-rate charge and discharge curve and the large noise in the test process, the DVA method often produces burrs, which affects the quality of the DV curve and leads to inaccurate peak position judgment, which in turn affects the in-depth analysis of the aging mechanism.

[0004] On the other hand, DRT technology aims to quantitatively analyze the dynamic changes of battery internal resistance and obtain the internal state information of the battery through relaxation time distribution. However, DRT technology may ignore the impedance changes caused by the solid electrolyte interface film (SE I film) in some cases, and the growth and change of SE I film plays a vital role in the aging process of the battery. Therefore, the applicability and comprehensiveness of DRT technology in analyzing the battery aging mechanism are also subject to certain limitations.

[0005] Based on this, it is urgent to improve the existing battery aging mechanism identification method to solve the defects of the aforementioned technology. Summary of the invention

[0006] The purpose of the present invention is to provide a method for overcoming the technical problems of low identification efficiency and ambiguous results in identifying battery aging mechanisms using DVA (differential voltage analysis) and DRT (relaxation time distribution technology) in view of the deficiencies in the prior art.

[0007] In order to achieve the above technical objectives, this application implements the following technical solutions:

[0008] A method for identifying a battery aging mechanism comprises the following steps:

[0009] S101, fitting the battery capacity attenuation curve during the battery aging process according to the first model, and determining whether the battery aging mechanism is only caused by the solid electrolyte interface film;

[0010] S201. When the battery aging mechanism of the battery is not caused only by the solid electrolyte interface film, the peaks and valleys of the IC curve and the DV curve during the battery aging process are analyzed to determine whether the battery aging mechanism is the second aging mechanism; the second aging mechanism is the loss of the negative electrode active material of the battery or the loss of the positive electrode active material of the battery;

[0011] S301. Decomposing the electrochemical impedance spectra of the battery before and after aging by using relaxation time distribution technology, and comparing the electrochemical impedance spectrum of the battery before aging with the electrochemical impedance spectrum of the battery after aging to obtain the change result of the electrochemical impedance spectrum of the battery.

[0012] The above technical solution produces the following technical effects:

[0013] This application takes into account that the existing DVA and DRT methods for battery aging mechanism identification do not explicitly detect the solid electrolyte interface membrane, and the impedance value of the solid electrolyte interface membrane often cannot be directly decomposed according to the DRT, and generally does not take into account that in a good state, the aging of the battery is mainly caused by the formation and growth of the solid electrolyte interface membrane. Therefore, this application first analyzes whether the battery aging mechanism is directly caused by the solid electrolyte interface membrane, avoiding the subsequent complicated detection of other battery aging mechanisms. In summary, this application further improves the limitations of the two existing battery aging mechanism detection methods, making its results more purposeful and improving the identification efficiency and accuracy.

[0014] As a further improvement to the battery aging mechanism identification method of the present invention, the mathematical expression of the first model is:

[0015] ξ(n)=J·n z

[0016] Among them, n is the cumulative throughput capacity of the battery, ξ(n) is the battery capacity decay rate, and J and z are the battery aging mechanism identification parameters.

[0017] As a further improvement to the battery aging mechanism identification method of the present invention, the method for determining whether the battery aging mechanism is caused only by the solid electrolyte interface film in step S101 is:

[0018] If the identification parameter z=0.5 in the first model after fitting, it is determined that the aging mechanism of the lithium battery is only caused by the solid electrolyte interface film;

[0019] If the identification parameter z in the fitted first model is greater than 0.5, it is determined that the aging mechanism of the lithium battery is not only caused by the solid electrolyte interface film.

[0020] As a further improvement to the battery aging mechanism identification method of the present invention, the steps of the peak and valley acquisition method of the battery DV curve in step S201 are as follows:

[0021] S202, plotting the OCV-Q curves of the battery before and after aging, differentiating the OCV-Q curve of the battery before aging to obtain the DV curve of the battery before aging; and differentiating the OCV-Q curve of the battery after aging to obtain the DV curve of the battery after aging;

[0022] S203, performing polynomial fitting and secondary derivation on the DV curve of the battery before aging based on the peak and valley of the DV curve obtained initially in each battery capacity interval, to obtain the peak and valley position of the DV curve of the battery before aging corresponding to each battery capacity;

[0023] The DV curve of the battery after aging is subjected to polynomial fitting and secondary derivation based on each battery capacity interval where the peak and valley of the DV curve are located, so as to obtain the peak and valley position of the DV curve of the battery after aging corresponding to each battery capacity.

[0024] As a further improvement to a battery aging mechanism identification method of the present invention, the steps of determining that the battery aging mechanism is the second aging mechanism method in step S201 are as follows:

[0025] Match the peaks and valleys of the IC curve to the peaks and valleys of the DV curve one by one;

[0026] When the phase change process corresponding to the peak and valley of the IC curve is a negative electrode reaction, the reduction in width between the peak and valley of the DV curve before battery aging and the peak and valley of the DV curve after battery aging corresponding to the peak and valley of the IC curve is the loss of the negative electrode active material of the battery;

[0027] When the phase change process corresponding to the peak and valley of the IC curve is a positive electrode reaction, the reduction in width between the peak and valley of the DV curve before battery aging and the peak and valley of the DV curve after battery aging corresponding to the peak and valley of the IC curve is the loss of the positive electrode active material of the battery.

[0028] As a further improvement to the battery aging mechanism identification method of the present invention, the step of determining that the battery aging mechanism is the second aging mechanism method in step S201 further includes:

[0029] When the peak and valley of the IC curve correspond to the phase change process of LiC 6 and LiC 12 When the transition occurs, the reduction in width between the peak and valley of the DV curve before battery aging and the peak and valley of the DV curve after battery aging corresponding to the peak and valley of the IC curve is the loss of active lithium in the battery.

[0030] As a further improvement to the battery aging mechanism identification method of the present invention, the battery in step S201 is a three-electrode battery with lithium-plated copper wire embedded inside.

[0031] As a further improvement to a battery aging mechanism identification method of the present invention, the electrochemical impedance spectra of the battery before and after aging decomposed by step S301 include one or more of ohmic impedance, solid electrolyte interface film impedance, charge transfer impedance, and diffusion impedance.

[0032] As a further improvement to a battery aging mechanism identification method of the present invention, the ohmic impedance and the solid electrolyte interface film impedance are separated by drawing a Nyquist plot through an electrochemical impedance spectrum; the Nyquist plot is used to identify the ohmic impedance.

[0033] As a further improvement to the battery aging mechanism identification method of the present invention, the frequency test range of the electrochemical impedance spectroscopy is 1000 Hz-0.01 Hz. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings described herein are used to provide further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0035] In the attached picture:

[0036] Figure 1 It is a flow chart of the battery aging mechanism identification method of the present invention;

[0037] Figure 2 The IC curve drawn in Example 2 of the present invention;

[0038] Figure 3 It is the DU curve drawn in Example 2 of the present invention;

[0039] Figure 4 The electrochemical impedance spectrum of the battery drawn in Example 2 of the present invention;

[0040] Figure 5 The Nyquist fitting curve is drawn in Example 2 of the present invention. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application. The terms used herein in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0042] In the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected" and "fixed" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0043] In order to facilitate the explanation of the technical solution of this application, this application will explain some technical names as follows to facilitate the understanding of the technical solution of this application:

[0044] 1. Weibull distribution: It is a flexible statistical distribution widely used in reliability analysis and life data modeling. Its probability density function and cumulative distribution function are defined as follows:

[0045] Probability density function (PDF):

[0046]

[0047] Cumulative Distribution Function (CDF):

[0048]

[0049] 2. Fitting: Use numerical methods such as nonlinear least squares method to fit the model to real-time data. The real-time data here refers to the data in the battery capacity attenuation curve during battery aging.

[0050] 3. Cumulative throughput capacity: In this application, it is the total capacity of charging / discharging, the unit can be milliampere hour (mAh) or other units

[0051] 4. Capacity decay rate: It is the relative capacity of the battery, which can be expressed as 1 for 100% capacity, 0.9 for 90% capacity, etc.

[0052] 5. Solid electrolyte interface film (SEI film): SEI film is a solid electrolyte interface formed after the negative electrode of a lithium-ion battery comes into contact with the electrolyte. It protects the negative electrode, preventing lithium ions from continuing to react with the electrolyte, thereby preventing the deposition of lithium metal and the deterioration of battery performance. The SEI film will continue to grow and mature during the charge and discharge process of the battery. Although this process is beneficial in the early stages, over time, the excessive growth of the SEI film will increase the internal resistance of the negative electrode, resulting in battery capacity decay. The increase in internal resistance not only affects the battery's charge and discharge efficiency, but also affects the overall battery performance, and is one of the causes of battery aging.

[0053] 6. IC curve: In battery testing, the IC curve is a graph of the relationship between current density and voltage obtained by differentiating the Q-OCV curve (capacity-open circuit voltage curve).

[0054] 7. IC curve peaks and valleys: The peaks of the IC curve peaks and valleys represent the change in the reaction rate of the battery at different voltages. Each peak corresponds to the beginning of a phase change process or chemical reaction. In lithium batteries, the reaction mainly involves the embedding and de-embedding of lithium ions in the positive and negative electrode materials. According to the peak position of the IC curve, the material in which the reaction occurs can be preliminarily determined.

[0055] 8. Positive electrode phase change and negative electrode phase change: When the IC curve shows a clear peak in the higher voltage range (usually corresponding to the lithium ion insertion / extraction reaction of the positive electrode material), it usually indicates that the positive electrode material has undergone a phase change. For example, LiCoO 2 Materials such as lithium will have specific voltage characteristics when lithium is inserted and removed. On the contrary, when the IC curve has a peak in the lower voltage range (usually corresponding to the lithium ion insertion reaction of the negative electrode material), it means that the negative electrode material has undergone a phase change. For example, graphite will undergo a phase change from LiC to 6 To LiC 12 transformation.

[0056] 9. DV curve: It is a curve obtained by differential calculation of the current, voltage and capacity signals of lithium-ion batteries during the charging and discharging process. Specifically: DV curve is the differential expression of the relationship between battery capacity (Q) and open circuit voltage (0CV), that is, the derivative of the relationship curve between capacity and voltage. Its mathematical expression is:

[0057]

[0058] The DV curve reflects the rate of change of the battery voltage at different capacity points, so it can be used to judge the electrochemical reaction process of the battery. The peaks and valleys in the curve provide important information related to the battery phase change, material properties and health status.

[0059] 10. Peaks and valleys of the DV curve: refers to the local maximum (peak) and minimum (valley) values ​​that appear in the DV curve; the peak-to-valley peak value indicates the maximum value of the battery voltage change rate at a specific capacity point. Usually, during the charge and discharge process, when a phase change occurs (such as lithium insertion or deinsertion of the corresponding material), the voltage change rate increases rapidly, forming a peak.

[0060] 11. Electrochemical Impedance Spectrum (EIS): It is an important test technology for analyzing the performance of electrochemical systems (such as lithium-ion batteries, fuel cells, etc.). It evaluates its impedance characteristics by applying a small-amplitude AC voltage signal and measuring the system's response to the signal. At the same time, EIS measures the response of the signal at different frequencies, so it can provide information about various dynamic processes of the battery, including interface reactions, ion diffusion, and the influence of the electrolyte membrane.

[0061] 12. Impedance: Electrochemical impedance is a complex number, usually composed of a real part (resistance) and an imaginary part (reactance). It can describe the internal resistance of the battery, interface resistance, diffusion process and electrochemical reaction kinetics.

[0062] 13. Nyquist Plot is a specific type of complex plane graph used to represent the results of electrochemical impedance spectroscopy. Its characteristic is that the real part of the impedance is plotted on the X-axis and the imaginary part of the impedance is plotted on the Y-axis.

[0063] Although the present application is disclosed as follows in the form of a preferred embodiment, it is not intended to limit the claims. Any person skilled in the art may make several possible changes and modifications without departing from the concept of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

[0064] The present application takes into account that when the battery is intact in appearance and the voltage is normal, the aging of the battery is mainly caused by the formation and growth of the solid electrolyte interface film, and there is no need for complicated detection. In the prior art, although the mechanism of the growth of the solid electrolyte interface film on the battery aging can be identified in the process of battery aging mechanism analysis using DVA (differential voltage analysis) and DRT (relaxation time distribution technology), it is impossible to quantify. DVA can identify lithium ion loss (LLI), but lithium ion loss (LLI) includes Li consumed by the growth of the solid electrolyte interface film (SEI film), Li precipitation, and other side reactions of Li. In the process of DRT (relaxation time distribution technology) separating the battery impedance, although it involves the analysis of the solid electrolyte interface film impedance, due to the overlap of the peaks of the electrolyte interface film impedance and the ohmic impedance in the DRT spectrum, the solid electrolyte interface film impedance is often directly included in the ohmic resistance, resulting in the lack of content of the prior art analysis of the battery aging mechanism, making its results more vague. This lack of understanding makes the prior art lack in efficiency and accuracy of aging analysis. Therefore, it is of great practical significance and application value to carry out research and development of more advanced technologies targeting the battery aging mechanism, especially in the effectiveness of accurate peak identification and internal resistance analysis.

[0065] Existing technical solutions generally do not take into account that in a good state, the aging of the battery is mainly caused by the formation and growth of the solid electrolyte interface film, and there is no need for complicated testing. This lack of understanding makes the existing technology lacking in efficiency and accuracy in aging analysis. Therefore, the present application has generated the invention motivation to solve this technical problem. Furthermore, the present application has made improvements to the existing battery aging mechanism identification method. Specifically, the improved battery aging mechanism identification method of the present application includes the following steps:

[0066] S101, fitting the battery capacity attenuation curve during the battery aging process according to the first model, and determining whether the battery aging mechanism is only caused by the solid electrolyte interface film;

[0067] S201. When the battery aging mechanism of the battery is not caused only by the solid electrolyte interface film, the peaks and valleys of the IC curve and the DV curve during the battery aging process are analyzed to determine whether the battery aging mechanism is the second aging mechanism; the second aging mechanism is the loss of the negative electrode active material of the battery or the loss of the positive electrode active material of the battery;

[0068] S301. Decomposing the electrochemical impedance spectra of the battery before and after aging by using relaxation time distribution technology, and comparing the electrochemical impedance spectrum of the battery before aging with the electrochemical impedance spectrum of the battery after aging to obtain the change result of the electrochemical impedance spectrum of the battery.

[0069] Through the above-mentioned improvement on the existing battery aging mechanism identification method, the present application fits the battery capacity attenuation curve during the battery aging process through the first model to determine whether the battery aging mechanism is only caused by the solid electrolyte interface membrane. On the one hand, it can analyze whether the battery is aged by the inevitable solid electrolyte interface membrane under normal operation, thereby saving the subsequent detection of abnormal battery positive and negative electrode active materials or battery impedance.

[0070] Furthermore, the mathematical expression of the first model is:

[0071] ξ(n)=J·n z

[0072] Among them, n is the cumulative throughput capacity of the battery, ξ(n) is the battery capacity decay rate, and J and z are the battery aging mechanism identification parameters. Among them, the first model is a mathematical model fitted by the Weibull distribution function through the existing battery capacity decay curve. Among them, the Weibull distribution is a flexible statistical distribution that is widely used in reliability analysis and life data modeling. Its probability density function and cumulative distribution function are defined as follows:

[0073] Probability density function (PDF):

[0074]

[0075] Cumulative Distribution Function (CDF):

[0076]

[0077] Among them, λ is the scale parameter and k is the shape parameter.

[0078] Furthermore, the present application relates the decrease in capacity of existing batteries after use to the characteristics of the Weibull distribution and the behavior of lithium batteries (as a preferred battery type, but the technical solution of the present application is applicable to the aging mechanism decomposition of lithium-ion batteries, and also includes any battery composed of electrodes that undergo phase change during the charge and discharge process.) Among them, the relationship between battery life and capacity is highlighted.

[0079] It is known that during the use of a battery, its capacity decreases as its cumulative throughput capacity changes, which is very similar to the time or intensity factor in the Weibull distribution. From the reference "Mechanism Model and State Estimation of Automotive Lithium-ion Batteries", it can be seen that when the battery runs long enough, t and n are equivalent, that is, time t is proportional to the cumulative throughput capacity n. Therefore, this application starts from the cumulative distribution function of the Weibull distribution and establishes a relationship between the battery capacity decay rate and battery use, where:

[0080]

[0081] and It indicates the rate at which the effective capacity of the battery changes; F(n) is the cumulative distribution function (CDF) that equates the time parameter to the battery capacity. It indicates the probability of failure below the capacity. Since this probability is related to failure, the relationship 1-F(n) represents the survival probability. This relationship shows that after a battery has experienced a lot of charge and discharge, its capacity decay rate is inversely proportional to the survival probability, that is, the lower the survival probability, the faster the capacity decay.

[0082] Furthermore, considering that the application of Weibull distribution lies in its cumulative distribution function (CDF) is When the above relationship is derived, the decay function is related to the capacity decay rate. At this time, ξ(n) represents the degree of capacity attenuation, which is consistent with the form of Weibull distribution. Through the above derivation and appropriate mathematical transformation, the following mathematical model is obtained:

[0083] ξ(n)=J·n 2

[0084] J and z in the above expression are parameters that need to be determined by data fitting in the above mathematical model (i.e., the first model) in this application. JJ represents the initial decay rate of the battery, and describes the strength of the relationship between decay and cumulative capacity. Further, it can be seen from the reference "Mechanism Model and State Estimation of Automotive Lithium-ion Batteries" that when time t is long enough, the thickness of the solid electrolyte interface film is proportional to t 0.5 , and t and n are equivalent, that is, time t is proportional to the cumulative throughput capacity n; it can be seen that if the identification parameter z=0.5 in the first model after fitting, it is determined that the aging mechanism of the lithium battery is only caused by the solid electrolyte interface film; if the identification parameter z>0.5 in the first model after fitting, it is determined that the aging mechanism of the lithium battery is not only caused by the solid electrolyte interface film. Taking lithium-ion batteries with graphite as negative electrodes as an example, the growth of solid electrolyte interface films is inevitable because the electrolyte will continue to react with lithium-embedded carbon; and when z>0.5, it indicates that the actual attenuation rate of the battery is faster than the aging caused by the growth of the solid electrolyte interface film, so there are other aging mechanisms. Therefore, when z>0.5 appears, the present application will perform the next step of detection on the battery in the detection process (that is, the detection links of step S201 and step S301)

[0085] Furthermore, when the battery aging mechanism of the battery is not caused only by the solid electrolyte interface film, the peaks and valleys of the IC curve and the DV curve during the battery aging process are analyzed, and the present application determines whether the battery aging mechanism is the second aging mechanism through step S021; wherein the second aging mechanism is the loss of active materials in the negative electrode of the battery or the loss of active materials in the positive electrode of the battery.

[0086] In the specific implementation process, the existing technical solution has provided a method for analyzing the battery aging mechanism through the peaks and valleys of the DV curve - the DVA method. Among them, the DVA method obtains the differential voltage (DV) curve by differentially analyzing the relationship between the capacity (Q) and the open circuit voltage (0CV) of the lithium-ion battery under different charge and discharge states. This process usually first performs multiple low-rate charge and discharge tests on the battery and records the open circuit voltage (0CV) at different capacity points. Secondly, a Q-OCV curve is drawn based on the recorded data to reflect the state of the battery and its changes. Finally, the Q-OCV curve is differentiated to obtain the DV curve. The peak on the DV curve represents the change in the reaction rate of the battery, which is then used to judge the aging state and phase change information, and then determine the battery aging mechanism and its influencing factors.

[0087] The present application found that in the process of using the DVA method to detect the battery aging mechanism, the resolution of the Q-OCV curve was insufficient due to the relatively small number of points for data collection at low-rate charge and discharge. Sparse sampling may make it impossible to accurately capture key phase change information, affecting the accuracy of peak position judgment. At the same time, in the actual testing process, the noise introduced by the environment and internal and external factors of the battery will affect the accuracy of voltage measurement, which in turn causes burrs on the DV curve. These burrs may form pseudo-peaks, affect the position and height judgment of the real peak, and cause the analysis of the aging mechanism to deviate from the actual situation. In addition, the DVA method fails to fully consider the impact of the solid electrolyte interface film (SEI film) on battery performance and aging, and the SEI film is very important in the aging process of the battery. Failure to quantitatively evaluate the contribution of its ultra-thin film to battery impedance and aging may lead to a one-sided analysis of the battery state. (The technical solution of step S101 of this application alleviates this technical problem)

[0088] Therefore, the present application improves the existing DVA method. Specifically, while solving the DV curve, the present application also draws the IC curve before and after battery aging. Specifically, the present application first obtains the low-rate charge and discharge curves of the battery in a fresh state (before aging) and after aging (the low-rate charge and discharge, the rate should be lower than 0.2C) to draw the Q-OCV curve (capacity-voltage curve), and then differentiates to obtain the IC curve. Peak search to solve the peak position U1, U2...Un, which is the phase change platform of the whole battery. The peak value of the IC curve here corresponds to the specific moment when the electrode material undergoes a phase change. Therefore, the IC curve can directly display the phase change process of the positive and negative electrode materials during the charging and discharging process of the battery.

[0089] Furthermore, before drawing the IC curve, a three-electrode battery is made by embedding a lithium-plated copper wire inside the full battery, so that the influence of the capacity loss of active lithium in the battery on battery aging can be analyzed by combining the phase change of the three electrodes with the DU curve. In the specific implementation process, the negative electrode ground to lithium potential φa1, φa2...φan and the positive electrode ground to lithium potential φc1, φc2...φcn under the full battery phase change platform are tested to determine the phase change process occurring at this voltage.

[0090] Furthermore, the steps of determining that the aging mechanism of the battery is the second aging mechanism method are as follows:

[0091] The peaks and valleys of the IC curve are matched one by one with the peaks and valleys of the DV curve; when the phase change process corresponding to the peaks and valleys of the IC curve is a negative electrode reaction, the reduction in width between the peaks and valleys of the DV curve before battery aging and the peaks and valleys of the DV curve after battery aging corresponding to the peaks and valleys of the IC curve is the loss of the negative electrode active material of the battery; when the phase change process corresponding to the peaks and valleys of the IC curve is a positive electrode reaction, the reduction in width between the peaks and valleys of the DV curve before battery aging and the peaks and valleys of the DV curve after battery aging corresponding to the peaks and valleys of the IC curve is the loss of the positive electrode active material of the battery.

[0092] In addition, since the aging mechanism of the three-electrode lithium-ion battery is preferably detected in the present application, there is a phase transition process corresponding to the peak and valley of the IC curve when LiC 6 and LiC 12 When the transition occurs, the reduction in width between the peak and valley of the DV curve before battery aging and the peak and valley of the DV curve after battery aging corresponding to the peak and valley of the IC curve is the loss of active lithium in the battery.

[0093] Furthermore, the steps of the peak and valley acquisition method of the DV curve of the battery in step S201 are as follows:

[0094] S202, plotting the OCV-Q curves of the battery before and after aging, differentiating the OCV-Q curve of the battery before aging to obtain the DV curve of the battery before aging; and differentiating the OCV-Q curve of the battery after aging to obtain the DV curve of the battery after aging;

[0095] S203, performing polynomial fitting and secondary derivation on the DV curve of the battery before aging according to each battery capacity interval where the peak value of the DV curve is initially obtained, to obtain the peak and valley position of the DV curve of the battery before aging corresponding to each battery capacity;

[0096] The DV curve of the battery after aging is subjected to polynomial fitting and secondary derivation based on each battery capacity interval where the peak value of the DV curve is located, so as to obtain the peak and valley position of the DV curve of the battery after aging corresponding to each battery capacity.

[0097] Among them, in step S203, the DV curve of the battery before aging is subjected to polynomial fitting and quadratic derivation in each battery capacity interval according to the peak and valley of the DV curve (specifically the peak value) obtained initially. Specifically, the method is as follows: visually determine the approximate peak position of the DV curve before aging: [x1, x2], [x3, x4]..., [x2n-1, x2n] On the OCV-SOC curve, find the interval [x1, x2], [x3, x4]...[x2n-1, x2n], divide the OCV-SOC curve into sections, perform polynomial fitting, and quadratically derivate to obtain the inflection points Q1, Q2...Qn, which are the positions of the peak and valley (specifically the peak value) of the DV curve of the lithium battery before aging. Where the fitting function is a polynomial, such as a cubic polynomial, the highest degree of x is 3, and the function is: Y=ax 3 +bx 2 +cx+d; further, in step S203, the DV curve of the battery after aging is subjected to polynomial fitting and quadratic derivation in each battery capacity interval where the peak and valley (specifically the peak value) of the DV curve is initially obtained. Specifically, the method is as follows: determine the approximate peak and valley (specifically the peak value) position of the DV curve after aging - [x1', x2'], [x3', x4'] ..., [x2n-1', x2n′], find the interval [x1', x2'], [x3', x4'] ..., [x2n-1', x2n′] on the OCV-SOC curve, perform polynomial fitting on the OOV-SOC curve in interval segments, and quadratically derivate to obtain the inflection points Q1', Q2' ... Qn', which are the positions of the peak and valley (specifically the peak value) of the DV curve of the lithium battery after aging.

[0098] Specifically, the present application adopts the method of first fitting the OCV-SOC curve and then solving the peak position of the DV curve, which has higher accuracy and can avoid the problem of many DV curve glitches and impact on peak position judgment caused by sparse sampling points and large test noise in the low-rate charge and discharge curve. Therefore, the present application achieves the optimization of the DVA analysis method.

[0099] Further, the electrochemical impedance spectra of the battery before and after aging decomposed by step S301 include one or more of ohmic impedance, solid electrolyte interface film impedance, charge transfer impedance, and diffusion impedance. In the process of obtaining the electrochemical impedance spectrum before and after battery aging, the frequency test range is 1000Hz-0.01Hz; the impedance is decomposed by DRT technology to obtain ohmic impedance Rs, solid electrolyte interface (SEI) film impedance Rse i, charge transfer impedance Rct, and diffusion impedance Rd respectively. Since the ohmic impedance Rs and the solid electrolyte interface (SEI) film impedance Rsei are both caused by physical reactions, the response speeds of the two are similar, and they are easily coupled and overlapped into one peak and cannot be distinguished, so it is necessary to separate them by drawing the Nyquist diagram through the electrochemical impedance spectrum; the Nyquist diagram is used to identify the ohmic impedance. Among them, the ohmic impedance Rs is first solved by the Nyquist diagram fitting result, and then the solid electrolyte interface (SEI) film impedance Rsei can be separated.

[0100] Furthermore, the present application further verifies the technical effect of the present application through the above-mentioned specific embodiments.

[0101] Example 1

[0102] Obtain the aged battery and confirm that the appearance is intact and the voltage is stable. Extract the battery capacity decay curve and fit it with the first model. The fitting results show that the identification parameter z = 0.5. Output result: The cause of battery aging is the growth of the solid electrolyte interface film, and no further detection and analysis will be performed.

[0103] Example 2

[0104] The difference from Example 1 is that another aged lithium battery is obtained to confirm that the appearance is intact and the voltage is low. The battery capacity decay curve is extracted and fitted with the first model to obtain the identification parameter z=0.8, and the SEI film growth is determined to be one of the reasons for battery aging.

[0105] Furthermore, the 0.05 charge and discharge curves of the battery in the fresh state and after aging are obtained, and the Q-OCV curve is plotted with the discharge data, and then the IC curve is obtained by differentiation. The peak-to-valley position is solved by peak search, and the peak-to-valley peaks of the IC curve are obtained as follows: U1 = 3.18V, U2 = 3.205V, U3 = 3.235, U4 = 3.270, U5 = 3.310, reference Figure 2 ; By embedding lithium-plated copper wire inside the full battery, a three-electrode lithium-ion battery was made, and the negative electrode / positive electrode ground-to-lithium potential φa and φc under the full battery phase change platform were tested. In addition, the above-mentioned aged battery is a lithium battery, the positive electrode is lithium iron phosphate, and the negative electrode is graphite. There is no corresponding peak for lithium iron phosphate, and all peaks correspond to the negative electrode phase change reaction. When U1=3.180, the corresponding graphite negative electrode potential is 0.22V, and it is determined that C 6 To LiC24 phase change process; when U1 = 3.310, the corresponding graphite negative electrode potential is 0.10V, and it is determined that LiC 12 To LiC 6 phase change process.

[0106] Furthermore, the OCV-Q curve (voltage-capacity curve) of the lithium battery before and after aging is plotted, and the DV curve of the lithium battery before and after aging is obtained by differentiation. Figure 3 The peak positions of each peak and valley of the DV curve are solved, and the peak spacing is solved by the peak positions. The reduction ratio of the peak spacing is used to represent the attenuation ratio of the positive electrode active material or the negative electrode active material or the active lithium.

[0107] The process of solving the peak position of the DV curve is as follows: visually observe the DV curve of the lithium battery before aging, and judge that the approximate peak and valley positions are [1.8, 2.2] and [0.4, 0.6]. On the corresponding OCV-SOC curve before aging, perform third-order polynomial fitting on the interval curves of [1.8, 2.2] and [0.4, 0.6] respectively, and obtain the following structure:

[0108] 1) Fitting result in the interval [1.8, 2.2]: y = -0.2051x 3 +1.2535x 2 -2.2387x+1.0983:

[0109] 2) Fitting result in the interval [0.4, 0.6]: y = -1.2263x 3 +1.6996x 2 -1.2735x+0.3365;

[0110] Among them, the inflection point Q1 = -1.2535 / (3*(-0.2051)) = 2.0372, the inflection point Q2 = -1.6996 / (3*(-1.2263)) = 0.4620;

[0111] Similarly, visually observe the DV curve of the lithium battery after aging and determine the approximate peak positions [1.7, 1.9] and [0.4, 0.6]. On the corresponding OCV-SOC curve after aging, perform third-order polynomial fitting on the interval curves of [1.7, 1.9] and [0.4, 0.6] and obtain the following results:

[0112] 1) Fitting result in the interval [1.7, 1.9]: y = -0.1841x 3 +0.8829x 2 -0.9961x-0.1015;

[0113] 2) Fitting result in the interval [0.4, 0.6]: y = -1.3308x 3 +1.8321x2 -0.7158x+0.0637;

[0114] Among them, the inflection point Q1=1.7986, and the inflection point Q2=0.4589.

[0115] It can be seen that the total discharge capacity of the battery before aging is Q0 = 2.9959, and the total discharge capacity after aging is Q0' = 2.6473. The expansion and contraction of the U1 peak of the IC curve corresponds to the change in the spacing from the DV curve Q1 to Q0, indicating the loss of active lithium; the expansion and contraction of the U2 peak of the IC curve corresponds to the change in the spacing from the DV curve Q2 to Q1, indicating the loss of negative electrode active materials, so:

[0116] Active lithium loss = ((QO-Q1)-(Q0'-Q1')) / (QO-Q1) = 11.47%;

[0117] Negative electrode active material loss = ((Q1-Q2)-(Q1'-Q2')) / (Q1-Q2) = 14.95%:

[0118] In summary, it is determined that the loss of negative electrode active materials is the main cause of battery aging.

[0119] Furthermore, the electrochemical impedance spectrum of the battery before and after aging is obtained, and the frequency test range is 0.01Hz-1000Hz; the impedance is decomposed by DRT technology to obtain the impedance data before battery aging (specifically, Figure 4 As shown in Figure 2, Rs = 4.33mΩ, Rsei = 2.19mΩ, Rct = 3.01mΩ, Rd = 8.96mΩ, after aging, Rs + Rsei = 10.17mΩ (the two peaks merge and are difficult to separate), Rct = 3.53mΩ, Rd = 9.10mΩ. By EIS fitting (specifically as shown in Figure 2, Rs = 4.33mΩ, Rsei = 2.19mΩ, Rct = 3.01mΩ, Rd = 8.96mΩ), after aging, Rs + Rsei = 10.17mΩ (the two peaks merge and are difficult to separate), Rct = 3.53mΩ, Rd = 9.10mΩ. Figure 5 As shown, Rs is solved by fitting the Nyquist diagram, and then Rsei is separated. The result is that after aging, Rs=6.34, and then Rsei=3.83 is separated.

[0120] Finally, the aging mechanism of lithium batteries is summarized as follows: the negative electrode active material loss is 14.95%, the active lithium loss is 11.47%, the ohmic impedance increases by 46%, the SEI film impedance increases by 75%, the charge transfer impedance increases by 17%, and the diffusion impedance increases by 2%.

[0121] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for identifying a battery aging mechanism, characterized in that: The following steps are involved: S101, fitting the battery capacity attenuation curve during the battery aging process according to the first model, and determining whether the battery aging mechanism is only caused by the solid electrolyte interface film; S201, when the battery aging mechanism of the battery is not caused only by the solid electrolyte interface film, analyzing the peaks and valleys of the IC curve and the DV curve during the battery aging process to determine whether the aging mechanism of the battery is a second aging mechanism; the second aging mechanism is the loss of the negative electrode active material of the battery or the loss of the positive electrode active material of the battery; S301, decomposing the electrochemical impedance spectra of the battery before and after aging by using relaxation time distribution technology, and comparing the electrochemical impedance spectrum of the battery before aging with the electrochemical impedance spectrum of the battery after aging to obtain a change result of the electrochemical impedance spectrum of the battery.

2. A battery aging mechanism identification method according to claim 1, characterized in that: The mathematical expression of the first model is: ξ(n)=J·n z Among them, n is the cumulative throughput capacity of the battery, ξ(n) is the battery capacity decay rate, and J and z are the battery aging mechanism identification parameters.

3. A battery aging mechanism identification method according to claim 2, characterized in that: The method for determining whether the battery aging mechanism is caused only by the solid electrolyte interface membrane in step S101 is: If the identification parameter z in the first model after fitting is 0.5, it is determined that the aging mechanism of the lithium battery is only caused by the solid electrolyte interface film; If the identification parameter z in the first model after fitting is greater than 0.5, it is determined that the aging mechanism of the lithium battery is not only caused by the solid electrolyte interface film.

4. A battery aging mechanism identification method according to claim 1, characterized in that: The steps of the method for obtaining the peak and valley of the DV curve of the battery in step S201 are as follows: S202, plotting the OCV-Q curves of the battery before and after aging, differentiating the OCV-Q curve of the battery before aging to obtain the DV curve of the battery before aging; and differentiating the OOV-Q curve of the battery after aging to obtain the DV curve of the battery after aging; S203, performing polynomial fitting and secondary derivation on the DV curve of the battery before aging according to each battery capacity interval where the peak and valley of the DV curve are located, to obtain the peak and valley position of the DV curve of the battery before aging corresponding to each battery capacity; The DV curve of the battery after aging is subjected to polynomial fitting and secondary derivation based on each battery capacity interval where the peak and valley of the DV curve are located, so as to obtain the peak and valley position of the DV curve of the battery after aging corresponding to each battery capacity.

5. A battery aging mechanism identification method according to claim 1, characterized in that: The steps of determining in step S201 that the aging mechanism of the battery is the second aging mechanism are as follows: The peaks and valleys of the IC curve are matched one by one with the peaks and valleys of the DV curve; When the phase change process corresponding to the peak and valley of the IC curve is a negative electrode reaction, the reduction in width between the peak and valley of the DV curve before battery aging and the peak and valley of the DV curve after battery aging corresponding to the peak and valley of the IC curve is the loss of the negative electrode active material of the battery; When the phase change process corresponding to the peak and valley of the IC curve is a positive electrode reaction, the reduction in width between the peak and valley of the DV curve before battery aging and the peak and valley of the DV curve after battery aging corresponding to the peak and valley of the IC curve is the loss of the positive electrode active material of the battery.

6. A battery aging mechanism identification method according to claim 5, characterized in that: The battery in step S201 is a three-electrode battery with lithium-plated copper wire embedded inside.

7. A battery aging mechanism identification method according to claim 6, characterized in that: The step of determining in step S201 that the aging mechanism of the battery is the second aging mechanism method further includes: When the peak and valley of the IC curve correspond to the phase transition process of LiC6 and LiC 12 When the battery is in a transition state, the reduction in width between the peak and valley of the DV curve before aging and the peak and valley of the DV curve after aging corresponding to the peak and valley of the IC curve is the loss of active lithium in the battery.

8. A battery aging mechanism identification method according to claim 1, characterized in that: The electrochemical impedance spectra of the battery before and after aging decomposed in step S301 include one or more of ohmic impedance, solid electrolyte interface film impedance, charge transfer impedance, and diffusion impedance.

9. A battery aging mechanism identification method according to claim 8, characterized in that: The ohmic impedance and the solid electrolyte interface film impedance are separated by drawing a Nyquist plot through electrochemical impedance spectroscopy: the Nyquist plot is used to identify the ohmic impedance.

10. A battery aging mechanism identification method according to claim 1, characterized in that: The frequency test range of the electrochemical impedance spectroscopy is 1000 Hz-0.01 Hz.

Images