Cycle life prediction method based on electrochemical structural phase transition under operating conditions

Abstract

This invention relates to a cycle life prediction method based on electrochemical structural phase transitions under operating conditions, belonging to the field of electric vehicle and battery management technology. It aims to solve the problem that existing methods neglect the characteristics of power battery discharge rate and depth of discharge changing with actual driving conditions and driver intentions, leading to difficulties in predicting the cycle life of electric vehicle power batteries under driving conditions and long prediction times. This method first collects actual user driving conditions and fits discharge conditions, directly equating the discharge conditions to actual vehicle mileage. Then, based on research on lithium battery electrochemical structural phase transitions, an electrochemical cycle life model is established. Next, the electrochemical cycle life model is modified according to the discharge conditions to obtain a cycle life prediction model based on operating conditions. Finally, the battery structural morphology parameters under operating conditions are extracted, and the cycle life prediction model based on operating conditions is further modified to obtain the final cycle life prediction model.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle and battery management technology, and relates to a method for predicting cycle life based on phase transitions of electrochemical structural morphology under operating conditions. Background Technology

[0002] Over time, due to factors such as changes in ambient temperature and increased charge-discharge cycles, a series of electrochemical reactions occur within lithium-ion batteries. The reduction of lithium ions, loss of active materials, and electrode wear all contribute to performance degradation. Furthermore, the charge-discharge current of lithium iron phosphate (LFP) batteries changes with driving conditions and driver behavior, further reducing energy output and cycle life. Therefore, real-time monitoring is a crucial technology for ensuring the efficient and safe operation of lithium-ion batteries and electric vehicles. However, research on power battery testing and evaluation methods in my country started relatively late, particularly in cycle life evaluation. Due to a lack of prior work and data accumulation, the understanding of battery cycle life degradation mechanisms is insufficient, resulting in simplistic cycle life evaluation methods that cannot meet the rapidly evolving needs of power battery applications.

[0003] Current methods for predicting the cycle life of electric vehicle (EV) power batteries based on electrochemical performance neglect the fact that the battery's discharge rate and depth of discharge vary with actual driving conditions and driver intent. This leads to difficulties in predicting the cycle life of EV power batteries under driving conditions and results in long prediction times. To address this, we can directly equate actual vehicle mileage by collecting data from users' actual driving conditions and fitting discharge conditions. Furthermore, by combining this with research on the phase transitions of lithium-ion battery electrochemical structures and optimizing the prediction performance under driving conditions, we aim to accurately predict cycle life and shorten prediction time for practical engineering applications. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a method for predicting cycle life based on the phase transition of electrochemical structural morphology under operating conditions, so as to realize the prediction of battery cycle life and shorten the prediction time.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A cycle lifetime prediction method based on electrochemical structural phase transitions under operating conditions includes the following steps:

[0007] Step 1: Collect the user's actual driving conditions and fit the discharge conditions, directly converting the discharge conditions into the actual vehicle mileage.

[0008] Step 2: Based on the study of phase transitions in the electrochemical structure of lithium batteries, establish an electrochemical cycle life model;

[0009] Step 3: Based on the discharge conditions in Step 1, modify the electrochemical cycle life model in Step 2 to obtain a cycle life prediction model based on the operating conditions.

[0010] Step 4: Extract the battery structural morphology parameters under operating conditions, and further revise the cycle life prediction model based on operating conditions in Step 3 to obtain the final cycle life prediction model.

[0011] Furthermore, in step 1, the methods for collecting the user's actual driving conditions include:

[0012] Target cities were selected based on environmental climate, topographic features, and transportation characteristics.

[0013] Select a certain number of pure electric vehicles from each typical city;

[0014] Big data collection of driving data throughout the year;

[0015] The actual driving conditions collected are decomposed into specific time intervals to obtain several vehicle speed points containing initial velocity and acceleration.

[0016] The proportion of vehicle speed points is statistically analyzed, and a vehicle speed condition is formed by weighting these points.

[0017] The weighted and recombined operating data is initially cleaned, and operating data within a specific speed and acceleration range is selected.

[0018] Through cyclic processing, the operating conditions are obtained that are basically consistent with the user's actual daily driving time.

[0019] Furthermore, in step 2, the method for establishing the electrochemical cycle life model includes:

[0020] An electrochemical cycle lifetime model was initially established based on the classical cycle lifetime model.

[0021] The model parameters are determined using the least squares fitting method.

[0022] Furthermore, in step 3, the methods for modifying the electrochemical cycle life model include:

[0023] The cumulative discharge capacity under different discharge rates, i.e. under varying current, is equivalent to the equivalent cumulative discharge capacity under a unit discharge rate.

[0024] The electrochemical cycle life model is modified based on the equivalent cumulative discharge capacity.

[0025] Furthermore, in step 4, the methods for extracting the battery structural morphology parameters under operating conditions include:

[0026] The chromatographic morphology of lithium-ion batteries under different cycle conditions was analyzed.

[0027] Obtain the battery structural morphology parameters that represent the battery's equivalent capacity decay rate.

[0028] Furthermore, the battery structural morphological parameters include:

[0029] Change in the percentage of active material area in the longitudinal section, ΔS;

[0030] Change in cell thickness Δh.

[0031] Furthermore, in step 4, the methods for further refining the cycle life prediction model based on operating conditions include:

[0032] Based on the chromatographic structure and characteristics of the battery components, the relationship between the battery's electrochemical performance and the chromatographic structure is derived.

[0033] The equivalent capacity decay rate X is obtained by using the change in the proportion of active material area in the longitudinal section ΔS and the change in cell thickness Δh as quantitative indicators. loss ;

[0034] Using formula Q loss＝ Q loss电 +X loss The modified lithium battery electrochemical cycle life model, where Q loss Q represents the battery's capacity degradation rate. loss电 This represents the capacity decay rate obtained from the electrical performance cycle life model.

[0035] Furthermore, the equivalent capacity decay rate X loss Represented as:

[0036] X loss＝ λQ loss电 .

[0037] Furthermore, the morphological characteristic coefficient λ of the battery structure is calculated by the following formula:

[0038] λ=k1·ΔS+k2·Δh

[0039] Where k1 and k2 are constants for univariate linear fitting of λ.

[0040] Furthermore, the final cycle life prediction model is expressed as follows:

[0041]

[0042] Where α, β, z, K1, and K2 are constants obtained through experimental fitting, N is the number of cycles, I1 is the discharge current at a 1C discharge rate, t is the time of one driving condition, and n (k) For the discharge rate under driving conditions, T and T (k)All values ​​represent the temperature during driving conditions. Δt(k) represents the time interval into which the driving conditions are divided into i time intervals, where k is the discrete form of each time segment, and R is the gas constant, which is 8.3145 J·mol⁻¹. -1 ·K -1

[0043] The beneficial effects of this invention are as follows:

[0044] (1) By collecting actual driving conditions and fitting discharge conditions, the discharge conditions are directly equivalent to the actual vehicle mileage, which more accurately reflects the actual usage and thus improves the accuracy of cycle life prediction.

[0045] (2) By modifying the electrochemical cycle life model, the amount of computation is reduced, thereby shortening the prediction time.

[0046] (3) This method can be applied to different types of power batteries for electric vehicles and has strong applicability.

[0047] (4) This method can be applied to predict the cycle life of power batteries for electric vehicles, providing guidance for the design and optimization of battery management systems, thereby improving the safety and reliability of electric vehicles.

[0048] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0049] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0050] Figure 1 This is a schematic diagram of the optimized solution;

[0051] Figure 2 Analysis of battery cycle degradation at different temperatures;

[0052] Figure 3 Cyclic degradation analysis at different charge / discharge rates;

[0053] Figure 4 Cyclic decay analysis under different DODs;

[0054] Figure 5 Cyclic decay analysis under different operating conditions;

[0055] Figure 6 Analysis of battery internal resistance changes under long-term cycling;

[0056] Figure 7 This is a comparison of electrode morphology changes before and after cycling. Detailed Implementation

[0057] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0058] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0059] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0060] 1. Analysis of the cycle life degradation mechanism and influencing factors of lithium-ion power batteries:

[0061] 1) The effect of temperature on cycle life

[0062] See Figure 2 Temperature significantly impacts the cycle life of lithium-ion batteries because it directly affects the lithium-ion intercalation / deintercalation process at the positive and negative electrodes, electrolyte decomposition, SEI film thickness, and self-discharge rate. LiFePO4 batteries exhibit poor low-temperature performance. Within a reasonable range, appropriately increasing the temperature can accelerate Li+ diffusion, bringing the actual battery capacity closer to the theoretical capacity. However, higher temperatures lead to a higher self-discharge rate, a thicker SEI film, and instability in the SEI film at high temperatures, along with a series of side reactions in the electrolyte. Therefore, high temperatures accelerate battery capacity decay.

[0063] 2) The effect of charge / discharge rate on cycle life

[0064] See Figure 3 The higher the charge / discharge rate, the faster the Li+ diffusion rate needs to be; however, the Li+ insertion / extraction rate is structurally limited. When the charge / discharge rate is very high, the local Li+ concentration gradient in the carbon layer increases, generating localized internal stress, leading to the breakage of C / C bonds and the exposure of new carbon layers, forming a new SEI film. This is one of the reasons for capacity degradation under high charge / discharge rates.

[0065] 3) The effect of discharge depth on cycle life

[0066] See Figure 4 The depth of discharge (DOD) reflects the degree of lithium-ion insertion / extraction; the greater the DOD, the more lithium-ions are extracted from the carbon anode. Prolonged cycle charging and discharging at a high DOD results in a low Li+ concentration in the anode and a more disordered internal structure, making the electrode structure prone to damage and collapse. During recharging, the re-insertion of lithium-ions becomes more difficult, leading to a decrease in battery capacity and consequently, capacity degradation. On the other hand, when the battery's state of charge (SOC) is very low (DOD is very high), the battery's internal resistance increases rapidly.

[0067] 4) The impact of driving conditions on battery cycle life

[0068] See Figure 5 Under driving conditions, the output current of the battery pack varies from 0-400A depending on demand, and the commonly used constant current cycle life model cannot meet the requirements. Studies on the impact of different operating conditions on the SOC, terminal voltage and their consistency, and operating current of electric vehicle power battery packs revealed that the battery's operating current varies significantly with different vehicle operating conditions. For example, under long-distance driving conditions, the operating voltage fluctuation is small, and the SOC decreases slowly; under hillside driving conditions and traffic jam conditions, the operating voltage fluctuation is large, and the SOC decreases rapidly. Simultaneously, different discharge currents result in different battery heat generation, causing temperature changes in the battery compartment, which in turn affects battery performance.

[0069] 2. Analysis of the cycle life degradation performance of lithium-ion batteries:

[0070] 1) Battery internal resistance:

[0071] See Figure 6 During charging and discharging, the degree of side reactions increases, and a passivation film gradually covers the surface of the active materials of the positive and negative electrodes. This film increases the ohmic internal resistance of the active materials. At the same time, the electrolyte concentration decreases, the diffusion rate of the electrolyte participating in the flow reaction decreases, and the polarization internal resistance increases.

[0072] 2) Internal structural form:

[0073] Geometric Characteristics: Changes in the internal structural morphology of lithium-ion power batteries can be reflected in three aspects: geometric characteristics, distribution of active materials, and density changes. During the charging and discharging process, other side reactions occur in lithium-ion power batteries. The decomposition of the internal electrolyte produces gas, resulting in a bulging phenomenon in the battery's geometric structure. Furthermore, when the charging and discharging method or operating environment of a lithium-ion power battery malfunctions, lithium ions can deposit on the battery plates, forming lithium dendrites that can pierce the separator, creating a micro-short circuit between the positive and negative electrodes. This leads to self-discharge and capacity decay, which manifests geometrically as a spatial longitudinal connection between the positive electrode, separator, and negative electrode.

[0074] Distribution of active materials: As we know from the working principle of lithium-ion power batteries, during the flow reaction inside the battery, liquid phase mass transfer, material transformation and new phase formation will occur at the electrode-solution interface. After cycling for a period of time, the distribution of surface active materials on the electrode plates will be uneven.

[0075] Density Changes: As the number of cycles increases, lithium-ion power batteries not only experience a decrease in charge and discharge capacity, but the destruction of the internal component structure also causes changes in the density of the active material. The positive electrode consists of an aluminum current collector and lithium iron phosphate active material. When the charge and discharge voltage is too high, the protective Al2O3 layer on the aluminum foil surface is damaged, which accelerates the corrosion rate of the current collector, causing the positive electrode active material to separate and detach from the current collector.

[0076] In summary, battery life degradation is directly manifested in the destruction of the internal morphological structure of the battery. By fully analyzing the deformation and degradation processes experienced by the components of the power battery through structural morphology analysis, and combining this with the many existing detection methods for the internal structure of the battery, such as in-situ SEM, XRD, AFM, infrared diffraction, and X-ray imaging, the morphological change parameters of the battery degradation process can be quantified. By combining capacity testing and battery internal resistance testing, an electrochemical cycle life model of the battery can be constructed. With the correction of essential characteristic parameters, a complete lithium-ion battery cycle life prediction model can be obtained.

[0077] 3. Model Establishment

[0078] The main factors affecting the cycle performance of lithium-ion power batteries include ambient temperature (T), discharge rate (RateC), depth of discharge (DOD), and number of cycles (N). The cycle life degradation rate model for lithium-ion power batteries is initially established based on the classic cycle life model, using an electrochemical cycle life model.

[0079] Based on existing literature, a classic cycle life model was established. Using raw data on battery cycle life degradation, modeling and calculation analysis were performed using Matlab and Origin to plot the cycle life prediction model and determine its parameters.

[0080] Q loss =f(Q) full T, C rate (DOD, N)

[0081] Model parameters are determined using fitting methods such as the least squares method. Without altering the internal failure mechanism of the battery, the total effective capacity that the battery can release during its lifespan is constant. Therefore, the cumulative discharge capacity is used as a parameter to describe the battery capacity decay.

[0082] Q loss =a(T, C) rate ,DOD)·A h Z

[0083] A h Defined as the cumulative discharge capacity of the battery, its calculation method is as follows:

[0084] A h =N·DOD·Q full

[0085] Combining the classical electrochemical cycle life model, the following is the lithium iron phosphate power cycle life model with undetermined coefficients:

[0086]

[0087] In the formula, α, k1, k2, and z are constants.

[0088] This paper analyzes the effects of various factors (temperature, discharge rate, and depth of discharge) on the classical cycle life model, summarizing the accelerating effects of temperature, discharge rate, and depth of discharge on capacity decay. The classical electrochemical model does not address the individual effects of rate and depth of discharge, resulting in poor fitting accuracy. Therefore, under arbitrary conditions, the combined accelerating effect of all factors on lifetime decay can be expressed as follows:

[0089]

[0090] In the formula, α, β, γ, k1, k2, and z are all constants. The formula can be obtained by performing regression analysis and fitting the experimental data obtained through experiments.

[0091] 4. Model optimization:

[0092] 1) Driving condition correction strategy based on big data fitting:

[0093] Operating conditions acquisition:

[0094] First, typical operating conditions for pure electric vehicle users are extracted. Target cities are selected based on dimensions such as environment, climate, terrain features, and traffic characteristics. A certain number of pure electric vehicles are randomly selected from each of the selected typical cities, and annual driving data is collected using big data analytics.

[0095] Due to differences in driving routes and habits among users, to preserve data characteristics, the collected actual driving conditions are decomposed into several speed points containing initial velocity and acceleration at specific time intervals. Then, the proportion of speed points is statistically analyzed, and weights are applied to form a single speed condition, ensuring that user driving characteristics are not lost.

[0096] After initial cleaning of the weighted and recombined driving data, a segment of continuous vehicle speeds within the acceleration range with roughly equal initial and final speeds is selected. Peaks or troughs that do not meet the criteria are removed using a combination of driving condition and constant speed methods until all speed points have been cycled through. After cycling through all speed points in the previous speed range, the cycle begins for the next speed range until all speed ranges have been cycled through. This repeated cycle yields driving conditions that are essentially consistent with the user's actual daily driving time.

[0097] By collecting real-world user driving conditions through big data, extracting and retaining user driving characteristics, and finally fitting a driving condition that is closer to the actual driving conditions of users, research or evaluation of cycle life prediction based on this driving condition will be more accurate.

[0098] Predictive model analysis under driving conditions:

[0099] In actual driving of an electric vehicle, the battery discharge current is a variable over time t, and the equivalent cumulative discharge capacity released at different discharge currents is not the same. The cumulative discharge capacity reflects the degree of battery capacity decay. The cumulative discharge capacity at different discharge rates (i.e., under varying currents) can be equated to the equivalent cumulative discharge capacity at a single discharge rate. The equivalence is as follows: Cumulative discharge capacity A at an nC discharge rate. hn The cumulative discharge capacity A at a 1C discharge rate h1 The effect on battery life degradation is the same; that is, at nC discharge rate, the capacity degradation rate is:

[0100]

[0101] When the life loss is equivalent (i.e., Q) loss1 =Q lossn The cumulative discharge capacity at an nC discharge rate is equivalent to the cumulative discharge capacity at a 1C discharge rate, which, when rearranged, yields:

[0102]

[0103] Substituting the values ​​into the calculation, the capacity decay model at nC discharge rate is obtained as follows:

[0104]

[0105] The cumulative ampere-hours discharged by the battery can be calculated using the following formula:

[0106] A h =n·(DOD)·Q full =It / 3600

[0107] Taking big data fitting conditions as an example, under a certain driving condition, the capacity decay percentage is:

[0108]

[0109] t represents the driving time under one driving condition, and n (t) T represents the discharge rate under driving conditions. (t) n represents the temperature during driving conditions. (t) T (t) All of these are variables related to time t, and I1 is the discharge current at a 1C discharge rate.

[0110] Assume a driving condition lasts for a duration of t, and divides it into i time intervals of Δti. The discharge rate of each time interval is n. (t) Then the discrete form is:

[0111]

[0112] The capacity decay model for N driving conditions is as follows:

[0113]

[0114] The constants α, z, k1, k2, etc. can be obtained through experimental fitting.

[0115] 2) Further refine the structural morphology parameters extracted under the working conditions:

[0116] See Figure 7 The tomographic morphology of lithium-ion batteries under different cycling conditions was analyzed to obtain battery structural morphology parameters representing the equivalent capacity decay rate, thus correcting the electrochemical cycle life model. Based on the tomographic morphology and characteristics of the battery components, the relationship between battery electrochemical performance and tomographic morphology was derived. The equivalent capacity decay rate X was obtained using the change in the proportion of active material area in the longitudinal section (ΔS) and the change in cell thickness (Δh) as quantitative indicators. loss To modify the electrochemical cycle life model of lithium-ion batteries, namely:

[0117] Q loss =Q loss电 +X loss

[0118] X loss It can be represented as:

[0119] X loss =λQ loss电

[0120] The battery structural morphology characteristic coefficient can be composed of the change S in the average area ratio of active material in the longitudinal section of the cell before and after cycling and the change h in the cell thickness. By extracting the structural morphology characteristic parameters ΔS and Δh under different cycling conditions, calculating the structural morphology attenuation coefficient, and performing a univariate linear fit, the influence coefficient can be obtained.

[0121] λ=k1·ΔS+k2·Δh

[0122] Final cycle life prediction model combining morphological feature parameters:

[0123]

[0124] The constants α, z, k1, k2, etc. can be obtained through experimental fitting.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for predicting cycle lifetime based on electrochemical structural phase transitions under operating conditions, characterized in that: Includes the following steps: Step 1: Collect the user's actual driving conditions and fit the discharge conditions, directly converting the discharge conditions into the actual vehicle mileage. Step 2: Based on the study of phase transitions in the electrochemical structure of lithium batteries, establish an electrochemical cycle life model; Step 3: Based on the discharge conditions in Step 1, modify the electrochemical cycle life model in Step 2 to obtain a cycle life prediction model based on the operating conditions. Step 4: Extract the battery structural morphology parameters under operating conditions, and further revise the cycle life prediction model based on operating conditions in Step 3 to obtain the final cycle life prediction model.

2. The cycle life prediction method based on electrochemical structural phase transition under operating conditions according to claim 1, characterized in that: In step 1, the methods for collecting the user's actual driving conditions include: Target cities were selected based on environmental climate, topographic features, and transportation characteristics. Select a certain number of pure electric vehicles from each typical city; Big data collection of driving data throughout the year; The actual driving conditions collected are decomposed into specific time intervals to obtain several vehicle speed points containing initial velocity and acceleration. The proportion of vehicle speed points is statistically analyzed, and a vehicle speed condition is formed by weighting these points. The weighted and recombined operating data is initially cleaned, and operating data within a specific speed and acceleration range is selected. Through cyclic processing, the operating conditions are obtained that are basically consistent with the user's actual daily driving time.

3. The cycle life prediction method based on electrochemical structural phase transition under operating conditions according to claim 1, characterized in that: In step 2, the methods for establishing the electrochemical cycle life model include: An electrochemical cycle lifetime model was initially established based on the classical cycle lifetime model. The model parameters are determined using the least squares fitting method.

4. The cycle life prediction method based on electrochemical structural phase transition under operating conditions according to claim 1, characterized in that: In step 3, the methods for modifying the electrochemical cycle lifetime model include: The cumulative discharge capacity under different discharge rates, i.e. under varying current, is equivalent to the equivalent cumulative discharge capacity under a unit discharge rate. The electrochemical cycle life model is modified based on the equivalent cumulative discharge capacity.

5. The cycle life prediction method based on electrochemical structural phase transition under operating conditions according to claim 1, characterized in that: In step 4, the methods for extracting the battery structural morphology parameters under operating conditions include: The chromatographic morphology of lithium-ion batteries under different cycle conditions was analyzed. Obtain the battery structural morphology parameters that represent the battery's equivalent capacity decay rate.

6. The cycle life prediction method based on electrochemical structural phase transition under operating conditions according to claim 5, characterized in that: The battery structural morphological parameters include: Change in the percentage of active material area in the longitudinal section, ΔS; Change in cell thickness Δh.

7. The cycle life prediction method based on electrochemical structural phase transition under operating conditions according to claim 1, characterized in that: In step 4, the methods for further refining the cycle life prediction model based on operating conditions include: Based on the chromatographic structure and characteristics of the battery components, the relationship between the battery's electrochemical performance and the chromatographic structure is derived. The equivalent capacity decay rate X is obtained by using the change in the proportion of active material area in the longitudinal section ΔS and the change in cell thickness Δh as quantitative indicators. loss ; Using formula Q loss =Q loss电 +X loss The modified lithium battery electrochemical cycle life model, where Q loss Q represents the battery's capacity degradation rate. loss电 This represents the capacity decay rate obtained from the electrical performance cycle life model.

8. The cycle life prediction method based on electrochemical structural phase transition under operating conditions according to claim 7, characterized in that: The equivalent capacity decay rate X loss Represented as: X loss＝ λQ loss电 。 9. The cycle life prediction method based on electrochemical structural morphology phase transition under operating conditions according to claim 8, characterized in that: The battery structural morphological characteristic coefficient λ is calculated by the following formula: λ=k1·ΔS+k2·Δh Where k1 and k2 are constants for univariate linear fitting of λ.

10. The cycle life prediction method based on electrochemical structural phase transition under operating conditions according to claim 1, characterized in that: The final cycle life prediction model is expressed as follows: Where α, β, z, K1, and K2 are constants obtained through experimental fitting, N is the number of cycles, I1 is the discharge current at a 1C discharge rate, t is the time of one driving condition, and n (k) For the discharge rate under driving conditions, T and T (k) All values ​​represent the temperature during driving conditions. Δt(k) represents the time interval into which the driving conditions are divided into i time intervals, where k is the discrete form of each time segment, and R is the gas constant, which is 8.3145 J·mol⁻¹. -1 ·K -1 .

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