Cycle life prediction method based on electrochemical structure form phase change under working condition

Abstract

The invention relates to a cycle life prediction method based on electrochemical structure form phase change under a working condition, and belongs to the technical field of electric automobile and battery management. The invention aims to solve the problems that the cycle life of the power battery for the electric vehicle under the driving condition is difficult to predict and the prediction time is long because the existing method ignores the characteristic that the discharge rate and the discharge depth of the power battery change along with the actual driving condition and the intention of a driver. The method comprises the following steps: firstly, collecting the actual driving condition of a user, fitting the discharging condition, and directly equalizing the discharging condition into the real vehicle driving mileage; then, establishing an electrochemical cycle life model according to the phase change research of the electrochemical structure of the lithium battery; then, the electrochemical cycle life model is corrected according to the discharge working condition, and a cycle life prediction model based on the working condition is obtained; and finally, extracting structural form parameters of the battery under the working condition, and further correcting the cycle life prediction model based on the working condition to obtain a final cycle life prediction model.

Description

Technical Field

[0001] The invention belongs to the technical field of electric vehicles and battery management, and relates to a cycle life prediction method based on electrochemical structural morphology phase change under working conditions. Background Art

[0002] Due to the accumulation of time, changes in ambient temperature, and the increase in charge and discharge cycles, a series of electrochemical reactions will occur inside the lithium battery. The reduction of lithium ions, the loss of active materials, and the loss of electrodes will cause the performance degradation of lithium-ion batteries. At the same time, the charge and discharge current of lithium iron phosphate power batteries will not only change with the changes in driving conditions and driver behavior, but these factors will also reduce the energy output and cycle life of lithium-ion batteries. Therefore, real-time monitoring is one of the key technologies to ensure the efficient and safe operation of lithium-ion power batteries and electric vehicles. However, my country's research on power battery test and evaluation methods started late, especially in the evaluation of cycle life. Due to the lack of preliminary work and data accumulation, the attenuation mechanism of battery cycle life has not been studied in depth, resulting in a single cycle life evaluation method that cannot meet the rapidly developing power battery application needs.

[0003] The current prediction method of cycle life of power batteries for electric vehicles based on electrochemical performance ignores the characteristics that the discharge rate and discharge depth of power batteries change with actual driving conditions and driver intentions, which leads to the difficulty of predicting the cycle life of power batteries for electric vehicles under driving conditions and the long prediction time. To this end, by collecting the actual driving conditions of users and fitting the discharge conditions, the actual vehicle mileage can be directly equivalent. On this basis, the prediction effect under the electrochemical structure phase change of lithium batteries and the optimization of driving conditions are further combined to accurately predict its cycle life and shorten the prediction time. It is applied to engineering practice. Summary of the invention

[0004] In view of this, the object of the present invention is to provide a cycle life prediction method based on electrochemical structural morphology phase change under working conditions, so as to realize the prediction of battery cycle life and shorten the prediction time.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] The cycle life prediction method based on the electrochemical structural morphology phase change under working conditions includes the following steps:

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

[0008] Step 2: Establish an electrochemical cycle life model based on the electrochemical structural phase change research of lithium batteries;

[0009] Step 3: According to the discharge operating condition in step 1, the electrochemical cycle life model in step 2 is modified to obtain a cycle life prediction model based on the operating condition;

[0010] Step 4: Extract the battery structure morphology parameters under the working conditions, further modify the cycle life prediction model based on the working conditions in step 3, and obtain the final cycle life prediction model.

[0011] Furthermore, in step 1, the method of collecting the user's actual driving conditions includes:

[0012] Select target cities based on environmental climate, terrain characteristics, and traffic characteristics;

[0013] Screen out a certain number of pure electric vehicles in each typical city;

[0014] Big data collects driving data throughout the year;

[0015] Decompose the collected actual driving conditions at specific time intervals to obtain a number of vehicle speed points including initial velocity and acceleration;

[0016] Count the proportions of vehicle speed points and add weights accordingly to form a vehicle speed condition;

[0017] Perform preliminary cleaning on the weighted reorganized operating condition data, and select the operating condition data within a specific vehicle speed range and acceleration range;

[0018] Through cyclic processing, a working condition that is basically consistent with the user's actual daily driving time is obtained.

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

[0020] The electrochemical cycle life model was initially established based on the classical cycle life model;

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

[0022] Furthermore, in step 3, the electrochemical cycle life model is corrected in the following manner:

[0023] The cumulative discharge capacity at different discharge rates, i.e., at variable current, is equivalent to the equivalent cumulative discharge capacity at unit rate;

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

[0025] Furthermore, in step 4, the method of extracting the battery structure morphology parameters under the working condition includes:

[0026] Analyze the tomographic structure morphology of lithium-ion batteries under different cycle conditions;

[0027] Obtain the battery structural morphology parameters representing the battery equivalent capacity attenuation rate.

[0028] Furthermore, the battery structure morphology parameters include:

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

[0030] Cell thickness change Δh.

[0031] Furthermore, in step 4, the method of further correcting the cycle life prediction model based on the working condition includes:

[0032] According to the corresponding chromatographic structure morphology and characteristics of the battery components, the relationship between the battery electrochemical performance and chromatographic structure morphology is deduced;

[0033] The change in the active material area ratio of the longitudinal section ΔS and the change in the cell thickness Δh are used as quantitative indicators to obtain the equivalent capacity attenuation rate X loss ;

[0034] Using the formula Q loss＝ Q loss电 +X loss Modified lithium battery electrochemical cycle life model, where Q loss is the capacity decay rate of the battery, Q loss电 It is the capacity attenuation rate obtained from the electrical performance cycle life model.

[0035] Furthermore, the equivalent capacity attenuation rate X loss It is expressed as:

[0036] X loss＝ λQ loss电 .

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

[0038] λ=k1·ΔS+k2·Δh

[0039] Among them, k1 and k2 are constants for univariate linear fitting of λ.

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

[0041]

[0042] Among them, α, β, z, K1, 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 driving time, n (k) is the discharge rate of the driving condition, T and T (k)are the temperatures in the driving condition, Δt(k) is the time period that divides the driving condition into i time intervals, k is the discrete form of each time segment, and R is the gas constant, which is 8.3145 J·mol -1 ·K -1

[0043] The beneficial effects of the present invention are:

[0044] (1) By collecting actual driving conditions and fitting the 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 calculation 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 the cycle life prediction 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 present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0050] Figure 1 Schematic diagram of the optimization scheme;

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

[0052] Figure 3 Cycle attenuation analysis at different charge and discharge rates;

[0053] Figure 4 It is the cycle attenuation analysis under different DOD;

[0054] Figure 5 Cyclic attenuation analysis under different working conditions;

[0055] Figure 6 Analysis of battery internal resistance changes under long cycles;

[0056] Figure 7 Comparison of electrode morphology changes before and after cycling. DETAILED DESCRIPTION

[0057] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways 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 only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0058] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0059] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

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

[0061] 1) Effect of temperature on cycle life

[0062] See also Figure 2 Temperature factors have a great impact on the cycle life of lithium-ion batteries, because temperature is directly related to the deintercalation process of lithium ions in the positive and negative electrodes, electrolyte decomposition, SEI film thickness, and self-discharge rate. LiFePO4 batteries have poor low-temperature performance. Within a reasonable range, increasing the temperature appropriately can accelerate the diffusion rate of Li+, and the actual capacity of the battery is close to the theoretical capacity. However, the higher the temperature, the greater the self-discharge rate, the thicker the SEI film, and the SEI film is unstable at high temperatures, and the electrolyte also undergoes a series of side reactions, so high temperatures will accelerate the capacity decay of the battery.

[0063] 2) Effect of charge and discharge rate on cycle life

[0064] See also Figure 3 The higher the charge and discharge rate, the faster the Li+ diffusion rate is required. However, the Li+ deintercalation rate is limited by the structure. When the charge and discharge rate is very high, the Li+ concentration gradient in the local carbon layer will increase, resulting in local internal stress, causing CC bonds to break, resulting in new carbon layers exposed, and forming new SEI films. This is also one of the reasons for capacity decay under high-rate charge and discharge.

[0065] 3) Effect of discharge depth on cycle life

[0066] See also Figure 4 The depth of discharge reflects the degree of lithium ion deintercalation. The greater the depth of discharge, the more lithium ions are extracted from the carbon negative electrode. When the battery is charged and discharged for a long time at a large depth of discharge, the concentration of Li+ in the negative electrode is low, and the internal structure of the negative electrode is relatively disordered, which can easily cause damage and collapse of the electrode structure. When charging again, it becomes more difficult for lithium ions to be re-intercalated, resulting in a decrease in the battery charging capacity, which in turn leads to capacity decay. On the other hand, when the battery SOC is very small (the depth of discharge DOD is very large), the internal resistance of the battery increases rapidly.

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

[0068] See also Figure 5 Under driving conditions, the output current distribution range of the battery pack is 0-400A, which varies with demand, and the commonly used constant current cycle life model cannot meet the requirements. When studying the impact of different operating conditions on the SOC, terminal voltage, consistency and working current of electric vehicle power battery packs, it was found that under different operating conditions, the working current of the battery varies greatly with the different vehicle operating conditions. For example, under long-distance conditions, the working voltage fluctuates less and the SOC decreases slowly; under hillside driving conditions and traffic jams, the working voltage fluctuates more and the SOC decreases rapidly. At the same time, the heat generated by the battery is different under different discharge currents, causing the temperature of the battery compartment to change, which in turn affects the performance of the battery.

[0069] 2. Analysis of lithium-ion battery cycle life attenuation performance:

[0070] 1) Battery internal resistance:

[0071] See also Figure 6 During the charge and discharge process, the degree of side reactions increases, and the surface of the positive and negative electrode active materials is gradually covered with a passivation film, which increases the ohmic internal resistance of the active materials; at the same time, the electrolyte concentration decreases, the diffusion rate of the electrolyte involved in the current reaction decreases, and the polarization internal resistance increases.

[0072] 2) Internal structure:

[0073] Geometric characteristics: Changes in the internal structure of lithium-ion power batteries can be reflected in three aspects: geometric characteristics, active material distribution, and density changes. During the charge and discharge reaction of lithium-ion power batteries, other side reactions will occur, the internal electrolyte will decompose to produce gas, and the battery will show bloating in the geometric structure. In addition, when the charging and discharging method and working environment of lithium-ion power batteries are abnormal, lithium ions will be deposited on the battery plates to form lithium dendrites, which will pierce the diaphragm and cause a micro-short circuit between the positive and negative electrodes, resulting in self-discharge and capacity attenuation of the battery. In terms of geometric structure, the positive electrode, diaphragm, and negative electrode are vertically connected in space.

[0074] Distribution of active substances: From the working principle of lithium-ion power batteries, it is known that when the flow reaction is taking place inside the battery, liquid phase mass transfer, material transformation and new phase formation will occur at the interface between the electrode and the solution. After a period of circulation, the distribution of surface active substances on the electrode plates will become uneven.

[0075] Density change: As the number of cycles of lithium-ion power batteries increases, not only does the charge and discharge capacity decay, but the destruction of the internal component structure of the battery will cause changes in the density of the active material. The positive electrode is composed of an aluminum current collector and lithium iron phosphate active material. When the charge and discharge voltage is too high, the Al2O3 protective layer on the surface of the aluminum foil is destroyed, which will accelerate the corrosion rate of the current collector and cause the positive electrode active material to separate and fall off from the current collector.

[0076] In summary, life decay is directly reflected in the destruction of the internal morphological structure of the battery. Through a full analysis of the battery structure and morphology, the deformation and degradation process experienced by the components of the power battery, combined with the many detection methods for the internal structure and morphology of the battery that have been derived, such as in-situ SEM, XRD, AFM, infrared diffraction, and X-ray imaging, the internal structure of the battery can be directly observed and monitored, thereby quantifying the morphological change parameters of the battery decay process, combining capacity detection and battery internal resistance detection to build a battery electrochemical cycle life model, and obtaining a complete lithium-ion battery cycle life prediction model by correcting the essential characteristic parameters.

[0077] 3. Model building

[0078] The factors affecting the cycle performance of lithium-ion power batteries mainly include ambient temperature T, discharge rate RateC, discharge depth DOD and number of cycles N. The lithium-ion power battery cycle life attenuation rate model first establishes an electrochemical cycle life model based on the classic cycle life model.

[0079] Combined with existing literature, a classic cycle life model is established. Based on the original data of battery cycle life attenuation, Matlab and Origin modeling and calculation analysis are used to draw a cycle life prediction model and determine the parameters of the model:

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

[0081] The model parameters are determined by fitting methods such as the least squares method. Under the premise of not changing the internal failure mechanism of the battery, the total effective capacity that the battery can release during its life is certain. Therefore, the cumulative discharge capacity is used as a parameter to describe the battery capacity attenuation:

[0082] Q loss =a(T,C rate ,DOD)·A h Z

[0083] A h Defined as the cumulative discharge capacity of the battery, it is calculated as follows:

[0084] A h =N·DOD·Q full

[0085] Combining the classical electrochemical cycle life model, the lithium iron phosphate power cycle life model with undetermined coefficients can be obtained as follows:

[0086]

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

[0088] The effects of various factors (temperature, discharge rate, and discharge depth) in the classic cycle life model on the model are analyzed, and the accelerating effects of temperature, discharge rate, and discharge depth on capacity decay are summarized. The classic electrochemical model does not involve the separate effects of rate and depth, and the fitting accuracy is poor. Therefore, under any conditions, the comprehensive accelerating effect of various factors on life decay can be expressed as:

[0089]

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

[0091] 4. Model optimization:

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

[0093] Working condition acquisition:

[0094] First, extract the typical operating conditions of pure electric vehicle users. Select target cities based on environmental climate, terrain characteristics, traffic characteristics and other dimensions, randomly select a certain number of pure electric vehicles from each typical city, and collect driving data throughout the year.

[0095] Due to the differences in driving routes and driving habits of different users, in order to retain data characteristics, the actual driving conditions collected are decomposed at specific time intervals to obtain several speed points including initial velocity and acceleration. Then, the proportion of speed points is counted and weighted to form a speed condition to ensure that the user's driving characteristics are not lost.

[0096] After the weighted reorganized working condition data is initially cleaned, a certain section of continuous vehicle speed is selected, which is within the acceleration range and the first and last speeds are basically equal. The working condition method and the constant speed method are combined to delete the peaks or troughs that do not meet the conditions until all speed points are cycled; after cycling all speed points in the previous speed range, the next speed range is cycled until all speed ranges are cycled. After repeated cycles, the working condition that is basically consistent with the actual daily driving time of the user is obtained.

[0097] By collecting actual user driving conditions through big data, extracting and retaining user driving characteristics, and ultimately fitting conditions that are closer to the user's actual driving conditions, research or evaluation on cycle life prediction based on this condition will be more accurate.

[0098] Prediction model analysis under driving conditions:

[0099] The battery discharge current during the actual driving of electric vehicles is a variable of time t, and the equivalent cumulative discharge capacity that can be released under different discharge currents is not the same. The cumulative discharge capacity reflects the degree of battery capacity attenuation. The cumulative discharge capacity under different discharge rates, that is, under variable current, can be equivalent to the equivalent cumulative discharge capacity under unit rate. The equivalent idea is: the cumulative discharge capacity A under nC discharge rate hn The cumulative discharge capacity A at 1C discharge rate h1 The effect on battery life decay is the same, that is, at nC discharge rate, the capacity decay rate is:

[0100]

[0101] When the life loss is equal (i.e. Q loss1 =Q lossn ), the cumulative discharge capacity at nC discharge rate is equivalent to the cumulative discharge capacity at 1C discharge rate, and the result is:

[0102]

[0103] Substituting 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 the big data fitting condition as an example, under a driving condition, the capacity decay percentage is:

[0108]

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

[0110] Assume that a driving condition lasts for t, and divide it into i time periods with a time interval of △ti. The discharge rate of each time segment is n (t) , then the discrete form is:

[0111]

[0112] The capacity decay model of N driving conditions is:

[0113]

[0114] Constants such as α, z, k1, k2, etc. can be obtained through experimental fitting.

[0115] 2) Further correction of structural morphological parameters under extraction conditions:

[0116] See also Figure 7 , the tomographic structure morphology of lithium-ion batteries under different cycle conditions is analyzed, the battery structure morphology parameters representing the battery equivalent capacity attenuation rate are obtained, and the electrochemical cycle life model is corrected; according to the tomographic structure morphology corresponding to the battery components and their characteristics, the relationship between the battery electrochemical performance and the tomographic structure morphology is introduced, and the change in the area ratio of the active material in the longitudinal section ΔS and the change in the thickness of the battery cell Δh are used as quantitative indicators to obtain the equivalent capacity attenuation rate X 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 expressed as:

[0119] X loss =λQ loss电

[0120] The battery structure morphology characteristic coefficient can be composed of the change S of the average area ratio of the active material in the longitudinal section of the battery cell before and after the cycle and the change h of the battery cell thickness. The battery structure morphology characteristic parameters ΔS and Δh under different cycle conditions are extracted, and the structural morphology attenuation coefficient is calculated. The influence coefficient can be obtained by performing a univariate linear fitting:

[0121] λ=k1·ΔS+k2·Δh

[0122] The final cycle life prediction model combining morphological characteristic parameters:

[0123]

[0124] Constants such as 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 solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A cycle life prediction method based on electrochemical structural morphology phase change under working conditions, characterized by: The following steps are involved: Step 1: Collect the user's actual driving conditions, fit the discharge conditions, and directly convert the discharge conditions into actual vehicle mileage; Step 2: Establish an electrochemical cycle life model based on the electrochemical structural phase change research of lithium batteries; Step 3: According to the discharge operating condition in step 1, the electrochemical cycle life model in step 2 is modified to obtain a cycle life prediction model based on the operating condition; Step 4: Extract the battery structure morphology parameters under the working conditions, further modify the cycle life prediction model based on the working conditions in step 3, and obtain the final cycle life prediction model.

2. The cycle life prediction method based on electrochemical structural morphology phase change under working conditions according to claim 1 is characterized by: In step 1, the method of collecting the user's actual driving conditions includes: Select target cities based on environmental climate, terrain characteristics, and traffic characteristics; Screen out a certain number of pure electric vehicles in each typical city; Big data collection of driving data throughout the year; Decompose the collected actual driving conditions at specific time intervals to obtain a number of vehicle speed points including initial velocity and acceleration; Count the proportions of vehicle speed points and add weights accordingly to form a vehicle speed condition; Perform preliminary cleaning on the weighted reorganized operating condition data, and select the operating condition data within a specific vehicle speed range and acceleration range; Through cyclic processing, a working condition that is basically consistent with the user's actual daily driving time is obtained.

3. The cycle life prediction method based on electrochemical structural morphology phase change under working conditions according to claim 1 is characterized by: In step 2, the method of establishing the electrochemical cycle life model includes: The electrochemical cycle life model was initially established based on the classical cycle life model; The model parameters were determined using the least squares fitting method.

4. The cycle life prediction method based on electrochemical structural morphology phase change under working conditions according to claim 1 is characterized in that: In step 3, the method of correcting the electrochemical cycle life model includes: The cumulative discharge capacity at different discharge rates, i.e., at variable current, is equivalent to the equivalent cumulative discharge capacity at unit rate; The electrochemical cycle life model is modified according to the equivalent cumulative discharge capacity.

5. The cycle life prediction method based on electrochemical structural morphology phase change under working conditions according to claim 1 is characterized in that: In step 4, the method of extracting the battery structure morphology parameters under the working condition includes: Analyze the tomographic structure morphology of lithium-ion batteries under different cycle conditions; Obtain the battery structural morphology parameters representing the battery equivalent capacity attenuation rate.

6. The cycle life prediction method based on electrochemical structural morphology phase change under working conditions according to claim 5 is characterized by: The battery structure morphology parameters include: Change in the active material area ratio of the longitudinal section ΔS; Cell thickness change Δh.

7. The cycle life prediction method based on electrochemical structural morphology phase change under working conditions according to claim 1 is characterized by: In step 4, the method of further correcting the cycle life prediction model based on the working condition includes: According to the corresponding chromatographic structure morphology and characteristics of the battery components, the relationship between the battery electrochemical performance and chromatographic structure morphology is deduced; The change in the active material area ratio of the longitudinal section ΔS and the change in the cell thickness Δh are used as quantitative indicators to obtain the equivalent capacity attenuation rate X loss ; Using the formula Q loss =Q loss电 +X loss Modified lithium battery electrochemical cycle life model, where Q loss is the capacity decay rate of the battery, Q loss电 It is the capacity attenuation rate obtained from the electrical performance cycle life model.

8. The cycle life prediction method based on electrochemical structural morphology phase change under working conditions according to claim 7 is characterized by: The equivalent capacity attenuation rate X loss It is expressed as: X loss＝ λQ loss电 。 9. The cycle life prediction method based on electrochemical structural morphology phase change under working conditions according to claim 8 is characterized by: The battery structure morphology characteristic coefficient λ is calculated by the following formula: λ=k1·ΔS+k2·Δh Among them, k1 and k2 are constants for univariate linear fitting of λ.

10. The cycle life prediction method based on electrochemical structural morphology phase change under working conditions according to claim 1 is characterized by: The final cycle life prediction model is expressed as: Among them, α, β, z, K1, 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 driving time, n (k) is the discharge rate of the driving condition, T and T (k) are the temperatures in the driving condition, Δt(k) is the time period that divides the driving condition into i time intervals, k is the discrete form of each time segment, and R is the gas constant, which is 8.3145 J·mol -1 ·K -1 .

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