Battery degradation analysis method, device, equipment and storage medium

Abstract

The present invention relates to the technical field of lithium-ion batteries, and discloses a battery degradation analysis method, device, equipment and storage medium. The method is used to calculate the aging coefficient of the battery under the current use state by establishing a battery aging model. The charging impedance model and the discharging impedance model are combined with the actual charge and discharge cycle period to calculate the charging impedance and the discharging impedance of the battery under the current state. Using the degradation parameter, the first cycle period and the natural logarithm, the expected capacity attenuation of the battery after a certain cycle, that is, the battery capacity after degradation, is deduced. Finally, the obtained battery aging coefficient, charging impedance, discharging impedance and battery capacity after degradation are integrated and analyzed to obtain the overall degradation analysis result of the battery. It realizes accurate simulation and prediction of the performance changes of the battery under different use conditions, providing a basis for scientific maintenance recommendations and service life estimation.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium-ion batteries, and in particular to a battery degradation analysis method, device, equipment and storage medium. Background Art

[0002] In recent years, the performance estimation of lithium-ion batteries has mainly revolved around SOH and RUL. SOH diagnosis and RUL prediction are hot spots and difficulties in lithium-ion battery research, and a large number of literatures have carried out related research. According to the different means of implementing the performance estimation method of lithium-ion batteries, the performance estimation methods of lithium-ion batteries can be divided into two categories: experience-based methods and performance-based methods. The experience-based method mainly uses the empirical knowledge of the degradation process of lithium-ion batteries to give a rough estimate of battery performance (such as cycle life, battery capacity, open circuit voltage and internal resistance, etc.). This method can be further subdivided into the cycle number method, the ampere-hour method and the event-oriented aging accumulation method. The cycle number method sets different failure thresholds according to different application scenarios of lithium-ion batteries. When the number of cycles of lithium-ion batteries reaches the set failure threshold, the battery is considered to be failed. The ampere-hour method is to count the total ampere-hours of each charge and discharge of the battery since it leaves the factory. When the total ampere-hours reaches a given threshold, the battery is considered to be failed. Considering the different working conditions, the total ampere-hours can be multiplied by a weighting parameter. The event-oriented aging accumulation method mainly sets various events that may cause battery performance degradation, and sets an attenuation value for each type of event. The final battery life is the normal service life minus the attenuation value of various events. The experience-based method estimates the battery performance quickly, but due to the complex and changeable working conditions of the actual battery, the internal performance of the battery varies greatly, the battery performance prediction error is large, and the robustness is poor. Performance-based lithium-ion battery performance estimation methods include model-based methods. Model-based methods mainly realize SOH diagnosis and RUL prediction based on the positive and negative electrode materials of the battery, the load carried, and the battery degradation mechanism. Model-based methods include degradation mechanism models, equivalent circuit models, and experience-based models. However, these three types of methods face problems such as poor robustness, poor description of the comprehensive characteristics of the battery by the model, and how to optimize the ORPF of battery performance estimation.

[0003] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of the present invention is to provide a battery degradation analysis method, device, equipment and storage medium, aiming to solve the technical problems of large battery performance prediction error and poor robustness in the prior art.

[0005] To achieve the above object, the present invention provides a battery degradation analysis method, which comprises the following steps:

[0006] Acquire an aging model, wherein the aging model includes a correspondence between an aging parameter and a charge-discharge cycle period and an aging coefficient;

[0007] According to the aging model, a battery aging coefficient is obtained;

[0008] Obtaining a charging impedance model and a discharging impedance model, wherein the charging impedance model includes a corresponding relationship between the charging impedance and the charging cycle period and the charging coefficient, and the discharging impedance model includes a corresponding relationship between the discharging impedance and the discharging cycle period and the discharging coefficient, wherein the charging impedance is the sum of the charge transfer resistance and the electrolyte resistance during charging, and the discharging impedance is the sum of the charge transfer resistance and the electrolyte resistance during discharging;

[0009] According to the charging impedance model, the battery charging impedance is obtained;

[0010] According to the discharge impedance model, the battery discharge impedance is obtained;

[0011] According to the degradation parameter, the first cycle period and the natural logarithm, the battery capacity after degradation is obtained;

[0012] A battery degradation analysis result is obtained according to the battery aging coefficient, the battery charging impedance, the battery discharging impedance and the battery capacity after degradation.

[0013] Optionally, it also includes:

[0014] Determining whether an actual value corresponding to the charge-discharge cycle exists;

[0015] When the actual value corresponding to the charge-discharge cycle period exists, the battery aging coefficient is obtained according to the actual value corresponding to the charge-discharge cycle period;

[0016] When the actual value corresponding to the charge and discharge cycle period does not exist, the battery aging coefficient is obtained according to the aging model and the charge and discharge cycle period.

[0017] Optionally, it also includes:

[0018] Determining whether an actual value corresponding to the charging cycle exists;

[0019] When the actual value corresponding to the charging cycle period exists, obtaining the battery charging impedance according to the actual value corresponding to the charging cycle period;

[0020] When an actual value corresponding to the charging cycle period does not exist, the battery charging impedance is obtained according to the charging impedance model and the charging cycle period.

[0021] Optionally, it also includes:

[0022] Determining whether an actual value corresponding to the discharge cycle exists;

[0023] When the actual value corresponding to the discharge cycle period exists, obtaining the battery discharge impedance according to the actual value corresponding to the discharge cycle period;

[0024] When the actual value corresponding to the discharge cycle period does not exist, the battery discharge impedance is obtained according to the discharge impedance model and the discharge cycle period.

[0025] Optionally, obtaining the battery capacity after degradation according to the degradation parameter, the first cycle period and the natural logarithm includes:

[0026] Obtaining a degraded battery capacity model according to the second cycle period, the degradation parameter, the product of the degradation parameter and the second cycle period, and the natural logarithm;

[0027] Obtaining a preset degraded battery capacity model according to the degraded battery capacity model and a quotient of the degradation parameter and the second cycle period;

[0028] Obtaining a target degraded battery capacity model according to the preset degraded battery capacity model, the second cycle period, the degradation parameter and the natural logarithm;

[0029] The degradation parameter, the first cycle period, and the natural logarithm are input into the target degraded battery capacity model to obtain the degraded battery capacity.

[0030] Optionally, obtaining a battery degradation analysis result according to the battery aging coefficient, the battery charging impedance, the battery discharging impedance and the degraded battery capacity includes:

[0031] The aging coefficient, the battery charging impedance, the battery discharging impedance and the battery capacity after degradation are cleaned, outliers and missing values ​​are removed and normalized to obtain processed data;

[0032] The processed data is input into a battery degradation prediction model to obtain the battery degradation analysis result, wherein the battery degradation prediction model includes a corresponding relationship between the battery degradation data and the aging coefficient, the battery charging impedance, the battery discharging impedance and the battery capacity after degradation.

[0033] Optionally, before inputting the processed data into a battery degradation prediction model to obtain the battery degradation analysis result, the method further includes:

[0034] According to the nonlinear function, a preset battery degradation prediction model is obtained;

[0035] According to the numerical optimization algorithm, the best estimated values ​​of the unknown parameters are obtained;

[0036] Substituting the best estimated value of the unknown parameter into the nonlinear function to obtain the fitting value at each observation point;

[0037] According to the fitted values ​​and the actual observed values, the coefficient of determination, the mean square error and the mean absolute error are obtained;

[0038] The battery degradation prediction model is obtained according to the determination coefficient, the mean square error and the mean absolute error.

[0039] In addition, to achieve the above object, the present invention also provides a battery degradation analysis device, the battery degradation analysis device comprising:

[0040] An acquisition module, used for acquiring an aging model, wherein the aging model includes a correspondence between an aging parameter and a charge-discharge cycle period and an aging coefficient;

[0041] A calculation module, used for obtaining a battery aging coefficient according to the aging model;

[0042] The acquisition module is further used to acquire a charging impedance model and a discharging impedance model, wherein the charging impedance model includes a corresponding relationship between the charging impedance and the charging cycle period and the charging coefficient, and the discharging impedance model includes a corresponding relationship between the discharging impedance and the discharging cycle period and the discharging coefficient, wherein the charging impedance is the sum of the charge transfer resistance and the electrolyte resistance during charging, and the discharging impedance is the sum of the charge transfer resistance and the electrolyte resistance during discharging;

[0043] The calculation module is further used to obtain the battery charging impedance according to the charging impedance model;

[0044] The calculation module is further used to obtain the battery discharge impedance according to the discharge impedance model;

[0045] The calculation module is further used to obtain the battery capacity after degradation according to the degradation parameter, the first cycle period and the natural logarithm;

[0046] The calculation module is further used to obtain a battery degradation analysis result according to the battery aging coefficient, the battery charging impedance, the battery discharging impedance and the degraded battery capacity.

[0047] In addition, to achieve the above-mentioned purpose, the present invention also proposes a battery degradation analysis device, which includes: a memory, a processor, and a battery degradation analysis program stored in the memory and executable on the processor, wherein the battery degradation analysis program is configured to implement the steps of the battery degradation analysis method described above.

[0048] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, on which a battery degradation analysis program is stored, and when the battery degradation analysis program is executed by a processor, the steps of the battery degradation analysis method described above are implemented.

[0049] The present invention proposes a battery degradation analysis method, device, equipment and storage medium, the method comprising: obtaining an aging model, the aging model comprising a corresponding relationship between an aging parameter and a charge-discharge cycle and an aging coefficient; obtaining a battery aging coefficient according to the aging model; obtaining a charging impedance model and a discharging impedance model, the charging impedance model comprising a corresponding relationship between charging impedance and a charge cycle and a charging coefficient, the discharging impedance model comprising a corresponding relationship between discharging impedance and a discharge cycle and a discharge coefficient, wherein the charging impedance is the sum of the charge transfer resistance and the electrolyte resistance during charging, and the discharging impedance is the sum of the charge transfer resistance and the electrolyte resistance during discharging; obtaining a battery charging impedance according to the charging impedance model; obtaining a battery discharging impedance according to the discharging impedance model; obtaining a battery capacity after degradation according to the degradation parameter, the first cycle and the natural logarithm; obtaining a battery degradation analysis result according to the battery aging coefficient, the battery charging impedance, the battery discharging impedance and the battery capacity after degradation. The aging coefficient is calculated by the battery aging model, and the charging impedance model and the discharging impedance model are combined with the actual charge-discharge cycle to calculate the charging impedance and the discharging impedance of the battery in the current state. Using degradation parameters, the first cycle period, and natural logarithm, the expected capacity attenuation of the battery after a certain number of cycles, i.e., the battery capacity after degradation, is calculated. Finally, the obtained battery aging coefficient, charging impedance, discharging impedance, and battery capacity after degradation are integrated and analyzed to obtain the overall degradation analysis results of the battery. This achieves accurate simulation and prediction of battery performance changes under different usage conditions, providing a basis for scientific maintenance recommendations and service life estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic diagram of the structure of a battery degradation analysis device in a hardware operating environment involved in an embodiment of the present invention;

[0051] Figure 2 It is a flow chart of the first embodiment of the battery degradation analysis method of the present invention;

[0052] Figure 3 A curve diagram of a battery complete charging process in the first embodiment of the battery degradation analysis method of the present invention;

[0053] Figure 4 It is a charging current curve diagram of the B5 battery in the first embodiment of the battery degradation analysis method of the present invention;

[0054] Figure 5It is a charging current curve diagram of a B6 battery in the first embodiment of the battery degradation analysis method of the present invention;

[0055] Figure 6 It is a charging current curve diagram of the B7 battery in the first embodiment of the battery degradation analysis method of the present invention;

[0056] Figure 7 It is a charging current curve diagram of a B18 battery in the first embodiment of the battery degradation analysis method of the present invention;

[0057] Figure 8 Schematic diagram of a resistance impedance equivalent circuit in the first embodiment of the battery degradation analysis method of the present invention;

[0058] Fig. 9 is a curve diagram of impedance variation in the first embodiment of the battery degradation analysis method of the present invention;

[0059] Fig.10 It is a capacity degradation curve diagram of a lithium-ion battery at different cycles in the first embodiment of the battery degradation analysis method of the present invention;

[0060] Fig.11 It is a flow chart of the second embodiment of the battery degradation analysis method of the present invention;

[0061] Fig.12 It is a flow chart of the third embodiment of the battery degradation analysis method of the present invention;

[0062] Fig.13 It is a structural block diagram of the battery degradation analysis device of the present invention.

[0063] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0064] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0065] Reference Figure 1 , Figure 1 The figure is a schematic diagram of the structure of a battery degradation analysis device in the hardware operating environment involved in the embodiment of the present invention.

[0066] like Figure 1As shown, the battery degradation analysis device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (Wireless-Fidelity, Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM), or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk storage. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0067] Those skilled in the art will understand that Figure 1 The structure shown in does not constitute a limitation on the battery degradation analysis device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0068] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a battery degradation analysis program.

[0069] exist Figure 1 In the battery degradation analysis device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the battery degradation analysis device of the present invention can be set in the battery degradation analysis device, and the battery degradation analysis device calls the battery degradation analysis program stored in the memory 1005 through the processor 1001, and executes the battery degradation analysis method provided by the embodiment of the present invention.

[0070] The embodiment of the present invention provides a battery degradation analysis method, referring to Figure 2 , Figure 2 FIG. 1 is a flow chart of a first embodiment of a battery degradation analysis method according to the present invention.

[0071] In this embodiment, the battery degradation analysis method includes the following steps:

[0072] Step S10: obtaining an aging model, wherein the aging model includes a correspondence between an aging parameter and a charge-discharge cycle period and an aging coefficient;

[0073] It should be noted that the executor of the method of this embodiment can be a device with sufficient computing power, capable of running corresponding algorithms and storing relevant data, including but not limited to the following devices: embedded systems, laboratory testing equipment, data center servers, personal computers or workstations, mobile devices and host computers. The present embodiment and the following embodiments are described below using the host computer as an example.

[0074] It is understandable that the host computer can obtain the battery aging model in a variety of ways. The aging model is constructed based on the performance decay data of the battery under different charge and discharge cycles, and includes the relationship between the aging parameters, the number of charge and discharge cycles and the corresponding aging coefficient.

[0075] The specific steps to obtain the aging model may be as follows:

[0076] Experimental acquisition: Through a large number of actual battery charge and discharge cycle tests in the laboratory, various performance indicators of the battery under different cycle numbers, such as capacity retention rate, internal resistance change, etc., are collected, and an aging model is fitted based on these measured data.

[0077] Software import: The aging model algorithm developed and verified by the battery manufacturer or research institution is provided to the host computer in the form of software for import and use. The model usually exists in the form of mathematical functions or data tables.

[0078] Online update: If the host computer is connected to a cloud database or advanced battery management system (BMS), the latest aging model data can be obtained from the server in real time to facilitate accurate status assessment and prediction based on the actual working conditions of the current battery.

[0079] Self-learning and optimization: For a host computer with AI functions, it can learn and analyze the battery operation data it monitors, gradually optimize and self-update the aging model to adapt to a wider range of battery types and usage environments.

[0080] Step S20: Obtaining a battery aging coefficient according to the aging model;

[0081] It should be noted that this embodiment selects four lithium-ion batteries (B5, B6, B7 and B18) from NASA's (PCOE) battery data set, and each battery data set has three different parameter sets at 24 degrees Celsius. The charging process of the four groups of batteries undergoes constant current (CC) mode and constant voltage (CV) mode in turn. The battery CC mode means that the battery is charged to 4.2V at a constant current of 1.5A, and then the CV mode is adopted. The CV mode means that after the battery is charged to a voltage of 4.2V, it is charged at a constant voltage of 4.2V until the charging current drops to 20mA. The discharge process only undergoes the CC mode, that is, the battery refers to the battery being discharged to a cut-off voltage at a constant current of 2A, such as Figure 3 As shown, Figure 3 This is a complete battery charging process curve diagram in the first embodiment of the battery degradation analysis method of the present invention, which shows the change process of the battery charging and discharging terminal voltage and current in one cycle. When performing subsequent RUL prediction, the battery capacity is normalized according to the following formula:

[0082]

[0083] In reality, every time a user uses a battery, the current is sometimes fully discharged to the cutoff voltage, but more often it is discharged to a non-cutoff voltage, at which time there is still some charge inside the battery. If charging is performed on this basis, the battery charging starting voltage varies greatly, and it is extremely difficult to find the battery degradation characteristics from the charging curve in the battery CC mode. However, users usually charge the battery fully, so the CV mode curve parameters in each charging process can be well preserved, and it is possible to extract battery degradation information from them. This embodiment organizes the charging CV data of the four battery groups B5, B6, B7 and B18 in three stages with cycle numbers of 1, 80 and 160, and obtains the current curve in the charging CV mode, such as Figure 4 As shown, Figure 4 This is a charging current curve diagram of the B5 battery in the first embodiment of the battery degradation analysis method of the present invention, as shown in FIG. Figure 5 As shown, Figure 5 FIG. 1 is a charging current curve diagram of a B6 battery in the first embodiment of the battery degradation analysis method of the present invention. Figure 6 As shown, Figure 6 FIG. 1 is a charging current curve diagram of the B7 battery in the first embodiment of the battery degradation analysis method of the present invention, as shown in FIG. Figure 7 As shown, Figure 7 This is a charging current curve diagram of a B18 battery in the first embodiment of the battery degradation analysis method of the present invention. Figure 4 , Figure 5 , Figure 6 , Figure 7The constant voltage charging current curves of the four lithium-ion battery groups B5, B6, B7 and B18 in different cycle periods are shown respectively. It can be seen from the figure that:

[0084] (1) During any cycle of charging of the four groups of batteries, the current in the CV charging mode decreases exponentially. The current drops sharply in the early stage, the current decrease rate begins to slow down in the middle stage, and the current becomes stable in the later stage until it reaches 20mA.

[0085] (2) For any group of batteries, the three different stages with cycle numbers of 1, 80 and 160 are very close in the early and late stages, but there are regular differences in the middle stage, that is, as the cycle number increases, the duration of the current decline in the middle stage begins to increase.

[0086] Generally speaking, this is because as the battery cycle increases, the performance of the positive and negative electrodes of the battery declines, the electrolyte is lost, and the diaphragm ages, resulting in an increase in the internal resistance of the battery and a decrease in charging efficiency during the constant voltage 4.2V charging process. Therefore, an exponential function can be considered to simulate the charging current drop law in CV mode:

[0087] I(t)=Bexp(-At)+C;

[0088] Wherein, t is the sampling time (t=0 corresponds to the start time of the CV mode), I is the charging current, and A, B, and C are model parameters.

[0089] Parameter A varies with the cycle period, and its variation can be fitted by a quadratic polynomial (i.e., aging model):

[0090] A(k)=a 0 k 2 +b 0 k+c 0 ;

[0091] In the formula, a 0 , b 0 , c 0 is the model parameter, k is the cycle period. The actual value at time k When it exists, A(k) (i.e., battery aging coefficient) is equal to If the actual value at time k When it does not exist, A(k) (i.e., the battery aging coefficient) is calculated using the above quadratic polynomial (i.e., the aging model).

[0092] Step S30: obtaining a charging impedance model and a discharging impedance model, wherein the charging impedance model includes a corresponding relationship between the charging impedance and the charging cycle period and the charging coefficient, and the discharging impedance model includes a corresponding relationship between the discharging impedance and the discharging cycle period and the discharging coefficient, wherein the charging impedance is the sum of the charge transfer resistance and the electrolyte resistance during charging, and the discharging impedance is the sum of the charge transfer resistance and the electrolyte resistance during discharging;

[0093] It should be noted that if Figure 8 As shown, Figure 8 Schematic diagram of the resistance impedance equivalent circuit in the first embodiment of the battery degradation analysis method of the present invention. The lithium-ion battery equivalent impedance circuit mainly includes the charge transfer resistor R ct , electrolyte resistance R e and Warburg impedance R w , where the Warburg impedance R w The effect on battery degradation process is minimal. Theoretically, the charge transfer resistance R ct and electrolyte resistance R e There is a high correlation between battery capacity and cycle period. In order to analyze the change law of battery impedance, this embodiment sorts out the battery impedance of the charging and discharging process of the four battery groups B5, B6, B7 and B18 in all cycle periods to obtain battery impedance change data, such as Fig. 9 As shown, Fig. 9 This is a curve diagram of impedance change in the first embodiment of the battery degradation analysis method of the present invention. It can be seen from the data set that:

[0094] (1) Regardless of charging or discharging mode, the charge transfer resistance R of the four battery groups ct , electrolyte resistance R e Both increase with the increase of cycle period.

[0095] (2) Battery charge transfer resistance R measured in charge mode and discharge mode in the same cycle ct The values ​​are obviously different, the electrolyte resistance R e There are also clear differences.

[0096] (3) Battery charge transfer resistance R ct The overall increase, but there are obvious fluctuations, the same electrolyte resistance R e The same problem exists. This is because no matter the charge transfer resistance R ct Or the electrolyte resistance R e The value of is very small, and the noise in the measurement process brings a large error to the measurement.

[0097] In general, the experimental results show that the battery impedance shows an overall increasing trend as the cycle period increases. Considering the noise problem in the measurement process, this embodiment finds that the charge transfer resistance R ct and electrolyte resistance R e The overall increasing trend of the sum of the charging impedance R is more stable. In some cycles, there are two missing impedance values. ct +R e The sum of the discharge impedance R ct +R e Modeled as the following quadratic polynomial: R ct +R e This is modeled by the following quadratic polynomial:

[0098] R c (k) = a 1 k 2 +b 1 k+c 1 ;

[0099] R d (k) = a 2 k 2 +b 2 k+c 2 ;

[0100] In the formula, R c (k) and R d (k) are the battery charging impedance (R ct +R e ) and battery discharge impedance (R ct +R e ), a 1 、b 1 、c 1 and a 2 、b 2 、c 2 They are R c (k) and R d (k) model parameters (i.e., charging coefficient and discharging coefficient). The actual value at the time of cycle k and If it exists, then R c (k) and R d (k) are equal to the actual values and If the actual value and If it does not exist, it is calculated using the above two polynomials.

[0101] Step S40: Obtaining the battery charging impedance according to the charging impedance model;

[0102] In the specific implementation, according to the above R c (k) = a 1 k 2 +b 1 k+c 1 , and calculate the battery charging impedance.

[0103] Step S50: Obtaining the battery discharge impedance according to the discharge impedance model;

[0104] In the specific implementation, according to the above R d (k) = a 2 k 2 +b 2 k+c 2 , the battery discharge impedance is calculated.

[0105] Step S60: Obtaining the battery capacity after degradation according to the degradation parameter, the first cycle period and the natural logarithm;

[0106] It should be noted that, as the battery cycle increases, the performance of the positive and negative electrodes of the battery declines, the electrolyte is lost, and the diaphragm ages. In theory, the battery capacity will gradually decrease, while the battery impedance value will gradually increase. In order to verify this conclusion, this example sorted out the battery capacities of the four battery groups B5, B6, B7 and B18 under all cycle cycles to obtain the battery capacity degradation curve, as shown in Figure 1. Fig.10 As shown, Fig.10 This is a graph showing the capacity degradation of a lithium-ion battery at different cycles in the first embodiment of the battery degradation analysis method of the present invention. It can be seen from the graph that:

[0107] As the cycle increases, the capacity of the four battery groups shows an overall downward trend. Taking the B5 battery as an example, the battery capacity drops from 1.8565Ah to 1.3251Ah from the 1st cycle to the 168th cycle.

[0108] As the number of cycles increases, the battery capacity sometimes rebounds locally. Taking the B6 battery as an example, the battery capacity decreases regularly from the 78th to the 89th cycle, dropping to 1.4417Ah, but rebounds to 1.5936Ah in the 90th cycle, with a capacity increase of 0.14Ah.

[0109] But in general, as the cycle period increases, the battery capacity shows a regular decrease.

[0110] For 18650 lithium-ion batteries, Bloom et al. conducted accelerated aging experiments on the batteries and proposed a battery capacity degradation model based on the changes in battery temperature T, SOC and Δ% SOC:

[0111]

[0112] In the formula, Q is the battery capacity after degradation, t is the number of weeks the battery is used, and B 0 is the pre-exponential coefficient cycle period, E a is the battery energy, and z is the correction parameter. This model does not give the relationship between the cycle period and capacity. According to the internal electrochemical mechanism of the battery, through a large number of experimental analyses, an empirical model based on battery capacity degradation is proposed:

[0113] Q(k+1)=ηQ(k)+β 1 exp(-β 2 / Δt k );

[0114] Where Q(k+1) is the battery charging capacity of the kth cycle, η represents the percentage of the charge released during battery discharge to the charge charged during battery charging, and Δt k is the interval time from the kth to k+1th cycle, β 1 and β 2 are the model parameters that need to be determined.

[0115] Foreign literature proposed a simplified practical dual-exponential battery degradation model:

[0116] Q=ae b*k +ce d*k ;

[0117] In the formula, Q is the battery capacity after degradation, and k is the cycle period. a, b, c, d are model parameters. All four parameters contain noise and change slightly with the battery cycle. Considering the rapid capacity decay phenomenon in the late stage of lithium-ion battery degradation, the above model is improved by ae b*k Partially corrected to ae b / k , the following modified model is proposed:

[0118] Q=ae b / k +ce d*k ;

[0119] The model is further modified as follows:

[0120]

[0121] According to the degradation parameters a, b, c, d, the first cycle k, through the above model The degraded battery capacity Q is calculated.

[0122] Step S70: obtaining a battery degradation analysis result according to the battery aging coefficient, the battery charging impedance, the battery discharging impedance and the degraded battery capacity.

[0123] It should be noted that the aging coefficient reflects the overall health status and performance degradation of the battery after a certain number of charge and discharge cycles. This coefficient can be used to understand the remaining effective working capacity of the battery in its life cycle.

[0124] Battery charging impedance: As the battery ages, its internal charge transfer resistance and electrolyte resistance increase, resulting in reduced charging efficiency and increased energy loss. Analyzing changes in charging impedance helps assess the battery's charging performance loss.

[0125] Battery discharge impedance: Similar to charging impedance, the increase in discharge impedance means that the energy conversion efficiency of the battery decreases during the discharge process, which directly affects the actual output power and endurance of the battery. Monitoring the change in discharge impedance is crucial to understanding the performance degradation of the battery during the discharge phase.

[0126] Battery capacity after degradation: This is a core indicator that directly measures the decline in battery energy storage capacity, that is, the percentage of actual available energy of the battery after completing a certain number of cycles compared to the initial capacity.

[0127] Combining the above four factors, it is possible to comprehensively judge the overall degradation level of the battery in its current state and predict possible problems in the future. For example, it is possible to evaluate whether the battery is approaching the end of its design life and whether it needs to be replaced in advance, or optimize the battery usage strategy and maintenance plan based on these parameters to extend the battery life and ensure safe and stable operation.

[0128] Furthermore, it also includes: judging whether an actual value corresponding to the charge and discharge cycle exists; when the actual value corresponding to the charge and discharge cycle exists, obtaining the battery aging coefficient according to the actual value corresponding to the charge and discharge cycle; when the actual value corresponding to the charge and discharge cycle does not exist, obtaining the battery aging coefficient according to the aging model and the charge and discharge cycle.

[0129] It should be understood that when performing battery aging analysis, it is first determined whether there are actually observed data points in the aging model for the charge and discharge cycle experienced by the current battery. The specific steps are as follows:

[0130] Check the actual value: Check the historical records or real-time monitoring data to confirm whether there is a clear actual observed value for the current charge and discharge cycle of the battery. If there is an actual value, directly use this actual observed aging parameter (i.e., the aging coefficient corresponding to the charge and discharge cycle) to evaluate the aging degree of the battery.

[0131] Prediction and estimation: When the actual value corresponding to the charge and discharge cycle does not exist, it means that there may not be actual measurements or records for this specific number of cycles. At this time, it is necessary to use the existing aging model to make an estimate. The aging model is built based on a large amount of experimental data. According to the mathematical relationship between the charge and discharge cycle and the aging coefficient, the corresponding battery aging coefficient under the charge and discharge cycle can be calculated through interpolation, fitting or other prediction methods.

[0132] Furthermore, it also includes: judging whether an actual value corresponding to the charging cycle exists; when the actual value corresponding to the charging cycle exists, obtaining the battery charging impedance according to the actual value corresponding to the charging cycle; when the actual value corresponding to the charging cycle does not exist, obtaining the battery charging impedance according to the charging impedance model and the charging cycle.

[0133] It is understandable that when analyzing the battery charging impedance, it is first determined whether there are actually observed data points in the charging cycle that the current battery is experiencing:

[0134] When the actual value exists: If the actual charging impedance value of the battery in the charging cycle is known and recorded, the actual measured data is directly used as the charging impedance of the current battery in the charging cycle.

[0135] When the actual value does not exist: When the actual charging impedance value corresponding to the charging cycle is not recorded or cannot be obtained, it is necessary to use a pre-established charging impedance model for calculation. The charging impedance model is built based on a large amount of experimental data and can describe the relationship between charging impedance and charging cycle. Therefore, when a specific charging cycle parameter is given, the corresponding battery charging impedance value under the charging cycle can be estimated by interpolation, fitting or other prediction methods based on the charging impedance model.

[0136] This processing method ensures that the charging impedance changes of the battery at different charging cycle stages can be accurately evaluated and predicted regardless of whether real-time monitoring data is available, thereby providing a comprehensive and precise analysis of the battery health status.

[0137] Furthermore, it also includes: judging whether an actual value corresponding to the discharge cycle period exists; when the actual value corresponding to the discharge cycle period exists, obtaining the battery discharge impedance according to the actual value corresponding to the discharge cycle period; when the actual value corresponding to the discharge cycle period does not exist, obtaining the battery discharge impedance according to the discharge impedance model and the discharge cycle period.

[0138] It is understandable that when analyzing the battery discharge impedance, first confirm whether there are actually observed data points in the discharge cycle that the current battery has experienced:

[0139] In the case where an actual value exists: if the actual discharge impedance value of the battery in the specific discharge cycle has been recorded, the measured data is directly used as the discharge impedance of the battery in the current discharge cycle.

[0140] When there is no actual value: When the actual discharge impedance value corresponding to the discharge cycle is not obtained, it is necessary to use a pre-built discharge impedance model for calculation. This model is based on a large amount of experimental data and can depict the relationship between the discharge impedance and the discharge cycle. Therefore, when a specific discharge cycle parameter is given, the discharge impedance value corresponding to the battery under the discharge cycle can be estimated by interpolation, fitting or other prediction techniques based on the discharge impedance model.

[0141] In this embodiment, an aging model of the battery is obtained, which describes the relationship between the aging parameters and the charge / discharge cycle and the corresponding aging coefficient. A charging impedance model and a discharge impedance model are obtained, which are respectively associated with the charge / discharge cycle, the corresponding charge / discharge coefficient, and the sum of the charge transfer resistance and the electrolyte resistance during the charge / discharge process. The aging coefficient of the battery is determined according to the actual recorded charge / discharge cycle data (if any) or through the aging model. For the charging impedance, if there is an actual value of the charging cycle, it is used directly; otherwise, it is calculated according to the charging impedance model. Similarly, for the discharge impedance, if there is a measured value for the discharge cycle, the measured value is used; if there is no measured value, the discharge impedance model is used for calculation. The degradation parameter, the first cycle and the natural logarithm are used to calculate the battery capacity after degradation. The obtained aging coefficient, charging impedance, discharge impedance and battery capacity after degradation are combined to perform a degradation analysis of the battery, thereby obtaining a specific quantitative result of the degree of battery degradation. By establishing a detailed aging model and impedance model, it is possible to more accurately simulate and predict changes in battery performance over time and usage conditions, rather than relying solely on a single measurement point. Whether there is actual measurement data or not, the battery state parameters can be effectively estimated, which improves the applicability and flexibility of the analysis method. Combined with the impedance parameter, an important indicator reflecting the internal physical and chemical reactions, it helps to understand and evaluate the overall health status and remaining service life of the battery from multiple dimensions, improving the accuracy of the prediction.

[0142] refer to Fig.11 , Fig.11 FIG. 4 is a flow chart of a second embodiment of a battery degradation analysis method according to the present invention.

[0143] Based on the above first embodiment, in this embodiment, step S60 includes:

[0144] Step S601: obtaining a degraded battery capacity model according to a second cycle period, a degradation parameter, a product of the degradation parameter and the second cycle period, and the natural logarithm;

[0145] In the specific implementation, a degraded battery capacity model is proposed, as shown in the following formula:

[0146] Q=ae b*k +ce d*k ;

[0147] Where k is the second cycle period, and a, b, c, d are degradation parameters.

[0148] Step S602: obtaining a preset degraded battery capacity model according to the degraded battery capacity model and the quotient of the degradation parameter and the second cycle period;

[0149] In the specific implementation, the above formula is improved and ae b*k Partially corrected to ae b / k , a preset degradation battery capacity model is proposed, as shown in the following formula:

[0150] Q=ae b / k +ce d*k ;

[0151] Step S603: obtaining a target degraded battery capacity model according to the preset degraded battery capacity model, the second cycle period, the degradation parameter and the natural logarithm;

[0152] In the specific implementation, the preset degraded battery capacity model is further improved to obtain the target degraded battery capacity model, as shown in the following formula:

[0153]

[0154] Step S604: inputting the degradation parameter, the first cycle period and the natural logarithm into the target degraded battery capacity model to obtain the degraded battery capacity.

[0155] In a specific implementation, the degraded battery capacity is calculated based on the degradation parameter, the first cycle period, the natural logarithm, and the above-mentioned target degraded battery capacity model.

[0156] This embodiment constructs a model (degraded battery capacity model) that reflects the degradation of battery capacity with cycle period according to the second cycle period, degradation parameters and their product, combined with natural logarithm. On this basis, the initial degraded battery capacity model is adjusted by using the quotient of the degradation parameter and the second cycle period to generate a preset degraded battery capacity model, which may take into account the battery capacity change law under different aging stages or special working conditions. The target degraded battery capacity model is derived again according to the preset degraded battery capacity model, the second cycle period, degradation parameters and natural logarithm, which is a more accurate or more adaptable prediction model. The degradation parameters, the first cycle period and the natural logarithm actually used are input into the target degraded battery capacity model to obtain the expected degraded battery capacity. Through the above multi-step modeling process, the capacity attenuation of the battery under different cycle periods can be simulated and predicted more finely, and the prediction accuracy is improved. The model can be dynamically adjusted for different use stages and conditions (such as changes in the second cycle period), ensuring that the health status and remaining capacity of the battery can be accurately evaluated under various circumstances.

[0157] refer to Fig.12 , Fig.12 FIG. 4 is a flow chart of a third embodiment of a battery degradation analysis method according to the present invention.

[0158] Based on the above first embodiment, in this embodiment, step S70 includes:

[0159] Step S701: cleaning the aging coefficient, the battery charging impedance, the battery discharging impedance and the degraded battery capacity, removing abnormal values ​​and missing values, and normalizing them to obtain processed data;

[0160] It should be noted that in the process of battery performance analysis and modeling, preprocessing data such as aging coefficient, battery charging impedance, battery discharging impedance, and battery capacity after degradation is a crucial step, including:

[0161] Cleaning: Remove invalid or erroneous data entries, such as values ​​that deviate significantly from the normal range, which may be outliers caused by measurement errors or other reasons.

[0162] Missing value processing: For missing data points, you can adopt a filling strategy, such as using the mean, median, nearest neighbor interpolation, linear interpolation, etc. to fill them, or use appropriate statistical methods to predict missing values ​​based on the characteristics of the data distribution.

[0163] Normalization: In order to eliminate the impact of dimensional differences between different parameters, make various parameters comparable, and facilitate machine learning algorithm training, all parameters are converted to the same scale. Common normalization methods include min-max scaling, Z-score standardization, and maximum absolute value normalization.

[0164] Step S702: inputting the processed data into a battery degradation prediction model to obtain the battery degradation analysis result, wherein the battery degradation prediction model includes a correspondence between the battery degradation data and the aging coefficient, the battery charging impedance, the battery discharging impedance and the degraded battery capacity.

[0165] It is understandable that the cleaned, outlier-processed and normalized data (including aging coefficient, battery charging impedance, battery discharging impedance and battery capacity after degradation) is input into a pre-trained battery degradation prediction model, which is built based on a large amount of historical battery performance data. By learning the intrinsic correspondence between battery degradation data and these key parameters, the model can predict the future degradation trend of the battery.

[0166] Furthermore, before inputting the processed data into the battery degradation prediction model to obtain the battery degradation analysis result, it also includes: obtaining a preset battery degradation prediction model based on a nonlinear function; obtaining the best estimate of the unknown parameter based on a numerical optimization algorithm; substituting the best estimate of the unknown parameter into the nonlinear function to obtain the fitting value at each observation point; obtaining the determination coefficient, mean square error and mean absolute error based on the fitting value and the actual observation value; and obtaining the battery degradation prediction model based on the determination coefficient, the mean square error and the mean absolute error.

[0167] It should be noted that before obtaining the battery degradation analysis results, a series of model building and optimization steps are required:

[0168] Establish a preset battery degradation prediction model: First, according to the characteristics of the battery performance data, select a suitable nonlinear function to describe the relationship between the aging coefficient, battery charging impedance, battery discharging impedance, and the battery capacity after degradation and battery degradation, so as to construct a preliminary battery degradation prediction model.

[0169] Numerical optimization to solve unknown parameters: Using numerical optimization algorithms (such as gradient descent, genetic algorithm, particle swarm optimization, etc.), by continuously adjusting the unknown parameters in the model, find the best parameter estimates that make the model fit the actual observed data to ensure that the model has the best explanatory power for historical data.

[0170] Fitting verification after model parameter estimation: Substitute the optimal parameters into the nonlinear function, calculate the theoretical fitting value at each known observation point, and compare it with the actual observation value.

[0171] Evaluate the goodness of fit of the model: Calculate the coefficient of determination (R 2 ), mean square error (MSE) and mean absolute error (MAE). The coefficient of determination measures the percentage of the variation in the model to explain the variable; the mean square error reflects the square average of the difference between the model's predicted value and the actual value; and the mean absolute error directly indicates the average deviation between the predicted value and the actual value.

[0172] Determine the final battery degradation prediction model: Based on the results of the above evaluation indicators, if the determination coefficient is high and the mean square error and mean absolute error are small, it means that the model can simulate the battery degradation process well, thus confirming that this model is an effective battery degradation prediction model. Input the processed actual data into this model to obtain accurate battery degradation analysis results.

[0173] In this embodiment, the original data such as the aging coefficient, battery charging impedance, battery discharging impedance and battery capacity after degradation are first cleaned to remove outliers and missing values, and then normalized to make each parameter comparable on the same scale, thereby obtaining processed data that can be used for model analysis. A suitable nonlinear function is selected to establish a preset battery degradation prediction model, and a numerical optimization algorithm, such as a gradient descent method, a genetic algorithm, etc., is used to solve the unknown parameters in the model to find the best estimate to ensure that the model can fit the actual observed data to the greatest extent. Substitute the obtained optimal parameters into the nonlinear function, calculate the theoretical fitting value at each observation point, and compare it with the actual observation value. Calculate the determination coefficient (R) for evaluating the quality of the model based on the fitting value and the actual observation value. 2 ), mean square error (MSE) and mean absolute error (MAE), these three indicators respectively reflect the quality of model fitting, the size of the difference between the predicted value and the true value, and the average level of deviation. The final battery degradation prediction model is determined according to the above evaluation indicators, that is, the validity and accuracy of the model are confirmed under the premise of ensuring a high determination coefficient, a small mean square error and a mean absolute error. The data preprocessing link helps to improve data quality, reduce noise interference, and ensure the reliability and stability of subsequent analysis results. The use of nonlinear functions to construct a model can more accurately capture the nonlinear change law of battery performance degradation, and improve the scope of application and prediction accuracy of the prediction model. The model performance is evaluated by the three dimensions of determination coefficient, mean square error and mean absolute error to ensure that the model can not only effectively explain historical data, but also make accurate predictions on future battery performance degradation trends.

[0174] Reference Fig.13 , Fig.13It is a structural block diagram of the battery degradation analysis device of the present invention.

[0175] like Fig.13 As shown, the battery degradation analysis device proposed in the embodiment of the present invention includes:

[0176] An acquisition module 131 is used to acquire an aging model, wherein the aging model includes a correspondence between an aging parameter and a charge-discharge cycle period and an aging coefficient;

[0177] A calculation module 132, used to obtain a battery aging coefficient according to the aging model;

[0178] The acquisition module 131 is further used to acquire a charging impedance model and a discharging impedance model, wherein the charging impedance model includes a corresponding relationship between the charging impedance and the charging cycle period and the charging coefficient, and the discharging impedance model includes a corresponding relationship between the discharging impedance and the discharging cycle period and the discharging coefficient, wherein the charging impedance is the sum of the charge transfer resistance and the electrolyte resistance during charging, and the discharging impedance is the sum of the charge transfer resistance and the electrolyte resistance during discharging;

[0179] The calculation module 132 is further used to obtain the battery charging impedance according to the charging impedance model;

[0180] The calculation module 132 is further used to obtain the battery discharge impedance according to the discharge impedance model;

[0181] The calculation module 132 is further used to obtain the battery capacity after degradation according to the degradation parameter, the first cycle period and the natural logarithm;

[0182] The calculation module 132 is further configured to obtain a battery degradation analysis result according to the battery aging coefficient, the battery charging impedance, the battery discharging impedance and the degraded battery capacity.

[0183] This embodiment calculates the aging coefficient through the battery aging model, combines the charging impedance model and the discharging impedance model with the actual charge and discharge cycle period, and calculates the charging impedance and discharging impedance of the battery in the current state. Using the degradation parameter, the first cycle period and the natural logarithm, the expected capacity decay of the battery after a certain cycle, that is, the battery capacity after degradation, is calculated. Finally, the obtained battery aging coefficient, charging impedance, discharge impedance and battery capacity after degradation are integrated and analyzed to obtain the overall degradation analysis result of the battery. It achieves accurate simulation and prediction of the performance changes of the battery under different usage conditions, providing a basis for scientific maintenance recommendations and service life estimation.

[0184] In one embodiment, the calculation module 132 is further used to determine whether an actual value corresponding to the charge-discharge cycle exists;

[0185] When the actual value corresponding to the charge-discharge cycle period exists, the battery aging coefficient is obtained according to the actual value corresponding to the charge-discharge cycle period;

[0186] When the actual value corresponding to the charge and discharge cycle period does not exist, the battery aging coefficient is obtained according to the aging model and the charge and discharge cycle period.

[0187] In one embodiment, the calculation module 132 is further used to determine whether an actual value corresponding to the charging cycle period exists;

[0188] When the actual value corresponding to the charging cycle period exists, obtaining the battery charging impedance according to the actual value corresponding to the charging cycle period;

[0189] When an actual value corresponding to the charging cycle period does not exist, the battery charging impedance is obtained according to the charging impedance model and the charging cycle period.

[0190] In one embodiment, the calculation module 132 is further used to determine whether an actual value corresponding to the discharge cycle period exists;

[0191] When the actual value corresponding to the discharge cycle period exists, obtaining the battery discharge impedance according to the actual value corresponding to the discharge cycle period;

[0192] When the actual value corresponding to the discharge cycle period does not exist, the battery discharge impedance is obtained according to the discharge impedance model and the discharge cycle period.

[0193] In one embodiment, the calculation module 132 is further used to obtain a degraded battery capacity model according to the second cycle period, the degradation parameter, the product of the degradation parameter and the second cycle period, and the natural logarithm;

[0194] Obtaining a preset degraded battery capacity model according to the degraded battery capacity model and the quotient of the degradation parameter and the second cycle period;

[0195] Obtaining a target degraded battery capacity model according to the preset degraded battery capacity model, the second cycle period, the degradation parameter and the natural logarithm;

[0196] The degradation parameter, the first cycle period, and the natural logarithm are input into the target degraded battery capacity model to obtain the degraded battery capacity.

[0197] In one embodiment, the calculation module 132 is further used to clean the aging coefficient, the battery charging impedance, the battery discharging impedance and the degraded battery capacity, remove abnormal values ​​and missing values, and perform normalization to obtain processed data;

[0198] The processed data is input into a battery degradation prediction model to obtain the battery degradation analysis result, wherein the battery degradation prediction model includes a corresponding relationship between the battery degradation data and the aging coefficient, the battery charging impedance, the battery discharging impedance and the battery capacity after degradation.

[0199] In one embodiment, the calculation module 132 is further used to obtain a preset battery degradation prediction model according to a nonlinear function;

[0200] According to the numerical optimization algorithm, the best estimated values ​​of the unknown parameters are obtained;

[0201] Substituting the best estimated value of the unknown parameter into the nonlinear function to obtain the fitting value at each observation point;

[0202] According to the fitted values ​​and the actual observed values, the coefficient of determination, the mean square error and the mean absolute error are obtained;

[0203] The battery degradation prediction model is obtained according to the determination coefficient, the mean square error and the mean absolute error.

[0204] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0205] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0206] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0207] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A battery degradation analysis method, characterized in that: The method comprises the following steps: Acquire an aging model, wherein the aging model includes a correspondence between aging parameters, charge-discharge cycles, and an aging coefficient; Obtaining a battery aging coefficient according to the aging model; Obtaining a charging impedance model and a discharging impedance model, wherein the charging impedance model includes a corresponding relationship between charging impedance, a charging cycle period, and a charging coefficient, and the discharging impedance model includes a corresponding relationship between discharging impedance, a discharging cycle period, and a discharging coefficient, wherein the charging impedance is the sum of the charge transfer resistance and the electrolyte resistance during charging, and the discharging impedance is the sum of the charge transfer resistance and the electrolyte resistance during discharging; According to the charging impedance model, the battery charging impedance is obtained; According to the discharge impedance model, the battery discharge impedance is obtained; Obtain the battery capacity after degradation according to the degradation parameter, the first cycle period and the natural logarithm; Obtaining the battery capacity after degradation according to the degradation parameter, the first cycle period, and the natural logarithm includes: Obtaining a degraded battery capacity model according to the second cycle period, the degradation parameter, the product of the degradation parameter and the second cycle period, and the natural logarithm; Obtaining a preset degraded battery capacity model according to the degraded battery capacity model and a quotient of the degradation parameter and the second cycle period; Obtaining a target degraded battery capacity model according to the preset degraded battery capacity model, the second cycle period, the degradation parameter, and the natural logarithm; Inputting the degradation parameter, the first cycle period, and the natural logarithm into the target degraded battery capacity model to obtain the degraded battery capacity; Obtaining a battery degradation analysis result according to the battery aging coefficient, the battery charging impedance, the battery discharging impedance, and the degraded battery capacity; Obtaining a battery degradation analysis result according to the battery aging coefficient, the battery charging impedance, the battery discharging impedance, and the degraded battery capacity includes: performing a cleaning process, removing outliers and missing values, and normalizing the aging coefficient, the battery charging impedance, the battery discharging impedance, and the degraded battery capacity to obtain processed data; Inputting the processed data into a battery degradation prediction model to obtain the battery degradation analysis result, wherein the battery degradation prediction model includes a correspondence between the battery degradation data and the aging coefficient, the battery charging impedance, the battery discharging impedance, and the degraded battery capacity; Before inputting the processed data into the battery degradation prediction model to obtain the battery degradation analysis result, the method further includes: According to the nonlinear function, a preset battery degradation prediction model is obtained; According to the numerical optimization algorithm, the best estimated value of the unknown parameters is obtained; Substituting the best estimated value of the unknown parameter into the nonlinear function to obtain the fitting value at each observation point; According to the fitted values ​​and the actual observed values, the coefficient of determination, the mean square error and the mean absolute error are obtained; The battery degradation prediction model is obtained according to the determination coefficient, the mean square error, and the mean absolute error.

2. The battery degradation analysis method according to claim 1, wherein: Also includes: Determining whether an actual value corresponding to the charge-discharge cycle exists; When the actual value corresponding to the charge-discharge cycle period exists, obtaining the battery aging coefficient according to the actual value corresponding to the charge-discharge cycle period; When the actual value corresponding to the charge and discharge cycle does not exist, the battery aging coefficient is obtained according to the aging model and the charge and discharge cycle.

3. The battery degradation analysis method according to claim 1, wherein: Also includes: Determining whether an actual value corresponding to the charging cycle exists; When the actual value corresponding to the charging cycle period exists, obtaining the battery charging impedance according to the actual value corresponding to the charging cycle period; When an actual value corresponding to the charging cycle period does not exist, the battery charging impedance is obtained according to the charging impedance model and the charging cycle period.

4. The battery degradation analysis method according to claim 1, wherein: Also includes: Determining whether an actual value corresponding to the discharge cycle exists; When the actual value corresponding to the discharge cycle period exists, obtaining the battery discharge impedance according to the actual value corresponding to the discharge cycle period; When an actual value corresponding to the discharge cycle period does not exist, the battery discharge impedance is obtained according to the discharge impedance model and the discharge cycle period.

5. A battery degradation analysis device, characterized in that: The battery degradation analysis device comprises: An acquisition module, configured to acquire an aging model, wherein the aging model includes a correspondence between aging parameters, charge-discharge cycles, and an aging coefficient; A calculation module, configured to obtain a battery aging coefficient according to the aging model; The acquisition module is further configured to acquire a charging impedance model and a discharging impedance model, wherein the charging impedance model includes a corresponding relationship between the charging impedance, the charging cycle period, and the charging coefficient, and the discharging impedance model includes a corresponding relationship between the discharging impedance, the discharging cycle period, and the discharging coefficient, wherein the charging impedance is the sum of the charge transfer resistance and the electrolyte resistance during charging, and the discharging impedance is the sum of the charge transfer resistance and the electrolyte resistance during discharging; The calculation module is further configured to obtain the battery charging impedance according to the charging impedance model; The calculation module is further configured to obtain the battery discharge impedance according to the discharge impedance model; The calculation module is further configured to obtain the battery capacity after degradation based on the degradation parameter, the first cycle period, and the natural logarithm; The calculation module is further configured to obtain a degraded battery capacity model based on a second cycle period, a degradation parameter, a product of the degradation parameter and the second cycle period, and the natural logarithm; obtain a preset degraded battery capacity model based on the degraded battery capacity model and a quotient of the degradation parameter and the second cycle period; obtain a target degraded battery capacity model based on the preset degraded battery capacity model, the second cycle period, the degradation parameter, and the natural logarithm; and input the degradation parameter, the first cycle period, and the natural logarithm into the target degraded battery capacity model to obtain the degraded battery capacity; The calculation module is further configured to obtain a battery degradation analysis result based on the battery aging coefficient, the battery charging impedance, the battery discharging impedance, and the degraded battery capacity; The calculation module is further configured to clean, remove outliers and missing values, and normalize the aging coefficient, the battery charging impedance, the battery discharging impedance, and the battery capacity after degradation to obtain processed data; and input the processed data into a battery degradation prediction model to obtain the battery degradation analysis result, wherein the battery degradation prediction model includes a correspondence between the battery degradation data and the aging coefficient, the battery charging impedance, the battery discharging impedance, and the battery capacity after degradation; The calculation module is also used to obtain a preset battery degradation prediction model based on a nonlinear function; obtain the best estimated values ​​of unknown parameters based on a numerical optimization algorithm; substitute the best estimated values ​​of the unknown parameters into the nonlinear function to obtain the fitting values ​​at each observation point; obtain the determination coefficient, mean square error and mean absolute error based on the fitting values ​​and actual observation values; and obtain the battery degradation prediction model based on the determination coefficient, the mean square error and the mean absolute error.

6. A battery degradation analysis device, characterized in that: The device includes: a memory, a processor, and a battery degradation analysis program stored in the memory and executable on the processor, wherein the battery degradation analysis program is configured to implement the steps of the battery degradation analysis method according to any one of claims 1 to 4.

7. A storage medium, characterized in that: The storage medium stores a battery degradation analysis program, which, when executed by a processor, implements the steps of the battery degradation analysis method according to any one of claims 1 to 4.

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