Method for predicting short-time pulse power performance and energy performance degradation, electronic device and medium

Abstract

The application discloses a kind of for short time pulse power performance and energy performance attenuation prediction method, electronic equipment and medium.The method can include: constructing energy attenuation model and power attenuation model;Determine the parameters of energy attenuation model and power attenuation model respectively;Accuracy verification is carried out to energy attenuation model and power attenuation model of confirming parameter respectively;According to the energy attenuation model and power attenuation model after accuracy verification, the prediction of battery aging trend is carried out.The present application establishes the quantitative relationship of energy attenuation and capacity, power attenuation and inverse resistance by experiment calibration and mathematical modeling, realizes high-precision online prediction by combining correlation and deviation analysis.

Description

Technical Field

[0001] This invention relates to the field of lithium-ion battery aging prediction technology, and more specifically, to a method, electronic device, and medium for predicting short-time pulse power performance and energy performance degradation. Background Technology

[0002] Lithium-ion batteries are widely used in electric vehicles, energy storage systems, and other fields due to their high energy density and long cycle life. However, their aging problem directly affects battery performance and safety. The aging process involves complex electrochemical, physical, and thermodynamic coupling mechanisms, mainly manifested as capacity decay, increased internal resistance, and decreased thermal stability.

[0003] The aging of lithium-ion batteries is the result of multiple mechanisms, mainly including material structure degradation, interfacial side reactions, and kinetic imbalance. Cathode material structure degradation generally manifests as structural phase transitions, transition metal dissolution, and particle breakage, leading to loss of active material and obstruction of lithium-ion diffusion pathways. During cycling, the anode material continuously thickens due to electrolyte reduction reactions, consuming active lithium and increasing interfacial impedance. Electrode materials undergo volume expansion and contraction during lithium-ion insertion / extraction; long-term cycling leads to particle cracks and conductive network breakage, further reducing the effective reaction area and electronic conductivity.

[0004] Battery aging is affected by both the material system and operating conditions. High-rate charging and discharging exacerbate polarization, accelerate SEI film growth and lithium dendrite formation, deep cycling (such as DOD>80%) significantly increases positive electrode structural stress and negative electrode volume deformation, high SOC storage accelerates positive electrode oxidation decomposition and electrolyte gas production, high temperature (>45℃) accelerates side reactions, while low temperature (<0℃) leads to lithium metal deposition.

[0005] Battery aging is primarily assessed by capacity and internal resistance. Capacity decay is the most direct indicator, reflecting the loss of active lithium and material failure, and is typically evaluated using remaining capacity (SOH). Increased internal resistance includes ohmic internal resistance (electrolyte resistance) and polarization internal resistance (SEI film resistance), which can be obtained through electrochemical impedance spectroscopy (EIS). A capacity reduction to 80% of its initial value and an internal resistance increase to 100% of its initial value are considered end-of-life (EOL) criteria.

[0006] Currently, battery life prediction is divided into mechanism-driven and data-driven approaches. Equivalent circuit models and electrochemical models suffer from computational complexity and strong parameter dependencies, making it difficult to meet the real-time estimation requirements of the entire vehicle. Data-driven approaches do not fully integrate mechanism analysis with external characteristic parameter modeling, have high operating condition requirements, and the amount of data directly affects prediction accuracy.

[0007] Lithium-ion batteries are widely used in new energy vehicles, and their energy and power performance directly affects the vehicle's driving range and power output. In existing technologies, the assessment of battery state of health (SOH) is mainly based on capacity decay and internal resistance growth, but there is a lack of accurate models for predicting energy and power decay.

[0008] Therefore, it is necessary to develop a method, electronic device, and medium for predicting the degradation of short-time pulse power performance and energy performance.

[0009] The information disclosed in the background section of this invention is intended only to enhance the understanding of the general background of this invention, and should not be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art. Summary of the Invention

[0010] This invention proposes a method, electronic device, and medium for predicting the power and energy performance degradation of short-time pulses. It can establish quantitative relationships between energy degradation and capacity, and between power degradation and the reciprocal of internal resistance through experimental calibration and mathematical modeling. Combined with correlation and deviation analysis, it can achieve high-precision online prediction.

[0011] In a first aspect, embodiments of this disclosure provide a method for predicting the degradation of short-time pulse power performance and energy performance, comprising: Construct energy decay models and power decay models; Determine the parameters for the energy decay model and the power decay model respectively; The accuracy of the energy decay model and power decay model for the confirmed parameters was verified respectively. Battery aging trends are predicted based on the energy decay model and power decay model after accuracy verification.

[0012] Preferably, the energy decay model is as follows: E'=aQ' c +b In the formula, E' represents the energy decay retention rate of the battery, Q' represents the nominal capacity decay retention rate of the battery, and a, b, and c are the model parameters to be solved.

[0013] Preferably, the power attenuation model is as follows: P'=d( ) 2 +e( )+f In the formula, P' represents the power decay retention rate of the battery, R' represents the battery internal resistance decay retention rate, and d, e, and f are the model parameters to be solved.

[0014] Preferably, determining the parameters of the energy decay model and the power decay model respectively includes: An aging experiment was conducted on the battery samples, and calibration was performed once every N cycles to obtain the calibration results. Calculate the health coefficient of the maximum pulse current value based on the calibration content, including capacity, energy, 50% SOC DC internal resistance, and test current; The parameters of the energy decay model and the power decay model are obtained by fitting the calibration content and the health coefficient.

[0015] Preferably, the calibration content includes the current state calibration capacity, energy, 50% SOC DC internal resistance, and the maximum pulse current value obtained by the test current.

[0016] Preferably, the health coefficient is the current calibrated value of the calculation parameter divided by the initial value.

[0017] Preferably, the aging test conditions are 25°C, 1C or StepC charging within the operating voltage range, 1C discharging cycle, and more than 2000 cycles.

[0018] Preferably, the accuracy verification of the energy decay model and power decay model for the confirmed parameters includes: Deviation and correlation analyses were performed on the model's predicted values ​​and the actual test values. If the root mean square error between the predicted and measured values ​​is ≤0.05 and the correlation coefficient Rm is within a certain range, the model is considered to be in good condition. 2 If the value is ≥0.97, the accuracy verification is passed.

[0019] Secondly, embodiments of this disclosure also provide an electronic device, the electronic device comprising: Memory, which stores executable instructions; A processor that executes the executable instructions in the memory to implement the method for predicting short-time pulse power performance and energy performance degradation.

[0020] Thirdly, embodiments of this disclosure also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for predicting short-time pulse power performance and energy performance degradation.

[0021] Its beneficial effects are as follows: (1) The present invention directly converts the capacity and internal resistance monitored online into energy and power attenuation signals; (2) This invention proposes a linear function relationship between energy decay and capacity decay and a quadratic function relationship between power decay and the reciprocal of internal resistance. The model is simple and highly accurate. (3) The present invention can realize the real-time linkage between battery aging parameters and vehicle performance, thereby improving the user experience.

[0022] The methods and apparatus of the present invention have other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0023] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same parts.

[0024] Figure 1 A flowchart illustrating the steps of a method for predicting short-time pulse power performance and energy performance degradation according to an embodiment of the present invention is shown.

[0025] Figure 2 A schematic diagram of the calibrated capacity energy with different numbers of cycles is shown according to an embodiment of the present invention.

[0026] Figure 3 A schematic diagram of the calibrated DC internal resistance with different numbers of cycles is shown according to an embodiment of the present invention.

[0027] Figure 4 A schematic diagram of short-time pulses Imax with different numbers of cycles is shown according to an embodiment of the present invention.

[0028] Figure 5 A schematic diagram showing the average voltage and the reciprocal of the internal resistance for different numbers of cycles according to an embodiment of the present invention is shown.

[0029] Figure 6 A schematic diagram showing the capacity energy and the retention rate of internal resistance and Imax as a function of cycling according to an embodiment of the present invention is illustrated.

[0030] Figure 7 A graph showing the relationship between energy decay and capacity decay according to an embodiment of the present invention is shown.

[0031] Figure 8 A graph showing the relationship between the attenuation of short-time maximum pulse capability and the attenuation of the reciprocal of internal resistance according to an embodiment of the present invention is provided. Detailed Implementation

[0032] Preferred embodiments of the invention will now be described in more detail. While preferred embodiments of the invention are described below, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0033] Figure 1A flowchart illustrating the steps of a method for predicting short-time pulse power performance and energy performance degradation according to an embodiment of the present invention is shown.

[0034] like Figure 1 As shown, the method for predicting short-time pulse power performance and energy performance degradation includes: Step 101: Construct the energy decay model and the power decay model; Step 102: Determine the parameters of the energy decay model and the power decay model respectively; Step 103: Verify the accuracy of the energy decay model and power decay model for the confirmed parameters, respectively. Step 104: Predict the battery aging trend based on the energy decay model and power decay model after accuracy verification.

[0035] In one example, the energy decay model is as follows: E'=aQ' c +b In the formula, E' represents the energy decay retention rate of the battery, Q' represents the nominal capacity decay retention rate of the battery, and a, b, and c are the model parameters to be solved.

[0036] In one example, the power attenuation model is: P'=d( ) 2 +e( )+f In the formula, P' represents the power decay retention rate of the battery, R' represents the battery internal resistance decay retention rate, and d, e, and f are the model parameters to be solved.

[0037] In one example, the parameters for determining the energy decay model and the power decay model include: An aging experiment was conducted on the battery samples, and calibration was performed once every N cycles to obtain the calibration results. Calculate the health coefficient of the maximum pulse current value based on the calibration content, including capacity, energy, 50% SOC DC internal resistance, and test current; The parameters of the energy decay model and the power decay model are obtained by fitting the calibration content and the health coefficient.

[0038] In one example, the calibration includes the current state calibrated capacity, energy, 50% SOC DC internal resistance, and the maximum pulse current value obtained from the test current.

[0039] In one example, the health coefficient is the current calibrated value of the calculated parameter divided by the initial value.

[0040] In one example, the aging test conditions were 25°C, 1C or StepC charging within the operating voltage range, 1C discharging cycle, and cycling to >2000 cycles.

[0041] In one example, accuracy verification is performed on the energy decay model and power decay model for the confirmed parameters, respectively, including: Deviation and correlation analyses were performed on the model's predicted values ​​and the actual test values. If the root mean square error between the predicted and measured values ​​is ≤0.05 and the correlation coefficient Rm is within a certain range, the model is considered to be in good condition. 2 If the value is ≥0.97, the accuracy verification is passed.

[0042] Specifically, this invention obtains different aging states during standard cyclic operating conditions, and then periodically conducts capacity energy calibration, internal resistance calibration, and power calibration tests to obtain their respective health coefficients, i.e., the retention rate values ​​of each parameter after aging. Then, it derives the parameters to be determined in the health coefficient relationship model, and verifies the reliability of the model by combining correlation and deviation analysis.

[0043] The relationship between battery energy, battery nominal capacity, and battery average voltage is as follows: E=Q×V av (1) In the formula, E represents the battery's energy, Q represents the battery's nominal capacity, and V... av This indicates the average battery voltage.

[0044] Energy aging focuses on capacity aging and changes in average voltage over time. (2) (3) (4) In the formula, t is the pulse time, which is typically required to be 10s, 30s, or 60s. max This represents the maximum pulse current, R is the battery's DC internal resistance, and P... max This represents the maximum pulse power, and Ve is the boundary voltage, which represents the upper voltage limit V. max Or lower voltage limit V min V0 is the OCV corresponding to the start of the pulse.

[0045] Short-duration, high-rate pulses are prone to over- and under-voltage risks. Typically, discharge and charge Pmax are calculated using the following formula: P max-dis =I max ×V min (5) P max-cha =I max ×V0 (6) According to equation (3), the power attenuation characteristic can be equivalently represented as the study of I.max The relationship between the inverse of the internal resistance and the decay is obtained as follows: P max =f(I max ) = f( (7) Based on the relationship between energy and capacity, average voltage, and the reciprocal relationship between power and internal resistance, a relationship model is obtained for their respective health coefficients.

[0046] This invention is applicable to NCM (nickel-cobalt-manganese layered polymer material as cathode) ternary systems.

[0047] S1. Establish an energy decay model: E'=aQ' c +b (8) In the formula, E' represents the energy decay retention rate of the battery, Q' represents the nominal capacity decay retention rate of the battery, and a, b, and c are the model parameters to be solved.

[0048] The power attenuation model is as follows: P'=d( ) 2 +e( )+f (9) In the formula, P' represents the power decay retention rate of the battery, which can be obtained from the decay retention rate of Imax, R' represents the battery internal resistance decay retention rate, and d, e, and f are the model parameters to be solved.

[0049] S2. Determine the parameters in the energy decay model and the power decay model: (1) Sample screening: Before testing, it is necessary to select batteries with consistent capacity, with a capacity difference of <2% and an internal resistance difference of <2%, and the capacity should be close to the median value of mass-produced batteries. (2) Conduct aging test: The test conditions are usually 25℃, 1C or StepC charging within the working voltage range, 1C discharging cycle, and cycling to >2000 cycles; (3) Calibrate the health status: Perform calibration once every 100 cycles, and record the current calibration capacity, energy, DC internal resistance at 50% SOC, and the test current to obtain the maximum pulse current value. Among them, pay attention to the pulse power corresponding to 80% SOC during charging and 10% SOC during discharging. If abnormal data points occur during the experiment, they need to be analyzed, confirmed, and then removed. (4) Confirm the health coefficient: The health coefficient is a coefficient that expresses the various types of decay. It is a parameter that fits the energy and power decay. The health coefficient includes the capacity decay coefficient, energy decay coefficient, internal resistance decay coefficient, and maximum pulse current decay coefficient. The maximum pulse current decay coefficient is equivalent to the power decay coefficient. Calculate the average discharge voltage and the reciprocal of the internal resistance per cycle, as well as the health coefficients of capacity, energy, internal resistance, and maximum pulse current, based on the data recorded in (3). Health coefficient = (10) E' corresponds to the health coefficient: energy at calibration divided by initial energy; Q' corresponds to the health coefficient: the capacity at calibration divided by the initial capacity; The health coefficient corresponding to P' is the maximum pulse current during calibration divided by the initial maximum pulse current. The health coefficient corresponding to R' is the internal resistance at calibration divided by the initial internal resistance.

[0050] (5) Calculate the model parameters: Based on the parameters recorded in (3) and (4), fit the values ​​of the undetermined parameters a, b, c, d, e, and f in the model.

[0051] S3. Verify the accuracy of each attenuation model in S2, i.e., perform deviation analysis and correlation analysis between the model predictions and actual test values. The root mean square error between the predicted and measured values ​​should be ≤0.05, and the correlation coefficient R0 should be within a certain range. 2 A value of ≥0.97 is sufficient to verify that the model has been successfully established.

[0052] S4. Based on the energy and power degradation model that has been verified with high accuracy, the subsequent battery aging trend can be predicted.

[0053] The present invention also provides an electronic device, comprising: a memory storing executable instructions; and a processor executing the executable instructions in the memory to implement the above-described method for predicting short-time pulse power performance and energy performance degradation.

[0054] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for predicting short-time pulse power performance and energy performance degradation.

[0055] To facilitate understanding of the solutions and effects of the embodiments of the present invention, three specific application examples are given below. Those skilled in the art should understand that these examples are merely for the purpose of understanding the present invention, and any specific details therein are not intended to limit the present invention in any way.

[0056] Example 1

[0057] Establish an energy decay model: E'=aQ' c +b In the formula, E' represents the energy decay retention rate of the battery, Q' represents the nominal capacity decay retention rate of the battery, and a, b, and c are the model parameters to be solved.

[0058] Establish a power attenuation model: P'=d( )2 +e( )+f In the formula, P' represents the power decay retention rate of the battery, R' represents the battery internal resistance decay retention rate, and d, e, and f are the model parameters to be solved.

[0059] Determine the parameters in the energy decay model and the power decay model, including: (1) Sample screening: The capacity difference is less than 2% and the internal resistance difference is less than 2% (1.9%). The consistency requirements are met and there are no abnormal appearances or other defects. Experiments will be carried out. (2) Conduct aging test: Place the battery in a 25±2℃ temperature chamber, fully charge it with constant current and constant voltage in Step C, let it stand for 30 minutes, discharge it to the lower voltage limit at 1C, and cycle it to 2000~3000 times. Figure 2 A schematic diagram of the calibrated capacity energy with different numbers of cycles is shown according to an embodiment of the present invention.

[0060] Figure 3 A schematic diagram of the calibrated DC internal resistance with different numbers of cycles is shown according to an embodiment of the present invention.

[0061] Figure 4 Short pulses I with different numbers of cycles are shown according to an embodiment of the present invention. max A schematic diagram.

[0062] (3) Determine health status: such as Figures 2-4 As shown, after every 100 cycles, the energy of 1 / 3C discharge capacity is recorded as the calibration capacity energy; based on the current capacity, it is adjusted to 50% SOC, and the DC internal resistance is obtained by HPPC method; based on the current capacity, it is adjusted to 10% SOC, and the maximum pulse discharge capability is obtained by pulse discharge for 30s to the lower voltage limit, denoted as I. max -dis, adjust to 80% SOC, pulse charge for 30 seconds to reach the voltage limit to obtain the maximum pulse charging capacity, denoted as I. max -cha,I max As a power performance parameter; (4) Confirm health coefficients: Calculate the average discharge voltage and the reciprocal of the internal resistance per cycle, as well as the health coefficients of capacity, energy, internal resistance, and maximum pulse current. Health coefficient =

[0063] (5) Calculate the model parameters: Based on the parameters recorded in (3) and (4), the values ​​of the undetermined parameters a, b, c, d, e and f in the model are obtained, as shown in Table 1.

[0064] Table 1. Energy decay and power decay model parameters

[0065] Figure 5 A schematic diagram showing the average voltage and the reciprocal of the internal resistance for different numbers of cycles according to an embodiment of the present invention is shown.

[0066] Figure 6 The capacity energy, internal resistance, and I are shown according to an embodiment of the present invention. max A schematic diagram of retention rate with cycling.

[0067] S3. Verify the accuracy of each attenuation model according to E=Q×V av It can be seen that the ratio of energy to capacity is the average voltage, and according to Figure 5 It was found that the average voltage continued to decrease, but the decrease was very small, resulting in minimal impact on energy. Figure 6 The medium-capacity energy curve confirms that the energy prediction model based on capacity is a linear function model, conforming to E'=aQ'+b, and the model fit coefficient R0 is [value missing]. 2 >0.999.

[0068] According to P max =f(I max ) = f( It can be known that I max Strongly correlated with the reciprocal of internal resistance, discharge I max The health coefficient, which is the reciprocal of the internal resistance, follows a linear function relationship. (Charging I) max The relationship between the health coefficient and the reciprocal of the internal resistance conforms to a quadratic function, satisfying P'=a( ) 2 +b( )+c, model fit coefficient R 2 >0.98.

[0069] S4. Based on the energy and power degradation model that has been verified with high accuracy, the subsequent battery aging trend can be predicted.

[0070] Based on existing aging methods, the capacity decay and internal resistance increase can be estimated. Then, based on the energy decay model and power decay model, the current energy and power health status can be obtained, as shown in Table 2.

[0071] Table 2 Predicted values ​​of health status after energy and power aging

[0072] Based on current mature methods for estimating capacity and internal resistance, this invention predicts the aging degradation of remaining energy and power performance, which have a more direct impact on overall vehicle performance. At the same time, this model is embedded in the battery management system (BMS), which can monitor changes in internal resistance and capacity in real time, calculate energy and power degradation, and thus guide the response and control of the vehicle's power output strategy, achieving more accurate early warning and management of range and pulse undervoltage risks.

[0073] Example 2

[0074] This disclosure provides an electronic device comprising: a memory storing executable instructions; and a processor executing the executable instructions in the memory to implement the above-described method for predicting short-time pulse power performance and energy performance degradation.

[0075] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0076] This memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0077] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory.

[0078] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0079] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0080] Example 3

[0081] This disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for predicting short-time pulse power performance and energy performance degradation.

[0082] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present disclosure are performed.

[0083] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0084] Those skilled in the art should understand that the above description of the embodiments of the present invention is only intended to illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any of the examples given.

[0085] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for predicting the degradation of short-time pulse power performance and energy performance, characterized in that, include: Construct energy decay models and power decay models; Determine the parameters for the energy decay model and the power decay model respectively; The accuracy of the energy decay model and power decay model for the confirmed parameters was verified respectively. Battery aging trends are predicted based on the energy decay model and power decay model after accuracy verification.

2. The method for predicting the degradation of short-time pulse power performance and energy performance according to claim 1, characterized in that, The energy decay model is as follows: E'=aQ' c +b In the formula, E' represents the energy decay retention rate of the battery, Q' represents the nominal capacity decay retention rate of the battery, and a, b, and c are the model parameters to be solved.

3. The method for predicting the degradation of short-time pulse power performance and energy performance according to claim 1, characterized in that, The power attenuation model is as follows: P'=d( ) 2 +e( )+f In the formula, P' represents the power decay retention rate of the battery, R' represents the battery internal resistance decay retention rate, and d, e, and f are the model parameters to be solved.

4. The method for predicting the degradation of short-time pulse power performance and energy performance according to claim 1, characterized in that, The parameters for the energy decay model and the power decay model are determined separately, including: An aging experiment was conducted on the battery samples, and calibration was performed once every N cycles to obtain the calibration results. Calculate the health coefficient of the maximum pulse current value based on the calibration content, including capacity, energy, 50% SOC DC internal resistance, and test current; The parameters of the energy decay model and the power decay model are obtained by fitting the calibration content and the health coefficient.

5. The method for predicting the degradation of short-time pulse power performance and energy performance according to claim 4, characterized in that, The calibration content includes the current state calibration capacity, energy, 50% SOC DC internal resistance, and the maximum pulse current value obtained from the test current.

6. The method for predicting the degradation of short-time pulse power performance and energy performance according to claim 5, characterized in that, The health coefficient is calculated by dividing the current calibrated value of the parameter by the initial value.

7. The method for predicting the degradation of short-time pulse power performance and energy performance according to claim 4, characterized in that, The aging test conditions are 25℃, 1C or StepC charging within the operating voltage range, 1C discharging cycle, and more than 2000 cycles.

8. The method for predicting the degradation of short-time pulse power performance and energy performance according to claim 1, characterized in that, Accuracy verification was performed on the energy decay model and power decay model for the confirmed parameters, including: Deviation and correlation analyses were performed on the model's predicted values ​​and the actual test values. If the root mean square error between the predicted and measured values ​​is ≤0.05 and the correlation coefficient Rm is within a certain range, the model is considered to be in good condition. 2 If the value is ≥0.97, the accuracy verification is passed.

9. An electronic device, characterized in that, The electronic device includes: Memory, which stores executable instructions; A processor that executes the executable instructions in the memory to implement the method for predicting short-time pulse power performance and energy performance degradation as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method for predicting short-time pulse power performance and energy performance degradation as described in any one of claims 1-8.

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