A method and medium for evaluating mechanical extrusion stability of a lithium battery

Abstract

The application discloses a lithium battery mechanical extrusion stability evaluation method and a medium. The method can comprise the following steps: acquiring basic parameters of a lithium battery, applying extrusion force in the width and thickness directions respectively, and acquiring extrusion deformation and a temperature gradient; calculating strain energy density, internal resistance change rate and temperature change rate; calculating an extrusion capacity parameter according to the strain energy density, the internal resistance change rate and the temperature change rate; and determining the current state of the battery according to the extrusion capacity parameter. The application evaluates the mechanical extrusion stability of the lithium battery.

Description

Technical Field

[0001] This invention relates to the field of battery mechanical safety testing, and more specifically, to a method and medium for evaluating the mechanical extrusion stability of lithium batteries. Background Technology

[0002] With the rapid development of new energy technologies, lithium-ion batteries have been widely used in electric vehicles, energy storage systems, and portable electronic devices. However, batteries may suffer various forms of mechanical damage during use, such as compression, impact, and vibration. This damage can lead to internal short circuits, thermal runaway, and serious safety accidents such as battery fires or explosions. To ensure battery safety, domestic and international organizations have developed various corresponding safety testing standards for lithium-ion batteries to conduct appropriate safety performance tests.

[0003] Existing thermal runaway early warning methods often rely on temperature thresholds or temperature change rate thresholds for judgment, but this approach has significant limitations and cannot comprehensively reflect the mechanism of thermal runaway. Furthermore, traditional testing methods depend solely on mechanical indicators (such as deformation rate ≥15%) to determine failure, failing to capture early signs of thermal runaway (such as micro-short circuits and electrolyte leaks), resulting in insufficient early warning accuracy. Simultaneously, due to the complex changes within the battery, data from a single sensor often cannot reflect the internal state of the battery in a timely manner, failing to accurately warn of potential thermal runaway risks in the early stages. Current technologies typically rely on single-dimensional data analysis, ignoring the correlations between multiple feature data, which may lead to low accuracy in thermal runaway identification; they also lack the ability to analyze complex features: many existing technologies fail to fully utilize advanced algorithms such as nonlinear analysis and neural networks to uncover potential correlations in multi-dimensional feature data, thus failing to accurately predict the risk of thermal runaway.

[0004] Currently, a method for evaluating the mechanical extrusion stability of lithium batteries still needs to be developed.

[0005] 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

[0006] This invention proposes a method and medium for evaluating the mechanical extrusion stability of lithium batteries. By combining the collaborative analysis capabilities of multi-dimensional data, it improves the accuracy and response speed of early warning. It can accurately correlate the co-evolution law of mechanical deformation, thermal runaway and electrochemical failure, and realize the dynamic prediction of mechanical failure threshold and early identification of critical safety risks. This improves the accuracy and timeliness of thermal runaway early warning and provides a more reliable guarantee for the safe application of batteries.

[0007] In a first aspect, embodiments of this disclosure provide a method for evaluating the mechanical extrusion stability of lithium batteries, including: To obtain the basic parameters of the lithium battery, extrusion pressure is applied in the width and thickness directions respectively, and the extrusion deformation and temperature gradient are obtained. Calculate the strain energy density, rate of change of internal resistance, and rate of change of temperature; The extrusion capacity parameters are calculated based on strain energy density, internal resistance change rate and temperature change rate. The current state of the battery is determined based on the compression capacity parameters.

[0008] Preferably, calculating the strain energy density based on the extrusion deformation includes: The strain of the battery is calculated based on the extrusion deformation, and the stress is calculated based on the extrusion force and cell size. Strain energy density is calculated based on strain and stress.

[0009] Preferably, the strain energy density is:

[0010] in, For extrusion deformation, Let σ be the strain energy density, σ be the stress, and ε be the strain.

[0011] Preferably, the rate of change of internal resistance is:

[0012] in, R is the rate of change of internal resistance. t Let be the internal resistance of the battery at time t.

[0013] Preferably, the rate of temperature change is:

[0014] in, Let T(t) be the rate of temperature change, and T(t) be the temperature of the battery at time t.

[0015] Preferably, the extrusion capacity parameter is:

[0016] in, For strain energy density, For the rate of temperature change, Let α be the rate of change of internal resistance, and β be the weights corresponding to strain energy density, temperature change rate, and internal resistance change rate, respectively. , and The threshold values ​​are the strain energy density, temperature change rate, and internal resistance change rate.

[0017] Preferably, α, β, and γ are iteratively updated based on real-time data through Bayesian optimization: Define the objective function as the early warning accuracy: f(α, β, γ) = Number of correct warnings / Total number of tests; Based on maximizing the objective function, Bayesian optimization is used to find the optimal weight combination that maximizes f(α, β, γ).

[0018] Preferably, determining the current state of the battery based on the compression capacity parameter includes: When the first threshold is less than or equal to the crushing capacity parameter, the lithium battery is determined to be in a dangerous state. When the second threshold is less than or equal to the extrusion capacity parameter and less than the first threshold, the lithium battery is determined to be in a level three risk state. When the third threshold is less than or equal to the extrusion capacity parameter and less than the second threshold, the lithium battery is determined to be in a level 2 risk state. When the fourth threshold is less than or equal to the extrusion capacity parameter and less than the third threshold, the lithium battery is determined to be in a level one risk state. When the compression capacity parameter is less than the fourth threshold, the lithium battery is determined to be in a safe state.

[0019] Preferably, the first threshold > the second threshold > the third threshold > the fourth threshold.

[0020] Secondly, embodiments of this disclosure also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the described method for evaluating the mechanical extrusion stability of lithium batteries.

[0021] Its beneficial effects are as follows: 1. This invention quantitatively assesses the safety status of battery cells by establishing quantitative extrusion capability parameters. By integrating multiple sensors and devices such as displacement sensors, force sensors, data acquisition instruments, data synchronization systems, early warning systems, and edge computing units, it achieves multi-dimensional monitoring of batteries, overcomes the limitations of existing technologies based on single-dimensional data, and significantly improves the accuracy and reliability of thermal runaway early warning. 2. Based on the thermal runaway model of power batteries and real-time data acquisition, this invention can identify the risk of thermal runaway of batteries in a timely manner by dynamically monitoring and analyzing parameters such as strain energy density, temperature change rate and internal resistance change rate, thereby realizing early warning of thermal runaway and effectively reducing response delay. 3. This invention establishes a complete thermal runaway risk assessment system through comprehensive analysis of the mechanical, thermal, and electrochemical characteristics of batteries, which can more accurately identify the safety status of batteries and improve the intelligence level of the system. 4. This invention employs advanced methods such as dynamic weight allocation and Bayesian optimization to achieve intelligent processing of multi-dimensional data, improve the quality of data preprocessing, and solve the problems of inadequate data cleaning, missing value imputation, and outlier handling in existing technologies. 5. By collecting and analyzing multi-dimensional characteristic data of the battery in real time, this invention can more comprehensively reflect the complex changes inside the battery, effectively overcoming the limitations of relying on data from a single sensor in the prior art, and improving the accuracy and timeliness of thermal runaway early warning.

[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 evaluating the mechanical extrusion stability of a lithium battery according to an embodiment of the present invention is shown.

[0025] Figure 2 A schematic diagram showing the data of extrusion in the thickness direction of a No. 1 NCM ternary lithium battery according to an embodiment of the present invention is illustrated.

[0026] Figure 3 A schematic diagram showing the data of extrusion in the thickness direction of a No. 2 NCM ternary lithium battery according to an embodiment of the present invention is provided.

[0027] Figure 4 A schematic diagram showing the data of a No. 1 NCM ternary lithium battery extruded in the width direction according to an embodiment of the present invention is illustrated.

[0028] Figure 5 A schematic diagram showing the data of a No. 2 NCM ternary lithium battery extruded in the width direction according to an embodiment of the present invention is illustrated.

[0029] Figure 6 A schematic diagram showing the data of a No. 1 LFP lithium iron phosphate battery extruded in the thickness direction according to an embodiment of the present invention is illustrated.

[0030] Figure 7 A schematic diagram showing the data of a No. 2 LFP lithium iron phosphate battery extruded in the thickness direction according to an embodiment of the present invention is illustrated.

[0031] Figure 8 A schematic diagram showing the data of a No. 1 LFP lithium iron phosphate battery extruded in the width direction according to an embodiment of the present invention is illustrated.

[0032] Figure 9 A schematic diagram showing the data of a No. 2 LFP lithium iron phosphate battery extruded in the width direction according to an embodiment of the present invention is illustrated. Detailed Implementation

[0033] 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.

[0034] Figure 1 A flowchart illustrating the steps of a method for evaluating the mechanical extrusion stability of a lithium battery according to an embodiment of the present invention is shown.

[0035] like Figure 1 As shown, the method for evaluating the mechanical extrusion stability of lithium batteries includes: Step 101: Obtain the basic parameters of the lithium battery, apply extrusion pressure in the width and thickness directions respectively, and obtain the extrusion deformation and temperature gradient; Step 102: Calculate the strain energy density, the rate of change of internal resistance, and the rate of change of temperature; Step 103: Calculate the extrusion capacity parameters based on strain energy density, internal resistance change rate, and temperature change rate; Step 104: Determine the current state of the battery based on the compression capacity parameters.

[0036] In one example, calculating the strain energy density based on the extrusion deformation includes: The strain of the battery is calculated based on the extrusion deformation, and the stress is calculated based on the extrusion force and cell size. Strain energy density is calculated based on strain and stress.

[0037] In one example, the strain energy density is:

[0038] in, For extrusion deformation, Let σ be the strain energy density, σ be the stress, and ε be the strain.

[0039] In one example, the rate of change of internal resistance is:

[0040] in, R is the rate of change of internal resistance. t Let be the internal resistance of the battery at time t.

[0041] In one example, the rate of temperature change is:

[0042] in, Let T(t) be the rate of temperature change, and T(t) be the temperature of the battery at time t.

[0043] In one example, the extrusion capacity parameter is:

[0044] in, For strain energy density, For the rate of temperature change, Let α be the rate of change of internal resistance, and β be the weights corresponding to strain energy density, temperature change rate, and internal resistance change rate, respectively. , and The threshold values ​​are the strain energy density, temperature change rate, and internal resistance change rate.

[0045] In one example, Bayesian optimization is used to iteratively update α, β, and γ based on real-time data: Define the objective function as the early warning accuracy: f(α, β, γ) = Number of correct warnings / Total number of tests; Based on maximizing the objective function, Bayesian optimization is used to find the optimal weight combination that maximizes f(α, β, γ).

[0046] In one example, determining the current state of the battery based on the crushing capacity parameter includes: When the first threshold is less than or equal to the crushing capacity parameter, the lithium battery is determined to be in a dangerous state. When the second threshold is less than or equal to the extrusion capacity parameter and less than the first threshold, the lithium battery is determined to be in a level three risk state. When the third threshold is less than or equal to the extrusion capacity parameter and less than the second threshold, the lithium battery is determined to be in a level 2 risk state. When the fourth threshold is less than or equal to the extrusion capacity parameter and less than the third threshold, the lithium battery is determined to be in a level one risk state. When the compression capacity parameter is less than the fourth threshold, the lithium battery is determined to be in a safe state.

[0047] In one example, the first threshold > the second threshold > the third threshold > the fourth threshold.

[0048] Specifically, the basic parameters of the lithium battery are obtained, including weight, internal resistance, voltage, width, and thickness. The battery weight is obtained using a weighing device, the voltage is measured using a voltmeter, the internal resistance is measured using an internal resistance meter, and the width and thickness are measured using vernier calipers. Based on the obtained data, the battery volume and density are calculated, with the following parameters: weight range of 2000-5000g, internal resistance range of 0.5-2.0mΩ, voltage range of 3.0-4.5V, width range of 100-200mm, and thickness range of 10-20mm.

[0049] Extrusion pressure was applied in both the width and thickness directions to obtain extrusion deformation and temperature gradient. A dynamic testing system was used to apply the extrusion pressure at a speed of 2 mm / s, and various parameters during the extrusion process, including load value, deformation, and temperature, were monitored and recorded in real time. Extrusion parameters were adjusted according to a preset extrusion scheme to ensure stable test conditions.

[0050] The strain on the battery surface is monitored in real time using high-precision strain sensors. The strain of the battery is calculated based on the extrusion deformation, and the stress is calculated based on the extrusion force and cell size, thereby obtaining the strain energy density. Strain energy density is an important indicator for measuring the stress on the internal structure of a battery under mechanical load. It reflects the energy stored inside the battery due to deformation. Its calculation formula is as follows:

[0051] in, For extrusion deformation, Let σ be the strain energy density, σ be the stress, and ε be the strain.

[0052] The battery's internal resistance is monitored in real time using a high-precision internal resistance sensor, and the rate of change of internal resistance is calculated. This rate of change reflects the dynamic characteristics of the electrochemical reactions within the battery and is closely related to the battery's health. The calculation formula is as follows:

[0053] Among them, R t Let be the internal resistance of the battery at time t.

[0054] High-precision data acquisition is used to monitor the battery surface temperature in real time and calculate the temperature change rate. The temperature change rate reflects the speed at which the battery temperature changes over time and is an important indicator for early warning of thermal runaway. The calculation formula is as follows:

[0055] Where T(t) is the temperature of the battery at time t.

[0056] The temperature gradient in this invention is primarily obtained through real-time acquisition using a battery surface temperature sensor. Since thermal runaway in lithium batteries originates from internal short-circuit heat generation, there is a significant lag in heat conduction from the cell interior to the surface. Therefore, in the early and middle stages of the extrusion process, the surface temperature often remains relatively constant (as shown in Examples 1-2, the surface temperature is maintained at 24-27°C), at which point the rate of temperature change is close to zero. This does not mean that no heat is generated inside the battery, but rather that the surface temperature has not yet reflected the internal thermal state.

[0057] To improve the accuracy of temperature acquisition, those skilled in the art can use built-in temperature lines (such as K-type thermocouples embedded between the positive and negative electrodes) to directly monitor the core temperature of the battery cell, or use infrared thermal imaging technology to monitor the surface temperature distribution. When using built-in temperature lines, temperature change signals can be captured earlier, and the weight of the temperature change rate in the extrusion capability parameter can be increased accordingly.

[0058] The extrusion capacity parameters are:

[0059] in, For strain energy density, For the rate of temperature change, The rate of change of internal resistance; , and These are the threshold values ​​for each parameter.

[0060] Bayesian optimization iteratively updates α, β, and γ using real-time data, adapting the weight allocation to different battery states and environmental conditions. The objective function is defined as maximizing warning accuracy: f(α, β, γ) = number of correct warnings / total number of tests. Bayesian optimization is used to find the optimal weight combination that maximizes f(α, β, γ), and the adjustment strategy is shown in Table 1.

[0061] Table 1

[0062] The initial parameters for the battery BOL are set as follows: α=0.6, β=0.2, γ=0.2, and the extrusion capacity parameter is:

[0063] Based on the extrusion capacity parameter W t Determine the current battery status: 80%≤W t At that time, it was determined that the lithium battery was currently in a dangerous state; 60%≤W t When the percentage is less than 80%, the lithium battery is determined to be at level three risk. 30%≤W t When the level is less than 60%, the lithium battery is determined to be at level two risk. 5%≤W t When the percentage is less than 30%, the lithium battery is considered to be at Level 1 risk.

[0064] In W t When the level is less than 5%, the lithium battery is considered to be currently safe.

[0065] 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 evaluating the mechanical crush stability of lithium batteries.

[0066] 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.

[0067] Example 1

[0068] Step 1: Fully charge the two NCM ternary lithium batteries to SOC=100% and let them stand for 24 hours to ensure the electrochemical state is stable; Step 2: Apply a compressive load at a speed of 2 mm / s, and simultaneously collect data on deformation, stress, temperature, and internal resistance; Step 3: Calculate the strain energy density. Calculate the strain of the battery based on the extrusion deformation, calculate the strain energy using the strain, and combine this with the temperature gradient and sudden increase in internal resistance to calculate the strain energy density. If the strain energy density is less than the preset threshold (3.66 MJ / m³), return to step 2 for retesting; if the strain energy density is greater than the preset threshold, proceed to step 4. Step 4: Determine the battery's safety status based on the sudden increase in internal resistance. Calculate the rate of change of internal resistance and determine if it exceeds a preset threshold (5%). If it does not exceed the preset threshold, the battery is considered safe; if it exceeds the preset threshold, the battery is considered dangerous. Step 5: Determine the battery's safety status based on temperature fluctuations. Calculate the temperature change rate to determine if it exceeds a preset threshold (1.0℃ / s). If it does not exceed the preset threshold, the battery is considered safe; if it exceeds the preset threshold, the battery is considered dangerous. Step 6: Dynamic threshold adjustment. The weights are adjusted based on SOC (State of Charge) and SOH (State of Health) to calculate the extrusion capacity parameters:

[0069] in, For strain energy density, For the rate of temperature change, This represents the rate of change of internal resistance.

[0070] Based on the extrusion capacity parameter W t Determine the current battery status: 80%≤W t At that time, it was determined that the lithium battery was currently in a dangerous state; 60%≤W t When the percentage is less than 80%, the lithium battery is determined to be at level three risk. 30%≤W t When the level is less than 60%, the lithium battery is determined to be at level two risk. 5%≤W t When the percentage is less than 30%, the lithium battery is considered to be at Level 1 risk.

[0071] In W t When the level is less than 5%, the lithium battery is considered to be currently safe.

[0072] Figure 2 A schematic diagram showing the data of extrusion in the thickness direction of a No. 1 NCM ternary lithium battery according to an embodiment of the present invention is illustrated.

[0073] Table 2 shows the extrusion data of the No. 1 NCM ternary lithium battery in the thickness direction. Figure 2 As shown.

[0074] Table 2

[0075] Figure 3 A schematic diagram showing the data of extrusion in the thickness direction of a No. 2 NCM ternary lithium battery according to an embodiment of the present invention is provided.

[0076] Table 3 shows the extrusion data of the No. 2 NCM ternary lithium battery in the thickness direction. Figure 3 As shown.

[0077] Table 3

[0078] Figure 4 A schematic diagram showing the data of a No. 1 NCM ternary lithium battery extruded in the width direction according to an embodiment of the present invention is illustrated.

[0079] Table 4 shows the data on the width-direction extrusion of the No. 1 NCM ternary lithium battery. Figure 4 As shown.

[0080] Table 4

[0081] Figure 5 A schematic diagram showing the data of a No. 2 NCM ternary lithium battery extruded in the width direction according to an embodiment of the present invention is illustrated.

[0082] Table 5 shows the data on the width-direction extrusion of the No. 2 NCM ternary lithium battery. Figure 5 As shown.

[0083] Table 5

[0084] Example 2

[0085] Step 1: Fully charge the two LFP lithium iron phosphate batteries to SOC=100% and let them stand for 24 hours to ensure the electrochemical state is stable; Step 2: Apply a compressive load at a speed of 2 mm / s, and simultaneously collect data on deformation, stress, temperature, and internal resistance; Step 3: Calculate the strain energy density. Calculate the strain of the battery based on the extrusion deformation, calculate the strain energy using the strain, and combine this with the temperature gradient and sudden increase in internal resistance to calculate the strain energy density; Step 4: Determine the battery's safety status based on the sudden increase in internal resistance. Calculate the rate of change of internal resistance and determine if it exceeds a preset threshold (5%). If it does not exceed the preset threshold, the battery is considered safe; if it exceeds the preset threshold, the battery is considered dangerous. Step 5: Determine the battery's safety status based on temperature fluctuations. Calculate the rate of temperature change to determine if it exceeds a preset threshold (1℃ / s). If it does not exceed the preset threshold, the battery is considered safe; if it exceeds the preset threshold, the battery is considered dangerous. Step 6: Dynamic threshold adjustment. The weights are adjusted based on SOC (State of Charge) and SOH (State of Health) to calculate the extrusion capacity parameters:

[0086] in, For strain energy density, For the rate of temperature change, This represents the rate of change of internal resistance.

[0087] Based on the extrusion capacity parameter W t Determine the current battery status: 80%≤W t At that time, it was determined that the battery was currently in a dangerous state; 60%≤W t When the battery level is less than 80%, the battery is determined to be at level three risk. 30%≤W t When the battery level is below 60%, the battery is determined to be at level 2 risk. 5%≤W t When the battery level is less than 30%, the battery is considered to be at Level 1 risk.

[0088] In W t When the battery level is below 5%, the battery is considered to be in a safe condition.

[0089] Figure 6 A schematic diagram showing the data of a No. 1 LFP lithium iron phosphate battery extruded in the thickness direction according to an embodiment of the present invention is illustrated.

[0090] Table 6 shows the extrusion data of the No. 1 LFP lithium iron phosphate battery in the thickness direction. Figure 6 As shown.

[0091] Table 6

[0092] Figure 7 A schematic diagram showing the data of a No. 2 LFP lithium iron phosphate battery extruded in the thickness direction according to an embodiment of the present invention is illustrated.

[0093] Table 7 shows the extrusion data of LFP lithium iron phosphate battery No. 2 in the thickness direction. Figure 7 As shown.

[0094] Table 7

[0095] Figure 8 A schematic diagram showing the data of a No. 1 LFP lithium iron phosphate battery extruded in the width direction according to an embodiment of the present invention is illustrated.

[0096] Table 8 shows the data on the width-direction extrusion of the No. 1 LFP lithium iron phosphate battery. Figure 8 As shown.

[0097] Table 8

[0098] Figure 9 A schematic diagram showing the data of a No. 2 LFP lithium iron phosphate battery extruded in the width direction according to an embodiment of the present invention is illustrated.

[0099] Table 9 shows the data on the width-direction extrusion of the No. 2 LFP lithium iron phosphate battery. Figure 9 As shown.

[0100] Table 9

[0101] This invention fully considers the hysteresis characteristics of surface temperature monitoring. Through dynamic weight allocation, it achieves risk assessment by relying on mechanical and electrochemical parameters in the early stage when temperature signals are missing, and responds quickly in the later stage when temperature signals appear. This solves the limitations of single temperature threshold early warning and improves the adaptability to batteries with different chemical systems.

[0102] Example 3

[0103] This disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the described method for evaluating the mechanical extrusion stability of lithium batteries.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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 evaluating the mechanical extrusion stability of lithium batteries, characterized in that, include: To obtain the basic parameters of the lithium battery, extrusion pressure is applied in the width and thickness directions respectively, and the extrusion deformation and temperature gradient are obtained. Calculate the strain energy density, rate of change of internal resistance, and rate of change of temperature; The extrusion capacity parameters are calculated based on strain energy density, internal resistance change rate and temperature change rate. The current state of the battery is determined based on the compression capacity parameters.

2. The method for evaluating the mechanical extrusion stability of lithium batteries according to claim 1, wherein, The strain energy density is calculated based on the extrusion deformation, including: The strain of the battery is calculated based on the extrusion deformation, and the stress is calculated based on the extrusion force and cell size. Strain energy density is calculated based on strain and stress.

3. The method for evaluating the mechanical extrusion stability of lithium batteries according to claim 2, wherein, The strain energy density is: in, For extrusion deformation, Let σ be the strain energy density, σ be the stress, and ε be the strain.

4. The method for evaluating the mechanical extrusion stability of lithium batteries according to claim 1, wherein, The rate of change of internal resistance is: in, R is the rate of change of internal resistance. t Let be the internal resistance of the battery at time t.

5. The method for evaluating the mechanical extrusion stability of lithium batteries according to claim 1, wherein, The rate of temperature change is: in, Let T(t) be the rate of temperature change, and T(t) be the temperature of the battery at time t.

6. The method for evaluating the mechanical extrusion stability of lithium batteries according to claim 1, wherein, The extrusion capacity parameters are: in, For strain energy density, For the rate of temperature change, Let α be the rate of change of internal resistance, and β be the weights corresponding to strain energy density, temperature change rate, and internal resistance change rate, respectively. , and The threshold values ​​are the strain energy density, temperature change rate, and internal resistance change rate.

7. The method for evaluating the mechanical extrusion stability of lithium batteries according to claim 6, wherein, α, β, and γ are iteratively updated based on real-time data using Bayesian optimization. Define the objective function as the early warning accuracy: f(α, β, γ) = Number of correct warnings / Total number of tests; Based on maximizing the objective function, Bayesian optimization is used to find the optimal weight combination that maximizes f(α, β, γ).

8. The method for evaluating the mechanical extrusion stability of lithium batteries according to claim 1, wherein, Determining the current state of the battery based on the compression capacity parameters includes: When the first threshold is less than or equal to the crushing capacity parameter, the lithium battery is determined to be in a dangerous state. When the second threshold is less than or equal to the extrusion capacity parameter and less than the first threshold, the lithium battery is determined to be in a level three risk state. When the third threshold is less than or equal to the extrusion capacity parameter and less than the second threshold, the lithium battery is determined to be in a level 2 risk state. When the fourth threshold is less than or equal to the extrusion capacity parameter and less than the third threshold, the lithium battery is determined to be in a level one risk state. When the compression capacity parameter is less than the fourth threshold, the lithium battery is determined to be in a safe state.

9. The method for evaluating the mechanical extrusion stability of lithium batteries according to claim 8, wherein, First threshold > Second threshold > Third threshold > Fourth threshold.

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 evaluating the mechanical crush stability of a lithium battery as described in any one of claims 1-9.

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