Battery safety performance evaluation method, device, equipment, storage medium and product

Abstract

This application provides a method, apparatus, device, storage medium, and product for evaluating battery safety performance, relating to the field of lithium battery technology. The method includes: acquiring a battery to be tested; performing a multi-stage thermal triggering test on the battery to be tested, the thermal triggering test including stepped heating at different heating rates; collecting multi-dimensional parameters of the battery to be tested during the thermal triggering test, including the battery's voltage drop point, thermal runaway trigger temperature, and valve opening time; and obtaining a consistency index of the battery to be tested based on the multi-dimensional parameters and a preset weighted scoring model, the consistency index being used to characterize the safety performance of the battery to be tested. This method, by combining thermal triggering testing with parameter quantitative analysis, significantly improves the ability to identify differences in thermal runaway characteristics, reduces the risk of system-level thermal runaway, and provides closed-loop feedback for battery process optimization and material development.

Description

Technical Field

[0001] This application relates to the field of lithium battery technology, and in particular to a method, apparatus, equipment, storage medium and product for evaluating battery safety performance. Background Technology

[0002] Lithium-ion batteries, as a core technology for modern energy storage, have been widely used in electric vehicles, energy storage systems, and consumer electronics. In the large-scale application of power batteries and energy storage batteries, the safety performance of battery packs or modules directly determines the reliability of the entire system and user safety.

[0003] Current battery testing methods widely used in the industry primarily focus on electrochemical parameters such as capacity and internal resistance, while neglecting the differences in thermal runaway characteristics of individual cells under extreme conditions. This testing method cannot effectively identify potential safety performance deviations in battery batches, making it difficult to quantify and control system safety risks after battery assembly.

[0004] Therefore, existing technologies lack a standardized and quantitative evaluation method for the safety performance of lithium batteries. Summary of the Invention

[0005] This application provides a method, apparatus, equipment, storage medium, and product for evaluating battery safety performance, in order to solve the technical problem that the prior art lacks a standardized and quantitative evaluation method for the safety performance of lithium batteries.

[0006] In a first aspect, this application provides a method for evaluating battery safety performance, including:

[0007] Obtain the battery to be tested;

[0008] The battery under test was subjected to a multi-stage thermal trigger test, which included stepped heating at different heating rates.

[0009] During the thermal trigger test, multi-dimensional parameters of the battery under test are collected, including the voltage drop point, thermal runaway trigger temperature, and valve opening time.

[0010] Based on multi-dimensional parameters and a pre-defined weighted scoring model, the consistency index of the battery under test is obtained. The consistency index is used to characterize the safety performance of the battery under test.

[0011] Secondly, this application provides a battery safety performance evaluation device, comprising:

[0012] The acquisition module is used to acquire the battery to be tested;

[0013] The thermal trigger test module is used to perform multi-stage thermal trigger tests on the battery under test. The thermal trigger test includes stepped heating at different heating rates.

[0014] The data acquisition module is used to acquire multi-dimensional parameters of the battery under test during the thermal trigger test. These parameters include the voltage drop point, thermal runaway trigger temperature, and valve opening time of the battery under test.

[0015] The evaluation module is used to obtain the consistency index of the battery under test based on multi-dimensional parameters and a preset weighted scoring model. The consistency index is used to characterize the safety performance of the battery under test.

[0016] Thirdly, this application provides an electronic device, including: a processor and a memory communicatively connected to the processor;

[0017] The memory stores the instructions that the computer executes;

[0018] The processor executes computer-executable instructions stored in memory to implement any of the methods of the first aspect.

[0019] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method of any one of the first aspects.

[0020] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method of any one of the first aspects.

[0021] The battery safety performance evaluation method, apparatus, equipment, storage medium, and products provided in this application solve the technical problem that traditional detection methods cannot reflect the differences in battery thermal runaway characteristics through multi-stage thermal triggering testing and multi-dimensional parameter acquisition. By controlling the heating rate in stages, the complex thermal environment in actual applications is simulated, thus comprehensively covering external heat sources and internal heat generation triggering mechanisms. The acquisition of multi-dimensional parameters further reveals the dynamic characteristics of individual cells during thermal runaway, identifies early signals of thermal runaway through voltage drop points, and quantifies the differences in thermal stability of battery materials through thermal runaway trigger temperatures. The consistency index calculated based on a weighted scoring model can objectively characterize the dispersion of battery safety performance, providing data support for the safe design of battery packs. The combination of thermal triggering testing and parameter quantification analysis significantly improves the ability to identify differences in thermal runaway characteristics, reduces the risk of system-level thermal runaway, and provides closed-loop feedback for battery process optimization and material development. Attached Figure Description

[0022] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0023] Figure 1 This is a schematic diagram of the detection method for a single battery cell;

[0024] Figure 2 A schematic flowchart illustrating a battery safety performance evaluation method provided in this application embodiment;

[0025] Figure 3 This is a schematic diagram of the structure of a battery safety performance evaluation device provided in an embodiment of this application;

[0026] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0027] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0028] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0029] It should be noted that the battery safety performance evaluation method, apparatus, equipment, storage medium and products provided in this application can be used in the field of lithium battery technology, or in any field other than lithium battery technology. The application fields of the battery safety performance evaluation method, apparatus, equipment, storage medium and products in this application are not limited.

[0030] This application specifically addresses the testing of batch safety consistency of batteries in large-scale production scenarios such as power batteries and energy storage systems. Lithium-ion batteries, as a core technology of modern energy storage, are widely used in electric vehicles, energy storage systems, and consumer electronics. In the large-scale application of power and energy storage batteries, the safety performance of battery packs or modules directly determines the reliability of the entire system and user safety. For example, in the electric vehicle field, differences in the thermal runaway characteristics of individual batteries may lead to localized overheating within the module, triggering a chain reaction that could cause fires or explosions. In energy storage systems, insufficient safety consistency of individual batteries may lead to thermal runaway risks for the entire energy storage power station, threatening grid security. However, current industry-standard battery testing methods primarily focus on electrochemical parameters such as capacity and internal resistance, neglecting the differences in thermal runaway characteristics of individual batteries under extreme conditions. This testing method cannot effectively identify potential safety performance deviations in battery batches, making it difficult to quantify and control system safety risks after battery assembly. Furthermore, with the rapid iteration of battery material systems (such as high-nickel cathodes and silicon-carbon anodes) and manufacturing processes, the thermal runaway triggering mechanisms and safety performance differences of individual batteries are becoming increasingly significant.

[0031] In existing technologies, the testing of individual battery cells mainly relies on the testing of conventional electrochemical parameters such as capacity, internal resistance, and self-discharge rate. These parameters are sorted to achieve performance consistency of the battery pack. Figure 1 This is a schematic diagram of the detection method for a single battery cell, as shown below. Figure 1 As shown, existing battery cell testing methods include: screening based on limited capacity, limited internal resistance, and limited voltage drop under different environments such as high temperature, low temperature, or room temperature; and screening battery cells by charging and discharging the batteries and comparing the changes in capacity, voltage, and current during the charging and discharging process.

[0032] However, current technologies rely on electrochemical parameters such as capacity and internal resistance for individual battery cell testing, but these parameters cannot reflect the differences in thermal runaway characteristics under extreme conditions. For example, in electric vehicle battery modules, if the thermal runaway trigger temperatures of individual cells differ significantly, it may lead to localized overheating within the module and trigger a chain reaction, but traditional sorting methods cannot identify such risks. Furthermore, thermal runaway testing only uses "pass" as the criterion, without quantitative analysis of parameters such as trigger temperature and voltage drop rate, making it impossible to distinguish the safety performance of individual cells. This limitation is particularly prominent in high-energy-density batteries (such as high-nickel cathode systems) because their thermal runaway triggering mechanisms are complex, and current technologies lack standardized evaluation methods, making it difficult to guide process optimization through consistency evaluation models.

[0033] The battery safety performance evaluation method, apparatus, equipment, storage medium, and product provided in this application apply a simulated thermal runaway external heat source to a single battery cell through a multi-stage thermal triggering test, covering both external heat source triggering and internal heat generation triggering mechanisms using different heating rates. During the test, high-precision sensors collect multi-dimensional parameters in real time, such as voltage drop point, thermal runaway trigger temperature, and valve opening time, recording key characteristics of the single battery cell during the thermal runaway process. Based on the collected multi-dimensional parameters and combined with a preset weighted scoring model, the consistency index of the tested battery is calculated to quantify the safety performance differences of single batteries within a batch. By combining thermal triggering testing with data acquisition, dynamic monitoring and quantitative analysis of battery thermal runaway characteristics are achieved, aiming to solve the aforementioned technical problems in existing technologies.

[0034] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0035] Figure 2 This is a flowchart illustrating a battery safety performance evaluation method provided in an embodiment of this application, as shown below. Figure 2 As shown, the method includes:

[0036] S201. Obtain the battery to be tested.

[0037] S202. Perform multi-stage thermal triggering tests on the battery under test.

[0038] In this embodiment of the application, the thermal triggering test includes stepped heating at different heating rates.

[0039] In one example, a multi-stage thermal triggering test may include a test method that simulates the thermal runaway process of the battery under test by controlling the heating rate in stages (e.g., rapidly heating to the trigger temperature and then switching to slow heating), for example, using 15 Heat up to 400 / min Then switched to 1.5 / min stepped temperature increase.

[0040] S203. Collect multi-dimensional parameters of the battery under test during the thermal trigger test.

[0041] In this embodiment, the multi-dimensional parameters include the voltage drop point, thermal runaway trigger temperature, and valve opening time of the battery under test.

[0042] In one example, the multi-dimensional parameters are several key indicators collected during the multi-stage thermal triggering test of the battery under test, such as the voltage drop point (…). Thermal runaway trigger temperature ( ), valve opening time (s), etc.

[0043] S204. Based on multi-dimensional parameters and a preset weighted scoring model, obtain the consistency index of the battery under test.

[0044] In this embodiment of the application, the consistency index is used to characterize the safety performance of the battery under test.

[0045] In one example, the consistency index is a quantitative indicator calculated using a weighted scoring model, used to characterize the dispersion of the safety performance of individual tested batteries within a batch.

[0046] In one implementation scenario, the consistency evaluation of the safety performance of a single lithium-ion battery cell may include: 1. Performing a multi-stage thermal triggering test on the battery under test: ① Using a controllable heating element with a power accuracy of ±1% attached to the surface of the single battery cell under test, at 15 Heat up to 400 / min Afterwards (simulating the heat generation process during thermal runaway), switch to 1.5. The temperature is increased in a stepped manner at 20°C / min (each step is held for 15 minutes to simulate self-generated heat after thermal runaway), and the thermal runaway trigger temperature, voltage, and time are recorded. The battery cells under test are observed using the simulation module. ② A controllable heating element with a power accuracy of ±1% is attached to the surface of the individual battery cell under test, and the temperature is increased at 20°C / min. Simultaneously overcharge the battery at 10℃ / min, 10℃ / min, and 5℃ / min until thermal runaway is triggered, and record the thermal runaway trigger temperature, voltage, and time of the battery under test.

[0047] 2. Collect multi-dimensional parameters of the battery under test during the thermal trigger test, which may include: monitoring points: voltage drop point ( ), voltage drop to 0V point, large surface temperature rise rate (≥3) / s), thermal runaway trigger temperature ( ), Valve opening time (s). Analysis points: thermal runaway trigger temperature A, time interval between the voltage drop to 0V and the thermal runaway trigger point B, time interval between two voltage drop points C, valve opening time D.

[0048] 3. Based on multi-dimensional parameters and a pre-defined weighted scoring model, the consistency index of the battery under test is obtained. The key parameter set includes:

[0049]

[0050] The calculation of the consistency index (CI) of the battery under test may include:

[0051]

[0052] In the formula, For parameter weights, These are measured values. This is the batch average. The standard deviation is denoted as . It is classified as Grade A consistency.

[0053] In another implementation scenario, the battery safety performance evaluation method may include: Step 1: Stepped thermal triggering, the battery cell to be tested is processed as follows: the surface bonding power accuracy of the battery cell to be tested is... The controllable heating element is used to conduct a multi-stage thermal triggering test. The multi-stage thermal triggering test specifically includes: Stage 1: at 15 The temperature is increased to 400℃ per minute to simulate the heat generation process that triggers thermal runaway. The thermal runaway trigger temperature, voltage, and time are recorded. Second stage: Switch to 1.5... The temperature is increased in steps at 20°C / min (each step held for 15 minutes) to simulate the self-generating heat process after thermal runaway, while continuously monitoring voltage and temperature changes; Third stage: [The temperature is increased at 20°C / min in stages (each stage held for 15 minutes)] / min, 10 / min、5 Simultaneously overcharge at three heating rates per minute until thermal runaway is triggered, and record the trigger temperature, voltage, and time under each condition.

[0054] Acquire the thermal runaway parameters of the individual cells under test under different thermal triggering conditions, including but not limited to trigger temperature, voltage drop rate, and valve opening time. The thermal runaway trigger temperature is the temperature at which the battery under test first experiences a voltage drop during heating; for example, the temperature at which a battery under test experiences a voltage drop of 15°C. Heat up to 200 / min The voltage suddenly drops, and the trigger temperature is 200°C. Step heating is a process in which the temperature is gradually increased at a constant rate, for example, from 15... / min switched to 1.5 The heating rate is measured in seconds per minute. Overcharging is defined as injecting electrical energy beyond the rated capacity of the battery under test using an external power source, for example, at a rate of 20... A heating rate of / min combined with overcharge current triggers thermal runaway.

[0055] By simulating extreme thermal environments that the tested batteries may encounter in real-world applications (such as localized overheating within the module and overcharging abuse), the thermal runaway triggering characteristics of the tested battery cells are quantified. The combination of stepped temperature rise and overcharging can cover different triggering mechanisms (such as external heat sources and internal heat generation), thereby comprehensively evaluating the thermal runaway risk of the battery. Multi-condition testing reveals the differences in safety performance between individual cells, providing a data foundation for subsequent consistency evaluation.

[0056] Step 2: Multi-dimensional data acquisition and consistency evaluation model construction. This involves collecting a set of multi-dimensional parameters of the battery under test during the thermal triggering test. From this set of parameters, monitoring points (voltage drop point, large-area temperature rise rate, valve opening time, etc.) and analysis points (trigger temperature, voltage drop interval, valve opening time) are extracted. Threshold ranges and weights are set based on the correlation between the multi-dimensional parameters and the materials and processes of the battery cells under test. For example, the trigger temperature threshold is... (Weight 40%). The consistency index (CI) of the tested battery cell is calculated using the following formula:

[0057]

[0058] In the formula, As a consistency index, For parameter weights, These are measured values. This is the batch average. The standard deviation is used. Based on the consistency index of the individual battery cells under test, the batch consistency level of the tested battery cells is determined. For example, CI < 5% is grade A. The consistency index and grade evaluation result of the tested battery batch are output. The consistency index (CI) is a quantitative indicator reflecting the differences in safety performance of individual tested batteries within a batch. For example, a batch of tested batteries with a CI value of 3% has better consistency than a batch with a CI value of 8%. The weighted score includes: assigning weights according to the importance of parameters (e.g., trigger temperature weight 40%) and calculating the comprehensive score. For example, a larger deviation in trigger temperature will significantly increase the CI value.

[0059] In another implementation scenario, multiple sets of thermal triggering conditions are designed based on the application scenario of the battery under test (such as electric vehicles, energy storage systems, and consumer electronics) to cover the extreme thermal environments of different scenarios. For example, for the high-temperature operating environment of energy storage systems, a high-temperature gradient (such as 300°C) is added. ~500 ) and prolonged low-speed heating (e.g., 0.5) Thermal triggering conditions ( / min). For electric vehicle fast charging scenarios, test conditions are added to simultaneously trigger overcharge rate and high temperature. By dynamically adjusting the thermal triggering conditions, the complex thermal runaway triggering mechanism of the battery under test in real-world applications is simulated.

[0060] By matching the thermal environment of real-world application scenarios, thermal triggering experiments can more accurately reveal the differences in the safety performance of individual batteries under specific conditions. High-temperature gradient testing in energy storage systems can identify the risk of thermal runaway in batteries under long-term high-temperature operation, while overcharge-high-temperature synchronous triggering testing in electric vehicle scenarios can simulate the safety characteristics under fast-charging abuse. This significantly improves the scenario relevance of thermal runaway parameters, providing a more realistic evaluation basis for battery pack safety design.

[0061] In another implementation scenario, machine learning algorithms (such as random forests and support vector machines) are introduced to train a model using historical thermal runaway test data. This model dynamically adjusts the weighting of various parameters of the battery under test (such as trigger temperature, voltage drop rate, and valve opening time). The model automatically optimizes the weights based on the battery material system (such as high-nickel cathode or silicon-carbon anode) or process differences (such as electrode coating thickness and electrolyte formulation), making the weighted scoring more consistent with the thermal runaway characteristics of different battery types.

[0062] Traditional fixed-weighted models struggle to adapt to the varying thermal runaway characteristics of different material systems or processes. Dynamically weighted models, however, use a data-driven approach to automatically adjust parameter weights based on battery characteristics. When the tested battery is a high-nickel cathode battery with high sensitivity to thermal runaway trigger temperature, the model automatically increases the weight of the trigger temperature, thus more accurately reflecting differences in safety performance. This significantly improves the adaptability and accuracy of the evaluation model, providing more flexible data support for materials research and development and process optimization.

[0063] In another implementation scenario, correlation analysis (such as Pearson correlation coefficient and principal component analysis) is introduced between thermal runaway parameters based on the existing weighted scoring to identify the coupling relationships between parameters. For example, there may be a negative correlation between trigger temperature and valve opening time (the higher the trigger temperature, the shorter the valve opening time). Correlation analysis is used to adjust the parameter weight allocation to avoid scoring distortion caused by redundancy or conflict between parameters.

[0064] Traditional weighted scoring models assume that all parameters are independent, but in reality, there are complex correlations between thermal runaway parameters. Correlation analysis can eliminate the influence of redundant parameters. For example, when trigger temperature and voltage drop rate are highly correlated, the weight of voltage drop rate can be reduced to avoid double-counting. This method significantly improves the scientific rigor of the scoring model, reduces evaluation bias caused by parameter coupling, and thus more accurately reflects the consistency of safety performance of individual cells within a batch.

[0065] In another implementation scenario, real-time monitoring technologies (such as high-speed thermal imaging and high-frequency voltage sampling) are introduced into multi-stage thermal triggering tests to dynamically adjust the data acquisition frequency of multi-dimensional parameters of the battery under test. For example, low-frequency sampling (e.g., 1Hz) is used before thermal runaway is triggered, and high-frequency sampling (e.g., 100Hz) is switched after triggering to capture key transient parameters such as voltage drop and temperature rise rate. At the same time, adaptive filtering algorithms (such as Kalman filtering) are used to eliminate noise interference.

[0066] Traditional fixed-frequency data acquisition may miss transient characteristics triggered by thermal runaway (such as microsecond-level changes in voltage sags), while real-time monitoring and adaptive sampling techniques can accurately capture the dynamic changes of key parameters. By identifying the precise time point of voltage sags through high-frequency sampling and combining it with adaptive filtering to eliminate environmental noise, the accuracy and reliability of data acquisition are significantly improved, providing higher-quality input data for subsequent consistency evaluation.

[0067] In another implementation scenario, a correlation model is established between battery manufacturing process parameters (such as electrode coating thickness, electrolyte injection volume, formation process conditions, etc.) and thermal runaway parameters (such as trigger temperature, valve opening time, etc.). Through regression analysis or neural networks, the impact of different combinations of process parameters on the consistency of thermal runaway is predicted, for example, predicting the contribution of electrode coating non-uniformity to the standard deviation of trigger temperature.

[0068] Traditional thermal runaway consistency assessments rely solely on test results, making it difficult to trace process defects. By employing a process-thermal runaway correlation model, key factors influencing safety performance consistency can be directly identified. For example, if the model shows that fluctuations in electrolyte injection volume are the primary cause of temperature differences, the electrolyte injection process can be optimized accordingly. This method significantly improves the efficiency and relevance of process optimization, providing a closed-loop feedback mechanism for battery production quality control.

[0069] The battery safety performance evaluation method provided in this embodiment solves the technical problem that traditional detection methods cannot reflect the differences in battery thermal runaway characteristics through multi-stage thermal triggering tests and multi-dimensional parameter acquisition. By controlling the heating rate in stages, it simulates the complex thermal environment in actual applications, thus comprehensively covering external heat sources and internal heat generation triggering mechanisms. The acquisition of multi-dimensional parameters further reveals the dynamic characteristics of individual cells during thermal runaway, identifies early signals of thermal runaway through voltage drop points, and quantifies the differences in thermal stability of battery materials through thermal runaway trigger temperatures. The consistency index calculated based on a weighted scoring model can objectively characterize the dispersion of battery safety performance, providing data support for the safe design of battery packs. The combination of thermal triggering tests and parameter quantification analysis significantly improves the ability to identify differences in thermal runaway characteristics, reduces the risk of system-level thermal runaway, and provides closed-loop feedback for battery process optimization and material development.

[0070] Optionally, a multi-stage thermal triggering test is performed on the battery under test, including: initially heating the battery under test at a first heating rate using a controllable heating element until the battery under test reaches a preset temperature; after the battery under test reaches the preset temperature, switching the controllable heating element to a second heating rate to perform stepped heating on the battery under test.

[0071] In one example, a controllable heating element is used to control the heating rate in stages to simulate the thermal runaway triggering process of the battery under test in a real-world application. The initial heating stage uses a first heating rate (e.g., 15...). Rapidly heat to the preset temperature (e.g., 400°C / min) This simulates the external heat source triggering mechanism of the battery under test; it switches to a low-speed stepped heating method with a second heating rate (e.g., 1.5). (r / min) simulates the self-heating process of the battery under test after thermal runaway. A staged heating method covers both external heat sources and internal heat generation as triggering mechanisms to ensure comprehensive thermal triggering testing. The first heating rate is the initial heating rate, for example, 15... / min. The second heating rate is the heating rate of the stepped heating stage, for example, 1.5 / min. The preset temperature is the target temperature set in the multi-stage thermal triggering test, for example, 400. .

[0072] By controlling the staged heating rate, a comprehensive coverage of the thermal runaway triggering mechanism is achieved. Initial high-speed heating simulates external heat sources triggering thermal runaway, while low-speed stepped heating simulates the development of thermal runaway caused by self-generated heat. This combination of staged heating rates significantly improves the ability to identify differences in thermal runaway characteristics, providing more accurate data support for the safe design of battery packs.

[0073] Optionally, after the battery under test is heated in a stepped manner, the method further includes: performing an overcharge test on the battery under test, and obtaining the thermal runaway trigger parameters of the battery under test under the overcharge test conditions.

[0074] In one example, an overcharge test is added after stepped heating to simulate the thermal runaway risk of the battery under test under abuse conditions (such as overcharging). For example, after stepped heating, an overcurrent is injected into the battery under test through an external power source, while the voltage drop point and trigger temperature are monitored, and thermal runaway parameters under overcharge conditions are recorded. By combining an external heat source and a dual triggering mechanism of overcharge abuse, the coverage of thermal triggering testing is further expanded. The overcharge test involves injecting electrical energy exceeding the rated capacity of the battery under test through an external power source, for example, at 20... A heating rate of / min combined with overcharge current triggers thermal runaway.

[0075] The introduction of overcharge testing enables a quantitative assessment of thermal runaway risk under abuse conditions. Overcharge testing can simulate the thermal runaway characteristics of batteries under fast charging or misoperation scenarios, revealing the differences in safety performance of individual batteries under overcharge conditions. By expanding the triggering mechanism of thermal trigger testing, the comprehensiveness of the evaluation results is significantly improved, providing more practical data support for the safe design of battery packs.

[0076] Optionally, based on multi-dimensional parameters and combined with a preset weighted scoring model, the consistency index of the battery under test is obtained, including: calculating the standardized dispersion index within the batch corresponding to the battery under test according to the preset parameter threshold range and weight allocation, combined with the preset weighted scoring model; wherein, the preset parameter threshold range includes at least one of the following: the fluctuation range of electrode coating thickness, the fluctuation range of electrolyte injection volume; the standardized dispersion index includes at least one of the following: the intra-batch parameter dispersion calculated based on standard deviation, the parameter coupling degree based on principal component analysis.

[0077] In one example, multi-dimensional parameters are processed by pre-setting parameter threshold ranges and weight assignments. For example, the threshold for trigger temperature is 110±20. The weight of the valve opening time threshold is 40%; the threshold for valve opening time is 500±50s, with a weight of 15%. A quantitative evaluation of the consistency of safety performance is achieved by calculating the standardized dispersion index (such as CI value) of multi-dimensional parameters of the batteries under test within a batch. The preset parameter threshold ranges include: the allowable fluctuation range of each parameter, for example, the threshold for trigger temperature is 110±20. Standardized dispersion index is the degree of dispersion of a parameter within a batch, calculated using statistical methods (such as standard deviation), for example, the CI value.

[0078] For example, a process-thermal runaway consistency prediction model can be established by incorporating the fluctuation range of process parameters (such as electrode coating thickness and electrolyte injection volume) into a preset parameter threshold range. For instance, if the electrode coating thickness fluctuation range is ±5μm, it can be correlated with the threshold range of the trigger temperature to analyze the impact of process parameters on thermal runaway characteristics. The fluctuation range of electrode coating thickness can include the allowable thickness deviation in the coating process of the positive and negative electrodes of the battery under test, for example, ±5μm. The fluctuation range of electrolyte injection volume can include the allowable deviation of the electrolyte injection volume during the assembly process of the battery under test, for example, ±5%.

[0079] For example, by combining standard deviation calculation and principal component analysis, the parameter dispersion and parameter coupling of the tested batteries within a batch can be comprehensively evaluated. For instance, standard deviation calculation quantifies the dispersion of trigger temperature, while principal component analysis identifies the negative correlation between trigger temperature and valve opening time, thereby optimizing weight allocation. Specifically, standard deviation calculation quantifies the dispersion of multi-dimensional parameters, such as the standard deviation of trigger temperature. Principal component analysis extracts the coupling relationships between multi-dimensional parameters through dimensionality reduction methods, such as the correlation between trigger temperature and valve opening time.

[0080] By pre-setting parameter threshold ranges and weight allocations, the objective quantification of safety performance differences among individual cells within a batch was achieved. A high weight allocation for trigger temperature prioritizes thermal runaway risk, while the weight allocation for valve opening time reflects the effectiveness of explosion-proof design. The calculation of standardized dispersion indices significantly improved the scientific rigor of the evaluation results, providing data support for battery screening and process optimization. By incorporating process parameters into pre-set parameter threshold ranges, the correlation between thermal runaway parameters and process defects was analyzed. Process parameters can be specifically optimized to reduce the thermal runaway risk of individual cells within a batch. The establishment of a process-thermal runaway correlation model significantly improved the efficiency and targeting of process optimization. By combining standard deviation calculation and principal component analysis, a comprehensive evaluation of parameter dispersion and coupling was achieved. Principal component analysis eliminates the influence of redundant parameters, avoiding scoring distortion due to parameter redundancy. Multi-dimensional analysis significantly improved the scientific rigor of the scoring model, providing more accurate data support for process optimization.

[0081] Optionally, before performing multi-stage thermal triggering tests on the battery under test, the method further includes: adjusting the heating rate and test conditions of the multi-stage thermal triggering test according to the thermal environment characteristics of the application scenario corresponding to the battery under test, so as to obtain the adjusted heating rate and adjusted test conditions.

[0082] In one example, by dynamically adjusting the heating rate and test conditions, the multi-stage thermal triggering test is made more closely resemble the actual application scenario of the battery under test. For example, for the high-temperature operating environment of energy storage systems, a temperature of 300°C is set... ~500 The high temperature gradient and 0.5 Low-speed temperature rise per minute; 20 is set for the overuse of fast charging in electric vehicles. The heating rate per minute is synchronized with the overcharge triggering condition. The thermal environment characteristics of the application scenario include extreme thermal environments that the battery under test may encounter in actual applications, such as high-temperature operation of energy storage systems or abuse of fast charging in electric vehicles.

[0083] By dynamically adjusting test conditions, the thermal triggering test was matched with real-world application scenarios. The high-temperature gradient test in energy storage systems can simulate the risk of thermal runaway under long-term high-temperature operation, while the overcharge-high-temperature synchronous triggering test in electric vehicle scenarios can simulate the safety characteristics under fast-charging abuse. Through scenario-related optimization, the practicality of the evaluation results was significantly improved, providing more accurate data support for the safe design of battery packs.

[0084] Optionally, after performing multi-stage thermal triggering tests on the battery under test, the method further includes: updating the preset parameter threshold range and weight allocation based on the adjusted test conditions and the preset process parameter fluctuation range, so as to update the preset weighted scoring model.

[0085] In one example, by combining dynamically adjusted test conditions and process parameter fluctuation ranges, the preset parameter threshold ranges and weight allocations are dynamically updated. For instance, in an energy storage system scenario, high-temperature gradient test results can be fed back to the process parameter fluctuation range to optimize the threshold allocation for electrode coating thickness. The dynamically adjusted test conditions are the heating rate and test conditions adjusted based on the thermal environment characteristics of the application scenario. The process parameter fluctuation range refers to the allowable deviations of process parameters such as electrode coating thickness and electrolyte injection volume.

[0086] By dynamically updating the parameter threshold range and weight allocation, closed-loop optimization of the evaluation model is achieved. The combination of dynamically adjusted test conditions and process parameter fluctuation range can reflect the impact of material or process improvements on thermal runaway characteristics in real time, thereby significantly improving the adaptability and accuracy of the evaluation model and providing a closed-loop feedback mechanism for battery production quality control.

[0087] Figure 3 This is a schematic diagram of the structure of a battery safety performance evaluation device provided in an embodiment of this application, as shown below. Figure 3 As shown, the battery safety performance evaluation device 30 provided in this embodiment includes:

[0088] Acquisition module 301 is used to acquire the battery to be tested;

[0089] The thermal trigger test module 302 is used to perform multi-stage thermal trigger tests on the battery under test. The thermal trigger test includes stepped heating at different heating rates.

[0090] The acquisition module 303 is used to acquire multi-dimensional parameters of the battery under test during the thermal trigger test. The multi-dimensional parameters include the voltage drop point, thermal runaway trigger temperature and valve opening time of the battery under test.

[0091] Evaluation module 304 is used to obtain the consistency index of the battery under test based on multi-dimensional parameters and a preset weighted scoring model. The consistency index is used to characterize the safety performance of the battery under test.

[0092] In one possible implementation, the thermal trigger test module 302 is specifically used to: initially heat the battery under test at a first heating rate using a controllable heating element until the battery under test reaches a preset temperature; after the battery under test reaches the preset temperature, switch the controllable heating element to a second heating rate to perform stepped heating of the battery under test.

[0093] In one possible implementation, the battery safety performance evaluation device is also specifically used to: perform an overcharge test on the battery under test and obtain the thermal runaway trigger parameters of the battery under test under the overcharge test conditions.

[0094] In one possible implementation, the evaluation module 304 is specifically used to: calculate the standardized dispersion index within the batch corresponding to the battery under test based on the preset parameter threshold range and weight allocation, combined with the preset weighted scoring model; wherein, the preset parameter threshold range includes at least one of the following: the fluctuation range of electrode coating thickness, the fluctuation range of electrolyte injection volume; the standardized dispersion index includes at least one of the following: the intra-batch parameter dispersion calculated based on standard deviation, the parameter coupling degree based on principal component analysis.

[0095] In one possible implementation, the battery safety performance evaluation device is further specifically used to: adjust the heating rate and test conditions of the multi-stage thermal triggering test according to the thermal environment characteristics of the application scenario corresponding to the battery under test, so as to obtain the adjusted heating rate and adjusted test conditions.

[0096] In one possible implementation, the battery safety performance evaluation device is further specifically used to: update the preset parameter threshold range and weight allocation based on the adjusted test conditions and the preset process parameter fluctuation range, so as to update the preset weighted scoring model.

[0097] The battery safety performance evaluation device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0098] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 40 may include a memory 401 and a processor 402. Optionally, the electronic device may also include a transceiver 403, wherein the memory 401 and the processor 402 communicate with each other; for example, the memory 401, the processor 402 and the transceiver 403 may communicate via a communication bus 404, the memory 401 is used to store a computer program, and the processor 402 executes the computer program to implement the method of the above embodiments.

[0099] Optionally, the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps in the method embodiments disclosed in this application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0100] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the methods in any of the above method embodiments.

[0101] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the methods in any of the above method embodiments.

[0102] All or part of the steps in the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable memory. When the program is executed, it performs the steps of the above method embodiments; and the aforementioned memory (storage medium) includes: read-only memory (ROM), RAM, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof.

[0103] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processing unit of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0104] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0105] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0106] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

[0107] In this application, the term "comprising" and its variations can refer to non-limiting inclusion; the term "or" and its variations can refer to "and / or". The terms "first", "second", etc., in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. In this application, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0108] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0109] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0110] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0111] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0112] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0113] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0114] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0115] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0116] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for evaluating battery safety performance, characterized in that, The method includes: Obtain the battery to be tested; The battery under test is subjected to a multi-stage thermal triggering test, which includes stepped heating at different heating rates. During the thermal triggering test, multi-dimensional parameters of the battery under test are collected, including the voltage drop point, thermal runaway trigger temperature, and valve opening time of the battery under test. Based on the multi-dimensional parameters and combined with a preset weighted scoring model, the consistency index of the battery under test is obtained, and the consistency index is used to characterize the safety performance of the battery under test.

2. The method according to claim 1, characterized in that, The multi-stage thermal triggering test on the battery under test includes: The battery under test is initially heated at a first heating rate using a controllable heating element until it reaches a preset temperature. After the battery under test reaches the preset temperature, the controllable heating element is switched to the second heating rate to perform stepped heating on the battery under test.

3. The method according to claim 2, characterized in that, After the step heating of the battery under test, the method further includes: An overcharge test is performed on the battery under test to obtain the thermal runaway trigger parameters of the battery under test under the overcharge test conditions.

4. The method according to claim 1, characterized in that, The process of obtaining the consistency index of the battery under test based on the multi-dimensional parameters and a preset weighted scoring model includes: Based on the preset parameter threshold range and weight allocation, and combined with the preset weighted scoring model, the standardized dispersion index within the batch corresponding to the battery under test is calculated; wherein, the preset parameter threshold range includes at least one of the following: the fluctuation range of electrode coating thickness and the fluctuation range of electrolyte injection volume; the standardized dispersion index includes at least one of the following: the intra-batch parameter dispersion calculated based on standard deviation and the parameter coupling degree based on principal component analysis.

5. The method according to claim 4, characterized in that, Before performing the multi-stage thermal triggering test on the battery under test, the method further includes: Based on the thermal environment characteristics of the application scenario corresponding to the battery under test, the heating rate and test conditions of the multi-stage thermal triggering test are adjusted to obtain the adjusted heating rate and adjusted test conditions.

6. The method according to claim 5, characterized in that, After performing the multi-stage thermal triggering test on the battery under test, the method further includes: Based on the adjusted test conditions and the fluctuation range of the preset process parameters, the preset parameter threshold range and weight allocation are updated to update the preset weighted scoring model.

7. A battery safety performance evaluation device, characterized in that, The device includes: The acquisition module is used to acquire the battery to be tested; A thermal trigger test module is used to perform multi-stage thermal trigger tests on the battery under test, the thermal trigger test including stepped heating at different heating rates; The acquisition module is used to acquire multi-dimensional parameters of the battery under test during the thermal triggering test. The multi-dimensional parameters include the voltage drop point, thermal runaway trigger temperature, and valve opening time of the battery under test. The evaluation module is used to obtain the consistency index of the battery under test based on the multi-dimensional parameters and a preset weighted scoring model. The consistency index is used to characterize the safety performance of the battery under test.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

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