Electrolyte fire sensitivity experiment method

Abstract

The invention discloses an electrolyte fire sensitivity experiment method, and relates to the technical field of fire protection, and the method comprises the steps: setting a plurality of ignition sources according to the concentration data of a gas-liquid mixture, carrying out the ignition according to the energy gradient from low to high, and generating the minimum ignitable energy and the ignition source parameter data; carrying out an ignition experiment on the minimum ignitable energy and the ignition source parameter data, recording flame propagation speed, ignition delay and combustion duration, and generating combustion process time sequence data; arranging and analyzing the time sequence data of the combustion process, calculating a fire sensitivity index, and repeating experiments under the conditions of different cavity temperatures, ventilation speeds, electrolyte volumes and ignition source types to generate multi-condition fire sensitivity data; and sorting and analyzing the multi-condition fire sensitivity data to generate a fire sensitivity evaluation result. The method provides a scientific decision basis for safety assessment and electrolyte design.

Description

Technical Field

[0001] This invention relates to the field of fire protection technology, and in particular to a method for testing the fire sensitivity of electrolytes. Background Technology

[0002] Electrolytes, as the core medium in electrochemical energy storage and chemical reactions, directly affect safety and stability through their chemical properties and physical performance. With the widespread application of lithium-ion batteries, sodium-ion batteries, and other novel energy storage devices, the thermal stability and flammability of electrolytes under different environments have become a key research focus. In existing technologies, the assessment of electrolyte fire risks largely relies on empirical experiments and simple ignition tests, determining flammability by directly igniting electrolyte samples or observing combustion behavior through combustion experiments.

[0003] While existing technologies can provide qualitative or semi-quantitative information on the combustion characteristics of electrolytes, limitations remain in experimental methods. The lack of systematic data acquisition and analysis methods for flame propagation behavior, ignition delay, and combustion duration under various conditions results in a lack of comparability and quantitative assessment of fire sensitivities under different temperatures, ventilation conditions, and ignition energies. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an experimental method for electrolyte fire sensitivity to solve the problem that fire sensitivity cannot be quantitatively evaluated.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: This invention provides a method for testing the fire sensitivity of electrolytes, comprising, Weigh a fixed volume of the electrolyte sample to be tested, record the initial state, and generate sample physical parameter data; Based on the physical parameter data of the sample, the electrolyte sample is placed in a controllable sealed cavity, the temperature and ventilation conditions are adjusted, gas-liquid mixture description data is generated, and the vapor concentration is measured to generate gas-liquid mixture concentration data. Based on the concentration data of the gas-liquid mixture, multiple ignition sources are set and ignited sequentially from low to high energy gradient to generate minimum ignition energy and ignition source parameter data. Ignition experiments were conducted on the minimum ignition energy and ignition source parameters, and flame propagation speed, ignition delay and combustion duration were recorded to generate combustion process time series data. The time series data of the combustion process were organized and analyzed to calculate the fire sensitivity index. The experiment was repeated under different cavity temperatures, ventilation speeds, electrolyte volumes and ignition source types to generate multi-condition fire sensitivity data. The fire sensitivity data under multiple conditions are collected and analyzed to generate fire sensitivity assessment results.

[0007] In a preferred embodiment of the electrolyte fire sensitivity test method of the present invention, the specific steps for generating sample physical parameter data are as follows: Weigh a fixed volume of the electrolyte sample to be tested, record the initial state of color, transparency and temperature, and generate initial record data; Based on the initial recorded data, the density, viscosity, and conductivity of the electrolyte sample were measured to generate the first stage of physical parameter data; Numerical correction and correlation feature combination are performed on the physical parameter data of the first stage to generate sample physical parameter data.

[0008] In a preferred embodiment of the electrolyte fire sensitivity test method of the present invention, the specific steps for generating the gas-liquid mixture descriptive data are as follows: Based on the physical parameter data of the sample, the electrolyte sample is placed into a controllable sealed cavity, and the initial temperature, humidity and background vapor baseline of the cavity are collected to generate the initial input data for volatilization. Based on the initial input data of volatilization, the heating power of the cavity, the ventilation flow rate and the atomization mode are adjusted, and the temperature and ventilation conditions are controlled in a closed loop to generate steady-state vapor concentration curve data. Based on the steady-state vapor concentration curve data, the trend and stability of vapor concentration changes in the cavity are analyzed, the parameters of the gas-liquid mixture are recorded, and descriptive data of the gas-liquid mixture are generated.

[0009] In a preferred embodiment of the electrolyte fire sensitivity test method of the present invention, the specific steps for generating the concentration data of the gas-liquid mixture are as follows: Based on the gas-liquid mixture description data, the chamber temperature, humidity, and initial vapor concentration are set to form the initial measurement state; A gas sensor array is deployed at the initial measurement state to continuously collect vapor concentration data and generate real-time concentration time series data. Smoothing, normalization, and trend analysis are performed on real-time concentration time series data to generate gas-liquid mixture concentration data.

[0010] As a preferred embodiment of the electrolyte fire sensitivity test method of the present invention, the specific steps for generating the minimum ignition energy and ignition source parameter data are as follows: Based on the concentration data of the gas-liquid mixture, the ignition energy range and ignition source type to be tested are set, and the initial parameter set of the ignition source is generated. Using the initial parameter set of the ignition source, the mixture is loaded sequentially into the gas-liquid mixture according to the energy gradient from low to high, and the ignition state and flame response are recorded to generate ignition experiment time series data. Energy response analysis was performed on the ignition experiment time series data to identify the minimum energy required to ignite and the corresponding ignition source parameters. The data were then processed to generate minimum ignitable energy and ignition source parameter data.

[0011] In a preferred embodiment of the electrolyte fire sensitivity test method of the present invention, the specific steps for generating combustion process time series data are as follows: Ignition experiments were conducted on the minimum ignitable energy and the corresponding ignition source parameters, and the flame initiation signal was recorded. Using a high-speed camera, infrared temperature sensor and optical flame detection, the flame propagation speed, ignition delay and combustion duration are recorded in real time to generate raw combustion data; The raw combustion data is time-synchronized, outlier removal is performed, and continuous processing is applied to generate time-series data of the combustion process.

[0012] In a preferred embodiment of the electrolyte fire sensitivity test method of the present invention, the specific steps for calculating the fire sensitivity index are as follows: Denoising, outlier removal, and time alignment are performed on the combustion process time series data to generate clean combustion sequence data. Flame propagation rate curves, ignition delay distributions, and combustion duration curves are extracted from clean combustion sequence data and integrated to generate a combustion feature dataset. Based on the combustion feature dataset, flame propagation rate, ignition delay, and combustion duration are calculated and integrated to generate a fire sensitivity index.

[0013] As a preferred embodiment of the electrolyte fire sensitivity experimental method of the present invention, the specific steps for generating multi-condition fire sensitivity data are as follows: Based on the preliminary fire sensitivity index, multiple sets of experimental conditions were set for cavity temperature, ventilation speed, electrolyte volume and ignition source type, and a multi-condition experimental parameter set was generated. Ignition experiments were conducted sequentially according to the multi-condition experimental parameter set, and the combustion process time series data under each condition were recorded to generate multi-condition raw combustion data. Denoising, time alignment, and feature extraction are performed on the raw combustion data under multiple conditions. Flame propagation rate, ignition delay, and combustion duration are calculated to generate multi-condition fire sensitivity data.

[0014] In a preferred embodiment of the electrolyte fire sensitivity test method of the present invention, the specific steps for generating the fire sensitivity assessment results are as follows: Normalize and correct the multi-condition fire sensitivity data to generate corrected multi-condition fire sensitivity data. Based on the corrected multi-condition fire sensitivity data, the flame propagation rate, ignition delay and combustion duration are numerically integrated to generate multi-condition comprehensive index data. The multi-condition comprehensive index data are scored and sorted according to the values ​​to generate fire sensitivity assessment results.

[0015] As a preferred embodiment of the electrolyte fire sensitivity test method of the present invention, the step of numerically integrating flame propagation rate, ignition delay, and combustion duration based on the corrected multi-condition fire sensitivity data to generate multi-condition comprehensive index data is as follows. The corrected multi-condition fire sensitivity data were summarized and screened, and the flame propagation rate, ignition delay and combustion duration under each experimental condition were extracted to generate integrated initial data. The flame propagation rate, ignition delay, and combustion duration in the integrated initial data are standardized to generate a standardized index dataset. A non-linear mapping is performed on the standardized indicator dataset to generate sensitivity scores, which are then integrated to generate multi-condition comprehensive indicator data.

[0016] The beneficial effects of this invention are as follows: by recording the flame propagation speed, ignition delay and combustion duration through ignition experiments, and generating time series data of the combustion process, combustion characteristics are extracted and fire sensitivity indexes are calculated, realizing a precise quantitative analysis of the combustion process. This allows for comparison of the effects of different temperatures, ventilation speeds, electrolyte volumes and ignition source types on combustion behavior, making the electrolyte fire sensitivity assessment comparable and repeatable, and providing a scientific basis for safety assessment and electrolyte design. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the electrolyte fire sensitivity test method.

[0019] Figure 2 A flowchart for generating concentration data of gas-liquid mixtures.

[0020] Figure 3 A flowchart for generating time series data of the combustion process.

[0021] Figure 4 A flowchart generated from the fire sensitivity assessment results. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figures 1-4 As an embodiment of the present invention, this embodiment provides a method for testing the fire sensitivity of an electrolyte, comprising the following steps: S1. Weigh a fixed volume of the electrolyte sample to be tested, record the initial state, and generate sample physical parameter data.

[0026] S1.1 Weigh a fixed volume of the electrolyte sample to be tested, record the initial state of color, transparency and temperature, and generate initial record data.

[0027] Specifically, weigh a fixed volume of the electrolyte sample to be tested, for example, 5 ml, and place it in a clean, transparent beaker. Record the sample mass using a calibrated electronic balance, observe the sample color using a colorimeter or spectrophotometer, measure the sample transparency using a transparency meter, and simultaneously measure the sample temperature using a standard thermometer. Organize and record the recorded color, transparency, and temperature values ​​to generate initial data.

[0028] It should also be noted that fixed volume refers to accurately weighing the electrolyte sample to be tested by volume, so that the sample volume remains consistent in each experiment. For example, sampling can be carried out using a graduated cylinder, pipette, or precision syringe to ensure that the electrolyte volume is 10 ml, 20 ml, or other set values, and this should be noted in the experimental record. The purpose of fixed volume is to ensure that the experimental conditions are consistent each time, so that the generated sample physical parameter data, gas-liquid mixture description data, and subsequent fire sensitivity indicators are comparable and repeatable. Experimental conditions refer to the combination of environmental and operational parameters that need to be set and controlled manually during combustion or ignition experiments. These conditions are used to define the experimental scenario and ensure the repeatability of the experiment. Specifically, they include chamber temperature, ventilation speed, electrolyte volume, and ignition source type. Multiple experimental scenarios can be formed by setting different values ​​or types to observe combustion behavior and collect corresponding data under various conditions. For example, the chamber temperature can be set to 20℃, 40℃, or 60℃, the ventilation speed can be set to 0.5 m / s or 1 m / s, the electrolyte volume can be set to 50 ml or 100 ml, and the ignition source type can be a spark or a high-energy electric arc.

[0029] S1.2. Based on the initial recorded data, the density, viscosity and conductivity of the electrolyte sample are measured to generate the first stage physical parameter data.

[0030] Specifically, based on the initial recorded data, a fixed volume of electrolyte sample, such as 10 ml, is taken. The sample density is measured using a specific gravity bottle or a digital density meter, and the value is recorded. The electrolyte viscosity is measured using a rotational viscometer at a standard rotational speed, and the viscosity value is recorded. The electrolyte conductivity is measured using a conductivity meter, and the conductivity value is recorded. The density, viscosity, and conductivity measurement results are then compiled to generate the first-stage physical parameter data.

[0031] S1.3 Perform numerical correction and correlation feature combination on the physical parameter data of the first stage to generate sample physical parameter data.

[0032] Specifically, for the physical parameter data in the first stage, the density, viscosity, and conductivity values ​​are corrected according to standard temperature and pressure conditions. For example, the measured density value is adjusted to the standard temperature condition of 25 degrees Celsius, and the viscosity and conductivity data are corrected accordingly based on temperature. The corrected density, viscosity, and conductivity data are then combined with related features, such as forming a ratio feature between density and viscosity, and forming a product feature between conductivity and density, to generate the sample physical parameter data.

[0033] It should also be noted that standard temperature and pressure conditions refer to a unified reference state used to calibrate and compare physical parameter data, such as a temperature of 25 degrees Celsius and an atmospheric pressure of 101.3 kPa. The temperature of 25 degrees Celsius is the room temperature standard, which facilitates the comparability of density, viscosity, and conductivity values ​​between different experiments; the atmospheric pressure of 101.3 kPa is the normal pressure condition, which can eliminate the influence of air pressure on liquid volume and flow characteristics, thereby ensuring that the physical parameter data of the generated samples are consistent and comparable, which is beneficial for subsequent description of gas-liquid mixtures and fire sensitivity analysis.

[0034] S2. Based on the physical parameter data of the sample, place the electrolyte sample into a controllable sealed cavity, adjust the temperature and ventilation conditions, generate gas-liquid mixture description data, and measure the vapor concentration to generate gas-liquid mixture concentration data.

[0035] S2.1. Based on the sample physical parameter data, place the electrolyte sample into a controllable sealed cavity, and collect the initial temperature, humidity and background vapor baseline of the cavity to generate initial volatilization input data.

[0036] Specifically, based on the physical parameter data of the sample, the electrolyte sample is weighed according to a fixed volume and carefully transferred into a controllable sealed cavity. After the electrolyte sample is placed, the initial temperature and initial humidity of the cavity are measured by temperature and humidity sensors, and the background vapor concentration baseline of the cavity is recorded by a gas detector. The temperature, humidity and background vapor measurement values ​​are sorted to generate the initial input data for volatilization.

[0037] S2.2. Based on the initial input data of volatilization, adjust the cavity heating power, ventilation flow rate and atomization mode, and control the temperature and ventilation conditions in a closed loop to generate steady-state vapor concentration curve data.

[0038] Specifically, based on the initial input data of evaporation, the heating power of the cavity is adjusted, for example, within the range of 50 to 150 watts, and the ventilation flow rate is adjusted, for example, within the range of 0.1 to 1 meter per second. Different atomization modes, such as continuous atomization or intermittent atomization, are used to monitor the temperature and ventilation conditions inside the cavity in real time. At the same time, feedback adjustments are made based on the real-time monitoring results until the temperature and ventilation conditions remain stable. The vapor concentration data of the cavity changing over time is continuously collected to generate steady-state vapor concentration curve data.

[0039] S2.3. Based on the steady-state vapor concentration curve data, analyze the trend and stability of vapor concentration changes in the cavity, record the parameters of the gas-liquid mixture, and generate gas-liquid mixture description data.

[0040] Specifically, based on the steady-state vapor concentration curve data, the vapor concentration data changing over time are organized in chronological order of sampling time, and the cavity temperature and ventilation conditions at each sampling point are labeled; the vapor concentration values ​​at each sampling point are arithmetically averaged to obtain the average vapor concentration; the maximum and minimum concentration values ​​are extracted, and the peak value and fluctuation range are calculated; the standard deviation is used to calculate the degree of concentration fluctuation; by comparing the concentration changes at continuous sampling points, the trend of concentration change over time is analyzed, and the time required for the concentration curve to reach steady state is determined; the cavity temperature, ventilation conditions, and concentration analysis results at each sampling point are summarized and organized, the corresponding gas-liquid mixture parameters are recorded, and gas-liquid mixture descriptive data are generated.

[0041] It should also be noted that sampling time refers to the time point corresponding to each recorded measurement value during the acquisition of combustion process time series data, usually expressed in seconds, milliseconds, or microseconds; the time interval between each sampling point is the sampling period, and the sampling time series is the set of time points arranged sequentially according to the sampling period, used to mark the position of each measurement value on the time axis, so as to perform operations such as noise reduction, outlier removal, linear interpolation, and trend analysis; for example, if the combustion process uses a sampling frequency of 1kHz to record the flame propagation speed, then the sampling time interval between every two adjacent sampling points is 1ms, and the sampling time series increases sequentially from 0ms, 1ms, and 2ms.

[0042] S2.4. Based on the description data of the gas-liquid mixture, set the cavity temperature, humidity and initial vapor concentration to form the initial measurement state.

[0043] Specifically, based on the description data of the gas-liquid mixture, the chamber temperature, humidity, and initial vapor concentration corresponding to the electrolyte sample to be tested are selected as measurement parameters. The chamber temperature and humidity are set to the target values ​​using temperature and humidity control devices, respectively, for example, the chamber temperature is set to 60℃ and the humidity is set to 50%. The vapor concentration in the chamber is adjusted to the initial vapor concentration, for example, 200ppm, using gas injection or extraction methods. At the same time, the stability of each parameter in the chamber is confirmed by thermometer, hygrometer, and gas concentration sensor, forming the initial measurement state.

[0044] It should also be noted that gas injection or extraction methods refer to adjusting the composition and concentration of the gas in the cavity by controlling an external gas source to introduce inert gas, air, or the gas to be tested into the cavity, or by extracting gas from the cavity using an extraction device. For example, to adjust the vapor concentration in the cavity to the target initial vapor concentration, a gas mixture containing the vapor to be tested can be slowly injected into the cavity while monitoring the vapor concentration in the cavity in real time. When the concentration reaches the set value, the injection is stopped. If the vapor concentration in the cavity is too high, the gas volume or concentration is reduced using an extraction device until the target concentration is reached.

[0045] S2.5. Arrange a gas sensor array at the start of the measurement to continuously collect vapor concentration data and generate real-time concentration time series data.

[0046] Specifically, at the initial measurement state, based on the gas-liquid mixture description data, a gas sensor array is arranged at multiple locations inside the cavity. For example, the center of the top of the cavity is used to collect the concentration of the rising vapor concentration area, the middle of the cavity near the electrolyte sample surface is used to collect the initial vapor volatilization concentration, and the four corners of the bottom of the cavity are used to detect possible vapor deposition and local concentration differences, thus forming a gas sensor array covering the upper and lower layers and different directions to achieve comprehensive vapor concentration acquisition. The sampling frequency is determined based on the cavity size and sensor response time, for example, once per second, and the vapor concentration signal of each sensor is continuously recorded through the data acquisition interface. At the same time, the recorded data is organized according to the sampling time sequence to generate real-time concentration time series data.

[0047] S2.6. Smooth, normalize, and perform trend analysis on the real-time concentration time series data to generate gas-liquid mixture concentration data.

[0048] Specifically, for real-time concentration time series data, after processing according to the sampling time sequence, the detected outliers are smoothed using the moving average method, so that abnormal fluctuations are replaced by the average value of neighboring sampling points, thereby reducing the impact of spikes or abrupt changes on the real-time concentration time series data, generating a smoothed concentration series. Based on the cavity vapor concentration measurement range, the smoothed concentration series is normalized. Using a linear mapping method, the minimum concentration value in the concentration series is mapped to 0, and the maximum concentration value is mapped to 1, generating a normalized concentration series. A sliding window trend analysis method is used to calculate the average slope of the normalized concentration series within each window, extracting the increase or decrease trend of concentration over time. By analyzing the concentration change trend over time, a sliding window linear regression method is used to determine the time when the concentration reaches a steady state. Simultaneously, within the steady-state time period, the difference between the maximum and minimum values ​​of the concentration series is calculated using an extreme value calculation method to obtain the concentration fluctuation range, for example, 0.01 mol / m³. 3 Up to 0.05 mol / m 3 During the analysis, the corresponding cavity temperature and ventilation conditions were recorded, and the concentration data of the gas-liquid mixture were compiled.

[0049] It should also be noted that the vapor concentration measurement range of the chamber refers to the concentration range in which the sensor or detection method used to measure the vapor concentration generated by the evaporation of the electrolyte can respond reliably and accurately; for example, for commonly used gas sensors, the vapor concentration measurement range can be 0 ppm to 1000 ppm, capable of detecting changes from trace amounts of vapor close to zero to high concentrations of vapor, and outputting linear or calibrated concentration values ​​within the vapor concentration measurement range. The sliding window trend analysis method is used to process the normalized cavity vapor concentration time series. By calculating the concentration change rate within each window, the rising or falling trend of vapor concentration over time is obtained, further determining the steady state and fluctuation range, and providing a basis for generating gas-liquid mixture concentration data.

[0050] S3. Based on the concentration data of the gas-liquid mixture, set multiple ignition sources and ignite them sequentially from low to high energy gradient to generate minimum ignition energy and ignition source parameter data.

[0051] S3.1 Based on the gas-liquid mixture concentration data, set the ignition energy range and ignition source type to be tested, and generate the initial parameter set of the ignition source.

[0052] Specifically, based on the gas-liquid mixture concentration data, the flammability characteristics corresponding to different concentration ranges are read to determine the ignition energy range to be tested. For example, ignition energy ranges of 5mJ to 20mJ, 20mJ to 50mJ, and 50mJ to 100mJ are set according to low, medium, and high concentration ranges, respectively. Based on the cavity temperature and ventilation conditions recorded in the gas-liquid mixture concentration data, the type of ignition source to be used is selected, such as an electric spark ignition source, a hot wire ignition source, or an open flame ignition source. After determining the ignition energy range and ignition source type, each set of ignition energy and ignition source type is numbered, and the ignition energy setting value, ignition duration setting value, and ignition source type information are recorded to generate an initial parameter set for the ignition source.

[0053] S3.2 Using the initial parameter set of the ignition source, load the gas-liquid mixture sequentially from low to high energy gradient, and record the ignition state and flame response to generate ignition experiment time series data.

[0054] Specifically, based on the initial parameter set of the ignition source, the ignition energy and ignition source type are selected sequentially from low to high according to the pre-numbered energy gradient. For each energy level, the ignition energy value (e.g., 5mJ, 10mJ, and 20mJ) and ignition duration are set, and the trigger position of the ignition source in the gas-liquid mixture is located before triggering the ignition operation. Each ignition simultaneously records the ignition state (unignited, momentary ignition, or continuous combustion), and flame response data, including flame brightness curves, peak temperature, and combustion duration, is collected synchronously with timestamps. After completing the experiment at the current energy level, the process is switched to the next energy level, repeating the same steps until all energy levels and ignition source types in the initial parameter set have been traversed. The timestamp records of each experiment are merged and organized in chronological order to generate ignition experiment time series data.

[0055] It should also be noted that the energy gradient refers to an energy sequence arranged in a predetermined manner from smallest to largest ignition energy, used to progressively increase the ignition energy to determine the ignition status of the gas-liquid mixture under different energy conditions. The energy gradient usually increases in a fixed increment or proportionally. For example, when using a fixed increment, it can be arranged in the example energy sequence of 5mJ, 10mJ, 15mJ, and 20mJ. When using a fixed proportional increase, it can be arranged in the example energy sequence of 5mJ, 10mJ, 20mJ, and 40mJ. Through the energy gradient, each level of ignition energy can have a clear upper and lower order and form a continuous energy increase process, which is used for subsequent progressive loading of ignition energy.

[0056] S3.3 Perform energy response analysis on the ignition experiment time series data, identify the minimum energy required to initiate ignition and the corresponding ignition source parameters, and organize the data to generate minimum ignitable energy and ignition source parameter data.

[0057] Specifically, the ignition experiment time series data is imported according to the test number and time sequence. Event judgment is performed on the flame response signal and temperature signal of each test. The threshold judgment method is used to identify ignition events. That is, when the flame brightness or temperature exceeds the preset flame brightness threshold after triggering and remains above the preset duration, it is judged as "ignition". The ignition energy values ​​corresponding to all tests that have "ignited" are listed and sorted from low to high energy. The lowest energy of the first "ignition" record is identified as the candidate minimum ignitable energy. The ignition source parameters of the candidate minimum ignitable energy are tested at least twice under the same conditions to confirm stability. Verification tests are performed on adjacent low energy levels under the candidate energy to eliminate sporadic ignition phenomena. The minimum ignitable energy value that has been repeatedly confirmed and the corresponding ignition source type, ignition duration, cavity temperature, ventilation conditions and test number are organized into a structured record to generate minimum ignitable energy and ignition source parameter data.

[0058] It should also be noted that when setting the flame brightness threshold, the flame brightness baseline signal is continuously collected under non-ignition conditions, and brightness data for at least 5 to 10 seconds is recorded in the order of sampling time. The arithmetic mean method is used to calculate the average brightness and brightness standard deviation of the recorded brightness baseline data. For example, the baseline average brightness is 0.02 and the brightness standard deviation is 0.01. The preset flame brightness threshold is determined by a fixed multiple. For example, the baseline average brightness is taken as 5 times as the flame brightness threshold. If the baseline average brightness is 0.02, the preset flame brightness threshold is set to 0.10. The flame brightness threshold is written into the ignition event judgment process as the basis for judging the ignition state. The preset duration can be determined by statistically analyzing the time period during which the brightness remains stable in a normal burning video: first, continuously extract the average brightness of each frame, use the difference method to calculate the brightness change between adjacent frames, and statistically analyze the longest stable interval where the brightness change remains within a very small range; then, use the typical duration of the longest stable interval as the duration threshold, for example, 0.3 to 0.5 seconds in a normal burning scene.

[0059] S4. Conduct ignition experiments on the minimum ignitable energy and ignition source parameters, and record the flame propagation speed, ignition delay and combustion duration to generate combustion process time series data.

[0060] S4.1 Conduct an ignition experiment based on the minimum ignitable energy and corresponding ignition source parameter data, and record the flame initiation signal.

[0061] Specifically, based on the minimum ignitable energy and corresponding ignition source parameter data, the ignition source of the corresponding energy is placed in the gas-liquid mixture to trigger the ignition device to start. The flame area is captured in real time using a high-speed camera and an optical flame detector. The brightness signal of each frame is continuously collected. The brightness change between adjacent frames is calculated by the differential method. The time point when the brightness first exceeds the preset flame brightness threshold is recorded as the flame initiation signal, generating flame initiation signal data.

[0062] S4.2. Employs a high-speed camera, infrared temperature sensor, and optical flame detection to record flame propagation speed, ignition delay, and combustion duration in real time, generating raw combustion data.

[0063] Specifically, based on the flame initiation signal, after the electrolyte sample is ignited, a high-speed camera is arranged around the cavity to capture a continuous sequence of images of the combustion process. At the same time, an infrared temperature sensor is installed above the sample to record the flame temperature change over time, and an optical flame detector is used to collect the flame emission signal in real time. The position of the flame front is measured over time using the image sequence, the flame propagation speed is calculated using the inter-frame displacement method, and the ignition delay time and combustion duration are determined by the signals from the infrared temperature sensor and the optical flame detector, generating raw combustion data that includes the flame propagation speed, ignition delay, and combustion duration.

[0064] It should also be noted that the inter-frame displacement method processes a continuous sequence of images captured by a high-speed camera, measures the positional change of the flame front between adjacent frames, and calculates the propagation speed of the flame front by combining the time interval between image acquisitions.

[0065] S4.3 Perform time synchronization, outlier removal, and continuous processing on the raw combustion data to generate combustion process time series data.

[0066] Specifically, the raw combustion data is aligned with the data recorded by the high-speed camera, infrared temperature sensor, and optical flame detector according to the acquisition timestamp to ensure that the data from different devices are synchronized on a unified time axis. Abnormal data points that deviate significantly from the trend during the combustion process are identified and removed, such as data exceeding twice the standard deviation of the average of consecutive data. The data after removing the abnormalities is processed for continuity, including using linear interpolation or spline interpolation methods to fill in missing time points, to obtain continuous records of flame propagation speed, ignition delay, and combustion duration corresponding to each sampling time point, and then organized to generate time series data of the combustion process.

[0067] It should be noted that by conducting ignition experiments on the minimum ignitable energy and ignition source parameters, and simultaneously recording the flame propagation speed, ignition delay, and combustion duration in real time, and generating time series data of the combustion process, this method overcomes the limitations of existing technologies that rely solely on single ignition tests or simple observation of whether a flame occurs. It achieves dynamic and continuous quantification of the combustion process, captures the dynamic characteristics of the entire flame development process, transforms them into quantifiable indicators, and enables refined analysis of combustion behavior. This provides a scientific basis for fire sensitivity assessment under different cavity temperatures, ventilation speeds, electrolyte volumes, and ignition source types, while also improving the comparability and repeatability of experimental data.

[0068] S5. Organize and analyze the time series data of the combustion process, calculate the fire sensitivity index, and repeat the experiment under different cavity temperatures, ventilation speeds, electrolyte volumes, and ignition source types to generate multi-condition fire sensitivity data.

[0069] S5.1 Denoising, outlier removal, and time alignment are performed on the combustion process time series data to generate clean combustion sequence data.

[0070] Specifically, a moving average filtering method is used to denoise the flame propagation speed, ignition delay, and combustion duration of the combustion process time series data. This involves calculating the average value of each sampling point with the values ​​of several points before and after it to obtain a smoothed time series. A local statistical method is used to calculate the mean difference between each sampling point and its adjacent points. If the mean difference exceeds twice the standard deviation of the mean difference, it is marked as an outlier and removed, and the time series index is updated. The data is then uniformly sorted according to the sampling time, and a linear interpolation method is used to calculate and fill the time gaps caused by the removal of outliers, generating continuous, smooth, and clean combustion sequence data after outlier removal.

[0071] S5.2 Extract flame propagation rate curves, ignition delay distributions, and combustion duration curves from clean combustion sequence data, and integrate them to generate a combustion feature dataset.

[0072] Specifically, for clean combustion sequence data, flame propagation speed data are extracted according to the sampling time order to form a flame propagation rate curve. The time difference method is used to calculate the time difference between the ignition signal occurrence time and the ignition signal time to form an ignition delay distribution. The combustion duration is extracted from the time interval from the start of the flame to the end of combustion to form a combustion duration curve. The flame propagation rate curve, ignition delay distribution and combustion duration curve are sorted and integrated according to the sampling time to generate a combustion feature dataset.

[0073] It should also be noted that the time difference method is a method that calculates the difference between the time of occurrence of the latter event and the time of occurrence of the former event by recording the timestamps of two events. It is used to quantify the delay or response time between events. For example, in a combustion experiment, the ignition delay is obtained by measuring the difference between the time of occurrence of the ignition signal and the time of occurrence of the ignition signal.

[0074] S5.3. Based on the combustion characteristic dataset, calculate the flame propagation rate, ignition delay, and combustion duration, and integrate them to generate a fire sensitivity index.

[0075] Specifically, the flame propagation rate curve, ignition delay distribution, and combustion duration curve are extracted sequentially from the combustion feature dataset, and then arranged according to the sampling time order. The average flame propagation rate is calculated, expressed as: ; in, This represents the average flame propagation rate. Indicates the total number of sampling points. Indicates the index of the sampling point. Indicates the first Flame propagation speed at each sampling point; The average ignition delay is calculated using the following expression: ; in, This represents the average ignition delay. Indicates the first Ignition delay at each sampling point; The average combustion duration is calculated using the following expression: ; in, This represents the average duration of combustion. Indicates the first The duration of combustion at each sampling point; The standard deviation of flame propagation rate is calculated using the following expression: ; in, This represents the standard deviation of the flame propagation rate; The standard deviation of ignition delay is calculated using the following expression: ; in, Indicates the standard deviation of ignition delay; The standard deviation of combustion duration is calculated using the following expression: ; in, This represents the standard deviation of the combustion duration; By integrating the average and standard deviation of the flame propagation rate curve, ignition delay distribution, and combustion duration curve, fire sensitivity index data is generated.

[0076] S5.4 Based on the preliminary fire sensitivity index, set multiple sets of experimental conditions for cavity temperature, ventilation speed, electrolyte volume and ignition source type, and generate a multi-condition experimental parameter set.

[0077] Specifically, referring to the trends in flame propagation rate, ignition delay, and combustion duration in the preliminary fire sensitivity indicators, and combining the measured influence ranges of cavity temperature, ventilation velocity, electrolyte volume, and ignition source type on combustion behavior, the selectable ranges for cavity temperature, ventilation velocity, electrolyte volume, and ignition source type are determined. For example, the cavity temperature is 20 to 60°C, the ventilation velocity is 0.1 to 1.0 m / s, the electrolyte volume is 50 to 200 mL, and the ignition source type is electric spark or hot wire. Discrete values ​​are set according to the step size of each parameter, for example, the cavity temperature is taken every 10°C, the ventilation velocity is taken every 0.2 m / s, and the electrolyte volume is taken every 50 mL. The parameters are combined using the full permutation method to generate multiple sets of experimental conditions, generating a multi-condition experimental parameter set, and the specific values ​​of cavity temperature, ventilation velocity, electrolyte volume, and ignition source type corresponding to each set of experimental conditions are recorded.

[0078] S5.5. Perform ignition experiments sequentially according to the multi-condition experimental parameter set, record the combustion process time series data under each condition, and generate multi-condition raw combustion data.

[0079] Specifically, according to the multi-condition experimental parameter set, specific combinations of chamber temperature, ventilation speed, electrolyte volume, and ignition source type are selected in sequence. The selected conditions are loaded into the chamber, and ignition is performed using the minimum ignitable energy and corresponding ignition source parameters. High-speed cameras, infrared temperature sensors, and optical flame detectors are used to record the flame propagation position, ignition signal, and combustion duration in real time. The combustion process under each set of conditions is observed, and the time series data corresponding to each experimental condition are compiled to generate multi-condition raw combustion data.

[0080] S5.6. Denoise, time-align, and feature-extract the raw combustion data under multiple conditions, calculate the flame propagation rate, ignition delay, and combustion duration, and generate multi-condition fire sensitivity data.

[0081] Specifically, for the original combustion data under multiple conditions, a moving average filtering method is used to denoise the flame propagation location, ignition signal, and combustion duration. This involves calculating the average value of each sampling point with several preceding and following sampling points to obtain a smooth time series. A local statistical method is used to calculate the mean difference between each sampling point and its adjacent points. If the mean difference exceeds twice the standard deviation of the mean difference, it is marked as an outlier and removed, and the time series index is updated. The data channels are uniformly sorted according to the sampling time, and a linear interpolation method is used to fill the time gaps caused by the removal of outliers, resulting in continuous, smooth, and clean combustion sequence data after outlier removal. From the clean combustion sequence data after outlier removal, the flame propagation rate curve, ignition delay distribution, and combustion duration curve are extracted sequentially. By calculating the average flame propagation rate, average ignition delay, average combustion duration, and corresponding standard deviations, multi-condition fire sensitivity data is generated.

[0082] It should be noted that by organizing and analyzing the time series data of the combustion process, and repeating the experiment under various chamber temperatures, ventilation speeds, electrolyte volumes, and ignition source types, multi-condition fire sensitivity data was generated. This enabled a multi-dimensional quantitative assessment of the electrolyte's fire sensitivity, overcoming the limitation of existing technologies that can only obtain combustion characteristics under a single condition. It reveals the comprehensive impact of different environmental and operating conditions on flame propagation rate, ignition delay, and combustion duration, making the fire sensitivity assessment comparable and repeatable, and providing a scientific basis for electrolyte safety design and risk management.

[0083] S6. Organize and analyze the multi-condition fire sensitivity data to generate fire sensitivity assessment results.

[0084] S6.1 Normalize and correct the multi-condition fire sensitivity data to generate corrected multi-condition fire sensitivity data.

[0085] Specifically, for multi-condition fire sensitivity data, the minimum and maximum values ​​of each index under all experimental conditions are mapped to the minimum and maximum values, respectively. For example, the minimum value of flame propagation rate is mapped to 0 and the maximum value is mapped to 1. The flame propagation rate, ignition delay, and combustion duration are linearly mapped according to the method of minimum value corresponding to 0 and maximum value corresponding to 1, to obtain normalized index values. The normalized flame propagation rate, ignition delay, and combustion duration are then integrated to generate corrected multi-condition fire sensitivity data.

[0086] S6.2 Based on the corrected multi-condition fire sensitivity data, the flame propagation rate, ignition delay and combustion duration are numerically integrated to generate multi-condition comprehensive index data.

[0087] S6.2.1 Summarize and screen the corrected multi-condition fire sensitivity data, extract the flame propagation rate, ignition delay and combustion duration under each experimental condition, and generate integrated initial data.

[0088] Specifically, for the corrected multi-condition fire sensitivity data, the flame propagation rate, ignition delay, and combustion duration are organized according to each set of experimental conditions. The flame propagation rate, ignition delay, and combustion duration corresponding to each experimental condition are extracted and arranged in the order of the experimental conditions. The completeness of the flame propagation rate, ignition delay, and combustion duration is checked, and missing or abnormal data are removed. The organized flame propagation rate, ignition delay, and combustion duration are integrated to generate integrated initial data, which may include corresponding fire sensitivity indicators under different cavity temperatures, ventilation speeds, electrolyte volumes, and ignition source types.

[0089] S6.2.2 Standardize the flame propagation rate, ignition delay, and combustion duration in the integrated initial data to generate a standardized index dataset.

[0090] Specifically, for the flame propagation rate, ignition delay, and combustion duration in the integrated initial data, the values ​​under each experimental condition are extracted sequentially, and linear mapping is performed according to the minimum and maximum value range of each index to map the index values ​​to a unified dimension range, such as 0 to 1. The flame propagation rate, ignition delay, and combustion duration under each experimental condition are processed in the same way to generate standardized flame propagation rate, standardized ignition delay, and standardized combustion duration for each experimental condition. The processed flame propagation rate, ignition delay, and combustion duration are integrated according to the experimental conditions to generate a standardized index dataset, which includes standardized index values ​​corresponding to different cavity temperatures, ventilation speeds, electrolyte volumes, and ignition source types.

[0091] S6.2.3 Perform nonlinear mapping on the standardized indicator dataset to generate sensitivity scores, and integrate them to generate multi-condition comprehensive indicator data.

[0092] Specifically, for each experimental condition in the standardized index dataset, the flame propagation rate, ignition delay, and combustion duration are mapped to sensitivity scores using a nonlinear mapping method, such as exponential or power mapping. Sensitivity scores under the same experimental condition are then integrated, for example, by summing them according to a set weight ratio, to obtain a comprehensive index for each experimental condition. Multi-condition experimental data are processed sequentially, and the comprehensive indices for all experimental conditions are compiled and summarized to generate multi-condition comprehensive index data. The sensitivity scores for each experimental condition and the integration results are recorded.

[0093] S6.3. Score the multi-condition comprehensive index data and sort them according to the value to generate the fire sensitivity assessment results.

[0094] Specifically, each experimental condition and its corresponding comprehensive index value are read sequentially from the multi-condition comprehensive index data. The comprehensive index values ​​of each experimental condition are sorted, with the experimental conditions with larger values ​​placed first. The sorted experimental conditions and their corresponding comprehensive index values ​​are recorded to generate fire sensitivity assessment results. For example, the experimental condition with the highest comprehensive index value is listed first, and so on, forming a complete sorting table. The integrated scores corresponding to the flame propagation rate, ignition delay, and combustion duration of each experimental condition are retained.

[0095] In summary, this invention achieves a refined quantitative analysis of the combustion process by recording flame propagation speed, ignition delay, and combustion duration through ignition experiments, generating time-series data of the combustion process, extracting combustion characteristics, and calculating fire sensitivity indices. This allows for comparison of the effects of different temperatures, ventilation speeds, electrolyte volumes, and ignition source types on combustion behavior, making the electrolyte fire sensitivity assessment comparable and repeatable, and providing a scientific basis for safety assessment and electrolyte design.

[0096] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method of electrolyte fire sensitivity testing, the method comprising: The application relates to a method for evaluating fire sensitivity of electrolyte, comprising the following steps: ​ a fixed volume of electrolyte sample is weighed, and initial physical parameters are recorded; the electrolyte sample is placed in a controllable closed cavity according to the physical parameters, and the temperature and ventilation conditions are adjusted, gas-liquid mixture description data are generated, and the vapor concentration data are measured; a plurality of ignition sources are set according to the gas-liquid mixture concentration data, and the ignition sources are ignited from low to high in energy gradient, and the minimum ignitable energy and the ignition source parameter data are generated; ignition experiments are carried out on the minimum ignitable energy and the ignition source parameter data, and the flame propagation speed, ignition delay and combustion duration are recorded, and the combustion process time sequence data are generated; the combustion process time sequence data are sorted and analyzed, the fire sensitivity index is calculated, and the experiments are repeated under different conditions of cavity temperature, ventilation speed, electrolyte volume and ignition source type, and the multi-condition fire sensitivity data are generated; the multi-condition fire sensitivity data are sorted and analyzed, and the fire sensitivity evaluation results are generated.

2. The method of claim 1, wherein the electrolyte fire sensitivity experiment is performed by: The specific steps of generating the sample physical parameter data are as follows: a fixed volume of electrolyte sample is weighed, and the initial color, transparency and temperature are recorded to generate initial record data; the density, viscosity and conductivity of the electrolyte sample are measured according to the initial record data to generate the first-stage physical parameter data; the first-stage physical parameter data are numerically corrected and combined with the characteristic groups to generate the sample physical parameter data.

3. The method of claim 1, wherein the electrolyte fire sensitivity experiment is performed by: The specific steps of generating the gas-liquid mixture description data are as follows: the electrolyte sample is placed in a controllable closed cavity according to the sample physical parameter data, and the initial temperature, humidity and background vapor baseline of the cavity are collected to generate the initial volatilization input data; the heating power, ventilation flow and atomization mode of the cavity are adjusted according to the initial volatilization input data, and the temperature and ventilation conditions are closed-loop controlled to generate the steady-state vapor concentration curve data; the vapor concentration change trend and stability in the cavity are analyzed according to the steady-state vapor concentration curve data, the gas-liquid mixture parameters are recorded, and the gas-liquid mixture description data are generated.

4. The electrolyte fire sensitivity test method of claim 1, wherein: The specific steps of generating the gas-liquid mixture concentration data are as follows: the cavity temperature, humidity and initial vapor concentration are set according to the gas-liquid mixture description data to form a measurement starting state; a gas sensor array is arranged under the measurement starting state, the vapor concentration is continuously collected, and real-time concentration time sequence data are generated; the real-time concentration time sequence data are smoothed, normalized and trend analyzed to generate the gas-liquid mixture concentration data.

5. The method of claim 1, wherein the electrolyte fire sensitivity experiment is performed by: The specific steps of generating the minimum ignitable energy and the ignition source parameter data are as follows: ​ the ignition energy range and the ignition source type to be tested are set according to the gas-liquid mixture concentration data to generate the initial ignition source parameter set; the initial ignition source parameter set is loaded into the gas-liquid mixture in energy gradient from low to high, and the ignition state and the flame response are recorded to generate the ignition experiment time sequence data; energy response analysis is carried out on the ignition experiment time sequence data, the lowest energy and the corresponding ignition source parameter that can cause ignition are identified, and the minimum ignitable energy and the ignition source parameter data are generated.

6. The electrolyte fire sensitivity test method of claim 1, wherein: The specific steps of generating the combustion process time sequence data are as follows: The minimum ignitable energy and the corresponding ignition source parameter data are subjected to ignition experiment, and a flame initiation signal is recorded; A high-speed camera, an infrared temperature sensor, and optical flame detection are used to record the flame propagation speed, ignition delay, and combustion duration in real time, and generate original combustion data; The original combustion data is subjected to time synchronization, abnormal point elimination, and continuous processing to generate time series data of the combustion process.

7. The electrolyte fire sensitivity test method of claim 1, wherein: The calculation of the fire sensitivity index includes the following steps, The time series data of the combustion process is subjected to denoising, abnormal point elimination, and time alignment to generate clean combustion sequence data; The flame propagation rate curve, ignition delay distribution, and combustion duration curve are extracted from the clean combustion sequence data, and integrated to generate a combustion feature data set; According to the combustion feature data set, the flame propagation rate, ignition delay, and combustion duration are calculated, and integrated to generate the fire sensitivity index.

8. The electrolyte fire sensitivity test method of claim 1, wherein: The generation of multi-condition fire sensitivity data includes the following steps, According to the preliminary fire sensitivity index, a plurality of experimental conditions of the cavity temperature, ventilation speed, electrolyte volume, and ignition source type are set to generate a multi-condition experimental parameter set; According to the multi-condition experimental parameter set, ignition experiments are sequentially performed, and the time series data of the combustion process under each condition is recorded to generate multi-condition original combustion data; The multi-condition original combustion data is subjected to denoising, time alignment, and feature extraction, and the flame propagation rate, ignition delay, and combustion duration are calculated to generate multi-condition fire sensitivity data.

9. The electrolyte fire sensitivity test method of claim 1, wherein: The generation of the fire sensitivity evaluation result includes the following steps, The multi-condition fire sensitivity data is subjected to normalization correction to generate corrected multi-condition fire sensitivity data; According to the corrected multi-condition fire sensitivity data, the flame propagation rate, ignition delay, and combustion duration are numerically integrated to generate multi-condition comprehensive index data; The multi-condition comprehensive index data is scored and sorted according to the numerical value to generate the fire sensitivity evaluation result.

10. The method of claim 9, wherein the electrolyte fire sensitivity experiment is performed by: According to the corrected multi-condition fire sensitivity data, the flame propagation rate, ignition delay, and combustion duration are numerically integrated to generate multi-condition comprehensive index data, including the following steps, ​ The corrected multi-condition fire sensitivity data is summarized and screened to extract the flame propagation rate, ignition delay, and combustion duration under each experimental condition to generate integrated initial data; The flame propagation rate, ignition delay, and combustion duration in the integrated initial data are subjected to standardization processing to generate a standardized index data set; The standardized index data set is subjected to nonlinear mapping to generate a sensitivity score, and integrated to generate multi-condition comprehensive index data.

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