An active safety protection control system for a test chamber

Abstract

This invention discloses an active safety protection and control system for a test chamber, relating to the field of protection and control technology. It includes a multi-source data acquisition module, a correlation feature analysis module, a trend prediction module, a safety risk assessment module, and an active protection module. The correlation feature analysis module extracts primary and secondary correlation data from battery test data, quantifies the degree of correlation between them, and obtains the correlation degree between the primary and secondary correlation data. This allows for the quantification of the correlation strength between the core temperature and internal resistance of the battery cell, enabling in-depth analysis of temperature correlation data, effectively improving the accuracy of risk identification and reducing the probability of false alarms and missed alarms. Simultaneously, by combining the correlation degree with the current temperature deviation to correct the temperature prediction results, it achieves an adaptive effect of automatically increasing the correction magnitude at high temperatures and automatically reducing the false alarm rate at low temperatures, improving the accuracy of thermal runaway prediction, increasing response time for active protection, and ensuring the effectiveness of the test chamber's safety protection capabilities.

Description

Technical Field

[0001] This invention relates to the field of protection and control technology, specifically to an active safety protection and control system for an experimental cabin. Background Technology

[0002] With the increasing severity of the energy crisis and environmental pollution, new energy vehicles have gained a rapid development opportunity. Lithium-ion batteries, in particular, are a type of battery that uses lithium metal or lithium alloys as positive and negative electrode materials and employs a non-aqueous electrolyte solution. Due to the highly reactive chemical properties of lithium metal, its processing, storage, and use require very strict environmental controls. With the advancement of science and technology, lithium-ion batteries have become mainstream and are widely used in various fields. To ensure the safety of lithium-ion batteries, testing is necessary. However, lithium-ion batteries themselves have inherent safety issues, and improving the safety of lithium-ion battery testing is an urgent problem to be solved.

[0003] In current battery testing processes, the safety protection system of the test chamber usually uses the surface temperature of the cell or the ambient temperature inside the test chamber as monitoring indicators. It lacks analysis of temperature-related data changes, such as the impact of internal resistance changes on thermal runaway. This can easily lead to misjudgments and missed judgments, posing significant safety hazards. At the same time, predicting temperature based solely on historical data changes has low accuracy, often triggering protection only when the temperature rises sharply. This results in delayed thermal runaway warnings and reduces the effectiveness of safety protection. Summary of the Invention

[0004] The purpose of this invention is to provide an active safety protection and control system for an experimental cabin, which solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an active safety protection and control system for an experimental chamber, comprising a multi-source data acquisition module, a correlation feature analysis module, a change trend prediction module, a safety risk assessment module, and an active protection module;

[0006] The multi-source data acquisition module is used to collect data from different dimensions of battery testing to obtain battery test data.

[0007] The correlation feature analysis module first extracts primary and secondary correlation data from the battery test data, and then quantifies the correlation between the primary and secondary correlation data based on the change information of the primary and secondary correlation data to obtain the correlation degree between the primary and secondary correlation data.

[0008] After obtaining the correlation between the primary data and the secondary related data, the trend prediction module uses it as a correction coefficient to correct the primary data for future time. Combined with the deviation of the primary data at the current time from its baseline value, the change in the primary data for future time is obtained.

[0009] The safety risk assessment module sets different change rate thresholds according to the battery testing stage, compares the changes in the main data over future time with the change rate thresholds, assesses the degree of safety risk, and introduces the degree of deviation of secondary related data from their initial values ​​as an influencing factor to adjust the safety risk and obtain a risk level score. Finally, the active protection module divides the risk level score into different protection levels and takes corresponding protective measures according to different protection levels.

[0010] Optionally, the main data extracted by the correlation feature analysis module includes the core temperature of the battery cell, and the secondary correlation data includes the internal resistance;

[0011] After obtaining the core temperature and internal resistance of the battery cell, the changes in the core temperature and internal resistance of the battery cell are first calculated per unit time to obtain the changes in the core temperature and internal resistance of the battery cell. Then, the ratio of the changes in the core temperature and internal resistance of the battery cell is calculated to obtain the rate of change influence term, which reflects the correlation strength when the two change.

[0012] Next, the absolute value of the change in internal resistance is calculated and compared with the historical average of internal resistance to obtain the relative change term. The relative change magnitude of secondary correlation data is quantified, and the relative change term is combined with the change rate influence term. An internal resistance influence coefficient is introduced to obtain the correlation degree A between the cell core temperature and internal resistance. R .

[0013] Optionally, the trend prediction module first obtains the basic change in the core temperature of the battery cell per unit time in the future through time series analysis based on historically collected core temperature data of the battery cell.

[0014] Next, the correlation A between the core temperature of the battery cell and its internal resistance is calculated. R As a correction factor, and combined with the ratio of the current cell core temperature to the reference temperature, the basic change in cell core temperature per unit time in the future is corrected to obtain the change in cell core temperature ΔT per unit time in the future. pred .

[0015] Optionally, the safety risk assessment module pre-sets the temperature change rate threshold for the corresponding stage based on the different testing stages of the battery; then, it calculates the ratio between the predicted core temperature change of the battery cell and the temperature change rate threshold for the corresponding stage to obtain the basic risk value; at the same time, it introduces the relative change of internal resistance and the initial benchmark value as an aging influencing factor to correct the basic risk value, and finally obtains the risk level score E.

[0016] Optionally, the active protection module pre-sets multiple risk level threshold ranges, matches the calculated risk level score with the corresponding range to determine the current risk level, and then automatically triggers corresponding graded protection measures according to different risk levels. The grade classification is as follows:

[0017] When the risk level score E < 0.5, it indicates that the test status is normal. Keep the current test parameters unchanged and carry out the test as originally planned.

[0018] When the risk level score E is between 0.5 and 0.8, it is considered a low protection level. A yellow audible and visual warning will be activated, and abnormal parameters will be highlighted on the monitoring interface. The test condition settings and battery status will be manually checked to confirm whether the fluctuation is normal.

[0019] When the risk level score E is between 0.8 and 1.2, it is considered a medium protection level. A red audible and visual warning will be activated, and corresponding measures will be taken according to different testing stages. Then, a risk assessment will be conducted again. If the risk level is medium for two consecutive tests, the maintenance personnel will be notified to conduct a shutdown inspection.

[0020] When the risk level E≥1.2, it is a high protection level. A red audible and visual warning is activated, the test control console is locked, and only emergency stop is allowed. The electrical connection between the battery and the test equipment is automatically cut off, the inert gas injection system inside the chamber is activated, and the pressure relief valve on the top of the test chamber is opened to maintain stable pressure inside the chamber.

[0021] Optionally, the testing phases include a fast charging test phase, a cycle life test phase, and a thermal abuse test phase.

[0022] Optionally, when the protection level is medium, corresponding measures are taken according to the test stage. During the fast charging test stage, the charging current is reduced by 10%-15% while maintaining the charging voltage unchanged. During the cycle life test stage, the charging and discharging cycle is paused for 5 minutes, the in-cabin pre-cooling system is started, and the cycle current is reduced by 5%. During the thermal abuse test stage, the thermal abuse heating program is paused, the in-cabin forced ventilation system is started, and manual inspection is carried out after the temperature drops to a safe range.

[0023] Optionally, the active protection module allows managers to intervene and adjust protection measures at any risk level. It also sets up a misoperation lockout mechanism. When a conflict is detected between the manager's operation and the protection strategy for the current risk level, the system will prompt the manager to confirm the operation again before proceeding.

[0024] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0025] I. This invention uses a correlation feature analysis module to determine primary and secondary correlation data. Primary data includes the core temperature of the battery cell, and secondary correlation data includes internal resistance. Then, the changes in the core temperature and internal resistance are calculated, and the ratio between the two is calculated to obtain the rate of change influence term, which reflects the correlation strength when the two change. Next, the absolute value of the change in internal resistance is calculated and compared with the historical average of internal resistance to obtain the relative change term, quantifying the relative change amplitude of the secondary correlation data. The relative change term is combined with the rate of change influence term to obtain the correlation degree between the core temperature of the battery cell and the internal resistance, quantifying the correlation strength between the secondary correlation data and the primary data. This enables the analysis of temperature-related data changes, improves the accuracy of risk identification, reduces false positives and false negatives, and lowers safety hazards.

[0026] Second, this invention corrects the predicted temperature value for future time by combining the correlation between the core temperature of the battery cell and its internal resistance. At the same time, it considers the impact of the deviation between the current core temperature of the battery cell and the reference core temperature of the battery cell on the temperature prediction. This achieves an adaptive effect of automatically increasing the correction range in high-temperature scenarios and automatically reducing the false alarm rate in low-temperature scenarios. As a result, it can more accurately predict thermal runaway, leave more time for active safety protection actions, and improve the safety protection effect.

[0027] Third, this invention sets temperature change rate thresholds differently according to different testing stages, so that test data can match the risk characteristics of different testing scenarios, avoiding the problems of excessive warnings or delayed warnings due to fixed thresholds. It also introduces battery aging factors to quantify the amplification effect of the rate of change of battery internal resistance relative to the initial value on safety risks, thereby improving the effectiveness of safety protection. Attached Figure Description

[0028] Figure 1 This is a flowchart of the system of the present invention;

[0029] Figure 2 This is a schematic diagram of the safety risk assessment module of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] For examples, please refer to Figure 1 and Figure 2 This embodiment provides an active safety protection and control system for an experimental chamber, including a multi-source data acquisition module, a correlation feature analysis module, a change trend prediction module, a safety risk assessment module, and an active protection module;

[0032] The multi-source data acquisition module collects data from different dimensions of battery testing to obtain battery test data;

[0033] The correlation feature analysis module first extracts primary and secondary correlation data from the battery test data. Primary data includes the core cell temperature A, as thermal runaway is a core risk in battery testing, and temperature data is the most direct indicator of thermal runaway; therefore, this data needs to be analyzed in detail. Secondary correlation data includes voltage V, internal resistance R, and cell expansion pressure P. Since changes in internal resistance R have the greatest impact on cell aging and thermal runaway, internal resistance R is selected as secondary correlation data. Then, based on the changes in primary and secondary correlation data, the correlation degree between the two is quantified to obtain the correlation score between the primary and secondary correlation data. The specific process is as follows:

[0034]

[0035] In the above formula, A R This represents the correlation between the core temperature and internal resistance of the battery cell. A larger absolute value indicates a stronger correlation; a positive value indicates a positive correlation, and a negative value indicates a negative correlation. X∈{V,R,P}, such as A... V <0 indicates a negative correlation between voltage decrease and temperature increase, A R >0 indicates a positive correlation between the increase in internal resistance and the increase in the core temperature of the battery cell. A P >0 indicates a positive correlation between pressure rise and temperature rise. In practical applications, multiple secondary correlation data can be selected for calculation to obtain the correlation between data from different dimensions and the core temperature of the battery cell.

[0036] k R The value of R is the internal resistance influence coefficient, which is 0.8. Since the change in internal resistance R is a direct electrochemical signal of cell aging and thermal runaway, the value of R is set to 0.8 when internal resistance R is a secondary correlation data.

[0037] C(t) is the secondary related data value at the current time t, C(t-1) is the secondary related data value at time t-1, and C(t)-C(t-1) is the change in the secondary related data.

[0038] A(t) represents the principal data value at time t, A(t-1) represents the principal data value at time t-1, and A(t) - A(t-1) represents the change in the difference between the principal data values. This is the rate of change effect term, used to quantify the dynamic changes of secondary and primary data, reflecting the correlation strength between the two when they change. For example, the larger the value of this term, the greater the real-time response amplitude of the internal resistance when the temperature changes by 1℃. If the result of this term is positive, it means that the secondary and primary data change in the same direction, that is, when the value of the primary data increases, the internal resistance R will also increase. Conversely, if the result of this term is negative, it means that the secondary and primary data change in opposite directions, that is, when the core temperature A of the cell increases, the internal resistance R will decrease.

[0039] |C(t)-C(t-1)| represents the absolute change in the secondary related data;

[0040] C avg This refers to the historical average of secondary related data, representing the average value of the data over a past period. This is a relative change term, reflecting the relative magnitude of change in secondary related data. It is used to eliminate differences in magnitude between different secondary related data, converting data with different dimensions into dimensionless data, so that data from different dimensions can be compared. This term is compared with the change rate influence term. Multiplication can combine the synchronicity and relative magnitude of changes in secondary and primary data to quantify the true correlation strength between them, thereby enabling the analysis of temperature-related data changes and reducing misjudgments and omissions. In practice, the selection of secondary and primary data can be changed according to actual testing needs, and the correlation strength between multiple secondary and primary data can be calculated simultaneously.

[0041] The trend prediction module obtains the correlation A between the core temperature and internal resistance of the battery cell. R Then, the trend prediction module uses this as a correction factor to adjust the main data for future time periods. Combined with the degree of deviation of the main data from its baseline value, the change in the main data for future time periods is obtained. The specific process is as follows:

[0042]

[0043] In the above formula, ΔT pred This represents the change in the core temperature of the battery cell per unit time in the future. A positive value indicates an increase in temperature based on the change in the reference temperature, while a negative value indicates a decrease in temperature. For example, if the rate of change of the core temperature of the battery cell in the future time is known to be 5℃ / min based on historical data, after correction, the rate of change of the core temperature of the battery cell in the future time will change, possibly to 3℃ / min or 7℃ / min.

[0044] ΔT base This is the basic change in the core temperature of the battery cell per unit time in the future. It is generated solely based on the time pattern of historical core temperature data of the battery cell and reflects the basic predicted value of the core temperature of the battery cell.

[0045] A R The correlation between the core temperature and internal resistance of the battery cell;

[0046] β is the correction influence coefficient, with a value of 0.2, which is derived based on battery type and system testing, and is used to control the degree of influence of correlation on the prediction results;

[0047] H(t) is the core temperature of the battery cell at the current time t;

[0048] H ref The reference cell core temperature represents the battery temperature under ideal conditions.

[0049] The correlation correction term is based on the correlation A between the cell core temperature and internal resistance. R Correcting the basic change in the core temperature of the battery cell improves the accuracy of battery temperature prediction.

[0050] When the correlation A between the core temperature and internal resistance of the battery cell R When the value is greater than 0, the predicted rise in cell core temperature can be amplified; conversely, the correlation between cell core temperature and internal resistance A is reduced. R When the value is less than 0, the predicted rise in the core temperature of the battery cell will be reduced.

[0051] Simultaneously, the cell core temperature H(t) at the current time t and the reference cell core temperature H are introduced. ref The ratio of these values ​​allows for a greater temperature correction as the current temperature increases. The core temperature of the battery cell at current time t, H(t), is greater than the reference core temperature of the battery cell, Hreference. ref When the value of this correction term is increased, it reflects the enhanced influence of internal resistance changes on temperature at high temperatures; conversely, when the core temperature of the cell at the current time t is less than the reference core temperature of the cell H, the correction term is increased. ref When this happens, the value of the correction term will be reduced, decreasing the correlation correction term's impact on the future unit time fundamental change in cell core temperature ΔT. base This reduces the false alarm rate in low-temperature scenarios.

[0052] By combining the correlation between the core temperature and internal resistance of the battery cell, A R The temperature prediction for future time is corrected, and the impact of the deviation between the current core temperature of the battery cell and the core temperature of the reference battery cell on the temperature prediction is taken into account. This conforms to the physical law that the safety risk increases with the strengthening of the correlation characteristics before the battery thermal runaway. As a result, thermal runaway can be predicted more accurately, more time is reserved for active safety protection actions, the safety of the test chamber is ensured, and the safety protection effect is improved.

[0053] The safety risk assessment module obtains the change in core cell temperature ΔT per unit time in the future. predSubsequently, the safety risk assessment module sets different change thresholds according to the testing phase of the test chamber. The changes in key data over future time are compared with these thresholds to assess safety risks. The degree to which secondary related data deviates from its initial value is also introduced as an influencing factor to adjust for safety risks, ultimately yielding a risk level score. The specific process is as follows:

[0054]

[0055] In the above formula, E represents the risk level score, and the higher the value, the higher the safety risk.

[0056] ΔT pred This represents the change in the core temperature of the battery cell per unit time in the future.

[0057] C(t) represents the secondary associated data value at the current time t;

[0058] C base The initial reference value for the associated data, taking internal resistance as an example, represents the initial internal resistance value of the lithium battery when it leaves the factory;

[0059] γ is the aging effect coefficient, with a value of 0.5, ranging from 0 to 1, determined based on sample tests and battery type. The aging effect is used to quantify the impact of battery aging on safety risks. This represents the rate of change of internal resistance relative to the initial value, i.e., the aging rate. The larger the value, the higher the safety risk, because the thermal stability of the aged battery decreases, and the risk of thermal runaway at the same temperature is higher.

[0060] ΔT th,i The temperature change rate threshold for stage i is used. In actual battery testing, the battery will go through different stages, such as fast charging test stage, cycle life test stage and thermal abuse test stage. The temperature of each stage is different. In order to avoid misjudgment or omission, a differentiated threshold is set according to the temperature change pattern. At the same time, it needs to be adjusted according to different battery types. The threshold for each stage is set according to the experiment.

[0061] The temperature change rate threshold during the fast charging test is 5℃ / min. During fast charging, the electrochemical reaction rate inside the battery is fast, which can easily generate local hot spots. By limiting the temperature rise rate, we can ensure that the battery thermal management system has sufficient response time during the fast charging test.

[0062] The temperature change rate threshold during the cycle life test is 3℃ / min. The cycle life test requires the battery to remain stable during multiple charge and discharge cycles. Excessive temperature fluctuations will accelerate material aging. A rapid rise in the internal temperature of the battery will cause uneven expansion and contraction of the electrode structure, affecting the cycle life. Compared with the fast charging stage, the cycle test generally has a lower power but a longer duration, so the temperature rise rate is lower. If the temperature change rate exceeds the threshold, it may indicate that the internal side reaction is aggravated. Timely warning can terminate the test and analyze the cause of aging.

[0063] The temperature change rate threshold for the thermal abuse test is 8℃ / min. The goal of the thermal abuse test is to simulate the performance of the battery under extreme thermal conditions and test the stability and safety of the battery under rapid temperature changes. The rate of temperature change in this stage is usually faster to accelerate the test process and observe whether the battery will malfunction, fail or have a safety accident under overheating conditions.

[0064] By setting temperature change rate thresholds differently according to different testing stages, the test data can be matched with the risk characteristics of different testing scenarios, avoiding the problems of excessive warnings or delayed warnings due to fixed thresholds. Furthermore, by introducing battery aging factors, the amplification effect of the rate of change of battery internal resistance relative to the initial value on safety risks is quantified, adapting to the entire life cycle of the battery from new manufacturing to aging and degradation, thereby improving the accuracy of the results data.

[0065] After obtaining the risk level score E, the active protection module sets thresholds based on experimental tests, classifying the risk level score E into different protection levels. Corresponding protective measures are then implemented according to each protection level to achieve the effect of active safety protection, as detailed below:

[0066] When the risk level score E < 0.5, it indicates that the test status is normal. Keep the current test parameters unchanged and execute the test as originally planned.

[0067] When the risk level score E is between 0.5 and 0.8, it is considered a low protection level. A yellow audible and visual warning will be activated, and abnormal parameters will be highlighted on the monitoring interface. The test condition settings and battery status will be manually checked to confirm whether the fluctuation is normal.

[0068] When the risk level score E is between 0.8 and 1.2, the protection level is medium, and a red audible and visual warning is activated. Response measures are taken according to different testing stages. For example, during the fast charging test, the charging current is reduced by 10%-15%, while maintaining the charging voltage to avoid sudden power outages affecting battery performance testing. During the cycle life test, the charge / discharge cycle is paused for 5 minutes, the pre-cooling system inside the chamber is activated, and the cycle current is reduced by 5%. During the thermal abuse test, the thermal abuse heating program is immediately paused, the forced ventilation system inside the chamber is activated, and manual inspection is performed after the temperature drops to a safe range.

[0069] When the protection level is medium and countermeasures are taken, the correlation A between the core temperature and internal resistance of the battery cell is recalculated. R The change in core temperature of the battery cell per unit time in the future, ΔT pred If the risk level is classified as E, and it is medium protection level twice in a row, the maintenance personnel should be notified to inspect the equipment.

[0070] When the risk level E≥1.2, it is a high protection level, and a red audible and visual warning is activated. At this time, regardless of the stage, the test control console is locked and only emergency stop is allowed. The electrical connection between the battery and the test equipment is automatically cut off, the inert gas injection system in the chamber is activated, the nitrogen concentration in the chamber is increased to more than 95%, oxygen is isolated to suppress thermal runaway, the pressure relief valve on the top of the test chamber is opened to release any combustible gases that may be generated in the chamber, and the pressure in the chamber is kept stable.

[0071] The active protection module also includes manual operation commands, which have higher priority than the automatic response logic. This allows managers to adjust active protection measures at any risk level to respond to various unexpected situations. At the same time, a misoperation lockout mechanism is set up. When a conflict is detected between the manager's operation and the preset strategy for the current risk level, it must be confirmed twice before execution to avoid human error from escalating the risk and to ensure the stability of the active safety protection of the test chamber.

[0072] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An active safety protection control system for an experimental cabin, characterized in that, It includes a multi-source data acquisition module, a correlation feature analysis module, a trend prediction module, a security risk assessment module, and a proactive protection module; The multi-source data acquisition module is used to collect data from different dimensions of battery testing to obtain battery test data. The correlation feature analysis module first extracts primary and secondary correlation data from the battery test data. The primary data includes the core temperature of the battery cell, and the secondary correlation data includes the internal resistance. Then, based on the change information of the primary and secondary correlation data, the correlation degree between the two is quantified to obtain the correlation degree between the primary and secondary correlation data. After obtaining the correlation between the primary data and the secondary related data, the trend prediction module uses it as a correction coefficient to correct the primary data for future time. Combined with the deviation of the primary data at the current time from its baseline value, the change in the primary data for future time is obtained. The safety risk assessment module sets different change rate thresholds according to the battery testing stage, compares the changes in the main data over future time with the change rate thresholds, assesses the degree of safety risk, and introduces the degree of deviation of secondary related data from their initial values ​​as an influencing factor to adjust the safety risk and obtain a risk level score. Finally, the active protection module divides the risk level score into different protection levels and takes corresponding protective measures according to different protection levels.

2. The active safety protection control system for the test chamber according to claim 1, characterized in that: After obtaining the core temperature and internal resistance of the battery cell, the changes in the core temperature and internal resistance of the battery cell are first calculated per unit time to obtain the changes in the core temperature and internal resistance of the battery cell. Then, the ratio of the changes in the core temperature and internal resistance of the battery cell is calculated to obtain the rate of change influence term, which reflects the correlation strength when the two change. Next, the absolute value of the change in internal resistance is calculated and compared with the historical average of internal resistance to obtain the relative change term. The relative change magnitude of secondary correlation data is quantified, and the relative change term is combined with the change rate influence term. An internal resistance influence coefficient is introduced to obtain the correlation degree A between the cell core temperature and internal resistance. R .

3. The active safety protection control system for the test chamber according to claim 2, characterized in that: The trend prediction module first uses historically collected core temperature data of the battery cell to obtain the basic change in core temperature of the battery cell per unit time in the future through time series analysis. Next, the correlation A between the core temperature of the battery cell and its internal resistance is calculated. R As a correction factor, and combined with the ratio of the current cell core temperature to the reference temperature, the basic change in cell core temperature per unit time in the future is corrected to obtain the change in cell core temperature ΔT per unit time in the future. pred .

4. The active safety protection control system for the test chamber according to claim 3, characterized in that: The safety risk assessment module pre-sets the temperature change rate threshold for the corresponding stage of the battery test; then it calculates the ratio of the predicted core temperature change of the battery cell to the temperature change rate threshold for the corresponding stage to obtain the basic risk value. At the same time, the relative change of internal resistance and the initial benchmark value is introduced as an aging influencing factor to correct the basic risk value, and finally the risk level score E is obtained.

5. The active safety protection control system for the test chamber according to claim 4, characterized in that: The active protection module pre-sets multiple risk level threshold ranges, matches the calculated risk level score with the corresponding range to determine the current risk level, and then automatically triggers corresponding graded protection measures according to different risk levels. The levels are divided as follows: When the risk level score E < 0.5, it indicates that the test status is normal. Keep the current test parameters unchanged and carry out the test as originally planned. When the risk level score E is between 0.5 and 0.8, it is considered a low protection level. A yellow audible and visual warning will be activated, and abnormal parameters will be highlighted on the monitoring interface. The test condition settings and battery status will be manually checked to confirm whether the fluctuation is normal. When the risk level score E is between 0.8 and 1.2, it is considered a medium protection level. A red audible and visual warning will be activated, and corresponding measures will be taken according to different testing stages. Then, a risk assessment will be conducted again. If the risk level is medium for two consecutive tests, the maintenance personnel will be notified to conduct a shutdown inspection. When the risk level E≥1.2, it is a high protection level. A red audible and visual warning is activated, the test control console is locked, and only emergency stop is allowed. The electrical connection between the battery and the test equipment is automatically cut off, the inert gas injection system inside the chamber is activated, and the pressure relief valve on the top of the test chamber is opened to maintain stable pressure inside the chamber.

6. The active safety protection control system for the test chamber according to claim 5, characterized in that: The testing phases include fast charging testing, cycle life testing, and thermal abuse testing.

7. The active safety protection control system for the test chamber according to claim 6, characterized in that: When the protection level is medium, take corresponding measures according to the test stage. During the fast charging test, reduce the charging current by 10%-15% while maintaining the charging voltage. During the cycle life test, pause the charge and discharge cycle for 5 minutes, start the cabin pre-cooling system, and reduce the cycle current by 5%. During the heat abuse test phase, the heat abuse heating procedure is suspended, the forced ventilation system inside the chamber is activated, and a manual inspection is carried out after the temperature drops to a safe range.

8. The active safety protection control system for the test chamber according to claim 1, characterized in that: The active protection module allows managers to intervene and adjust protection measures at any risk level. It also has a misoperation lockout mechanism. When a conflict is detected between a manager's operation and the protection strategy for the current risk level, the operation must be confirmed again before execution to avoid human error.

Images