Control system based on battery thermal runaway and vehicle

Abstract

The invention provides a battery thermal runaway-based control system and a vehicle, the battery thermal runaway-based control system comprises a test terminal and a test integrated plate, the test integrated plate comprises a pole piece, a test optical fiber sensor, a base body and a collector plate, the pole piece is arranged at the top of the test integrated plate and is in contact with an electrode of a battery to be tested, the base body is arranged between the pole piece and the collector plate, and the test optical fiber sensor is arranged on the base body; the base body is provided with a groove, the test optical fiber sensor is arranged in the groove, the collector plate is in contact with an external circuit of a battery to be tested, and the test terminal is configured to obtain temperature data in a preset time period; inputting the temperature data into a battery thermal runaway test model to obtain thermal runaway risk parameters of the to-be-tested battery; and based on the current time and the thermal runaway risk parameter, determining an early warning strategy of the to-be-detected battery. The temperature of the pole piece is monitored through the testing integrated plate, the thermal runaway risk parameter and the early warning strategy are obtained through the testing terminal in combination with model analysis, and the safety performance of the power battery is improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a control system and a vehicle based on battery thermal runaway. Background Art

[0002] At present, power batteries are the core components of new energy vehicles, and their technological development is the key support for the global automotive industry's electrification transformation. Low-carbon and environmental protection policies promote the development of electric vehicles and drive the progress of the power battery industry. High-energy-density batteries can solve mileage anxiety. The energy density of solid-state batteries far exceeds that of traditional lithium-ion batteries, and solid-state electrolytes are non-flammable and have no leakage risk, which can greatly reduce the probability of thermal runaway.

[0003] Electric vehicles face shortcomings such as high prices and limited range. Lithium-ion batteries also face issues such as accelerated aging from rapid charging and temperature sensitivity. Charging and discharging generate heat, and thermal runaway can cause safety issues such as explosions and fires. Lithium-ion battery thermal runaway early warning technology is a preventative measure.

[0004] Although solid-state batteries are considered safe and reliable, studies have shown that certain solid-state batteries can experience rapid and severe thermal runaway, placing higher demands on thermal safety performance. Summary of the Invention

[0005] In view of this, the purpose of the present application is to provide a control system and vehicle based on battery thermal runaway to overcome at least one of the above-mentioned defects.

[0006] In the first aspect, an embodiment of the present application provides a control system based on battery thermal runaway, the system comprising: a test terminal and a test integrated board, the test integrated board comprising a pole piece, a test optical fiber sensor, a substrate and a current collecting plate, the pole piece being arranged on the top of the test integrated board and contacting the electrode of the battery to be tested, the substrate being arranged between the pole piece and the current collecting plate, the substrate being provided with a groove, the test optical fiber sensor being arranged inside the groove and being used to monitor the temperature of each area on the pole piece, the current collecting plate being in contact with the external circuit of the battery to be tested, wherein the test terminal is configured to: obtain temperature data within a preset time period; input the temperature data into a battery thermal runaway test model to obtain a thermal runaway risk parameter of the battery to be tested, the battery thermal runaway test model being used to characterize the relationship between temperature data and thermal runaway risk parameters; and determine an early warning strategy for the battery to be tested based on the current time and the thermal runaway risk parameters. In an optional embodiment of the present application, the substrate further includes a plurality of through holes, which divide the substrate into a plurality of regions, each through hole corresponding to a region of the substrate, each region of the substrate corresponding to each region of the pole piece, and a cooling material is arranged in each through hole. The test terminal is further configured to: receive temperature data sent by the test optical fiber sensor, the temperature data including the pole piece temperature of the corresponding pole piece position detected by each optical fiber sensor; input the pole piece temperature of the corresponding pole piece position detected by each optical fiber sensor into the battery thermal runaway test model to obtain the thermal runaway risk parameters of the battery to be tested, the thermal runaway risk parameters including the predicted time interval for thermal runaway and the corresponding thermal runaway occurrence region on the pole piece.

[0007] In an optional embodiment of the present application, the time interval includes an upper limit and a lower limit, and the test terminal is further configured to: calculate a first difference between the current time and the upper limit and a second difference between the current time and the lower limit to determine a minimum difference; determine whether the minimum difference is less than a preset value; if the minimum difference is less than the preset value, directly start the cooling material to cool the electrode, and the cooling material is arranged in a through hole pre-set in the substrate, and the through holes are distributed in an array on the substrate, covering all areas of the substrate, so that the cooling material contacts all areas of the electrode; if the minimum difference is not less than the preset value, control the thermal management system to cool the electrode temperature of the battery, so that the electrode temperature is controlled within a reasonable range.

[0008] In an optional embodiment of the present application, the test terminal is further configured to: calculate the theoretical heating time required for the current electrode temperature to rise to the thermal runaway critical temperature based on the thermal runaway critical temperature of the battery; calculate the time difference between the theoretical heating time and the time when the cooling material starts to cool down in combination with the response time of the cooling material; determine the buffer time required for the thermal management system to reduce the battery temperature to a safe range based on the cooling efficiency of the thermal management system; and determine the preset value based on the theoretical heating time, the time difference and the buffer time.

[0009] In an optional embodiment of the present application, the test terminal is further configured to: collect temperature data of different areas of the battery pole piece and calculate the temperature rise rate of each area; identify the battery thermal runaway law by comparing the temperature rise rate and speed threshold of different areas; determine the area where the temperature rise rate exceeds the preset temperature rise rate threshold and the temperature is higher than the critical temperature threshold as the thermal runaway area; based on the temperature data of the battery pole piece corresponding to the thermal runaway area and the pole piece area distribution, predict the flame spread direction and the fire spread area.

[0010] In an optional embodiment of the present application, the test terminal is further configured to: detect the temperature change of the electrode in real time when the cooling material is started to cool down; if the temperature does not drop to a safe range, trigger the battery protection mechanism to cut off the power supply.

[0011] In an optional embodiment of the present application, the test terminal is further configured to: obtain a training sample set, the training sample set including multiple training samples, each training sample including sample temperature data and sample thermal runaway risk parameters; use the sample temperature data as the input of an initial battery thermal runaway test model, and use the sample thermal runaway risk parameters as the output of the initial battery thermal runaway test model to train the initial battery thermal runaway test model.

[0012] In the second aspect, an embodiment of the present application also provides a control method based on battery thermal runaway, which is applied to a control system based on battery thermal runaway. The test integrated board includes a pole piece, a test optical fiber sensor, a substrate and a current collecting plate. The pole piece is arranged on the top of the test integrated board and contacts the electrode of the battery to be tested. The substrate is arranged between the pole piece and the current collecting plate. The substrate is provided with a groove. The test optical fiber sensor is arranged inside the groove for monitoring the temperature of each area on the pole piece. The current collecting plate contacts the external circuit of the battery to be tested. The method includes: obtaining temperature data within a preset time period; inputting the temperature data into a battery thermal runaway test model to obtain a thermal runaway risk parameter of the battery to be tested, and the battery thermal runaway test model is used to characterize the relationship between temperature data and thermal runaway risk parameters; based on the current time and the thermal runaway risk parameters, determining the early warning strategy of the battery to be tested. In a third aspect, an embodiment of the present application further provides a vehicle, comprising the control system based on battery thermal runaway as described above.

[0013] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described above are executed.

[0014] The control system and vehicle based on battery thermal runaway provided by the embodiment of the present application include a test terminal and a test integrated board. The test integrated board includes a pole piece, a test optical fiber sensor, a substrate and a current collecting plate. The pole piece is arranged on the top of the test integrated board and contacts the electrode of the battery to be tested. The substrate is arranged between the pole piece and the current collecting plate. A groove is provided on the substrate. The test optical fiber sensor is arranged inside the groove and is used to monitor the temperature of each area on the pole piece. The current collecting plate contacts the external circuit of the battery to be tested. The test terminal is configured to: obtain temperature data within a preset time period; input the temperature data into the battery thermal runaway test model to obtain the thermal runaway risk parameters of the battery to be tested. The battery thermal runaway test model is used to characterize the relationship between the temperature data and the thermal runaway risk parameters; based on the current time and the thermal runaway risk parameters, determine the early warning strategy of the battery to be tested. The present application monitors the pole piece temperature through the test integrated board and uses the test terminal combined with the model analysis to obtain the thermal runaway risk parameters and early warning strategy, effectively assessing the battery thermal runaway risk and improving the safety performance of the power battery.

[0015] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A schematic diagram of the structure of the integrated testing board provided in an embodiment of the present application; Figure 2 One of the flow charts of the test terminal according to the embodiment of the present application determining the early warning strategy of the battery to be tested; Figure 3 The second flowchart of the test terminal according to the embodiment of the present application determining the early warning strategy of the battery to be tested; Figure 4 A flow chart for determining preset values ​​for a test terminal provided in an embodiment of the present application; Figure 5 A flow chart of the test terminal provided in an embodiment of the present application for determining the direction of flame spread and the area of ​​fire spread. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for which protection is claimed, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, each other embodiment obtained by those skilled in the art without making creative work falls within the scope of protection of the present application.

[0019] First, the application scenarios to which this application is applicable are introduced. This application can be applied in the field of vehicles.

[0020] Research has found that high-energy-density batteries can provide electric vehicles with longer driving range, alleviating range anxiety and extending the battery life of electronic devices. Traditional lithium-ion batteries are limited by the energy density bottleneck of their liquid electrolytes (150 to 260 watt-hours per kilogram) and safety risks. Solid-state batteries, on the other hand, can easily reach an energy density of 300 to 500 watt-hours per kilogram, far exceeding that of traditional lithium-ion batteries. Furthermore, solid-state electrolytes are non-flammable and pose no risk of leakage, significantly reducing the probability of thermal runaway.

[0021] However, with the development of electric vehicles, negative impacts are gradually becoming apparent. High prices, limited range, short cycle life, and limited safety and reliability are prominent shortcomings. Currently, the dual demands for fast charging and long life of lithium-ion batteries are increasing. However, fast charging often accelerates battery aging, particularly the formation of lithium metal plating on the negative electrode, leading to a significant decrease in capacity and performance. Due to their inherent design, lithium-ion batteries for electric vehicles operate optimally between 20°C and 40°C. Temperatures that are too high or too low can affect battery performance and pose safety risks. The charging and discharging process of power batteries generates heat, which in turn causes the battery's temperature to rise uncontrollably, eventually leading to explosion and fire. When the temperature in a specific flammable area rises to a certain level, reaching the ignition point of the material, the power battery will ignite.

[0022] In addition, when the battery is in abnormal operating conditions, such as overcharging, over-discharging, or used in a high-temperature environment, the electrode material will undergo corresponding side reactions with the electrolyte and produce a large amount of gas. At the same time, the battery shell will continue to expand as the internal air pressure increases. When the battery cannot expand to break through the critical volume state, the battery will explode and catch fire, causing serious safety problems.

[0023] Although solid-state batteries are considered safe and reliable, the thermal runaway of certain solid-state batteries is fast and severe, which places higher demands on their thermal safety performance.

[0024] Based on this, the embodiment of the present application provides a control system and vehicle based on battery thermal runaway, which tests the integrated board containing the pole piece, tests the optical fiber sensor and other components to monitor the pole piece temperature. After the test terminal obtains the temperature data, it inputs the battery thermal runaway test model to obtain the risk parameters, and determines the early warning strategy based on this, which can effectively evaluate the battery thermal runaway risk and improve the safety performance of the power battery.

[0025] See also Figure 1 , Figure 1 This is a schematic diagram of the structure of the test integrated board provided in the embodiment of the present application. Figure 1 As shown in , the test integrated board provided in the embodiment of the present application includes a pole piece 1, a test optical fiber sensor 2, a substrate 3 and a current collecting plate 4.

[0026] Here, the electrode piece 1 is arranged on the top of the test integrated board and contacts the electrode of the battery to be tested. The substrate 3 is arranged between the electrode piece 1 and the current collecting plate 4. A groove 5 is provided on the substrate 3. The test optical fiber sensor 2 is arranged inside the groove 5 to monitor the temperature of each area on the electrode piece 1. The current collecting plate 4 contacts the external circuit of the battery to be tested.

[0027] Taking the soft-pack solid-state battery as an example, the stacking order from positive to negative is: positive current collector (aluminum foil) - positive active material layer - solid electrolyte layer - negative active material layer - negative current collector (copper foil). In the above installation position, the electrode is in contact with the electrode of the battery to be tested to ensure good electrical connection, ensure stable and accurate current transmission, and make the test results truly reflect the battery status; the substrate is placed between the electrode and the current collector, which not only provides structural support to ensure mechanical strength, but also effectively isolates to avoid short circuits and ensure test safety; the groove of the substrate provides a stable installation position for the test optical fiber sensor, ensuring accurate and stable temperature monitoring and protecting the sensor from physical damage; the test optical fiber sensor monitors the temperature of each area of ​​the electrode, and can obtain data in real time and accurately, assisting in thermal runaway warning and mechanism research, and improving battery safety performance; the current collector is in contact with the external circuit of the battery to be tested, which facilitates current extraction and data acquisition and analysis, so that the test results are close to reality, providing a reliable basis for battery performance evaluation and optimization.

[0028] Based on the design concept of an integrated functional electrode, a serpentine-shaped fiber optic sensor was used to monitor the in-situ temperature of power battery cells. The fiber optic sensor's non-destructive implantation enabled highly reliable in-situ temperature monitoring, accurately measuring the battery's internal temperature. The battery was then disassembled, characterized, and tested after cycle aging to further verify the performance of the integrated functional electrode.

[0029] Matrix Design and Fabrication: 3D printing (additive manufacturing) is used to create a matrix with specially designed grooves. The matrix is ​​made of biodegradable polylactic acid plastic, formed through a spin-curing process similar to squeezing toothpaste. These precisely designed grooves act as rails to secure the optical fiber, allowing it to separately detect temperature and deformation signals during battery operation, preventing interference between the two data types.

[0030] Fiber Optic Arrangement and Temperature Monitoring: The optical fiber is arranged in a snake-like pattern, entering from below the negative electrode and exiting from below the positive electrode. Combined with a high-precision detection technology called Optical Frequency Domain Reflectometry (OFDR), this arrangement can scan temperature changes at various points within the battery in real time, much like a full-body CT scan, accurately capturing heating conditions in different areas.

[0031] Battery Assembly and Testing: A specialized electrode sheet with an ultra-thin conductive layer and a single-sided graphite coating was developed. This electrode sheet is tightly bonded to a substrate with optical fibers, forming a novel integrated sensing and power supply component. Finally, these components, along with separators, electrolytes, and other materials, are packaged into soft-pack batteries according to standard procedures. Comprehensive testing of charge-discharge performance, cycle life, and other aspects of the battery design verifies the reliability of the entire design in real-world use.

[0032] Here, the specific details include: A three-dimensional sensing substrate was constructed using additive manufacturing technology. A polylactic acid (PLA) substrate with a precise 0.2mm×0.2mm groove array was fabricated using a fused deposition modeling (FDM) process. The crystallinity of the PLA material (≥35%) was precisely controlled through a spin-curing process, ensuring a heat deformation temperature (HDT) of 55°C while matching the operating temperature range of lithium-ion batteries (-20°C to 60°C). The grooves were arranged periodically with a spacing of 1.5mm and a gradient depth design (0.1-0.3mm). Combined with UV-curable adhesive, the grooves were mechanically secured to a 125-micron diameter optical fiber and optimized for strain transfer efficiency. By matching the substrate's structural stiffness (elastic modulus of 3.5 GPa) with the modulus of the fiber coating, the device successfully achieved temperature and strain measurement sensitivities of 12 picometers per degree Celsius (pm / °C) and 1.2 picometers per microstrain (pm / με).

[0033] Fiber optic sensing system integration: Based on a serpentine path optimization algorithm, a sensing network topology with 92% coverage was constructed. The fiber optic sensor is protected by a three-layer polyimide coating. Laser micromachining creates a 0.3mm φ microhole (in engineering and mechanical drawing, φ represents the diameter symbol for circular objects) at the end of the current collector. The optical fiber enters the bottom of the negative electrode nickel foil current collector (8μm thick) and, after a figure-eight fold, exits the positive electrode aluminum foil current collector (10μm thick). Incorporating optical frequency domain reflectometry (OFDR) technology, using a wavelength-scanning laser (tuning range 1520-1570nm) and 0.1µm spatial resolution, distributed monitoring of the internal battery temperature field is achieved. Frequency division multiplexing technology improves temperature measurement accuracy to ±0.5°C, with a spatial resolution of 2mm and a sampling frequency of 1Hz, meeting the requirements of dynamic operating condition monitoring.

[0034] Integrated pole piece fabrication and verification: A single-sided graphite coating (surface density 12 mg / cm², milligrams per square centimeter) was prepared on a 6μm ultra-thin copper foil current collector (indicating a copper foil current collector thickness of 6 microns) using a magnetron sputtering process. A mechanical-electrochemical coupling interface was formed between the PLA substrate and the pole piece by hot pressing (temperature 80°C, pressure 5 MPa, time 30 seconds). The interface contact resistance was less than 0.5Ω·cm² (Ohm·square centimeter is a unit related to resistivity or contact resistance, indicating the resistance value per unit area. "Interface contact resistance <0.5Ω·cm²" indicates that the contact resistance of the mechanical-electrochemical coupling interface formed between the PLA substrate and the pole piece is less than 0.5 ohm·square centimeter). After vacuum drying (85°C, 12 hours), the integrated electrode was assembled into a 20Ah soft-pack battery with a separator (Celgard 2320) and an electrolyte (1M LiPF6 in EC / DEC, representing a mixture of ethylene carbonate (EC) and diethyl carbonate (DEC) with a molar concentration of 1 mol / L of lithium hexafluorophosphate (LiPF6). Constant-current charge-discharge (1C rate) and electrochemical impedance spectroscopy (EIS, frequency range 100kHz-10mHz) tests were performed to verify the influence of the matrix structure on the ion transfer number (t+ > 0.4) and interfacial stability (capacity retention > 95% after 100 cycles), providing reliability assurance for embedded fiber optic sensing.

[0035] For details, please refer to Figure 2 , Figure 2 This is one of the flow charts of the test terminal determining the early warning strategy of the battery to be tested provided in the embodiment of the present application. Figure 2 As shown in the figure, the test terminal is configured as follows: S101, obtaining temperature data within a preset time period; Here, the test terminal receives the temperature data sent by the test optical fiber sensor.

[0036] The temperature data includes the pole piece temperature at the corresponding pole piece position detected by each optical fiber sensor.

[0037] The test terminal receives temperature data from the fiber optic sensor in real time, ensuring accurate and timely data transmission and avoiding data loss or delay. For example, using a high-speed, stable communication protocol such as Ethernet ensures that temperature data can be transmitted quickly and accurately to the test terminal.

[0038] Specifically, the substrate 3 also includes multiple through holes 6, which divide the substrate 3 into multiple areas. Each through hole 6 corresponds to an area of ​​the substrate 3, and each area of ​​the substrate 3 corresponds to each area of ​​the pole piece 1. Cooling material is set in each through hole 6.

[0039] Here, multiple through-holes are rationally arranged on the substrate, dividing the substrate into multiple regions. This ensures that each through-hole precisely corresponds to a region of the substrate, and that each region of the substrate corresponds to each region of the electrode. For example, based on the size and structural characteristics of the electrode, the through-holes are evenly distributed so that each region is of moderate size. This ensures effective monitoring of each region of the electrode while facilitating the placement of cooling materials.

[0040] Furthermore, each through-hole is filled with a suitable cooling material. The selection of the cooling material should take into account factors such as thermal conductivity, stability, and cost. For example, a phase-change material with high thermal conductivity can be used. When the battery temperature rises, the phase-change material absorbs heat and undergoes a phase change, effectively reducing the temperature of the corresponding area of ​​the electrode.

[0041] By dividing the substrate and the pole piece into corresponding areas through the through-holes, refined management of different areas of the pole piece is achieved, which facilitates independent monitoring and cooling control of the temperature of each area, improves the pertinence and effectiveness of battery thermal management, and the cooling material filled in the through-holes can absorb the heat generated by the pole piece in time, reduce the pole piece temperature, effectively prevent local overheating, reduce the possibility of thermal runaway, and improve the safety performance and service life of the battery.

[0042] For example, a reasonable time interval can be set based on comprehensive considerations of battery characteristics, test requirements, and actual application scenarios. This could be the time it takes for a battery to complete a charge and discharge cycle, or a period of operation under specific operating conditions. For example, for electric vehicle batteries that are frequently used daily, the preset time period could be the duration of a typical vehicle trip (such as commuting).

[0043] Fiber optic sensors are placed at key locations on the battery, such as the surface of the pole piece, to ensure accurate data collection reflecting the battery's true temperature condition. For example, multiple fiber optic sensors can be evenly distributed on the battery pole piece to obtain temperature information from different areas of the pole piece.

[0044] Set an appropriate temperature data collection frequency based on the speed at which battery thermal runaway occurs and the test accuracy requirements. If battery thermal runaway develops rapidly, a higher collection frequency, such as once per second, may be required. If the battery state is relatively stable, the collection frequency can be appropriately reduced, such as once per minute.

[0045] Here, by reasonably determining the preset time period, accurately selecting the temperature collection points, and setting the appropriate collection frequency, the temperature changes of the battery within a specific time period can be comprehensively and accurately obtained, providing a reliable data basis for subsequent analysis and helping to more accurately assess the thermal runaway risk of the battery.

[0046] S102 : Input the temperature data into a battery thermal runaway test model to obtain a thermal runaway risk parameter of the battery to be tested.

[0047] The battery thermal runaway test model is used to characterize the relationship between temperature data and thermal runaway risk parameters; Preferably, the pole piece temperature at the corresponding pole piece position detected by each optical fiber sensor is input into the battery thermal runaway test model to obtain the thermal runaway risk parameter of the battery to be tested.

[0048] Thermal runaway risk parameters include the predicted time interval for thermal runaway to occur and the corresponding thermal runaway occurrence area on the electrode.

[0049] The collected temperature data is cleaned to remove noise, outliers, and other interfering data to ensure accuracy and consistency. For example, a filtering algorithm is used to smooth the temperature data to eliminate fluctuations caused by sensor measurement errors. Furthermore, the data is normalized to bring temperature data of different dimensions into a specific range for easier model processing.

[0050] The preprocessed temperature data is organized in the format required by the model and input into the battery thermal runaway test model. Ensure the integrity and correctness of the input data to avoid inaccurate model output results due to missing or incorrect data.

[0051] The battery thermal runaway test model is activated. The model analyzes and calculates the input temperature data based on its internal algorithms and parameters, ultimately outputting thermal runaway risk parameters for the battery under test. These parameters may include the probability of thermal runaway, the time interval during which thermal runaway is likely to occur, and the severity of thermal runaway. This application uses the predicted time interval for thermal runaway and the corresponding thermal runaway region on the electrode as examples, and these parameters can be adjusted in real time based on actual conditions.

[0052] This application improves data quality by preprocessing temperature data, enabling the model to analyze based on more accurate data. The battery thermal runaway test model uses input temperature data to quickly and accurately calculate thermal runaway risk parameters, helping testers more intuitively understand the battery's thermal runaway risk level.

[0053] The test terminal receives and analyzes real-time temperature data, promptly identifying abnormal battery temperatures. By predicting the time interval and area where thermal runaway may occur, it provides early warning for battery safety management, allowing personnel to take appropriate measures to prevent thermal runaway accidents.

[0054] Moreover, based on the temperature data of the specific location detected by each fiber optic sensor, the model can more accurately assess the thermal runaway risk of the battery, determine the specific area where thermal runaway may occur, and improve the safety and reliability of the battery.

[0055] S103: Determine a warning strategy for the battery to be tested based on the current time and the thermal runaway risk parameter.

[0056] Based on the thermal runaway risk parameters output by the battery thermal runaway test model, determine the time interval in which the battery may experience thermal runaway. For example, if the model predicts an 80% probability of thermal runaway and it is expected to occur within the next 2-3 hours, then the 2-3 hours thermal runaway prediction time interval is used.

[0057] Compare the current time with the time interval to determine whether the current time is within the predicted time interval and the time interval between the start and end of the predicted time interval. For example, if the current time is one hour away from the start of the predicted time interval, it indicates that the battery is facing a high risk of thermal runaway.

[0058] For details, please refer to Figure 3 , Figure 3 The second flow chart of the test terminal determining the warning strategy of the battery to be tested provided in the embodiment of the present application. Figure 3 As shown in the figure, the test terminal is also configured as follows: S201, calculating a first difference between the current time and the upper limit and a second difference between the current time and the lower limit, and determining a minimum difference; The time interval includes an upper limit and a lower limit.

[0059] In an optional embodiment, after obtaining the thermal runaway prediction time range (including the upper and lower limits) and the current time, the test terminal uses a built-in time calculation algorithm to calculate a first difference between the current time and the upper limit (current time minus the upper limit; if the result is negative, the absolute value is used), and a second difference between the current time and the lower limit (similarly, the absolute value is used). For example, if the current time is 10:00, the upper limit of the thermal runaway prediction time range is 10:30, and the lower limit is 11:00, the first difference is 30 minutes, and the second difference is 60 minutes.

[0060] Determine the minimum difference: compare the calculated first difference and the second difference, and select the smaller value as the minimum difference. In the above embodiment, the minimum difference is 30 minutes.

[0061] By calculating the difference between the current time and the upper and lower limits of the thermal runaway prediction time range and determining the minimum difference, the shortest time interval to the occurrence of thermal runaway can be intuitively reflected, providing a basis for subsequent judgment on whether emergency measures need to be taken immediately.

[0062] S202, determining whether the minimum difference is less than a preset value; S203: If the minimum difference is less than the preset value, directly start the cooling material to cool the electrode.

[0063] The cooling material is placed in pre-set through-holes on the substrate. The through-holes are distributed in an array on the substrate, covering all areas of the substrate so that the cooling material contacts all areas of the pole piece. If the minimum difference is determined to be less than a preset value, the test terminal immediately sends a start signal to the coolant control system on the substrate. The coolant is placed within pre-set through-holes in the substrate, which are arranged in an array to cover all areas of the substrate, ensuring uniform contact between the coolant and the electrode. Upon receiving the signal, the coolant control system rapidly activates the coolant, for example by controlling the circulation of the coolant or triggering the phase change material, to rapidly cool the electrode.

[0064] While the cooling material is being started, the test terminal continuously receives the electrode temperature data sent by the test fiber optic sensor, detecting the electrode temperature changes in real time. For example, temperature data is collected every 10 seconds and the temperature change trend is recorded.

[0065] When the risk of thermal runaway is imminent, immediately activating the cooling material can rapidly reduce the electrode temperature, effectively curbing the development of thermal runaway. Real-time monitoring of electrode temperature changes can promptly understand the cooling effect and provide data support for whether further measures are needed.

[0066] Here, the test terminal is also configured as: When the cooling material is started to cool down, the temperature change of the electrode is detected in real time; if the temperature does not drop to a safe range, the battery protection mechanism is triggered to cut off the power supply.

[0067] In this step, a safe range of electrode temperature is pre-set based on the safety performance requirements of the battery. For example, for lithium-ion batteries, the safe range can be set to 20°C-50°C.

[0068] The test terminal compares the real-time electrode temperature with the safe range. If the temperature does not fall within the safe range, the battery protection mechanism is immediately triggered, sending a power-off command to the battery management system. Upon receiving the command, the battery management system quickly disconnects the battery from the external circuit, halting the battery's charge and discharge processes.

[0069] Through this application, when cooling measures cannot reduce the electrode temperature to a safe range, the power supply can be cut off in time to prevent the battery from continuing to work and generate more heat, further reducing the risk of thermal runaway and ensuring the safety of personnel and equipment.

[0070] S204: If the minimum difference is not less than a preset value, the thermal management system is controlled to cool the electrode temperature of the battery so that the electrode temperature is controlled within a reasonable range.

[0071] If the minimum difference is determined to be no less than a preset value, the test terminal sends a control command to the thermal management system to adjust its operating parameters, such as reducing the speed of the cooling fan and adjusting the flow rate of the coolant, to lower the temperature of the battery electrode and keep it within a reasonable range. For example, the electrode temperature can be controlled between 30°C and 40°C.

[0072] During the operation of the thermal management system, the test terminal continuously monitors the temperature changes of the electrode and adjusts the working parameters of the thermal management system in a timely manner according to the actual temperature conditions to ensure that the electrode temperature is always within a reasonable range.

[0073] When the risk of thermal runaway is relatively low, cooling by controlling the thermal management system can effectively control the electrode temperature while ensuring the normal operation of the battery, preventing potential safety problems caused by excessive temperature, and at the same time improving the battery's efficiency and life.

[0074] For details, please refer to Figure 4 , Figure 4 This is a flow chart of determining a preset value for a test terminal provided in an embodiment of the present application. Figure 4 As shown in , the test terminal can determine the preset value in the following ways: S301, based on the thermal runaway critical temperature of the battery, calculating the theoretical heating time required for the current electrode temperature to rise to the thermal runaway critical temperature; Here, by consulting the battery's technical specifications, experimental data, or industry standards, the critical temperature for thermal runaway of that type of battery can be accurately determined. For example, for a certain type of ternary lithium battery, its critical temperature for thermal runaway has been verified to be 200°C through extensive experiments.

[0075] The test fiber optic sensor is used to collect the current temperature data of the electrode in real time and ensure the accuracy and reliability of the data. For example, the sensor collects temperature data once per second and takes the average of the multiple collected data as the current electrode temperature.

[0076] Based on the battery's thermal characteristic parameters, such as heat capacity and thermal resistance, combined with the current electrode temperature and the critical temperature of thermal runaway, the theoretical heating time is calculated using heat conduction and thermodynamics related formulas. By accurately calculating the theoretical heating time, we can understand the time required for the battery to develop from the current state to thermal runaway without external intervention, which helps to provide early warning and take measures.

[0077] S302, calculating the time difference between the theoretical heating time and the time when the cooling material starts to cool down, based on the response time of the cooling material; The time it takes for the cooling material to effectively cool down after receiving the start signal, i.e., the cooling material's response time, can be determined through experimental testing or by consulting the cooling material's product manual. For example, the response time of a phase change cooling material may be 5 seconds.

[0078] The theoretical heating time calculated in step S301 is subtracted from the response time of the cooling material to obtain the time difference between the theoretical heating time and the time when the cooling material starts to cool down. For example, if the theoretical heating time is 60 seconds and the response time of the cooling material is 5 seconds, the time difference is 55 seconds.

[0079] Taking into account the response time of the cooling material, the calculated time difference can more accurately reflect the actual time the battery can be used to heat up after the cooling measures are initiated, which helps to evaluate whether the cooling measures can take effect in a timely manner and avoid an increased risk of thermal runaway due to delayed cooling response.

[0080] S303, determining the buffer time required for the thermal management system to reduce the battery temperature to a safe range based on the cooling efficiency of the thermal management system; Through experimental testing, the time required for the thermal management system to reduce the battery temperature from a higher temperature to a safe range is measured under different operating conditions (such as different battery temperatures and ambient temperatures), and the cooling efficiency of the thermal management system is calculated. For example, if the battery temperature is 80°C and the ambient temperature is 25°C, the thermal management system takes 20 minutes to reduce the battery temperature to 40°C (the upper limit of the safe range). The cooling efficiency under these operating conditions can be calculated.

[0081] Determine the battery's safe temperature range based on the battery's safety performance requirements and usage scenarios. For example, for this battery, the safe temperature range is set at 20°C to 40°C.

[0082] Assuming the current electrode temperature is higher than the upper limit of the safety range, the thermal management system calculates the buffer time required to reduce the battery temperature to the safe range based on the cooling efficiency of the thermal management system and the temperature difference between the current electrode temperature and the upper limit of the safety range. For example, if the current electrode temperature is 60°C, the upper limit of the safety range is 40°C, and the thermal management system cooling efficiency is 1°C per minute, the buffer time is 20 minutes.

[0083] By determining the buffer time required by the thermal management system to reduce the battery temperature to a safe range, we can understand the time required for the battery temperature to return to a safe state after taking cooling measures, ensuring that the battery operates within a safe temperature range.

[0084] S304: Determine the preset value based on the theoretical heating time, time difference and buffer time.

[0085] For example, the theoretical heating time reflects the development trend of thermal runaway of the battery itself, the time difference reflects the response delay of the cooling measures, and the buffer time reflects the cooling ability of the thermal management system.

[0086] Based on the analysis results, combined with the battery's safety requirements and application scenarios, determine an appropriate preset value. This value should ensure sufficient time to implement effective cooling measures and avoid thermal runaway accidents if the battery experiences thermal runaway risk. For example, considering the three time factors mentioned above, the preset value could be set to one hour. If the minimum difference is less than one hour, the thermal runaway risk is considered imminent and emergency measures are required.

[0087] By determining thermal runaway risk parameters and evaluating the relationship between the current time and this range, this application can accurately determine the urgency of the battery thermal runaway risk. Based on this determination, a reasonable early warning strategy can be formulated to promptly and effectively alert relevant personnel to take appropriate measures to avoid battery thermal runaway accidents and ensure the safety of personnel and equipment. Furthermore, different early warning strategies can be flexibly adjusted based on the risk level, improving the relevance and effectiveness of early warnings.

[0088] In an optional embodiment, the test terminal can also predict the flame spread direction and the fire spread area.

[0089] See also Figure 5 , Figure 5 The flow chart of determining the flame spreading direction and the fire spreading area of ​​the test terminal provided in the embodiment of the present application. Figure 5 As shown in , the test terminal can predict the direction of flame spread and the area of ​​fire spread by: S401, collecting temperature data of different areas of the battery electrode and calculating the temperature rise rate of each area; Multiple temperature sensors, such as thermocouples or fiber optic temperature sensors, are evenly distributed across different areas of the battery electrode. These sensors should be highly accurate and fast-responding, enabling real-time and accurate temperature data collection in each area. For example, temperature data can be collected every 1 second and transmitted to the data processing system.

[0090] For each area, the temperature difference between adjacent time points is calculated based on the collected temperature data, and then divided by the time interval to obtain the temperature rise rate of the area.

[0091] S402, identifying battery thermal runaway patterns by comparing temperature rise rates and speed thresholds in different regions; Based on the battery type, specifications, and temperature variation characteristics under normal operating conditions, set a reasonable temperature rise rate threshold through experiments or empirical data. For example, for a certain lithium-ion battery, the temperature rise rate generally does not exceed 0.5°C / s during normal charge and discharge, so the rate threshold can be set to 0.8°C / s.

[0092] The calculated temperature rise rate for each region is compared with the set speed threshold. If the temperature rise rate in a region exceeds the speed threshold, the region is marked as an abnormal region. At the same time, the occurrence time, location distribution, and temperature rise rate trends of multiple abnormal regions are observed, and the correlation between them is analyzed to identify the pattern of battery thermal runaway. For example, if the temperature rise rates of multiple adjacent regions exceed the speed threshold at the same time and the temperature rise rates show a gradually increasing trend, it may indicate that thermal runaway is spreading from these regions.

[0093] By comparing the temperature rise rate and speed threshold, this application can quickly identify abnormal areas on the battery pole piece, and through analysis of the abnormal areas, gain an in-depth understanding of the laws of battery thermal runaway, such as the starting position, diffusion direction and speed of thermal runaway, providing a basis for predicting the direction of flame spread and taking effective prevention and control measures.

[0094] S403, determining a region where the temperature rise rate exceeds a preset temperature rise rate threshold and the temperature is higher than a critical temperature threshold as a thermal runaway region; A critical temperature threshold is set based on the battery's critical thermal runaway temperature and safety requirements. For example, for the lithium-ion battery mentioned above, whose critical thermal runaway temperature is 200°C, the critical temperature threshold can be set at 180°C to ensure that timely measures can be taken when the temperature approaches the critical thermal runaway temperature.

[0095] Within the marked abnormal areas, we further screen out areas where the temperature rise rate exceeds the preset temperature rise rate threshold and the temperature is above the critical temperature threshold. These areas are identified as thermal runaway areas. For example, if the temperature rise rate in a certain area is 1.2°C / s (exceeding the rate threshold of 0.8°C / s) and the temperature is 190°C (exceeding the critical temperature threshold of 180°C), then this area is identified as a thermal runaway area.

[0096] S404: Predict the flame spread direction and the fire spread area based on the temperature data of the battery pole pieces corresponding to the thermal runaway area and the pole piece area distribution.

[0097] Based on the location and number of thermal runaway regions identified, their distribution on the pole piece is analyzed. For example, it is observed whether the thermal runaway regions are concentrated in a specific area or distributed in a linear, circular, or other manner.

[0098] Predicting the direction of flame spread based on the electrode area distribution: Considering the electrode material properties, structural layout, heat conduction and gas flow within the battery, combined with the distribution of the thermal runaway area, the possible direction of flame spread is predicted. For example, if the thermal runaway area is concentrated at one end of the battery, and the electrode has more flammable materials and gas channels at that end, the flame is likely to spread in that direction.

[0099] Based on the predicted spread direction and the thermal radiation range of the thermal runaway area, combined with the regional distribution of the pole pieces, the possible fire spread area is determined. For example, the thermal radiation model calculates the thermal radiation intensity of the thermal runaway area to the surrounding area. When the thermal radiation intensity reaches a certain value, the area is considered to be a fire, thus determining the fire spread area.

[0100] By predicting the flame spread direction and fire spread area based on the temperature data of the battery pole pieces corresponding to the thermal runaway area and the pole piece area distribution, it is possible to understand the possible development trend of thermal runaway accidents in advance, which helps to reduce the losses caused by fire accidents.

[0101] Based on the analysis of the temperature rise rate during the constant current discharge phase, as well as the temperature data of the battery's geometric center and the positive tab area, the team used supervised learning and time series models to identify thermal runaway patterns, regression models and physical information neural networks to predict temperature distribution, and image recognition and reinforcement learning models to predict flame propagation paths.

[0102] The specific process is as follows: Thermal Safety Analysis Foundation: The system collects key battery data during continuous discharge, including overall temperature rise rate, core temperature, and temperature changes at the positive terminal. This real-time monitoring data provides detailed foundational information for subsequent analysis, effectively providing a "body temperature monitoring system" for the battery.

[0103] Intelligent Prediction Model Construction: Through intelligent analysis systems (supervised learning) and historical data modeling (time series analysis), we summarize the evolutionary patterns of battery overheating and fire. Simultaneously, we apply temperature prediction algorithms (regression models) and models incorporating physical laws (physical neural networks) to accurately deduce the temperature distribution of various battery components, creating a "heat map" for the battery.

[0104] Safety protection system optimization: A flame tracking system has been developed, leveraging image recognition technology to capture flame morphological characteristics and combining it with an intelligent decision-making model (reinforcement learning) to predict the direction of fire spread. This multi-dimensional analysis solution not only reveals the mechanism of battery thermal runaway but also provides a scientific basis for designing safer battery protection systems, effectively equipping batteries with a "fire warning navigator."

[0105] Afterwards, the model is trained and optimized. Experimental validation and real-time testing are used to verify the accuracy of the model's predictions and test its real-time prediction capabilities. If the final result meets the set prediction threshold, it is accepted for use. If further refinement is required, the previous steps are repeated.

[0106] Specific details include: Intelligent prediction model construction: Build a hybrid prediction architecture based on the TensorFlow framework.

[0107] ① The supervised learning module uses a random forest algorithm (number of decision trees = 200) to classify and identify the thermal runaway stage. Feature importance analysis shows that the cathode oxygen evolution reaction rate (d[O2] / dt) contributes 37% to the classification; ② The time series prediction module uses an LSTM network (128 hidden units) to model temperature evolution, achieving an RMSE of less than 1.2°C within a 1s prediction window. ③ A physical information neural network (PINN) integrates Fourier's law of thermal conductivity and the Newman electrochemical model to construct a three-dimensional temperature field reconstruction model (mesh size 0.5 mm³). This model was validated on a 4Ah ternary soft-pack battery, demonstrating a maximum relative error of less than 4.5%. Using a Bayesian optimization algorithm for combined parameter tuning, the final model achieved a temperature distribution prediction coefficient (R²) greater than 0.93 under UN38.3 test conditions.

[0108] Safety protection system optimization: A flame propagation analysis system based on multispectral vision uses a Phantom VEO710 high-speed camera (frame rate 20,000 fps) to capture the trajectory of the flame front. It then uses a ResNet-50 convolutional neural network to extract characteristic parameters such as the flame area expansion rate (dA / dt = 152 cm² / s) and propagation angle (θ = 67 ± 8°). A reinforcement learning module uses the PPO algorithm to build a fire spread prediction model. After 1,000 strategy iterations, the path prediction accuracy increased to 89%. Based on this analysis, a tiered protection strategy was designed: ① Arrange a variable porosity aerogel insulation layer (thermal conductivity <0.02W / m·K) at the module level to achieve a heat spread delay time of >300s; ② A system-level, directional spray system is integrated, using fuzzy PID control of coolant flow (with an adjustment accuracy of ±5%) to increase thermal runaway propagation blocking efficiency to 92%. Verified according to the GB / T 31485 standard, this protection system can reduce the propagation rate of thermal runaway to 0.8 m / min, and achieve an early warning response time of 18 ± 3 seconds.

[0109] The third step was to evaluate the accuracy of the model's predictions through experimental verification and real-time testing, and to test its real-time predictive capabilities. If the model's predictions met the set prediction threshold, it was considered practical. If further improvement was needed, the previous optimization steps were repeated. This closed-loop research process ensured the model's reliability and practicality, providing an effective technical solution for battery safety management and thermal runaway warning.

[0110] In an optional embodiment, the test terminal is further configured to: Get the training sample set.

[0111] The training sample set includes multiple training samples, each training sample includes sample temperature data and sample thermal runaway risk parameters; In a controlled laboratory environment, various tests are performed on batteries (such as overcharge, over-discharge, short circuit, high temperature, etc.), and temperature data and thermal runaway risk parameters under different operating conditions are recorded. Data is collected from actual operating battery systems to ensure that the data covers normal operation and abnormal conditions. Virtual data is generated using a battery thermal runaway simulation model to supplement the deficiencies in experimental and actual operation data.

[0112] Use high-precision temperature sensors (such as thermocouples, fiber optic sensors, etc.) to monitor the temperature of battery electrodes, surfaces and key parts in real time, record time series data, and define and measure parameters related to thermal runaway risks, such as temperature rise rate, temperature gradient, gas generation rate, voltage change, etc.

[0113] Remove noisy data, outliers, and erroneous data, standardize temperature data and thermal runaway risk parameters to eliminate dimensional differences and facilitate model training, and enhance the data set through interpolation, noise addition, etc. to improve the generalization ability of the model.

[0114] The collected data is divided into training, validation, and test sets. Typically, the training set accounts for a larger proportion (e.g., 70%), while the validation and test sets each account for a certain proportion (e.g., 15% and 15%). Each sample is labeled with the corresponding thermal runaway risk parameter to ensure accuracy and consistency.

[0115] The sample temperature data is used as the input of the initial battery thermal runaway test model, and the sample thermal runaway risk parameter is used as the output of the initial battery thermal runaway test model to train the initial battery thermal runaway test model.

[0116] Among them, according to the complexity of the problem and the characteristics of the data, select the appropriate model architecture, such as deep learning models (such as convolutional neural networks CNN, recurrent neural networks RNN, Transformer, etc.), machine learning models (such as random forests, support vector machines SVM, etc.) or hybrid models, and randomly initialize the model parameters or use pre-trained weights (if a pre-trained model is available).

[0117] Use sample temperature data as the input of the model and sample thermal runaway risk parameters as the output of the model. Select an appropriate loss function based on the output type, such as mean squared error (MSE) for regression problems and cross entropy loss for classification problems. Select an appropriate optimizer (such as Adam, SGD, etc.) and learning rate to optimize the model parameters. Input the training set data into the model, and continuously adjust the model parameters through backpropagation and gradient descent algorithms to minimize the loss function.

[0118] During the training process, the validation set is regularly used to evaluate the model performance, monitor overfitting and underfitting, and the model's hyperparameters (such as learning rate, batch size, number of network layers, etc.) are adjusted based on the validation results to improve model performance. If the validation set performance no longer improves after several rounds of training, training is stopped early to prevent overfitting.

[0119] The control system and vehicle based on battery thermal runaway provided in the embodiment of the present application are composed of a test terminal and a test integrated board. In the test integrated board, the pole piece is placed on the top and contacts the electrode of the battery to be tested, the substrate is arranged between the pole piece and the current collecting plate and the substrate has a groove, the test optical fiber sensor is placed in the groove, and the current collecting plate contacts the external circuit of the battery to be tested. The test terminal first obtains the temperature data within a preset time period, and then inputs it into the battery thermal runaway test model to obtain the thermal runaway risk parameters of the battery to be tested. Finally, the early warning strategy is determined based on the current time and the thermal runaway risk parameters. By monitoring the pole piece temperature with the help of the test integrated board and combining it with model analysis, the safety performance of the power battery is effectively improved.

[0120] This application monitors the internal temperature distribution of the battery in real time, systematically studies the temperature evolution law before and after battery aging, and accurately locates the thermal runaway area of ​​the battery, which can effectively improve the thermal runaway safety performance of the battery cell and fundamentally solve the thermal runaway safety hazards. Statistical analysis is used to conduct in-depth mining of the processed data to determine the specific impact of different charging strategies on battery aging, revealing the dynamic characteristics of the chemical changes inside the battery during the charging process, improving the safety performance of the power battery, adapting to the rapid development of electric vehicle power battery systems, and greatly helping the rapid development of electric vehicle technology. At the same time, it greatly improves product consistency, can reduce the product testing link in the battery production process, simplify some manufacturing processes, and thus effectively improve production efficiency.

[0121] The integrated test board not only effectively decouples fiber optic signals for non-destructive temperature monitoring but also exhibits excellent corrosion resistance, enabling distributed in-situ battery measurements. These characteristics give this design significant advantages in battery health status monitoring, providing reliable technical support for battery safety management and performance optimization. This improves the accuracy and efficiency of the cell material design process, enhances the safety and pass rate of power battery manufacturing, and significantly enhances product quality. Furthermore, it can directly guide design improvements for electric vehicles and power batteries from a mechanistic perspective, helping to extend the lifespan and safety of these vehicles and batteries, further promoting their commercial application.

[0122] (3) The above method can shorten the cycle from product conception to production, reduce errors and lower costs. At the same time, it can form an empirical analysis model that can be applied to the design and optimization of other related manufacturing fields. In the first embodiment, a method for calibrating and verifying the temperature characteristics of an optical fiber sensor is implemented as follows: The optical fiber sensor segment to be measured is placed in a specially designed high-temperature-resistant container within a Constance horizontal high-temperature furnace. Silicone oil is injected into the container cavity as a heat transfer medium. A K-type thermocouple is simultaneously inserted into the silicone oil, forming a temperature cross-validation system with the furnace's temperature control system. A heating program is initiated to control the silicone oil temperature rise rate at 2°C / min. The optical fiber strain signal and thermocouple-measured temperature data are collected within the 20-70°C temperature range. Based on the distributed measurement characteristics of OFDR, three measurement points (spacing ≥10 cm) can be randomly selected within the sensing segment without inscribing the fiber Bragg grating (FBG). The microstrain (με) value at each point is obtained by demodulating the Rayleigh scattering signal. Experimental measurements show a linear response between temperature and strain, with a fitting proportionality coefficient of 9.98 με / °C (measurement unit length 1 mm). Comparisons between the thermocouple-measured temperature and the fiber-inverted temperature data reveal a maximum deviation of ≤±0.5°C, verifying the reliability of this sensing method. This example demonstrates that the use of silicone oil medium combined with OFDR analysis algorithm can achieve non-destructive optical fiber temperature sensing calibration, which is suitable for the construction of distributed temperature monitoring systems in complex environments.

[0123] In the second example, the specific implementation steps are as follows: a soft-pack battery is constructed using an NCM523 ternary cathode material system, and a 50μm-thick IFE functional layer is implanted at the cathode-separator interface. The assembled battery is placed in a constant temperature environment of 25±2°C and subjected to 1C constant current charge-discharge cycle testing (voltage window 3.0-4.2V) using a Blue Electric test system, while simultaneously collecting discharge capacity and coulombic efficiency data.

[0124] The test cell implanted with the IFE exhibited linear capacity decay over 400 cycles, with an initial discharge capacity of 1053 mAh (based on a 0.2C rate capacity calibration value) and a capacity retention of 83.38% (corresponding to 878 mAh) at the end of the cycle. Notably, the Coulombic efficiency remained stable at 99.8% ± 0.1% throughout the test, indicating that the IFE implantation did not induce side reactions or abnormal lithium loss. Comparative experimental data showed that this capacity decay trend was consistent with that of a control cell without the IFE, demonstrating that the introduction of the IFE functional layer did not cause additional performance impairment. Further electrochemical impedance spectroscopy (EIS) analysis revealed that the cell bulk impedance (Rb) increased from an initial 5.8 mΩ to 9.2 mΩ, the SEI film impedance (Rsei) increased from 12.3 mΩ to 21.5 mΩ, and the charge transfer impedance (Rct) increased by 64%. This impedance evolution is consistent with typical aging characteristics characterized by structural degradation of the cathode material and continuous reconstruction of the anode SEI film. Combined with differential capacity curve analysis, it can be confirmed that the main causes of capacity decay are the loss of positive and negative electrode active materials and the degradation of interface dynamics. This example demonstrates that the IFE functional layer is compatible with the conventional aging mechanisms of existing battery systems while maintaining interface stability.

[0125] In the third example, the specific implementation process is as follows: NCM523 lithium-ion soft-pack batteries were placed in a constant temperature environment at 25°C and subjected to constant current charge-discharge cycling tests at a 1C rate (voltage range 3.0-4.2V). The test was paused every 50 cycles, and a high-precision battery analysis system was used to collect charge and discharge data. IC curves were calculated through differential processing (dQ / dV), and the post-cycling electrochemical impedance spectrum of the battery was simultaneously recorded.

[0126] In the first 400 cycles, the main peak intensity of the IC curve decreased from the initial value of 1.25V / A to 0.87V / A, a decrease of 30.4%, indicating that the cumulative effect of the loss of active materials (LAM) of the positive and negative electrodes was significant. At the same time, the main peak potential shifted left from 3.65V to 3.58V, and the corresponding charge transfer impedance (Rct) increased by more than 60%, and the electrochemical window widened to ±15mV, confirming that the internal resistance of the battery increased exponentially with the number of cycles. By correlating the IC curve offset with the impedance spectrum data, the competitive mechanism of active lithium loss and interfacial side reactions can be quantitatively analyzed. This example verifies the effectiveness of the IC analysis method for diagnosing battery aging modes and provides a basis for key kinetic parameters for thermal runaway warning.

[0127] Based on the same inventive concept, the embodiments of the present application also provide a control method based on battery thermal runaway corresponding to the control system based on battery thermal runaway. Since the principle of solving the problem by the method in the embodiments of the present application is similar to the above-mentioned control system based on battery thermal runaway in the embodiments of the present application, the implementation of the method can refer to the implementation of the test terminal in the system, and the repeated parts will not be repeated.

[0128] The present application also provides a control method based on battery thermal runaway, which is applied to a control system based on battery thermal runaway. The test integrated board includes a pole piece, a test optical fiber sensor, a substrate, and a current collecting plate. The pole piece is arranged on the top of the test integrated board and contacts the electrode of the battery to be tested. The substrate is arranged between the pole piece and the current collecting plate. The substrate is provided with a groove. The test optical fiber sensor is arranged inside the groove and is used to monitor the temperature of various areas on the pole piece. The current collecting plate contacts the external circuit of the battery to be tested. The method includes: Get temperature data within a preset time period; Inputting the temperature data into a battery thermal runaway test model to obtain a thermal runaway risk parameter of the battery to be tested, wherein the battery thermal runaway test model is used to characterize the relationship between the temperature data and the thermal runaway risk parameter; Based on the current time and the thermal runaway risk parameter, a warning strategy for the battery to be tested is determined.

[0129] An embodiment of the present application also provides a vehicle, comprising the above-mentioned control system based on battery thermal runaway.

[0130] The embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the computer program can execute the above-mentioned Figure 2 The specific implementation of the steps executed by the test terminal in the method embodiment shown can be found in the method embodiment, and will not be repeated here.

[0131] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0132] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, the indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms.

[0133] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0134] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0135] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0136] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The scope of protection of the present application is not limited thereto. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-mentioned embodiments within the technical scope disclosed in the present application, or perform equivalent replacements for some of the technical features thereof. However, these modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A control system based on battery thermal runaway, characterized in that: The test integrated board includes a test terminal and a test integrated board, wherein the test integrated board includes a pole piece, a test optical fiber sensor, a substrate and a current collecting plate. The pole piece is arranged on the top of the test integrated board and contacts the electrode of the battery to be tested. The substrate is arranged between the pole piece and the current collecting plate. The substrate is provided with a groove. The test optical fiber sensor is arranged inside the groove to monitor the temperature of each area on the pole piece. The current collecting plate contacts the external circuit of the battery to be tested. The test terminal is configured as follows: Get temperature data within a preset time period; Inputting the temperature data into a battery thermal runaway test model to obtain a thermal runaway risk parameter of the battery to be tested, wherein the battery thermal runaway test model is used to characterize the relationship between the temperature data and the thermal runaway risk parameter; Based on the current time and the thermal runaway risk parameter, a warning strategy for the battery to be tested is determined.

2. The control system according to claim 1, characterized in that: The substrate further includes a plurality of through holes, which divide the substrate into a plurality of regions. Each through hole corresponds to a region of the substrate, and each region of the substrate corresponds to each region of the pole piece. Cooling material is disposed in each through hole. The test terminal is further configured to: Receiving temperature data sent by the test optical fiber sensor, the temperature data including the pole piece temperature at the corresponding pole piece position detected by each optical fiber sensor; The pole piece temperature at the corresponding pole piece position detected by each optical fiber sensor is input into the battery thermal runaway test model to obtain the thermal runaway risk parameters of the battery to be tested. The thermal runaway risk parameters include the predicted time interval for thermal runaway to occur and the corresponding thermal runaway occurrence area on the pole piece.

3. The control system according to claim 2, characterized in that: The time interval includes an upper limit and a lower limit, The test terminal is further configured to: Calculating a first difference between the current time and the upper limit and a second difference between the current time and the lower limit to determine a minimum difference; Determining whether the minimum difference is less than a preset value; If the minimum difference is less than a preset value, the cooling material is directly activated to cool the electrode. The cooling material is set in the through holes pre-set on the substrate. The through holes are distributed in an array on the substrate, covering all areas of the substrate, so that the cooling material contacts all areas of the electrode. If the minimum difference is not less than a preset value, the thermal management system is controlled to cool the electrode temperature of the battery so that the electrode temperature is controlled within a reasonable range.

4. The control system according to claim 3, characterized in that: The test terminal is further configured to: Based on the thermal runaway critical temperature of the battery, calculate the theoretical heating time required to increase the current electrode temperature to the thermal runaway critical temperature; Calculate the difference between the theoretical heating time and the time when the cooling material starts to cool down, based on the response time of the cooling material; Determine the buffer time required for the thermal management system to reduce the battery temperature to a safe range based on the cooling efficiency of the thermal management system; The preset value is determined according to the theoretical heating time, the time difference and the buffer time.

5. The control system according to claim 3, characterized in that: The test terminal is further configured to: Collect temperature data from different areas of the battery electrode and calculate the temperature rise rate of each area; Identify battery thermal runaway patterns by comparing temperature rise rates and speed thresholds in different areas; Determine an area where the temperature rise rate exceeds a preset temperature rise rate threshold and the temperature is higher than a critical temperature threshold as a thermal runaway area; Based on the temperature data of the battery pole pieces corresponding to the thermal runaway area and the pole piece area distribution, the flame spread direction and the fire spread area are predicted.

6. The control system according to claim 3, characterized in that: The test terminal is further configured to: When starting the cooling material to cool down, the temperature change of the pole piece is detected in real time; If the temperature does not drop to a safe range, the battery protection mechanism is triggered to cut off the power supply.

7. The control system according to claim 1, characterized in that: The test terminal is further configured to: Acquire a training sample set, wherein the training sample set includes a plurality of training samples, each training sample including sample temperature data and a sample thermal runaway risk parameter; The sample temperature data is used as input of an initial battery thermal runaway test model, and the sample thermal runaway risk parameter is used as output of the initial battery thermal runaway test model, so as to train the initial battery thermal runaway test model.

8. A control method based on battery thermal runaway, characterized in that: Applied to a control system based on battery thermal runaway, the test integrated board includes a pole piece, a test optical fiber sensor, a substrate, and a current collecting plate. The pole piece is arranged on the top of the test integrated board and contacts the electrode of the battery to be tested. The substrate is arranged between the pole piece and the current collecting plate. The substrate is provided with a groove. The test optical fiber sensor is arranged inside the groove and is used to monitor the temperature of each area on the pole piece. The current collecting plate contacts the external circuit of the battery to be tested. The method includes: Get temperature data within a preset time period; Inputting the temperature data into a battery thermal runaway test model to obtain a thermal runaway risk parameter of the battery to be tested, wherein the battery thermal runaway test model is used to characterize the relationship between the temperature data and the thermal runaway risk parameter; Based on the current time and the thermal runaway risk parameter, a warning strategy for the battery to be tested is determined.

9. A vehicle, characterized in that: The vehicle includes the battery thermal runaway-based control system as described above.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which is used to execute the steps of the method according to claim 8 when executed by a processor.

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