Battery box thermal runaway detection method and system

Abstract

The invention provides a battery box thermal runaway detection method. The method comprises the following steps: S1, monitoring CO2 concentration, smoke double-spectrum characteristics and temperature parameters in a battery box in real time; s2, analyzing a CO2 concentration change rate, a smoke double-light rate ratio and a temperature rise rate through a multi-source data fusion algorithm; s3, when the parameters obtained in the step S1 and the step S2 meet preset threshold values at the same time, triggering an alarm and executing corresponding emergency treatment measures; and S4, uploading the data to an external monitoring system in real time. The battery box thermal runaway detection system is used for realizing the method and comprises a data acquisition module, a data processing unit, a linkage output module and a communication module. According to the method, whether thermal runaway happens to the battery pack or not can be accurately judged, early warning is triggered in the early stage of thermal runaway, real thermal runaway smoke, dust and water mist can be effectively distinguished, and the false alarm rate is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of battery safety monitoring, and in particular to a method and system for detecting thermal runaway of a battery box. Background Art

[0002] With the widespread adoption of lithium-ion batteries in electric vehicles and energy storage systems, safety concerns are becoming increasingly prominent. Thermal runaway, the most serious safety hazard in battery systems, is typically triggered by factors such as internal short circuits, overcharging, or high temperatures in individual cells. This self-accelerating exotherm can easily lead to fires if not promptly suppressed.

[0003] Traditional thermal runaway detection relies primarily on electrochemical or semiconductor sensors for carbon monoxide, hydrogen, VOCs, and other gases, as well as smoke and temperature monitoring. However, these sensors generally suffer from inherent drawbacks such as short lifespans (electrochemical and semiconductor sensors typically only last 2-3 years), susceptibility to poisoning by ambient gases (such as silane and sulfide), and high false alarm rates. Summary of the Invention

[0004] In order to solve the technical problems existing in the background technology, the present invention proposes a battery box thermal runaway detection method and system.

[0005] The present invention proposes a method for detecting thermal runaway of a battery box, comprising the following steps: S1, real-time monitoring of CO2 concentration, smoke dual-spectrum characteristics and temperature parameters in the battery box; S2. Analyze the CO2 concentration change rate, smoke dual-light rate ratio, and temperature rise rate through multi-source data fusion algorithm; S3. When the parameters obtained in step S1 and step S2 simultaneously meet the preset thresholds, an alarm is triggered and corresponding emergency measures are executed; S4. Upload data to the external monitoring system in real time.

[0006] Preferably, the parameters in S3 are divided into two groups, namely parameter group 1 and parameter group 2. Parameter group 1 includes: CO2 concentration, CO2 change rate, red light value, blue light value and red / blue light rate ratio; parameter group 2 includes: temperature value and temperature rise rate; the triggering of the alarm requires that the obtained parameters simultaneously meet the preset threshold and the duration of either parameter group 1 or parameter group 2 reaches a preset time.

[0007] Preferably, the alarm levels are divided into three levels, from low to high: Level 1, Level 2 and Level 3; the triggering of each alarm level requires that the preset threshold of the corresponding level is met at the same time and the duration of either parameter group 1 or parameter group 2 reaches the preset time.

[0008] Preferably, the emergency response measures include level reporting, cutting off high voltage, starting the cooling system and starting the fire extinguishing device. When a level 1 alarm is triggered, an early warning is reported and the mode of high-speed data acquisition is switched to; when a level 2 alarm is triggered, the high voltage is cut off and the cooling system is started; when a level 3 alarm is triggered, the fire extinguishing device is started.

[0009] Preferably, the preset thresholds for each alarm level include: Level 1 alarm: CO2 concentration ≥700ppm, CO2 change rate ≥20ppm / 10s, red light value ≥6, blue light value ≥12, red / blue light rate ratio ≥3, temperature value ≥70℃, temperature rise rate ≥1℃ / s; Level 2 alarm: CO2 concentration ≥1000ppm, CO2 change rate ≥25ppm / 10s, red light value ≥6, blue light value ≥12, red / blue light rate ratio ≥3, temperature value ≥75℃, temperature rise rate ≥1.5℃ / s; Level 3 alarm: CO2 concentration ≥1500ppm, CO2 change rate ≥30ppm / 10s, red light value ≥6, blue light value ≥12, red / blue light rate ratio ≥3, temperature value ≥80℃, temperature rise rate ≥1.5℃ / s.

[0010] Preferably, the CO2 concentration is detected using a CO2 sensor and a piecewise linear compensation formula: , real-time dynamic calibration of detection data, where: is the CO2 concentration after temperature compensation; The original concentration detected by the CO2 sensor; is the temperature coefficient; T is the current ambient temperature; The reference temperature set for the CO2 sensor.

[0011] Preferably, the smoke dual-spectrum feature detection uses a dual-spectrum smoke sensor and adopts a dual-wavelength compensation formula: , real-time dynamic calibration of detection data, where: is the smoke characteristic ratio after temperature compensation; is the ratio of the original signals of the red and blue channels; is the temperature compensation factor.

[0012] Preferably, the temperature detection uses a temperature sensor, and there are three temperature sensors, and triple redundant temperature verification is adopted. The specific steps are: the three temperature sensors collect temperature data at the same time, and compare the three sets of temperature data in real time. When the difference in readings between any two temperature sensors exceeds a threshold, the sensor validity verification process is triggered, and a majority voting algorithm is used to isolate the faulty temperature sensor.

[0013] Preferably, the specific steps of the majority voting algorithm are as follows: S11. If the readings of any two temperature sensors tend to be consistent, but the third one deviates, the third temperature sensor is determined to be faulty; if all sets of data are inconsistent, the self-diagnosis mode is activated; S12: Automatically isolate the faulty temperature sensor, switch to healthy temperature sensor data, and report the fault.

[0014] The present invention proposes a battery box thermal runaway detection system, which is used to implement the above method, and includes: Data acquisition module, used to collect CO2 concentration, smoke dual-spectrum characteristics and temperature parameters in the battery box; The data processing unit is used to obtain data from the data acquisition module and execute multi-source data fusion algorithms and alarm determination; Linkage output module, triggering corresponding emergency measures according to the alarm level; Communication module, used to upload data to the remote monitoring system.

[0015] Preferably, the data acquisition module includes three temperature sensors, one CO2 sensor, and one dual-spectrum smoke sensor.

[0016] Preferably, the data processing unit is a micro control unit.

[0017] Through multi-parameter collaborative monitoring and comprehensive analysis using a multi-source data fusion algorithm, the present invention can accurately determine whether a battery pack has thermal runaway and trigger an early warning in the early stages of thermal runaway, providing a critical time window for emergency response, significantly improving the safety of the battery system, and offering reliable protection for battery safety management. The CO2 concentration change rate and temperature rise rate are collaboratively analyzed to trigger an alarm earlier than traditional methods, and the dual-spectrum characteristics of smoke can effectively distinguish between real thermal runaway smoke and dust and water mist, reducing the false alarm rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a judgment logic block diagram of the dual-spectrum smoke sensor in the battery box thermal runaway detection method proposed by the present invention; Figure 2 This is a schematic structural diagram of a battery box thermal runaway detection system proposed by the present invention; Figure 3 This is a flow chart of the data processing unit in the battery box thermal runaway detection system proposed by the present invention. DETAILED DESCRIPTION

[0019] The present invention proposes a method for detecting thermal runaway of a battery box, comprising the following steps: S1. Install a CO2 sensor, a dual-spectrum smoke sensor, and a temperature sensor on the top of the battery box to monitor the CO2 concentration, smoke dual-spectrum characteristics, and temperature parameters in the battery box in real time. S2. Analyze the CO2 concentration change rate, smoke dual-light rate ratio, and temperature rise rate through multi-source data fusion algorithm; S3. When the parameters obtained in step S1 and step S2 simultaneously meet the preset thresholds, an alarm is triggered and corresponding emergency measures are executed; S4. Upload data to the external monitoring system in real time to achieve remote early warning.

[0020] Furthermore, in S3, the obtained parameters are divided into two groups, namely parameter group 1 and parameter group 2. Parameter group 1 includes: CO2 concentration, CO2 change rate, red light value, blue light value and red / blue light rate ratio, and parameter group 2 includes: temperature value and temperature rise rate; the alarm level is divided into three levels, from low to high: level 1, level 2 and level 3; the triggering of each alarm level requires that the corresponding level preset threshold is met at the same time and either parameter group 1 or parameter group 2 lasts for 3 seconds. When the level 1 alarm is triggered, the level is reported and the high-speed data acquisition mode is switched at the same time; when the level 2 alarm is triggered, the corresponding linkage output is executed, the high voltage is cut off and the cooling system is started; when the level 3 alarm is triggered, the fire extinguishing device is started. The thresholds of the alarms of each level are shown in the following table:

[0021] Furthermore, the CO2 sensor is based on non-dispersive infrared (NDIR) technology, which monitors CO2 concentration (range 0-5000ppm, accuracy ±30ppm) by detecting the absorption characteristics of a specific infrared band, thereby accurately identifying whether the battery pack has thermal runaway. Its core formula is: , where A represents absorbance, is the absorption coefficient, C is the gas concentration, and L is the optical path length. The CO2 sensor has an infrared light source, a measurement channel, and a reference channel. When working, the built-in infrared light source first emits a 4.26 The characteristic wavelength of broadband infrared light, when the light passes through the gas to be measured, the CO2 molecules will selectively absorb the light energy of this specific wavelength; the measurement channel inside the CO2 sensor is equipped with a 4.26 The narrowband filter detects the residual light intensity I after absorption, and the reference channel set inside the CO2 sensor detects the reference wavelength light intensity that is not absorbed. By comparing the light intensity signals of the two channels, using the formula The CO2 concentration can be accurately calculated, where k is the calibration coefficient compensated for temperature and pressure.

[0022] The working principle of the dual-spectrum smoke sensor is based on the Mie scattering principle, which realizes smoke recognition through dual-wavelength optical detection. Its core formula is the scattering ratio ,in Honghe The blue represents the signal changes of the red light (950nm) and blue light (450nm) channels. When working, the built-in red light LED and blue light LED emit light beams alternately. When smoke particles enter the optical darkroom, particles of different sizes will produce differential scattering: large particles (such as fire smoke) have a stronger scattering effect on red light ( Red light is significantly increased, while small particles (such as dust or water mist) scatter blue light more significantly ( By calculating the dual-wavelength scattering ratio R in real time and combining it with the threshold judgment (when R ≥ 3, it is judged as fire smoke), the smoke type can be effectively distinguished. The specific judgment process is as follows: Figure 1 shown.

[0023] The temperature sensors are provided with three and triple redundant temperature calibration is adopted. The steps are as follows: the three temperature sensors collect temperature data simultaneously and compare the three sets of temperature data in real time. When the reading difference between any two temperature sensors exceeds 10°C, that is: When the sensor validity verification process is triggered, the majority voting algorithm is used to isolate the faulty temperature sensor. The specific steps of the majority voting algorithm are as follows: S11. If the readings of any two temperature sensors tend to be consistent, but the third one deviates, then the third temperature sensor is determined to be faulty, that is: and If the temperature sensor deviates, Fault; if the three sets of data are inconsistent, the self-diagnosis mode will be activated; S12: Automatically isolate the faulty temperature sensor, switch to healthy temperature sensor data, and report the fault through the communication interface.

[0024] At the same time, multi-sensor temperature collaborative compensation technology is used to dynamically calibrate the CO2 sensor and dual-spectrum smoke sensor through real-time temperature data to improve monitoring accuracy. The specific methods are as follows: 1. Use piecewise linear compensation formula: , the detection data of the CO2 sensor is calibrated in real time, where: is the CO2 concentration after temperature compensation; The original concentration detected by the CO2 sensor; is the temperature coefficient (unit: % / °C), which indicates the concentration deviation ratio caused by each degree Celsius change; T is the current ambient temperature (obtained by the temperature sensor); The reference temperature set for the CO2 sensor (usually 25°C).

[0025] 2. Use dual wavelength compensation formula: , real-time dynamic calibration of dual-spectrum smoke sensor detection data, where: is the smoke characteristic ratio after temperature compensation; is the original signal ratio of the red light (950nm) and blue light (450nm) channels; is the temperature compensation factor.

[0026] Reference Figure 2 The present invention proposes a battery box thermal runaway detection system for implementing the above method, comprising: a data acquisition module, a data processing unit, a linkage output module and a communication module, wherein: The data acquisition module includes three temperature sensors, a CO2 sensor, and a dual-spectrum smoke sensor. It collects CO2 concentration, smoke dual-spectrum characteristics, and temperature parameters within the battery compartment. The data processing unit collects data from the data acquisition module, executes multi-source data fusion algorithms, and determines alarm levels. The linkage output module triggers appropriate emergency measures based on the alarm level. The communication module uploads this data to the remote monitoring system.

[0027] Reference Figure 3 ,The data processing unit is the micro control unit (MUC), and its specific work flow is as follows: The microcontrol unit (MUC) collects CO2 concentration, smoke dual-spectral characteristics and temperature parameters in the battery box in real time, and uses the built-in multi-source data fusion algorithm to calculate the CO2 concentration change rate, smoke dual-light rate ratio and temperature rise rate; when the obtained value meets the level 1 alarm threshold and either parameter group 1 or parameter group 2 lasts for 3 seconds, the level 1 alarm is triggered, and the level is reported, and the mode is switched to high-rate data acquisition mode; when the obtained value meets the level 2 alarm threshold and either parameter group 1 or parameter group 2 lasts for 3 seconds, the level 2 alarm is triggered and the corresponding linkage output is executed; when the obtained value meets the level 3 alarm threshold and either parameter group 1 or parameter group 2 lasts for 3 seconds, the level 3 alarm is triggered and the fire extinguishing device is activated.

[0028] As can be seen from the above, the present invention has the following advantages: 1. Multi-parameter collaborative monitoring During battery thermal runaway, large quantities of CO2 are generated, and changes in its concentration show a significant correlation with the progress of thermal runaway. This invention utilizes a CO2 sensor based on the NDIR principle. This sensor monitors CO2 concentration (range: 0-5000ppm, accuracy: ±30ppm) by detecting the absorption characteristics of specific infrared bands, thereby accurately identifying whether the battery pack is experiencing thermal runaway. To further enhance detection reliability, a dual-spectrum smoke sensor is employed to capture the dual-spectral characteristics of smoke. The specific principle is as follows: the dual-spectrum smoke sensor utilizes a dual-channel design (450nm blue LED + 950nm red LED). By analyzing the scattering characteristics of smoke particles at different wavelengths, it effectively distinguishes true thermal runaway smoke from interference sources (such as dust and water vapor), thereby reducing false alarms. Simultaneously, three temperature sensors (range: -40°C to 150°C, response time: ≤0.8s) monitor the internal temperature and temperature change rate of the battery pack in real time. The temperature sensors are used to dynamically calibrate the dual-spectrum smoke sensor and CO2 sensor to eliminate data errors caused by ambient temperature drift. Through multi-parameter collaborative monitoring, early warning can be triggered in the early stages of thermal runaway, providing a critical time window for emergency response and significantly improving the safety of the battery system.

[0029] 2. Composite alarm level judgment The composite alarm level is judged by different sensor data thresholds and change rates, and duration judgment (lasting 3 seconds) is introduced, with strong anti-interference ability and strong environmental adaptability; 3. Triple redundant temperature detection Cross calibration is achieved through three high-precision temperature sensors; 4. Dynamic calibration Compensate CO2 concentration and smoke data in real time based on current temperature to improve accuracy; 5. Long life and low maintenance cost Using a non-contact detection principle, it consumes no chemical substances and has a lifespan of up to 10 years. Furthermore, it offers more stable performance in harsh conditions such as high and low temperatures, and humidity fluctuations, making it easier to maintain and lowering the cost of use throughout its lifecycle, making it an ideal choice for long-term monitoring.

[0030] In summary, compared with traditional methods, the present invention can trigger an early warning in the early stage of thermal runaway, provide a critical time window for emergency response, greatly improve the safety of the battery system, and provide reliable protection for battery safety management; it also has a low false alarm rate, strong anti-interference ability, and a long working life.

[0031] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for detecting thermal runaway of a battery box, characterized in that: The following steps are involved: S1, real-time monitoring of CO2 concentration, smoke dual-spectrum characteristics and temperature parameters in the battery box; S2. Analyze the CO2 concentration change rate, smoke dual-light rate ratio and temperature rise rate through multi-source data fusion algorithm; S3. When the parameters obtained in step S1 and step S2 simultaneously meet the preset thresholds, an alarm is triggered and corresponding emergency measures are executed; S4. Upload data to the external monitoring system in real time.

2. The battery box thermal runaway detection method according to claim 1, characterized in that: The parameters in S3 are divided into two groups, namely parameter group 1 and parameter group 2, among which: parameter group 1 includes: CO2 concentration, CO2 change rate, red light value, blue light value and red / blue light rate ratio; parameter group 2 includes: temperature value and temperature rise rate; the triggering of the alarm requires that the obtained parameters simultaneously meet the preset thresholds and the duration of either parameter group 1 or parameter group 2 reaches the preset time.

3. The battery box thermal runaway detection method according to claim 2, characterized in that: The alarm levels are divided into three levels, from low to high: Level 1, Level 2 and Level 3; the triggering of each alarm level requires that the preset threshold of the corresponding level is met at the same time and the duration of either parameter group 1 or parameter group 2 reaches the preset time.

4. The battery box thermal runaway detection method according to claim 3, characterized in that: Emergency response measures include level reporting, cutting off high voltage, starting the cooling system and starting the fire extinguishing device. When a level 1 alarm is triggered, an early warning is reported and the mode of high-speed data acquisition is switched to. When a level 2 alarm is triggered, the high voltage is cut off and the cooling system is started. When a level 3 alarm is triggered, the fire extinguishing device is started.

5. The battery box thermal runaway detection method according to claim 4, characterized in that: The preset thresholds for each alarm level include: Level 1 alarm: CO2 concentration ≥700ppm, CO2 change rate ≥20ppm / 10s, red light value ≥6, blue light value ≥12, red / blue light rate ratio ≥3, temperature value ≥70℃, temperature rise rate ≥1℃ / s; Level 2 alarm: CO2 concentration ≥1000ppm, CO2 change rate ≥25ppm / 10s, red light value ≥6, blue light value ≥12, red / blue light rate ratio ≥3, temperature value ≥75℃, temperature rise rate ≥1.5℃ / s; Level 3 alarm: CO2 concentration ≥1500ppm, CO2 change rate ≥30ppm / 10s, red light value ≥6, blue light value ≥12, red / blue light rate ratio ≥3, temperature value ≥80℃, temperature rise rate ≥1.5℃ / s.

6. The battery box thermal runaway detection method according to claim 1, characterized in that: The CO2 concentration is detected using a CO2 sensor and a piecewise linear compensation formula: , real-time dynamic calibration of detection data, where: is the CO2 concentration after temperature compensation; The original concentration detected by the CO2 sensor; is the temperature coefficient; T is the current ambient temperature; The reference temperature set for the CO2 sensor.

7. The battery box thermal runaway detection method according to claim 1, characterized in that: The dual-spectrum smoke feature detection uses a dual-spectrum smoke sensor and a dual-wavelength compensation formula: , real-time dynamic calibration of detection data, where: is the smoke characteristic ratio after temperature compensation; is the ratio of the original signals of the red and blue channels; is the temperature compensation factor.

8. The battery box thermal runaway detection method according to claim 1, characterized in that: The temperature detection uses three temperature sensors and adopts triple redundant temperature verification. The specific steps are as follows: the three temperature sensors collect temperature data simultaneously and compare the three sets of temperature data in real time. When the difference between the readings of any two temperature sensors exceeds a threshold, the sensor validity verification process is triggered and the majority voting algorithm is used to isolate the faulty temperature sensor. Preferably, the specific steps of the majority voting algorithm are as follows: S11. If the readings of any two temperature sensors tend to be consistent, but the third one deviates, the third temperature sensor is determined to be faulty. If all sets of data are inconsistent, the self-diagnosis mode is activated. S12: Automatically isolate the faulty temperature sensor, switch to healthy temperature sensor data, and report the fault.

9. A battery box thermal runaway detection system, used to implement the method according to any one of claims 1 to 8, characterized in that: include: Data acquisition module, used to collect CO2 concentration, smoke dual-spectrum characteristics and temperature parameters in the battery box; The data processing unit is used to obtain data from the data acquisition module and execute multi-source data fusion algorithms and alarm determination; Linkage output module, triggering corresponding emergency measures according to the alarm level; Communication module, used to upload data to the remote monitoring system.

10. The battery box thermal runaway detection system according to claim 9, characterized in that: The data acquisition module includes three temperature sensors, one CO2 sensor, and one dual-spectrum smoke sensor; preferably, the data processing unit is a micro control unit.

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