Battery box thermal runaway detection method and system

Abstract

The application provides a battery box thermal runaway detection method, which comprises the following steps: S1, monitoring the CO2 concentration, smoke dual-spectrum characteristics and temperature parameters in the battery box in real time; S2, analyzing the CO2 concentration change rate, smoke dual-spectrum rate ratio and temperature rise rate through a multi-source data fusion algorithm; S3, when the parameters obtained in steps S1 and S2 simultaneously satisfy preset threshold values, triggering an alarm and executing corresponding emergency treatment measures; and S4, uploading data to an external monitoring system in real time. The application also provides a battery box thermal runaway detection system for realizing the above method, which comprises a data acquisition module, a data processing unit, a linkage output module and a communication module. The application can accurately judge whether a battery pack has thermal runaway, trigger an early warning in an early stage of thermal runaway, effectively distinguish real thermal runaway smoke from dust and water mist, and reduce the false alarm rate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery safety monitoring, and particularly relates to a battery box thermal runaway detection method and system. BACKGROUND

[0002] With the large-scale application of lithium-ion batteries in electric vehicles and energy storage systems, the safety problem thereof is increasingly prominent. Thermal runaway, as the most serious safety hazard of the battery system, is usually caused by internal short circuit of a single battery cell, overcharge or high temperature, etc. If this self-accelerating heat release process is not inhibited in time, it is easy to cause a fire accident.

[0003] Traditional thermal runaway detection mainly relies on electrochemical or semiconductor sensors such as carbon monoxide, hydrogen, VOC, and smoke and temperature monitoring. However, such sensors generally have inherent defects such as short service life (electrochemical and semiconductor sensors usually only last for 2-3 years), easy poisoning by environmental gases (such as silane and sulfide), and high false alarm rate. SUMMARY

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

[0005] The battery box thermal runaway detection method proposed by the present application comprises the following steps:

[0006] S1, real-time monitoring of CO2 concentration, smoke dual-spectrum characteristics and temperature parameters in the battery box;

[0007] S2, analysis of CO2 concentration change rate, smoke dual-light rate ratio and temperature rise rate by a multi-source data fusion algorithm;

[0008] S3, when the parameters obtained in steps S1 and S2 simultaneously satisfy the preset threshold, triggering an alarm and executing corresponding emergency handling measures;

[0009] S4, real-time uploading of data to an external monitoring system.

[0010] 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, and parameter group 2 includes temperature value and temperature rise rate. The triggering of the alarm requires that the obtained parameters simultaneously satisfy the preset threshold and the duration of any one of parameter group 1 or parameter group 2 reaches the preset time.

[0011] Preferably, the alarm levels are divided into three levels, from low to high, namely level 1, level 2 and level 3. The triggering of each alarm level requires that the corresponding level preset threshold is satisfied and the duration of any one of parameter group 1 or parameter group 2 reaches the preset time.

[0012] Preferably, the emergency treatment measures include grade reporting, high voltage cut-off, cooling system start-up and fire extinguishing device start-up, when a level 1 alarm is triggered, a warning is reported and switched to a high-speed data acquisition mode; 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.

[0013] Preferably, the preset threshold values of each alarm level include:

[0014] Level 1 alarm: CO2 concentration ≥ 700 ppm, CO2 change rate ≥ 20 ppm / 10s, red light value ≥ 6, blue light value ≥ 12, red / blue light rate ratio ≥ 3, temperature value ≥ 70℃, temperature rise rate ≥ 1℃ / s;

[0015] Level 2 alarm: CO2 concentration ≥ 1000 ppm, CO2 change rate ≥ 25 ppm / 10s, red light value ≥ 6, blue light value ≥ 12, red / blue light rate ratio ≥ 3, temperature value ≥ 75℃, temperature rise rate ≥ 1.5℃ / s;

[0016] Level 3 alarm: CO2 concentration ≥ 1500 ppm, CO2 change rate ≥ 30 ppm / 10s, red light value ≥ 6, blue light value ≥ 12, red / blue light rate ratio ≥ 3, temperature value ≥ 80℃, temperature rise rate ≥ 1.5℃ / s.

[0017] Preferably, the CO2 concentration detection uses a CO2 sensor, and uses a piecewise linear compensation formula: C cal =C raw ×[1+α(T-T ref )], which is dynamically calibrated in real time for detection data, wherein:

[0018] C cal is the CO2 concentration after temperature compensation;

[0019] C raw is the original concentration detected by the CO2 sensor;

[0020] α is the temperature coefficient;

[0021] T is the current ambient temperature;

[0022] T ref is the reference temperature set for the CO2 sensor.

[0023] Preferably, the smoke dual-spectrum feature detection uses a dual-spectrum smoke sensor, and uses a dual-wavelength compensation formula: R corr =V red / V blue ×β(T), which is dynamically calibrated in real time for detection data, wherein:

[0024] R corra ratio of the smoke feature after temperature compensation;

[0025] V red / V blue a ratio of the original signals of the red and blue channels;

[0026] β(T) is a temperature compensation factor.

[0027] Preferably, temperature detection employs temperature sensors, and three temperature sensors are provided, and triple-redundant temperature verification is employed, and the specific steps are as follows: three temperature sensors simultaneously collect temperature data, and three sets of temperature data are compared in real time, when the readings of any two temperature sensors differ by more than a threshold value, a sensor effectiveness verification process is triggered, and a majority voting algorithm is employed to isolate the faulty temperature sensor.

[0028] Preferably, the specific steps of the majority voting algorithm are as follows:

[0029] S11, if the readings of any two temperature sensors tend to be consistent, and the third one deviates, it is determined that the third temperature sensor is faulty; if all sets of data are inconsistent, a self-diagnosis mode is started;

[0030] S12, the faulty temperature sensor is automatically isolated, the data of the healthy temperature sensor is switched to, and the fault is reported.

[0031] The present application proposes a battery box thermal runaway detection system for realizing the above method, comprising:

[0032] A data acquisition module is configured to acquire CO2 concentration, smoke dual-spectrum features and temperature parameters in the battery box.

[0033] A data processing unit is configured to acquire data of the data acquisition module, and execute a multi-source data fusion algorithm and alarm determination.

[0034] A linkage output module is configured to trigger corresponding emergency measures according to the alarm level.

[0035] A communication module is configured to upload data to a remote monitoring system.

[0036] Preferably, the data acquisition module comprises three temperature sensors, one CO2 sensor and one dual-spectrum smoke sensor.

[0037] Preferably, the data processing unit is a micro control unit.

[0038] The application can accurately judge whether the battery pack has thermal runaway by multi-parameter cooperative monitoring and multi-source data fusion algorithm comprehensive analysis, trigger early warning in the early stage of thermal runaway, provide a key time window for emergency disposal, greatly improve the safety of the battery system, and provide reliable guarantee for battery safety management; and the CO2 concentration change rate and temperature rise rate are analyzed cooperatively, the alarm is triggered earlier than the traditional method, the smoke dual-spectrum feature can effectively distinguish real thermal runaway smoke from dust and water mist, and the false alarm rate is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 A judgment logic block diagram of the dual-spectrum smoke sensor in the battery box thermal runaway detection method provided by the application;

[0040] Figure 2 A structural schematic diagram of the battery box thermal runaway detection system provided by the application;

[0041] Figure 3 A work flow chart of the data processing unit in the battery box thermal runaway detection system provided by the application. DETAILED DESCRIPTION

[0042] The battery box thermal runaway detection method provided by the application comprises the following steps:

[0043] S1, installing a CO2 sensor, a dual-spectrum smoke sensor and a temperature sensor on the top of the battery box to monitor the CO2 concentration, the smoke dual-spectrum feature and the temperature parameter in the battery box in real time;

[0044] S2, analyzing the CO2 concentration change rate, the smoke dual-spectrum rate ratio and the temperature rise rate by a multi-source data fusion algorithm;

[0045] S3, when the parameters obtained in steps S1 and S2 meet the preset threshold value at the same time, triggering an alarm and executing corresponding emergency treatment measures;

[0046] S4, uploading data to an external monitoring system in real time to realize remote early warning.

[0047] Furthermore, in S3, the obtained parameters are divided into two groups: 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 alarm levels are divided into three levels, from low to high: level 1, level 2, and level 3. Each alarm level must be triggered by simultaneously meeting the corresponding preset threshold and either parameter group 1 or parameter group 2 for 3 seconds. When a level 1 alarm is triggered, the level is reported, and the system switches to high-rate data acquisition mode. When a level 2 alarm is triggered, the corresponding linkage output is executed, the high voltage is cut off, and the cooling system is activated. When a level 3 alarm is triggered, the fire extinguishing device is activated. The threshold values ​​for each alarm level are shown in the table below.

[0048]

[0049] Furthermore, the CO2 sensor is based on non-dispersive infrared (NDIR) technology. It 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 experienced thermal runaway. Its core formula is A=α·C·L, where A represents absorbance, α is the absorption coefficient, C is the gas concentration, and L is the optical path length. The CO2 sensor internally includes an infrared light source, a measurement channel, and a reference channel. During operation, the built-in infrared light source first emits broadband infrared light containing a characteristic wavelength of 4.26μm. When the light passes through the gas being measured, CO2 molecules selectively absorb this specific wavelength of light energy. The measurement channel, located inside the CO2 sensor, is equipped with a 4.26μm narrowband filter to detect the residual light intensity I after absorption. The reference channel, also located inside the CO2 sensor, detects the intensity of the unabsorbed reference wavelength light. By comparing the light intensity signals of the two channels, the formula is used. The CO2 concentration can then be accurately calculated, where k is a calibration coefficient compensated for by temperature and pressure.

[0050] The dual-spectrum smoke sensor operates based on the Mie scattering principle, using dual-wavelength optical detection to identify smoke. Its core formula is the scattering ratio R = ΔV_red / ΔV_blue, where ΔV_red and ΔV_blue represent the signal changes in the red (950nm) and blue (450nm) channels, respectively. During operation, the built-in red and blue LEDs alternately emit light beams. When smoke particles enter the optical dark chamber, particles of different sizes exhibit differentiated scattering: larger particles (such as fire smoke) scatter red light more strongly (ΔV_red increases significantly), while smaller particles (such as dust or water mist) scatter blue light more noticeably (ΔV_blue increases). By calculating the dual-wavelength scattering ratio R in real time and combining it with a threshold judgment (R ≥ 3 indicates fire smoke), the smoke type can be effectively distinguished. The specific judgment process is as follows... Figure 1As shown.

[0051] The system has three temperature sensors with triple redundancy temperature verification. The steps are as follows: all three temperature sensors simultaneously collect temperature data and compare the three sets of temperature data in real time. When the difference between the readings of any two temperature sensors exceeds 10°C, i.e.: When this occurs, the sensor validity verification process is triggered, and a majority voting algorithm is used to isolate the faulty temperature sensor. The specific steps of the majority voting algorithm are as follows:

[0052] S11. If any two temperature sensors show consistent readings, while the third deviates, then the third temperature sensor is considered faulty. and If the temperature deviates, the temperature sensor is considered faulty. Fault; if all three sets of data are inconsistent, then self-diagnosis mode will be activated;

[0053] S12. Automatically isolate faulty temperature sensors, switch to healthy temperature sensor data, and report the fault through the communication interface.

[0054] Meanwhile, multi-sensor temperature collaborative compensation technology is adopted to dynamically calibrate the CO2 sensor and dual-spectrum smoke sensor using real-time temperature data, thereby improving monitoring accuracy. The specific method is as follows:

[0055] 1. Piecewise linear compensation formula is adopted: C cal =C raw ×[1+α(TT ref The CO2 sensor's detection data is dynamically calibrated in real time, where:

[0056] C cal This represents the CO2 concentration after temperature compensation.

[0057] C raw The CO2 sensor detected the original concentration;

[0058] α is the temperature coefficient (unit: % / ℃), which represents the proportion of concentration deviation caused by a change of one degree Celsius;

[0059] T represents the current ambient temperature (obtained by a temperature sensor).

[0060] T ref The reference temperature set for the CO2 sensor (usually 25°C).

[0061] 2. Using the dual-wavelength compensation formula: R corr =V red / V blue ×β(T) is used for real-time dynamic calibration of the dual-spectral smoke sensor detection data, where:

[0062] R corr is the temperature-compensated smoke feature ratio;

[0063] V red / V blue is the raw signal ratio of red light (950 nm) and blue light (450 nm) channels;

[0064] β(T) is a temperature compensation factor.

[0065] With reference to Figure 2 , the battery box thermal runaway detection system for implementing the above method comprises a data acquisition module, a data processing unit, a linkage output module and a communication module, wherein:

[0066] The data acquisition module comprises three temperature sensors, one CO2 sensor and one dual-spectrum smoke sensor; the data acquisition module is used to acquire the CO2 concentration, smoke dual-spectrum features and temperature parameters in the battery box. The data processing unit is used to acquire the data of the data acquisition module and execute a multi-source data fusion algorithm and alarm level determination. The linkage output module triggers corresponding emergency measures according to the alarm level. The communication module is used to upload data to a remote monitoring system.

[0067] With reference to Figure 3 , the data processing unit is a micro control unit (MUC), and the specific working process is as follows:

[0068] The micro control unit (MUC) acquires the CO2 concentration, smoke dual-spectrum features and temperature parameters in the battery box in real time, and calculates the CO2 concentration change rate, smoke dual-spectrum rate ratio and temperature rise rate by using the built-in multi-source data fusion algorithm. When the obtained values meet the 1st alarm threshold value, and any one of parameter group 1 or parameter group 2 lasts for 3 seconds, the 1st alarm is triggered, and the level is reported, and the high-speed data acquisition mode is switched to. When the obtained values meet the 2nd alarm threshold value, and any one of parameter group 1 or parameter group 2 lasts for 3 seconds, the 2nd alarm is triggered, and the corresponding linkage output is executed. When the obtained values meet the 3rd alarm threshold value, and any one of parameter group 1 or parameter group 2 lasts for 3 seconds, the 3rd alarm is triggered, and the fire extinguishing device is started.

[0069] As can be seen from the above, the present application has the following advantages:

[0070] 1. Multi-parameter collaborative monitoring

[0071] In the process of battery thermal runaway, CO2 will be generated in large quantities, and its concentration change is significantly related to the thermal runaway process. The application adopts a CO2 sensor based on the NDIR principle to monitor the CO2 concentration (range 0-5000 ppm, accuracy ± 30 ppm) by detecting the absorption characteristics of a specific infrared band, thereby accurately identifying whether the battery pack has occurred thermal runaway. And to further improve the detection reliability, a dual-spectrum smoke sensor is used to collect the dual-spectrum characteristics of the smoke, the specific principle is: the dual-spectrum smoke sensor adopts a dual-channel design (450nm blue light LED + 950nm red light LED), by analyzing the scattering characteristics of smoke particles at different wavelengths, effectively distinguishing between real thermal runaway smoke and interference sources (such as dust, water vapor, etc.), reducing the false alarm rate. At the same time, 3 temperature sensors (range -40℃ ~ 150℃, response time ≤0.8s) monitor the internal temperature and temperature change rate of the battery pack in real time. And use the temperature sensor to dynamically calibrate the dual-spectrum smoke sensor and the CO2 sensor, eliminate the data error caused by environmental temperature drift. Through multi-parameter collaborative monitoring, it can trigger an early warning in the early stage of thermal runaway, providing a key time window for emergency disposal, greatly improving the safety of the battery system.

[0072] 2、Composite alarm level judgment

[0073] Composite alarm level judgment is performed through different sensor data thresholds and change rates, and a duration determination (3 seconds) is introduced, which has strong anti-interference ability and strong environmental adaptability;

[0074] 3、Three redundant temperature detection

[0075] Cross calibration is achieved through 3 high-precision temperature sensors;

[0076] 4、Dynamic calibration

[0077] CO2 concentration and smoke data are compensated in real time according to the current temperature, improving accuracy;

[0078] 5、Long service life, low maintenance cost

[0079] Non-contact detection principle is adopted, no chemical consumption, service life can reach 10 years. And the performance is more stable under harsh conditions such as high and low temperature, humidity change, maintenance is simpler, and the use cost is lower in the whole life cycle, which is an ideal choice for long-term monitoring.

[0080] As shown above, the application can trigger an early warning in the early stage of thermal runaway compared with the traditional method, providing a key time window for emergency disposal, greatly improving the safety of the battery system, and providing reliable protection for battery safety management; The false alarm rate is low, the anti-interference ability is strong, and the service life is long.

[0081] The above merely describes preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art, according to the technical solution and inventive concept of the present application, makes equivalent replacement or change within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A battery case thermal runaway detection method, characterized by, Comprise the following steps: S1, real-time monitoring CO2 concentration, smoke dual-spectrum characteristics and temperature parameters in the battery box; temperature detection adopts temperature sensor, and the temperature sensor is provided with three, and three redundant temperature verification is adopted, the specific steps are: three temperature sensors collect temperature data at the same time, and three groups of temperature data are compared in real time, when the reading difference of any two temperature sensors exceeds the threshold value, the sensor effectiveness verification process is triggered, and 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 degrees of any two temperature sensors tend to be consistent, and the third one deviates, the third temperature sensor is judged to be faulty; if each group of data is inconsistent, the self-diagnosis mode is started; S12, automatically isolate the faulty temperature sensor, switch to the healthy temperature sensor data, and report the fault; S2, analyze CO2 concentration change rate, smoke dual-light speed rate ratio and temperature rise rate through multi-source data fusion algorithm; S3, when the parameters obtained in steps S1 and S2 meet the preset threshold value at the same time, trigger alarm and execute corresponding emergency treatment measures, specifically including: The parameters are divided into two groups, parameter group 1 and parameter group 2, wherein: parameter group 1 includes: CO2 concentration, CO2 change rate, red light value, blue light value and red / blue light speed rate ratio; parameter group 2 includes: temperature value and temperature rise rate; the triggering of alarm needs the obtained parameters to meet the preset threshold value at the same time and the continuous time of parameter group 1 or parameter group 2 reaches the preset time; S4, real-time data upload to external monitoring system.

2. The battery pack thermal runaway detection method of claim 1, wherein, The alarm level is divided into three levels, from low to high in turn: 1st, 2nd and 3rd; the triggering of each alarm level needs to meet the corresponding level preset threshold value at the same time and the continuous time of parameter group 1 or parameter group 2 reaches the preset time.

3. The battery pack thermal runaway detection method of claim 2, wherein, The emergency treatment measures include level reporting, cutting off high voltage, starting cooling system and starting fire extinguishing device, when 1st level alarm is triggered, the pre-alarm is reported and the high-speed data acquisition mode is switched to; when 2nd level alarm is triggered, the high voltage is cut off and the cooling system is started; when 3rd level alarm is triggered, the fire extinguishing device is started.

4. The battery pack thermal runaway detection method of claim 3, wherein, The preset threshold values of each alarm level include: 1st level alarm: CO2 concentration≥700ppm, CO2 change rate≥20ppm / 10s, red light value≥6, blue light value≥12, red / blue light speed rate ratio≥3, temperature value≥70℃, temperature rise rate≥1℃ / s; 2nd level alarm: CO2 concentration≥1000ppm, CO2 change rate≥25ppm / 10s, red light value≥6, blue light value≥12, red / blue light speed rate ratio≥3, temperature value≥75℃, temperature rise rate≥1.5℃ / s; 3rd level alarm: CO2 concentration≥1500ppm, CO2 change rate≥30ppm / 10s, red light value≥6, blue light value≥12, red / blue light speed rate ratio≥3, temperature value≥80℃, temperature rise rate≥1.5℃ / s.

5. The battery pack thermal runaway detection method of claim 1, wherein, CO2 concentration detection uses CO2 sensor and adopts piecewise linear compensation formula: C cal =C raw ×[1+α(T-T ref )], real-time dynamic calibration is performed on detection data, wherein: C cal C02 concentration after temperature compensation; C raw Raw concentration for CO2 sensor detection; α is the temperature coefficient; T is the current environmental temperature; T ref Reference temperature set for the CO2 sensor.

6. The battery pack thermal runaway detection method of claim 1, wherein the smoke Dual-spectrum feature detection uses dual-spectrum smoke sensor and adopts dual-wavelength compensation formula: R corr = V red / V blue × β(T), real-time dynamic calibration of detection data, wherein: R corr is the temperature-compensated smoke feature ratio; V red / V blue is the raw signal ratio for the red and blue channels; β(T) is the temperature compensation factor.

7. A battery case thermal runaway detection system for implementing the method of any one of claims 1-6, characterized by, Comprise: Data acquisition module, for collecting CO2 concentration, smoke dual-spectrum characteristics and temperature parameters in the battery box; A data processing unit is configured to acquire data of the data acquisition module, and execute a multi-source data fusion algorithm and an alarm determination. A linkage output module is configured to trigger corresponding emergency measures according to an alarm level. A communication module is configured to upload data to a remote monitoring system.

8. The battery pack thermal runaway detection system of claim 7, wherein, The data acquisition module includes three temperature sensors, one CO2 sensor, and one dual-spectrum smoke sensor.

9. The battery pack thermal runaway detection system of claim 8, wherein, The data processing unit is a micro control unit.

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