Battery module anti-reburning control system for electrochemical energy storage station

Abstract

The invention provides a battery module anti-reburning control system for an electrochemical energy storage station, and relates to the technical field of battery module monitoring. Determining concentration increase characteristics corresponding to thermal runaway gas of the battery module according to the concentration data, and determining a module smoldering factor of the battery module based on the concentration increase characteristics; acquiring real-time temperature data of the battery module, extracting temperature trend characteristics of the battery module from the corresponding real-time temperature data, and performing reburning prediction on the battery module by using the temperature trend characteristics to obtain a reburning predicted temperature of the battery module; and the opening degree of a fire extinguishing agent injection valve and the opening degree of a ventilation valve at the position where each battery module is located are obtained, and the corresponding opening degree of the fire extinguishing agent injection valve and the opening degree of the ventilation valve are adjusted according to the corresponding module smoldering factor and the reburning predicted temperature. Therefore, the overall response capability of the anti-reburning control system is improved.

Description

Technical Field

[0001] The present application relates to the technical field of battery module monitoring, and more specifically, to a battery module anti-reignition control system for an electrochemical energy storage station. Background Art

[0002] The anti-reignition control system of the electrochemical energy storage station is designed to ensure that the battery module can promptly identify and respond to potential fire risks during operation by integrating advanced monitoring technology and automated control strategies, thereby reducing the possibility of re-ignition. The core of this system includes real-time data monitoring, fire extinguishing agent injection, ventilation control and emergency response mechanism. By deploying temperature sensors, voltage monitoring equipment and gas detectors, the system can monitor the re-ignition status of the battery module in real time. When an abnormal situation is detected, the opening of the fire extinguishing agent injection valve is automatically adjusted, and combined with the adjustment of the ventilation valve, the safety of the internal and external environment of the battery module is ensured.

[0003] In the process of implementing this anti-re-ignition control system, the adjustment of the fire extinguishing agent injection and ventilation system requires extremely high sensitivity and response speed. Under high temperature or abnormal conditions, the system needs to respond quickly to activate the fire extinguishing device or increase ventilation to avoid the potential spread of fire. However, excessive injection or ventilation may cause other safety hazards. Therefore, how to adaptively adjust the fire extinguishing agent injection and ventilation system based on the thermal runaway gas concentration and temperature data of the battery module to improve the overall responsiveness of the anti-re-ignition control system is a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a battery module anti-reignition control system for an electrochemical energy storage station, which can adaptively adjust the fire extinguishing agent injection and ventilation system according to the thermal runaway gas concentration and temperature data of the battery module to improve the overall response capability of the anti-reignition control system.

[0005] The present application provides a battery module anti-reignition control system for an electrochemical energy storage station, the control system comprising: Gas concentration monitoring module, used to monitor the thermal runaway gas concentration of each battery module in the electrochemical energy storage station; A smoldering feature extraction module, used to determine the concentration growth characteristics corresponding to the thermal runaway gas of each battery module in the electrochemical energy storage station according to the monitored concentration data of the thermal runaway gas, and determine the module smoldering factor of each battery module in the electrochemical energy storage station based on the concentration growth characteristics corresponding to the thermal runaway gas of each battery module; A re-ignition temperature prediction module is used to collect real-time temperature data of each battery module in the electrochemical energy storage station, extract temperature trend characteristics of each battery module from the corresponding real-time temperature data, and use the corresponding temperature trend characteristics to predict the re-ignition of each battery module to obtain the predicted re-ignition temperature of each battery module; The valve opening control module is used to obtain the fire extinguishing agent injection valve opening and ventilation valve opening of each battery module, and adjust the corresponding fire extinguishing agent injection valve opening and ventilation valve opening according to the corresponding module smoldering factor and re-ignition predicted temperature.

[0006] In this embodiment, the thermal runaway gas concentration of each battery module in the electrochemical energy storage station is monitored by a gas detector.

[0007] In this embodiment, determining the concentration growth characteristics corresponding to the thermal runaway gas of each battery module in the electrochemical energy storage station according to the monitored concentration data of the thermal runaway gas specifically includes: For each battery module in the electrochemical energy storage station, fitting the concentration data of the thermal runaway gas of the monitored battery module to obtain a concentration fitting curve of the thermal runaway gas of the battery module; Growth characteristics are extracted from the concentration fitting curve to obtain the concentration growth characteristics corresponding to the thermal runaway gas of the battery module, and then the concentration growth characteristics corresponding to the thermal runaway gas of each battery module in the electrochemical energy storage station are obtained.

[0008] In this embodiment, the concentration fitting curve is subjected to growth feature extraction to obtain the concentration growth feature corresponding to the thermal runaway gas of the battery module, which specifically includes: Segmenting the concentration fitting curve to obtain a plurality of concentration curve segments; Determine the concentration growth rate corresponding to each concentration curve segment; The concentration growth characteristics corresponding to the thermal runaway gas of the battery module are determined by all concentration growth rates.

[0009] In this embodiment, determining the module smoldering factor of each battery module in the electrochemical energy storage station based on the concentration growth characteristics corresponding to the thermal runaway gas of each battery module specifically includes: For each battery module in the electrochemical energy storage station, obtaining the volume fraction of various gases in the thermal runaway gas of the battery module; The module smoldering factor of the battery module is determined according to the concentration growth characteristics corresponding to the thermal runaway gas of the battery module and the volume fraction of various gases in the thermal runaway gas of the battery module, and then the module smoldering factor of each battery module in the electrochemical energy storage station is obtained.

[0010] In this embodiment, the real-time temperature data of each battery module in the electrochemical energy storage station is collected by a temperature sensor.

[0011] In this embodiment, extracting the temperature trend characteristics of each battery module from the corresponding real-time temperature data specifically includes: For the real-time temperature data of each battery module, a temperature curve of the battery module is drawn according to the real-time temperature data; Feature extraction is performed on the temperature curve to obtain the temperature trend feature of the battery module, and then the temperature trend feature of each battery module is obtained.

[0012] In this embodiment, the re-ignition prediction of each battery module is performed using the corresponding temperature trend characteristics, and the re-ignition prediction temperature of each battery module is obtained specifically including: Get pre-trained simulation software for battery module re-ignition prediction; The temperature trend characteristics corresponding to each battery module are input into the simulation software to perform reignition prediction, thereby obtaining the reignition prediction temperature of each battery module.

[0013] In this embodiment, the fire extinguishing agent injection valve opening and the ventilation valve opening of each battery module are obtained through an angle sensor.

[0014] In this embodiment, adjusting the corresponding fire extinguishing agent injection valve opening and ventilation valve opening according to the corresponding module smoldering factor and re-ignition predicted temperature specifically includes: For each battery module, when the module smoldering factor of the battery module exceeds a preset threshold, the ventilation valve opening is increased and the fire extinguishing agent is continuously sprayed; When the module smoldering factor of the battery module is lower than the preset threshold, the ventilation valve is kept open; When the predicted re-ignition temperature of the battery module exceeds the preset safety value, the fire extinguishing agent injection valve opening is increased and the fire extinguishing agent injection time is increased; When the predicted re-ignition temperature of the battery module is lower than the preset safety value, keep the fire extinguishing agent injection valve open.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: The thermal runaway gas concentration of each battery module in the electrochemical energy storage station is monitored by a gas concentration monitoring module; the smoldering feature extraction module determines the concentration growth characteristics corresponding to the thermal runaway gas of each battery module in the electrochemical energy storage station based on the monitored thermal runaway gas concentration data, and determines the module smoldering factor of each battery module in the electrochemical energy storage station based on the concentration growth characteristics corresponding to the thermal runaway gas of each battery module; the re-ignition temperature prediction module collects the real-time temperature data of each battery module in the electrochemical energy storage station, extracts the temperature trend characteristics of each battery module from the corresponding real-time temperature data, and uses the corresponding temperature trend characteristics to predict the re-ignition of each battery module to obtain the re-ignition predicted temperature of each battery module; the valve opening control module obtains the fire extinguishing agent injection valve opening and the ventilation valve opening of each battery module, and adjusts the fire extinguishing agent injection valve opening and the ventilation valve opening according to the module smoldering factor and the re-ignition predicted temperature of the battery module.

[0016] It can be seen that in the present application, the fire extinguishing agent injection and ventilation systems can be adaptively adjusted according to the thermal runaway gas concentration and temperature data of the battery module; wherein, by real-time monitoring of the concentration changes of the thermal runaway gas, the potential risk of fire re-ignition can be timely identified, and the calculation of the module smoldering factor can quantify the safety risk of each battery module; then, by real-time monitoring of temperature data and extraction of temperature trend characteristics, the abnormal temperature rise trend of the battery module can be timely identified, and the potential re-ignition risk can be warned early, and the calculation of the re-ignition prediction temperature can help identify which battery modules face a higher risk of re-ignition, thereby giving priority to monitoring and protecting high-risk battery modules; finally, by real-time acquisition of valve opening data, and timely adjustment of ventilation and fire extinguishing strategies based on the module smoldering factor and the re-ignition prediction temperature, the abnormal conditions of the battery module can be effectively responded to, the re-ignition risk can be reduced, and the overall response efficiency and effect can be improved.

[0017] In summary, the technical solution adopted in the present application can adaptively adjust the fire extinguishing agent injection and ventilation system according to the thermal runaway gas concentration and temperature data of the battery module to improve the overall response capability of the anti-reignition control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0019] Figure 1 It is a module structure diagram of a battery module anti-reignition control system for an electrochemical energy storage station provided in accordance with the present application; Figure 2 It is a schematic diagram of a process for determining the module smoldering factor of each battery module provided in the present application; Figure 3 It is a schematic diagram of the process of extracting the temperature trend characteristics of each battery module provided in this application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] The embodiment of the present application provides a battery module re-ignition prevention control system for an electrochemical energy storage station, the core of which is to monitor the thermal runaway gas concentration of each battery module in the electrochemical energy storage station through a gas concentration monitoring module; a smoldering feature extraction module determines the concentration growth characteristics corresponding to the thermal runaway gas of each battery module in the electrochemical energy storage station according to the monitored thermal runaway gas concentration data, and determines the module smoldering factor of each battery module in the electrochemical energy storage station based on the concentration growth characteristics corresponding to the thermal runaway gas of each battery module; a re-ignition temperature prediction module collects the real-time temperature data of each battery module in the electrochemical energy storage station, extracts the temperature trend characteristics of each battery module from the corresponding real-time temperature data, and uses the corresponding temperature trend characteristics to predict the re-ignition of each battery module to obtain the re-ignition predicted temperature of each battery module; a valve opening control module obtains the fire extinguishing agent injection valve opening and the ventilation valve opening of each battery module, and adjusts the corresponding fire extinguishing agent injection valve opening and the ventilation valve opening according to the corresponding module smoldering factor and the re-ignition predicted temperature. The present application adopts the above-mentioned technical solution to adaptively adjust the fire extinguishing agent injection and ventilation system according to the thermal runaway gas concentration and temperature data of the battery module, so as to improve the overall response capability of the anti-reignition control system.

[0022] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 As shown, this figure is a module structure diagram of a battery module anti-reignition control system for an electrochemical energy storage station according to this embodiment of the present application. The processing system includes: a gas concentration monitoring module 100, a smoldering feature extraction module 200, a re-ignition temperature prediction module 300, and a valve opening control module 400, which are respectively described as follows: The gas concentration monitoring module 100 is used to monitor the thermal runaway gas concentration of each battery module in the electrochemical energy storage station.

[0023] It should be noted that in the present application, thermal runaway gases mainly refer to hydrogen, methane, ethylene and carbon monoxide; in specific implementation, the thermal runaway gas concentration of each battery module in the electrochemical energy storage station can be monitored by a gas detector; a gas detector is installed at each battery module position in the electrochemical energy storage station, and the gas detector can monitor a variety of thermal runaway gases in real time.

[0024] The smoldering feature extraction module 200 is used to determine the concentration growth characteristics corresponding to the thermal runaway gas of each battery module in the electrochemical energy storage station based on the concentration data of the monitored thermal runaway gas, and determine the module smoldering factor of each battery module in the electrochemical energy storage station based on the concentration growth characteristics corresponding to the thermal runaway gas of each battery module.

[0025] In this embodiment, the concentration growth characteristics of the thermal runaway gas corresponding to each battery module in the electrochemical energy storage station can be determined according to the monitored concentration data of the thermal runaway gas in the following manner, namely: For each battery module in the electrochemical energy storage station, fitting the concentration data of the thermal runaway gas of the monitored battery module to obtain a concentration fitting curve of the thermal runaway gas of the battery module; Growth characteristics are extracted from the concentration fitting curve to obtain the concentration growth characteristics corresponding to the thermal runaway gas of the battery module, and then the concentration growth characteristics corresponding to the thermal runaway gas of each battery module in the electrochemical energy storage station are obtained.

[0026] In specific implementation, first, for each battery module in the electrochemical energy storage station, the concentration data of the thermal runaway gas of the monitored battery module can be fitted, that is, the concentration data of the thermal runaway gas can be fitted by linear regression, so that the concentration fitting curve of the thermal runaway gas of the battery module can be obtained, and the concentration fitting curve can accurately reflect the changing trend of the thermal runaway gas concentration over time; then, the growth feature of the concentration fitting curve can be extracted to obtain the concentration growth feature corresponding to the thermal runaway gas of the battery module. The above method can be used to obtain the concentration growth feature corresponding to the thermal runaway gas of each battery module in the electrochemical energy storage station, and the concentration growth feature is used to represent the feature of the growth trend fluctuation of the thermal runaway gas concentration.

[0027] In this embodiment, the growth feature extraction of the concentration fitting curve is performed to obtain the concentration growth feature corresponding to the thermal runaway gas of the battery module in the following manner, namely: Segmenting the concentration fitting curve to obtain a plurality of concentration curve segments; Determine the concentration growth rate corresponding to each concentration curve segment; The concentration growth characteristics corresponding to the thermal runaway gas of the battery module are determined by all concentration growth rates.

[0028] In specific implementation, first, the concentration fitting curve can be segmented according to the characteristics of the concentration fitting curve, that is, the inflection point in the concentration fitting curve can be identified, and the concentration fitting curve can be divided into different sections according to the slope change, thereby obtaining a plurality of concentration curve segments; then, the concentration growth rate corresponding to each concentration curve segment can be determined, that is, the slope of the concentration curve segment can be calculated, and the slope can be used as the concentration growth rate corresponding to the concentration curve segment, thereby obtaining the concentration growth rate corresponding to each concentration curve segment, and the concentration growth rate is used to represent the growth rate of the thermal runaway gas concentration in the corresponding time period; finally, the concentration growth characteristics corresponding to the thermal runaway gas of the battery module are determined by all the concentration growth rates, that is, the standard deviation of all the concentration growth rates is used as the concentration growth characteristics corresponding to the thermal runaway gas of the battery module.

[0029] Preferably, in this embodiment, the module smoldering factor of each battery module in the electrochemical energy storage station is determined based on the concentration growth characteristics of the thermal runaway gas corresponding to each battery module, referring to Figure 2 As described above, the figure is a schematic diagram of a process for determining the module smoldering factor of each battery module in some embodiments of the present application. In this embodiment, the module smoldering factor of each battery module can be determined by the following steps: First, in step S21, for each battery module in the electrochemical energy storage station, the volume fraction of each gas in the thermal runaway gas of the battery module is obtained; Then, in step S22, the module smoldering factor of the battery module is determined according to the concentration growth characteristics corresponding to the thermal runaway gas of the battery module and the volume fractions of various gases in the thermal runaway gas of the battery module, thereby obtaining the module smoldering factor of each battery module in the electrochemical energy storage station.

[0030] In specific implementation, first, for each battery module in the electrochemical energy storage station, the volume fraction of various gases in the thermal runaway gas of the battery module is obtained, and the gas detector can be used to monitor the thermal runaway gas components released by the battery module in real time, and record the volume fractions of the main thermal runaway gases (i.e., hydrogen, methane, ethylene, and carbon monoxide); then, the module smoldering factor of the battery module can be determined according to the concentration growth characteristics corresponding to the thermal runaway gas of the battery module and the volume fractions of various gases in the thermal runaway gas of the battery module. The module smoldering factor indicates the probability of the existence of a smoldering condition in the battery module. The larger the module smoldering factor, the greater the possibility of the existence of a smoldering condition, and the greater the possibility of the battery module re-igniting. In actual implementation, the module smoldering factor of the battery module can be determined according to the following formula: in, represents the module smoldering factor of the i-th battery module, represents the weighted average of the volume fractions of various gases in the thermal runaway gas of the i-th battery module, represents the concentration growth characteristics of the thermal runaway gas of the i-th battery module, and Represents the empirical weight coefficient, and the module smoldering factor of each battery module in the electrochemical energy storage station can be obtained by the above method.

[0031] It should be noted that by real-time monitoring of the concentration changes of thermal runaway gases, potential risks of fire re-ignition can be identified in a timely manner, and the calculation of the module smoldering factor can quantify the safety risks of each battery module, helping managers to prioritize high-risk modules, thereby optimizing resource allocation and safety management strategies.

[0032] The re-ignition temperature prediction module 300 is used to collect real-time temperature data of each battery module in the electrochemical energy storage station, extract the temperature trend characteristics of each battery module from the corresponding real-time temperature data, use the corresponding temperature trend characteristics to predict the re-ignition of each battery module, and obtain the re-ignition prediction temperature of each battery module.

[0033] In specific implementation, real-time temperature data of each battery module in the electrochemical energy storage station is collected through temperature sensors; temperature sensors are installed at key positions of each battery module, and the temperature sensors are connected to the control system to ensure that the control system can read and store temperature data in real time, and the collection frequency of temperature data is set, usually at a time interval of one second or shorter, to ensure that the temperature changes of the battery module can be captured in time.

[0034] Preferably, in this embodiment, the temperature trend characteristics of each battery module are extracted from the corresponding real-time temperature data, referring to Figure 3 As described above, the figure is a schematic diagram of a process for extracting temperature trend characteristics of each battery module in some embodiments of the present application. In this embodiment, the temperature trend characteristics of each battery module can be extracted by the following steps: First, in step S31, for the real-time temperature data of each battery module, a temperature curve of the battery module is drawn according to the real-time temperature data; Then, in step S32, feature extraction is performed on the temperature curve to obtain the temperature trend feature of the battery module, and then the temperature trend feature of each battery module is obtained.

[0035] In the specific implementation, first, for the real-time temperature data of each battery module, the temperature curve of the battery module can be drawn based on the real-time temperature data, that is, with time as the horizontal axis and the real-time temperature data of each battery module as the vertical axis, the temperature curve of each battery module can be drawn, and data visualization tools (such as Matplotlib, Plotly, etc.) can be used to generate a temperature curve graph to clearly show the temperature change; then, the temperature curve is feature extracted, that is, the overall slope of the temperature curve is calculated, and the overall slope is used as the temperature trend feature of the battery module. The temperature trend feature is used to represent the characteristics of the overall temperature trend change of the battery module. The temperature trend characteristics of each battery module can be obtained in the above manner.

[0036] In this embodiment, the corresponding temperature trend characteristics are used to predict the re-ignition of each battery module, and the re-ignition prediction temperature of each battery module can be obtained in the following manner, namely: Get pre-trained simulation software for battery module re-ignition prediction; The temperature trend characteristics corresponding to each battery module are input into the simulation software to perform reignition prediction, thereby obtaining the reignition prediction temperature of each battery module.

[0037] In the specific implementation, first, obtain the simulation software for battery module reignition prediction to ensure that the software has been fully trained and has the ability to predict battery module reignition; then, organize the temperature trend characteristics extracted from each battery module into a format suitable for input into the simulation software, and standardize the temperature trend characteristics according to the requirements of the simulation software, so as to input the organized temperature trend characteristics into the simulation software one by one, start the simulation software, run the reignition prediction model, calculate according to the input temperature trend characteristics, and obtain the reignition prediction temperature of each battery module. The reignition prediction temperature is the temperature that the battery module may reach after reignition through prediction.

[0038] It should be noted that real-time monitoring of temperature data and extraction of temperature trend characteristics can promptly identify abnormal temperature rise trends in battery modules and provide early warning of potential re-ignition risks. The calculation of re-ignition prediction temperature can help identify which battery modules face a higher risk of re-ignition. Managers can better assess the safety status of each battery module, thereby prioritizing monitoring and protection of high-risk battery modules and optimizing resource allocation.

[0039] The valve opening control module 400 is used to obtain the fire extinguishing agent injection valve opening and ventilation valve opening of each battery module, and adjust the corresponding fire extinguishing agent injection valve opening and ventilation valve opening according to the corresponding module smoldering factor and re-ignition predicted temperature.

[0040] In specific implementation, the opening of the fire extinguishing agent injection valve and the opening of the ventilation valve at the position of each battery module can be obtained through the angle sensor; in this application, the selected angle sensor is a tilt sensor, which can accurately measure the opening and closing angles of the fire extinguishing agent injection valve and the ventilation valve, thereby connecting the angle sensor to the control system to ensure that the angle data can be read in real time.

[0041] In this embodiment, the corresponding fire extinguishing agent injection valve opening and ventilation valve opening are adjusted according to the corresponding module smoldering factor and re-ignition predicted temperature in the following manner, namely: For each battery module, when the module smoldering factor of the battery module exceeds a preset threshold, the ventilation valve opening is increased and the fire extinguishing agent is continuously sprayed; When the module smoldering factor of the battery module is lower than the preset threshold, the ventilation valve is kept open; When the predicted re-ignition temperature of the battery module exceeds the preset safety value, the fire extinguishing agent injection valve opening is increased and the fire extinguishing agent injection time is increased; When the predicted re-ignition temperature of the battery module is lower than the preset safety value, keep the fire extinguishing agent injection valve open.

[0042] In specific implementation, first, the threshold value of the module smoldering factor of each battery module can be set according to historical data and safety standards, and the safety value of the re-ignition prediction temperature of the battery module can be set according to historical data to ensure safe operation; then, the module smoldering factor and the re-ignition prediction temperature of each battery module are continuously monitored to ensure the real-time and accuracy of the data; further, for each battery module, when the module smoldering factor exceeds the preset threshold, the ventilation valve opening is increased to promote air circulation, reduce the thermal runaway gas concentration of the battery module, and continuously spray the fire extinguishing agent to prevent thermal runaway; when the module smoldering factor is lower than the preset threshold, the ventilation valve opening is maintained to maintain the current ventilation state to avoid waste of resources caused by excessive ventilation; when the re-ignition prediction temperature exceeds the preset safety value, the fire extinguishing agent injection valve opening is increased to quickly spray the fire extinguishing agent, and the fire extinguishing agent injection time is increased to reduce the battery module temperature to prevent re-ignition; when the re-ignition prediction temperature is lower than the preset safety value, the fire extinguishing agent injection valve opening is maintained to ensure the use efficiency of the fire extinguishing agent while avoiding unnecessary injection.

[0043] It should be noted that by acquiring valve opening data in real time and adjusting ventilation and fire extinguishing strategies in a timely manner based on the module smoldering factor and re-ignition predicted temperature, we can effectively respond to abnormal conditions of the battery module, reduce the risk of re-ignition, and improve the overall response efficiency and effectiveness.

[0044] It can be seen that in the present application, the fire extinguishing agent injection and ventilation systems can be adaptively adjusted according to the thermal runaway gas concentration and temperature data of the battery module; wherein, by real-time monitoring of the concentration changes of the thermal runaway gas, the potential risk of fire re-ignition can be timely identified, and the calculation of the module smoldering factor can quantify the safety risk of each battery module; then, by real-time monitoring of temperature data and extraction of temperature trend characteristics, the abnormal temperature rise trend of the battery module can be timely identified, and the potential re-ignition risk can be warned early, and the calculation of the re-ignition prediction temperature can help identify which battery modules face a higher risk of re-ignition, thereby giving priority to monitoring and protecting high-risk battery modules; finally, by real-time acquisition of valve opening data, and timely adjustment of ventilation and fire extinguishing strategies based on the module smoldering factor and the re-ignition prediction temperature, the abnormal conditions of the battery module can be effectively responded to, the re-ignition risk can be reduced, and the overall response efficiency and effect can be improved.

[0045] In summary, the technical solution adopted in the present application can adaptively adjust the fire extinguishing agent injection and ventilation system according to the thermal runaway gas concentration and temperature data of the battery module to improve the overall response capability of the anti-reignition control system.

[0046] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0047] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0048] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

Claims

1. A battery module anti-reignition control system for an electrochemical energy storage station, characterized in that: The control system includes: a gas concentration monitoring module, which is used to monitor the thermal runaway gas concentration of each battery module in the electrochemical energy storage station; a smoldering feature extraction module, which is used to determine the concentration growth characteristics corresponding to the thermal runaway gas of each battery module in the electrochemical energy storage station based on the monitored thermal runaway gas concentration data, and determine the module smoldering factor of each battery module in the electrochemical energy storage station based on the concentration growth characteristics corresponding to the thermal runaway gas of each battery module; a re-ignition temperature prediction module, which is used to collect real-time temperature data of each battery module in the electrochemical energy storage station, extract the temperature trend characteristics of each battery module from the corresponding real-time temperature data, use the corresponding temperature trend characteristics to predict the re-ignition of each battery module, and obtain the re-ignition predicted temperature of each battery module; a valve opening control module, which is used to obtain the fire extinguishing agent injection valve opening and the ventilation valve opening at the location of each battery module, and adjust the corresponding fire extinguishing agent injection valve opening and the ventilation valve opening according to the corresponding module smoldering factor and the re-ignition predicted temperature.

2. A battery module anti-reignition control system for an electrochemical energy storage station as claimed in claim 1, characterized in that: The thermal runaway gas concentration of each battery module in the electrochemical energy storage station is monitored by a gas detector.

3. A battery module anti-reignition control system for an electrochemical energy storage station as claimed in claim 1, characterized in that: Determining the concentration growth characteristics corresponding to the thermal runaway gas of each battery module in the electrochemical energy storage station based on the monitored concentration data of the thermal runaway gas specifically includes: for each battery module in the electrochemical energy storage station, fitting the monitored concentration data of the thermal runaway gas of the battery module to obtain a concentration fitting curve of the thermal runaway gas of the battery module; extracting growth characteristics of the concentration fitting curve to obtain the concentration growth characteristics corresponding to the thermal runaway gas of the battery module, and further obtaining the concentration growth characteristics corresponding to the thermal runaway gas of each battery module in the electrochemical energy storage station.

4. A battery module anti-reignition control system for an electrochemical energy storage station as claimed in claim 3, characterized in that: Extracting growth characteristics of the concentration fitting curve to obtain concentration growth characteristics corresponding to the thermal runaway gas of the battery module specifically includes: segmenting the concentration fitting curve to obtain multiple concentration curve segments; determining the concentration growth rate corresponding to each concentration curve segment; and determining the concentration growth characteristics corresponding to the thermal runaway gas of the battery module through all concentration growth rates.

5. A battery module anti-reignition control system for an electrochemical energy storage station as claimed in claim 1, characterized in that: Determining the module smoldering factor of each battery module in the electrochemical energy storage station based on the concentration growth characteristics corresponding to the thermal runaway gas of each battery module specifically includes: for each battery module in the electrochemical energy storage station, obtaining the volume fraction of various gases in the thermal runaway gas of the battery module; determining the module smoldering factor of the battery module according to the concentration growth characteristics corresponding to the thermal runaway gas of the battery module and the volume fraction of various gases in the thermal runaway gas of the battery module, and then obtaining the module smoldering factor of each battery module in the electrochemical energy storage station.

6. A battery module anti-reignition control system for an electrochemical energy storage station as claimed in claim 1, characterized in that: The real-time temperature data of each battery module in the electrochemical energy storage station is collected by a temperature sensor.

7. A battery module anti-reignition control system for an electrochemical energy storage station as claimed in claim 1, characterized in that: Extracting the temperature trend characteristics of each battery module from the corresponding real-time temperature data specifically includes: for the real-time temperature data of each battery module, drawing a temperature curve of the battery module based on the real-time temperature data; performing feature extraction on the temperature curve to obtain the temperature trend characteristics of the battery module, and then obtaining the temperature trend characteristics of each battery module.

8. A battery module anti-reignition control system for an electrochemical energy storage station as claimed in claim 1, characterized in that: Using the corresponding temperature trend characteristics to predict the reignition of each battery module to obtain the reignition prediction temperature of each battery module specifically includes: obtaining pre-trained simulation software for battery module reignition prediction; inputting the temperature trend characteristics corresponding to each battery module into the simulation software to perform reignition prediction, and then obtaining the reignition prediction temperature of each battery module.

9. A battery module anti-reignition control system for an electrochemical energy storage station as claimed in claim 1, characterized in that: The angle sensor is used to obtain the opening of the fire extinguishing agent injection valve and the opening of the ventilation valve at the location of each battery module.

10. A battery module anti-reignition control system for an electrochemical energy storage station as claimed in claim 1, characterized in that: The corresponding fire extinguishing agent injection valve opening and ventilation valve opening are adjusted according to the corresponding module smoldering factor and re-ignition predicted temperature, specifically including: for each battery module, when the module smoldering factor of the battery module exceeds the preset threshold, the ventilation valve opening is increased, and the fire extinguishing agent is continuously sprayed; when the module smoldering factor of the battery module is lower than the preset threshold, the ventilation valve opening is maintained; when the re-ignition predicted temperature of the battery module exceeds the preset safety value, the fire extinguishing agent injection valve opening is increased, and the fire extinguishing agent injection time is increased; when the re-ignition predicted temperature of the battery module is lower than the preset safety value, the fire extinguishing agent injection valve opening is maintained.

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