A lithium battery gas detection system based on thermal runaway monitoring

Abstract

The present invention provides a lithium battery gas detection system based on thermal runaway monitoring, the system comprising a data acquisition module, a data analysis module, an alarm module and a communication module; the data acquisition module is used to obtain gas concentration data related to thermal runaway; the data analysis module is used to analyze the obtained gas concentration data to identify thermal runaway risks; the alarm module is used to trigger emergency alarm measures when it is identified that the lithium battery has a thermal runaway risk; the communication module is used to complete communication between the system and the user; the present invention can identify and warn of the thermal runaway risk of lithium batteries earlier and more accurately through comprehensive real-time monitoring and historical data analysis, and provide risk prediction of various factors affecting the thermal runaway risk of lithium batteries, thereby effectively preventing potential safety hazards and guiding users to take appropriate countermeasures in a timely manner.

Description

Technical Field

[0001] The present invention relates to the field of energy storage battery monitoring systems, and in particular to a lithium battery gas detection system based on thermal runaway monitoring. Background Art

[0002] Lithium batteries are widely used in portable electronic devices, electric vehicles, energy storage systems and other fields due to their high energy density and long cycle life. However, during use, lithium batteries may suffer from thermal runaway due to a variety of factors, causing fire or explosion, posing serious safety hazards. Therefore, monitoring and early warning of thermal runaway of lithium batteries has become a key issue that needs to be solved urgently. Current thermal runaway monitoring technologies mostly rely on a single hazard threshold for risk assessment, lack a comprehensive assessment of multiple risk factors, and are difficult to warn of potential thermal runaway risks in a timely and accurate manner.

[0003] After consulting the relevant disclosed technical solutions, the technology with publication number CN113720965A proposes a system and method for determining thermal runaway of energy storage batteries through gas detection monitoring; the system includes a plurality of battery packs, a plurality of lithium battery modules are arranged in the battery packs, and CO concentration detectors, H2 concentration detectors and CH4 concentration detectors are installed between adjacent lithium battery modules; it also includes a data receiving system, and the data receiving system is electrically connected to the CO concentration detector, H2 concentration detector and CH4 concentration detector through data transmission cables respectively; the scheme provides a system and method for timely detecting the concentration of multi-component mixed gas to determine thermal runaway of energy storage batteries; however, the scheme only makes judgments through fixed thresholds and cannot pre-identify and warn potential thermal runaway risks. Summary of the invention

[0004] The purpose of the present invention is to address the current deficiencies and propose a lithium battery gas detection system based on thermal runaway monitoring.

[0005] The present invention adopts the following technical solution:

[0006] A lithium battery gas detection system based on thermal runaway monitoring, the system comprising a data acquisition module, a data analysis module, an alarm module and a communication module;

[0007] The data acquisition module is used to obtain gas concentration data related to thermal runaway; the data analysis module is used to analyze the obtained gas concentration data to identify thermal runaway risks; the alarm module is used to trigger emergency alarm measures when it is identified that the lithium battery has a thermal runaway risk; the communication module is used to complete the communication between the system and the user;

[0008] The data analysis module includes a data acquisition unit, a short-term risk identification unit and a long-term risk prediction unit; the data acquisition unit is used to obtain real-time gas concentration data from the data acquisition module and perform preprocessing operations; the short-term risk identification unit is used to analyze the real-time gas concentration data to detect the real-time thermal runaway risk of the lithium battery; the long-term risk prediction unit is used to predict the risk factors that may cause thermal runaway of the lithium battery in the future based on historical maintenance records, and provide alarm notifications for related risk factors;

[0009] The emergency alarm module includes an alarm notification unit and a measure execution unit; when the sound and light alarm unit identifies that the lithium battery has a thermal runaway risk, it reminds the user to take timely measures through the sound and light alarm device; the measure execution unit is used to automatically execute the preset emergency response measures when it identifies that the lithium battery has a thermal runaway risk;

[0010] Furthermore, the short-term risk identification unit analyzes and detects the real-time thermal runaway risk of the lithium battery in the following manner:

[0011] S21: pre-establishing a plurality of thermal runaway gas data models, wherein the plurality of thermal runaway gas data models include concentration change data of various thermal runaway-related gases under different thermal runaway scenarios;

[0012] S22: Setting a collection time window, and continuously acquiring gas concentration data within the collection time window with the current time as the cutoff time;

[0013] S23: Align the gas concentration data in the current acquisition time window to the pre-established thermal runaway gas data model according to the time axis through the dynamic time warping method, and evenly set m time points in the time axis; calculate the similarity Sim between the gas concentration data in the environmental data in the acquisition time window and the pre-established multiple thermal runaway gas data models:

[0014]

[0015]

[0016] Among them, R i is the similarity between the ith thermal runaway related gas and a thermal runaway gas data model, n is the number of thermal runaway related gases, C current，i，j is the gas concentration data of the i-th gas concentration at the j-th time point in the current acquisition time window; C model，i，j is the gas concentration data of the i-th gas concentration at the j-th time point in a thermal runaway gas data model, R max，i is the preset maximum error threshold of the i-th thermal runaway related gas, used to normalize the data;

[0017] S24: Setting a dangerous concentration threshold for each type of thermal runaway-related gas, where the dangerous concentration threshold is a concentration limit at which there is a serious risk of thermal runaway; obtaining the maximum similarity max(Sim) between each thermal runaway gas data model and the gas concentration data within the acquisition time window; and calculating the thermal runaway risk coefficient:

[0018]

[0019] Among them, q i is the maximum value of the ith thermal runaway related gas in the current acquisition time window, q i ′ is the preset dangerous concentration threshold of the i-th thermal runaway related gas, a i is the measurement uniform coefficient of the preset i-th thermal runaway related gas; All the current collection time window The maximum value in ;

[0020] S25: Setting a thermal runaway risk alarm threshold, and within a continuous acquisition time window, when the thermal runaway risk coefficient is greater than the thermal runaway risk alarm threshold, determining that the lithium battery has a thermal runaway risk at this time;

[0021] Furthermore, the long-term risk prediction unit includes a maintenance record acquisition subunit, a risk assessment subunit and a suggestion generation subunit; the maintenance record acquisition subunit is used to acquire historical maintenance record data with timestamps from a maintenance database or a record system; the historical maintenance record data includes various risk factors causing thermal runaway; the risk assessment subunit is used to perform a comprehensive assessment on each risk factor to acquire a risk assessment parameter of each risk factor; the suggestion generation subunit provides the user with different degrees of alarm notifications according to the risk assessment parameters of each risk factor;

[0022] Furthermore, the risk assessment subunit sets a record analysis period and analyzes the number of times each risk factor causes thermal runaway of the lithium battery within the record analysis period to obtain risk assessment parameters of each risk factor;

[0023] A lithium battery gas detection method based on thermal runaway detection, the method comprising the following steps:

[0024] S1: Obtain real-time thermal runaway related gas concentration data;

[0025] S2: Identify the risk of thermal runaway of lithium batteries based on real-time gas concentration data analysis;

[0026] S3: Trigger emergency alarm measures when the risk of thermal runaway of the lithium battery is identified;

[0027] S4: Generates alert notifications of risk factors based on historical maintenance record data;

[0028] S5: Provide the lithium battery thermal runaway risk identification information and risk factor alarm notification information to the user.

[0029] The beneficial effects achieved by the present invention are:

[0030] The present invention uses multi-level data analysis and dynamic time warping methods to identify the thermal runaway risk of lithium batteries earlier and more accurately. Compared with the prior art that only relies on a single danger threshold for risk assessment, the present invention provides a more comprehensive risk assessment through analysis of multiple models and comprehensive formulas, and can provide early warning before the risk occurs. By analyzing various risk factors during the record analysis cycle of historical maintenance records and providing users with different degrees of alarm notifications, the safety and reliability of lithium batteries are improved, and users are guided to take preventive and countermeasures in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The present invention can be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily drawn to scale, but the emphasis is placed on illustrating the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0032] Figure 1 It is a schematic diagram of the overall module of the present invention.

[0033] Figure 2 It is a schematic flow chart of the lithium battery gas detection method based on thermal runaway detection of the present invention.

[0034] Figure 3 This is a schematic diagram of the workflow of the short-term risk identification unit of the present invention. DETAILED DESCRIPTION

[0035] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with its embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention; for those skilled in the art, other systems, methods and / or features of the present embodiment will become apparent after reviewing the following detailed description; it is intended that all such additional systems, methods, features and advantages are included in this specification; included within the scope of the present invention and protected by the appended claims; additional features of the disclosed embodiments are described in the following detailed description, and these features will be apparent from the following detailed description.

[0036] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right" and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, it is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or component referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limitations on this patent. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0037] Embodiment 1:

[0038] like Figure 1 As shown, this embodiment provides a lithium battery gas detection system based on thermal runaway monitoring, the system includes a data acquisition module, a data analysis module, an alarm module and a communication module;

[0039] The data acquisition module is used to obtain gas concentration data related to thermal runaway; the data analysis module is used to analyze the obtained gas concentration data to identify thermal runaway risks; the alarm module is used to trigger emergency alarm measures when it is identified that the lithium battery has a thermal runaway risk; the communication module is used to complete the communication between the system and the user;

[0040] Specifically, the data acquisition module includes a sensor array composed of a plurality of gas sensing units, and the sensor array is used to obtain thermal runaway-related gas concentration data at the location of the lithium battery;

[0041] The data analysis module includes a data acquisition unit, a short-term risk identification unit and a long-term risk prediction unit; the data acquisition unit is used to obtain real-time gas concentration data from the data acquisition module and perform preprocessing operations; the short-term risk identification unit is used to analyze the real-time gas concentration data to detect the real-time thermal runaway risk of the lithium battery; the long-term risk prediction unit is used to predict the risk factors that may cause thermal runaway of the lithium battery in the future based on historical maintenance records, and provide alarm notifications for related risk factors;

[0042] The emergency alarm module includes an alarm notification unit and a measure execution unit; when the sound and light alarm unit identifies that the lithium battery has a thermal runaway risk, it reminds the user to take timely measures through the sound and light alarm device; the measure execution unit is used to automatically execute the preset emergency response measures when it identifies that the lithium battery has a thermal runaway risk;

[0043] Furthermore, the emergency response measures executed by the measure execution unit include but are not limited to:

[0044] Automatic power off: cut off the power supply of lithium battery to prevent further battery thermal runaway reaction;

[0045] Cooling system start: Activate the battery cooling system to reduce battery temperature and slow down or prevent further development of thermal runaway;

[0046] Exhaust system start: Turn on the exhaust system to discharge harmful gases around the battery to ensure environmental safety;

[0047] Emergency evacuation: In public places or industrial environments, trigger emergency evacuation signals to ensure that people evacuate dangerous areas in a timely manner;

[0048] Further, such as Figure 3 As shown, the short-term risk identification unit analyzes and detects the real-time thermal runaway risk of the lithium battery in the following manner:

[0049] S21: pre-establishing a plurality of thermal runaway gas data models, wherein the plurality of thermal runaway gas data models include concentration change data of various thermal runaway-related gases under different thermal runaway scenarios;

[0050] S22: Setting a collection time window, and continuously acquiring gas concentration data within the collection time window with the current time as the cutoff time;

[0051] S23: Align the gas concentration data in the current acquisition time window to the pre-established thermal runaway gas data model according to the time axis through the dynamic time warping method, and evenly set m time points in the time axis; calculate the similarity Sim between the gas concentration data in the environmental data in the acquisition time window and the pre-established multiple thermal runaway gas data models:

[0052]

[0053]

[0054] Among them, R i is the similarity between the ith thermal runaway related gas and a thermal runaway gas data model, n is the number of thermal runaway related gases, C current，i，j is the gas concentration data of the i-th gas concentration at the j-th time point in the current acquisition time window; C model，i，j is the gas concentration data of the i-th gas concentration at the j-th time point in a thermal runaway gas data model, R max，i is the preset maximum error threshold of the i-th thermal runaway related gas, used to normalize the data;

[0055] S24: Setting a dangerous concentration threshold for each type of thermal runaway-related gas, where the dangerous concentration threshold is a concentration limit at which there is a serious risk of thermal runaway; obtaining the maximum similarity max(Sim) between each thermal runaway gas data model and the gas concentration data within the acquisition time window; and calculating the thermal runaway risk coefficient:

[0056]

[0057] Among them, q i is the maximum value of the ith thermal runaway related gas in the current acquisition time window, q i ′ is the preset dangerous concentration threshold of the i-th thermal runaway related gas, a i is the measurement uniform coefficient of the preset i-th thermal runaway related gas; All the current collection time window The maximum value in ;

[0058] S25: Setting a thermal runaway risk alarm threshold, and within a continuous acquisition time window, when the thermal runaway risk coefficient is greater than the thermal runaway risk alarm threshold, determining that the lithium battery has a thermal runaway risk at this time;

[0059] This solution uses multi-level data analysis and dynamic time warping methods to identify the thermal runaway risk of lithium batteries earlier and more accurately. Compared with the existing technology that only relies on a single danger threshold for risk assessment, this solution provides a more comprehensive risk assessment through multiple models and comprehensive formula analysis, which can provide early warning before the risk occurs to prevent adverse consequences caused by thermal runaway.

[0060] Embodiment 2:

[0061] This embodiment should be understood to include at least all the features of any of the above embodiments, and further improve upon them;

[0062] This embodiment provides a lithium battery gas detection system based on thermal runaway monitoring, the system comprising a data acquisition module, a data analysis module, an alarm module and a communication module;

[0063] The data acquisition module is used to obtain gas concentration data related to thermal runaway; the data analysis module is used to analyze the obtained gas concentration data to identify thermal runaway risks; the alarm module is used to trigger emergency alarm measures when it is identified that the lithium battery has a thermal runaway risk; the communication module is used to complete the communication between the system and the user;

[0064] The data analysis module includes a data acquisition unit, a short-term risk identification unit and a long-term risk prediction unit; the data acquisition unit is used to obtain real-time gas concentration data from the data acquisition module and perform preprocessing operations; the short-term risk identification unit is used to analyze the real-time gas concentration data to detect the real-time thermal runaway risk of the lithium battery; the long-term risk prediction unit is used to predict the risk factors that may cause thermal runaway of the lithium battery in the future based on historical maintenance records, and provide alarm notifications for related risk factors;

[0065] Furthermore, the long-term risk prediction unit includes a maintenance record acquisition subunit, a risk assessment subunit and a suggestion generation subunit; the maintenance record acquisition subunit is used to acquire historical maintenance record data with timestamps from a maintenance database or a record system; the historical maintenance record data includes various risk factors causing thermal runaway; the risk assessment subunit is used to perform a comprehensive assessment on each risk factor to acquire a risk assessment parameter of each risk factor; the suggestion generation subunit provides the user with different degrees of alarm notifications according to the risk assessment parameters of each risk factor;

[0066] Furthermore, the risk assessment subunit completes the risk assessment of each risk factor that may cause thermal runaway in the following manner:

[0067] S41: Setting a record analysis cycle, wherein the record analysis cycle is a time period for analyzing historical maintenance record data by looking back from the current time as a time cutoff point;

[0068] S42: Divide the record analysis period into k time periods on average, wherein the first time period is the time period farthest from the current time, and the kth time period is the time period closest to the current time;

[0069] S43: Calculate the risk assessment parameters of each risk factor. The specific calculation process is as follows:

[0070] For a risk factor:

[0071]

[0072] Among them, E is the risk assessment parameter of the risk factor, u l is the number of times the risk factor causes thermal runaway of the lithium battery in the first time period;

[0073] Furthermore, the suggestion generation subunit provides different degrees of alarm notifications according to different risk assessment threshold intervals of the risk assessment parameters of each risk factor; for each risk factor, a specific alarm notification is generated as follows:

[0074] Assume that β1, β2 and β3 are the boundary values ​​of each pre-set risk assessment threshold interval, and satisfy β1<β2<β3, then:

[0075] When β1≤E<β2, a primary alarm notification is generated for the risk element to remind the staff to perform routine inspections on the risk element and maintain regular maintenance;

[0076] When β2≤E<β3, a medium-level alarm notification is generated for the risk element, reminding the staff to conduct a detailed inspection of the risk factor and implement measures such as preventive battery replacement or enhanced monitoring to prevent potential problems from worsening;

[0077] When E≥β3, a high-level alarm notification is generated for the risk element, reminding the staff to conduct a comprehensive inspection and repair of the lithium battery, and to take measures such as deactivating and replacing the battery to prevent the risk of thermal runaway;

[0078] By setting multiple independent time periods in the record analysis cycle and setting weights according to the distance between different time periods and the current time, the impact of risk factors on the current risk status can be more accurately reflected; alarm notifications of different degrees are generated according to the size of the risk assessment parameters of each risk factor, so that the risk of thermal runaway of lithium batteries can be prevented more timely and effectively;

[0079] Furthermore, the communication module realizes the communication between the system and the user, and the specific communication content includes the real-time acquired thermal runaway related gas concentration data, the thermal runaway risk information identified by the short-term risk identification unit, and the alarm notification information provided by the long-term risk prediction unit; through the effective communication between the system and the user, it is ensured that the user can timely understand and deal with the potential thermal runaway risk of the lithium battery;

[0080] like Figure 2 As shown, this embodiment also provides a lithium battery gas detection method based on thermal runaway detection, the method comprising the following steps:

[0081] S1: Obtain real-time thermal runaway related gas concentration data;

[0082] S2: Identify the risk of thermal runaway of lithium batteries based on real-time gas concentration data analysis;

[0083] S3: Trigger emergency alarm measures when the risk of thermal runaway of the lithium battery is identified;

[0084] S4: Generates alert notifications of risk factors based on historical maintenance record data;

[0085] S5: Provide the lithium battery thermal runaway risk identification information and risk factor alarm notification information to the user.

[0086] The contents disclosed above are only preferred feasible embodiments of the present invention, and do not limit the protection scope of the present invention. Therefore, all equivalent technical changes made using the contents of the present invention specification and drawings are included in the protection scope of the present invention. In addition, the elements therein can be updated as technology develops.

Claims

1. A lithium battery gas detection system based on thermal runaway monitoring, characterized in that: The system includes a data acquisition module, a data analysis module, an alarm module and a communication module; The data acquisition module is used to obtain gas concentration data related to thermal runaway; the data analysis module is used to analyze the obtained gas concentration data to identify thermal runaway risks; the alarm module is used to trigger emergency alarm measures when it is identified that the lithium battery has a thermal runaway risk; the communication module is used to complete the communication between the system and the user; The data analysis module includes a data acquisition unit, a short-term risk identification unit and a long-term risk prediction unit; the data acquisition unit is used to obtain real-time gas concentration data from the data acquisition module and perform preprocessing operations; the short-term risk identification unit is used to analyze the real-time gas concentration data to detect the real-time thermal runaway risk of the lithium battery; the long-term risk prediction unit is used to predict the risk factors that may cause thermal runaway of the lithium battery in the future based on historical maintenance records, and provide alarm notifications for related risk factors; The alarm module includes an alarm notification unit and a measure execution unit; when the alarm notification unit identifies that the lithium battery has a thermal runaway risk, it reminds the user to take timely measures through an audible and visual alarm device; the measure execution unit is used to automatically execute preset emergency response measures when it identifies that the lithium battery has a thermal runaway risk; The short-term risk identification unit analyzes and detects the real-time thermal runaway risk of the lithium battery in the following manner: S21: pre-establishing a plurality of thermal runaway gas data models, wherein the plurality of thermal runaway gas data models include concentration change data of various thermal runaway-related gases under different thermal runaway scenarios; S22: Setting a collection time window, and continuously acquiring gas concentration data within the collection time window with the current time as the cutoff time; S23: The gas concentration data in the current acquisition time window is aligned to the pre-established thermal runaway gas data model according to the time axis through the dynamic time warping method, and is evenly set in the time axis. time point; Calculate the similarity between the gas concentration data in the environmental data within the acquisition time window and multiple pre-established thermal runaway gas data models : ； ； in, For the The similarity between a thermal runaway related gas and a thermal runaway gas data model, is the number of types of gases related to thermal runaway, The number of The gas concentration is Gas concentration data at each time point; For a thermal runaway gas data model The gas concentration is Gas concentration data at each time point, For the pre-set The maximum error threshold of the thermal runaway related gas is used to normalize the data; S24: Setting a dangerous concentration threshold for each type of thermal runaway-related gas, where the dangerous concentration threshold is a concentration limit at which there is a serious risk of thermal runaway; obtaining the maximum similarity between each thermal runaway gas data model and the gas concentration data within the acquisition time window ; Calculate the thermal runaway risk factor: ； in, For the The maximum value of the thermal runaway related gas in the current acquisition time window, For the preset The dangerous concentration thresholds of the thermal runaway related gases are For the preset Unified coefficients for the measurement of various thermal runaway related gases; All the current collection time window The maximum value in ; S25: Setting a thermal runaway risk alarm threshold, and within a continuous acquisition time window, when the thermal runaway risk coefficient is greater than the thermal runaway risk alarm threshold, determining that the lithium battery has a thermal runaway risk at this time; The long-term risk prediction unit includes a maintenance record acquisition subunit, a risk assessment subunit and a suggestion generation subunit; the maintenance record acquisition subunit is used to obtain historical maintenance record data with timestamps from a maintenance database or a record system; the historical maintenance record data includes various risk factors that cause thermal runaway; the risk assessment subunit is used to perform a comprehensive assessment of each risk factor to obtain the risk assessment parameters of each risk factor; the suggestion generation subunit provides the user with different degrees of alarm notifications according to the risk assessment parameters of each risk factor.

2. A lithium battery gas detection system based on thermal runaway monitoring according to claim 1, characterized in that: The risk assessment subunit sets a record analysis period and analyzes the number of times each risk factor causes thermal runaway of the lithium battery within the record analysis period to obtain risk assessment parameters of each risk factor.

3. A lithium battery gas detection method based on thermal runaway detection, characterized in that: The method is used for a lithium battery gas detection system based on thermal runaway monitoring as claimed in claim 2, and the method comprises the following steps: S1: Obtain real-time thermal runaway related gas concentration data; S2: Identify the risk of thermal runaway of lithium batteries based on real-time gas concentration data analysis; S3: Trigger emergency alarm measures when the risk of thermal runaway of the lithium battery is identified; S4: Generates alert notifications of risk factors based on historical maintenance record data; S5: Provide the lithium battery thermal runaway risk identification information and risk factor alarm notification information to the user.

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