New energy automobile lithium battery intelligent packaging system based on Internet of Things technology

Abstract

The invention relates to the field of lithium battery monitoring, and discloses a new energy automobile lithium battery intelligent packaging system based on the Internet of Things technology, and the system comprises a plurality of key modules: a temperature sensor array module which is formed by arranging a plurality of temperature sensors in a lithium battery packaging box and senses the temperature of each part of a battery in real time; the temperature acquisition and transmission module is connected with the sensor array, collects temperature data, stores the temperature data in a dictionary form, and attaches corresponding lithium battery numbers; the gateway data processing module is used for receiving the data of the acquisition module, judging real-time temperature according to a preset safety and early warning temperature threshold value, and then deeply analyzing according to different analysis modes; the temperature alarm module determines whether to start an early warning mechanism or trigger danger alarm according to the analysis result; and finally, the monitoring center module receives gateway data, is responsible for displaying and storing temperature data, also has alarm and remote control functions, and ensures safety monitoring and management of the lithium battery in an omnibearing manner.

Description

Technical Field

[0001] The present invention relates to the field of lithium battery monitoring, and in particular to an intelligent packaging system for lithium batteries of new energy vehicles based on Internet of Things technology. Background Art

[0002] In the field of new energy vehicles, lithium batteries are the core power source, and their safety and stability are of vital importance. During the use of lithium batteries, temperature has a significant impact on their performance and life. The existing lithium battery packaging box temperature monitoring solution has many defects, which brings hidden dangers to the safe operation of new energy vehicles.

[0003] Currently, most monitoring solutions rely on single-point or limited-area temperature sensors, which makes the reflection of the actual temperature distribution inside the battery pack extremely limited. During the charging and discharging process of the battery, the temperature of different parts varies significantly. Single-point or a small number of sensors cannot fully capture these temperature changes, resulting in inaccurate temperature monitoring. For example, in the corners or deep internal areas of the battery, heat may accumulate but cannot be detected in time.

[0004] At the same time, the existing solutions have a slow response speed. When the battery temperature is abnormal, it is difficult to detect the abnormal temperature in time due to unreasonable sensor layout and delays in data transmission and processing. Especially in high temperature environments, this untimely monitoring may lead to serious accidents such as battery thermal runaway and short circuit. For example, in the hot summer, the battery temperature rises rapidly. If it is not discovered and measures are not taken in time, it is very easy to cause dangerous situations such as fire, posing a serious threat to personnel and property safety.

[0005] In summary, the existing lithium battery packaging box temperature monitoring solution needs to be improved urgently to meet the needs of new energy vehicles for safe and efficient operation of lithium batteries. Summary of the invention

[0006] The purpose of the present invention is to provide a smart packaging system for lithium batteries of new energy vehicles based on Internet of Things technology to solve at least one of the above technical problems.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A smart packaging system for lithium batteries of new energy vehicles based on Internet of Things technology, including: a temperature sensor array module, a temperature acquisition and transmission module, a gateway data processing module, a temperature alarm module and a monitoring center module;

[0009] The temperature sensor array module is composed of multiple temperature sensors and is arranged inside the lithium battery packaging box to sense the temperature information of different parts of the lithium battery in real time;

[0010] A temperature collection and transmission module, connected to the temperature sensor array, for collecting temperature data sensed by the temperature sensor array, and storing the temperature data and the corresponding lithium battery numbers in the form of a dictionary;

[0011] The gateway data processing module is used to receive the battery temperature and serial number information from the temperature acquisition module, and further analyze it according to different analysis modes based on the preset safety temperature threshold and warning temperature threshold;

[0012] The temperature alarm module is used to determine whether to start the early warning mechanism or trigger the danger alarm according to the results of further analysis of the analysis mode;

[0013] The monitoring center module receives temperature data from the gateway data processing module, displays and stores it, and provides alarm and remote control functions.

[0014] In a further solution, the working process of the gateway data processing module includes:

[0015] The real-time temperature value collected by each temperature sensor is recorded as , and then compare each real-time temperature value with the preset safety temperature threshold and warning temperature threshold; where the warning temperature threshold is less than the safety temperature threshold;

[0016] If each real-time temperature value is less than the warning temperature threshold, analysis is performed according to mode 1;

[0017] If at least one real-time temperature value is greater than or equal to the warning temperature threshold, analysis is performed according to mode 2.

[0018] In a further solution, the analysis process of Mode 1 includes:

[0019] Will Substituting into the formula: , calculate the temperature index ;

[0020] in, Indicates The coefficient of change of the real-time temperature value collected by a temperature sensor is expressed as: ; In the formula Indicates The real-time temperature value collected by the temperature sensor, Represents the real-time temperature gradient value, the expression is: , Indicates the maximum value of the temperatures collected by multiple temperature sensors. Indicates the minimum value of the temperatures collected by multiple temperature sensors. Indicates the number of times the current temperature sensor has sampled in the last unit time. Indicates that the current temperature sensor is in The temperature value at the time of sampling, Indicates the average value of the temperature collected by the current temperature sensor. , is the preset weight coefficient;

[0021] The calculated temperature index With preset temperature index threshold For comparison; if , it is judged that there is no safety hazard in the current lithium battery;

[0022] like , it is judged that there is a safety hazard in the lithium battery and the early warning mechanism is activated.

[0023] In a further embodiment, the analysis process of Mode 1 further includes:

[0024] The calculated The coefficient of variation of the real-time temperature value collected by the temperature sensor With the preset temperature index range Make a comparison;

[0025] like , the area where the current temperature sensor is located is judged to be a medium-risk area for thermal runaway, and a trend forecast analysis is performed;

[0026] like , the area where the current temperature sensor is located is judged to be a high-risk area for thermal runaway, and a danger alarm is triggered;

[0027] like , then the area where the current temperature sensor is located is judged to be a safe area.

[0028] In a further solution, the process of trend prediction analysis for the risk area of ​​thermal runaway is as follows:

[0029] Get the real-time temperature change coefficient of the area in the risk area of ​​thermal runaway per unit time Curves of change over time ;

[0030] The trend of the risk area of ​​thermal runaway is obtained through the predictive analysis model:

[0031] The expression of the predictive analysis model is:

[0032] ;

[0033] In the formula Indicates the trend index of the risk area of ​​thermal runaway, , are the starting point and end point of the unit time respectively. is the maximum value of the real-time temperature value change coefficient per unit time, is the minimum value of the real-time temperature change coefficient per unit time, It is a preset fixed coefficient.

[0034] A further solution is to calculate the trend index The preset trend index threshold interval Make a comparison;

[0035] like , it is judged that the state of the risk area in thermal runaway tends to be stable;

[0036] like , it is judged that the state of the risk area in thermal runaway tends to be optimized;

[0037] like , it is judged that the state of the risk area in thermal runaway is tending to deteriorate, and the early warning mechanism is activated to send early warning information to the monitoring personnel.

[0038] A further solution is that when it is determined that the state of the area at risk of thermal runaway is deteriorating, the early warning mechanism is activated to send early warning information to the monitoring personnel;

[0039] The data collection frequency of the temperature collection and transmission module was adjusted, and the collection frequency of the area was increased to twice the original frequency in order to focus on monitoring the temperature changes in the area.

[0040] In a further solution, the analysis process of the second mode includes:

[0041] The warning temperature threshold will be exceeded Real-time temperature value The corresponding area is marked as a medium-risk area for thermal runaway, and the real-time temperature value of the medium-risk area for thermal runaway is With safety temperature threshold Make comparisons;

[0042] When the real-time temperature value of the thermal runaway risk area Reaching or exceeding the safety temperature threshold When the high-risk area for thermal runaway is detected, the area is marked as such and a danger alarm is triggered to notify the monitoring center and on-site operators. Otherwise, the early warning mechanism is activated to send early warning information to the monitoring personnel.

[0043] Beneficial effects of the present invention:

[0044] (1) The temperature sensor array module in the present invention can accurately sense the temperature of different parts of the lithium battery in real time through a multi-position layout, overcoming the limitation of single-point monitoring, comprehensively grasping the battery temperature status, and facilitating the discovery of local hot spots or anomalies. At the same time, the temperature acquisition and transmission module stores the data and numbers in a dictionary form, providing a clear and orderly data structure for subsequent processing, which is convenient for query, retrieval and processing; the gateway data processing module then performs fine processing on the battery temperature data based on the preset threshold and analysis mode, can distinguish different temperature ranges, identify situations approaching or exceeding the threshold, improve the system's sensitivity to temperature anomalies, detect potential risks in advance, and provide a basis for response measures at different stages. In combination with the temperature alarm module, based on the analysis results, timely warnings are issued when the temperature approaches the warning threshold, and danger alarms are triggered when the temperature exceeds the safety threshold, effectively preventing thermal runaway and reducing safety risks;

[0045] (2) The present invention receives, stores and displays temperature data through the monitoring center module, which is convenient for operators to intuitively observe real-time and historical trends and provide visual support for fault analysis; the modules work together to form a closed-loop monitoring, analysis, early warning and control process, which reduces the impact of temperature abnormalities on battery performance, reduces safety hazards, extends battery life, ensures the normal operation and safe use of new energy vehicles, optimizes lithium battery management, reduces maintenance and risk costs caused by battery failures, and provides strong support for the development of new energy vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present invention will be further described below in conjunction with the accompanying drawings.

[0047] Figure 1 is a network architecture diagram of the present invention;

[0048] Figure 2 It is a system structure block diagram of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] See also Figure 1-Figure 2 As shown, the present invention is a smart packaging system for lithium batteries of new energy vehicles based on Internet of Things technology, including: a temperature sensor array module, a temperature acquisition and transmission module, a gateway data processing module, a temperature alarm module and a monitoring center module;

[0051] The temperature sensor array module is composed of multiple temperature sensors and is arranged inside the lithium battery packaging box to sense the temperature information of different parts of the lithium battery in real time;

[0052] The temperature acquisition and transmission module is connected to the temperature sensor array, and is used to collect the temperature data sensed by the temperature sensor array, and store the temperature data and the corresponding lithium battery number in the form of a dictionary; wherein, storage in the form of a dictionary means storing the temperature data and the corresponding lithium battery number according to a specific key-value pair structure. In programming, a dictionary is a data structure that can store multiple key-value pairs. For example, suppose there are three lithium batteries, numbered "001", "002" and "003", and the corresponding temperature data are 25°C, 30°C and 28°C respectively; storage in the form of a dictionary means that the lithium battery number is the key, and the temperature data is the corresponding value; in this way, the corresponding temperature data can be found quickly and easily according to the lithium battery number. In subsequent data transmission and processing, and when the system determines the battery temperature status and other operations, this storage form can improve the efficiency of data retrieval and use;

[0053] The gateway data processing module is used to receive the battery temperature and serial number information from the temperature acquisition module, and further analyze it according to different analysis modes based on the preset safety temperature threshold and warning temperature threshold;

[0054] The temperature alarm module is used to determine whether to start the early warning mechanism or trigger the danger alarm according to the results of further analysis of the analysis mode;

[0055] The monitoring center module receives temperature data from the gateway data processing module, displays and stores it, and provides alarm and remote control functions.

[0056] In the present invention, firstly, accurate temperature monitoring and data management are realized through the temperature sensor array module. The multi-position layout of the temperature sensor array module can sense the temperature of different parts of the lithium battery in real time and accurately, overcome the limitation of single-point monitoring, fully grasp the temperature status of the battery, and facilitate the discovery of local hot spots or anomalies; the temperature acquisition and transmission module stores the data and numbers in the form of a dictionary, providing a clear and orderly data structure for subsequent processing, which is convenient for query, retrieval and processing;

[0057] Secondly, the gateway data processing module provides multi-level temperature analysis and risk assessment. Based on preset thresholds and analysis modes, the gateway data processing module processes the battery temperature data in a refined manner, distinguishes different temperature ranges, identifies situations approaching or exceeding the threshold, and improves the system's sensitivity to temperature anomalies. It can detect potential risks in advance, provide a basis for response measures at different stages, and ensure safe and stable battery use.

[0058] Thirdly, it has the functions of timely alarm and early warning. Based on the analysis results, the temperature alarm module can issue an early warning when the temperature approaches the warning threshold, allowing users or personnel to take precautions in advance. When the safety threshold is exceeded, a danger alarm is triggered to notify the monitoring center and on-site personnel, effectively preventing thermal runaway and reducing safety risks.

[0059] Then, with convenient monitoring and remote operation functions, the monitoring center module receives, stores and displays temperature data, allowing operators to intuitively observe real-time and historical trends, providing visual support for fault analysis;

[0060] Finally, through the collaboration of various modules, a closed-loop monitoring, analysis, early warning and control process is formed, which can reduce the impact of abnormal temperature on battery performance, reduce safety hazards, extend battery life, ensure the normal operation and safe use of new energy vehicles, optimize lithium battery management, and reduce maintenance and risk costs caused by battery failures, providing strong support for the development of new energy vehicles.

[0061] The working process of the gateway data processing module includes:

[0062] The real-time temperature value collected by each temperature sensor is recorded as , and then compare each real-time temperature value with the preset safety temperature threshold and warning temperature threshold; where the warning temperature threshold is less than the safety temperature threshold;

[0063] If each real-time temperature value is less than the warning temperature threshold, analysis is performed according to mode 1;

[0064] If at least one real-time temperature value is greater than or equal to the warning temperature threshold, analysis is performed according to mode 2.

[0065] In the present invention, by comparing the real-time temperature value of each temperature sensor with a preset threshold, the temperature state can be accurately determined; when all real-time temperature values ​​are lower than the warning temperature threshold, mode one analysis is enabled, which can perform in-depth processing on temperature data within the normal range and mine potential information; and when one or more real-time temperature values ​​exceed the warning temperature threshold, mode two analysis is switched to enable the system to quickly focus on abnormal temperature data; this dual-mode working process can effectively distinguish between normal and abnormal situations, realize refined management of lithium battery temperature, improve the response speed to temperature anomalies, help ensure the safe and stable operation of lithium batteries, and reduce the risks caused by temperature anomalies.

[0066] The analysis process of Mode 1 includes:

[0067] Will Substituting into the formula: , calculate the temperature index ;

[0068] in, Indicates The coefficient of change of the real-time temperature value collected by a temperature sensor is expressed as: ; In the formula Indicates The real-time temperature value collected by the temperature sensor, Represents the real-time temperature gradient value, the expression is: , Indicates the maximum value of the temperatures collected by multiple temperature sensors. Indicates the minimum value of the temperatures collected by multiple temperature sensors. Indicates the number of times the current temperature sensor has sampled in the last unit time. Indicates that the current temperature sensor is in The temperature value at the time of sampling, Indicates the average value of the temperature collected by the current temperature sensor. , is the preset weight coefficient;

[0069] The calculated temperature index With preset temperature index threshold For comparison; if , it is judged that there is no safety hazard in the current lithium battery;

[0070] like , it is judged that there is a safety hazard in the lithium battery and the early warning mechanism is activated.

[0071] In this embodiment, in order to analyze the potential risks of lithium batteries in a safe temperature state and prevent the dangers caused by thermal runaway in advance, the lithium batteries at the warning temperature threshold are analyzed by the formula Calculate and get the temperature index Through the above method, multiple temperature values ​​collected by the temperature sensor array can be processed in a summed and averaged manner, thereby achieving the purpose of accurately reflecting the actual temperature of the lithium battery; at the same time, in order to consider the changes in the corresponding area of ​​each temperature sensor, it is obvious that the greater the change, the more abnormal the temperature of the corresponding area of ​​the temperature sensor, so the exponential driving is used for reaction, which can react quickly and achieve the purpose of effective evaluation. Specifically, through the formula Calculate the first The coefficient of variation of the real-time temperature value collected by the temperature sensor Obviously, the above formula can express the temperature variation in space, which is reflected by the ratio of real-time temperature to temperature gradient, while considering the degree of temperature fluctuation in time series, which is reflected by the standard deviation of real-time temperature, thus helping to evaluate the stability of temperature variation of lithium batteries at different locations and the relative importance of these variations in space; when the coefficient of variation When it is large, it means that in some areas, not only is the temperature gradient relatively small, there may be local hot spots, and the heat is difficult to diffuse, but the temperature itself fluctuates greatly, and the real-time temperature standard deviation is large; therefore, the risk of thermal runaway will increase, because the heat in the local high-temperature area continues to accumulate and the temperature is unstable, which can easily cause a series of exothermic reactions inside the battery to get out of control;

[0072] For example, if the ratio of real-time temperature to temperature gradient in a certain area of ​​the battery is large, indicating that heat is easily accumulated near this area, and at the same time the standard deviation of the real-time temperature is also large, indicating that the temperature fluctuations in this area are frequent and large, then this area may be a potential high-risk area for thermal runaway.

[0073] The analysis process of Mode 1 also includes:

[0074] The calculated The coefficient of variation of the real-time temperature value collected by the temperature sensor With the preset temperature index range Make a comparison;

[0075] like , the area where the current temperature sensor is located is judged to be a medium-risk area for thermal runaway, and a trend forecast analysis is performed;

[0076] like , the area where the current temperature sensor is located is judged to be a high-risk area for thermal runaway, and a danger alarm is triggered;

[0077] like , then the area where the current temperature sensor is located is judged to be a safe area.

[0078] The present invention uses the above technical solution to safely divide the area corresponding to each temperature sensor, thereby triggering a danger alarm for the high-risk area of ​​thermal runaway in a focused manner and immediately processing it. For the medium-risk area of ​​thermal runaway, a trend prediction analysis is performed before deciding whether to activate the early warning mechanism to avoid false early warnings caused by normal fluctuations.

[0079] The process of trend prediction analysis for the risk area of ​​thermal runaway is as follows:

[0080] Get the real-time temperature change coefficient of the area in the risk area of ​​thermal runaway per unit time Curves of change over time ;

[0081] The trend of the risk area of ​​thermal runaway is obtained through the predictive analysis model:

[0082] The expression of the predictive analysis model is:

[0083] ;

[0084] In the formula Indicates the trend index of the risk area of ​​thermal runaway, , are the starting point and end point of the unit time respectively. is the maximum value of the real-time temperature value change coefficient per unit time, is the minimum value of the real-time temperature change coefficient per unit time, It is a preset fixed coefficient.

[0085] The present invention, through The real-time temperature value change coefficient per unit time is calculated by integration, and then the extreme value is used As an index impact, a predictive analysis model is obtained , obviously If it is large, it means that the current lithium battery thermal management system fails to effectively control the spatial and temporal changes of temperature, resulting in significant fluctuations and cumulative effects of the comprehensive indicators in time and space; for example, if If the range is large, it means that the overall comprehensive index is large during this period, which may mean that the temperature change relationship of the lithium battery at different locations and at different times has been in a relatively complex or undesirable state; if the range is also large, it means that this undesirable state is also accompanied by large fluctuations, suggesting that the thermal management system may not be able to balance the temperature changes, easily causing local overheating or overcooling, affecting battery performance and life.

[0086] The calculated trend index The preset trend index threshold interval Make a comparison;

[0087] like , it is judged that the state of the risk area in thermal runaway tends to be stable;

[0088] like , it is judged that the state of the risk area in thermal runaway tends to be optimized;

[0089] like , it is judged that the state of the risk area in thermal runaway is tending to deteriorate, and the early warning mechanism is activated to send early warning information to the monitoring personnel.

[0090] When it is judged that the state of the area at risk of thermal runaway is deteriorating, the early warning mechanism is activated to send early warning information to the monitoring personnel;

[0091] The data collection frequency of the temperature collection and transmission module was adjusted, and the collection frequency of the area was increased to twice the original frequency in order to focus on monitoring the temperature changes in the area.

[0092] The present invention, through the above technical solution, determines that the area tends to deteriorate and issues an early warning, and then increases the collection frequency of the temperature collection and transmission module in the area, which can capture temperature changes more intensively and accurately, and timely discover potential risk upgrades, thereby buying time for early intervention.

[0093] The analysis process of Mode 2 includes:

[0094] The warning temperature threshold will be exceeded Real-time temperature value The corresponding area is marked as a medium-risk area for thermal runaway, and the real-time temperature value of the medium-risk area for thermal runaway is With safety temperature threshold Make comparisons;

[0095] When the real-time temperature value of the thermal runaway risk area Reaching or exceeding the safety temperature threshold When the high-risk area for thermal runaway is detected, the area is marked as such and a danger alarm is triggered to notify the monitoring center and on-site operators. Otherwise, the early warning mechanism is activated to send early warning information to the monitoring personnel.

[0096] The present invention clearly distinguishes different risk levels by marking and judging risk areas. Once the real-time temperature reaches or exceeds the safety threshold, it is promptly updated to a high-risk area and triggers a danger alarm, which can quickly notify relevant personnel and respond quickly to prevent thermal runaway accidents. When the safety threshold is not reached, the early warning mechanism can be activated in time to provide key information to the monitoring personnel, prompting them to take corresponding measures, effectively reducing the risk of thermal runaway and ensuring the safe and stable operation of lithium batteries and related systems.

[0097] It should be noted that the calculation formulas and various parameters involved in the calculation in the present invention have been dimensionally processed in advance, and the process of dimensionless processing is well known in the industry and will not be described here.

[0098] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A smart packaging system for lithium batteries of new energy vehicles based on Internet of Things technology, characterized in that: include: Temperature sensor array module, temperature acquisition and transmission module, gateway data processing module, temperature alarm module and monitoring center module; The temperature sensor array module is composed of multiple temperature sensors and is arranged inside the lithium battery packaging box to sense the temperature information of different parts of the lithium battery in real time; A temperature collection and transmission module, connected to the temperature sensor array, for collecting temperature data sensed by the temperature sensor array, and storing the temperature data and the corresponding lithium battery numbers in the form of a dictionary; The gateway data processing module is used to receive the battery temperature and number information from the temperature acquisition module, and further analyze it according to different analysis modes based on the preset safety temperature threshold and warning temperature threshold; The temperature alarm module is used to determine whether to start the early warning mechanism or trigger the danger alarm according to the results of further analysis of the analysis mode; The monitoring center module receives temperature data from the gateway data processing module, displays and stores it, and provides alarm and remote control functions.

2. The intelligent packaging system for new energy vehicle lithium batteries based on Internet of Things technology according to claim 1 is characterized in that: The working process of the gateway data processing module includes: The real-time temperature value collected by each temperature sensor is recorded as , and then compare each real-time temperature value with the preset safety temperature threshold and warning temperature threshold; where the warning temperature threshold is less than the safety temperature threshold; If each real-time temperature value is less than the warning temperature threshold, analysis is performed according to mode 1; If at least one real-time temperature value is greater than or equal to the warning temperature threshold, analysis is performed according to mode 2.

3. The intelligent packaging system for new energy vehicle lithium batteries based on Internet of Things technology according to claim 2 is characterized in that: The analysis process of Mode 1 includes: Will Substituting into the formula: , calculate the temperature index ; in, Indicates The coefficient of change of the real-time temperature value collected by a temperature sensor is expressed as: ; In the formula Indicates The real-time temperature value collected by the temperature sensor, Represents the real-time temperature gradient value, the expression is: , Indicates the maximum value of the temperatures collected by multiple temperature sensors. Indicates the minimum value of the temperatures collected by multiple temperature sensors. Indicates the number of times the current temperature sensor has sampled in the last unit time. Indicates that the current temperature sensor is in The temperature value at the time of sampling, Indicates the average value of the temperature collected by the current temperature sensor. , is the preset weight coefficient; The calculated temperature index With preset temperature index threshold For comparison; if , it is judged that there is no safety hazard in the current lithium battery; like , it is judged that there is a safety hazard in the lithium battery and the early warning mechanism is activated.

4. The intelligent packaging system for new energy vehicle lithium batteries based on Internet of Things technology according to claim 3 is characterized in that: The analysis process of Mode 1 also includes: The calculated The coefficient of variation of the real-time temperature value collected by the temperature sensor With the preset temperature index range Make a comparison; like , the area where the current temperature sensor is located is judged to be a medium-risk area for thermal runaway, and a trend forecast analysis is performed; like , the area where the current temperature sensor is located is judged to be a high-risk area for thermal runaway, and a danger alarm is triggered; like , then the area where the current temperature sensor is located is judged to be a safe area.

5. The intelligent packaging system for new energy vehicle lithium batteries based on Internet of Things technology according to claim 4 is characterized in that: The process of trend prediction analysis for the risk area of ​​thermal runaway is as follows: Get the real-time temperature change coefficient of the area in the risk area of ​​thermal runaway per unit time Curves of change over time ; The trend of the risk area of ​​thermal runaway is obtained through the predictive analysis model: The expression of the predictive analysis model is: ； In the formula Indicates the trend index of the risk area of ​​thermal runaway, , are the starting point and end point of the unit time respectively. is the maximum value of the real-time temperature value change coefficient per unit time, is the minimum value of the real-time temperature change coefficient per unit time, It is a preset fixed coefficient.

6. The intelligent packaging system for new energy vehicle lithium batteries based on Internet of Things technology according to claim 7 is characterized in that: The calculated trend index The preset trend index threshold interval Make a comparison; like , it is judged that the state of the risk area in thermal runaway tends to be stable; like , it is judged that the state of the risk area in thermal runaway tends to be optimized; like , it is judged that the state of the risk area in thermal runaway is tending to deteriorate, and the early warning mechanism is activated to send early warning information to the monitoring personnel.

7. The intelligent packaging system for new energy vehicle lithium batteries based on Internet of Things technology according to claim 6 is characterized in that: When it is judged that the state of the area at risk of thermal runaway is deteriorating, the early warning mechanism is activated to send early warning information to the monitoring personnel; The data collection frequency of the temperature collection and transmission module was adjusted, and the collection frequency of the area was increased to twice the original frequency in order to focus on monitoring the temperature changes in the area.

8. The intelligent packaging system for new energy vehicle lithium batteries based on Internet of Things technology according to claim 2 is characterized in that: The analysis process of Mode 2 includes: The warning temperature threshold will be exceeded Real-time temperature value The corresponding area is marked as a medium-risk area for thermal runaway, and the real-time temperature value of the medium-risk area for thermal runaway is and safety temperature threshold Make comparisons; When the real-time temperature value of the thermal runaway risk area Reaching or exceeding the safety temperature threshold When the high-risk area for thermal runaway is detected, the area is marked as such and a danger alarm is triggered to notify the monitoring center and on-site operators. Otherwise, the early warning mechanism is activated to send early warning information to the monitoring personnel.

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