A thermal safety management system and method for battery energy storage

Abstract

The present invention relates to the technical field of battery control and management, and particularly to a thermal safety management system and method for battery energy storage. The method includes the following steps: obtaining a battery energy storage device; dividing the battery energy storage device into battery packs to generate a set of energy storage battery packs; detecting the internal temperature of the battery packs in the set of energy storage battery packs to obtain the internal temperature of the battery packs; quantitatively counting the temperature values of the internal temperature of the battery packs to generate a battery pack temperature statistical value; judging whether the battery pack temperature statistical value is abnormally high and marking the battery packs to obtain abnormally high temperature battery packs. Through data processing technology, pattern recognition technology and deep learning technology, the present invention realizes the sub - monitoring of battery units for the battery energy storage device, and evaluates the degree of battery thermal runaway by monitoring multiple parameters of the energy storage battery, so as to realize the dynamic thermal safety control of the battery energy storage device, thereby improving the thermal safety management efficiency of battery energy storage.

Description

Technical Field

[0001] The present invention relates to the technical field of battery control and management, and particularly to a thermal safety management system and method for battery energy storage. Background Art

[0002] Early energy storage batteries mainly focused on energy density and cycle life. With the application of energy storage batteries in electric vehicles and large-scale energy storage systems, thermal safety issues have gradually become the focus of research. Energy storage batteries generate heat during the charging and discharging processes. If the temperature is not properly controlled, it will lead to a decline in battery performance or even thermal runaway, triggering safety accidents. Therefore, energy storage battery thermal management technology has emerged, aiming to control the battery temperature through effective heat exchange means to ensure the safe operation of the battery. Energy storage battery thermal management technology requires monitoring the battery operating status according to the battery management system and performing hierarchical thermal safety management. However, the existing thermal safety management technology for battery energy storage fails to conduct detailed monitoring of battery cells in the battery energy storage device, making it difficult to accurately perform thermal management on battery cells. Moreover, it fails to evaluate the degree of battery thermal runaway by monitoring multiple parameters of the energy storage battery, resulting in low timeliness and efficiency of the thermal safety management of battery energy storage. Summary of the Invention

[0003] Based on this, it is necessary to provide a thermal safety management system and method for battery energy storage to solve at least one of the above technical problems.

[0004] To achieve the above object, a thermal safety management method for battery energy storage, the method includes the following steps:

[0005] Step S1: Obtain a battery energy storage device; divide the battery energy storage device into battery packs to generate a set of energy storage battery packs; detect the internal temperature of the battery packs in the set of energy storage battery packs to obtain the internal temperature of the battery packs;

[0006] Step S2: Quantify and statistically analyze the internal temperature of the battery packs to generate a temperature statistical value of the battery packs; determine whether there is an abnormal temperature for the temperature statistical value of the battery packs and mark the battery packs to obtain battery packs with abnormal temperature; detect the internal resistance of the battery cells in the battery packs with abnormal temperature to generate the internal resistance values of the battery cells; monitor the diffused gas of the battery packs with abnormal temperature to obtain diffused gas characteristics;

[0007] Step S3: Record the change in the internal resistance of the battery cells to obtain internal resistance change data; detect the abnormal temperature change of the battery packs with abnormal temperature according to the internal resistance change data to generate abnormal temperature change data; evaluate the degree of battery thermal runaway based on the diffused gas characteristics and the abnormal temperature change data to obtain the degree of battery thermal runaway; perform dynamic thermal safety control on the battery packs with abnormal temperature according to the degree of battery thermal runaway to generate thermal safety control measures;

[0008] Step S4: Monitor the battery energy storage device in real time to obtain real-time battery monitoring data; perform thermal runaway early warning on the real-time battery monitoring data to generate a thermal runaway early warning signal; execute thermal safety control measures on the battery energy storage device based on the thermal runaway early warning signal to complete the thermal safety management operation of the battery energy storage.

[0009] The present invention divides the battery energy storage device into battery groups by obtaining it, generating a set of energy storage battery groups, and having a clear understanding of the structure of the battery energy storage device, providing a basis for subsequent temperature detection and abnormality judgment; detecting the internal temperature of the battery groups in the set of energy storage battery groups and obtaining the internal temperature of the battery groups, providing an accurate data basis for temperature value quantification statistics and abnormality judgment, thereby ensuring the safe operation of the battery energy storage device. Quantify and statistically analyze the temperature values of the internal temperature of the battery groups to clarify the temperature distribution of the battery groups; perform temperature abnormality judgment on the temperature statistical values of the battery groups and mark the battery groups to obtain temperature-abnormal battery groups, which can timely discover potential safety hazards existing in the battery energy storage device; perform internal resistance detection of battery cells and monitoring of diffused gases in the battery groups on the temperature-abnormal battery groups to generate internal resistance values of battery cells and diffused gas characteristics, providing necessary parameters for evaluating the degree of battery thermal runaway and enhancing the safety monitoring ability of the battery energy storage device. Record the changes in the internal resistance of the battery cells for the internal resistance values of the battery cells, enabling the monitoring of the performance changes of the battery cells and providing data support for subsequent detection of abnormal temperature changes; perform detection of abnormal temperature changes on the temperature-abnormal battery groups according to the internal resistance change data to generate abnormal temperature change data, accurately grasping the temperature dynamics of the battery groups and providing real-time data for evaluating the degree of battery thermal runaway; evaluate the degree of battery thermal runaway based on the diffused gas characteristics and abnormal temperature change data, which can scientifically evaluate the safety status of the battery energy storage device and provide a basis for dynamic thermal safety control; perform dynamic thermal safety control on the temperature-abnormal battery groups according to the degree of battery thermal runaway to generate thermal safety control measures, ensuring that the battery energy storage device can be timely and effectively processed in case of abnormalities and improving the safety performance of the battery energy storage device. Monitor the battery energy storage device in real time to obtain real-time battery monitoring data, continuously tracking the operating state of the battery energy storage device and providing real-time data for thermal runaway early warning; perform thermal runaway early warning on the real-time battery monitoring data, which can timely issue a warning to remind relevant personnel to take actions; execute thermal safety control measures on the battery energy storage device based on the thermal runaway early warning signal to complete the thermal safety management operation of the battery energy storage, ensuring that the battery energy storage device can be effectively controlled and processed when facing the risk of thermal runaway and guaranteeing the safe and stable operation of the battery energy storage device. Therefore, the present invention realizes the sub-monitoring of battery cells for the battery energy storage device through data processing technology, pattern recognition technology, and deep learning technology, and evaluates the degree of battery thermal runaway by monitoring multiple parameters of the energy storage battery, realizing the dynamic thermal safety control of the battery energy storage device, thereby improving the thermal safety management efficiency of the battery energy storage.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain a battery energy storage device;

[0012] Step S12: Determine the energy storage battery area of the battery energy storage device to obtain the energy storage battery area; measure the area of the energy storage battery area to generate battery area data;

[0013] Step S13: Divide the battery area data into multi-component battery areas to obtain battery group division data; encode and merge the battery group division data to obtain an energy storage battery group set;

[0014] Step S14: Detect the battery group area temperature of the energy storage battery group set to obtain the battery group area temperature; extract the internal temperature within the group from the battery group area temperature to obtain the internal temperature of the battery group.

[0015] The acquisition of the battery energy storage device in the present invention is the starting point of the entire battery energy storage management process, ensuring the direct control of the battery energy storage device and the basis for subsequent operations; comprehensively managing and monitoring the battery energy storage device provides the necessary prerequisite conditions for subsequent operations such as area determination and temperature detection; determining the energy storage battery area of the battery energy storage device and obtaining the energy storage battery area provides a clear definition for the spatial layout of the battery energy storage device, accurately identifying and managing each battery area; measuring the area of the energy storage battery area and generating battery area data provides an important parameter basis for subsequent battery group division and thermal management; dividing the battery area data into multi-component battery areas and obtaining battery group division data realizes the refined management of the internal structure of the battery energy storage device, enabling each battery area to be individually identified and monitored; encoding and merging the battery group division data to obtain an energy storage battery group set integrates the information of each battery area through encoding to form a complete battery energy storage device data set; detecting the battery group area temperature of the energy storage battery group set and obtaining the battery group area temperature provides basic data for the temperature monitoring of the battery energy storage device, ensuring the real-time grasp of the thermal state of the battery energy storage device; extracting the internal temperature within the group from the battery group area temperature and obtaining the internal temperature of the battery group further refines the temperature data, enabling accurate monitoring of the internal temperature of each battery group and providing accurate temperature data for subsequent temperature anomaly judgment and thermal safety control.

[0016] Preferably, step S2 includes the following steps:

[0017] Step S21: Quantify the internal temperature values of each group of the battery group to obtain the internal temperature values of each group; summarize and statistically analyze the internal temperature values of each group to generate a battery group temperature statistical value;

[0018] Step S22: Perform temperature feature mapping for each battery pack in the energy storage battery pack set based on the battery pack temperature statistical value to obtain the temperature mapping features of each group; generate a temperature heat map for the temperature mapping features of each group to obtain the battery pack temperature heat map;

[0019] Step S23: Judge the temperature anomaly of the battery pack temperature heat map to obtain the abnormal block of the heat map; mark the battery packs in the energy storage battery pack set based on the abnormal block of the heat map to obtain the temperature-abnormal battery packs;

[0020] Step S24: Monitor the battery cell voltage of the temperature-abnormal battery packs to obtain the battery cell voltage value; monitor the battery cell current of the temperature-abnormal battery packs to obtain the battery cell current value;

[0021] Step S25: Calculate the internal resistance of the battery cells based on the battery cell voltage value and the battery cell current value to generate the internal resistance value of the battery cells;

[0022] Step S26: Monitor the diffused gas of the temperature-abnormal battery packs to obtain the diffused gas characteristics.

[0023] The present invention quantifies the temperature values within each group of the battery pack to obtain the temperature values within each group, providing precise data support for the temperature monitoring of the battery pack and enabling a detailed analysis of the temperature status of each battery group. The temperature values within each group are summarized and statistically analyzed to generate a battery pack temperature statistical value, specifically aggregating the temperature data of all battery groups, providing comprehensive temperature distribution information for subsequent temperature feature mapping and anomaly judgment. Based on the battery pack temperature statistical value, temperature feature mapping of each battery group in the energy storage battery pack set is performed, converting the temperature data into feature information and providing a basis for generating a temperature heat map. The temperature heat map of the battery group is generated from the temperature mapping features of each group, obtaining the battery pack temperature heat map, which visually displays the temperature distribution of the battery pack, making the temperature anomaly area more intuitive and easy to identify. Temperature anomaly judgment is performed on the battery pack temperature heat map, and by analyzing the abnormal blocks of the heat map, the temperature area with problems can be quickly located. Based on the abnormal blocks of the heat map, battery group marking is performed on the energy storage battery pack set, providing a clear abnormal battery group identifier for subsequent battery cell monitoring and safety control, improving the safety management efficiency of the battery energy storage device. Battery cell voltage monitoring is performed on the temperature abnormal battery group to clearly evaluate the working state of the battery cell. Battery cell current monitoring is performed on the temperature abnormal battery group, providing data support for the analysis of the current characteristics of the battery cell and enabling further diagnosis of the abnormal situation of the battery cell. Battery cell internal resistance calculation is performed based on the battery cell voltage value and the battery cell current value, obtaining the internal resistance of the battery cell through calculation, providing key parameters for the health status evaluation and performance prediction of the battery cell, and timely detecting abnormal problems of the battery cell. Battery pack diffusion gas monitoring is performed on the temperature abnormal battery group, specifically monitoring the diffusion gas inside the battery pack, which can analyze the chemical reaction state of the battery pack and provide important information for the thermal runaway risk assessment of the battery pack, thereby preventing thermal runaway events of the battery pack.

[0024] Preferably, step S26 includes the following steps:

[0025] Step S261: Monitor the diffusion situation of the temperature abnormal battery pack to obtain the diffusion monitoring situation; identify the diffusion gas from the diffusion monitoring situation to generate diffusion gas situation data;

[0026] Step S262: Judge the gas color from the diffusion gas situation data to obtain the diffusion gas color; determine the gas concentration based on the diffusion gas color for the diffusion gas situation data to generate the gas concentration status;

[0027] Step S263: Identify the gas components from the diffusion gas situation data according to the gas concentration status to obtain the gas component data; identify the diffusion mode from the gas component data to generate the gas diffusion mode; detect the gas diffusion range for the gas diffusion mode to obtain the gas diffusion range;

[0028] Step S264: Integrate the gas concentration state, gas diffusion mode, and gas diffusion range to obtain the diffusion gas characteristics of the battery pack.

[0029] The present invention monitors the diffusion situation of the temperature-abnormal battery pack, which can track the internal diffusion changes of the battery pack in real time and provide key information for battery safety monitoring; identify the diffusion gas for the diffusion monitoring situation. By identifying the diffusion gas, the chemical changes inside the battery pack can be accurately captured; judge the gas color for the data of the diffusion gas situation. Through the analysis of the gas color, the properties and chemical components of the gas can be preliminarily judged; determine the gas concentration for the data of the diffusion gas situation based on the diffusion gas color. By determining the gas concentration, the activity degree of the internal chemical reaction of the battery pack can be evaluated; identify the gas components for the data of the diffusion gas situation according to the gas concentration state. By analyzing the gas components, the internal chemical reaction process of the battery pack can be deeply understood; identify the gas diffusion mode for the gas component data. By identifying the gas diffusion mode, the propagation path of the gas inside the battery pack can be clarified; detect the gas diffusion range for the gas diffusion mode. By detecting the gas diffusion range, the safety hazard area inside the battery pack can be evaluated; integrate the gas concentration state, gas diffusion mode, and gas diffusion range to obtain the diffusion gas characteristics of the battery pack. This step forms a comprehensive description of the diffusion gas characteristics of the battery pack by integrating key gas parameters, providing data support for the safety monitoring and risk assessment of the battery pack.

[0030] Preferably, step S3 includes the following steps:

[0031] Step S31: Continuously monitor the internal resistance of the battery cell to obtain the battery internal resistance monitoring data; mark the monitoring time period for the battery internal resistance monitoring data to generate the internal resistance monitoring time period.

[0032] Step S32: Record the internal resistance value in the initial period for the internal resistance value of the battery cell based on the internal resistance monitoring time period to obtain the internal resistance value in the initial period; record the internal resistance value in the last period for the internal resistance value of the battery cell according to the internal resistance monitoring time period to obtain the internal resistance value in the last period.

[0033] Step S33: Subtract the internal resistance value in the last period from the internal resistance value in the initial period to generate the internal resistance change data.

[0034] Step S34: Detect the temperature abnormal change for the temperature-abnormal battery pack according to the internal resistance change data to generate the temperature abnormal change data.

[0035] Step S35: Evaluate the degree of battery thermal runaway based on the diffusion gas characteristics and the temperature abnormal change data to obtain the degree of battery thermal runaway.

[0036] Step S36: Perform dynamic thermal safety control on the battery pack with abnormal temperature according to the degree of battery thermal runaway, and generate thermal safety control measures.

[0037] The present invention continuously monitors the internal resistance of the battery cell, can track the change of the internal resistance of the battery cell in real time, marks the monitoring time period for the internal resistance monitoring data of the battery, and identifies the trend of battery performance change by marking the internal resistance data of different time periods; records the internal resistance value in the initial time period based on the internal resistance monitoring time period, provides a reference point for the baseline of battery performance, and clarifies the starting state for evaluating battery performance; records the internal resistance value in the last time period based on the internal resistance monitoring time period, provides data for the final state of battery performance, and thus evaluates the change and degradation of battery performance. Subtract the internal resistance value in the last time period from the internal resistance value in the initial time period, and by calculating the change amount of the internal resistance, the degradation degree of battery performance can be quantified, providing a key index for the evaluation of the battery health state; detect the abnormal temperature change of the battery pack with abnormal temperature according to the internal resistance change data, and by analyzing the relationship between the internal resistance change and the abnormal temperature, the abnormal temperature change of the battery pack can be identified; evaluate the degree of battery thermal runaway based on the diffusion gas characteristics and abnormal temperature change data, and by comprehensively considering the data of gas diffusion and abnormal temperature, the risk degree of battery thermal runaway can be accurately evaluated; perform dynamic thermal safety control on the battery pack with abnormal temperature according to the degree of battery thermal runaway, specifically by implementing corresponding thermal safety control measures according to the evaluation result of the thermal runaway risk, can prevent and control the occurrence of battery thermal runaway events, and ensure the safe and stable operation of the battery energy storage device.

[0038] Preferably, step S34 includes the following steps:

[0039] Step S341: Perform frequency domain conversion on the internal resistance change data to obtain internal resistance change frequency data; identify the frequency jitter characteristics of the internal resistance change frequency data to generate internal resistance frequency jitter characteristics;

[0040] Step S342: Perform jitter time mapping on the internal resistance monitoring time period according to the internal resistance frequency jitter characteristics to obtain internal resistance frequency jitter time;

[0041] Step S343: Perform temperature mode recognition on the battery pack with abnormal temperature based on the internal resistance frequency jitter time to obtain a frequency jitter-temperature mode; judge the temperature sensitivity of the battery pack for the frequency jitter-temperature mode to generate temperature sensitivity data;

[0042] Step S344: Record the duration of abnormal temperature change of the battery pack with abnormal temperature according to the temperature sensitivity data to obtain the duration of abnormal temperature change; monitor the abnormal temperature change value of the battery pack with abnormal temperature based on the temperature sensitivity data to obtain the abnormal temperature change degree value;

[0043] Step S345: Integrate the abnormal temperature change duration and the temperature abnormal change degree value to generate temperature abnormal change data.

[0044] The present invention performs frequency domain conversion on the internal resistance change data. By converting the time domain data into frequency domain data, it can reveal the periodic characteristics of the battery internal resistance change; identify the frequency jitter characteristics of the internal resistance change frequency data. By identifying the frequency jitter in the internal resistance change, it can capture the subtle dynamics of the battery performance change; perform jitter time mapping on the internal resistance monitoring time period according to the internal resistance frequency jitter characteristics. By associating the frequency jitter characteristics with specific time points, it can determine the specific moment of the battery performance change, providing accurate time information for the real-time monitoring and early warning of the battery state; perform temperature mode recognition on the temperature abnormal battery pack based on the internal resistance frequency jitter time. By analyzing the relationship between the internal resistance frequency jitter and the temperature change, it can identify the temperature change mode of the battery pack, providing an important reference basis for battery thermal management; judge the temperature sensitivity of the battery pack for the frequency jitter-temperature mode. By evaluating the sensitivity of the battery pack to temperature changes, it can provide key parameters for the thermal stability evaluation of the battery pack; record the abnormal temperature change duration for the temperature abnormal battery pack according to the temperature sensitivity data. By recording the duration of the abnormal temperature, it can evaluate the stability and safety of the battery pack in the abnormal state; monitor the temperature abnormal change value for the temperature abnormal battery pack based on the temperature sensitivity data. By monitoring the degree of the temperature abnormal change, it can quantify the severity of the temperature abnormality of the battery pack; integrate the abnormal temperature change duration and the temperature abnormal change degree value to form temperature abnormal change characteristics. By integrating the duration and degree of the temperature abnormality, it can comprehensively describe the temperature abnormal characteristics of the battery pack, enhancing the temperature abnormal management ability of the battery energy storage device.

[0045] Preferably, step S35 includes the following steps:

[0046] Step S351: Determine the gas concentration amount of the diffusion gas characteristics to obtain the diffusion gas concentration amount; identify the diffusion mode type of the diffusion gas characteristics to obtain the gas diffusion mode type;

[0047] Step S352: Detect the gas diffusion space range of the diffusion gas characteristics based on the diffusion gas concentration amount and the gas diffusion mode type to generate diffusion space distribution data;

[0048] Step S353: Match the influence weight of the gas diffusion degree for the temperature abnormal battery pack according to the diffusion space distribution data to generate the gas diffusion influence weight value;

[0049] Step S354: Determine the abnormal temperature change period for the abnormal temperature change data to obtain the abnormal temperature change period; measure the temperature abnormal deviation value for the abnormal temperature change data to generate the temperature abnormal deviation value;

[0050] Step S355: Match the weight of the influence of the abnormal temperature change on the battery pack with abnormal temperature based on the abnormal temperature change period and the temperature abnormal deviation value to generate the temperature change influence weight value;

[0051] Step S356: Calculate the battery thermal runaway weight based on the gas diffusion influence weight value and the temperature change influence weight value to obtain the battery thermal runaway weight scoring data; divide the battery thermal runaway weight scoring data by the runaway degree to obtain the battery thermal runaway degree.

[0052] The present invention determines the gas concentration amount for the characteristics of the diffused gas, which can accurately quantify the concentration level of the gas; identifies the diffusion mode type for the characteristics of the diffused gas, and by identifying the diffusion mode of the gas, clarifies the propagation characteristics of the gas in the battery pack; detects the gas diffusion spatial range for the characteristics of the diffused gas based on the diffused gas concentration amount and the gas diffusion mode type, and by combining the gas concentration and the diffusion mode, can determine the spatial distribution of the gas in the battery pack; matches the weight of the influence of the gas diffusion degree on the battery pack with abnormal temperature according to the diffusion spatial distribution data, and by matching the spatial distribution of the gas diffusion with the abnormal temperature of the battery pack, can quantify the influence degree of the gas diffusion on the thermal safety of the battery, providing important weight data for the risk assessment of the battery thermal runaway; determines the abnormal temperature change period for the abnormal temperature change data, and by determining the periodicity of the abnormal temperature, can identify the regularity of the battery temperature abnormality, providing a time basis for predicting and controlling the abnormal temperature; measures the temperature abnormal deviation value for the abnormal temperature change data to generate the temperature abnormal deviation value, and by calculating the temperature deviation, can quantify the degree of the abnormal temperature; matches the weight of the influence of the abnormal temperature change on the battery pack with abnormal temperature based on the abnormal temperature change period and the temperature abnormal deviation value, and by combining the period and deviation of the abnormal temperature, can evaluate the comprehensive influence of the abnormal temperature on the battery performance, providing weight data in terms of temperature for the risk assessment of the battery thermal runaway; calculates the battery thermal runaway weight according to the gas diffusion influence weight value and the temperature change influence weight value, comprehensively considers the influence of the gas diffusion and the temperature change, and can obtain the comprehensive risk score of the battery thermal runaway, providing a quantitative risk assessment for the battery safety management; divides the battery thermal runaway weight scoring data by the runaway degree, which can provide clear guidance for the safety management and maintenance of the battery pack, ensuring the operation safety of the battery energy storage device.

[0053] Preferably, step S36 includes the following steps:

[0054] Step S361: Compare the degree of battery thermal runaway with a preset thermal runaway degree index. If the degree of battery thermal runaway is greater than the preset thermal runaway degree index, mark it as the severity of battery thermal runaway;

[0055] Step S362: Locate the thermal runaway battery cells in the battery pack with abnormal temperature based on the severity of battery thermal runaway, and generate a thermal runaway battery cell area;

[0056] Step S363: Cut off the battery cell circuit in the thermal runaway battery cell area, and generate a circuit cut-off measure;

[0057] Step S364: Evacuate the diffused gas in the thermal runaway battery cell area, and generate a diffused gas evacuation measure;

[0058] Step S365: Start the battery cooling device in the thermal runaway battery cell area, and generate a cooling device start measure;

[0059] Step S366: Integrate the circuit cut-off measure, the diffused gas evacuation measure, and the cooling device start measure for thermal safety control to generate a thermal safety control measure.

[0060] The present invention compares the degree of battery thermal runaway with a preset thermal runaway degree index. If the degree of battery thermal runaway is greater than the preset thermal runaway degree index, it is marked as the severity of battery thermal runaway. By setting a threshold to judge the thermal runaway risk level of the battery, it can provide a clear warning for battery safety management and ensure that corresponding emergency measures are taken when the battery thermal runaway risk is high; locate the thermal runaway battery cells in the battery pack with abnormal temperature based on the severity of battery thermal runaway. By accurately locating the thermal runaway battery cells, it can quickly identify the battery area that needs emergency treatment; cut off the battery cell circuit in the thermal runaway battery cell area. By cutting off the circuit connected to the thermal runaway battery cells, it can immediately stop the current flow, prevent the further spread of the thermal runaway phenomenon, and protect other parts of the battery pack from damage; evacuate the diffused gas in the thermal runaway battery cell area and generate a diffused gas evacuation measure. By evacuating the diffused gas in the thermal runaway battery cell area, it can reduce the explosion risk caused by gas accumulation, and at the same time reduce the internal pressure of the battery, providing additional protection for the safety of the battery pack; start the battery cooling device in the thermal runaway battery cell area and generate a cooling device start measure. By starting the cooling device to cool down the thermal runaway battery cells, it can effectively control and reduce the battery temperature, prevent the further deterioration of the thermal runaway phenomenon, and protect the structure and function of the battery pack from damage; by comprehensively considering the three measures of circuit cut-off, gas evacuation, and cooling start, a complete set of thermal safety control solutions is formed, which can systematically respond to battery thermal runaway events, minimize the impact of thermal runaway on the battery energy storage device, and ensure the thermal safety of the battery energy storage device.

[0061] Preferably, step S4 includes the following steps:

[0062] Step S41: Real-time monitor the state of the energy storage battery of the battery energy storage device to obtain real-time battery monitoring data;

[0063] Step S42: Identify the abnormal battery temperature state from the real-time battery monitoring data to obtain the abnormal battery temperature state; Determine the battery thermal runaway mode for the abnormal battery temperature state to generate the battery thermal runaway mode;

[0064] Step S43: Issue a thermal runaway warning for the battery thermal runaway mode to generate a thermal runaway warning signal;

[0065] Step S44: Locate the thermal runaway battery pack of the battery energy storage device based on the thermal runaway warning signal to obtain the position information of the thermal runaway battery pack; Execute thermal safety control measures for the position information of the thermal runaway battery pack to complete the thermal safety management operation of the battery energy storage.

[0066] The present invention real-time monitors the state of the energy storage battery of the battery energy storage device to obtain real-time battery monitoring data. By continuously collecting the operation state information of the battery energy storage device, it can ensure the real-time grasp of the battery performance and safety state; and provides a data basis for timely discovery of battery abnormalities, which is a key link in battery safety management; Identify the abnormal battery temperature state from the real-time battery monitoring data. Through the analysis of the monitoring data, it can quickly identify whether the battery has abnormal temperature, providing an early warning for battery thermal safety management; Determine the battery thermal runaway mode for the abnormal battery temperature state. By determining the thermal runaway mode, it can accurately evaluate the thermal runaway risk of the battery; Issue a thermal runaway warning for the battery thermal runaway mode, and by sending a warning signal, it can timely notify relevant personnel or systems to take emergency measures to prevent the occurrence or further deterioration of thermal runaway events and protect the safety of the battery energy storage device; Locate the thermal runaway battery pack of the battery energy storage device based on the thermal runaway warning signal. By accurately locating the thermal runaway battery pack, it can ensure the targeted execution of emergency measures and improve the efficiency of dealing with thermal runaway events; Execute thermal safety control measures for the position information of the thermal runaway battery pack to complete the thermal safety management operation of the battery energy storage. By executing specific thermal safety control measures, it can effectively control and handle thermal runaway events and ensure the safe and stable operation of the battery energy storage device.

[0067] In this specification, a thermal safety management system for battery energy storage is provided, which is used to execute the above-mentioned thermal safety management method for battery energy storage. The thermal safety management system for battery energy storage includes:

[0068] The battery energy storage device temperature detection module is used to obtain the battery energy storage device; divide the battery energy storage device into battery packs to generate a set of energy storage battery packs; detect the internal temperature of the battery packs in the set of energy storage battery packs to obtain the internal temperature of the battery packs.

[0069] The battery cell anomaly analysis module is used to perform quantitative statistics on the temperature values of the internal temperature of the battery packs to generate battery pack temperature statistical values; perform temperature anomaly judgment on the battery pack temperature statistical values and mark the battery packs to obtain temperature anomaly battery packs; detect the internal resistance of the battery cells in the temperature anomaly battery packs to generate the internal resistance values of the battery cells; monitor the diffused gas of the temperature anomaly battery packs to obtain diffused gas characteristics.

[0070] The battery thermal safety control module is used to record the change in the internal resistance of the battery cells based on the internal resistance values of the battery cells to obtain internal resistance change data; detect the temperature anomaly change of the temperature anomaly battery packs according to the internal resistance change data to generate temperature anomaly change data; evaluate the degree of battery thermal runaway based on the diffused gas characteristics and the temperature anomaly change data to obtain the degree of battery thermal runaway; perform dynamic thermal safety control on the temperature anomaly battery packs according to the degree of battery thermal runaway to generate thermal safety control measures.

[0071] The thermal runaway warning execution module is used to monitor the battery energy storage device in real time to obtain real-time battery monitoring data; issue a thermal runaway warning for the real-time battery monitoring data to generate a thermal runaway warning signal; execute thermal safety control measures on the battery energy storage device based on the thermal runaway warning signal to complete the thermal safety management operation of the battery energy storage.

[0072] Through the temperature detection module of the battery energy storage device, the battery energy storage device is acquired and the battery packs are divided to generate a set of energy storage battery packs, providing a clear understanding of the structure of the battery energy storage device and laying a foundation for subsequent temperature detection and abnormality judgment; the internal temperature of the battery packs in the set of energy storage battery packs is detected to obtain the internal temperature of the battery packs, providing an accurate data basis for temperature value quantification statistics and abnormality judgment, thus ensuring the safe operation of the battery energy storage device. Through the battery unit abnormality analysis module, temperature value quantification statistics are carried out on the internal temperature of the battery packs to clarify the temperature distribution of the battery packs; temperature abnormality judgment is performed on the temperature statistical values of the battery packs, and the battery packs are marked to obtain temperature-abnormal battery packs, enabling timely discovery of potential safety hazards in the battery energy storage device; internal resistance detection of the battery units and monitoring of the diffused gas in the battery packs are carried out on the temperature-abnormal battery packs to generate the internal resistance values of the battery units and the characteristics of the diffused gas, providing necessary parameters for evaluating the degree of battery thermal runaway and enhancing the safety monitoring ability of the battery energy storage device. Through the battery thermal safety control module, battery internal resistance change records are made for the internal resistance values of the battery units, enabling monitoring of the performance changes of the battery units and providing data support for subsequent detection of temperature abnormality changes; based on the internal resistance change data, temperature abnormality change detection is carried out on the temperature-abnormal battery packs to generate temperature abnormality change data, accurately grasping the temperature dynamics of the battery packs and providing real-time data for evaluating the degree of battery thermal runaway; based on the characteristics of the diffused gas and the temperature abnormality change data, the degree of battery thermal runaway is evaluated, enabling scientific evaluation of the safety state of the battery energy storage device and providing a basis for dynamic thermal safety control; according to the degree of battery thermal runaway, dynamic thermal safety control is carried out on the temperature-abnormal battery packs to generate thermal safety control measures, ensuring that the battery energy storage device can be timely and effectively processed in case of abnormality and improving the safety performance of the battery energy storage device. Through the thermal runaway warning execution module, the battery energy storage device is monitored in real time to obtain real-time battery monitoring data, continuously tracking the operating state of the battery energy storage device and providing real-time data for thermal runaway warning; thermal runaway warning is carried out on the real-time battery monitoring data, enabling timely warning and reminding relevant personnel to take actions; based on the thermal runaway warning signal, thermal safety control measures are executed on the battery energy storage device to complete the thermal safety management operation of the battery energy storage, ensuring that the battery energy storage device can be effectively controlled and processed when facing the risk of thermal runaway and guaranteeing the safe and stable operation of the battery energy storage device. Therefore, through data processing technology, pattern recognition technology and deep learning technology, the present invention realizes the sub-monitoring of battery units for the battery energy storage device, and evaluates the degree of battery thermal runaway by monitoring multiple parameters of the energy storage battery, realizing the dynamic thermal safety control of the battery energy storage device, thereby improving the thermal safety management efficiency of the battery energy storage. Description of the Drawings

[0073] Figure 1 It is a schematic flow chart of the steps of a thermal safety management method for battery energy storage;

[0074] Figure 2 is Figure 1 a schematic diagram of the detailed implementation steps of step S3 in

[0075] Figure 3 is Figure 2 a schematic diagram of the detailed implementation steps of step S35 in

[0076] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Specific embodiments

[0077] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0078] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0079] It should be understood that although the terms "first", "second", etc. may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed associated items.

[0080] To achieve the above object, please refer to Figures 1 to 3 , a thermal safety management method for battery energy storage, the method comprising the following steps:

[0081] Step S1: Obtain a battery energy storage device; divide the battery energy storage device into battery packs to generate a set of energy storage battery packs; detect the internal temperature of the battery packs in the set of energy storage battery packs to obtain the internal temperature of the battery packs;

[0082] Step S2: Quantify and statistically analyze the internal temperature of the battery pack to generate a battery pack temperature statistical value; determine whether there is a temperature anomaly for the battery pack temperature statistical value, and mark the battery pack to obtain a battery pack with a temperature anomaly; detect the internal resistance of the battery cells in the battery pack with a temperature anomaly to generate the internal resistance values of the battery cells; monitor the diffused gas of the battery pack with a temperature anomaly to obtain the diffused gas characteristics;

[0083] Step S3: Record the change in the internal resistance of the battery cells to obtain internal resistance change data; detect the temperature anomaly change for the battery pack with a temperature anomaly based on the internal resistance change data to generate temperature anomaly change data; evaluate the degree of battery thermal runaway based on the diffused gas characteristics and the temperature anomaly change data to obtain the degree of battery thermal runaway; perform dynamic thermal safety control on the battery pack with a temperature anomaly according to the degree of battery thermal runaway to generate thermal safety control measures;

[0084] Step S4: Monitor the battery energy storage device in real time to obtain real-time battery monitoring data; issue a thermal runaway warning for the real-time battery monitoring data to generate a thermal runaway warning signal; execute thermal safety control measures on the battery energy storage device based on the thermal runaway warning signal to complete the thermal safety management operation of the battery energy storage.

[0085] The present invention obtains a battery energy storage device, divides its battery packs, generates a set of energy storage battery packs, and has a clear understanding of the structure of the battery energy storage device, providing a basis for subsequent temperature detection and abnormality judgment; detects the internal temperature of the battery packs in the set of energy storage battery packs to obtain the internal temperature of the battery packs, providing an accurate data basis for temperature value quantification statistics and abnormality judgment, thereby ensuring the safe operation of the battery energy storage device. Quantifies and statistically analyzes the temperature values of the internal temperature of the battery packs to clarify the temperature distribution of the battery packs; judges the temperature abnormality of the battery pack temperature statistical values and marks the battery packs to obtain temperature-abnormal battery packs, enabling timely discovery of potential safety hazards in the battery energy storage device; detects the internal resistance of the battery cells and monitors the diffused gas of the battery packs in the temperature-abnormal battery packs to generate the internal resistance values of the battery cells and the characteristics of the diffused gas, providing necessary parameters for evaluating the degree of battery thermal runaway and enhancing the safety monitoring ability of the battery energy storage device. Records the change in the internal resistance of the battery cells based on the internal resistance values of the battery cells, enabling monitoring of the performance changes of the battery cells and providing data support for subsequent detection of temperature abnormality changes; detects the temperature abnormality changes of the temperature-abnormal battery packs according to the internal resistance change data to generate temperature abnormality change data, accurately grasping the temperature dynamics of the battery packs and providing real-time data for evaluating the degree of battery thermal runaway; evaluates the degree of battery thermal runaway based on the characteristics of the diffused gas and the temperature abnormality change data, enabling scientific evaluation of the safety status of the battery energy storage device and providing a basis for dynamic thermal safety control; performs dynamic thermal safety control on the temperature-abnormal battery packs according to the degree of battery thermal runaway to generate thermal safety control measures, ensuring that the battery energy storage device can be processed in a timely and effective manner under abnormal conditions and improving the safety performance of the battery energy storage device. Monitors the battery energy storage device in real time to obtain real-time battery monitoring data, continuously tracks the operating state of the battery energy storage device, and provides real-time data for thermal runaway early warning; issues a thermal runaway early warning for the real-time battery monitoring data, enabling timely warning and reminding relevant personnel to take actions; executes thermal safety control measures on the battery energy storage device based on the thermal runaway early warning signal to complete the thermal safety management operation of the battery energy storage, ensuring that the battery energy storage device can be effectively controlled and processed when facing the risk of thermal runaway and guaranteeing the safe and stable operation of the battery energy storage device. Therefore, the present invention realizes the sub-monitoring of battery cells for the battery energy storage device through data processing technology, pattern recognition technology, and deep learning technology, and evaluates the degree of battery thermal runaway by monitoring multiple parameters of the energy storage battery, realizing the dynamic thermal safety control of the battery energy storage device, thereby improving the thermal safety management efficiency of the battery energy storage.

[0086] In an embodiment of the present invention, with reference to Figure 1 shown in the figure, it is a schematic flow chart of the steps of a thermal safety management method for a battery energy storage of the present invention. In this example, the thermal safety management method for the battery energy storage includes the following steps:

[0087] Step S1: Obtain a battery energy storage device; divide the battery energy storage device into battery packs to generate a set of energy storage battery packs; detect the internal temperature of the battery packs in the set of energy storage battery packs to obtain the internal temperature of the battery packs.

[0088] In the embodiment of the present invention, information of the battery energy storage device is obtained through the high-voltage cascade technology. Specifically, through power electronics design, a grid-connected voltage of 6 - 35 kV can be achieved without passing through a transformer; among them, the energy storage system is a 12.5 MW / 25 MWh system, which consists of three phases A, B, and C. Each phase contains 42 H-bridge power units supporting 42 battery clusters. In total, there are 126 H-bridge power units in three phases and 126 clusters of battery clusters, storing a total of 25.288 MWh of electricity. The battery energy storage device is divided into battery packs. According to the high-voltage cascade scheme, each battery cluster consists of 224 battery cells connected in series; the specific system electrical structure is similar to that of a high-voltage SVG, consisting of three phases A, B, and C. Each phase contains 42 H-bridge power units supporting 42 battery clusters; and it is divided according to 42 battery clusters per phase, with a total of 126 battery clusters, forming a set of energy storage battery packs; the internal temperature of the battery packs in the set of energy storage battery packs is detected. The electrochemical impedance spectroscopy (EIS) technology is used to monitor the battery internal resistance, double-layer capacitance, diffusion, etc. EIS, as a non-destructive and non-implantable method, avoids the temperature delay phenomenon of using surface temperature sensors; in addition, based on the lithium battery internal temperature monitoring technology of NTC temperature sensors, the abnormal temperature rise condition inside the battery can be detected in real time and with high precision during the battery operation process; specifically, multiple temperature measurement probes are fixed at preset positions inside the battery, and each temperature measurement probe is connected to an external temperature recorder through a temperature wire. The temperature data at the preset positions inside the battery is collected in real time through the external temperature recorder to achieve the temperature detection inside the battery, thereby obtaining the internal temperature of the battery packs.

[0089] Step S2: Quantitatively statistically analyze the internal temperature of the battery packs to generate a battery pack temperature statistical value; determine whether there is an abnormal temperature in the battery pack temperature statistical value and mark the battery pack to obtain a battery pack with abnormal temperature; detect the internal resistance of the battery cells in the battery pack with abnormal temperature to generate the internal resistance value of the battery cells; monitor the diffused gas of the battery pack with abnormal temperature to obtain the diffused gas characteristics.

[0090] In the embodiments of the present invention, the temperature values inside the battery pack are quantitatively statistically analyzed. Specifically, through cloud data preprocessing technology, the temperature data collected by 24 temperature sensors installed in the battery pack are quantitatively statistically analyzed. Taking a ternary lithium battery pack as an example, each battery module contains 5 parallel monomer batteries to form a parallel unit, and then multiple parallel units are connected in series to form a module, which is composed of 91 large monomer batteries connected in series. Through data preprocessing, the temperature data of the temperature sensors corresponding to the battery serial numbers in each module are calculated, and the temperature statistical value of the battery pack is obtained. The temperature statistical value of the battery pack is judged for temperature abnormality, and the battery pack is marked. Specifically, a temperature acquisition abnormality judgment method based on temperature difference calculation is adopted. The temperature data TN collected by N temperature sensors within a time period t are extracted, and the total change amount SN of all adjacent temperature data TN collected by each sensor is calculated. Based on the total amount SN, the calibration parameter K, and the set threshold S0, the temperature sensors with abnormal temperature acquisition are judged, and the battery pack with temperature abnormality is obtained. The internal resistance of the battery unit of the battery pack with temperature abnormality is detected to generate the internal resistance value of the battery unit. The four-terminal measurement method is used to detect the internal resistance of the battery unit. This method is based on measuring the voltage drop across the battery and simultaneously providing a constant test current. Specifically, a constant current is provided to the battery unit, the voltage drop across the battery is measured, and the internal resistance is calculated through Ohm's law. If the battery voltage drop is 0.2V and the test current is 10A, the internal resistance value is 0.2V / 10A = 0.02 ohms. The diffusion gas of the battery pack with temperature abnormality is monitored to obtain the diffusion gas characteristics, and the gas signal monitoring technology is adopted to evaluate the change of the target gas concentration through the gas sensor S2. Specifically, simulation is carried out through the built-in model of COMSOL. Compared with surface temperature monitoring, the gas sensing method can detect battery thermal runaway much earlier than the critical time of thermal runaway propagation. For example, in a 55-gallon cylindrical space, through simulation and experimental verification, the gas sensing method can detect battery thermal runaway within about 85 seconds, while the critical time of thermal runaway propagation is about 710 seconds, and the diffusion gas characteristics are obtained.

[0091] Step S3: Record the change in battery internal resistance for the internal resistance value of the battery unit to obtain the internal resistance change data; detect the abnormal temperature change of the battery pack with temperature abnormality according to the internal resistance change data to generate the abnormal temperature change data; evaluate the degree of battery thermal runaway based on the diffusion gas characteristics and the abnormal temperature change data to obtain the degree of battery thermal runaway; perform dynamic thermal safety control on the battery pack with temperature abnormality according to the degree of battery thermal runaway to generate thermal safety control measures.

[0092] In an embodiment of the present invention, the internal resistance value of the battery unit is recorded to obtain internal resistance change data; through Electrochemical Impedance Spectroscopy (EIS) technology, parameters such as battery internal resistance, double-layer capacitance, and diffusion are monitored; specifically, Princeton Applied Research electrochemical technology is used to test a 2.8 Ah lithium-ion battery, and the internal resistance values of the battery under different charge and discharge states are recorded; for example, the initial internal resistance of the battery is 5 mΩ, and after 100 charge and discharge cycles, the change in the internal resistance value is recorded as 6 mΩ, indicating a 20% increase in internal resistance; according to the internal resistance change data, temperature anomaly detection is performed on the battery pack with abnormal temperature. Specifically, using the relationship between battery internal resistance and temperature, the abnormal change in temperature is inferred by monitoring the change in internal resistance. The impedance at a specific frequency is highly sensitive to the internal temperature of the battery and is related to SOC and SOH; in practical applications, if it is monitored that the internal resistance increases from 6 mΩ to 7 mΩ, it is identified that the internal temperature of the battery has increased abnormally; based on the diffusion gas characteristics and temperature anomaly change data, the degree of battery thermal runaway is evaluated to obtain the degree of battery thermal runaway; a gas signal monitoring technology is adopted, and the concentration changes of target gases (including CH 4 、C 3 H 8 and CO) are evaluated through gas sensors; combined with temperature anomaly change data, such as the increase in internal resistance and the increase in temperature, the degree of battery thermal runaway is evaluated; if the gas sensor detects a sudden increase in the CH 4 concentration, and at the same time the internal resistance increases and the temperature rises, this indicates that the battery is in the initial stage of thermal runaway; according to the degree of battery thermal runaway, dynamic thermal safety control is performed on the battery pack with abnormal temperature. The battery management system (BMS) is used in combination with traditional algorithms to detect the battery state, and various battery states such as temperature, voltage, state of charge (SOC), etc. are monitored; specifically, the evaluation result shows that the risk of battery thermal runaway is high, and the BMS will start the cooling system to reduce the battery temperature and at the same time cut off the connection between the battery and the external circuit.

[0093] Step S4: The battery energy storage device is monitored in real time to obtain real-time battery monitoring data; a thermal runaway early warning is performed on the real-time battery monitoring data to generate a thermal runaway early warning signal; based on the thermal runaway early warning signal, thermal safety control measures are executed on the battery energy storage device to complete the thermal safety management operation of the battery energy storage.

[0094] In an embodiment of the present invention, the battery energy storage device is monitored in real time, and the real-time monitoring of the battery energy storage device is realized by using the technical solution of the battery energy storage system data management and status monitoring platform; specifically, it includes data and parameters such as system frequency, energy storage SOC, energy storage working status, load information, etc.; through the system real-time monitoring function, a unique monitoring and analysis function of energy storage equipment is provided, such as power generation power, frequency, inverter information; information such as SOC, SOH, charge and discharge capacity, charge and discharge power, and battery consistency of energy storage batteries; for specific operations, the temperature of the battery pack is collected in real time through a temperature sensor, the total current of the battery pack is collected in real time through a current sensor, and the total energy of the battery pack is collected in real time through a voltage sensor; a thermal runaway early warning is carried out on the real-time monitoring data of the battery, and a thermal runaway early warning signal is generated; the gas-sensitive technology for monitoring and warning the thermal runaway of lithium-ion batteries is adopted, and the concentration change of target gases (including CH 4 、C 3 H 8 and CO) is evaluated through a gas sensor to realize early warning of thermal runaway; at the same time, combined with the diagnostic method of abnormal battery collection temperature, the parameters of the battery are measured under different SOC, different currents, different temperatures, and different aging degrees, including the ohmic internal resistance r 0 and the polarization internal resistance r p , the heat dissipation of the battery is controlled, and the heat generation amount and internal resistance change rate of the battery are calculated, so as to diagnose whether the battery collection temperature t is an abnormal value and generate a thermal runaway early warning signal. Based on the thermal runaway early warning signal, thermal safety control measures are executed on the battery energy storage device, and according to the lithium battery thermal runaway early warning technology based on multi-feature fusion, when the thermal runaway early warning signal is triggered, thermal safety control measures are executed; these measures include starting the cooling system to reduce the battery temperature and cutting off the connection between the battery and the external circuit to prevent the further spread of thermal runaway. For specific operations, if it is detected that the battery temperature rises abnormally, the system will automatically start the thermal management function in the battery management system (BMS), and control the battery temperature within a safe range by adjusting the charge and discharge plan, adjusting the battery working state or starting the liquid cooling system, etc., to ensure the safe operation of the battery energy storage device.

[0095] Preferably, step S1 includes the following steps:

[0096] Step S11: Obtain the battery energy storage device;

[0097] Step S12: Determine the energy storage battery area of the battery energy storage device to obtain the energy storage battery area; measure the area of the energy storage battery area to generate battery area data;

[0098] Step S13: Divide the battery area data into multi-component battery areas to obtain battery group division data; encode and merge the battery group division data to obtain a set of energy storage battery groups;

[0099] Step S14: Detect the battery pack area temperature of the energy storage battery pack set to obtain the battery pack area temperature; extract the internal temperature within the group from the battery pack area temperature to obtain the internal temperature of the battery pack.

[0100] In the embodiment of the present invention, through battery energy storage detection technology, the position and quantity of the battery energy storage device are determined; the energy storage battery area of the battery energy storage device is determined, specifically, the battery area is divided to determine the energy storage battery area; the battery is divided into multiple areas, and the service range of each area corresponds to that of a battery pack. The service area of each battery pack is determined by the Voronoi diagram division method; and with the area measurement technology, battery area area data is generated; the battery area area data is subjected to multi-component battery area division, and the battery pack division data is encoded and merged to obtain the energy storage battery pack set; the electrochemical measurement technology, such as the galvanostatic intermittent titration technique (GITT), is used to analyze the electrochemical characteristics of the batteries in different areas. According to the electrochemical specific surface area and the lithium ion chemical diffusion coefficient, the battery area is divided into different battery packs; specifically, according to the battery area area data, the service area of each battery swapping station is further divided into multiple battery packs, and each battery pack has similar electrochemical characteristics; then through the encoding and merging technology, the data of these battery packs are integrated to form the energy storage battery pack set; the battery pack area temperature of the energy storage battery pack set is detected. A temperature-sensitive resistor is arranged for each battery module by using a temperature detection circuit and a temperature detection method, and a bridge circuit is constructed; the resistance data is collected by the battery management unit and converted into temperature data; specifically, for each battery module in the energy storage battery pack set, a temperature-sensitive resistor is arranged and connected into a bridge circuit; the resistance value change of each temperature-sensitive resistor is collected by the battery management unit, and the temperature data of each battery module is obtained through conversion according to the resistance value change, so as to obtain the battery pack area temperature; then through the temperature anomaly detection method, the battery pack area temperature is analyzed to extract the internal temperature of each battery module.

[0101] Preferably, step S2 includes the following steps:

[0102] Step S21: Quantify the internal temperature values within each group of the battery pack to obtain the internal temperature values within each group; summarize and statistically analyze the internal temperature values within each group to generate a battery pack temperature statistical value.

[0103] Step S22: Perform temperature characteristic mapping on each battery pack of the energy storage battery pack set based on the battery pack temperature statistical value to obtain temperature mapping characteristics of each group; generate a battery pack temperature heat map from the temperature mapping characteristics of each group.

[0104] Step S23: Perform temperature anomaly judgment on the battery pack temperature heat map to obtain the abnormal blocks of the heat map; based on the abnormal blocks of the heat map, mark the battery packs in the energy storage battery pack set to obtain the temperature-abnormal battery packs;

[0105] Step S24: Monitor the battery cell voltages of the temperature-abnormal battery packs to obtain battery cell voltage values; monitor the battery cell currents of the temperature-abnormal battery packs to obtain battery cell current values;

[0106] Step S25: Calculate the internal resistance of the battery cells based on the battery cell voltage values and the battery cell current values to generate the internal resistance values of the battery cells;

[0107] Step S26: Monitor the diffused gases of the temperature-abnormal battery packs to obtain the diffused gas characteristics.

[0108] In an embodiment of the present invention, for a battery pack composed of 100 battery cells, an NTC thermistor is arranged in each battery cell. By measuring the resistance value of each thermistor and using a look-up table method or a formula method to convert the resistance value into a temperature value, the temperature values within each group are obtained. By summarizing these temperature values, the average temperature or the temperature distribution range is calculated to generate a battery pack temperature statistical value. Based on the battery pack temperature statistical value, a temperature characteristic mapping of each battery pack in the energy storage battery pack set is performed. Using the electrochemical impedance spectroscopy (EIS) technology, the impedance characteristics of different battery packs at different temperatures are analyzed, and the temperature statistical value is mapped onto the characteristic parameters of the battery pack. Then, using the temperature heat map generation technology, the temperature mapping characteristics are presented in a graphical manner to form a battery pack temperature heat map, so as to intuitively display the temperature distribution of each area of the battery energy storage device. The battery pack temperature heat map is judged for temperature anomalies to obtain abnormal blocks in the heat map. Based on the abnormal blocks in the heat map, the battery packs in the energy storage battery pack set are marked to obtain temperature-abnormal battery packs. By analyzing the high-temperature areas on the heat map and combining the early characteristics and warning methods of lithium-ion battery thermal runaway, it is judged whether there are temperature anomalies in the battery pack. Specifically, the temperature threshold is set to 60 °C, and the areas exceeding this threshold are marked as abnormal blocks. The battery packs corresponding to these abnormal blocks are marked to identify the temperature-abnormal battery packs. The battery cell voltages of the temperature-abnormal battery packs are monitored to obtain battery cell voltage values. The battery cell currents of the temperature-abnormal battery packs are monitored to obtain battery cell current values. Using a voltage sampling circuit and an AC transmitter, the voltage response signal and the phase difference signal of the battery cell are sampled in real time. These signals are sent to a microcontroller (MCU) through a wireless communication module and stored in the internal resistance calculation module of the MCU to monitor the voltage and current of each battery cell in real time, obtaining the battery cell voltage value and the current value. Based on the battery cell voltage value and the battery cell current value, the internal resistance of the battery cell is calculated to generate an internal resistance value of the battery cell. Through the internal resistance calculation module in the MCU, using a voltage continuous sampling algorithm and an impedance formula, the internal resistance of the battery cell is calculated. Specifically, according to Ohm's law and the phase relationship of the AC circuit, the internal resistance value of the battery cell is calculated. The diffusion gases of the temperature-abnormal battery packs are monitored to obtain diffusion gas characteristics. Using gas sensor technology, the gases diffused from the battery pack are monitored, such as CH 4 、C 3 H 8 and CO, etc., to evaluate the risk of battery thermal runaway; through the gas sensor, the change in the target gas concentration is evaluated to obtain diffusion gas characteristics.

[0109] Preferably, step S26 includes the following steps:

[0110] Step S261: Monitor the diffusion situation of the temperature-abnormal battery pack to obtain the diffusion monitoring situation; identify the diffusion gases from the diffusion monitoring situation to generate diffusion gas situation data;

[0111] Step S262: Perform gas color judgment on the diffusion gas condition data to obtain the diffusion gas color; determine the gas concentration based on the diffusion gas color for the diffusion gas condition data, and generate a gas concentration state;

[0112] Step S263: Identify the gas components for the diffusion gas condition data according to the gas concentration state to obtain gas component data; perform diffusion mode recognition on the gas component data to generate a gas diffusion mode; detect the gas diffusion range for the gas diffusion mode to obtain the gas diffusion range;

[0113] Step S264: Integrate the gas concentration state, the gas diffusion mode, and the gas diffusion range to form the diffusion gas characteristics of the battery pack.

[0114] In the embodiment of the present invention, the diffusion situation of the temperature-abnormal battery pack is monitored to obtain the diffusion monitoring situation; the diffusion gas is identified for the diffusion monitoring situation; the battery cell voltage and temperature are monitored by the battery cell management unit BMU in the BMS battery management system, and thermal management and abnormal alarm are performed; the gas sensor technology, such as a gas sensor, is used to evaluate the concentration change of target gases (including CH 4 , C 3 H 8 and CO); specifically, a battery pack composed of 100 battery cells is monitored, the concentration change of each gas is identified and recorded by a gas sensor to generate diffusion gas condition data; gas color judgment is performed on the diffusion gas condition data to obtain the diffusion gas color; the mixed gas component detection and recognition technology based on a nano-sensor matrix is adopted, and four gas components are distinguished through the promotion or inhibition effect of the mixed gas in the nano-sensor matrix; specifically, the spectral characteristics of the diffusion gas are analyzed to determine the gas color, and then the gas concentration state is determined according to the known gas concentration range corresponding to the color; the gas components are identified for the diffusion gas condition data according to the gas concentration state, the gas diffusion range is detected for the gas diffusion mode to obtain the gas diffusion range; the velocity streamline of the air flow and the hydrogen concentration evolution in the energy storage battery compartment are analyzed; specifically, through simulation and experimental data, the gas component data are identified, and then the gas diffusion mode is generated according to the velocity and range of gas diffusion, and the gas diffusion range is detected; the data integration technology is used to integrate the gas concentration state, the gas diffusion mode, and the gas diffusion range to form the diffusion gas characteristics of the battery pack.

[0115] As an example of the present invention, as shown in Figure 2 In this example, step S3 includes:

[0116] Step S31: Continuously monitor the internal resistance of the battery cell to obtain battery internal resistance monitoring data; mark the monitoring time period for the battery internal resistance monitoring data to generate an internal resistance monitoring time period.

[0117] Step S32: Record the internal resistance value during the initial period for the internal resistance value of the battery cell based on the internal resistance monitoring time period to obtain the internal resistance value during the initial period; record the internal resistance value during the last period for the internal resistance value of the battery cell according to the internal resistance monitoring time period to obtain the internal resistance value during the last period.

[0118] Step S33: Subtract the internal resistance value during the initial period from the internal resistance value during the last period to generate internal resistance change data.

[0119] Step S34: Detect the abnormal temperature change of the battery pack with abnormal temperature according to the internal resistance change data to generate abnormal temperature change data.

[0120] Step S35: Evaluate the degree of battery thermal runaway based on the diffusion gas characteristics and the abnormal temperature change data to obtain the degree of battery thermal runaway.

[0121] Step S36: Perform dynamic thermal safety control on the battery pack with abnormal temperature according to the degree of battery thermal runaway to generate thermal safety control measures.

[0122] In an embodiment of the present invention, the internal resistance of the battery cell is continuously monitored to obtain battery internal resistance monitoring data; the internal resistance monitoring function in the BMS (Battery Management System) is used to measure the internal resistance of the battery by real-time monitoring of the voltage and current of the battery cell; for example, the BMS can timely detect abnormal conditions of the battery cell through voltage monitoring, current monitoring and internal resistance monitoring; specifically, the BMS records the internal resistance value of each battery cell at a specific time point, such as once every 5 minutes, to generate an internal resistance monitoring time period; based on the internal resistance monitoring time period, the internal resistance value at the initial time period of the battery cell is recorded, and the internal resistance value at the end time period of the battery cell is recorded according to the internal resistance monitoring time period; at the beginning of the monitoring, the initial internal resistance value of the battery cell is recorded, such as recording the internal resistance value within the first minute of the start of the monitoring; at the end of the monitoring, the internal resistance value at the end time period is recorded, such as recording the internal resistance value within the last minute before the end of the monitoring; the internal resistance change data is generated by subtracting the internal resistance value at the end time period from the internal resistance value at the initial time period; and the internal resistance change data is obtained by calculating the difference between the internal resistance value at the end time period and the internal resistance value at the initial time period; specifically, if the internal resistance value at the initial time period is 50 mΩ and the internal resistance value at the end time period is 55 mΩ, the internal resistance change data is an increase of 5 mΩ; according to the internal resistance change data, temperature anomaly detection of the battery pack with temperature anomaly is carried out, and the temperature anomaly change of the battery pack is detected by using the internal resistance change data in combination with the temperature monitoring data of the battery; specifically, if the increase in internal resistance is accompanied by an abnormal increase in temperature, this indicates that there is a risk of thermal runaway in the battery pack; based on the diffusion gas characteristics and temperature anomaly change data, the degree of battery thermal runaway is evaluated to obtain the degree of battery thermal runaway; by analyzing the characteristics of the diffusion gas and temperature anomaly change data, the degree of battery thermal runaway is evaluated; for example, by monitoring the gases diffused from the battery pack, such as CH 4 、C 3 H 8 and CO, etc., to evaluate the risk of battery thermal runaway; according to the degree of battery thermal runaway, dynamic thermal safety control is carried out on the battery pack with temperature anomaly to generate thermal safety control measures. Using the thermal management function of the BMS, corresponding thermal safety control measures are executed according to the degree of battery thermal runaway; for example, if the risk of battery thermal runaway is high, the BMS will start the cooling system to reduce the battery temperature and at the same time cut off the connection between the battery and the external circuit to prevent the further spread of thermal runaway.

[0123] Preferably, step S34 includes the following steps:

[0124] Step S341: Perform frequency domain conversion on the internal resistance change data to obtain internal resistance change frequency data; perform frequency jitter feature recognition on the internal resistance change frequency data to generate internal resistance frequency jitter features;

[0125] Step S342: Perform jitter time mapping on the internal resistance monitoring time period according to the internal resistance frequency jitter feature to obtain internal resistance frequency jitter time;

[0126] Step S343: Based on the internal resistance frequency jitter time, perform temperature mode recognition on the battery pack with abnormal temperature to obtain the frequency jitter-temperature mode; perform battery pack temperature sensitivity judgment on the frequency jitter-temperature mode to generate temperature sensitivity data;

[0127] Step S344: Record the duration of abnormal temperature change for the battery pack with abnormal temperature according to the temperature sensitivity data to obtain the duration of abnormal temperature change; monitor the abnormal temperature change value of the battery pack with abnormal temperature based on the temperature sensitivity data to obtain the abnormal temperature change degree value;

[0128] Step S345: Integrate the duration of abnormal temperature change and the abnormal temperature change degree value to generate abnormal temperature change data.

[0129] In the embodiment of the present invention, the frequency domain method is used to convert the time domain signal into a frequency domain signal through Fourier transform, so as to identify the frequency characteristics of the internal resistance change; specifically, the fast Fourier transform (FFT) algorithm is used to perform frequency domain conversion on the battery internal resistance change data to obtain the internal resistance change frequency data; then, the key technology of identifying the test modal parameters is used through the frequency domain method to identify the frequency jitter characteristics in the internal resistance change frequency data to generate the internal resistance frequency jitter characteristics; by analyzing the distribution of the internal resistance frequency jitter characteristics in the frequency domain and combining the time-frequency mapping technology, the jitter time is determined to obtain the internal resistance frequency jitter time; based on the internal resistance frequency jitter time, perform temperature mode recognition on the battery pack with abnormal temperature to obtain the frequency jitter-temperature mode; utilize the correlation between the internal resistance frequency jitter time and the battery pack temperature data to identify the frequency jitter-temperature mode; specifically, by analyzing the correlation between the internal resistance frequency jitter time and the battery pack temperature data, the temperature mode is identified, and the temperature sensitivity of the mode is judged to generate temperature sensitivity data; record the duration of abnormal temperature change for the battery pack with abnormal temperature according to the temperature sensitivity data to obtain the duration of abnormal temperature change; specifically, according to the temperature sensitivity data, monitor and record the abnormal temperature change of the battery pack within a specific time period to obtain the duration of abnormal temperature change and the abnormal temperature change degree value; integrate the duration of abnormal temperature change and the abnormal temperature change degree value to generate abnormal temperature change data; by integrating the duration and the degree value of the abnormal temperature change, complete abnormal temperature change data is formed; specifically, integrate the duration of abnormal temperature change and the abnormal temperature change degree value to generate abnormal temperature change data, providing key parameters for battery thermal runaway assessment.

[0130] As an example of the present invention, refer to Figure 3 As shown, in this example, step S35 includes:

[0131] Step S351: Determine the gas concentration amount of the diffusion gas characteristics to obtain the diffusion gas concentration amount; identify the diffusion mode type of the diffusion gas characteristics to obtain the gas diffusion mode type;

[0132] Step S352: Detect the gas diffusion space range of the diffusion gas characteristics based on the diffusion gas concentration amount and the gas diffusion mode type, and generate diffusion space distribution data;

[0133] Step S353: Match the influence weight of the gas diffusion degree on the temperature-abnormal battery pack according to the diffusion space distribution data to generate a gas diffusion influence weight value;

[0134] Step S354: Determine the abnormal temperature change period of the abnormal temperature change data to obtain the abnormal temperature change period; measure the abnormal temperature deviation value of the abnormal temperature change data to generate an abnormal temperature deviation value;

[0135] Step S355: Match the influence weight of the abnormal temperature change on the temperature-abnormal battery pack based on the abnormal temperature change period and the abnormal temperature deviation value to generate a temperature change influence weight value;

[0136] Step S356: Calculate the battery thermal runaway weight according to the gas diffusion influence weight value and the temperature change influence weight value to obtain the battery thermal runaway weight scoring data; divide the runaway degree of the battery thermal runaway weight scoring data to obtain the battery thermal runaway degree.

[0137] In the embodiment of the present invention, the gas concentration amount of the diffusion gas characteristics is determined to obtain the diffusion gas concentration amount; the diffusion mode type of the diffusion gas characteristics is identified to obtain the gas diffusion mode type; the concentration amount determination and diffusion mode type identification of the gas diffused from the battery pack are carried out through a gas sensor array and a mode recognition algorithm, such as a backpropagation artificial neural network (BP-ANN); specifically, a high-resolution gas detection device is used to identify and quantify the gas components, and the BP-ANN algorithm is applied to classify and identify the gas concentration amount and the diffusion mode through the data collected by the sensor array; the gas diffusion space range of the diffusion gas characteristics is detected based on the diffusion gas concentration amount and the gas diffusion mode type to generate diffusion space distribution data; combining the gas concentration amount and the diffusion mode type, the gas diffusion space range in the battery pack is determined through simulation and experimental data; specifically, by analyzing CO 2Based on the relationship between the diffusion coefficient and the effective diffusion coefficient of gas molecules in coal and time, calculate the diffusion path and range of gas molecules in coal, and generate diffusion space distribution data; according to the diffusion space distribution data, perform gas diffusion degree influence weight matching on the temperature-abnormal battery pack to generate gas diffusion influence weight values; use time series analysis methods in statistics, such as smoothing techniques (such as moving average, exponential smoothing), to analyze the diffusion space distribution data to determine the influence weight of gas diffusion degree on the battery pack; specifically, by analyzing the gas diffusion space distribution data, calculate the contribution degree of gas diffusion to the thermal runaway risk of the battery pack, and generate gas diffusion influence weights; determine the abnormal temperature change period for the abnormal temperature change data, use infrared thermal imaging technology to monitor the temperature distribution of the new energy battery pack in real time, and determine the abnormal temperature change period and temperature anomaly deviation value by detecting the temperature difference inside the battery pack; specifically, monitor the temperature of the battery pack through the infrared thermal imaging module, analyze the time series of temperature data, and determine the abnormal temperature change period and temperature anomaly deviation value; analyze the abnormal temperature change period and temperature anomaly deviation value to determine the influence weight of temperature anomaly on the thermal runaway risk of the battery pack; specifically, process the abnormal temperature change data through statistical analysis methods, calculate the contribution degree of temperature anomaly to the thermal runaway risk of the battery pack, and generate temperature change influence weight values; calculate the battery thermal runaway weight according to the gas diffusion influence weight value and the temperature change influence weight value to obtain battery thermal runaway weight scoring data; combine the gas diffusion influence weight value and the temperature change influence weight value, calculate the weight scoring data of battery thermal runaway, and conduct a division of the runaway degree; specifically, by evaluating the thermal runaway risk of the battery, combine multiple parameters (such as temperature, gas concentration, voltage, internal resistance, etc.) for early warning to effectively improve the accuracy and sensitivity of the early warning system, and finally determine the degree of battery thermal runaway.

[0138] Preferably, step S36 includes the following steps:

[0139] Step S361: Compare the degree of battery thermal runaway with a preset thermal runaway degree index. If the degree of battery thermal runaway is greater than the preset thermal runaway degree index, mark it as the severity of battery thermal runaway;

[0140] Step S362: Locate the thermal runaway battery cells for the temperature-abnormal battery pack based on the severity of battery thermal runaway to generate a thermal runaway battery cell area;

[0141] Step S363: Cut off the circuit of the battery cells in the thermal runaway battery cell area and generate a circuit cut-off measure;

[0142] Step S364: Evacuate the diffused gas in the thermal runaway battery cell area and generate a diffused gas evacuation measure;

[0143] Step S365: Start the battery cooling device for the thermal runaway battery cell area and generate a cooling device startup measure;

[0144] Step S366: Integrate the circuit cut-off measure, the diffusion gas evacuation measure, and the cooling device startup measure for thermal safety control to generate a thermal safety control measure.

[0145] In the embodiment of the present invention, the degree of battery thermal runaway is compared with a preset thermal runaway degree index. If the degree of battery thermal runaway is greater than the preset thermal runaway degree index, it is marked as the severity of battery thermal runaway. A lithium battery safety evaluation method based on the thermal runaway risk index is adopted. By evaluating the battery thermal runaway risk index, the actual thermal runaway degree is compared with a preset safety threshold. For example, the preset thermal runaway degree index is that the battery temperature reaches 120 °C. If the monitored battery temperature exceeds this value, it is marked as the severity of battery thermal runaway. Based on the severity of battery thermal runaway, locate the thermal runaway battery cells in the battery pack with abnormal temperature to generate a thermal runaway battery cell area. Use an online battery thermal runaway warning method and system based on EIS parameter extraction to determine the specific location of the thermal runaway battery cells through the extraction of key parameters of the electrochemical impedance spectrum (EIS). Specifically, by analyzing the changes in the real and imaginary parts of the impedance, a specific battery cell area in the battery pack is located as the thermal runaway battery cell area. Cut off the battery cell circuit for the thermal runaway battery cell area and generate a circuit cut-off measure. According to the battery thermal runaway safety strategy, after determining the thermal runaway battery cell area, immediately perform a circuit cut-off operation to prevent the spread of thermal runaway. Specifically, the battery management system (BMS) controls the relay or fuse to cut off the circuit connection with the thermal runaway battery cell area to generate a circuit cut-off measure. Evacuate the diffusion gas for the thermal runaway battery cell area and generate a diffusion gas evacuation measure. Perform a gas evacuation operation on the thermal runaway battery cell area to reduce the gas accumulation caused by thermal runaway. For example, start the gas evacuation system in the battery compartment to extract the gas in the thermal runaway battery cell area to generate a diffusion gas evacuation measure. Start the battery cooling device for the thermal runaway battery cell area and generate a cooling device startup measure. Specifically, start the battery cooling device to reduce the temperature of the thermal runaway battery cell area. For example, cool the thermal runaway battery cell area through a coolant circulation system or use a phase change material for local cooling to generate a cooling device startup measure. Use data fusion technology to integrate the circuit cut-off measure, the diffusion gas evacuation measure, and the cooling device startup measure for thermal safety control to generate a thermal safety control measure.

[0146] Preferably, step S4 includes the following steps:

[0147] Step S41: Monitor the real-time state of the energy storage battery of the battery energy storage device to obtain real-time battery monitoring data;

[0148] Step S42: Identify the abnormal state of the battery temperature from the real-time monitoring data of the battery to obtain the abnormal state of the battery temperature; determine the battery thermal runaway mode for the abnormal state of the battery temperature to generate the battery thermal runaway mode;

[0149] Step S43: Issue a thermal runaway warning for the battery thermal runaway mode to generate a thermal runaway warning signal;

[0150] Step S44: Locate the thermal runaway battery pack for the battery energy storage device based on the thermal runaway warning signal to obtain the position information of the thermal runaway battery pack; execute thermal safety control measures on the position information of the thermal runaway battery pack to complete the thermal safety management operation of the battery energy storage.

[0151] In the embodiment of the present invention, the state of the energy storage battery of the battery energy storage device is monitored in real time. Specifically, the battery management system (BMS) is used to detect various state variables during the operation of the battery in real time, such as voltage, temperature, current, SOC, SOH, etc. In a specific embodiment, the BMS obtains the temperature frequency distribution table of each battery temperature probe through the temperature sensors arranged in the battery module; identifies the abnormal state of the battery temperature from the real-time monitoring data of the battery to obtain the abnormal state of the battery temperature; determines the battery thermal runaway mode for the abnormal state of the battery temperature to generate the battery thermal runaway mode; identifies the abnormal state of the battery temperature by analyzing the battery temperature curve and interval division; if the temperature of the monitoring point reaches the maximum temperature specified by the manufacturer (generally 60°C), it is determined as the battery thermal runaway mode; issues a thermal runaway warning for the battery thermal runaway mode, and through the multi-feature fusion lithium battery thermal runaway warning method of abnormal temperature rise rate and temperature drop after the safety valve is opened, timely and accurately discovers the opening time and position of the lithium battery safety valve to generate a thermal runaway warning signal. Locate the thermal runaway battery pack for the battery energy storage device based on the thermal runaway warning signal to obtain the position information of the thermal runaway battery pack; then combine the current temperature of the energy storage battery to obtain the comprehensive risk prediction index when the energy storage battery is in normal use, and reflect the degree of risk currently existing in the energy storage battery through it; and execute thermal safety control measures on the position information of the thermal runaway battery pack to complete the thermal safety management operation of the battery energy storage.

[0152] In this specification, a thermal safety management system for battery energy storage is provided for implementing the above-mentioned thermal safety management method for battery energy storage. The thermal safety management system for battery energy storage includes:

[0153] A temperature detection module for the battery energy storage device, which is used to obtain the battery energy storage device; divide the battery pack of the battery energy storage device to generate a set of energy storage battery packs; detect the internal temperature of the battery packs in the set of energy storage battery packs to obtain the internal temperature of the battery packs;

[0154] The battery cell anomaly analysis module is used to quantitatively statistically analyze the temperature values of the internal temperature of the battery pack to generate a battery pack temperature statistical value; perform a temperature anomaly judgment on the battery pack temperature statistical value and mark the battery pack to obtain a temperature-anomaly battery pack; detect the internal resistance of the battery cells of the temperature-anomaly battery pack to generate the internal resistance values of the battery cells; monitor the diffusion gas of the temperature-anomaly battery pack to obtain the diffusion gas characteristics;

[0155] The battery thermal safety control module is used to record the battery internal resistance change of the internal resistance values of the battery cells to obtain the internal resistance change data; detect the temperature anomaly change of the temperature-anomaly battery pack according to the internal resistance change data to generate the temperature anomaly change data; evaluate the degree of battery thermal runaway based on the diffusion gas characteristics and the temperature anomaly change data to obtain the degree of battery thermal runaway; perform dynamic thermal safety control on the temperature-anomaly battery pack according to the degree of battery thermal runaway to generate thermal safety control measures;

[0156] The thermal runaway warning execution module is used to perform real-time monitoring on the battery energy storage device to obtain the battery real-time monitoring data; perform a thermal runaway warning on the battery real-time monitoring data to generate a thermal runaway warning signal; execute the thermal safety control measures on the battery energy storage device based on the thermal runaway warning signal to complete the thermal safety management operation of the battery energy storage.

[0157] Through the temperature detection module of the battery energy storage device, the battery energy storage device is acquired and the battery packs are divided to generate a set of energy storage battery packs, providing a clear understanding of the structure of the battery energy storage device and laying a foundation for subsequent temperature detection and abnormal judgment; the internal temperature of the battery packs in the set of energy storage battery packs is detected to obtain the internal temperature of the battery packs, providing an accurate data basis for temperature value quantification statistics and abnormal judgment, thereby ensuring the safe operation of the battery energy storage device. Through the battery unit abnormal analysis module, temperature value quantification statistics are performed on the internal temperature of the battery packs to clarify the temperature distribution of the battery packs; temperature abnormal judgment is performed on the temperature statistical values of the battery packs, and the battery packs are marked to obtain temperature-abnormal battery packs, enabling timely discovery of potential safety hazards in the battery energy storage device; the internal resistance of the battery units in the temperature-abnormal battery packs is detected and the diffusion gas in the battery packs is monitored to generate the internal resistance values of the battery units and the diffusion gas characteristics, providing necessary parameters for evaluating the degree of battery thermal runaway and enhancing the safety monitoring ability of the battery energy storage device. Through the battery thermal safety control module, the change of the battery internal resistance is recorded based on the internal resistance values of the battery units, enabling monitoring of the performance change of the battery units and providing data support for subsequent detection of abnormal temperature changes; according to the internal resistance change data, abnormal temperature change detection is performed on the temperature-abnormal battery packs to generate abnormal temperature change data, accurately grasping the temperature dynamics of the battery packs and providing real-time data for evaluating the degree of battery thermal runaway; based on the diffusion gas characteristics and the abnormal temperature change data, the degree of battery thermal runaway is evaluated, enabling scientific evaluation of the safety state of the battery energy storage device and providing a basis for dynamic thermal safety control; according to the degree of battery thermal runaway, dynamic thermal safety control is performed on the temperature-abnormal battery packs to generate thermal safety control measures, ensuring that the battery energy storage device can be processed in a timely and effective manner under abnormal conditions and improving the safety performance of the battery energy storage device. Through the thermal runaway warning execution module, the battery energy storage device is monitored in real time to obtain real-time battery monitoring data, continuously tracking the operating state of the battery energy storage device and providing real-time data for thermal runaway warning; thermal runaway warning is performed on the real-time battery monitoring data, enabling timely warning to remind relevant personnel to take actions; based on the thermal runaway warning signal, thermal safety control measures are executed on the battery energy storage device to complete the thermal safety management operation of the battery energy storage, ensuring that the battery energy storage device can be effectively controlled and processed when facing the risk of thermal runaway and guaranteeing the safe and stable operation of the battery energy storage device. Therefore, through data processing technology, pattern recognition technology, and deep learning technology, the present invention realizes the sub-monitoring of battery units for the battery energy storage device, and evaluates the degree of battery thermal runaway by monitoring multiple parameters of the energy storage battery, realizing the dynamic thermal safety control of the battery energy storage device, thereby improving the thermal safety management efficiency of the battery energy storage.

[0158] Therefore, in all respects, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be embraced within the present invention.

[0159] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A thermal safety management method for battery energy storage, characterized in that: The following steps are involved: Step S1: obtaining a battery energy storage device; dividing the battery energy storage device into battery groups to generate a set of energy storage battery groups; Detecting the internal temperature of a battery pack of an energy storage battery pack to obtain the internal temperature of the battery pack; Step S2: quantifying and counting the internal temperature of the battery pack to generate a battery pack temperature statistic; determining temperature anomalies on the battery pack temperature statistic, and marking the battery pack to obtain a battery pack with abnormal temperature; Perform battery cell internal resistance detection on battery packs with abnormal temperature to generate battery cell internal resistance values; perform battery pack diffusion gas monitoring on battery packs with abnormal temperature to obtain diffusion gas characteristics; Step S3: recording the change of the internal resistance of the battery cell to obtain the internal resistance change data; performing temperature abnormality change detection on the temperature abnormal battery pack according to the internal resistance change data to generate the temperature abnormality change data; The battery thermal runaway degree is evaluated based on the diffusion gas characteristics and abnormal temperature change data to obtain the battery thermal runaway degree; dynamic thermal safety control is performed on the temperature abnormal battery pack according to the battery thermal runaway degree to generate thermal safety control measures; The detecting of abnormal temperature change of the battery pack with abnormal temperature according to the internal resistance change data comprises: Perform frequency domain conversion on the internal resistance change data to obtain internal resistance change frequency data; perform frequency jitter feature recognition on the internal resistance change frequency data to generate internal resistance frequency jitter features; According to the internal resistance frequency jitter characteristics, the internal resistance monitoring time period is mapped to a jitter time to obtain the internal resistance frequency jitter time; Based on the internal resistance frequency jitter time, the temperature pattern of the battery pack with abnormal temperature is identified to obtain the frequency jitter-temperature pattern; the temperature sensitivity of the battery pack is judged on the frequency jitter-temperature pattern to generate temperature sensitivity data; According to the temperature sensitivity data, the abnormal temperature change duration of the temperature-abnormal battery pack is recorded to obtain the abnormal temperature change duration; based on the temperature sensitivity data, the abnormal temperature change value of the temperature-abnormal battery pack is monitored to obtain the abnormal temperature change degree value; The abnormal temperature change duration and the abnormal temperature change degree values ​​are integrated into the abnormal temperature change characteristics to generate abnormal temperature change data; Step S4: monitor the battery energy storage device in real time to obtain real-time monitoring data of the battery; perform thermal runaway warning on the real-time monitoring data of the battery to generate a thermal runaway warning signal; and perform thermal safety control measures on the battery energy storage device based on the thermal runaway warning signal to complete the thermal safety management operation of the battery energy storage.

2. The thermal safety management method for battery energy storage according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire a battery energy storage device; Step S12: determining the energy storage battery area of ​​the battery energy storage device to obtain the energy storage battery area; calculating the area of ​​the energy storage battery area to generate battery area data; Step S13: Divide the battery area data into multiple battery areas to obtain battery group division data; encode and merge the battery group division data to obtain an energy storage battery group set; Step S14: Detect the battery pack area temperature of the energy storage battery pack set to obtain the battery pack area temperature; extract the internal temperature of the battery pack area temperature to obtain the internal temperature of the battery pack.

3. The thermal safety management method for battery energy storage according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: quantifying the temperature values ​​within each battery pack to obtain the temperature values ​​within each battery pack; summarizing and counting the temperature values ​​within each battery pack to generate battery pack temperature statistics; Step S22: mapping the temperature characteristics of each battery pack of the energy storage battery pack set based on the battery pack temperature statistics to obtain the temperature mapping characteristics of each group; generating a temperature heat map for the temperature mapping characteristics of each group to obtain the battery pack temperature heat map; Step S23: determining temperature anomalies on the battery pack temperature thermodynamic map to obtain abnormal blocks on the thermodynamic map; marking the energy storage battery packs based on the abnormal blocks on the thermodynamic map to obtain abnormal temperature battery packs; Step S24: monitoring the battery cell voltage of the battery pack with abnormal temperature to obtain the battery cell voltage value; monitoring the battery cell current of the battery pack with abnormal temperature to obtain the battery cell current value; Step S25: Calculating the internal resistance of the battery cell based on the battery cell voltage value and the battery cell current value to generate the internal resistance value of the battery cell; Step S26: monitoring the battery pack diffusion gas for the battery pack with abnormal temperature to obtain the diffusion gas characteristics.

4. The thermal safety management method for battery energy storage according to claim 3, characterized in that: Step S26 includes the following steps: Step S261: monitoring the battery pack diffusion condition of the battery pack with abnormal temperature to obtain the diffusion monitoring condition; identifying the diffusion gas based on the diffusion monitoring condition to generate diffusion gas condition data; Step S262: performing gas color judgment on the diffused gas condition data to obtain diffused gas color; performing gas concentration determination on the diffused gas condition data based on the diffused gas color to generate a gas concentration state; Step S263: performing gas composition identification on the diffused gas condition data according to the gas concentration state to obtain gas composition data; performing diffusion pattern identification on the gas composition data to generate a gas diffusion pattern; performing gas diffusion range detection on the gas diffusion pattern to obtain a gas diffusion range; Step S264: Integrate the gas concentration state, gas diffusion mode and gas diffusion range into the battery pack diffusion gas characteristics to obtain the diffusion gas characteristics.

5. The thermal safety management method for battery energy storage according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: continuously monitoring the internal resistance of the battery cell to obtain battery internal resistance monitoring data; marking the monitoring time period of the battery internal resistance monitoring data to generate an internal resistance monitoring time period; Step S32: recording the resistance value of the internal resistance of the battery cell in the initial period based on the internal resistance monitoring period to obtain the resistance value in the initial period; recording the resistance value of the internal resistance of the battery cell in the final period based on the internal resistance monitoring period to obtain the resistance value in the final period; Step S33: subtract the internal resistance value in the final period from the internal resistance value in the initial period to generate internal resistance change data; Step S34: performing temperature abnormality detection on the temperature abnormality battery pack according to the internal resistance change data, and generating temperature abnormality change data; Step S35: evaluating the degree of thermal runaway of the battery based on the diffusion gas characteristics and the abnormal temperature change data to obtain the degree of thermal runaway of the battery; Step S36: Perform dynamic thermal safety control on the battery pack with abnormal temperature according to the degree of thermal runaway of the battery, and generate thermal safety control measures.

6. The thermal safety management method for battery energy storage according to claim 5, characterized in that: Step S35 includes the following steps: Step S351: determining the gas concentration of the diffused gas feature to obtain the diffused gas concentration; identifying the diffusion mode type of the diffused gas feature to obtain the gas diffusion mode type; Step S352: performing gas diffusion space range detection on the diffusion gas characteristics based on the diffusion gas concentration and the gas diffusion mode type, and generating diffusion space distribution data; Step S353: performing gas diffusion influence weight matching on the battery pack with abnormal temperature according to the diffusion space distribution data, and generating a gas diffusion influence weight value; Step S354: determining the abnormal temperature change period of the abnormal temperature change data to obtain the abnormal temperature change period; calculating the abnormal temperature deviation value of the abnormal temperature change data to generate the abnormal temperature deviation value; Step S355: performing temperature abnormality change impact weight matching on the temperature abnormal battery pack based on the abnormal temperature change cycle and the temperature abnormality deviation value, and generating a temperature change impact weight value; Step S356: Calculate the battery thermal runaway weight according to the gas diffusion influence weight value and the temperature change influence weight value to obtain the battery thermal runaway weight scoring data; divide the battery thermal runaway weight scoring data into runaway degrees to obtain the battery thermal runaway degree.

7. The thermal safety management method for battery energy storage according to claim 5, characterized in that: Step S36 includes the following steps: Step S361: Compare the battery thermal runaway degree with a preset thermal runaway degree index. If the battery thermal runaway degree is greater than the preset thermal runaway degree index, mark it as the battery thermal runaway severity. Step S362: locating the thermal runaway battery cells of the battery pack with abnormal temperature based on the severity of the battery thermal runaway, and generating a thermal runaway battery cell area; Step S363: Cutting off the battery cell circuit in the thermal runaway battery cell area and generating circuit cutting measures; Step S364: evacuating the thermal runaway battery cell area with diffusion gas, and generating diffusion gas evacuation measures; Step S365: activating a battery cooling device for the thermal runaway battery cell area and generating cooling device activation measures; Step S366: Integrate the circuit cut-off measures, diffusion gas evacuation measures and cooling device startup measures for thermal safety control to generate thermal safety control measures.

8. The thermal safety management method for battery energy storage according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: monitoring the state of the energy storage battery of the battery energy storage device in real time to obtain real-time monitoring data of the battery; Step S42: identifying the abnormal battery temperature state of the battery real-time monitoring data to obtain the abnormal battery temperature state; determining the battery thermal runaway mode of the abnormal battery temperature state to generate the battery thermal runaway mode; Step S43: Perform a thermal runaway warning on the battery thermal runaway mode and generate a thermal runaway warning signal; Step S44: based on the thermal runaway warning signal, locate the thermal runaway battery pack of the battery energy storage device to obtain the location information of the thermal runaway battery pack; perform thermal safety control measures on the location information of the thermal runaway battery pack to complete the thermal safety management operation of the battery energy storage.

9. A thermal safety management system for battery energy storage, characterized in that: Used to execute the thermal safety management method of battery energy storage as claimed in claim 1, the thermal safety management system of battery energy storage comprises: The battery energy storage device temperature detection module is used to obtain the battery energy storage device; divide the battery energy storage device into battery groups to generate a set of energy storage battery groups; detect the internal temperature of the battery groups of the energy storage battery group set to obtain the internal temperature of the battery groups; The battery cell abnormality analysis module is used to quantify the temperature value of the internal temperature of the battery pack and generate the battery pack temperature statistics; to judge the temperature abnormality of the battery pack temperature statistics and mark the battery pack to obtain the temperature abnormal battery pack; to detect the battery cell internal resistance of the temperature abnormal battery pack and generate the battery cell internal resistance value; to monitor the battery pack diffusion gas of the temperature abnormal battery pack and obtain the diffusion gas characteristics; The battery thermal safety control module is used to record the change of the internal resistance of the battery unit and obtain the internal resistance change data; detect the abnormal temperature change of the temperature-abnormal battery pack based on the internal resistance change data and generate the abnormal temperature change data; evaluate the degree of thermal runaway of the battery based on the diffusion gas characteristics and the abnormal temperature change data and obtain the degree of thermal runaway of the battery; perform dynamic thermal safety control on the temperature-abnormal battery pack according to the degree of thermal runaway of the battery and generate thermal safety control measures; The thermal runaway warning execution module is used to monitor the battery energy storage device in real time to obtain real-time monitoring data of the battery; to issue a thermal runaway warning to the real-time monitoring data of the battery and generate a thermal runaway warning signal; and to execute thermal safety control measures on the battery energy storage device based on the thermal runaway warning signal to complete the thermal safety management of the battery energy storage.

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