Energy storage thermal management out-of-control early warning method and system

Abstract

The invention discloses an energy storage thermal management out-of-control early warning method and system, and relates to the technical field of energy storage devices. The method comprises the following steps: acquiring a temperature characteristic value array set and a gas characteristic value array set; constructing a temperature characteristic distribution sequence and a gas characteristic distribution sequence; heating analysis and thermal runaway trend analysis are carried out, fitting verification is carried out, and heating parameters, thermal runaway parameters and thermal runaway monitoring accuracy coefficients are obtained; collecting the thermal management parameters to obtain a thermal management parameter sequence; performing thermal management operation analysis and thermal management margin analysis, then performing thermal management out-of-control analysis to obtain thermal management out-of-control probability information, performing compensation correction, and performing judgment decision early warning. The technical problems that in the prior art, thermal runaway judgment is conducted only according to the temperature, the management side change trend is not considered, and missed judgment and erroneous judgment are caused are solved, the management side change trend is considered, analysis is conducted in combination with the temperature and gas, and the technical effect of improving the safety of the energy storage system is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of energy storage devices, and specifically to a method and system for early warning of out-of-control of thermal management of energy storage. Background Art

[0002] With the increasing number of energy storage power stations and the expansion of the scale of single stations, the importance of thermal management in energy storage power stations has become increasingly prominent. Thermal runaway is one of the serious safety issues that may occur in energy storage devices, which may cause fire or explosion of battery modules or even the entire energy storage power station. Therefore, early warning of thermal management runaway in energy storage power stations is of great significance to ensure the safe operation of energy storage power stations. In order to achieve early warning of thermal management runaway in energy storage power stations, it is necessary to monitor key parameters in energy storage power stations, such as temperature and pressure, and to determine whether there is a risk of thermal runaway by analyzing the changing trends of these parameters. Using temperature alone as an early detection parameter for thermal runaway is not ideal, because when the surface temperature of the energy storage device is low but the internal temperature is high, thermal runaway may have occurred, but traditional methods cannot accurately determine thermal runaway.

[0003] In summary, the existing technology only judges thermal runaway based on temperature, without considering the change trend on the management side, resulting in technical problems of missed judgment and wrong judgment. Summary of the invention

[0004] Based on this, it is necessary to provide a method and system for early warning of thermal management out of control of energy storage systems in response to the above technical problems, which can improve the safety of energy storage systems by considering the change trend on the management side and combining temperature and gas for analysis.

[0005] In a first aspect, a method for early warning of thermal management out of control of energy storage is provided, the method comprising: continuously monitoring and collecting temperature characteristic values ​​and gas characteristic values ​​of multiple monitoring points in the energy storage device at multiple monitoring time points through a monitoring module arranged in the energy storage device, and obtaining a temperature characteristic value array set and a gas characteristic value array set; constructing a temperature characteristic distribution sequence and a gas characteristic distribution sequence according to the temperature characteristic value array set and the gas characteristic value array set; based on the temperature characteristic distribution sequence and the gas characteristic distribution sequence, respectively performing heating analysis and thermal runaway trend analysis of the energy storage device, and obtaining temperature heating parameters, gas heating parameters, temperature thermal runaway parameters and Gas thermal runaway parameters are obtained, and fitting verification is performed to obtain heating parameters, thermal runaway parameters and thermal runaway monitoring accuracy coefficients; the thermal management parameters of the thermal management module in the energy storage device at the multiple monitoring time points are collected to obtain a thermal management parameter sequence; based on the thermal management parameter sequence and thermal parameters, thermal management operation analysis and thermal management margin analysis are performed to obtain normal thermal management parameters and thermal management margin parameters; based on the normal thermal management parameters, thermal runaway parameters and thermal management margin parameters, thermal management runaway analysis is performed to obtain thermal management runaway probability information, compensation correction is performed based on the thermal runaway monitoring accuracy coefficient, and discrimination, decision and early warning are performed based on the compensated thermal management runaway probability information.

[0006] In a second aspect, a thermal management out-of-control warning system for energy storage is provided, the system comprising: a characteristic value acquisition module, the characteristic value acquisition module is used to continuously monitor and collect temperature characteristic values ​​and gas characteristic values ​​of multiple monitoring points in the energy storage device at multiple monitoring time points through a monitoring module arranged in the energy storage device, and obtain a temperature characteristic value array set and a gas characteristic value array set; a distribution sequence construction module, the distribution sequence construction module is used to construct a temperature characteristic distribution sequence and a gas characteristic distribution sequence according to the temperature characteristic value array set and the gas characteristic value array set; a fitting verification module, the fitting verification module is used to perform heating analysis and thermal runaway trend analysis of the energy storage device based on the temperature characteristic distribution sequence and the gas characteristic distribution sequence, respectively, to obtain temperature heating parameters, gas heating parameters, temperature thermal runaway parameters and gas thermal parameters. out-of-control parameters, and perform fitting verification to obtain heating parameters, thermal runaway parameters and thermal runaway monitoring accuracy coefficients; a thermal management parameter sequence acquisition module, the thermal management parameter sequence acquisition module is used to collect the thermal management parameters of the thermal management module in the energy storage device at the multiple monitoring time points to obtain a thermal management parameter sequence; a thermal management analysis module, the thermal management analysis module is used to perform thermal management operation analysis and thermal management margin analysis based on the thermal management parameter sequence and thermal parameters, and obtain thermal management normal parameters and thermal management margin parameters; a compensation correction module, the compensation correction module is used to perform thermal management out-of-control analysis based on the thermal management normal parameters, thermal runaway parameters and thermal management margin parameters, obtain thermal management out-of-control probability information, perform compensation correction based on the thermal runaway monitoring accuracy coefficient, and make judgments, decisions and early warnings based on the compensated thermal management out-of-control probability information.

[0007] The above-mentioned energy storage thermal management out-of-control warning method and system solve the technical problem that in the prior art, thermal runaway is judged only based on temperature without considering the change trend on the management side, resulting in missed judgment and wrong judgment. By considering the change trend on the management side and combining temperature and gas for analysis, the technical effect of improving the safety of the energy storage system is achieved.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic diagram of a process of an energy storage thermal management out-of-control warning method in one embodiment; Figure 2 A schematic diagram of a flow chart for constructing a temperature characteristic distribution sequence and a gas characteristic distribution sequence of an energy storage thermal management out-of-control early warning method in one embodiment; Figure 3The structure block diagram of an energy storage thermal management out-of-control warning system in one embodiment.

[0010] Explanation of the reference numerals: characteristic value acquisition module 11 , distribution sequence construction module 12 , fitting verification module 13 , thermal management parameter sequence acquisition module 14 , thermal management analysis module 15 , compensation correction module 16 . DETAILED DESCRIPTION

[0011] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0012] like Figure 1 As shown, the present application provides a method for early warning of out-of-control of energy storage thermal management, the method comprising: By means of a monitoring module arranged in the energy storage device, at multiple monitoring time points, the temperature characteristic values ​​and gas characteristic values ​​of multiple monitoring points in the energy storage device are continuously monitored and collected to obtain a temperature characteristic value array set and a gas characteristic value array set; Energy storage thermal management runaway warning is an important measure to ensure the safe and stable operation of energy storage systems. In energy storage systems, since batteries and other energy storage components generate heat during operation, if thermal management cannot be effectively performed, thermal runaway may occur, leading to safety accidents such as fires. In this application, thermal runaway refers to the temperature being too high to continue working, rather than fires, such as excessively high mobile phone temperatures or high CPU temperatures. This application provides an energy storage thermal management runaway warning method to ensure the safe and stable operation of energy storage systems. By monitoring temperature characteristics, gas characteristics, and other means, thermal runaway events can be effectively prevented and controlled.

[0013] Monitoring modules are arranged at key locations inside the energy storage device, such as battery packs, heat exchangers, etc. The monitoring module is a device for real-time monitoring and data collection. According to specific needs and application scenarios, the target object is continuously and accurately collected. The application includes a temperature monitor and a gas detector to capture changes in temperature and gas concentration. According to the operating characteristics and safety requirements of the energy storage device, multiple monitoring time points are set. The monitoring time points are the time used to monitor the energy storage device. They can be fixed time intervals, such as every hour, every day, etc., or they can be triggered based on certain specific conditions, such as temperature changes exceeding a threshold, abnormal gas concentration, etc. Continuously monitor and collect temperature characteristic values ​​and gas characteristic values ​​of multiple monitoring points in the energy storage device. Multiple monitoring points are coordinate points where the monitoring module is arranged; the temperature characteristic value refers to the temperature information monitored by the monitoring module, and the gas characteristic value refers to the gas information monitored by the monitoring module, such as the concentration of carbon dioxide and hydrogen; the characteristic values ​​of the monitoring points are integrated to obtain a temperature characteristic value array set and a gas characteristic value array set. Through the above method, a monitoring module is deployed in the energy storage device to continuously monitor and collect temperature characteristic values ​​and gas characteristic values ​​of multiple monitoring points, so as to realize real-time monitoring and early warning of the safety status of the energy storage system.

[0014] Constructing a temperature characteristic distribution sequence and a gas characteristic distribution sequence according to the temperature characteristic value array set and the gas characteristic value array set; Based on the eigenvalue array, the eigenvalue distribution of all areas of the energy storage device is constructed to form an eigenvalue distribution image, in which the temperature characteristic distribution sequence describes the evolution of the temperature in the energy storage device over time. The gas characteristic distribution sequence describes the evolution of the gas concentration in the energy storage device over time. The above method provides an important basis for subsequent fault warning and disposal.

[0015] like Figure 2 As shown, a plurality of basic temperature characteristic distributions are constructed according to a plurality of temperature characteristic value arrays in the temperature characteristic value array set; Interpolating and rendering the temperature characteristic values ​​outside the multiple monitoring points in the multiple basic temperature characteristic distributions to obtain multiple temperature characteristic distributions and construct a temperature characteristic distribution sequence; The gas characteristic distribution sequence is constructed based on the gas characteristic value array set.

[0016] When constructing the basic temperature characteristic distribution, each array of temperature characteristic values ​​represents the temperature data at a time point, which is regarded as a two-dimensional matrix, in which rows represent different monitoring points, and columns represent different times or different sampling periods. The two-dimensional matrix is ​​regarded as multiple basic temperature characteristic distributions, and each distribution corresponds to the temperature data at a time point. The temperature characteristic values ​​outside the multiple monitoring points in the multiple basic temperature characteristic distributions are interpolated and rendered. Since the monitoring points are usually limited, in order to obtain a more detailed temperature distribution map, an interpolation algorithm is required to estimate the temperature values ​​between the monitoring points, and the temperature values ​​of unknown locations are estimated based on the temperature values ​​of the known monitoring points. The temperature characteristic values ​​outside the multiple monitoring points in each basic temperature characteristic distribution are interpolated and rendered, and a more complete temperature distribution map can be obtained, which describes the temperature distribution at different time points. Arranging all the interpolated and rendered temperature distribution maps in chronological order constitutes a temperature characteristic distribution sequence, which shows the evolution of the temperature in the energy storage device over time. Similar to the temperature characteristic value, the gas characteristic value array represents the gas data at a time point. As in the above method, the gas characteristic value array is regarded as a two-dimensional matrix, where the rows represent different monitoring points and the columns represent different times. Each two-dimensional matrix is ​​a basic gas characteristic distribution. Although the gas concentration usually has good spatial continuity, in some cases, such as when there is an obvious leakage source or diffusion boundary, interpolation may also be required to obtain a more accurate distribution. According to the staff's own judgment, the gas characteristic value arrays at each time point are arranged in chronological order to form a gas characteristic distribution sequence. The gas characteristic distribution sequence is constructed to show the evolution of gas concentration in the energy storage device over time. Through the above method, the temperature characteristic distribution sequence and the gas characteristic distribution sequence are constructed to provide basic data support for subsequent data analysis, anomaly detection and early warning.

[0017] Based on the temperature characteristic distribution sequence and the gas characteristic distribution sequence, respectively, a heating analysis and a thermal runaway trend analysis of the energy storage device are performed to obtain temperature heating parameters, gas heating parameters, temperature thermal runaway parameters and gas thermal runaway parameters, and fitting verification is performed to obtain heating parameters, thermal runaway parameters and thermal runaway monitoring accuracy coefficients; Based on the temperature characteristic distribution sequence and the gas characteristic distribution sequence, the heating analysis and thermal runaway trend analysis of the energy storage device are respectively performed, which refers to identifying the heating situation and thermal runaway trend. The temperature heating parameters reflect the heating situation of the energy storage device. The gas heating parameters are obtained by determining the types of gases closely related to the heating state of the energy storage device and focusing on the concentration changes of these gases; the temperature thermal runaway parameters analyze the diffusion of abnormal temperature growth in space to evaluate the diffusion speed and range of thermal runaway; the gas thermal runaway parameters monitor the abnormal growth pattern of the concentration of specific gases in the gas characteristic distribution sequence, which is related to the gas release generated during the thermal runaway process, and analyze the changes in the proportion of different gas components in the gas characteristic distribution to identify the gas composition change pattern related to thermal runaway; the thermal runaway trend is identified by slowfast, and the temperature characteristic distribution sequence and the gas characteristic distribution sequence can form a distribution image sequence in time series. The heating parameter refers to the temperature heating parameter and the gas heating parameter obtained after fitting, which is used to describe the heating situation of the energy storage device; fitting in this application refers to calculating the mean of the temperature thermal runaway parameter and the gas thermal runaway parameter, and the mean is used as the thermal runaway parameter; according to the deviation of the temperature thermal runaway parameter and the gas thermal runaway parameter from the final thermal runaway parameter, the thermal runaway monitoring accuracy coefficient is calculated, such as the deviation mean, that is, the difference between the thermal runaway parameter and the temperature thermal runaway parameter divided by the thermal runaway parameter. Through the above steps, the temperature heating parameter, the gas heating parameter, the temperature thermal runaway parameter and the gas thermal runaway parameter are obtained, and fitting verification is performed to obtain the heating parameter, the thermal runaway parameter and the thermal runaway monitoring accuracy coefficient. These parameters and coefficients will provide an important basis for the safety monitoring and fault warning of the energy storage device.

[0018] Sampling and downsampling the temperature characteristic distribution sequence according to a first sampling step length to obtain a first temperature characteristic distribution set; Sampling the temperature characteristic distribution sequence according to a second sampling step length to obtain a second temperature characteristic distribution set, wherein the second sampling step length is greater than the first sampling step length; According to the first temperature characteristic distribution set, performing a heating analysis on the energy storage device to obtain the temperature heating parameter, and according to the first temperature characteristic distribution set and the second temperature characteristic distribution set, performing a thermal runaway trend analysis on the energy storage device to obtain the temperature thermal runaway parameter; Based on the gas characteristic distribution sequence, a heating analysis and a thermal runaway trend analysis of the energy storage device are performed to obtain gas heating parameters and gas thermal runaway parameters.

[0019] The first sampling step is set to sample the temperature characteristic distribution sequence. The first sampling step refers to the time length or number of data points for selecting samples according to a fixed time interval or data point interval when sampling data. In the present application, the first sampling step is used to sample and downsample the temperature characteristic distribution sequence to obtain a first temperature characteristic distribution set, which includes the temperature characteristic distribution data selected according to the first sampling step; the temperature characteristic distribution sequence is sampled using the second sampling step, and the second sampling step is larger than the first sampling step, which means that the selected data points are sparser, and the second temperature characteristic distribution set is obtained, which includes the temperature characteristic distribution data selected according to the second sampling step, for subsequent thermal runaway trend analysis. The first temperature characteristic distribution set is used to perform heating analysis of the energy storage device. By comparing the temperature characteristic distributions at different time points, the areas and patterns of abnormal heating can be identified, thereby obtaining temperature heating parameters. To conduct thermal runaway trend analysis, it is necessary to consider both the first temperature characteristic distribution set, which contains more detailed data, and the second temperature characteristic distribution set, which contains sparser data but with a longer time span. By comparing and analyzing these two sets, the long-term trend of temperature change and potential thermal runaway risks can be identified. Based on the above analysis, the temperature thermal runaway parameters can be obtained, which describe the temperature changes of the energy storage device during the thermal runaway process, such as the temperature rise rate, the time to reach the critical temperature, etc. For the gas characteristic distribution sequence, the heating analysis and thermal runaway trend analysis are directly performed. By comparing the gas characteristic distribution at different time points, the gas change pattern related to heating and thermal runaway can be identified. Based on the analysis of the gas characteristic distribution sequence, the gas heating parameters, such as the gas concentration change rate; gas thermal runaway parameters, such as the abnormal growth of the concentration of a specific gas, can be obtained. Through the above method, the gas heating parameters and gas thermal runaway parameters are obtained, which lays a foundation for subsequent research.

[0020] Based on the thermal management data record of the energy storage device, a set of sample temperature characteristic distribution sequences is obtained, and the samples are processed according to the first sampling step length and the second sampling step length to obtain a plurality of sample first temperature characteristic distribution sets and a plurality of sample second temperature characteristic distribution sets; Based on the thermal management data record of the energy storage device, a sample temperature heating parameter set is obtained, and according to the probability of thermal runaway of the energy storage device, a sample temperature thermal runaway parameter set is obtained; Using the plurality of sample first temperature characteristic distribution sets and the sample temperature heating parameter set, constructing a temperature heating analysis path, analyzing the first temperature characteristic distribution set, and obtaining the temperature heating parameter; By using the multiple sample first temperature characteristic distribution sets, the multiple sample second temperature characteristic distribution sets and the sample temperature thermal runaway parameter set, a temperature thermal runaway analysis path is constructed based on a slowfast network, and the first temperature characteristic distribution set and the second temperature characteristic distribution set are analyzed to obtain the temperature thermal runaway parameters.

[0021] From the thermal management data record of the energy storage device, temperature-related data is extracted to construct a sample temperature feature distribution sequence set, which includes temperature data at different times and locations. The sample temperature feature distribution sequence set is processed according to the preset first sampling step and second sampling step. The first sampling step is usually used to obtain more detailed data features, while the second sampling step is used to capture the trend of changes over a longer time range. Based on the first sampling step, multiple sample first temperature feature distribution sets are obtained, and based on the second sampling step, multiple sample second temperature feature distribution sets are obtained. From the thermal management data record of the energy storage device, parameters related to temperature heating are extracted to construct a sample temperature heating parameter set, including temperature rise rate, local hot spot temperature, etc. According to the historical data or expert knowledge of thermal runaway of the energy storage device, parameters related to temperature thermal runaway are obtained to construct a sample temperature thermal runaway parameter set. These parameters may include the time to reach the critical temperature, the pattern of abnormal temperature growth, etc. Using multiple sample first temperature feature distribution sets and sample temperature heating parameter sets, a temperature heating analysis path is constructed to learn the mapping relationship from temperature feature distribution to temperature heating parameters. When analyzing the new first temperature feature distribution set, it is input into the temperature heating analysis path to obtain the corresponding temperature heating parameters. Using multiple sample first temperature feature distribution sets, multiple sample second temperature feature distribution sets and sample temperature thermal runaway parameter sets, a temperature thermal runaway analysis path is constructed based on a slowfast network. The slowfast network is a network structure that can process information of different time scales at the same time, and is very suitable for thermal runaway trend analysis. During the construction process, the first temperature feature distribution set is used to capture the temperature details in a short time, while the second temperature feature distribution set is used to capture the temperature change trend in a longer time range. When analyzing the new first temperature feature distribution set and the second temperature feature distribution set, they are input into the temperature thermal runaway analysis path to obtain the corresponding temperature thermal runaway parameters. The analysis path is constructed by the above method, and the temperature data is monitored and analyzed in real time to provide timely fault warnings and disposal suggestions.

[0022] According to the temperature heating parameter and the gas heating parameter, the heating parameter is obtained by calculation and fitting; According to the temperature thermal runaway parameter and the gas thermal runaway parameter, calculating and fitting to obtain the thermal runaway parameter; The temperature thermal runaway parameter, the gas thermal runaway parameter and the deviation of the thermal runaway parameter are verified respectively, and the thermal runaway monitoring accuracy coefficient is obtained by calculation.

[0023] According to the temperature heating parameters, such as the temperature rise rate, hot spot temperature, etc. and the gas heating parameters, such as the gas concentration change rate, gas type, etc., the heating parameters are calculated and fitted, and the average values ​​of these indicators are selected to obtain the heating parameters. For the temperature thermal runaway parameters and the gas thermal runaway parameters, a similar method is used to calculate a thermal runaway parameter, including the time to reach the critical temperature, the mode of abnormal temperature growth, the rapid increase of gas concentration, etc. When verifying the deviation of the thermal runaway parameters, a data set or simulation data of the actual thermal runaway event is required, including the known real thermal runaway parameters, and statistical methods such as mean square error, absolute error, etc. are used to quantify the difference between the thermal runaway parameters predicted by the model and the real thermal runaway parameters. The thermal runaway monitoring accuracy coefficient can be defined based on the deviation calculated in the previous step, and the proportion of correctly predicted thermal runaway events is calculated, such as the difference between the thermal runaway parameter and the temperature thermal runaway parameter to the thermal runaway parameter. These indicators are calculated for the temperature thermal runaway parameter, the gas thermal runaway parameter and the comprehensive thermal runaway parameter, and the thermal runaway monitoring accuracy coefficient is calculated based on these indicators. Through the above method, the accuracy coefficient of thermal runaway monitoring is obtained, which lays a foundation for the follow-up.

[0024] Collecting thermal management parameters of the thermal management module in the energy storage device at the multiple monitoring time points to obtain a thermal management parameter sequence; Collect the thermal management parameters of the thermal management module in the energy storage device at the multiple monitoring time points. The thermal management parameters can reflect the performance of the thermal management module and the thermal state of the energy storage device, including but not limited to temperature, gas, etc.; install corresponding sensors on the thermal management module of the energy storage device to monitor the selected parameters in real time, configure the data acquisition system to ensure that the sensor data can be collected in real time and accurately, obtain the thermal management parameters, arrange the thermal management parameters collected at all monitoring time points in chronological order, and form a thermal management parameter sequence, which contains the status information of the thermal management module at different time points, and can be used to analyze the thermal management performance of the energy storage device and predict potential thermal runaway risks. Through the above method, the thermal management parameter sequence is obtained, which lays the foundation for subsequent thermal management analysis.

[0025] Based on the thermal management parameter sequence and the thermal parameters, a thermal management operation analysis and a thermal management margin analysis are performed to obtain thermal management normal parameters and thermal management margin parameters; Thermal management operation analysis refers to observing the changing trend of the thermal management parameter sequence over time, judging whether there are abnormal fluctuations or long-term changing trends, evaluating the stability of thermal management parameters at different time points, checking whether there are unstable factors or periodic changes, and evaluating the performance of the thermal management module, such as cooling efficiency, temperature control accuracy, etc., based on the thermal management parameter sequence. Based on historical data and expert knowledge, the normal parameters of the thermal management module are determined, including temperature range, pressure range, flow range, etc. The thermal management margin parameters are the margins for the thermal management module to improve thermal management efficiency, such as the margin for the fan to reach the maximum speed, as a reserve resource for subsequent management of thermal runaway; the margin of the thermal management module is calculated based on the thermal management parameter sequence and normal parameters. Through the above method, the normal parameters and thermal management margin parameters of thermal management are obtained, which provides support for subsequent compensation and correction.

[0026] Based on the thermal management data record of the energy storage device, a sample thermal management parameter sequence set and a sample thermal parameter set are obtained, and according to the thermal management situation of the energy storage device, a sample thermal management normal parameter set is evaluated and obtained; The sample thermal management parameter sequence set, the sample thermal parameter set and the sample thermal management normal parameter set are used to train a thermal management normal analysis path, analyze and identify the thermal management parameter sequence and thermal parameters, and obtain the thermal management normal parameters; Based on the thermal management parameter sequence, outputting a maximum thermal management parameter; A thermal management margin parameter is calculated according to the maximum thermal management parameter.

[0027] From the thermal management data records of the energy storage device, extract the thermal management parameter sequence and thermal parameters of the historical period to form a sample thermal management parameter sequence set and a sample thermal parameter set. According to the design specifications of the energy storage device, historical operation data and expert experience, evaluate and determine the range or threshold of the normal thermal management parameters to form a sample thermal management normal parameter set. Select a statistical learning method to train the thermal management normal analysis path, and use the sample thermal management parameter sequence set, sample thermal parameter set and sample thermal management normal parameter set as training data to train the thermal management normal analysis model. Use the validation data set to verify the accuracy and performance of the model. If the performance is poor, it is necessary to adjust the model parameters, input the thermal management parameter sequence and thermal parameters to be analyzed into the trained thermal management normal analysis model, process and analyze the input data, determine whether the thermal management parameters are within the normal range, and output the thermal management normal parameters. Find the maximum value from the thermal management parameter sequence, that is, the maximum thermal management parameter, including the maximum temperature, maximum pressure, etc. The maximum thermal management parameter is used as the output result for subsequent analysis and calculation. According to the design specifications and safety requirements of the energy storage device, define the calculation method and standard of the thermal management margin parameter. The margin parameter usually represents the difference or ratio between the current thermal management parameter and the safety threshold. The thermal management margin parameter is calculated using the maximum thermal management parameter and the defined thermal management normal parameter range or threshold. For example, if the maximum temperature is 10°C away from the safety threshold, the thermal management margin parameter is 10°C. The calculated thermal management margin parameter is used as the output result to evaluate the thermal management safety performance of the energy storage device and formulate a maintenance plan. Through the above steps, based on the thermal management data record of the energy storage device, the thermal management parameters are obtained and analyzed, and the thermal management margin parameters are calculated, thereby providing data support and decision-making basis for the thermal management of the energy storage device.

[0028] A thermal management runaway analysis is performed based on the thermal management normal parameters, thermal runaway parameters and thermal management margin parameters to obtain thermal management runaway probability information, compensation correction is performed based on the thermal runaway monitoring accuracy coefficient, and a judgment decision warning is performed based on the compensated thermal management runaway probability information.

[0029] According to the design specifications of the energy storage device, historical operating data and expert experience, the thermal runaway threshold is set, including the upper limit of thermal management parameters such as temperature and pressure. Exceeding these thresholds will cause thermal runaway. According to the current thermal management parameters, such as temperature, pressure, etc., and the degree of proximity to the thermal runaway threshold, the thermal runaway risk index is calculated to indicate the degree of proximity of the current thermal management state to thermal runaway. Based on the thermal runaway risk index, normal thermal management parameters and thermal runaway parameters, the probability of thermal runaway under the current thermal management state, that is, the thermal management runaway probability, is evaluated, and the thermal management runaway probability information is obtained. The thermal runaway monitoring accuracy coefficient reflects the accuracy and reliability of the thermal runaway monitoring system, which is obtained by the above calculation. The thermal runaway monitoring accuracy coefficient is used to compensate and correct the thermal management runaway probability obtained by the preliminary evaluation. The compensation correction is to use 1 plus or minus the thermal runaway monitoring accuracy coefficient to amplify and reduce the thermal management runaway probability, and retain the amplified one to improve the safety of the thermal management runaway warning. The corrected thermal management runaway probability is closer to the actual situation, reducing the possibility of false alarms and missed alarms. According to the safety requirements, operation strategies and risk assessment results of the energy storage device, a threshold for judgment decision warning is set. When the probability of thermal management runaway after compensation exceeds this threshold, a warning will be triggered. Through the above method, the risk of thermal management runaway of the energy storage device is accurately assessed, and judgment decision warning is performed based on the information of thermal management runaway probability after compensation, thereby improving the safety and reliability of the energy storage device.

[0030] Based on the thermal runaway monitoring data records of the energy storage device, a sample thermal management normal parameter set, a sample thermal runaway parameter set and a sample thermal management margin parameter set are obtained, and a sample thermal management runaway probability information set is obtained; Using the sample thermal management normal parameter set, the sample thermal runaway parameter set, the sample thermal management margin parameter set and the sample thermal management runaway probability information set, a thermal management runaway predictor is constructed, and thermal management normal parameters, thermal runaway parameters and thermal management margin parameters are analyzed to obtain thermal management runaway probability information; Based on the thermal runaway monitoring accuracy coefficient, a compensation correction calculation is performed on the thermal management runaway probability information to obtain maximum thermal management runaway probability information and minimum heat pipe runaway probability information; The maximum thermal management runaway probability information is output as the compensated thermal management runaway probability information.

[0031] From the thermal runaway monitoring data record of the energy storage device, extract the thermal management normal parameters, thermal runaway parameters, thermal management margin parameters and corresponding thermal management runaway probability information of the historical period, organize the above data to form a sample thermal management normal parameter set, a sample thermal runaway parameter set, a sample thermal management margin parameter set and a sample thermal management runaway probability information set; select a neural network model to build a thermal management runaway predictor, use the sample thermal management normal parameter set, the sample thermal runaway parameter set, the sample thermal management margin parameter set and the sample thermal management runaway probability information set as training data to train the thermal management runaway predictor, and adjust the model parameters and optimize the algorithm so that the predictor can accurately predict the thermal management runaway probability. Input the real-time thermal management normal parameters, thermal runaway parameters and thermal management margin parameters into the trained thermal management runaway predictor. The predictor outputs the corresponding thermal management runaway probability information based on the input real-time data. Based on the thermal runaway monitoring accuracy coefficient, the predicted thermal management runaway probability information is compensated and corrected. The specific calculation method can be determined according to the actual situation, such as adjusting the probability value by multiplying by a correction factor. After compensation and correction, the maximum thermal management out-of-control probability information and the minimum thermal management out-of-control probability information can be obtained. In this application, we only care about the maximum thermal management out-of-control probability information, because it represents the risk in the worst case, and the maximum thermal management out-of-control probability information is output as the compensated thermal management out-of-control probability information. This result can be used for subsequent judgment and decision-making warnings. For example, when the maximum thermal management out-of-control probability exceeds a certain threshold, the corresponding early warning measures are triggered. The above method is used to improve the safety of thermal management out-of-control warnings. It solves the technical problem that the prior art only judges thermal runaway based on temperature, does not consider the change trend on the management side, and leads to missed judgments and wrong judgments. By considering the change trend on the management side and combining temperature and gas for analysis, the technical effect of improving the safety of the energy storage system is achieved.

[0032] In summary, the beneficial effects of this method include: 1. By continuously monitoring the temperature characteristic values ​​and gas characteristic values ​​of multiple monitoring points in the energy storage device and constructing temperature characteristic distribution sequences and gas characteristic distribution sequences, we can have a more comprehensive understanding of the thermal management status of the energy storage device. Based on these data, we can conduct heating analysis and thermal runaway trend analysis to more accurately determine whether the energy storage device has a thermal runaway risk, thereby improving the accuracy of early warning; 2. By using temperature feature distribution sets with different sampling steps for analysis, this method can capture the thermal management changes of energy storage devices at different time scales. In particular, through thermal runaway trend analysis, potential thermal runaway risks can be discovered in advance; 3. Compensation and correction based on the probability information of thermal management runaway and the accuracy coefficient of thermal runaway monitoring can more accurately assess the risk of thermal management runaway of energy storage devices. Through judgment, decision-making and early warning, corresponding safety measures can be taken in time to avoid or reduce the damage caused by thermal runaway events to the energy storage system and improve the safety of the system.

[0033] 4. Through thermal management operation analysis and thermal management margin analysis, we can understand the operation status and margin of the energy storage device in thermal management, and provide data support for optimizing the thermal management strategy.

[0034] like Figure 3 As shown, the embodiment of the present application includes a storage thermal management out-of-control warning system, the system comprising: A characteristic value acquisition module 11, which is used to continuously monitor and collect temperature characteristic values ​​and gas characteristic values ​​of multiple monitoring points in the energy storage device at multiple monitoring time points through a monitoring module arranged in the energy storage device, and obtain a temperature characteristic value array set and a gas characteristic value array set; A distribution sequence construction module 12, wherein the distribution sequence construction module 12 is used to construct a temperature characteristic distribution sequence and a gas characteristic distribution sequence according to the temperature characteristic value array set and the gas characteristic value array set; A fitting verification module 13, the fitting verification module 13 is used to perform heating analysis and thermal runaway trend analysis of the energy storage device based on the temperature characteristic distribution sequence and the gas characteristic distribution sequence, obtain temperature heating parameters, gas heating parameters, temperature thermal runaway parameters and gas thermal runaway parameters, and perform fitting verification to obtain heating parameters, thermal runaway parameters and thermal runaway monitoring accuracy coefficients; A thermal management parameter sequence acquisition module 14, which is used to collect thermal management parameters of the thermal management module in the energy storage device at the multiple monitoring time points to obtain a thermal management parameter sequence; A thermal management analysis module 15, the thermal management analysis module 15 is used to perform thermal management operation analysis and thermal management margin analysis based on the thermal management parameter sequence and thermal parameters, and obtain thermal management normal parameters and thermal management margin parameters; The compensation correction module 16 is used to perform thermal management runaway analysis based on the thermal management normal parameters, thermal runaway parameters and thermal management margin parameters, obtain thermal management runaway probability information, perform compensation correction based on the thermal runaway monitoring accuracy coefficient, and make judgment decisions and early warnings based on the compensated thermal management runaway probability information.

[0035] Furthermore, the embodiment of the present application also includes: A basic temperature characteristic distribution construction module, wherein the basic temperature characteristic distribution construction module is used to construct a plurality of basic temperature characteristic distributions according to a plurality of temperature characteristic value arrays in the temperature characteristic value array set; An interpolation rendering module, the interpolation rendering module is used to perform interpolation rendering on the temperature characteristic values ​​outside the multiple monitoring points in the multiple basic temperature characteristic distributions, obtain multiple temperature characteristic distributions, and construct a temperature characteristic distribution sequence; An array collection module, wherein the array collection module is used to construct the gas characteristic distribution sequence based on the gas characteristic value array collection.

[0036] Furthermore, the embodiment of the present application also includes: A sampling module, the sampling module is used to sample and downsample the temperature feature distribution sequence according to a first sampling step length to obtain a first temperature feature distribution set; A sampling module, the sampling module is used to sample the temperature characteristic distribution sequence according to a second sampling step length to obtain a second temperature characteristic distribution set, wherein the second sampling step length is greater than the first sampling step length; a thermal runaway trend analysis module, the thermal runaway trend analysis module being used to perform a heating analysis of the energy storage device according to the first temperature characteristic distribution set to obtain the temperature heating parameter, and to perform a thermal runaway trend analysis of the energy storage device according to the first temperature characteristic distribution set and the second temperature characteristic distribution set to obtain the temperature thermal runaway parameter; An energy storage device analysis module is used to perform heating analysis and thermal runaway trend analysis on the energy storage device based on the gas characteristic distribution sequence, and obtain gas heating parameters and gas thermal runaway parameters.

[0037] Furthermore, the embodiment of the present application also includes: A processing module, the processing module is used to obtain a set of sample temperature characteristic distribution sequence based on the thermal management data record of the energy storage device, and process according to the first sampling step length and the second sampling step length to obtain a plurality of sample first temperature characteristic distribution sets and a plurality of sample second temperature characteristic distribution sets; A sample temperature thermal runaway parameter set acquisition module, the sample temperature thermal runaway parameter set acquisition module is used to acquire a sample temperature heating parameter set based on the thermal management data record of the energy storage device, and acquire the sample temperature thermal runaway parameter set according to the probability of thermal runaway of the energy storage device; a temperature heating analysis path construction module, the temperature heating analysis path construction module is used to use the first temperature characteristic distribution sets of the multiple samples and the sample temperature heating parameter set to construct a temperature heating analysis path, analyze the first temperature characteristic distribution set, and obtain the temperature heating parameter; A temperature thermal runaway analysis path module, wherein the temperature thermal runaway analysis path module is used to adopt the multiple sample first temperature characteristic distribution sets, the multiple sample second temperature characteristic distribution sets and the sample temperature thermal runaway parameter set, based on a slowfast network, to construct a temperature thermal runaway analysis path, analyze the first temperature characteristic distribution set and the second temperature characteristic distribution set, and obtain the temperature thermal runaway parameters.

[0038] Furthermore, the embodiment of the present application also includes: A heating parameter calculation module, the heating parameter calculation module is used to calculate and fit the heating parameter according to the temperature heating parameter and the gas heating parameter; A thermal runaway parameter calculation module, the thermal runaway parameter calculation module is used to calculate and fit the thermal runaway parameter according to the temperature thermal runaway parameter and the gas thermal runaway parameter; A verification module, wherein the verification module is used to respectively verify the temperature thermal runaway parameter, the gas thermal runaway parameter and the deviation of the thermal runaway parameter, and calculate the thermal runaway monitoring accuracy coefficient.

[0039] Furthermore, the embodiment of the present application also includes: A thermal management situation acquisition module, the thermal management situation acquisition module is used to acquire a sample thermal management parameter sequence set and a sample thermal parameter set based on the thermal management data record of the energy storage device, and evaluate and acquire a sample thermal management normal parameter set according to the thermal management situation of the energy storage device; a thermal management normal analysis path training module, the thermal management normal analysis path training module being used to train a thermal management normal analysis path by using the sample thermal management parameter sequence set, the sample thermal parameter set and the sample thermal management normal parameter set, analyzing and identifying the thermal management parameter sequence and thermal parameters, and obtaining the thermal management normal parameters; a maximum thermal management parameter output module, the maximum thermal management parameter output module being used to output a maximum thermal management parameter based on the thermal management parameter sequence; A thermal management margin parameter module is used to calculate and obtain a thermal management margin parameter according to the maximum thermal management parameter.

[0040] Furthermore, the embodiment of the present application also includes: A runaway probability information set acquisition module, the runaway probability information set acquisition module is used to acquire a sample thermal management normal parameter set, a sample thermal runaway parameter set and a sample thermal management margin parameter set based on the thermal runaway monitoring data record of the energy storage device, and acquire a sample thermal management runaway probability information set; a thermal management runaway probability information acquisition module, the thermal management runaway probability information acquisition module being used to construct a thermal management runaway predictor by using the sample thermal management normal parameter set, the sample thermal runaway parameter set, the sample thermal management margin parameter set and the sample thermal management runaway probability information set, and to perform thermal management runaway analysis on the thermal management normal parameters, the thermal runaway parameters and the thermal management margin parameters to obtain thermal management runaway probability information; A compensation correction calculation module, the compensation correction calculation module is used to perform compensation correction calculation on the thermal management runaway probability information based on the thermal runaway monitoring accuracy coefficient to obtain maximum thermal management runaway probability information and minimum heat pipe runaway probability information; A compensation thermal management runaway probability information acquisition module is used to output the maximum thermal management runaway probability information as the compensation thermal management runaway probability information.

[0041] For a specific embodiment of an energy storage thermal management out-of-control warning system, please refer to the embodiment of an energy storage thermal management out-of-control warning method described above, which will not be repeated here. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0042] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0043] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A method for early warning of out-of-control thermal management of energy storage, It is characterized in that The method comprises: By means of a monitoring module arranged in the energy storage device, at multiple monitoring time points, the temperature characteristic values ​​and gas characteristic values ​​of multiple monitoring points in the energy storage device are continuously monitored and collected to obtain a temperature characteristic value array set and a gas characteristic value array set; Constructing a temperature characteristic distribution sequence and a gas characteristic distribution sequence according to the temperature characteristic value array set and the gas characteristic value array set; Based on the temperature characteristic distribution sequence and the gas characteristic distribution sequence, respectively, a heating analysis and a thermal runaway trend analysis of the energy storage device are performed to obtain temperature heating parameters, gas heating parameters, temperature thermal runaway parameters and gas thermal runaway parameters, and fitting verification is performed to obtain heating parameters, thermal runaway parameters and thermal runaway monitoring accuracy coefficients; Collecting thermal management parameters of the thermal management module in the energy storage device at the multiple monitoring time points to obtain a thermal management parameter sequence; Based on the thermal management parameter sequence and the thermal parameters, a thermal management operation analysis and a thermal management margin analysis are performed to obtain thermal management normal parameters and thermal management margin parameters; A thermal management runaway analysis is performed based on the thermal management normal parameters, thermal runaway parameters and thermal management margin parameters to obtain thermal management runaway probability information, compensation correction is performed based on the thermal runaway monitoring accuracy coefficient, and a judgment decision warning is performed based on the compensated thermal management runaway probability information.

2. The method according to claim 1, It is characterized in that According to the temperature characteristic value array set and the gas characteristic value array set, a temperature characteristic distribution sequence and a gas characteristic distribution sequence are constructed, including: Constructing a plurality of basic temperature characteristic distributions according to a plurality of temperature characteristic value arrays in the temperature characteristic value array set; Interpolating and rendering the temperature characteristic values ​​outside the multiple monitoring points in the multiple basic temperature characteristic distributions to obtain multiple temperature characteristic distributions and construct a temperature characteristic distribution sequence; The gas characteristic distribution sequence is constructed based on the gas characteristic value array set.

3. The method according to claim 1, It is characterized in that Based on the temperature characteristic distribution sequence and the gas characteristic distribution sequence, heat generation analysis and thermal runaway trend analysis of the energy storage device are performed respectively, including: Sampling and downsampling the temperature characteristic distribution sequence according to a first sampling step length to obtain a first temperature characteristic distribution set; Sampling the temperature characteristic distribution sequence according to a second sampling step length to obtain a second temperature characteristic distribution set, wherein the second sampling step length is greater than the first sampling step length; According to the first temperature characteristic distribution set, performing a heating analysis on the energy storage device to obtain the temperature heating parameter, and according to the first temperature characteristic distribution set and the second temperature characteristic distribution set, performing a thermal runaway trend analysis on the energy storage device to obtain the temperature thermal runaway parameter; Based on the gas characteristic distribution sequence, a heating analysis and a thermal runaway trend analysis of the energy storage device are performed to obtain gas heating parameters and gas thermal runaway parameters.

4. The method according to claim 3, It is characterized in that According to the first temperature characteristic distribution set, performing a heating analysis on the energy storage device to obtain the temperature heating parameter, and according to the first temperature characteristic distribution set and the second temperature characteristic distribution set, performing a thermal runaway trend analysis on the energy storage device to obtain the temperature thermal runaway parameter, including: Based on the thermal management data record of the energy storage device, a set of sample temperature characteristic distribution sequences is obtained, and the samples are processed according to the first sampling step length and the second sampling step length to obtain a plurality of sample first temperature characteristic distribution sets and a plurality of sample second temperature characteristic distribution sets; Based on the thermal management data record of the energy storage device, a sample temperature heating parameter set is obtained, and according to the probability of thermal runaway of the energy storage device, a sample temperature thermal runaway parameter set is obtained; Using the plurality of sample first temperature characteristic distribution sets and the sample temperature heating parameter set, constructing a temperature heating analysis path, analyzing the first temperature characteristic distribution set, and obtaining the temperature heating parameter; By using the multiple sample first temperature characteristic distribution sets, the multiple sample second temperature characteristic distribution sets and the sample temperature thermal runaway parameter set, a temperature thermal runaway analysis path is constructed based on a slowfast network, and the first temperature characteristic distribution set and the second temperature characteristic distribution set are analyzed to obtain the temperature thermal runaway parameters.

5. The method according to claim 3, It is characterized in that Perform fitting verification to obtain heating parameters, thermal runaway parameters and thermal runaway monitoring accuracy coefficients, including: According to the temperature heating parameter and the gas heating parameter, the heating parameter is obtained by calculation and fitting; According to the temperature thermal runaway parameter and the gas thermal runaway parameter, calculating and fitting to obtain the thermal runaway parameter; The temperature thermal runaway parameter, the gas thermal runaway parameter and the deviation of the thermal runaway parameter are verified respectively, and the thermal runaway monitoring accuracy coefficient is obtained by calculation.

6. The method according to claim 1, It is characterized in that Based on the thermal management parameter sequence and the thermal parameters, thermal management operation analysis and thermal management margin analysis are performed to obtain thermal management normal parameters and thermal management margin parameters, including: Based on the thermal management data record of the energy storage device, a sample thermal management parameter sequence set and a sample thermal parameter set are obtained, and according to the thermal management situation of the energy storage device, a sample thermal management normal parameter set is evaluated and obtained; The sample thermal management parameter sequence set, the sample thermal parameter set and the sample thermal management normal parameter set are used to train a thermal management normal analysis path, analyze and identify the thermal management parameter sequence and thermal parameters, and obtain the thermal management normal parameters; Based on the thermal management parameter sequence, outputting a maximum thermal management parameter; A thermal management margin parameter is calculated according to the maximum thermal management parameter.

7. The method according to claim 1, It is characterized in that Performing a thermal management runaway analysis based on the thermal management normal parameters, thermal runaway parameters and thermal management margin parameters to obtain thermal management runaway probability information, and performing compensation correction based on the thermal runaway monitoring accuracy coefficient, including: Based on the thermal runaway monitoring data records of the energy storage device, a sample thermal management normal parameter set, a sample thermal runaway parameter set and a sample thermal management margin parameter set are obtained, and a sample thermal management runaway probability information set is obtained; Using the sample thermal management normal parameter set, the sample thermal runaway parameter set, the sample thermal management margin parameter set and the sample thermal management runaway probability information set, a thermal management runaway predictor is constructed, and thermal management normal parameters, thermal runaway parameters and thermal management margin parameters are analyzed to obtain thermal management runaway probability information; Based on the thermal runaway monitoring accuracy coefficient, a compensation correction calculation is performed on the thermal management runaway probability information to obtain maximum thermal management runaway probability information and minimum heat pipe runaway probability information; The maximum thermal management runaway probability information is output as the compensated thermal management runaway probability information.

8. A thermal management out-of-control early warning system for energy storage, It is characterized in that The system comprises: A characteristic value acquisition module, wherein the characteristic value acquisition module is used to continuously monitor and collect temperature characteristic values ​​and gas characteristic values ​​of multiple monitoring points in the energy storage device at multiple monitoring time points through a monitoring module arranged in the energy storage device, and obtain a temperature characteristic value array set and a gas characteristic value array set; A distribution sequence construction module, the distribution sequence construction module is used to construct a temperature characteristic distribution sequence and a gas characteristic distribution sequence according to the temperature characteristic value array set and the gas characteristic value array set; A fitting verification module, the fitting verification module is used to perform heating analysis and thermal runaway trend analysis of the energy storage device based on the temperature characteristic distribution sequence and the gas characteristic distribution sequence, obtain temperature heating parameters, gas heating parameters, temperature thermal runaway parameters and gas thermal runaway parameters, and perform fitting verification to obtain heating parameters, thermal runaway parameters and thermal runaway monitoring accuracy coefficients; A thermal management parameter sequence acquisition module, the thermal management parameter sequence acquisition module is used to collect thermal management parameters of the thermal management module in the energy storage device at the multiple monitoring time points to obtain a thermal management parameter sequence; A thermal management analysis module, the thermal management analysis module is used to perform thermal management operation analysis and thermal management margin analysis based on the thermal management parameter sequence and thermal parameters, and obtain thermal management normal parameters and thermal management margin parameters; A compensation correction module, the compensation correction module is used to perform thermal management runaway analysis based on the thermal management normal parameters, thermal runaway parameters and thermal management margin parameters, obtain thermal management runaway probability information, perform compensation correction based on the thermal runaway monitoring accuracy coefficient, and make judgment decisions and early warnings based on the compensated thermal management runaway probability information.

Images