Battery thermal runaway focusing control method

Abstract

The invention provides a battery thermal runaway focusing control method, and relates to the technical field of battery state monitoring, and the method comprises the steps: carrying out the real-time monitoring of a target battery cabin, obtaining a battery cabin monitoring data set, carrying out the standardization processing and feature recognition, building a battery cabin thermal runaway prediction module, and carrying out the thermal runaway prediction. And obtaining a comprehensive thermal runaway prediction coefficient, judging whether the comprehensive thermal runaway prediction coefficient is smaller than a thermal runaway threshold, generating a thermal runaway early warning signal if the comprehensive thermal runaway prediction coefficient is larger than or equal to a preset thermal runaway threshold, and performing thermal runaway operation and maintenance on the target battery compartment. The technical problem that the safety and the operation and maintenance efficiency of the battery system are further affected due to inaccurate thermal runaway risk identification caused by single battery cabin state monitoring means in the prior art can be solved, and the safety and the reliability of the battery system are improved.

Description

Technical Field

[0001] The present application relates to the technical field of battery status monitoring, and in particular to a battery thermal runaway focusing control method. Background Art

[0002] Wide application of lithium-ion batteries: Lithium-ion batteries have advantages such as high energy density, no radiation, low self-discharge, and low memory effect. As the battery energy density increases and the charging speed increases, the safety issues of lithium-ion batteries are becoming increasingly prominent. Studies have shown that when batteries are subjected to abuse conditions, they may suffer safety problems such as thermal runaway. Thermal runaway is characterized by the generation of intense heat inside the battery, and may release high-temperature combustible fumes, releasing a large amount of energy through a chain reaction of chemical components. In a battery module, once a battery has thermal runaway, the heat will quickly spread to adjacent batteries, causing the thermal runaway to spread, which may lead to more serious safety accidents.

[0003] If you rely on a single monitoring method, such as monitoring only through temperature sensors or gas sensors, you may not be able to fully capture the complex changes in the battery compartment. This limitation will lead to inaccurate or delayed identification of thermal runaway risks, making it impossible to take effective prevention and control measures in time before a thermal runaway event occurs. This inaccuracy not only increases the risk of accidents in the battery system, but may also affect the operation and maintenance efficiency of the entire system, because false warnings or missed reports will lead to unnecessary downtime, maintenance or misoperation.

[0004] In summary, the existing battery compartment status monitoring method is single, which leads to inaccurate identification of thermal runaway risks, further affecting the safety and operation and maintenance efficiency of the battery system. Summary of the invention

[0005] The purpose of this application is to provide a battery thermal runaway focused control method to solve the technical problem that the existing battery compartment status monitoring method is single, resulting in inaccurate identification of thermal runaway risks, further affecting the safety and operation and maintenance efficiency of the battery system.

[0006] In view of the above problems, the present application provides a battery thermal runaway focusing control method.

[0007] The present application provides a battery thermal runaway focused control method, wherein the method includes: real-time monitoring of a target battery compartment according to a battery compartment monitoring unit to obtain a battery compartment monitoring data set; performing standardization processing and feature recognition according to the battery compartment monitoring data set to generate battery compartment temperature feature data, battery compartment gas feature data and battery compartment operation feature data; building a battery compartment thermal runaway prediction module, wherein the battery compartment thermal runaway prediction module includes a first thermal runaway prediction unit, a second thermal runaway prediction unit, a third thermal runaway prediction unit and a thermal runaway fusion prediction unit; performing thermal runaway prediction according to the battery compartment thermal runaway prediction module in combination with the battery compartment temperature feature data, the battery compartment gas feature data and the battery compartment operation feature data to obtain a comprehensive thermal runaway prediction coefficient; determining whether the comprehensive thermal runaway prediction coefficient is less than a predetermined thermal runaway threshold; generating a thermal runaway warning signal if the comprehensive thermal runaway prediction coefficient is greater than / equal to the predetermined thermal runaway threshold; and activating the thermal runaway focused control module according to the thermal runaway warning signal to perform thermal runaway operation and maintenance on the target battery compartment.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: By performing real-time monitoring of the target battery compartment according to the battery compartment monitoring unit, a battery compartment monitoring data set is obtained; standardization processing and feature recognition are performed according to the battery compartment monitoring data set to generate battery compartment temperature feature data, battery compartment gas feature data and battery compartment operation feature data; a battery compartment thermal runaway prediction module is built, wherein the battery compartment thermal runaway prediction module includes a first thermal runaway prediction unit, a second thermal runaway prediction unit, a third thermal runaway prediction unit and a thermal runaway fusion prediction unit; according to the battery compartment thermal runaway prediction module, the battery compartment temperature feature data, the battery compartment gas feature data and the battery compartment operation feature data are combined to predict the battery compartment thermal runaway. The thermal runaway prediction is performed based on the battery compartment operation characteristic data to obtain a comprehensive thermal runaway prediction coefficient; it is determined whether the comprehensive thermal runaway prediction coefficient is less than a predetermined thermal runaway threshold; if the comprehensive thermal runaway prediction coefficient is greater than / equal to the predetermined thermal runaway threshold, a thermal runaway warning signal is generated; and according to the thermal runaway warning signal, a thermal runaway focusing control module is activated to perform thermal runaway operation and maintenance on the target battery compartment, which effectively solves the technical problem that the prior art battery compartment status monitoring means is single, resulting in inaccurate thermal runaway risk identification, further affecting the safety of the battery system and the operation and maintenance efficiency, thereby improving the safety and reliability of the battery system.

[0009] 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 according to the contents of the specification, and 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 specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0011] Figure 1 A schematic flow chart of a battery thermal runaway focusing control method for the present application; Figure 2 A schematic diagram of the process of thermal runaway operation and maintenance in a battery thermal runaway focusing control method of the present application. DETAILED DESCRIPTION

[0012] The present application provides a battery thermal runaway focused control method to solve the technical problem that the existing battery compartment status monitoring method is single, resulting in inaccurate identification of thermal runaway risks, further affecting the safety and operation and maintenance efficiency of the battery system, thereby improving the safety and reliability of the battery system.

[0013] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0014] Embodiment 1 Please refer to the attached Figure 1 The present application provides a battery thermal runaway focusing control method, wherein the method specifically comprises the following steps: S1: Monitor the target battery compartment in real time according to the battery compartment monitoring unit to obtain a battery compartment monitoring data set; Specifically, according to the real-time monitoring of the target battery compartment by the battery compartment monitoring unit, the following battery compartment monitoring data sets can be obtained, including battery status parameters, voltage, through real-time monitoring, the voltage value of each battery in the battery compartment can be obtained, current, monitoring the current of the battery compartment can reflect the charging and discharging status of the battery, and whether there is abnormal current. Temperature, the operating temperature of the battery has an important impact on its performance and safety. By real-time monitoring the temperature of the battery compartment, temperature anomalies can be discovered in time to prevent safety issues such as thermal runaway. Environmental parameters, humidity, the humidity in the battery compartment may affect the performance and life of the battery. By monitoring the humidity, humidity anomalies can be discovered in time and corresponding measures can be taken. Air pressure, changes in air pressure may affect the sealing and safety of the battery. Real-time monitoring of air pressure helps to detect potential safety hazards in a timely manner. Each piece of monitoring data will be accompanied by a timestamp to record the exact time of data collection.

[0015] S2: performing standardization processing and feature recognition according to the battery compartment monitoring data set to generate battery compartment temperature feature data, battery compartment gas feature data and battery compartment operation feature data; Specifically, duplicate data can be removed by interpolation, deletion, or estimation based on other relevant features. Identify and process outliers, such as using statistical methods such as IQR rule, Z-score, etc. to identify and process extreme data. Normalize or standardize data of different dimensions to make each feature comparable. Normalization is to scale the data to the range of [0, 1] or [-1, 1]. Standardization is to convert the data into a distribution with a mean of 0 and a standard deviation of 1. For categorical data, such as the identification information of the battery compartment and the protection measure activation information, it can be converted by using unique hot encoding or label encoding. Extract temperature-related data from the monitoring data set. Calculate the statistical features of the temperature, such as the mean, maximum, minimum, standard deviation, etc., to generate battery compartment temperature feature data. If the monitoring data set contains gas composition or related sensor data, such as humidity, air pressure, etc., extract these data. Calculate the statistical features of the gas concentration, humidity, and air pressure, such as the mean, variance, etc., to generate battery compartment gas feature data. Analyze the changing trend and periodicity of the gas composition. Extract data related to the battery compartment operating status, such as charge and discharge current, voltage, etc. Calculate the statistical characteristics of these operating parameters, such as the average value, peak value, fluctuation range, etc. of current and voltage, and generate battery compartment operating characteristic data. Analyze the time series characteristics of operating parameters, such as periodic charge and discharge mode, abnormal operating mode, etc.

[0016] S3: Building a battery compartment thermal runaway prediction module, wherein the battery compartment thermal runaway prediction module includes a first thermal runaway prediction unit, a second thermal runaway prediction unit, a third thermal runaway prediction unit and a thermal runaway fusion prediction unit; Specifically, receive and process the temperature characteristic data of the battery compartment, including temperature statistical characteristics, temperature change rate, etc. Use machine learning algorithms such as SVM, random forest, etc. or deep learning models to learn the temperature data and establish a correlation model between temperature characteristics and thermal runaway. Output the thermal runaway prediction result based on the temperature characteristics. Receive and process the gas characteristic data of the battery compartment, such as statistical characteristics and change trends of gas concentration, humidity, air pressure, etc. Use models suitable for processing time series data, such as LSTM, GRU, etc., to learn the relationship between gas characteristics and thermal runaway. Output the thermal runaway prediction result based on gas characteristics. Receive and process the operation characteristic data of the battery compartment, including the statistical characteristics and time series characteristics of operation parameters such as current and voltage. Use algorithms such as regression analysis and neural networks to establish a prediction model between operation characteristics and thermal runaway. Output the thermal runaway prediction result based on the operation characteristics. Receive the output results of the first, second, and third thermal runaway prediction units. Use weighted average, voting mechanism or fusion algorithm, such as stacked ensemble learning, to fuse the results of each prediction unit. According to the fusion result, judge whether the battery compartment will have thermal runaway, and output the final prediction result. A comprehensive and efficient battery compartment thermal runaway prediction module can be constructed to provide strong protection for the safe operation of the battery system.

[0017] S4: performing thermal runaway prediction according to the battery compartment thermal runaway prediction module, combining the battery compartment temperature characteristic data, the battery compartment gas characteristic data and the battery compartment operation characteristic data, to obtain a comprehensive thermal runaway prediction coefficient; Specifically, the battery compartment temperature characteristic data is input into the first thermal runaway prediction unit. The battery compartment gas characteristic data is input into the second thermal runaway prediction unit. The battery compartment operation characteristic data is input into the third thermal runaway prediction unit. Each prediction unit processes the data according to its specific algorithm and model, and outputs a predicted value or probability of thermal runaway. The thermal runaway fusion prediction unit receives the outputs of the three prediction units. The fusion prediction unit can use weighted averaging, voting, or more complex fusion algorithms, such as weight-based fusion, decision tree fusion, etc. to integrate the results of the three prediction units. The result of the fusion is a comprehensive thermal runaway prediction coefficient, which can be a specific value indicating the degree of risk of thermal runaway, or a classification label, such as high risk, medium risk, or low risk. Finally, the thermal runaway fusion prediction unit will output a comprehensive thermal runaway prediction coefficient, which represents a comprehensive assessment of the thermal runaway risk of the battery compartment based on all currently available characteristic data.

[0018] S5: determining whether the comprehensive thermal runaway prediction coefficient is less than a predetermined thermal runaway threshold; Specifically, this threshold can be set based on historical data. The threshold can be a fixed value or a value that is dynamically adjusted according to the state of the battery compartment. The comprehensive thermal runaway prediction coefficient is directly compared with the predetermined thermal runaway threshold. If the comprehensive thermal runaway prediction coefficient is less than the predetermined thermal runaway threshold, it is considered that the thermal runaway risk of the current battery compartment is acceptable. If the comprehensive thermal runaway prediction coefficient is greater than or equal to the predetermined thermal runaway threshold, it means that the battery compartment has a higher risk of thermal runaway.

[0019] S6: if the comprehensive thermal runaway prediction coefficient is greater than / equal to the predetermined thermal runaway threshold, generating a thermal runaway warning signal; Specifically, the comprehensive thermal runaway prediction coefficient is continuously monitored and compared with a predetermined thermal runaway threshold. Once the comprehensive thermal runaway prediction coefficient is found to be greater than or equal to the predetermined thermal runaway threshold, the trigger condition is met. When the trigger condition is met, a thermal runaway warning signal is automatically generated. This warning signal can be an electronic alarm, an audible alarm, a light indication, or an obvious sign displayed on the monitoring interface.

[0020] S7: Activate a thermal runaway focus control module according to the thermal runaway warning signal to perform thermal runaway operation and maintenance on the target battery compartment.

[0021] Specifically, the thermal runaway focus control module first receives the thermal runaway warning signal and identifies the level and source of the signal. According to the severity of the warning signal, the urgency of operation and maintenance and the measures that need to be taken are determined. According to the pre-established thermal runaway emergency plan, the control module will automatically or prompt the operator to manually initiate a series of emergency measures. These measures include cutting off power, starting the emergency cooling system, isolating the battery compartment, etc. The control module will strengthen the real-time monitoring of the target battery compartment, including tracking of key parameters such as temperature, pressure, and gas composition. The collected data is analyzed in real time to assess the risk and development trend of thermal runaway. Based on the data analysis results, the control module will automatically adjust the environmental parameters in the battery compartment, such as increasing ventilation, lowering temperature, etc., to reduce the risk of thermal runaway.

[0022] Furthermore, step S3 of the present application also includes: Interacting with the battery compartment data management unit to construct the first thermal runaway prediction unit; Interacting with the battery compartment data management unit to construct the second thermal runaway prediction unit; Interacting with the battery compartment data management unit to construct the third thermal runaway prediction unit; The thermal runaway fusion prediction unit is established, wherein the thermal runaway fusion prediction unit includes a thermal runaway fusion prediction function, and the thermal runaway fusion prediction function is: ; Among them, TLCD represents the comprehensive thermal runaway prediction coefficient, TLCA represents the first thermal runaway prediction coefficient, TLCB represents the second thermal runaway prediction coefficient, and TLCC represents the third thermal runaway prediction coefficient; The battery compartment thermal runaway prediction module is obtained by fusing the first thermal runaway prediction unit, the second thermal runaway prediction unit, the third thermal runaway prediction unit and the thermal runaway fusion prediction unit.

[0023] Specifically, temperature characteristic data is obtained from the battery compartment data management unit. Based on these data, a model is trained using a machine learning or deep learning algorithm to construct a first thermal runaway prediction unit, which can output a thermal runaway prediction coefficient based on temperature characteristics. Gas characteristic data is obtained from the battery compartment data management unit. A model is trained using these data to construct a second thermal runaway prediction unit, which can output a thermal runaway prediction coefficient based on gas characteristics. Interactive battery compartment data management unit to construct a third thermal runaway prediction unit: Operation characteristic data is obtained from the battery compartment data management unit. A model is trained using these data to construct a third thermal runaway prediction unit, which can output a thermal runaway prediction coefficient based on operation characteristics. Create a fusion prediction unit that will receive the outputs from the first three prediction units. In the fusion prediction unit, a thermal runaway fusion prediction function is defined to calculate the comprehensive thermal runaway prediction coefficient. The specific function is, ; Among them, TLCD represents the comprehensive thermal runaway prediction coefficient, TLCA represents the first thermal runaway prediction coefficient, TLCB represents the second thermal runaway prediction coefficient, and TLCC represents the third thermal runaway prediction coefficient; the outputs of the first, second, and third thermal runaway prediction units are input into the thermal runaway fusion prediction unit. The thermal runaway fusion prediction function is used to calculate the comprehensive thermal runaway prediction coefficient. By integrating three prediction units and one fusion prediction unit, a complete battery compartment thermal runaway prediction module is obtained. This module can perform a comprehensive thermal runaway risk assessment based on a variety of characteristic data and output a comprehensive thermal runaway prediction coefficient.

[0024] Furthermore, the present application also includes: According to the battery compartment data management unit, retrieve the battery compartment temperature characteristic data record, the battery compartment temperature abnormality detection record and the temperature characteristic thermal runaway coefficient record; Using the battery compartment temperature characteristic data record as input data and the battery compartment temperature anomaly detection record as output data, a predetermined machine learning model is trained, and after each predetermined number of trainings, a mean square loss error of temperature anomaly detection is obtained; If the temperature anomaly detection mean square loss error is less than or equal to a predetermined mean square loss error threshold, generating a battery compartment temperature anomaly detection model; Taking the battery compartment temperature anomaly detection record as input information and the temperature characteristic thermal runaway coefficient record as output information, training the predetermined machine learning model, and obtaining a thermal runaway prediction mean square loss error each time the predetermined number of trainings is performed; If the thermal runaway prediction mean square loss error is less than or equal to the predetermined mean square loss error threshold, generating a temperature characteristic thermal runaway prediction model; The battery compartment temperature anomaly detection model and the temperature characteristic thermal runaway prediction model are fully connected to generate the first thermal runaway prediction unit.

[0025] Specifically, obtain the battery compartment temperature feature data records, which will be used as the input of the machine learning model. Obtain the battery compartment temperature anomaly detection records, which will be used as the target output when training the temperature anomaly detection model. Obtain the temperature feature thermal runaway coefficient records, which will be used as the target output when training the thermal runaway prediction model. Use machine learning algorithms such as support vector machines, random forests, or neural networks. Use the battery compartment temperature feature data records as input data. Use the battery compartment temperature anomaly detection records as output data. And after each predetermined number of trainings, calculate the mean square loss error of temperature anomaly detection. If the temperature anomaly detection mean square loss error is less than or equal to the predetermined mean square loss error threshold, it means that the model performance meets the requirements. Generate a battery compartment temperature anomaly detection model that can detect battery compartment temperature anomalies based on temperature feature data. Use the same machine learning algorithm and use the battery compartment temperature anomaly detection records as input information. Use the temperature feature thermal runaway coefficient records as output information. Start training the model, and after each predetermined number of trainings, obtain the mean square loss error of thermal runaway prediction. If the thermal runaway prediction mean square loss error is less than or equal to the predetermined mean square loss error threshold, it means that the model performance meets the requirements. Generate a temperature characteristic thermal runaway prediction model that can predict the thermal runaway coefficient based on the temperature anomaly detection record. Fully connect the battery compartment temperature anomaly detection model and the temperature characteristic thermal runaway prediction model. The output of the temperature anomaly detection model will be used as the input of the thermal runaway prediction model. By fully connecting the two trained models, the first thermal runaway prediction unit is obtained. This unit can receive the temperature characteristic data of the battery compartment, first detect temperature anomalies, and then predict the thermal runaway coefficient based on these anomalies.

[0026] Furthermore, step S4 of the present application also includes: Inputting the battery compartment temperature characteristic data into the first thermal runaway prediction unit to obtain a temperature characteristic thermal runaway prediction coefficient; Inputting the battery compartment gas characteristic data into the second thermal runaway prediction unit to obtain a gas characteristic thermal runaway prediction coefficient; Inputting the battery compartment operation characteristic data into the third thermal runaway prediction unit to obtain an operation characteristic thermal runaway prediction coefficient; Performing standardization processing according to the temperature characteristic thermal runaway prediction coefficient, the gas characteristic thermal runaway prediction coefficient and the operation characteristic thermal runaway prediction coefficient to obtain a first thermal runaway prediction coefficient, a second thermal runaway prediction coefficient and a third thermal runaway prediction coefficient; The first thermal runaway prediction coefficient, the second thermal runaway prediction coefficient and the third thermal runaway prediction coefficient are input into the thermal runaway fusion prediction unit, and the comprehensive thermal runaway prediction coefficient is output.

[0027] Specifically, the temperature characteristic data of the battery compartment is input into the first thermal runaway prediction unit. Through the processing of this unit, the temperature characteristic thermal runaway prediction coefficient is obtained. The gas characteristic data of the battery compartment is input into the second thermal runaway prediction unit. Through the processing of this unit, the gas characteristic thermal runaway prediction coefficient is obtained. The operation characteristic data of the battery compartment is input into the third thermal runaway prediction unit. Through the processing of this unit, the operation characteristic thermal runaway prediction coefficient is obtained. The temperature characteristic thermal runaway prediction coefficient, the gas characteristic thermal runaway prediction coefficient and the operation characteristic thermal runaway prediction coefficient are standardized. The standardized processing can eliminate the dimensional differences and numerical range differences between different characteristic data, so that each prediction coefficient is fused on the same scale. The standardized coefficients are respectively called the first thermal runaway prediction coefficient, the second thermal runaway prediction coefficient and the third thermal runaway prediction coefficient. The standardized first thermal runaway prediction coefficient, the second thermal runaway prediction coefficient and the third thermal runaway prediction coefficient are input into the thermal runaway fusion prediction unit. The thermal runaway fusion prediction unit fuses these coefficients according to the preset fusion prediction function. Through the processing of the thermal runaway fusion prediction unit, a comprehensive thermal runaway prediction coefficient is finally output.

[0028] Furthermore, the present application also includes: inputting the battery compartment gas characteristic data into a battery compartment gas anomaly detection model in the second thermal runaway prediction unit to generate gas anomaly detection characteristic data; The gas anomaly detection characteristic data is input into the gas characteristic thermal runaway prediction model in the second thermal runaway prediction unit, and the gas characteristic thermal runaway prediction coefficient is output.

[0029] Specifically, the battery compartment gas characteristic data is input into the battery compartment gas anomaly detection model in the second thermal runaway prediction unit. The model is trained to identify abnormal conditions that do not conform to the normal gas characteristic pattern, such as a sudden increase or decrease in gas concentration, or the abnormal appearance of a specific gas. The battery compartment gas anomaly detection model processes the input gas characteristic data and generates gas anomaly detection characteristic data. These characteristic data include information such as the detected abnormal point, abnormal degree, and abnormal type, which are crucial for subsequent thermal runaway prediction. Next, the gas anomaly detection characteristic data is input into the gas characteristic thermal runaway prediction model in the second thermal runaway prediction unit. The model is trained to predict the risk of thermal runaway in the battery compartment based on the abnormal characteristic data. The gas characteristic thermal runaway prediction model processes the input gas anomaly detection characteristic data and outputs a gas characteristic thermal runaway prediction coefficient. This coefficient represents the possibility or risk level of thermal runaway in the battery compartment based on the current gas characteristic data and the detected abnormal conditions.

[0030] Further, if Figure 2 As shown, step S7 of the present application also includes: The thermal runaway focusing control module includes a thermal runaway focusing unit, a thermal runaway operation and maintenance decision unit, and a thermal runaway control unit; Performing thermal runaway feature focusing according to the thermal runaway focusing unit to generate a thermal runaway feature focusing result; According to the thermal runaway feature focusing result, in combination with the thermal runaway operation and maintenance decision unit, a thermal runaway operation and maintenance feature decision is obtained; The thermal runaway operation and maintenance feature decision is transmitted to the thermal runaway control unit, and the thermal runaway control unit performs thermal runaway operation and maintenance of the target battery compartment based on the thermal runaway operation and maintenance feature decision.

[0031] Specifically, the thermal runaway focusing unit is responsible for analyzing and focusing on features related to thermal runaway. This includes collecting data from various sensors and monitoring systems to identify key parameters and trends related to thermal runaway risks. Through data analysis and feature extraction technology, a thermal runaway feature focusing result is generated. This result is a collection of one or more key indicators that indicate the potential risk and urgency of thermal runaway. The thermal runaway operation and maintenance decision unit receives the feature focusing results from the thermal runaway focusing unit. Based on these results, combined with preset operation and maintenance strategies and historical data, the unit formulates one or more thermal runaway operation and maintenance feature decisions. These decisions include emergency cooling measures, isolating potentially dangerous areas, notifying relevant personnel, etc. The thermal runaway control unit is responsible for receiving and executing instructions from the thermal runaway operation and maintenance decision unit. Based on the operation and maintenance feature decisions, the control unit will trigger corresponding control measures, such as starting the cooling system, shutting down the power supply of a specific area, and sounding an alarm.

[0032] Furthermore, the present application also includes: According to the battery compartment thermal runaway prediction module, multi-dimensional thermal runaway characteristic data is retrieved; Based on the multi-dimensional thermal runaway characteristic data, performing thermal runaway fitting propagation on the multi-dimensional thermal runaway characteristic data according to a thermal runaway propagation model in the thermal runaway focusing unit to generate fitted thermal runaway propagation characteristic data; Data fusion is performed based on the multi-dimensional thermal runaway characteristic data and the fitted thermal runaway propagation characteristic data to obtain the thermal runaway characteristic focusing result.

[0033] Specifically, multi-dimensional thermal runaway feature data are retrieved from the battery compartment thermal runaway prediction module. These data include but are not limited to temperature, pressure, gas composition, current and voltage, and other parameters, which reflect the thermal runaway risk of the battery compartment from different dimensions. The retrieved multi-dimensional thermal runaway feature data are input into the thermal runaway propagation model in the thermal runaway focusing unit. The thermal runaway propagation model is a trained mathematical model used to simulate and predict the propagation behavior of thermal runaway in the battery compartment. The thermal runaway propagation model is used to fit the multi-dimensional thermal runaway feature data to simulate the possible propagation path and speed of thermal runaway in the battery compartment. Through the fitting process, fitted thermal runaway propagation feature data are generated, which reflect the propagation characteristics of thermal runaway in time and space. The original multi-dimensional thermal runaway feature data and the fitted thermal runaway propagation feature data are prepared for data fusion. The original feature data and the fitted propagation feature data are fused using appropriate data fusion techniques, such as weighted averaging, Kalman filtering, neural network fusion, etc. The purpose of fusion is to comprehensively utilize the information of the two data sources to improve the accuracy and completeness of the thermal runaway feature description. After data fusion, the thermal runaway feature focusing result is obtained. This result is a data set that integrates multi-dimensional original features and fitted propagation features, which can more comprehensively and accurately describe the current thermal runaway state and trend of the battery compartment.

[0034] Furthermore, the present application also includes: Obtaining a multi-dimensional thermal runaway characteristic data record library of the target battery compartment; Performing time series processing according to the multi-dimensional thermal runaway characteristic data record library to generate a multi-dimensional thermal runaway characteristic record sequence; Loading the expanded thermal runaway feature data record library of the target battery compartment, and performing time-sequencing processing on the expanded thermal runaway feature data record library to obtain a multi-dimensional expanded thermal runaway feature record sequence; Performing supervised training on the Markov chain according to the multi-dimensional expanded thermal runaway feature record sequence to obtain an initial thermal runaway propagation model; The initial thermal runaway propagation model is incrementally learned according to the multi-dimensional thermal runaway feature record sequence to generate the thermal runaway propagation model.

[0035] Specifically, a multi-dimensional thermal runaway feature data record library of the target battery compartment is obtained. This record library may contain a variety of feature data related to thermal runaway, such as temperature, pressure, current, voltage, and gas composition. The multi-dimensional thermal runaway feature data record library is time-sequentially processed. The data is arranged in chronological order to generate a multi-dimensional thermal runaway feature record sequence. The extended thermal runaway feature data record library of the target battery compartment is loaded. This extended library contains more scenarios and more types of thermal runaway data, which is used to enhance the generalization ability of the model. The extended thermal runaway feature data record library is also time-sequentially processed to obtain a multi-dimensional extended thermal runaway feature record sequence. The Markov chain is supervised and trained using the multi-dimensional extended thermal runaway feature record sequence. The Markov chain is a random process in which the future state depends only on the current state. This model is suitable for describing phenomena with time-series dependence such as thermal runaway. Through supervised training, an initial thermal runaway propagation model is obtained. This model can predict the future thermal runaway state based on the current state. The initial thermal runaway propagation model is incrementally learned using the multi-dimensional thermal runaway feature record sequence. Incremental learning allows the model to continuously learn and improve as it continuously receives new data without having to retrain the entire model. Through incremental learning, the model can gradually adapt to the unique thermal runaway characteristics of the target battery compartment, improving the accuracy and reliability of the prediction. After incremental learning, the final thermal runaway propagation model is generated. This model combines the augmented data with the specific data of the target battery compartment to more accurately simulate and predict the propagation process of thermal runaway.

[0036] In summary, the battery thermal runaway focusing control method provided by the present application has the following technical effects: By performing real-time monitoring of the target battery compartment according to the battery compartment monitoring unit, a battery compartment monitoring data set is obtained; standardization processing and feature recognition are performed according to the battery compartment monitoring data set to generate battery compartment temperature feature data, battery compartment gas feature data and battery compartment operation feature data; a battery compartment thermal runaway prediction module is built, wherein the battery compartment thermal runaway prediction module includes a first thermal runaway prediction unit, a second thermal runaway prediction unit, a third thermal runaway prediction unit and a thermal runaway fusion prediction unit; according to the battery compartment thermal runaway prediction module, the battery compartment temperature feature data, the battery compartment gas feature data and the battery compartment operation feature data are combined to predict the battery compartment thermal runaway. The thermal runaway prediction is performed based on the battery compartment operation characteristic data to obtain a comprehensive thermal runaway prediction coefficient; it is determined whether the comprehensive thermal runaway prediction coefficient is less than a predetermined thermal runaway threshold; if the comprehensive thermal runaway prediction coefficient is greater than / equal to the predetermined thermal runaway threshold, a thermal runaway warning signal is generated; and according to the thermal runaway warning signal, a thermal runaway focusing control module is activated to perform thermal runaway operation and maintenance on the target battery compartment, which effectively solves the technical problem that the prior art battery compartment status monitoring means is single, resulting in inaccurate thermal runaway risk identification, further affecting the safety of the battery system and the operation and maintenance efficiency, thereby improving the safety and reliability of the battery system.

[0037] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0038] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A battery thermal runaway focusing control method, characterized in that: The method comprises: Perform real-time monitoring of the target battery compartment according to the battery compartment monitoring unit to obtain a battery compartment monitoring data set; Performing standardization processing and feature recognition on the battery compartment monitoring data set to generate battery compartment temperature feature data, battery compartment gas feature data, and battery compartment operation feature data; Building a battery compartment thermal runaway prediction module, wherein the battery compartment thermal runaway prediction module includes a first thermal runaway prediction unit, a second thermal runaway prediction unit, a third thermal runaway prediction unit and a thermal runaway fusion prediction unit; According to the battery compartment thermal runaway prediction module, thermal runaway prediction is performed in combination with the battery compartment temperature characteristic data, the battery compartment gas characteristic data and the battery compartment operation characteristic data to obtain a comprehensive thermal runaway prediction coefficient; Determining whether the comprehensive thermal runaway prediction coefficient is less than a predetermined thermal runaway threshold; If the comprehensive thermal runaway prediction coefficient is greater than / equal to the predetermined thermal runaway threshold, generating a thermal runaway warning signal; The thermal runaway focusing control module is activated according to the thermal runaway warning signal to perform thermal runaway operation and maintenance on the target battery compartment.

2. The method according to claim 1, characterized in that Build a battery compartment thermal runaway prediction module, including: Interacting with the battery compartment data management unit to construct the first thermal runaway prediction unit; Interacting with the battery compartment data management unit to construct the second thermal runaway prediction unit; Interacting with the battery compartment data management unit to construct the third thermal runaway prediction unit; The thermal runaway fusion prediction unit is established, wherein the thermal runaway fusion prediction unit includes a thermal runaway fusion prediction function, and the thermal runaway fusion prediction function is: ； Among them, TLCD represents the comprehensive thermal runaway prediction coefficient, TLCA represents the first thermal runaway prediction coefficient, TLCB represents the second thermal runaway prediction coefficient, and TLCC represents the third thermal runaway prediction coefficient; The battery compartment thermal runaway prediction module is obtained by fusing the first thermal runaway prediction unit, the second thermal runaway prediction unit, the third thermal runaway prediction unit and the thermal runaway fusion prediction unit.

3. The method according to claim 2, characterized in that The interactive battery compartment data management unit constructs the first thermal runaway prediction unit, including: According to the battery compartment data management unit, retrieve the battery compartment temperature characteristic data record, the battery compartment temperature abnormality detection record and the temperature characteristic thermal runaway coefficient record; Using the battery compartment temperature characteristic data record as input data and the battery compartment temperature anomaly detection record as output data, a predetermined machine learning model is trained, and after each predetermined number of trainings, a mean square loss error of temperature anomaly detection is obtained; If the temperature anomaly detection mean square loss error is less than or equal to a predetermined mean square loss error threshold, generating a battery compartment temperature anomaly detection model; Taking the battery compartment temperature anomaly detection record as input information and the temperature characteristic thermal runaway coefficient record as output information, training the predetermined machine learning model, and obtaining a thermal runaway prediction mean square loss error each time the predetermined number of trainings is performed; If the thermal runaway prediction mean square loss error is less than or equal to the predetermined mean square loss error threshold, generating a temperature characteristic thermal runaway prediction model; The battery compartment temperature anomaly detection model and the temperature characteristic thermal runaway prediction model are fully connected to generate the first thermal runaway prediction unit.

4. The method according to claim 1, characterized in that According to the battery compartment thermal runaway prediction module, thermal runaway prediction is performed in combination with the battery compartment temperature characteristic data, the battery compartment gas characteristic data and the battery compartment operation characteristic data to obtain a comprehensive thermal runaway prediction coefficient, including: Inputting the battery compartment temperature characteristic data into the first thermal runaway prediction unit to obtain a temperature characteristic thermal runaway prediction coefficient; Inputting the battery compartment gas characteristic data into the second thermal runaway prediction unit to obtain a gas characteristic thermal runaway prediction coefficient; Inputting the battery compartment operation characteristic data into the third thermal runaway prediction unit to obtain an operation characteristic thermal runaway prediction coefficient; Performing standardization processing according to the temperature characteristic thermal runaway prediction coefficient, the gas characteristic thermal runaway prediction coefficient and the operation characteristic thermal runaway prediction coefficient to obtain a first thermal runaway prediction coefficient, a second thermal runaway prediction coefficient and a third thermal runaway prediction coefficient; The first thermal runaway prediction coefficient, the second thermal runaway prediction coefficient and the third thermal runaway prediction coefficient are input into the thermal runaway fusion prediction unit, and the comprehensive thermal runaway prediction coefficient is output.

5. The method according to claim 4, characterized in that Inputting the battery compartment gas characteristic data into the second thermal runaway prediction unit to obtain a gas characteristic thermal runaway prediction coefficient includes: inputting the battery compartment gas characteristic data into a battery compartment gas anomaly detection model in the second thermal runaway prediction unit to generate gas anomaly detection characteristic data; The gas anomaly detection characteristic data is input into the gas characteristic thermal runaway prediction model in the second thermal runaway prediction unit, and the gas characteristic thermal runaway prediction coefficient is output.

6. The method according to claim 1, characterized in that Activating a thermal runaway focus control module according to the thermal runaway warning signal to perform thermal runaway operation and maintenance on the target battery compartment includes: The thermal runaway focusing control module includes a thermal runaway focusing unit, a thermal runaway operation and maintenance decision unit, and a thermal runaway control unit; Performing thermal runaway feature focusing according to the thermal runaway focusing unit to generate a thermal runaway feature focusing result; According to the thermal runaway feature focusing result, in combination with the thermal runaway operation and maintenance decision unit, a thermal runaway operation and maintenance feature decision is obtained; The thermal runaway operation and maintenance feature decision is transmitted to the thermal runaway control unit, and the thermal runaway control unit performs thermal runaway operation and maintenance of the target battery compartment based on the thermal runaway operation and maintenance feature decision.

7. The method according to claim 6, characterized in that Performing thermal runaway feature focusing according to the thermal runaway focusing unit to generate a thermal runaway feature focusing result includes: According to the battery compartment thermal runaway prediction module, multi-dimensional thermal runaway characteristic data is retrieved; Based on the multi-dimensional thermal runaway characteristic data, performing thermal runaway fitting propagation on the multi-dimensional thermal runaway characteristic data according to a thermal runaway propagation model in the thermal runaway focusing unit to generate fitted thermal runaway propagation characteristic data; Data fusion is performed based on the multi-dimensional thermal runaway characteristic data and the fitted thermal runaway propagation characteristic data to obtain the thermal runaway characteristic focusing result.

8. The method according to claim 7, characterized in that The method comprises: Obtaining a multi-dimensional thermal runaway characteristic data record library of the target battery compartment; Performing time series processing according to the multi-dimensional thermal runaway characteristic data record library to generate a multi-dimensional thermal runaway characteristic record sequence; Loading the expanded thermal runaway feature data record library of the target battery compartment, and performing time-sequencing processing on the expanded thermal runaway feature data record library to obtain a multi-dimensional expanded thermal runaway feature record sequence; Performing supervised training on the Markov chain according to the multi-dimensional expanded thermal runaway feature record sequence to obtain an initial thermal runaway propagation model; The initial thermal runaway propagation model is incrementally learned according to the multi-dimensional thermal runaway feature record sequence to generate the thermal runaway propagation model.

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