Battery life warning platform for a mobile energy storage device based on a thermal runaway analysis of a battery

Abstract

The present invention discloses a battery life warning platform for a mobile energy storage device based on an analysis of thermal runaway of a battery and relates to the field of battery safety.The platform includes: a module for generating a battery thermal runaway parameter set, which is used to retrieve historical battery thermal runaway logs and generate a battery thermal runaway parameter set; a device battery operating parameter acquisition module, which is used for real-time monitoring to acquire a device battery operating parameter; a parameter comparison module, which is used for comparison to generate a parameter comparison result; a battery life warning model construction module, which is used to construct a battery life warning model; a warning signal output module, which is used to acquire a battery status parameter and output a warning signal; and a smart alarm module, which is used to activate an alarm device and establish a connection with a remote terminal for alerting.This solves the technical problem of the low accuracy of prediction results in the existing battery life warning system for mobile energy storage devices, thereby achieving the technical effect of improving the accuracy and reliability of the warning and providing a solid guarantee for the safe operation of mobile energy storage devices.

Description

Battery Life Early Warning Platform for Mobile Energy Storage Devices Based on Battery Thermal Runaway Analysis Technical Field

[0001] This application relates to the field of battery safety, and in particular to a battery life early warning platform for mobile energy storage devices based on battery thermal runaway analysis. Background Technology

[0002] As a key component of mobile energy storage devices, the lifespan of batteries directly impacts the overall operational efficiency and safety of the equipment. Thermal runaway is an extreme phenomenon that occurs during battery charging and discharging due to factors such as internal short circuits, overcharging, over-discharging, and external high temperatures. The high temperatures generated during thermal runaway accelerate the rate of internal chemical reactions within the battery, leading to accelerated aging of battery materials and consequently shortening battery life. Existing methods for predicting battery life often rely on experience, simple mathematical models, or statistical methods. These methods tend to ignore the impact of thermal runaway on battery life or simply simplify the process. Because they do not fully consider the impact of thermal runaway on battery life, they cannot accurately predict the performance degradation of batteries under high-temperature environments, resulting in significant errors in the prediction results.

[0003] Currently, among related technologies, the prediction accuracy of battery life warning for mobile energy storage devices is relatively low. Technical issues

[0004] This application provides a battery life early warning platform for mobile energy storage devices based on battery thermal runaway analysis. By analyzing historical battery thermal runaway logs, generating a set of battery thermal runaway parameters, monitoring the mobile energy storage device in real time, comparing parameters, constructing a battery life early warning model, and outputting early warning signals, this application achieves the technical effect of improving the accuracy and reliability of early warning and providing strong protection for the safe operation of mobile energy storage devices.

[0005] This application provides a battery life early warning platform for mobile energy storage devices based on battery thermal runaway analysis, including:

[0006] A battery thermal runaway parameter set generation module is used to retrieve historical battery thermal runaway logs to analyze mobile energy storage devices and generate a battery thermal runaway parameter set, which includes thermal runaway response parameters and thermal runaway trigger parameters.

[0007] The device battery operating parameter acquisition module is used to acquire device battery operating parameters by real-time monitoring of the mobile energy storage device.

[0008] A parameter comparison module is used to compare the device battery operating parameters with the thermal runaway response parameters using the thermal runaway triggering parameters as constraints, and generate parameter comparison results.

[0009] A battery life warning model construction module is used to construct a battery life warning model and synchronize the parameter comparison results to the battery life warning model. The battery life warning model includes a battery life analysis channel and a battery life warning channel.

[0010] The warning signal output module is used to obtain battery status parameters through the battery life analysis channel, and input the battery status parameters to the battery life warning channel to output a warning signal.

[0011] The intelligent alarm module is used to receive the early warning signal from the early warning platform and activate the alarm device, and then connect to a remote terminal through the alarm device to provide intelligent alarms for the battery life of the mobile energy storage device.

[0012] In a possible implementation, the battery thermal runaway parameter set generation module includes:

[0013] The historical thermal runaway event extraction unit is used to analyze the historical battery thermal runaway log to extract historical thermal runaway events of the mobile energy storage device. The historical thermal runaway events include thermal runaway time data, thermal runaway location data, and thermal runaway battery status.

[0014] A battery short-circuit trigger record generation unit is used to perform trigger analysis based on the thermal runaway battery state and generate a battery short-circuit trigger record.

[0015] A battery overcharge / discharge trigger record generation unit is used to perform trigger analysis based on the thermal runaway battery state and the thermal runaway time data to generate battery overcharge trigger records and battery overdischarge trigger records.

[0016] An ambient temperature trigger record generation unit is used to perform trigger analysis based on the thermal runaway location data and generate an ambient temperature trigger record.

[0017] A thermal runaway trigger parameter determination unit is used to integrate the battery short circuit trigger record, the battery overcharge trigger record, the battery over-discharge trigger record, and the ambient temperature trigger record to determine the thermal runaway trigger parameters.

[0018] In a possible implementation, the battery thermal runaway parameter set generation module includes:

[0019] A thermal runaway temperature recording data acquisition unit is used to acquire thermal runaway temperature recording data based on the changes in battery state of the mobile energy storage device identified by the historical thermal runaway events. The thermal runaway temperature recording data includes thermal runaway initiation temperature, thermal runaway peak temperature, and thermal runaway temperature change rate.

[0020] A thermal runaway voltage and current data acquisition unit is used to analyze the changes in power parameters during the historical thermal runaway events and acquire thermal runaway voltage data and thermal runaway current data.

[0021] A thermal runaway reaction rate data acquisition unit is used to calculate thermal runaway reaction rate data based on the thermal runaway voltage data and the thermal runaway current data.

[0022] A thermal runaway response parameter determination unit is used to integrate the thermal runaway temperature recording data and the thermal runaway response rate data to determine the thermal runaway response parameters.

[0023] In a possible implementation, the parameter comparison module includes:

[0024] Multiple trigger frequency determination units are used to determine multiple trigger frequencies based on the battery short circuit trigger record, the battery overcharge trigger record, the battery over-discharge trigger record, and the ambient temperature trigger record.

[0025] A constraint condition determination unit is used to extract trigger records that lead to battery thermal runaway based on the plurality of trigger frequencies, and determine the constraint conditions.

[0026] A thermal runaway critical value determination unit is used to analyze thermal runaway characteristics based on the thermal runaway initiation temperature, the thermal runaway peak temperature, and the thermal runaway temperature change rate, and to determine the thermal runaway critical value.

[0027] The comparison and judgment unit is used to compare the device battery operating parameters with the thermal runaway response parameters according to the constraints, and to determine whether the device battery operating parameters have reached the thermal runaway critical value.

[0028] A battery thermal runaway risk parameter acquisition unit is used to acquire battery thermal runaway risk parameters if the battery operating parameters of the device reach the thermal runaway critical value, which is considered as a risk of battery thermal runaway for the mobile energy storage device.

[0029] A parameter comparison result adding unit is used to add the battery thermal runaway risk parameter to the parameter comparison result.

[0030] In a possible implementation, the battery life warning model construction module includes:

[0031] A lifespan impact feature extraction unit is used to extract lifespan impact features based on the battery lifespan impact parameters of the mobile energy storage device.

[0032] Multiple decision tree construction units are used to construct multiple decision trees based on the lifetime impact features using random forest;

[0033] A battery life analysis channel construction unit is used to cross-validate the multiple decision trees to construct the battery life analysis channel.

[0034] A splitting criterion execution unit is configured to execute a splitting criterion on the plurality of decision trees according to the maximum depth and generate an execution result.

[0035] A battery life warning channel construction unit is used to perform unsupervised training on the battery life of mobile energy storage devices based on the execution results, and to construct the battery life warning channel based on the training results.

[0036] A channel fusion unit is used to fuse the battery life analysis channel and the battery life warning channel to construct the battery life warning model.

[0037] In a possible implementation, the warning signal output module includes:

[0038] A battery thermal runaway risk parameter input unit is used to extract the battery thermal runaway risk parameter from the parameter comparison result and input the battery thermal runaway risk parameter into the battery life analysis channel.

[0039] A battery life degradation rate data generation unit is used to calculate the changing trend of the battery thermal runaway risk parameter and the device battery operating parameters to generate battery life degradation rate data.

[0040] A battery remaining life data generation unit is used to perform a life assessment on a mobile energy storage device based on the battery life degradation rate data, and generate battery remaining life data.

[0041] A battery status parameter output unit is used to integrate the battery life degradation rate data and the remaining battery life data and output the battery status parameters through the battery life analysis channel.

[0042] In a possible implementation, the warning signal output module includes:

[0043] A battery status parameter parsing unit is used to parse the battery status parameters and obtain battery life percentage, battery cycle count, and battery usage time.

[0044] A threshold setting unit is configured to set a minimum battery life percentage threshold based on the battery life percentage, a remaining battery cycle threshold based on the battery cycle count, and a maximum battery usage time threshold based on the battery usage time.

[0045] A battery remaining life data extraction unit is used to extract the remaining percentage of battery life, the remaining number of battery cycles, and the battery usage time based on the battery life decline rate data.

[0046] The first warning signal generation unit is used to generate a first warning signal if the battery usage time is greater than or equal to the longest battery usage time threshold.

[0047] The second warning signal generation unit is used to determine whether the remaining number of battery cycles is less than the remaining battery cycle threshold if the battery usage time is less than the longest battery usage time threshold. If so, a second warning signal is generated.

[0048] The third warning signal generation unit is used to determine whether the remaining battery life percentage is less than or equal to the minimum battery life percentage threshold if the battery usage time is less than the longest battery usage time threshold and the remaining battery cycle count is greater than or equal to the remaining battery cycle threshold. If so, a third warning signal is generated.

[0049] The early warning response unit is used to perform an early warning response based on the first early warning signal, the second early warning signal, and the third early warning signal, and output the early warning signal.

[0050] In a possible implementation, the intelligent alarm module includes:

[0051] An abnormal parameter set acquisition unit is used to decompose the warning signal to acquire an abnormal parameter set, and send the abnormal parameter set to the remote terminal through the alarm device to issue an alarm.

[0052] The parameter set generation unit is used to adjust the initial battery usage strategy of the mobile energy storage device by transmitting the battery optimization strategy back through the remote terminal, and by executing the battery optimization strategy in combination with the battery operating parameters of the device, thereby generating an adjustment parameter set.

[0053] A parameter adjustment unit is used to adjust the thermal runaway time data, the thermal runaway location data, and the thermal runaway battery state according to the adjustment parameter set, and to obtain battery usage time adjustment parameters, battery usage location adjustment parameters, and battery usage state adjustment parameters.

[0054] A battery life data update unit is used to update the battery life data of the mobile energy storage device based on the battery usage time adjustment parameters, the battery usage location adjustment parameters, and the battery usage status adjustment parameters.

[0055] The proposed battery life early warning platform for mobile energy storage devices, based on battery thermal runaway analysis, utilizes a battery thermal runaway parameter set generation module to analyze historical battery thermal runaway logs and generate a battery thermal runaway parameter set. This set includes thermal runaway response parameters and thermal runaway trigger parameters. A device battery operating parameter acquisition module monitors the mobile energy storage device in real time to obtain its battery operating parameters. A parameter comparison module uses the thermal runaway trigger parameters as constraints to compare the device battery operating parameters with the thermal runaway response parameters, generating a parameter comparison result. Finally, a battery life early warning model is implemented. The module constructs a battery life early warning model and synchronizes the parameter comparison results to the model. The battery life early warning model includes a battery life analysis channel and a battery life early warning channel. The battery status parameters are obtained through the battery life analysis channel of the early warning signal output module, and the battery status parameters are input to the battery life early warning channel to output an early warning signal. The intelligent alarm module receives the early warning signal based on the early warning platform and activates the alarm device. The alarm device connects to a remote terminal to provide intelligent alarm for the battery life of the mobile energy storage device. This achieves the technical effect of improving the accuracy and reliability of the early warning and providing strong protection for the safe operation of the mobile energy storage device. Attached Figure Description

[0056] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the platform according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0057] Figure 1 is a schematic diagram of the structure of a mobile energy storage device battery life early warning platform based on battery thermal runaway analysis provided in an embodiment of this application;

[0058] Figure 2 is a schematic diagram of the early warning signal output module of the mobile energy storage device battery life early warning platform based on battery thermal runaway analysis provided in the embodiment of this application.

[0059] Explanation of reference numerals in the attached diagram: 10 for battery thermal runaway parameter set generation module, 20 for device battery operating parameter acquisition module, 30 for parameter comparison module, 40 for battery life early warning model construction module, 50 for early warning signal output module, and 60 for intelligent alarm module. Detailed Implementation

[0060] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application.

[0061] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0062] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same or different subsets of all possible embodiments and may be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, platform, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0063] This application provides a battery life early warning platform for mobile energy storage devices based on battery thermal runaway analysis, as shown in Figure 1. The platform includes:

[0064] A battery thermal runaway parameter set generation module 10 is used to retrieve historical battery thermal runaway logs to analyze mobile energy storage devices and generate a battery thermal runaway parameter set. This parameter set includes thermal runaway response parameters and thermal runaway trigger parameters. The mobile energy storage device refers to a mobile device capable of storing electrical energy and releasing it when needed, such as an electric vehicle or portable power source. Specifically, the battery thermal runaway parameter set generation module 10 accesses a database or data warehouse storing historical battery thermal runaway logs, performs a query operation, and retrieves relevant historical log data according to preset filtering conditions (such as mobile energy storage device model, battery type, etc.). The historical battery thermal runaway logs are preprocessed, including data cleaning (removing duplicate, erroneous, or invalid data), data transformation (converting data to a unified format or unit), and data imputation (filling in missing values), to improve data quality. By employing statistical methods, data mining algorithms, or expert knowledge, key parameters related to thermal runaway response and triggering are extracted from preprocessed data. Battery thermal runaway refers to the phenomenon where, during charging, discharging, storage, or transportation, the internal temperature of a battery rises rapidly due to factors such as internal short circuits, overcharging, over-discharging, or external high temperatures, triggering a series of uncontrollable chemical reactions and physical changes, potentially leading to serious consequences such as battery fire or explosion. Thermal runaway response parameters describe the physical or chemical parameters exhibited by the battery under thermal runaway conditions, such as the trends and thresholds of changes in battery temperature, voltage, current, and internal pressure. These parameters are crucial for determining whether a battery is in a thermal runaway state. Thermal runaway triggering parameters are specific conditions or parameter values ​​that can trigger battery thermal runaway, such as the rate of temperature rise and voltage fluctuation range. These parameters are key indicators for preventing battery thermal runaway. By using statistical analysis, machine learning algorithms, or physical models, the relationships, patterns of change, and triggering conditions of parameters are analyzed. The thermal runaway response parameters (such as the range or threshold of temperature and voltage of the battery during thermal runaway) and thermal runaway triggering parameters (such as the rate of temperature rise and voltage fluctuation range) obtained from the analysis are organized to generate a battery thermal runaway parameter set.

[0065] In one possible implementation, the battery thermal runaway parameter set generation module 10 includes: a historical thermal runaway event extraction unit, which analyzes the historical battery thermal runaway logs to extract historical thermal runaway events of the mobile energy storage device. These historical thermal runaway events include thermal runaway time data, thermal runaway location data, and thermal runaway battery status. Specifically, it filters historical thermal runaway event records related to the mobile energy storage device from the historical battery thermal runaway logs. Thermal runaway events refer to thermal runaway phenomena caused by internal or external factors in the battery, usually accompanied by a rapid increase in temperature and potential safety hazards. From the filtered historical thermal runaway event records, it extracts thermal runaway time data (e.g., year, month, day, hour, minute, second), thermal runaway location data (e.g., latitude and longitude, specific address), and thermal runaway battery status (e.g., voltage, current, temperature, remaining charge percentage, etc.). A battery short-circuit trigger record generation unit is also included, which performs trigger analysis based on the thermal runaway battery status to generate battery short-circuit trigger records. Specifically, the extracted thermal runaway battery state is analyzed to identify abnormal states related to short circuits. A battery short circuit refers to a direct connection between the positive and negative electrodes inside the battery for some reason, causing current to bypass the normal path and potentially triggering severe thermal runaway phenomena, such as sudden voltage drops and sudden current increases. Based on the short-circuit-related abnormal states, it is determined whether the thermal runaway is caused by a short circuit, and a corresponding battery short-circuit trigger record is generated. A battery overcharge / discharge trigger record generation unit is used to perform trigger analysis based on the thermal runaway battery state and the thermal runaway time data to generate battery overcharge trigger records and battery overdischarge trigger records. Specifically, the thermal runaway time data is correlated with the thermal runaway battery state. The charge and discharge states of the battery before and after the thermal runaway occur are analyzed. Based on the charge and discharge states before and after the thermal runaway, it is determined whether the thermal runaway was caused by overcharging or over-discharging, and corresponding battery overcharging trigger records and battery over-discharging trigger records are generated. Overcharging refers to the battery exceeding its designed maximum charging voltage or charging time during the charging process, which may lead to excessive internal pressure and temperature rise, thereby triggering thermal runaway. Over-discharging refers to the battery falling below its designed minimum discharge voltage or excessive depth of discharge during the discharge process, which may lead to changes in the internal material structure of the battery, triggering thermal runaway. An environmental temperature trigger record generation unit is used to perform trigger analysis based on the thermal runaway location data and generate environmental temperature trigger records.Specifically, the extracted thermal runaway location data is analyzed to obtain the ambient temperature information at the time of thermal runaway. Ambient temperature refers to the external temperature conditions during battery operation. The system analyzes whether the ambient temperature at the time of thermal runaway is abnormal (excessively high or low temperatures can affect battery performance and safety) and determines whether it is one of the external factors leading to thermal runaway. Based on the temperature analysis results, an ambient temperature trigger record is generated. A thermal runaway trigger parameter determination unit is used to integrate the battery short-circuit trigger record, the battery overcharge trigger record, the battery over-discharge trigger record, and the ambient temperature trigger record to determine the thermal runaway trigger parameters. Specifically, the battery short-circuit trigger record, battery overcharge trigger record, battery over-discharge trigger record, and ambient temperature trigger record are integrated. Based on the integrated trigger record, thermal runaway trigger parameters are determined, including voltage threshold, current threshold, temperature threshold, and charge / discharge time threshold. This implementation method identifies key factors leading to thermal runaway from multiple dimensions (short circuit, overcharge / discharge, and ambient temperature) by setting up a historical thermal runaway event extraction unit, a battery short circuit trigger record generation unit, a battery overcharge / discharge trigger record generation unit, an ambient temperature trigger record generation unit, and a thermal runaway trigger parameter determination unit. It generates corresponding trigger records and accurately determines thermal runaway trigger parameters by integrating these trigger records, thus achieving the technical effect of improving the accuracy of thermal runaway trigger parameter determination.

[0066] In one possible implementation, the battery thermal runaway parameter set generation module 10 includes: a thermal runaway temperature recording data acquisition unit, which is used to acquire thermal runaway temperature recording data based on the changes in the battery state of the mobile energy storage device identified by the historical thermal runaway events. The thermal runaway temperature recording data includes the thermal runaway initiation temperature, the thermal runaway peak temperature, and the thermal runaway temperature change rate. Specifically, based on the records of historical thermal runaway events, thermal runaway events related to changes in the battery state of the mobile energy storage device are identified. From the identified thermal runaway events, temperature-related data is extracted, including the thermal runaway initiation temperature (i.e., the battery temperature at the start of thermal runaway), the thermal runaway peak temperature (i.e., the highest temperature reached by the battery during thermal runaway), and the thermal runaway temperature change rate (i.e., the rate of temperature change over time). A thermal runaway voltage and current data acquisition unit is used to analyze the changes in electrical parameters within the historical thermal runaway events and acquire thermal runaway voltage data and thermal runaway current data. Specifically, the electrical parameters (voltage and current) during historical thermal runaway events are analyzed to extract thermal runaway voltage data (i.e., the voltage change of the battery during thermal runaway) and thermal runaway current data (i.e., the current change of the battery during thermal runaway). A thermal runaway reaction rate data acquisition unit is used to calculate and obtain thermal runaway reaction rate data based on the thermal runaway voltage and current data. Specifically, based on the acquired thermal runaway voltage and current data, differential processing or other mathematical operations are performed to obtain the thermal runaway reaction rate data. The thermal runaway reaction rate refers to the speed at which internal chemical reactions or physical processes occur during thermal runaway. A thermal runaway reaction parameter determination unit is used to integrate the thermal runaway temperature recording data and the thermal runaway reaction rate data to determine the thermal runaway reaction parameters. Specifically, the acquired thermal runaway temperature recording data and thermal runaway reaction rate data are integrated. Based on the integrated data, thermal runaway reaction parameters are determined. These parameters describe key characteristics of the battery during the thermal runaway process, such as temperature thresholds and reaction rate thresholds. This approach, by setting up a thermal runaway temperature recording data acquisition unit, a thermal runaway voltage and current data acquisition unit, a thermal runaway reaction rate data acquisition unit, and a thermal runaway reaction parameter determination unit, comprehensively analyzes battery thermal runaway events from multiple dimensions and obtains key thermal runaway reaction parameters. This achieves the technical effect of providing accurate data support for accurately predicting and assessing battery thermal runaway risks.

[0067] The device battery operating parameter acquisition module 20 is used to acquire the device battery operating parameters by real-time monitoring of the mobile energy storage device. These battery operating parameters describe various physical and chemical parameters exhibited by the battery during operation. Specifically, the device battery operating parameter acquisition module 20 is a module used to monitor the mobile energy storage device in real time and acquire its battery operating parameters. Upon startup, the device battery operating parameter acquisition module 20 performs initialization settings, including setting the data acquisition frequency, communication interface configuration, and data storage format. It establishes a communication connection with the mobile energy storage device via wired or wireless means, identifying the connected mobile energy storage device model, battery type, and related configuration information. It then starts the real-time monitoring program, acquiring data from the mobile energy storage device's battery according to the preset acquisition frequency, and reading battery operating parameters from the mobile energy storage device's battery management system or other relevant sensors, including but not limited to battery voltage, current, temperature, charging / discharging state, remaining charge percentage, and health status.

[0068] The parameter comparison module 30 is used to compare the device battery operating parameters with the thermal runaway response parameters using the thermal runaway trigger parameters as constraints, and generate a parameter comparison result. Specifically, the parameter comparison module 30 receives thermal runaway trigger parameters and thermal runaway response parameters from the battery thermal runaway parameter set generation module 10, and device battery operating parameters from the device battery operating parameter acquisition module 20. It matches the received device battery operating parameters with the thermal runaway trigger parameters to ensure consistency in data type and units, and performs comparative analysis to assess the similarity between the current battery state and the thermal runaway state. Based on the comparison results of the device battery operating parameters with the thermal runaway trigger and response parameters, the thermal runaway risk of the battery is assessed. If the device battery operating parameters are close to or exceed the thermal runaway trigger parameters, or are similar to the thermal runaway response parameters, the battery is considered to have a thermal runaway risk or is already in a thermal runaway state. Based on the comparative analysis and risk assessment results, a parameter comparison result is generated.

[0069] In one possible implementation, the parameter comparison module 30 includes: multiple trigger frequency determination units, which determine multiple trigger frequencies based on the battery short-circuit trigger record, the battery overcharge trigger record, the battery over-discharge trigger record, and the ambient temperature trigger record. Specifically, they statistically analyze the number and frequency of each trigger record occurring per unit time, analyze the specific impact of each trigger on the battery, including battery performance degradation and shortened lifespan, and output the trigger frequency and its impact analysis for each trigger record. A constraint condition determination unit is used to extract trigger records that lead to battery thermal runaway based on the multiple trigger frequencies and determine the constraint conditions. Specifically, it filters out the trigger records that ultimately lead to battery thermal runaway from all trigger records, extracts common features or conditions from the filtered trigger records, and sets constraint conditions (specific conditions or standards) for judging the risk of battery thermal runaway based on the extracted features or conditions. A thermal runaway critical value determination unit is used to analyze thermal runaway characteristics based on the thermal runaway initiation temperature, the thermal runaway peak temperature, and the thermal runaway temperature change rate to determine the thermal runaway critical value. The thermal runaway critical value refers to the value at which a certain parameter in the battery reaches or exceeds a specific value during thermal runaway, indicating that the battery has already or is about to experience thermal runaway. Specifically, based on parameters such as the thermal runaway initiation temperature, thermal runaway peak temperature, and thermal runaway temperature change rate, the characteristics of the battery during thermal runaway are analyzed. Based on the thermal runaway characteristic analysis, a thermal runaway critical value, such as a temperature threshold or a change rate threshold, is set. A comparison and judgment unit is used to compare the device battery operating parameters with the thermal runaway response parameters according to the constraints, and to determine whether the device battery operating parameters have reached the thermal runaway critical value. Specifically, the comparison and judgment unit acquires the device battery operating parameters and thermal runaway response parameters, compares them, determines whether the device battery operating parameters have reached or exceeded the set thermal runaway critical value, and outputs the judgment result. A battery thermal runaway risk parameter acquisition unit is used to acquire battery thermal runaway risk parameters if the device battery operating parameters reach the thermal runaway critical value, indicating that the mobile energy storage device has a battery thermal runaway risk. Specifically, when the device's battery operating parameters reach the thermal runaway critical value, a thermal runaway risk is identified. Parameters related to this risk, such as temperature, voltage, and current, are extracted from real-time device battery operating parameter data, and battery thermal runaway risk parameters are output. These parameters or indicators reflect the degree of battery thermal runaway risk. A parameter comparison result adding unit is used to add the battery thermal runaway risk parameters to the parameter comparison results.Specifically, the parameter comparison result is the final conclusion or report on the risk of battery thermal runaway, obtained by the parameter comparison module 30 after a series of comparisons, analyses, and judgments. This implementation achieves the comprehensiveness and accuracy of the parameter comparison module 30 by setting up multiple trigger frequency determination units, constraint condition determination units, thermal runaway critical value determination units, comparison and judgment units, battery thermal runaway risk parameter acquisition units, and parameter comparison result addition units, thus achieving the technical effect of improving the accuracy of parameter comparison.

[0070] A battery life early warning model construction module 40 is used to construct a battery life early warning model and synchronize the parameter comparison results to the model. The battery life early warning model includes a battery life analysis channel and a battery life early warning channel. Specifically, the battery life early warning model construction module 40 is used to select or design a battery life early warning model structure based on historical battery thermal runaway data, according to battery type, specifications, and usage environment. The battery life early warning model is a predictive model used to predict the remaining life of the battery and generate early warning signals. It can assess and predict the battery's health status based on historical data and real-time operating status. It includes two main channels: a battery life analysis channel and a battery life early warning channel. The battery life analysis channel analyzes the current state and future trends of the battery to assess and predict its health status. The battery life early warning channel generates early warning signals based on the analysis results. When the battery's health status falls below a preset threshold, the early warning channel triggers an early warning signal so that appropriate measures can be taken in a timely manner. The battery life warning model is initialized using historical data and expert knowledge. Initial parameters and thresholds are set. The model is trained using preprocessed historical battery thermal runaway data and the initial structure of the battery life warning model. The model parameters are adjusted through iterative optimization algorithms (such as gradient descent and genetic algorithms) to enable the model to better fit historical battery thermal runaway data and predict future trends. After training, the battery life warning model is obtained, and the parameter comparison results generated by the parameter comparison module 30 are synchronized to the battery life warning model.

[0071] In one possible implementation, the battery life warning model construction module 40 includes: a lifespan impact feature extraction unit, which extracts lifespan impact features based on the battery lifespan impact parameters of the mobile energy storage device. Lifespan impact features refer to various parameters or indicators that can affect battery lifespan, used to accurately predict the remaining battery lifespan. Specifically, it collects battery lifespan impact parameters of the mobile energy storage device, such as charge / discharge cycles, temperature, current, and voltage; preprocesses the collected data, such as missing value imputation, outlier handling, and data standardization; and extracts features closely related to battery lifespan, such as charging capacity decay rate and internal resistance growth rate, from the preprocessed data using statistical methods, domain knowledge, or machine learning algorithms. It also includes a multiple decision tree construction unit, which constructs multiple decision trees based on the lifespan impact features using a random forest. The decision tree is a machine learning algorithm that uses a tree structure to represent the relationship between features and labels in the data; the random forest is an ensemble learning method based on decision trees, which improves the accuracy and stability of the model by constructing multiple decision trees and combining their prediction results. Specifically, the extracted lifespan impact feature dataset is divided into training and test sets. Using the training set data, multiple decision trees are constructed using a random forest algorithm. During the construction of each decision tree, a subset of features and samples are randomly selected for training. A battery lifespan analysis channel construction unit is used to cross-validate the multiple decision trees and construct the battery lifespan analysis channel. Specifically, the test set data is used to cross-validate the multiple decision trees, evaluating the predictive performance of each tree. Based on the cross-validation results, the best-performing decision trees are selected for ensemble integration to construct the battery lifespan analysis channel. This channel is used to analyze the current state and future trends of the battery based on the input lifespan impact features. A splitting criterion execution unit is used to execute splitting criteria on the multiple decision trees according to the maximum depth and generate execution results. Specifically, to prevent overfitting, a maximum depth limit is set for the decision trees. During the construction of the decision trees, pruning is performed according to the maximum depth limit to ensure that the tree depth does not exceed the set maximum value. Simultaneously, during the splitting process at each node, the optimal splitting features and splitting points are selected based on splitting criteria such as information gain and Gini index. A battery life warning channel construction unit is used to perform unsupervised training on the battery life of mobile energy storage devices based on the execution results, and construct the battery life warning channel based on the training results.Specifically, based on the extracted lifespan impact features, unsupervised training is performed on the battery lifespan of mobile energy storage devices. Unsupervised training is used to discover potential structures and patterns in the data, without requiring labeled data. Based on the training results and domain knowledge, battery lifespan warning thresholds are set. When battery state parameters reach or exceed these thresholds, a warning signal is triggered. Based on the unsupervised training results and warning thresholds, a battery lifespan warning channel is constructed. This channel can generate warning signals in real time based on the input battery state parameters. A channel fusion unit is used to fuse the battery lifespan analysis channel and the battery lifespan warning channel to construct the battery lifespan warning model. Specifically, the battery lifespan analysis channel and the battery lifespan warning channel are integrated to form a complete battery lifespan warning model. This implementation method constructs an efficient, accurate, and reliable battery lifespan warning model by setting up a lifespan impact feature extraction unit, multiple decision tree construction units, a battery lifespan analysis channel construction unit, a splitting criterion execution unit, a battery lifespan warning channel construction unit, and a channel fusion unit. This achieves the technical effect of enabling the battery lifespan warning model to accurately predict the remaining battery lifespan and issue warning signals in a timely manner.

[0072] The warning signal output module 50 is used to acquire battery status parameters through the battery life analysis channel and input these parameters into the battery life warning channel to output a warning signal. Specifically, the warning signal output module 50 acquires battery status parameters of the mobile energy storage device from the battery life analysis channel. These battery status parameters are derived from the analysis of input parameter comparison results and reflect the current state or health status of the battery. The battery status parameters are input into the battery life warning channel, which judges the current state of the battery based on the input battery status parameters or extracted features. Based on preset thresholds in the battery life warning channel (such as battery capacity below a certain percentage, temperature exceeding a certain upper limit, etc.), it determines whether a warning signal needs to be generated. If the warning conditions are met, the warning signal output module 50 generates a corresponding warning signal and outputs it to the intelligent alarm module 60.

[0073] In one possible implementation, the early warning signal output module 50 includes: a battery thermal runaway risk parameter input unit, which extracts the battery thermal runaway risk parameter from the parameter comparison result and inputs the battery thermal runaway risk parameter into the battery life analysis channel. Specifically, the battery thermal runaway risk parameter is extracted from the parameter comparison result and input into the battery life analysis channel. A battery life degradation rate data generation unit is also included, which calculates the changing trend of the battery thermal runaway risk parameter and the device battery operating parameters to generate battery life degradation rate data. Specifically, the battery life degradation rate data generation unit receives the battery thermal runaway risk parameter and the device battery operating parameters, analyzes the changing trend of the parameters, calculates the rate of battery performance degradation, i.e., the battery life degradation rate, which describes the speed at which battery performance degrades over time and can be measured by indicators such as capacity decay rate or internal resistance increase rate. The calculation result is saved as battery life degradation rate data. A battery remaining life data generation unit is used to assess the lifespan of the mobile energy storage device based on the battery life degradation rate data and generate battery remaining life data. Specifically, the battery remaining life data generation unit receives battery life degradation rate data, assesses the battery life of the mobile energy storage device based on the battery's current performance state, usage history, and expected lifespan degradation rate, and generates battery remaining life data, which predicts the battery's continued usability or capacity in the future. A battery status parameter output unit is used to integrate the battery life degradation rate data and the battery remaining life data and output the battery status parameters through the battery life analysis channel. Specifically, the battery status parameter output unit integrates the battery life degradation rate data and the battery remaining life data, and outputs these integrated battery status parameters to the battery life warning channel through the battery life analysis channel. This implementation method, by setting up a battery thermal runaway risk parameter input unit, a battery life degradation rate data generation unit, a battery remaining life data generation unit, and a battery state parameter output unit, obtains a set of battery state parameters that comprehensively reflect information such as the current state of the battery, the rate of performance degradation, and the remaining life. This achieves the technical effect of providing accurate data input for the battery life warning channel, thereby improving the accuracy and reliability of battery life warning.

[0074] As shown in Figure 2, in one possible implementation, the warning signal output module 50 includes: a battery status parameter parsing unit, which parses the battery status parameters to obtain battery life percentage, battery cycle count, and battery usage time. Specifically, the battery status parameter parsing unit receives battery status parameters from the battery life analysis channel and extracts battery life percentage, battery cycle count, and battery usage time information from them. The battery life percentage represents the percentage of the battery's current lifespan relative to its original lifespan; the battery cycle count is the number of complete charge-discharge cycles the battery has undergone from its new state to its current state; and the battery usage time is the total time elapsed from its new state to its current state. A threshold setting unit is also included, which sets a minimum battery life percentage threshold based on the battery life percentage, a remaining battery cycle threshold based on the battery cycle count, and a maximum battery usage time threshold based on the battery usage time. Specifically, the threshold setting unit sets a minimum battery life percentage threshold based on factors such as battery type and usage scenario. When the remaining battery life is lower than the minimum battery life percentage threshold, the battery is considered to need replacement or repair. Based on battery design and manufacturer recommendations, a remaining battery cycle threshold is set. When the remaining battery cycle count is lower than the remaining battery cycle count threshold, the battery performance is considered to have significantly degraded. Based on battery usage history and maintenance records, a maximum battery usage time threshold is set. The maximum battery usage time threshold is the total time from when the battery is brand new until it reaches its lifespan limit. A battery remaining life data extraction unit is used to extract the remaining battery life percentage, remaining battery cycle count, and battery usage time from the battery remaining life data based on the battery life degradation rate data. Specifically, the battery remaining life data extraction unit receives the battery life degradation rate data and, based on the battery life degradation rate data and current battery state parameters, calculates and extracts the remaining battery life percentage, remaining battery cycle count, and battery usage time. A first warning signal generation unit is used to generate a first warning signal if the battery usage time is greater than or equal to the maximum battery usage time threshold. Specifically, the current battery usage time is compared with the maximum battery usage time threshold. If the current battery usage time is greater than or equal to the maximum battery usage time threshold, a first warning signal is generated, indicating that the battery has reached or exceeded the maximum usage time limit. A second warning signal generation unit is used to determine whether the remaining battery cycle count is less than the remaining battery cycle threshold if the current battery usage time is less than the maximum battery usage time threshold. If so, a second warning signal is generated.Specifically, for batteries whose usage time is less than the maximum battery usage time threshold, it is further determined whether the remaining battery cycle count is less than the remaining battery cycle threshold. If the remaining battery cycle count is less than the remaining battery cycle threshold, a second warning signal is generated, indicating that the battery cycle count is about to be exhausted. A third warning signal generation unit is used to determine whether the remaining battery life percentage is less than or equal to the minimum battery life percentage threshold if the battery usage time is less than the maximum battery usage time threshold and the remaining battery cycle count is greater than or equal to the remaining battery cycle threshold. If so, a third warning signal is generated. Specifically, for batteries whose usage time is less than the maximum battery usage time threshold and the remaining battery cycle count is greater than or equal to the remaining battery cycle threshold, it is further determined whether the remaining battery life percentage is less than or equal to the minimum battery life percentage threshold. If the remaining battery life percentage is less than or equal to the minimum battery life percentage threshold, a third warning signal is generated, indicating that the remaining battery life percentage has dropped to a low level. A warning response unit is used to respond to warnings based on the first warning signal, the second warning signal, and the third warning signal, and output the warning signal. Specifically, the early warning response unit receives the first, second, and third early warning signals, generates corresponding early warning signals based on different signal types, and sends these signals to the early warning platform. This implementation, through the establishment of a battery status parameter parsing unit, a threshold setting unit, a battery remaining life data extraction unit, a first early warning signal generation unit, a second early warning signal generation unit, a third early warning signal generation unit, and an early warning response unit, performs multi-dimensional analysis and judgment of battery status parameters. Combined with threshold settings, it accurately determines the battery's health status and usage, and generates corresponding early warning signals under different conditions, achieving a multi-level, comprehensive, and refined early warning effect for battery life.

[0075] The intelligent alarm module 60 is used to receive the warning signal from the warning platform, activate the alarm device, and connect to a remote terminal through the alarm device to provide intelligent alarms for the battery life of the mobile energy storage device. Specifically, the intelligent alarm module 60 is responsible for receiving warning signals, parsing the signal content, activating the alarm device, and sending alarm information to the remote terminal to improve the safety and reliability of battery management. Specifically, the intelligent alarm module 60 receives the warning signal from the warning signal output module 50 through the warning platform (a central platform for receiving, processing, and distributing warning signals). The warning signal contains specific information about the battery status, such as the risk of battery thermal runaway, the rate of battery life degradation, and the remaining battery life. The intelligent alarm module 60 parses the warning signal and, based on the parsed warning signal content, determines whether it is necessary to activate the alarm device (a physical device or system for generating alarm signals). If so, it sends an activation command to the alarm device, which includes a flashing light, a vibrator, etc., to attract the user's attention. The intelligent alarm module 60 generates detailed alarm information based on the parsed warning signal content, including the current battery status, existing risks, and suggested measures. The intelligent alarm module 60 sends the generated alarm information to a remote terminal via a pre-set communication protocol and connection method. The remote terminal can be a monitoring center, a management personnel's mobile device, or another remote monitoring system. Upon receiving the alarm information, the remote terminal displays or plays the alarm content so that relevant personnel can promptly understand the battery status and potential risks. This embodiment of the application employs techniques such as analyzing historical battery thermal runaway logs, generating a battery thermal runaway parameter set, real-time monitoring of mobile energy storage devices, parameter comparison, constructing a battery life warning model, and outputting warning signals to provide intelligent alarms for battery life. These techniques improve the accuracy and reliability of warnings, providing strong protection for the safe operation of mobile energy storage devices.

[0076] In one possible implementation, the intelligent alarm module 60 includes: an abnormal parameter set acquisition unit, which is used to decompose the warning signal to acquire an abnormal parameter set, and send the abnormal parameter set to the remote terminal for alarm through the alarm device. Specifically, the abnormal parameter set acquisition unit receives the warning signal from the warning signal output module 50, parses the warning signal, extracts parameters related to battery abnormalities, and forms an abnormal parameter set. The abnormal parameter set is a set of parameters related to the abnormal state of the battery, used to describe the current abnormal state of the battery, including abnormal battery temperature values, abnormal current values, abnormal voltage values, etc. The abnormal parameter set is sent to the remote terminal through the alarm device so that the management personnel can be informed of the abnormal battery situation in a timely manner. An adjustment parameter set generation unit is used to transmit the battery optimization strategy back through the remote terminal, execute the battery optimization strategy in combination with the battery operating parameters of the device, adjust the initial battery usage strategy of the mobile energy storage device, and generate an adjustment parameter set. Specifically, the parameter set generation unit receives battery optimization strategies from a remote terminal, including adjusting charging current, adjusting depth of discharge, and changing battery usage mode. Combining the device's battery operating parameters (such as current battery temperature and charge / discharge rate) with the received battery optimization strategies, it adjusts the initial battery usage strategy of the mobile energy storage device. Based on the adjustment results, it generates an adjustment parameter set, which is a set of parameters used to adjust the battery usage strategy, including new charging current values, depth of discharge limits, and battery usage modes. The parameter adjustment unit adjusts the thermal runaway time data, thermal runaway location data, and thermal runaway battery state according to the adjustment parameter set, obtaining battery usage time adjustment parameters, battery usage location adjustment parameters, and battery usage state adjustment parameters. Specifically, the parameter adjustment unit receives the adjustment parameter set and adjusts the parameters related to battery performance and usage mode in the thermal runaway time data, thermal runaway location data, and thermal runaway battery state based on the adjustment parameter set. It then extracts battery usage time adjustment parameters, battery usage location adjustment parameters, and battery usage state adjustment parameters from the adjusted parameters. A battery life data update unit is used to update the battery life data of the mobile energy storage device based on the battery usage time adjustment parameters, the battery usage location adjustment parameters, and the battery usage status adjustment parameters. Specifically, the battery life data update unit receives the battery usage time adjustment parameters, battery usage location adjustment parameters, and battery usage status adjustment parameters from the parameter adjustment unit, and updates the battery life data of the mobile energy storage device according to these adjustment parameters, including updating the estimated remaining battery life and adjusting the battery maintenance plan.This implementation method, by setting up an abnormal parameter set acquisition unit, an adjustment parameter set generation unit, a parameter adjustment unit, and a battery life data update unit, achieves the technical effect of dynamically adjusting the battery usage strategy according to the real-time status and usage of the battery, thereby extending the battery life and improving the battery performance and safety.

[0077] Although this application makes various references to certain modules in the platform according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0078] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A battery life early warning platform for mobile energy storage devices based on battery thermal runaway analysis, characterized in that, The platform includes: A battery thermal runaway parameter set generation module is used to retrieve historical battery thermal runaway logs to analyze mobile energy storage devices and generate a battery thermal runaway parameter set, which includes thermal runaway response parameters and thermal runaway trigger parameters. The device battery operating parameter acquisition module is used to acquire device battery operating parameters by real-time monitoring of the mobile energy storage device. A parameter comparison module is used to compare the device battery operating parameters with the thermal runaway response parameters using the thermal runaway triggering parameters as constraints, and generate parameter comparison results. A battery life warning model construction module is used to construct a battery life warning model and synchronize the parameter comparison results to the battery life warning model. The battery life warning model includes a battery life analysis channel and a battery life warning channel. The warning signal output module is used to obtain battery status parameters through the battery life analysis channel, and input the battery status parameters to the battery life warning channel to output a warning signal. The intelligent alarm module is used to receive the early warning signal from the early warning platform and activate the alarm device, and then connect to a remote terminal through the alarm device to provide intelligent alarms for the battery life of the mobile energy storage device.

2. The mobile energy storage device battery life early warning platform based on battery thermal runaway analysis as described in claim 1, characterized in that, The battery thermal runaway parameter set generation module includes: The historical thermal runaway event extraction unit is used to analyze the historical battery thermal runaway log to extract historical thermal runaway events of the mobile energy storage device. The historical thermal runaway events include thermal runaway time data, thermal runaway location data, and thermal runaway battery status. A battery short-circuit trigger record generation unit is used to perform trigger analysis based on the thermal runaway battery state and generate a battery short-circuit trigger record. A battery overcharge / discharge trigger record generation unit is used to perform trigger analysis based on the thermal runaway battery state and the thermal runaway time data to generate battery overcharge trigger records and battery overdischarge trigger records. An ambient temperature trigger record generation unit is used to perform trigger analysis based on the thermal runaway location data and generate an ambient temperature trigger record. A thermal runaway trigger parameter determination unit is used to integrate the battery short circuit trigger record, the battery overcharge trigger record, the battery over-discharge trigger record, and the ambient temperature trigger record to determine the thermal runaway trigger parameters.

3. The mobile energy storage device battery life early warning platform based on battery thermal runaway analysis as described in claim 2, characterized in that, The battery thermal runaway parameter set generation module includes: A thermal runaway temperature recording data acquisition unit is used to acquire thermal runaway temperature recording data based on the changes in battery state of the mobile energy storage device identified by the historical thermal runaway events. The thermal runaway temperature recording data includes thermal runaway initiation temperature, thermal runaway peak temperature, and thermal runaway temperature change rate. A thermal runaway voltage and current data acquisition unit is used to analyze the changes in power parameters during the historical thermal runaway events and acquire thermal runaway voltage data and thermal runaway current data. A thermal runaway reaction rate data acquisition unit is used to calculate thermal runaway reaction rate data based on the thermal runaway voltage data and the thermal runaway current data. A thermal runaway response parameter determination unit is used to integrate the thermal runaway temperature recording data and the thermal runaway response rate data to determine the thermal runaway response parameters.

4. The mobile energy storage device battery life early warning platform based on battery thermal runaway analysis as described in claim 3, characterized in that, The parameter comparison module includes: Multiple trigger frequency determination units are used to determine multiple trigger frequencies based on the battery short circuit trigger record, the battery overcharge trigger record, the battery over-discharge trigger record, and the ambient temperature trigger record. A constraint condition determination unit is used to extract trigger records that lead to battery thermal runaway based on the plurality of trigger frequencies, and determine the constraint conditions. A thermal runaway critical value determination unit is used to analyze thermal runaway characteristics based on the thermal runaway initiation temperature, the thermal runaway peak temperature, and the thermal runaway temperature change rate, and to determine the thermal runaway critical value. The comparison and judgment unit is used to compare the device battery operating parameters with the thermal runaway response parameters according to the constraints, and to determine whether the device battery operating parameters have reached the thermal runaway critical value. A battery thermal runaway risk parameter acquisition unit is used to acquire battery thermal runaway risk parameters if the battery operating parameters of the device reach the thermal runaway critical value, which is considered as a risk of battery thermal runaway for the mobile energy storage device. A parameter comparison result adding unit is used to add the battery thermal runaway risk parameter to the parameter comparison result.

5. The mobile energy storage device battery life early warning platform based on battery thermal runaway analysis as described in claim 1, characterized in that, The battery life early warning model construction module includes: A lifespan impact feature extraction unit is used to extract lifespan impact features based on the battery lifespan impact parameters of the mobile energy storage device. Multiple decision tree construction units are used to construct multiple decision trees based on the lifetime impact features using random forest; A battery life analysis channel construction unit is used to cross-validate the multiple decision trees to construct the battery life analysis channel. A splitting criterion execution unit is configured to execute a splitting criterion on the plurality of decision trees according to the maximum depth and generate an execution result. A battery life warning channel construction unit is used to perform unsupervised training on the battery life of mobile energy storage devices based on the execution results, and to construct the battery life warning channel based on the training results. A channel fusion unit is used to fuse the battery life analysis channel and the battery life warning channel to construct the battery life warning model.

6. The mobile energy storage device battery life early warning platform based on battery thermal runaway analysis as described in claim 4, characterized in that, The warning signal output module includes: A battery thermal runaway risk parameter input unit is used to extract the battery thermal runaway risk parameter from the parameter comparison result and input the battery thermal runaway risk parameter into the battery life analysis channel. A battery life degradation rate data generation unit is used to calculate the changing trend of the battery thermal runaway risk parameter and the device battery operating parameters to generate battery life degradation rate data. A battery remaining life data generation unit is used to perform a life assessment on a mobile energy storage device based on the battery life degradation rate data, and generate battery remaining life data. A battery status parameter output unit is used to integrate the battery life degradation rate data and the remaining battery life data and output the battery status parameters through the battery life analysis channel.

7. The mobile energy storage device battery life early warning platform based on battery thermal runaway analysis as described in claim 6, characterized in that, The warning signal output module includes: A battery status parameter parsing unit is used to parse the battery status parameters and obtain battery life percentage, battery cycle count, and battery usage time. A threshold setting unit is configured to set a minimum battery life percentage threshold based on the battery life percentage, a remaining battery cycle threshold based on the battery cycle count, and a maximum battery usage time threshold based on the battery usage time. A battery remaining life data extraction unit is used to extract the remaining percentage of battery life, the remaining number of battery cycles, and the battery usage time based on the battery life decline rate data. The first warning signal generation unit is used to generate a first warning signal if the battery usage time is greater than or equal to the longest battery usage time threshold. The second warning signal generation unit is used to determine whether the remaining number of battery cycles is less than the remaining battery cycle threshold if the battery usage time is less than the longest battery usage time threshold. If so, a second warning signal is generated. The third warning signal generation unit is used to determine whether the remaining battery life percentage is less than or equal to the minimum battery life percentage threshold if the battery usage time is less than the longest battery usage time threshold and the remaining battery cycle count is greater than or equal to the remaining battery cycle threshold. If so, a third warning signal is generated. The early warning response unit is used to perform an early warning response based on the first early warning signal, the second early warning signal, and the third early warning signal, and output the early warning signal.

8. The mobile energy storage device battery life early warning platform based on battery thermal runaway analysis as described in claim 2, wherein the intelligent alarm module comprises: An abnormal parameter set acquisition unit is used to decompose the warning signal to acquire an abnormal parameter set, and send the abnormal parameter set to the remote terminal through the alarm device to issue an alarm. The parameter set generation unit is used to adjust the initial battery usage strategy of the mobile energy storage device by transmitting the battery optimization strategy back through the remote terminal, and by executing the battery optimization strategy in combination with the battery operating parameters of the device, thereby generating an adjustment parameter set. A parameter adjustment unit is used to adjust the thermal runaway time data, the thermal runaway location data, and the thermal runaway battery state according to the adjustment parameter set, and to obtain battery usage time adjustment parameters, battery usage location adjustment parameters, and battery usage state adjustment parameters. A battery life data update unit is used to update the battery life data of the mobile energy storage device based on the battery usage time adjustment parameters, the battery usage location adjustment parameters, and the battery usage status adjustment parameters.

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