Thermal runaway safety analysis method and device of lithium ion battery and computer equipment

Abstract

The invention relates to a thermal runaway safety analysis method and device of a lithium ion battery and computer equipment. The method comprises the following steps: acquiring current temperature distribution information of a lithium ion battery to predict temperature distribution trend information of the lithium ion battery; based on the temperature distribution trend information, dynamic temperature characteristics and time periods of all thermal runaway temperature stages of the lithium ion battery are identified through a dynamic characteristic extraction division strategy, and then safety scores of all the thermal runaway temperature stages are identified through a battery safety evaluation strategy; and based on the safety score of each thermal runaway temperature stage, generating thermal runaway safety evaluation early warning information of each thermal runaway temperature stage according to each time period, and based on the safety score of each thermal runaway temperature stage and the thermal runaway safety evaluation early warning information, determining a thermal runaway safety analysis result of the lithium ion battery. By adopting the method, the early warning timeliness and early warning efficiency of thermal runaway can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of lithium ion battery safety, in particular to a thermal runaway safety analysis method and device for lithium ion batteries and a computer device. BACKGROUND

[0002] With the rapid development of new energy vehicles, large-scale energy storage and portable electronic devices, lithium ion batteries have become the mainstream energy storage carrier due to their high energy density and long cycle life. However, the battery may trigger thermal runaway (TR) under overcharge, short circuit, mechanical abuse or high temperature environment, leading to fire or even explosion, which seriously threatens personal and property safety. Therefore, how to analyze and warn the thermal runaway problem of lithium ion batteries is the current research focus.

[0003] The traditional technical solution is to set multiple fixed temperature thresholds (such as 60℃ warning, 80℃ alarm, 120℃ emergency shutdown) to respond to the thermal runaway risk of the battery. Usually, the BMS (battery management system) monitors the surface temperature of the battery in real time, and triggers the corresponding protection measures, but the thermal runaway time usually takes 5-15 minutes from the initial trigger to complete runaway, but the local temperature can rise from 80℃ to 800℃ in 30 seconds, making the response of the temperature threshold lag, thus resulting in low efficiency of the thermal runaway warning. SUMMARY

[0004] Therefore, it is necessary to provide a thermal runaway safety analysis method, device, computer device, computer readable storage medium and computer program product for lithium ion batteries to solve the above technical problems.

[0005] In a first aspect, the present application provides a thermal runaway safety analysis method for lithium ion batteries, comprising:

[0006] obtaining current temperature distribution information of the lithium ion battery, and predicting temperature distribution trend information of the lithium ion battery based on the current temperature distribution information; the current temperature distribution information is distribution information arranged according to temperature data corresponding to each time point of the lithium ion battery;

[0007] based on the temperature distribution trend information, identifying the dynamic temperature characteristics of each thermal runaway temperature stage of the lithium ion battery and the time period corresponding to each thermal runaway temperature stage through a dynamic feature extraction division strategy, and identifying the safety score of each thermal runaway temperature stage based on the dynamic temperature characteristics of each thermal runaway temperature stage through a battery safety evaluation strategy;

[0008] Based on the safety scores of each thermal runaway temperature stage, the thermal runaway safety evaluation early warning information of each thermal runaway temperature stage is generated according to the time period corresponding to each thermal runaway temperature stage, and the thermal runaway safety analysis result of the lithium ion battery is determined based on the safety scores of each thermal runaway temperature stage and the thermal runaway safety evaluation early warning information of each thermal runaway temperature stage.

[0009] Optionally, the temperature distribution trend information of the lithium ion battery is predicted based on the current temperature distribution information, including:

[0010] Based on the current temperature distribution information, a temperature distribution curve of the lithium ion battery is generated;

[0011] Based on the temperature distribution curve, a future temperature distribution curve of the temperature distribution curve in a future time period is generated through a linear trend prediction network;

[0012] The temperature distribution curve and the future temperature distribution curve are arranged to obtain the temperature distribution trend information of the lithium ion battery.

[0013] Optionally, based on the temperature distribution trend information, the dynamic temperature features of each thermal runaway temperature stage of the lithium ion battery and the time period corresponding to each thermal runaway temperature stage are identified through a dynamic feature extraction division strategy, including:

[0014] Based on the temperature distribution trend information, a feature temperature value corresponding to each feature time point of the temperature distribution trend information is extracted through a temperature feature value identification network;

[0015] Based on the feature time point corresponding to each feature temperature value, the temperature distribution trend information is divided into a thermal runaway temperature stage corresponding to each time period;

[0016] Based on the sub-temperature distribution curve of each thermal runaway temperature stage, the dynamic temperature features of each thermal runaway temperature stage are extracted through a feature extraction network.

[0017] Optionally, based on the dynamic temperature features of each thermal runaway temperature stage, the safety scores of each thermal runaway temperature stage are identified through a battery safety evaluation strategy, including:

[0018] Based on the time period corresponding to each thermal runaway stage, the time feature of each thermal runaway stage is extracted;

[0019] Based on the dynamic temperature features of each thermal runaway temperature stage, the time features of each thermal runaway stage, and the sub-temperature distribution curve of each thermal runaway temperature stage, the index evaluation value of each safety evaluation index of each thermal runaway temperature stage is identified through a battery safety evaluation strategy.

[0020] Based on the index evaluation value of each safety evaluation index of each thermal runaway temperature stage, by the weight value of each safety evaluation index of each thermal runaway temperature stage, the index weighted sum processing is performed according to the index type corresponding to each safety evaluation index, to obtain the safety score of each thermal runaway temperature stage.

[0021] Optionally, based on the safety score of each thermal runaway temperature stage, the thermal runaway safety evaluation early warning information of each thermal runaway temperature stage is generated according to the time period corresponding to each thermal runaway temperature stage, including:

[0022] Based on the safety score of each thermal runaway temperature stage, the safety risk level of each thermal runaway temperature stage is identified;

[0023] Based on the dynamic temperature characteristics of each thermal runaway temperature stage and the time period corresponding to each thermal runaway temperature stage, according to the safety evaluation early warning strategy corresponding to the safety risk level of each thermal runaway temperature stage, the thermal runaway safety evaluation early warning information of each thermal runaway temperature stage is generated.

[0024] Optionally, based on the safety score of each thermal runaway temperature stage and the thermal runaway safety evaluation early warning information of each thermal runaway temperature stage, the thermal runaway safety analysis result of the lithium ion battery is determined, including:

[0025] A thermal runaway safety analysis report template is obtained, and based on the thermal runaway safety evaluation early warning information of each thermal runaway temperature stage, according to the thermal runaway control strategy corresponding to the safety risk level corresponding to each thermal runaway temperature stage, a safety control scheme corresponding to each thermal runaway temperature stage is generated;

[0026] The safety score of each thermal runaway temperature stage, the thermal runaway safety evaluation early warning information of each thermal runaway temperature stage, and the safety control scheme corresponding to each thermal runaway temperature stage are filled into the thermal runaway safety analysis report template to obtain the thermal runaway safety analysis result of the lithium ion battery.

[0027] In a second aspect, the present application also provides a lithium ion battery thermal runaway safety analysis device, including:

[0028] The acquisition module is configured to acquire current temperature distribution information of a lithium ion battery, and predict temperature distribution trend information of the lithium ion battery based on the current temperature distribution information; the current temperature distribution information is distribution information arranged in distribution according to temperature data corresponding to each time point of the lithium ion battery;

[0029] The identification module is configured to identify, based on the temperature distribution trend information, dynamic temperature features of each thermal runaway temperature stage of the lithium ion battery by a dynamic feature extraction division strategy, and time periods corresponding to each thermal runaway temperature stage, and identify, based on the dynamic temperature features of each thermal runaway temperature stage, a safety score of each thermal runaway temperature stage by a battery safety evaluation strategy.

[0030] The determination module is configured to generate thermal runaway safety evaluation early warning information of each thermal runaway temperature stage based on the safety score of each thermal runaway temperature stage and the time period corresponding to each thermal runaway temperature stage, and determine a thermal runaway safety analysis result of the lithium ion battery based on the safety score of each thermal runaway temperature stage and the thermal runaway safety evaluation early warning information of each thermal runaway temperature stage.

[0031] Optionally, the acquisition module is specifically configured to:

[0032] generate a temperature distribution curve of the lithium ion battery based on the current temperature distribution information;

[0033] generate a future temperature distribution curve of the temperature distribution curve in a future time period based on the temperature distribution curve and a linear trend prediction network;

[0034] arrange the temperature distribution curve and the future temperature distribution curve to obtain temperature distribution trend information of the lithium ion battery.

[0035] Optionally, the identification module is specifically configured to:

[0036] extract, based on the temperature distribution trend information, a feature time point corresponding to each feature temperature value of the temperature distribution trend information by a temperature feature value identification network;

[0037] divide the temperature distribution trend information into thermal runaway temperature stages corresponding to each time period based on the feature time point corresponding to each feature temperature value;

[0038] extract, based on a sub-temperature distribution curve of each thermal runaway temperature stage, a dynamic temperature feature of each thermal runaway temperature stage by a feature extraction network.

[0039] Optionally, the identification module is specifically configured to:

[0040] extract a time feature of each thermal runaway stage based on a time period corresponding to each thermal runaway stage;

[0041] Based on the dynamic temperature characteristics of each thermal runaway temperature stage, the time characteristics of each thermal runaway stage, and the sub-temperature distribution curve of each thermal runaway temperature stage, through a battery safety evaluation strategy, an index evaluation value of each safety evaluation index of each thermal runaway temperature stage is identified.

[0042] Based on the index evaluation value of each safety evaluation index of each thermal runaway temperature stage, through the weight value of each safety evaluation index of each thermal runaway temperature stage, the index weighted sum processing is performed according to the index type corresponding to each safety evaluation index, to obtain the safety score of each thermal runaway temperature stage.

[0043] Optionally, the determining module is specifically configured to:

[0044] Based on the safety score of each thermal runaway temperature stage, the safety risk level of each thermal runaway temperature stage is identified.

[0045] Based on the dynamic temperature characteristics of each thermal runaway temperature stage and the time period corresponding to each thermal runaway temperature stage, according to the safety evaluation early warning strategy corresponding to the safety risk level of each thermal runaway temperature stage, the thermal runaway safety evaluation early warning information of each thermal runaway temperature stage is generated.

[0046] Optionally, the determining module is specifically configured to:

[0047] The thermal runaway safety analysis report template is obtained, and based on the thermal runaway safety evaluation early warning information of each thermal runaway temperature stage, according to the thermal runaway control strategy corresponding to the safety risk level of each thermal runaway temperature stage, the safety control scheme corresponding to each thermal runaway temperature stage is generated.

[0048] The safety score of each thermal runaway temperature stage, the thermal runaway safety evaluation early warning information of each thermal runaway temperature stage, and the safety control scheme corresponding to each thermal runaway temperature stage are filled into the thermal runaway safety analysis report template to obtain the thermal runaway safety analysis result of the lithium ion battery.

[0049] In a third aspect, the present application provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the method of any one of the first aspect when executing the computer program.

[0050] In a fourth aspect, the present application provides a computer readable storage medium. A computer program is stored thereon, and the computer program is executed by a processor to implement the steps of the method of any one of the first aspect.

[0051] In a fifth aspect, the present application provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of the method of any one of the first aspect.

[0052] The thermal runaway safety analysis method, device and computer equipment of the lithium ion battery, by obtaining the current temperature distribution information of the lithium ion battery, and based on the current temperature distribution information, predicting the temperature distribution trend information of the lithium ion battery; the current temperature distribution information is the distribution information arranged according to the temperature data corresponding to each time point of the lithium ion battery; based on the temperature distribution trend information, through the dynamic feature extraction division strategy, the dynamic temperature characteristics of each thermal runaway temperature stage of the lithium ion battery and the time period corresponding to each thermal runaway temperature stage are identified, and based on the dynamic temperature characteristics of each thermal runaway temperature stage, through the battery safety evaluation strategy, the safety score of each thermal runaway temperature stage is identified; based on the safety score of each thermal runaway temperature stage, according to the time period corresponding to each thermal runaway temperature stage, the thermal runaway safety evaluation warning information of each thermal runaway temperature stage is generated, and based on the safety score of each thermal runaway temperature stage and the thermal runaway safety evaluation warning information of each thermal runaway temperature stage, the thermal runaway safety analysis result of the lithium ion battery is determined. The scheme, by monitoring the current temperature distribution information of the lithium ion battery in real time, the temperature distribution trend information of the lithium ion battery is obtained by trend prediction, so as to avoid the inefficiency problem of thermal runaway temperature early warning analysis after collecting each temperature data, and then the dynamic feature extraction division strategy designed in the scheme is used to divide the temperature distribution trend information into different thermal runaway temperature stages, and the dynamic temperature characteristics of different thermal runaway temperature stages are analyzed respectively, which avoids the poor key dynamic feature recognition, fuzzy stage analysis and poor timeliness of thermal runaway temperature early warning in the prior art. Finally, the quantitative index of cross-system comparison and operation and maintenance decision guidance is provided, the safety score of battery thermal runaway is quantified as a value of 0-1, and it is suitable for different battery systems, different thermal runaway triggering modes and different working conditions. Finally, the thermal runaway safety analysis result containing the thermal runaway safety evaluation warning information of each thermal runaway temperature stage is generated, which not only improves the comprehensiveness and accuracy of the safety analysis of thermal runaway, but also effectively improves the timeliness and efficiency of the early warning of thermal runaway. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed in the embodiment or related art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creating any creative labor.

[0054] Figure 1 a flowchart of a method in an embodiment;

[0055] Figure 2 a flowchart of a thermal runaway safety analysis example of a lithium ion battery in an embodiment;

[0056] Figure 3 a block diagram of a thermal runaway safety analysis device of a lithium ion battery in an embodiment;

[0057] Figure 4 an internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0058] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0059] The thermal runaway safety analysis method of the lithium ion battery provided by the embodiment of the present application can be applied to the application environment of the thermal runaway safety analysis of the lithium ion battery. The method can be applied to a terminal, a server, or a system including a terminal and a server, and is realized through the interaction of the terminal and the server. The terminal can be, but is not limited to, various personal computers, notebook computers, medium-sized computers, etc. The terminal obtains the current temperature distribution information of the lithium ion battery through real-time monitoring, performs trend prediction to obtain the temperature distribution trend information of the lithium ion battery, thereby avoiding the inefficiency problem of thermal runaway temperature early warning analysis after collecting each temperature data, and then divides the temperature distribution trend information into different thermal runaway temperature stages through the dynamic feature extraction and division strategy designed in the present solution, and analyzes the dynamic temperature features of different thermal runaway temperature stages respectively, thereby avoiding the poor key dynamic feature recognition and fuzzy stage analysis of the prior art, resulting in poor timeliness of thermal runaway temperature early warning. Finally, the present solution provides a quantitative index that is comparable across systems and can guide operation and maintenance decisions, quantifies the safety of battery thermal runaway into a value of 0-1, and is applicable to different battery systems, different thermal runaway triggering modes, and different working conditions. Finally, the thermal runaway safety analysis result including the thermal runaway safety evaluation and early warning information of each thermal runaway temperature stage is generated, which not only improves the comprehensiveness and accuracy of the safety analysis of thermal runaway, but also effectively improves the timeliness and efficiency of the early warning of thermal runaway.

[0060] In an exemplary embodiment, as Figure 1As shown, a thermal runaway safety analysis method of a lithium ion battery is provided, and the method is taken as an example for description applied to a terminal, including the following steps S101 to S103. Wherein:

[0061] In step S101, current temperature distribution information of the lithium ion battery is acquired, and temperature distribution trend information of the lithium ion battery is predicted based on the current temperature distribution information.

[0062] The current temperature distribution information is distribution information arranged according to temperature data of the lithium ion battery at each time point.

[0063] In this embodiment, the terminal collects temperature data of the lithium ion battery in real time through a temperature sensor arranged on the lithium ion battery, and sorts the temperature data collected each time according to the time points of collection in time sequence to obtain the current temperature distribution information of the lithium ion battery. Then, the terminal predicts the temperature distribution trend based on the current temperature distribution information through a linear trend prediction technology to obtain the temperature distribution trend information of the lithium ion battery. The specific prediction process will be described in detail later.

[0064] In step S102, dynamic temperature features of each thermal runaway temperature stage of the lithium ion battery and time periods corresponding to each thermal runaway temperature stage are identified through a dynamic feature extraction division strategy based on the temperature distribution trend information, and safety scores of each thermal runaway temperature stage are identified through a battery safety evaluation strategy based on the dynamic temperature features of each thermal runaway temperature stage.

[0065] In this embodiment, the terminal identifies the dynamic temperature characteristics of each thermal runaway temperature stage of the lithium ion battery and the time period corresponding to each thermal runaway temperature stage based on the temperature distribution trend information through a dynamic feature extraction division strategy, and identifies the safety score of each thermal runaway temperature stage based on the dynamic temperature characteristics of each thermal runaway temperature stage through a battery safety evaluation strategy. The dynamic feature extraction division strategy is a strategy that divides the temperature distribution information into four temperature stages according to five preset characteristic temperature values, and extracts the dynamic temperature characteristics corresponding to each temperature stage, and the specific extraction process will be described in detail later. The five characteristic temperature values are the thermal abuse start temperature (Tstart), the thermal runaway start temperature (Tonset), the maximum temperature of the temperature rise rate (T(dT)max), the maximum temperature (Tmax), and the end temperature (Tover). The four thermal runaway temperature stages are the induction period stage, the fast reaction period stage, the slow reaction period stage, and the post-reaction period stage. The battery safety evaluation strategy is to obtain the safety score of each thermal runaway temperature stage by normalizing the weight value corresponding to each evaluation index and the index type corresponding to different evaluation indexes after evaluating each stage according to eight evaluation indexes. The eight evaluation indexes are Δti, ΔTi, Δtr, ΔTr, Δts, ΔTs, Δtp, and ΔTp. The index type includes but is not limited to positive index and negative index. The specific normalization summation process will be described in detail later.

[0066] In step S103, based on the safety score of each thermal runaway temperature stage, the thermal runaway safety evaluation warning information of each thermal runaway temperature stage is generated according to the time period corresponding to each thermal runaway temperature stage, and based on the safety score of each thermal runaway temperature stage and the thermal runaway safety evaluation warning information of each thermal runaway temperature stage, the thermal runaway safety analysis result of the lithium ion battery is determined.

[0067] In this embodiment, the terminal generates the thermal runaway safety evaluation warning information of each thermal runaway temperature stage based on the safety score of each thermal runaway temperature stage according to the time period corresponding to each thermal runaway temperature stage, and determines the thermal runaway safety analysis result of the lithium ion battery based on the safety score of each thermal runaway temperature stage and the thermal runaway safety evaluation warning information of each thermal runaway temperature stage. The thermal runaway safety evaluation warning information of each thermal runaway temperature stage includes the thermal runaway warning content of each thermal runaway temperature stage at the start time of the stage, and the thermal runaway warning content includes information such as the thermal runaway temperature stage, the safety score, the duration, and the dynamic temperature characteristics. The specific generation process will be described in detail later.

[0068] Based on the above scheme, by monitoring the current temperature distribution information of the lithium ion battery in real time, the temperature distribution trend information of the lithium ion battery is obtained through trend prediction, so as to avoid the inefficiency problem of heat runaway temperature early warning analysis after collecting each temperature data, and then the dynamic feature extraction and division strategy designed by the scheme is used to divide the temperature distribution trend information into different heat runaway temperature stages, and the dynamic temperature characteristics of different heat runaway temperature stages are analyzed respectively, which avoids the poor key dynamic feature recognition, fuzzy stage analysis and poor timeliness of heat runaway temperature early warning of the prior art. Finally, the scheme provides a quantitative index that is comparable across systems and can guide operation and decision-making, quantifies the safety of battery heat runaway into a value of 0-1, and is suitable for different battery systems, different heat runaway triggering modes and different working conditions. Finally, the heat runaway safety analysis result containing the heat runaway safety evaluation early warning information of each heat runaway temperature stage is generated, which not only improves the comprehensiveness and accuracy of the safety analysis of heat runaway, but also effectively improves the timeliness and efficiency of the early warning of heat runaway.

[0069] Optionally, based on the current temperature distribution information, the temperature distribution trend information of the lithium ion battery is predicted, including: based on the current temperature distribution information, a temperature distribution curve of the lithium ion battery is generated; based on the temperature distribution curve, a future temperature distribution curve of the temperature distribution curve in a future period is generated through a linear trend prediction network; and the temperature distribution curve and the future temperature distribution curve are arranged to obtain the temperature distribution trend information of the lithium ion battery.

[0070] In this embodiment, the terminal performs linear fitting processing on the current temperature distribution information based on the current temperature distribution information through the algorithm corresponding to the linear fitting technology, to obtain the temperature distribution curve of the lithium ion battery. Then, the terminal generates a future temperature distribution curve of the temperature distribution curve in a future period based on the temperature distribution curve through a linear trend prediction network. Finally, the terminal arranges the temperature distribution curve and the future temperature distribution curve to obtain the temperature distribution trend information of the lithium ion battery. The linear trend prediction network is a linear trend prediction neural network based on a long short-term memory model, which is a neural network trained by a large amount of sample data of historical heat runaway temperature distribution information.

[0071] Based on the above scheme, by combining the linear fitting analysis of the currently collected temperature distribution information and predicting the temperature distribution trend information of the lithium ion battery, the analysis priority of the temperature distribution trend of the lithium ion heat runaway is improved, so as to avoid the inefficiency problem of heat runaway temperature early warning analysis after collecting each temperature data.

[0072] Optionally, based on the temperature distribution trend information, the dynamic characteristic extraction division strategy is used to identify the dynamic temperature characteristics of each thermal runaway temperature stage of the lithium ion battery and the time period corresponding to each thermal runaway temperature stage, including: based on the temperature distribution trend information, the feature time points corresponding to each feature temperature value of the temperature distribution trend information are extracted through a temperature feature value identification network; based on the feature time points corresponding to each feature temperature value, the temperature distribution trend information is divided into thermal runaway temperature stages corresponding to each time period; and based on the sub-temperature distribution curves of each thermal runaway temperature stage, the dynamic temperature characteristics of each thermal runaway temperature stage are extracted through a feature extraction network.

[0073] In this embodiment, the terminal extracts the feature time points corresponding to each feature temperature value of the temperature distribution trend information through a temperature feature value identification network based on the temperature distribution trend information. The temperature feature value identification network is a convolutional neural network based on a self-attention mechanism, which is used to identify the feature time points corresponding to the thermal abuse start temperature (Tstart), the thermal runaway start temperature (Tonset), the maximum temperature of the temperature rise rate (T(dT)max), the maximum temperature (Tmax), and the end temperature (Tover) in the temperature distribution trend information.

[0074] Then, the terminal divides the temperature distribution trend information into thermal runaway temperature stages corresponding to each time period based on the feature time points corresponding to each feature temperature value.

[0075] Finally, the terminal extracts the dynamic temperature characteristics of each thermal runaway temperature stage through a feature extraction network based on the sub-temperature distribution curves of each thermal runaway temperature stage. The feature extraction network is a convolutional neural network based on deep learning, which is used to extract the time variation characteristics and temperature variation characteristics of each sub-temperature distribution curve, and the time variation characteristics and temperature variation characteristics of each thermal runaway temperature stage are used as the dynamic temperature characteristics of each thermal runaway temperature stage.

[0076] Based on the above scheme, the five feature temperatures are extracted through the preset extraction strategy, the thermal runaway stage is subdivided, different thermal runaway temperature stages are obtained, and then feature analysis is performed, thereby improving the comprehensiveness of the thermal runaway temperature analysis.

[0077] Optionally, based on the dynamic temperature characteristics of each thermal runaway temperature stage, the safety scores of each thermal runaway temperature stage are identified through a battery safety evaluation strategy, including: based on the time period corresponding to each thermal runaway stage, extracting the time characteristics of each thermal runaway stage; based on the dynamic temperature characteristics of each thermal runaway temperature stage, the time characteristics of each thermal runaway stage, and the sub-temperature distribution curve of each thermal runaway temperature stage, identifying the index evaluation value of each safety evaluation index of each thermal runaway temperature stage through the battery safety evaluation strategy; based on the index evaluation value of each safety evaluation index of each thermal runaway temperature stage, through the weight value of each safety evaluation index of each thermal runaway temperature stage, the index weighting sum processing is performed according to the index type corresponding to each safety evaluation index, to obtain the safety score of each thermal runaway temperature stage.

[0078] In this embodiment, the terminal extracts the time characteristics of each thermal runaway stage based on the time period corresponding to each thermal runaway stage. The time characteristics are used to represent the stage start time of the thermal runaway stage, the stage duration, and other characteristics. Then, the terminal identifies the index evaluation value of each safety evaluation index of each thermal runaway temperature stage based on the dynamic temperature characteristics of each thermal runaway temperature stage, the time characteristics of each thermal runaway stage, and the sub-temperature distribution curve of each thermal runaway temperature stage through the battery safety evaluation strategy. The battery safety evaluation strategy is a model based on the entropy weight TOPSIS designed by the present scheme, which respectively evaluates the eight safety evaluation indexes according to the dynamic temperature characteristics of different thermal runaway temperature stages. The present scheme comprehensively considers the time characteristics and temperature characteristics by combining temperature-time coupling, which can effectively analyze the safety of thermal runaway by coupling analysis from the time dimension and temperature dimension under the condition of complex thermal abuse curve. The complex thermal abuse curve conditions include the situation that the thermal runaway starts late but the temperature value is high, and the situation that the thermal runaway starts early but the temperature value is low.

[0079] Then, the terminal identifies the index evaluation value of each safety evaluation index of each thermal runaway temperature stage based on the dynamic temperature characteristics of each thermal runaway temperature stage, the time characteristics of each thermal runaway stage, and the sub-temperature distribution curve of each thermal runaway temperature stage through the battery safety evaluation strategy. The battery safety evaluation strategy is a model based on the entropy weight TOPSIS designed by the present scheme, which respectively evaluates the eight safety evaluation indexes according to the dynamic temperature characteristics of different thermal runaway temperature stages. The present scheme comprehensively considers the time characteristics and temperature characteristics by combining temperature-time coupling, which can effectively analyze the safety of thermal runaway by coupling analysis from the time dimension and temperature dimension under the condition of complex thermal abuse curve. The complex thermal abuse curve conditions include the situation that the thermal runaway starts late but the temperature value is high, and the situation that the thermal runaway starts early but the temperature value is low.

[0080] Among the 8 indicators, the terminal needs to combine the positive indicators and negative indicators among them, and combine the actual situation of battery thermal runaway. For the induction period, it is the process of battery continuous heating under thermal conditions until triggering thermal runaway (Tonset), so the time change and temperature change of this process are as large as possible, which represents that the battery can reach thermal runaway later under thermal conditions, and the threshold temperature of thermal runaway is higher, that is, the positive indicator. The time and temperature change of the other three stages can be regarded as negative indicators, because after the battery has occurred thermal runaway, we hope that the temperature of battery thermal runaway is lower and the duration of thermal runaway is shorter, which has a crucial influence on safety.

[0081] The normalization formula of positive indicators and negative indicators is shown in formula (1) and formula (2).

[0082]

[0083]

[0084] wherein, represents the normalized data of the jth indicator of the ith battery, represents the jth indicator.

[0085] After normalization, the proportion needs to be calculated, as shown in formula (3).

[0086]

[0087] wherein, is the proportion of the ith battery indicator in the jth indicator.

[0088] Then, the information entropy of the jth indicator is calculated, as shown in formula (4).

[0089]

[0090] wherein, is the information entropy value of the jth indicator, and the information redundancy Dj=1-Ej is calculated by the calculated information entropy, and then the weight of each indicator is calculated , as shown in formula (5).

[0091]

[0092] wherein, wj is the weight value of the jth indicator.

[0093] After obtaining the weight coefficient of each indicator, the normalized data (i.e. is multiplied by the weight coefficient, and a weighted matrix is constructed, as shown in formula (6).

[0094]

[0095] wherein Z* is the weighted matrix. According to the data in the weighted matrix, the maximum value Z+ and the minimum value Z- are found respectively, and Di+ and Di- can be calculated, as shown in formula (7) and formula (8).

[0096]

[0097]

[0098] wherein Di+ and Di- represent the Euclidean distance of the jth index of the ith battery and the maximum value and the minimum value respectively, and finally, the comprehensive safety score of the final battery i is calculated from Di+ and Di-, as shown in formula (9).

[0099]

[0100] wherein the comprehensive safety score Si (0≤Si≤1). After obtaining the weight coefficient of each index, the normalized data zij (i.e. X'ij) is multiplied by the weight coefficient, and the weighted matrix is constructed, as shown in formula (6).

[0101]

[0102] wherein Z* is the weighted matrix. According to the data in the weighted matrix, the maximum value Z+ and the minimum value Z- are found respectively, and Di+ and Di- can be calculated, as shown in formula (7) and formula (8).

[0103]

[0104]

[0105] wherein Di+ and Di- represent the Euclidean distance of the jth index of the ith battery and the maximum value and the minimum value respectively, and finally, the comprehensive safety score of the final battery i is calculated from Di+ and Di-, as shown in formula (9).

[0106]

[0107] wherein the comprehensive safety score Si (0≤Si≤1).

[0108] Based on the above scheme, the entropy weight TOPSIS evaluation model is used to analyze 8 characteristic indexes, and the weight proportion of each index is obtained, and the comprehensive safety evaluation score is calculated by using the TOPSIS model, and the value is between 0-1. Through this analysis and calculation method, the abstract battery safety concept is calculated through the coupling analysis of time and temperature, and a precise quantitative index is obtained, which provides a reference idea for a more comprehensive and more accurate evaluation method of thermal runaway performance. According to the change degree of the thermal runaway curve, the information entropy is calculated, and the weight is calculated, this process is completely objective, compared with other evaluation methods, the interference of human factors is avoided, and the higher the weight is, the more important the corresponding index is. The calculation and analysis method of the application is suitable for different batteries, different thermal runaway triggering modes and other working conditions, and has wider applicability.

[0109] Optionally, based on the safety scores of the thermal runaway temperature stages, the thermal runaway safety evaluation and early warning information of the thermal runaway temperature stages is generated according to the time periods corresponding to the thermal runaway temperature stages, including: identifying the safety risk levels of the thermal runaway temperature stages based on the safety scores of the thermal runaway temperature stages; based on the dynamic temperature characteristics of the thermal runaway temperature stages and the time periods corresponding to the thermal runaway temperature stages, generating the thermal runaway safety evaluation and early warning information of the thermal runaway temperature stages according to the safety evaluation and early warning strategies corresponding to the safety risk levels of the thermal runaway temperature stages.

[0110] In this embodiment, the terminal identifies the safety risk levels of the thermal runaway temperature stages based on the safety scores of the thermal runaway temperature stages. The terminal predefines the safety score ranges corresponding to different safety risk levels, and identifies the safety risk levels of the thermal runaway temperature stages through range adaptation.

[0111] Then, the terminal generates the thermal runaway safety evaluation and early warning information of the thermal runaway temperature stages based on the dynamic temperature characteristics of the thermal runaway temperature stages and the time periods corresponding to the thermal runaway temperature stages, according to the safety evaluation and early warning strategies corresponding to the safety risk levels of the thermal runaway temperature stages. Different thermal runaway temperature stages correspond to different safety evaluation and early warning strategies at different safety risk levels. The safety evaluation and early warning strategy can alert the working staff of the thermal runaway temperature characteristics, the safety risk level, the current temperature change characteristics and the time change characteristics of the lithium ion battery.

[0112] Based on the above scheme, the thermal runaway safety evaluation and early warning information is generated for different thermal runaway temperature stages, thereby improving the timeliness of the thermal runaway temperature early warning for the working staff and the comprehensiveness of the early warning content, and the working staff has sufficient time to cope with the thermal runaway risk, thereby improving the control effect of the thermal runaway.

[0113] Optionally, based on the safety score of each thermal runaway temperature stage and the thermal runaway safety evaluation warning information of each thermal runaway temperature stage, a thermal runaway safety analysis result of the lithium ion battery is determined, including: obtaining a thermal runaway safety analysis report template, and based on the thermal runaway safety evaluation warning information of each thermal runaway temperature stage, generating a safety control scheme corresponding to each thermal runaway temperature stage according to the thermal runaway control strategy corresponding to the safety risk level corresponding to each thermal runaway temperature stage; filling the safety score of each thermal runaway temperature stage, the thermal runaway safety evaluation warning information of each thermal runaway temperature stage, and the safety control scheme corresponding to each thermal runaway temperature stage into the thermal runaway safety analysis report template to obtain the thermal runaway safety analysis result of the lithium ion battery.

[0114] In this embodiment, the terminal obtains a thermal runaway safety analysis report template, and based on the thermal runaway safety evaluation warning information of each thermal runaway temperature stage, generates a safety control scheme corresponding to each thermal runaway temperature stage according to the thermal runaway control strategy corresponding to the safety risk level corresponding to each thermal runaway temperature stage. The thermal runaway control strategy is a generation strategy of each safety control scheme preset by the staff on the terminal, which includes the adaptation of safety control schemes to different safety risk levels, thermal runaway temperature stages, and warning contents of thermal runaway safety evaluation warning information. The safety control scheme includes control schemes for the operating mode of the lithium ion battery and the operating state of the lithium ion battery (including automatic control schemes and manual control schemes).

[0115] Then, the terminal fills the safety score of each thermal runaway temperature stage, the thermal runaway safety evaluation warning information of each thermal runaway temperature stage, and the safety control scheme corresponding to each thermal runaway temperature stage into the thermal runaway safety analysis report template to obtain the thermal runaway safety analysis result of the lithium ion battery.

[0116] Based on the above scheme, through the thermal runaway safety evaluation warning information of each thermal runaway temperature stage, the safety control scheme corresponding to each thermal runaway temperature stage is generated according to the thermal runaway control strategy corresponding to the safety risk level corresponding to each thermal runaway temperature stage, which improves the safety control effect of the lithium ion battery in different thermal runaway temperature stages, ensures that the lower the battery thermal runaway temperature is, the better, and the shorter the thermal runaway duration is, the better.

[0117] The present application also provides a thermal runaway safety analysis example of a lithium ion battery, as shown in Figure 2 The specific processing process includes the following steps:

[0118] Step S201, obtaining current temperature distribution information of the lithium ion battery.

[0119] Step S202, generating a temperature distribution curve of the lithium ion battery based on the current temperature distribution information.

[0120] At step S203, a future temperature distribution curve of the temperature distribution curve in a future time period is generated based on the temperature distribution curve by a linear trend prediction network.

[0121] At step S204, the temperature distribution curve and the future temperature distribution curve are arranged to obtain temperature distribution trend information of the lithium ion battery.

[0122] At step S205, feature time points corresponding to feature temperature values of the temperature distribution trend information are extracted based on the temperature distribution trend information by a temperature feature value recognition network.

[0123] At step S206, the temperature distribution trend information is divided into thermal runaway temperature stages corresponding to each time period based on the feature time points corresponding to the feature temperature values.

[0124] At step S207, dynamic temperature features of each thermal runaway temperature stage are extracted based on sub-temperature distribution curves of each thermal runaway temperature stage by a feature extraction network.

[0125] At step S208, time features of each thermal runaway stage are extracted based on a time period corresponding to each thermal runaway stage.

[0126] At step S209, index evaluation values of each safety evaluation index of each thermal runaway temperature stage are identified based on the dynamic temperature features of each thermal runaway temperature stage, the time features of each thermal runaway stage, and the sub-temperature distribution curves of each thermal runaway temperature stage by a battery safety evaluation strategy.

[0127] At step S210, safety scores of each thermal runaway temperature stage are obtained by performing index weighted summation processing according to index types corresponding to each safety evaluation index based on the index evaluation values of each safety evaluation index of each thermal runaway temperature stage and weight values of each safety evaluation index of each thermal runaway temperature stage.

[0128] At step S211, safety risk levels of each thermal runaway temperature stage are identified based on the safety scores of each thermal runaway temperature stage.

[0129] At step S212, thermal runaway safety evaluation warning information of each thermal runaway temperature stage is generated according to a safety evaluation warning strategy corresponding to a safety risk level of each thermal runaway temperature stage based on the dynamic temperature features of each thermal runaway temperature stage and a time period corresponding to each thermal runaway temperature stage.

[0130] In step S213, a thermal runaway safety analysis report template is obtained, and based on the thermal runaway safety evaluation and early warning information of each thermal runaway temperature stage, a safety control scheme corresponding to each thermal runaway temperature stage is generated according to the thermal runaway control strategy corresponding to the safety risk level of each thermal runaway temperature stage.

[0131] In step S214, the safety score of each thermal runaway temperature stage, the thermal runaway safety evaluation and early warning information of each thermal runaway temperature stage, and the safety control scheme corresponding to each thermal runaway temperature stage are filled into the thermal runaway safety analysis report template to obtain the thermal runaway safety analysis result of the lithium ion battery.

[0132] It should be understood that, although each step in the flowchart involved in each embodiment as described above is displayed in sequence according to the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least part of other steps or steps or stages in other steps.

[0133] Based on the same inventive concept, the present application also provides a lithium ion battery thermal runaway safety analysis device for implementing the above-mentioned lithium ion battery thermal runaway safety analysis method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more lithium ion battery thermal runaway safety analysis device embodiments provided below can refer to the limitations of the lithium ion battery thermal runaway safety analysis method described above, which will not be repeated here.

[0134] In one exemplary embodiment, as shown in Figure 3 A lithium ion battery thermal runaway safety analysis device is provided, comprising: an acquisition module 310, an identification module 320, and a determination module 330, wherein:

[0135] The acquisition module 310 is configured to acquire current temperature distribution information of a lithium ion battery, and predict temperature distribution trend information of the lithium ion battery based on the current temperature distribution information; the current temperature distribution information is distribution information arranged according to temperature data corresponding to each time point of the lithium ion battery;

[0136] The identification module 320 is configured to identify, based on the temperature distribution trend information, dynamic temperature features of each thermal runaway temperature stage of the lithium ion battery by a dynamic feature extraction division strategy, and time periods corresponding to each thermal runaway temperature stage, and identify, based on the dynamic temperature features of each thermal runaway temperature stage, a safety score of each thermal runaway temperature stage by a battery safety evaluation strategy.

[0137] The determination module 330 is configured to generate thermal runaway safety evaluation warning information of each thermal runaway temperature stage according to the time period corresponding to each thermal runaway temperature stage based on the safety score of each thermal runaway temperature stage, and determine a thermal runaway safety analysis result of the lithium ion battery based on the safety score of each thermal runaway temperature stage and the thermal runaway safety evaluation warning information of each thermal runaway temperature stage.

[0138] Optionally, the acquisition module 310 is specifically configured to:

[0139] generate a temperature distribution curve of the lithium ion battery based on the current temperature distribution information;

[0140] generate a future temperature distribution curve of the temperature distribution curve in a future time period by a linear trend prediction network based on the temperature distribution curve;

[0141] arrange the temperature distribution curve and the future temperature distribution curve to obtain temperature distribution trend information of the lithium ion battery.

[0142] Optionally, the identification module 320 is specifically configured to:

[0143] extract feature time points corresponding to each feature temperature value of the temperature distribution trend information by a temperature feature value identification network based on the temperature distribution trend information;

[0144] divide the temperature distribution trend information into thermal runaway temperature stages corresponding to each time period based on the feature time points corresponding to each feature temperature value;

[0145] extract dynamic temperature features of each thermal runaway temperature stage by a feature extraction network based on a sub-temperature distribution curve of each thermal runaway temperature stage.

[0146] Optionally, the identification module 320 is specifically configured to:

[0147] extract a time feature of each thermal runaway stage based on a time period corresponding to each thermal runaway stage;

[0148] Based on the dynamic temperature characteristics of each thermal runaway temperature stage, the time characteristics of each thermal runaway stage, and the sub-temperature distribution curve of each thermal runaway temperature stage, through a battery safety evaluation strategy, an index evaluation value of each safety evaluation index of each thermal runaway temperature stage is identified;

[0149] Based on the index evaluation value of each safety evaluation index of each thermal runaway temperature stage, through the weight value of each safety evaluation index of each thermal runaway temperature stage, the index weighted sum processing is performed according to the index type corresponding to each safety evaluation index, to obtain the safety score of each thermal runaway temperature stage.

[0150] Optionally, the determination module 330 is specifically configured to:

[0151] Based on the safety score of each thermal runaway temperature stage, the safety risk level of each thermal runaway temperature stage is identified;

[0152] Based on the dynamic temperature characteristics of each thermal runaway temperature stage and the time period corresponding to each thermal runaway temperature stage, according to the safety evaluation early warning strategy corresponding to the safety risk level of each thermal runaway temperature stage, the thermal runaway safety evaluation early warning information of each thermal runaway temperature stage is generated.

[0153] Optionally, the determination module 330 is specifically configured to:

[0154] Obtain a thermal runaway safety analysis report template, and based on the thermal runaway safety evaluation early warning information of each thermal runaway temperature stage, according to the thermal runaway control strategy corresponding to the safety risk level corresponding to each thermal runaway temperature stage, generate a safety control scheme corresponding to each thermal runaway temperature stage;

[0155] Fill the safety score of each thermal runaway temperature stage, the thermal runaway safety evaluation early warning information of each thermal runaway temperature stage, and the safety control scheme corresponding to each thermal runaway temperature stage into the thermal runaway safety analysis report template to obtain the thermal runaway safety analysis result of the lithium ion battery.

[0156] Each module in the above lithium ion battery thermal runaway safety analysis device can be realized by software, hardware and their combinations. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so that the processor calls and executes the operations corresponding to each module.

[0157] In one exemplary embodiment, a computer device is provided, which can be a terminal, and its internal structure diagram can be as shown in Figure 4As shown in the figure. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through the system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with the external terminal in a wired or wireless manner. The wireless manner can be realized through WIFI, mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by the processor to realize a thermal runaway safety analysis method of a lithium ion battery. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0158] Those skilled in the art can understand that, Figure 4 The skilled in the art can understand that,

[0159] In one exemplary embodiment, a computer device is provided, comprising a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the steps of the thermal runaway safety analysis method of the lithium ion battery.

[0160] In one embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by the processor to realize the steps of the thermal runaway safety analysis method of the lithium ion battery.

[0161] In one embodiment, a computer program product is provided, comprising a computer program, and the computer program is executed by the processor to realize the steps of the thermal runaway safety analysis method of the lithium ion battery.

[0162] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0163] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0164] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0165] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A thermal runaway safety analysis method for lithium-ion batteries, characterized in that: The method comprises: Obtaining current temperature distribution information of the lithium-ion battery, and predicting temperature distribution trend information of the lithium-ion battery based on the current temperature distribution information; the current temperature distribution information is distribution information arranged according to temperature data corresponding to the lithium-ion battery at each time point; Based on the temperature distribution trend information, identifying the dynamic temperature characteristics of each thermal runaway temperature stage of the lithium-ion battery and the time period corresponding to each thermal runaway temperature stage through a dynamic feature extraction and classification strategy, and identifying the safety score of each thermal runaway temperature stage through a battery safety evaluation strategy based on the dynamic temperature characteristics of each thermal runaway temperature stage; Based on the safety score of each thermal runaway temperature stage and according to the time period corresponding to each thermal runaway temperature stage, thermal runaway safety evaluation warning information for each thermal runaway temperature stage is generated, and based on the safety score of each thermal runaway temperature stage and the thermal runaway safety evaluation warning information for each thermal runaway temperature stage, a thermal runaway safety analysis result of the lithium-ion battery is determined.

2. The method according to claim 1, characterized in that The predicting, based on the current temperature distribution information, the temperature distribution trend information of the lithium-ion battery includes: generating a temperature distribution curve of the lithium-ion battery based on the current temperature distribution information; Based on the temperature distribution curve, generating a future temperature distribution curve of the temperature distribution curve in a future period through a linear trend prediction network; The temperature distribution curve and the future temperature distribution curve are arranged to obtain temperature distribution trend information of the lithium-ion battery.

3. The method according to claim 2, characterized in that The method of identifying the dynamic temperature characteristics of each thermal runaway temperature stage of the lithium-ion battery and the time period corresponding to each thermal runaway temperature stage through a dynamic feature extraction and division strategy based on the temperature distribution trend information includes: Based on the temperature distribution trend information, extracting characteristic time points corresponding to characteristic temperature values ​​of the temperature distribution trend information through a temperature characteristic value recognition network; Based on the characteristic time points corresponding to the characteristic temperature values, dividing the temperature distribution trend information into thermal runaway temperature stages corresponding to the respective time periods; Based on the sub-temperature distribution curves of each thermal runaway temperature stage, the dynamic temperature features of each thermal runaway temperature stage are extracted through a feature extraction network.

4. The method according to claim 1, wherein The method of identifying the safety score of each thermal runaway temperature stage based on the dynamic temperature characteristics of each thermal runaway temperature stage through a battery safety evaluation strategy includes: Based on the time period corresponding to each thermal runaway stage, the time characteristics of each thermal runaway stage are extracted; Based on the dynamic temperature characteristics of each thermal runaway temperature stage, the time characteristics of each thermal runaway temperature stage, and the sub-temperature distribution curve of each thermal runaway temperature stage, the battery safety evaluation strategy is used to identify the indicator evaluation value of each safety evaluation indicator in each thermal runaway temperature stage; Based on the indicator evaluation values ​​of each safety evaluation indicator in each thermal runaway temperature stage, the weight values ​​of each safety evaluation indicator in each thermal runaway temperature stage are used to perform weighted summation processing according to the indicator type corresponding to each safety evaluation indicator to obtain the safety score of each thermal runaway temperature stage.

5. The method according to claim 1, wherein The generating of thermal runaway safety evaluation warning information for each thermal runaway temperature stage based on the safety score of each thermal runaway temperature stage and according to the time period corresponding to each thermal runaway temperature stage includes: identifying a safety risk level of each thermal runaway temperature stage based on the safety score of each thermal runaway temperature stage; Based on the dynamic temperature characteristics of each thermal runaway temperature stage and the time period corresponding to each thermal runaway temperature stage, thermal runaway safety evaluation and warning information for each thermal runaway temperature stage is generated according to the safety evaluation and warning strategy corresponding to the safety risk level of each thermal runaway temperature stage.

6. The method according to claim 5, characterized in that The step of determining the thermal runaway safety analysis result of the lithium-ion battery based on the safety score of each thermal runaway temperature stage and the thermal runaway safety evaluation warning information of each thermal runaway temperature stage includes: Obtain a thermal runaway safety analysis report template, and based on the thermal runaway safety evaluation warning information for each thermal runaway temperature stage, generate a safety control plan corresponding to each thermal runaway temperature stage according to the thermal runaway control strategy corresponding to the safety risk level corresponding to each thermal runaway temperature stage; The safety score of each thermal runaway temperature stage, the thermal runaway safety evaluation warning information of each thermal runaway temperature stage, and the safety control plan corresponding to each thermal runaway temperature stage are filled into the thermal runaway safety analysis report template to obtain the thermal runaway safety analysis result of the lithium-ion battery.

7. A thermal runaway safety analysis device for lithium-ion batteries, characterized in that: The device comprises: an acquisition module, configured to acquire current temperature distribution information of the lithium-ion battery and, based on the current temperature distribution information, predict temperature distribution trend information of the lithium-ion battery; the current temperature distribution information is distribution information arranged according to temperature data corresponding to the lithium-ion battery at each time point; an identification module, configured to identify, based on the temperature distribution trend information and using a dynamic feature extraction and classification strategy, the dynamic temperature characteristics of each thermal runaway temperature stage of the lithium-ion battery and the time period corresponding to each thermal runaway temperature stage, and identify, based on the dynamic temperature characteristics of each thermal runaway temperature stage and using a battery safety assessment strategy, a safety score for each thermal runaway temperature stage; a determination module for generating thermal runaway safety evaluation warning information for each thermal runaway temperature stage based on the safety score of each thermal runaway temperature stage and according to the time period corresponding to each thermal runaway temperature stage, and determining a thermal runaway safety analysis result of the lithium-ion battery based on the safety score of each thermal runaway temperature stage and the thermal runaway safety evaluation warning information for each thermal runaway temperature stage.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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