A method for evaluating the risk of thermal runaway of power batteries

Abstract

The present invention relates to a method for assessing the thermal runaway risk of a power battery. The method comprises S1, data acquisition, wherein the power battery comprises a plurality of single cells, and data of the single cells under actual operating conditions is acquired; S2, data classification, wherein the first type of data of charging of the vehicle in a stationary state and the second type of data of the vehicle in an operating state are acquired; S3, calculation of evaluation parameters, wherein the evaluation parameter A is calculated based on the first type of data by applying a single cell voltage fluctuation algorithm; and the evaluation parameter B is calculated based on the second type of data by applying a single cell voltage information entropy algorithm; S4, calculation of a thermal runaway risk score, wherein the evaluation parameters A and B are integrated by applying a machine learning algorithm to obtain a thermal runaway risk score; and S5, risk assessment, wherein the risk level of the single cells is classified according to the ranking of the thermal runaway risk scores. The present invention proposes a method for assessing the thermal runaway risk of a power battery, which can effectively assess the thermal runaway risk of a power battery.

Description

Technical Field

[0001] The present invention relates to the technical field of power battery testing for new energy vehicles, and in particular to a method for assessing the risk of thermal runaway of a power battery. Background Art

[0002] New energy vehicles are an important tool in achieving the vision of "carbon peak" and "carbon neutrality", and play an important role in promoting the development of the national economy and the transformation and upgrading of smart transportation. However, with the increasing number of new energy vehicles in my country, automobile safety issues such as vehicle spontaneous combustion, battery performance degradation, and vehicle charging failures can no longer be ignored. The safety issues caused by battery thermal runaway and its expansion are one of the main causes of power battery failure. If technical means can be used to assess the risk of power battery thermal runaway and issue safety warnings based on different risks, the vehicle driver can be informed in advance of the extent and scope of the danger, so that he can perceive the danger and respond in the first time. Summary of the Invention

[0003] In response to the above-mentioned problems in the prior art, the present invention proposes a power battery thermal runaway risk assessment method, which can effectively assess the thermal runaway risk of the power battery and prepare for subsequent early warning work.

[0004] Specifically, the present invention proposes a method for assessing the risk of thermal runaway of a power battery, comprising the steps of:

[0005] S1, data acquisition, wherein the power battery includes a plurality of single cells, and data of the single cells under actual operating conditions are acquired. The actual operating conditions include a vehicle equipped with the power battery being charged in a stationary state and the vehicle being in operation;

[0006] S2, data classification, classifying the data based on actual operating conditions, obtaining first-category data of the vehicle being charged in a stationary state and second-category data of the vehicle being in an operating state;

[0007] S3, calculating evaluation parameters, applying a single-cell voltage fluctuation algorithm to analyze and calculate evaluation parameter A based on the first type of data; and applying a single-cell voltage information entropy algorithm to analyze and calculate evaluation parameter B based on the second type of data;

[0008] S4, calculating a thermal runaway risk score, applying a machine learning algorithm to fuse the evaluation parameters A and B, and obtaining a thermal runaway risk score for each single cell of the power battery;

[0009] S5, risk assessment, classifying the risk levels of the single cells according to the thermal runaway risk score ranking.

[0010] According to one embodiment of the present invention, in step S3, the evaluation parameter C is calculated based on the first type of data by applying a cell voltage difference ratio algorithm;

[0011] In step S4, a machine learning algorithm is applied to fuse the evaluation parameters A, B, and C to obtain a thermal runaway risk score for each single cell of the power battery.

[0012] According to one embodiment of the present invention, in step S3, the evaluation parameter D is calculated by applying a single cell voltage range algorithm based on the second type of data;

[0013] In step S4, a machine learning algorithm is applied to fuse the evaluation parameters A, B, C, and D to obtain a thermal runaway risk score for each single cell of the power battery.

[0014] According to an embodiment of the present invention, in step S1, the acquired data at least includes the voltage, temperature, current, state of charge, battery charge and discharge status, serial number and corresponding time of the single battery.

[0015] According to an embodiment of the present invention, in step S2, the data of the single battery is divided into the first category of data and the second category of data based on the corresponding time, current and battery charge and discharge status.

[0016] According to one embodiment of the present invention, in step S3, the calculation steps of the cell voltage fluctuation algorithm include:

[0017] The first type of data is sliced according to the time dimension to generate time segments, and then divided into a new data matrix T based on the time segments:

[0018] T={(V 11 ,V 21 ,,V 31 ,…V n1 ),(V 12 ,V 22 ,,V 32 ,…V n2 ),…,(V 1k ,V 2k ,,V 3k ,…V nk )};

[0019] Wherein, V represents the corresponding data of the voltage of a single cell in the time segment after being sliced according to the time dimension, the subscript 1, 2, 3…n∈R represents the serial number of the single cell, n is the total number of single cells, the subscript 1, 2, 3…k∈R represents the serial number of the time matrix, k is the total number of time matrices sliced according to the time dimension; R is a positive integer;

[0020] Based on the data matrix T, the median of the voltage of the single battery is calculated in a single time matrix to obtain data matrices T1, T2, ...Tk:

[0021] T1=Median(V 11 ,V21,,V 31 ,…V n1 );

[0022] T2=Median(V 12 ,V 22 ,,V 32 ,…V n2 );

[0023] …

[0024] Tk=Median(V 1k ,V 2k ,,V 3k ,…V nk )

[0025] Calculate the standard deviation S of the data matrix T1, T2, ...Tn T1 , S T2 …S Tk , as the evaluation parameter A:

[0026]

[0027]

[0028] …

[0029]

[0030] The calculation steps of the cell voltage information entropy algorithm include:

[0031] The second type of data is sliced according to the time dimension, and the Shannon entropy e of each single cell voltage is calculated. s :

[0032]

[0033] The standard scores of the Shannon entropy of the different single-cell voltages under the same time matrix are calculated as the evaluation parameter B.

[0034] According to one embodiment of the present invention, the calculation steps of the single cell voltage difference ratio algorithm include:

[0035] Based on the data obtained from each of the single cells at adjacent time intervals, the absolute value of the voltage difference between the two is calculated, and the ratio Vr of the maximum value to the minimum value of the voltage difference is obtained as the evaluation parameter C:

[0036]

[0037] According to one embodiment of the present invention, in step S3, the calculation steps of the single cell voltage range difference algorithm include:

[0038] The maximum and minimum values of the voltages of different single cells of the power battery at any time are obtained, and then the difference between the maximum and minimum values is taken to obtain the voltage range of the single cell, which is used as the evaluation parameter D.

[0039] The present invention also provides a power battery thermal runaway risk assessment device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of any of the aforementioned power battery thermal runaway risk assessment methods are implemented.

[0040] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the aforementioned methods for assessing the risk of thermal runaway of a power battery.

[0041] The present invention provides a power battery thermal runaway risk assessment method, which obtains data of the power battery under actual operating conditions, applies a single cell voltage fluctuation algorithm and a single cell voltage information entropy algorithm to analyze and calculate corresponding evaluation parameters, and applies a machine learning algorithm to fuse the evaluation parameters to obtain a thermal runaway risk score for each single cell of the power battery, thereby effectively assessing the thermal runaway risk of the power battery and preparing for subsequent early warning work.

[0042] It is to be understood that both the foregoing general description and the following detailed description of the present invention are exemplary and explanatory and are intended to provide further explanation of the invention as claimed. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings are included to provide further explanation of the present invention, are incorporated into and constitute a part of this application, illustrate embodiments of the present invention, and together with this specification serve to explain the principles of the present invention. In the drawings:

[0044] Figure 1 A flowchart of a method for assessing thermal runaway risk of a power battery according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0045] It should be noted that, unless there is any conflict, the embodiments and features in the embodiments of this application can be combined with each other.

[0046] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is in no way intended to limit the present application and its application or use. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0047] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0048] Unless otherwise specified, the relative arrangement of the parts and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present application. Meanwhile, it should be understood that, for ease of description, the sizes of the various parts shown in the accompanying drawings are not drawn according to actual proportional relationships. Technology, methods and equipment known to those of ordinary skill in the relevant art may not be discussed in detail, but in appropriate cases, the technology, methods and equipment should be considered as a part of the specification. In all examples shown and discussed here, any specific value should be interpreted as being merely exemplary, rather than as a limitation. Therefore, other examples of exemplary embodiments can have different values. It should be noted that similar numbers and letters represent similar items in the following drawings, and therefore, once an item is defined in an accompanying drawing, it does not need to be further discussed in subsequent drawings.

[0049] In the description of this application, it should be understood that the directions or positional relationships indicated by directional words such as "front, back, up, down, left, right", "horizontal, vertical, vertical, horizontal" and "top, bottom" are usually based on the directions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description. Unless otherwise specified, these directional words do not indicate or imply that the device or element referred to must have a specific direction or be constructed and operated in a specific direction. Therefore, they cannot be understood as limiting the scope of protection of this application; the directional words "inside and outside" refer to the inside and outside relative to the outline of each component itself.

[0050] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is solely for the purpose of distinguishing the corresponding components. Unless otherwise stated, these terms have no special meaning and therefore should not be construed as limiting the scope of protection of this application. Furthermore, while the terms used in this application are selected from commonly known and commonly used terms, some terms mentioned in this specification may have been selected by the applicant at his or her discretion, and their detailed meanings are explained in the relevant sections of this description. Furthermore, this application should be understood not only by the actual terms used, but also by the meaning implied by each term.

[0051] As we all know, new energy vehicles generate and accumulate massive amounts of data during actual use. The information contained in it can be extracted through deep mining. After the relevant features are quantified, they can be used to assess the risk of thermal runaway of power batteries. With the application and continuous development of big data technology and machine learning technology, the feasibility and effectiveness of thermal runaway risk assessment based on massive data have been greatly improved. The present invention is based on big data and machine learning technology to process, analyze and self-learn massive amounts of data under actual operating conditions, and conduct thermal runaway risk assessment on power batteries, thereby ensuring the safe operation of power batteries and promoting industrial development.

[0052] Figure 1 A flowchart of a method for assessing thermal runaway risk of a power battery according to an embodiment of the present invention is shown.

[0053] As shown in the figure, the present invention provides a method for assessing the risk of thermal runaway of a power battery, comprising the following steps:

[0054] S1, data acquisition. The power battery includes multiple single cells. Data of all single cells under actual operating conditions are acquired. The actual operating conditions include charging of a vehicle with a power battery at rest and when the vehicle is in operation.

[0055] S2, data classification, classifies data based on actual operating conditions, obtaining first-category data for charging the vehicle when stationary and second-category data for the vehicle when in operation. Data from charging the vehicle when stationary is considered first-category data. Under the first-category operating condition, the power battery is in a charging state throughout the entire process, and its charging current depends on different charging strategies. The charging voltage curves of each single cell of the power battery can be used as the calculation basis for subsequent processing. Data from the power battery under loaded conditions is considered second-category data. When the vehicle is accelerating or traveling at a constant speed, the power battery is in a discharging state. When the vehicle is coasting or braking, the power battery is in a recharged state. Data obtained under these two conditions are collectively classified as second-category data.

[0056] S3, calculating evaluation parameters, applying a single-cell voltage fluctuation algorithm to analyze and calculate evaluation parameter A based on the first type of data; and applying a single-cell voltage information entropy algorithm to analyze and calculate evaluation parameter B based on the second type of data;

[0057] S4 calculates the thermal runaway risk score by applying a machine learning algorithm to the evaluation parameters A and B to obtain a thermal runaway risk score for each cell in the power battery. Specifically, the evaluation parameters A and B are used as inputs for model training in the machine learning algorithm. The output is a single-parameter thermal runaway risk score that incorporates the cell data contained in evaluation parameters A and B.

[0058] S5, risk assessment, classifies the risk levels of single cells according to the thermal runaway risk score.

[0059] Preferably, in step S1, the acquired data is cleaned to remove abnormal data. Because various abnormalities exist in actual operating conditions, such as abnormal feedback current and cell voltage drops, it is necessary to specify corresponding cleaning strategies for different abnormal data. Abnormal data includes misaligned data, overflow data, jump data, missing data, cell voltage drop data, and asynchronous data.

[0060] Preferably, in step S3, the evaluation parameter C is calculated based on the first type of data using a cell voltage differential ratio algorithm. In step S4, a machine learning algorithm is applied to combine the evaluation parameters A, B, and C to obtain a thermal runaway risk score for each cell of the power battery. It will be readily understood that the addition of evaluation parameter C to the evaluation parameters is intended to improve the accuracy of the thermal runaway risk assessment.

[0061] Preferably, in step S3, a single-cell voltage range algorithm is applied to analyze and calculate the evaluation parameter D based on the second type of data. In step S4, a machine learning algorithm is applied to fuse the evaluation parameters A, B, C, and D to obtain a thermal runaway risk score for each single cell of the power battery. Similarly, adding evaluation parameter D to the evaluation parameters and applying a machine learning algorithm to fuse the evaluation parameters A, B, C, and D can further improve the accuracy of the thermal runaway risk assessment.

[0062] Preferably, in step S1, the acquired data includes at least the voltage, temperature, current, state of charge (SOC), battery charge and discharge status, serial number, and the corresponding time of data acquisition. In other words, the acquired data includes the status data of the single battery at different times.

[0063] Preferably, in step S2, the data of the single battery cells is divided into first-category data and second-category data based on the corresponding time, the positive and negative current, and the battery charge and discharge status. Specifically, the corresponding time, the positive and negative current, and the battery charge and discharge status can be used to distinguish whether the vehicle is charging in a stationary state or in a running state. Therefore, the data can be divided into first-category data and second-category data based on these three factors.

[0064] Preferably, in step S3, the calculation steps of the cell voltage fluctuation algorithm include:

[0065] The first type of data is sliced according to the time dimension to generate time segments, which are then divided into a new data matrix T based on the time segments:

[0066] T={(V 11 ,V 21 ,,V 31 ,…V n1 ),(V 12 ,V 22 ,,V 32 ,…V n2 ),…,(V 1k ,V 2k ,,V 3k ,…V nk )};

[0067] Where V represents the corresponding data of a single cell voltage in the time segment after slicing according to the time dimension, the subscript 1, 2, 3…n∈R represents the sequence number of the single cell, n is the total number of single cells, the subscript 1, 2, 3…k∈R represents the sequence number of the time matrix, k is the total number of time matrices sliced according to the time dimension; R is a positive integer;

[0068] Based on the data matrix T, the median of the voltage of each cell is calculated in a single time matrix to obtain the data matrices T1, T2, ...Tk:

[0069] T1=Median(V 11 ,V 21 ,,V 31 ,…V n1 );

[0070] T2=Median(V 12 ,V 22 ,,V 32 ,…V n2 );

[0071] …

[0072] Tk=Median(V 1k ,V 2k ,,V 3k ,…Vnk )

[0073] Calculate the standard deviation S of the data matrix T1, T2, ...Tn T1 , S T2 …S Tk , as the evaluation parameter A:

[0074]

[0075]

[0076] …

[0077]

[0078] The calculation steps of the single cell voltage information entropy algorithm include:

[0079] Slice the second type of data according to the time dimension and calculate the Shannon entropy e of each single cell voltage s :

[0080]

[0081] Calculate the standard score of Shannon entropy of different single cell voltages under the same time matrix as the evaluation parameter B.

[0082] Preferably, the calculation steps of the cell voltage difference ratio algorithm include:

[0083] Based on the data obtained from each single cell at adjacent time intervals, the absolute value of the voltage difference between the two is calculated, and the ratio of the maximum to the minimum voltage difference, Vr, is obtained as the evaluation parameter C:

[0084]

[0085] Preferably, in step S3, the calculation steps of the single cell voltage range difference algorithm include:

[0086] The maximum and minimum values of the voltage of different single cells of the power battery at any time are obtained, and then the difference between the two is taken to obtain the voltage range of the single cell, which is used as the evaluation parameter D.

[0087] Preferably, the single cells are ranked according to the thermal runaway risk score based on the degree of risk of inducing thermal runaway and divided into four risk levels: extremely high, high, relatively high, and safe. More preferably, different warning signals are provided for each risk level, thereby indicating the varying degrees of thermal runaway risk in the power battery. For extremely high levels, a level 3 warning signal is generated and reported; for high levels, a level 2 warning signal is generated and reported; for relatively high levels, a level 1 warning signal is generated and reported; and for safe levels, no warning signal is generated. In actual operation, each level of warning signal can be used to provide safety warnings via hardware devices that generate audio and visual signals.

[0088] This invention designs multiple evaluation and analysis algorithms and applies machine learning methods to fuse multiple evaluation indicators to obtain thermal runaway risk assessment parameters for each power battery cell. These thermal runaway risk assessment parameters are then used as a grading standard for different levels of risk. This effectively assesses the thermal runaway risk of power batteries and enables safety warnings. By integrating multiple evaluation parameters into the thermal runaway risk assessment, the impact of biases in a single algorithm is avoided, improving the robustness of the algorithm.

[0089] The present invention also provides a power battery thermal runaway risk assessment device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of any one of the aforementioned power battery thermal runaway risk assessment methods are implemented.

[0090] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the aforementioned power battery thermal runaway risk assessment methods.

[0091] Among them, the specific implementation methods and technical effects of the power battery thermal runaway risk assessment device and computer-readable storage medium can be referred to the embodiments of the power battery thermal runaway risk assessment method provided by the above-mentioned present invention, and will not be repeated here.

[0092] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of the two. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps are generally described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. A skilled person may implement the described functionality in different ways for each specific application, but such implementation decisions should not be interpreted as resulting in a departure from the scope of the present invention.

[0093] The various illustrative logic modules and circuits described in conjunction with the embodiments disclosed herein may be implemented or executed using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0094] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read and write information from / to the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and storage medium may reside in a user terminal as discrete components.

[0095] In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functions may be stored on or transmitted via a computer-readable medium as one or more instructions or codes. Computer-readable media include both computer storage media and communication media, including any media that facilitates the transfer of a computer program from one location to another. A storage medium may be any available medium that can be accessed by a computer. By way of example and not limitation, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Any connection is also properly referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwaves, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwaves are included in the definition of medium. As used herein, disk and disc include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, where disks typically reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0096] It will be apparent to those skilled in the art that various modifications and variations may be made to the above exemplary embodiments of the present invention without departing from the spirit and scope of the present invention. Therefore, it is intended that the present invention cover modifications and variations of the present invention that fall within the scope of the appended claims and their equivalent technical solutions.

Claims

1. A method for assessing the risk of thermal runaway of a power battery, comprising the following steps: S1, data acquisition, wherein the power battery includes a plurality of single cells, and data of the single cells under actual operating conditions are acquired. The actual operating conditions include a vehicle equipped with the power battery being charged in a stationary state and the vehicle being in operation; S2, data classification, classifying the data based on actual operating conditions, obtaining first-category data of the vehicle being charged in a stationary state and second-category data of the vehicle being in an operating state; S3, calculating an evaluation parameter, applying a single cell voltage fluctuation algorithm to analyze and calculate an evaluation parameter A based on the first type of data; Based on the second type of data, the single cell voltage information entropy algorithm is applied to analyze and calculate the evaluation parameter B; S4, calculating a thermal runaway risk score, applying a machine learning algorithm to fuse the evaluation parameters A and B, and obtaining a thermal runaway risk score for each single cell of the power battery; S5, risk assessment, classifying the risk levels of the single cells according to the thermal runaway risk score ranking.

2. The power battery thermal runaway risk assessment method according to claim 1, wherein: In step S3, the evaluation parameter C is calculated based on the first type of data by applying a single cell voltage difference ratio algorithm; In step S4, a machine learning algorithm is applied to fuse the evaluation parameters A, B, and C to obtain a thermal runaway risk score for each single cell of the power battery.

3. The power battery thermal runaway risk assessment method according to claim 2, characterized in that: In step S3, the evaluation parameter D is calculated by applying a single cell voltage range algorithm based on the second type of data; In step S4, a machine learning algorithm is applied to fuse the evaluation parameters A, B, C, and D to obtain a thermal runaway risk score for each single cell of the power battery.

4. The power battery thermal runaway risk assessment method according to claim 1, wherein: In step S1 , the acquired data at least includes the voltage, temperature, current, state of charge, battery charge and discharge state, serial number and corresponding time of the single battery.

5. The power battery thermal runaway risk assessment method according to claim 4, characterized in that: In step S2, the data of the single battery is divided into the first category of data and the second category of data based on the corresponding time, current and battery charge and discharge status.

6. The power battery thermal runaway risk assessment method according to claim 1, wherein: In step S3, the calculation steps of the cell voltage fluctuation algorithm include: The first type of data is sliced according to the time dimension to generate time segments, and then divided into a new data matrix T based on the time segments: T={(V 11 ,V 21 ,,V 31 ,…V n1 ),(V 12 ,V 22 ,,V 32 ,…V n2 ),…,(V 1k ,V 2k ,,V 3k ,…V nk )}; Wherein, V represents the corresponding data of the voltage of a single cell in the time segment after being sliced according to the time dimension, the subscript 1, 2, 3…n∈R represents the serial number of the single cell, n is the total number of single cells, the subscript 1, 2, 3…k∈R represents the serial number of the time matrix, k is the total number of time matrices sliced according to the time dimension; R is a positive integer; Based on the data matrix T, the median of the voltage of the single battery is calculated in a single time matrix to obtain data matrices T1, T2, ...Tk: T1=Median(V 11 ,V 21 ,,V 31 ,…V n1 ); T2=Median(V 12 ,V 22 ,,V 32 ,…V n2 ); … Tk=Median(V 1k ,V 2k ,,V 3k ,…V nk ) Calculate the standard deviation S of the data matrix T1, T2, ...Tn T1 , S T2 …S Tk , as the evaluation parameter A: … The calculation steps of the cell voltage information entropy algorithm include: The second type of data is sliced according to the time dimension, and the Shannon entropy e of each single cell voltage is calculated. s : The standard scores of the Shannon entropy of the different single-cell voltages under the same time matrix are calculated as the evaluation parameter B.

7. The power battery thermal runaway risk assessment method according to claim 2, wherein: The calculation steps of the single cell voltage difference ratio algorithm include: Based on the data obtained from each of the single cells at adjacent time intervals, the absolute value of the voltage difference between the two is calculated, and the ratio Vr of the maximum value to the minimum value of the voltage difference is obtained as the evaluation parameter C:

8. The power battery thermal runaway risk assessment method according to claim 3, wherein: In step S3, the calculation steps of the single cell voltage range difference algorithm include: The maximum and minimum values of the voltages of different single cells of the power battery at any time are obtained, and then the difference between the maximum and minimum values is taken to obtain the voltage range of the single cell, which is used as the evaluation parameter D.

9. A power battery thermal runaway risk assessment device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the power battery thermal runaway risk assessment method according to any one of claims 1 to 8 are implemented.

10. 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 power battery thermal runaway risk assessment method according to any one of claims 1 to 8 are implemented.

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