Battery thermal management method, device, medium and equipment

Abstract

The present disclosure relates to a battery thermal management method, device, medium and equipment, which belongs to the field of battery management technology and can respond to thermal events quickly and accurately, and effectively provide thermal runaway warning. A battery thermal management method includes: obtaining real-time thermal performance data of a battery pack; clustering the real-time thermal performance data into normal data and abnormal data through a clustering algorithm; calculating the normal cluster center of the normal data and the abnormal cluster center of the abnormal data; calculating the distance between the normal cluster center and the abnormal cluster center; and providing thermal runaway warning based on the distance.

Description

Technical Field

[0001] The present disclosure relates to the technical field of battery management, and in particular, to a battery thermal management method, device, medium and equipment. Background Art

[0002] In order to solve the problem of battery thermal runaway, the common practice is to configure multiple temperature sensors and voltage sensors in the battery pack to collect the temperature and voltage of each module or cell, and monitor the thermal safety status of the battery pack in real time by monitoring the temperature value of each temperature sensor and the voltage value of the voltage sensor. However, this method has a slow response speed to thermal events and low accuracy. Summary of the invention

[0003] The purpose of the present disclosure is to provide a battery thermal management method, device, medium and equipment, which can quickly and accurately respond to thermal events and effectively provide thermal runaway warning.

[0004] According to a first embodiment of the present disclosure, a battery thermal management method is provided, including: acquiring real-time thermal performance data of a battery pack; clustering the real-time thermal performance data into normal class data and abnormal class data through a clustering algorithm; calculating a normal class cluster center of the normal class data and an abnormal class cluster center of the abnormal class data; calculating a distance between the normal class cluster center and the abnormal class cluster center; and performing a thermal runaway warning based on the distance.

[0005] Optionally, the calculation of the normal class cluster center of the normal class data and the abnormal class cluster center of the abnormal class data includes: initializing the normal class cluster center and the abnormal class cluster center; calculating the distance from each of the real-time thermal performance data to the normal class cluster center and the abnormal class cluster center; clustering each of the real-time thermal performance data to the cluster center that is closest to the real-time thermal performance data among the normal class cluster center and the abnormal class cluster center; calculating the sum of the squares of the distances from each of the real-time thermal performance data to its cluster center; if the sum of the squares of the distances is less than a set value, the clustering process ends; if the sum of the squares of the distances is greater than the set value, calculating the centroid of the real-time thermal performance data sets clustered to the same cluster center, and restarting clustering as a new cluster center.

[0006] Optionally, the real-time thermal performance data includes real-time temperature data of all temperature sensors arranged in the battery pack and real-time voltage data of all voltage sensors; then:

[0007] The normal data includes normal temperature data and normal voltage data, and the abnormal data includes abnormal temperature data and abnormal voltage data;

[0008] The normal cluster centers include a normal temperature cluster center and a normal voltage cluster center, and the abnormal cluster centers include an abnormal temperature cluster center and an abnormal voltage cluster center;

[0009] The distance between the normal class cluster center and the abnormal class cluster center includes: the normal-abnormal temperature distance between the normal temperature class cluster center and the abnormal temperature class cluster center, and the normal-abnormal voltage distance between the normal voltage class cluster center and the abnormal voltage class cluster center.

[0010] Optionally, the performing thermal runaway warning based on the distance includes: performing thermal runaway warning when the normal-abnormal temperature distance and the normal-abnormal voltage distance trigger at least two of the following conditions:

[0011] The normal-abnormal temperature distance is greater than a first preset threshold;

[0012] The rising rate of the normal-abnormal temperature distance is greater than a first preset rising rate threshold;

[0013] The voltage normal-abnormal distance is greater than a second preset threshold;

[0014] The rising rate of the voltage normal-abnormal distance is greater than a second preset rising rate threshold.

[0015] Optionally, the real-time thermal performance data includes all real-time temperature data, real-time single cell voltage data and real-time current data about the battery pack, then:

[0016] The normal data includes normal temperature data, normal cell voltage data and normal real-time state of charge SOC data, and the abnormal data includes abnormal temperature data, abnormal cell voltage data and abnormal SOC data;

[0017] The normal cluster centers include the normal temperature cluster center, the normal cell voltage cluster center, and the normal SOC cluster center, and the abnormal cluster centers include the abnormal temperature cluster center, the abnormal cell voltage cluster center, and the abnormal SOC cluster center;

[0018] The distance between the normal class cluster center and the abnormal class cluster center includes: the normal-abnormal temperature distance between the normal temperature class cluster center and the abnormal temperature class cluster center, the normal-abnormal voltage distance between the normal cell voltage class cluster center and the abnormal cell voltage class cluster center, and the normal-abnormal SOC distance between the normal SOC class cluster center and the abnormal SOC class cluster center.

[0019] Optionally, the performing thermal runaway warning based on the distance includes:

[0020] Thermal runaway warning is performed when the temperature normal-abnormal distance, the voltage normal-abnormal distance, and the SOC normal-abnormal distance trigger at least two of the following conditions:

[0021] The normal-abnormal temperature distance is greater than a first preset threshold;

[0022] The rising rate of the normal-abnormal temperature distance is greater than a first preset rising rate threshold;

[0023] The voltage normal-abnormal distance is greater than a second preset threshold;

[0024] The rising rate of the voltage normal-abnormal distance is greater than a second preset rising rate threshold;

[0025] The SOC normal-abnormal distance is greater than a third preset threshold;

[0026] The rising rate of the SOC normal-abnormal distance is greater than a third preset rising rate threshold.

[0027] According to a second embodiment of the present disclosure, a battery thermal management device is provided, including: an acquisition module, used to acquire real-time thermal performance data of a battery pack; a clustering module, used to cluster the real-time thermal performance data into normal class data and abnormal class data through a clustering algorithm; a first calculation module, used to calculate the normal class cluster center of the normal class data and the abnormal class cluster center of the abnormal class data; a second calculation module, used to calculate the distance between the normal class cluster center and the abnormal class cluster center; and an early warning module, used to issue a thermal runaway early warning based on the distance.

[0028] Optionally, the first calculation module is used to: initialize the normal class cluster center and the abnormal class cluster center; calculate the distance from each of the real-time thermal performance data to the normal class cluster center and the abnormal class cluster center; cluster each of the real-time thermal performance data to the cluster center that is closest to the real-time thermal performance data among the normal class cluster center and the abnormal class cluster center; calculate the sum of the squares of the distances from each of the real-time thermal performance data to its cluster center; if the sum of the squares of the distances is less than a set value, the clustering process ends; if the sum of the squares of the distances is greater than a set value, calculate the centroid of the real-time thermal performance data sets clustered to the same cluster center, and restart clustering as the new cluster center.

[0029] Optionally, the real-time thermal performance data includes real-time temperature data of all temperature sensors arranged in the battery pack and real-time voltage data of all voltage sensors; then:

[0030] The normal data includes normal temperature data and normal voltage data, and the abnormal data includes abnormal temperature data and abnormal voltage data;

[0031] The normal cluster centers include a normal temperature cluster center and a normal voltage cluster center, and the abnormal cluster centers include an abnormal temperature cluster center and an abnormal voltage cluster center;

[0032] The distance between the normal class cluster center and the abnormal class cluster center includes: the normal-abnormal temperature distance between the normal temperature class cluster center and the abnormal temperature class cluster center, and the normal-abnormal voltage distance between the normal voltage class cluster center and the abnormal voltage class cluster center.

[0033] Optionally, the performing thermal runaway warning based on the distance includes: performing thermal runaway warning when the normal-abnormal temperature distance and the normal-abnormal voltage distance trigger at least two of the following conditions:

[0034] The normal-abnormal temperature distance is greater than a first preset threshold;

[0035] The rising rate of the normal-abnormal temperature distance is greater than a first preset rising rate threshold;

[0036] The voltage normal-abnormal distance is greater than a second preset threshold;

[0037] The rising rate of the voltage normal-abnormal distance is greater than a second preset rising rate threshold.

[0038] Optionally, the real-time thermal performance data includes all real-time temperature data, real-time single cell voltage data and real-time current data about the battery pack, then:

[0039] The normal data includes normal temperature data, normal cell voltage data and normal real-time state of charge SOC data, and the abnormal data includes abnormal temperature data, abnormal cell voltage data and abnormal SOC data;

[0040] The normal cluster centers include the normal temperature cluster center, the normal cell voltage cluster center, and the normal SOC cluster center, and the abnormal cluster centers include the abnormal temperature cluster center, the abnormal cell voltage cluster center, and the abnormal SOC cluster center;

[0041] The distance between the normal class cluster center and the abnormal class cluster center includes: the normal-abnormal temperature distance between the normal temperature class cluster center and the abnormal temperature class cluster center, the normal-abnormal voltage distance between the normal cell voltage class cluster center and the abnormal cell voltage class cluster center, and the normal-abnormal SOC distance between the normal SOC class cluster center and the abnormal SOC class cluster center.

[0042] Optionally, the performing thermal runaway warning based on the distance includes:

[0043] Thermal runaway warning is performed when the temperature normal-abnormal distance, the voltage normal-abnormal distance, and the SOC normal-abnormal distance trigger at least two of the following conditions:

[0044] The normal-abnormal temperature distance is greater than a first preset threshold;

[0045] The rising rate of the normal-abnormal temperature distance is greater than a first preset rising rate threshold;

[0046] The voltage normal-abnormal distance is greater than a second preset threshold;

[0047] The rising rate of the voltage normal-abnormal distance is greater than a second preset rising rate threshold;

[0048] The SOC normal-abnormal distance is greater than a third preset threshold;

[0049] The rising rate of the SOC normal-abnormal distance is greater than a third preset rising rate threshold.

[0050] According to a third embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the steps of the method according to the first embodiment of the present disclosure are implemented.

[0051] According to a fourth embodiment of the present disclosure, an electronic device is provided, including: a memory on which a computer program is stored; and a processor for executing the computer program in the memory to implement the steps of the method according to the first embodiment of the present disclosure.

[0052] By adopting the above technical solution, the thermal performance data is first clustered using a clustering algorithm, and each type of thermal performance data is clustered into two types of data, namely normal data and abnormal data. Then, the distance between the clustering centers of the normal data and the abnormal data is compared to judge the risk of thermal runaway. Since the clustering algorithm is very intuitive and reliable in dividing normal data and abnormal data, the early warning of thermal runaway is highly accurate and fast.

[0053] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0055] Figure 1 is a flow chart of a battery thermal management method according to an embodiment of the present disclosure.

[0056] Figure 2 It is a schematic diagram of the cluster center calculation process.

[0057] Figure 3 is a schematic block diagram of a battery thermal management device according to an embodiment of the present disclosure.

[0058] Figure 4 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0059] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0060] Figure 1 FIG. 1 is a flow chart of a battery thermal management method according to an embodiment of the present disclosure. Figure 1 As shown, the method includes the following steps S11 to S15.

[0061] In step S11 , real-time thermal performance data of the battery pack is obtained.

[0062] The real-time thermal performance data may be directly obtained from all detection devices (such as temperature sensors, voltage sensors, etc.) arranged in the battery pack, or may be obtained from, for example, a battery management system.

[0063] In other embodiments, the real-time thermal performance data may include real-time temperature data of all temperature sensors disposed in the battery pack and real-time voltage data of all voltage sensors disposed in the battery pack.

[0064] In other embodiments, the real-time thermal performance data may include all real-time temperature data, real-time cell voltage data, and real-time current data about the battery pack.

[0065] In step S12, the real-time thermal performance data is clustered into normal data and abnormal data by using a clustering algorithm.

[0066] The clustering algorithm may be a K-means algorithm or other clustering algorithms. The present disclosure does not limit the specific form of the clustering algorithm as long as it can implement the data clustering operation.

[0067] For example, assuming that there are currently N real-time temperature data, through the clustering algorithm, n real-time temperature data among the N real-time temperature data are clustered as normal class data, and the remaining Nn real-time temperature data are clustered as abnormal class data.

[0068] When the real-time thermal performance data includes real-time temperature data and real-time voltage data, the real-time temperature data can be clustered into normal temperature data and abnormal temperature data, and the real-time voltage data can be clustered into normal voltage data and abnormal voltage data through a clustering algorithm.

[0069] In the case where the real-time thermal performance data includes all real-time temperature data, real-time cell voltage data and real-time current data about the battery pack, the real-time temperature data can be clustered into normal temperature data and abnormal temperature data, the real-time cell voltage data can be clustered into normal cell voltage data and abnormal cell voltage data, and the real-time SOC data can be clustered into normal SOC data and abnormal SOC data. Among them, the SOC of the single cell can be calculated by using the ampere-hour integration method, the open circuit voltage (OCV) curve table lookup method, etc.

[0070] In step S13, the normal class cluster center of the normal class data and the abnormal class cluster center of the abnormal class data are calculated.

[0071] In one embodiment, the normal class cluster center and the abnormal class cluster center can be initialized first, that is, the normal class cluster center and the abnormal class cluster center are assigned corresponding initial values; then, the distance from each real-time thermal performance data to the normal class cluster center and the abnormal class cluster center is calculated; then, each real-time thermal performance data is clustered to the cluster center that is closest to the real-time thermal performance data among the normal class cluster center and the abnormal class cluster center; then, the sum of the squares of the distances from each real-time thermal performance data to its cluster center is calculated; then, if the sum of the squares of the distances is less than the set value, the clustering process ends, and if the sum of the squares of the distances is greater than the set value, the centroid of the real-time thermal performance data sets clustered to the same cluster center is calculated, and clustering is restarted as the new cluster center.

[0072] Figure 2 It is a schematic diagram of the cluster center calculation process. A1 and A2 are abnormal data, B1 and B2 are normal data, and the cluster center C1 of the abnormal data and the cluster center C2 of the normal data can be calculated at every moment.

[0073] When the real-time thermal performance data includes real-time temperature data and real-time voltage data, the normal temperature cluster center T_C2 of the normal temperature data, the abnormal temperature cluster center T_C1 of the abnormal temperature data, the normal voltage cluster center V_C2 of the normal voltage data, and the abnormal voltage cluster center V_C1 of the abnormal voltage data will be calculated. The calculation process of these cluster centers is performed as above.

[0074] In the case where the real-time thermal performance data includes all real-time temperature data, real-time cell voltage data, and real-time current data about the battery pack, the temperature normal cluster center of the normal temperature data, the temperature abnormal cluster center of the abnormal temperature data, the cell voltage normal cluster center of the normal cell voltage data, the cell voltage abnormal cluster center of the abnormal cell voltage data, the SOC normal cluster center of the normal SOC data, and the SOC abnormal cluster center of the abnormal SOC data are calculated. The calculation process of these cluster centers is calculated as above.

[0075] In step S14, the distance between the normal class cluster center and the abnormal class cluster center is calculated.

[0076] When the real-time thermal performance data includes real-time temperature data and real-time voltage data, the distance between the normal temperature cluster center and the abnormal temperature cluster center is calculated to obtain the normal temperature-abnormal distance, and the distance between the normal voltage cluster center and the abnormal voltage cluster center is calculated to obtain the normal voltage-abnormal distance.

[0077] When the real-time thermal performance data includes all real-time temperature data, real-time cell voltage data and real-time current data about the battery pack, the distance between the normal temperature cluster center and the abnormal temperature cluster center will be calculated to obtain the normal-abnormal temperature distance, the distance between the normal cell voltage cluster center and the abnormal cell voltage cluster center will be calculated to obtain the normal-abnormal voltage distance, and the distance between the normal SOC cluster center and the abnormal SOC cluster center will be calculated to obtain the normal-abnormal SOC distance.

[0078] In step S15, a thermal runaway warning is performed based on the distance.

[0079] In the case where the real-time thermal performance data includes real-time temperature data and real-time voltage data, a thermal runaway warning can be issued when the normal-abnormal temperature distance and the normal-abnormal voltage distance trigger at least two of the following conditions: (1) the normal-abnormal temperature distance is greater than a first preset threshold; (2) the rising rate of the normal-abnormal temperature distance is greater than a first preset rising rate threshold; (3) the normal-abnormal voltage distance is greater than a second preset threshold; (4) the rising rate of the normal-abnormal voltage distance is greater than a second preset rising rate threshold. Moreover, an internal short circuit warning can be issued through the combination of the logical "AND" and "OR" of the above four conditions.

[0080] When the real-time thermal performance data includes all real-time temperature data, real-time single cell voltage data and real-time current data about the battery pack, a thermal runaway warning can be issued when the normal-abnormal temperature distance, the normal-abnormal voltage distance and the normal-abnormal SOC distance trigger at least two of the following conditions: (1) the normal-abnormal temperature distance is greater than a first preset threshold; (2) the rising rate of the normal-abnormal temperature distance is greater than a first preset rising rate threshold; (3) the normal-abnormal voltage distance is greater than a second preset threshold; (4) the rising rate of the normal-abnormal voltage distance is greater than the second preset rising rate threshold; (5) the normal-abnormal SOC distance is greater than a third preset threshold; (6) the rising rate of the normal-abnormal SOC distance is greater than the third preset rising rate threshold.

[0081] In the present disclosure, the rising rate of normal-abnormal distance of temperature refers to the rising rate of normal-abnormal distance of temperature in unit time; the rising rate of normal-abnormal distance of voltage refers to the rising rate of normal-abnormal distance of voltage in unit time; the rising rate of normal-abnormal distance of SOC refers to the rising rate of normal-abnormal distance of SOC in unit time.

[0082] By adopting the above technical solution, the thermal performance data is first clustered using a clustering algorithm, and each type of thermal performance data is clustered into two types of data, namely normal data and abnormal data. Then, the distance between the clustering centers of the normal data and the abnormal data is compared to judge the risk of thermal runaway. Since the clustering algorithm is very intuitive and reliable in dividing normal data and abnormal data, the early warning of thermal runaway is highly accurate and fast.

[0083] Figure 3 is a schematic block diagram of a battery thermal management device according to an embodiment of the present disclosure. Figure 3As shown, the device includes: an acquisition module 31, used to acquire real-time thermal performance data of the battery pack; a clustering module 32, used to cluster the real-time thermal performance data into normal class data and abnormal class data through a clustering algorithm; a first calculation module 33, used to calculate the normal class cluster center of the normal class data and the abnormal class cluster center of the abnormal class data; a second calculation module 34, used to calculate the distance between the normal class cluster center and the abnormal class cluster center; an early warning module 35, used to perform thermal runaway early warning based on distance.

[0084] By adopting the above technical solution, the thermal performance data is first clustered using a clustering algorithm, and each type of thermal performance data is clustered into two types of data, namely normal data and abnormal data. Then, the distance between the clustering centers of the normal data and the abnormal data is compared to judge the risk of thermal runaway. Since the clustering algorithm is very intuitive and reliable in dividing normal data and abnormal data, the early warning of thermal runaway is highly accurate and fast.

[0085] Optionally, the first calculation module 33 is used to: initialize the normal class cluster center and the abnormal class cluster center; calculate the distance from each real-time thermal performance data to the normal class cluster center and the abnormal class cluster center; cluster each real-time thermal performance data to the cluster center that is closest to the real-time thermal performance data among the normal class cluster center and the abnormal class cluster center; calculate the sum of the squares of the distances from each real-time thermal performance data to its cluster center; if the sum of the squares of the distances is less than a set value, the clustering process ends; if the sum of the squares of the distances is greater than the set value, calculate the centroid of the real-time thermal performance data sets clustered to the same cluster center, and restart clustering as the new cluster center.

[0086] Optionally, the real-time thermal performance data includes real-time temperature data of all temperature sensors arranged in the battery pack and real-time voltage data of all voltage sensors; then:

[0087] Normal data includes normal temperature data and normal voltage data, and abnormal data includes abnormal temperature data and abnormal voltage data;

[0088] The normal cluster centers include the normal temperature cluster center and the normal voltage cluster center, and the abnormal cluster centers include the abnormal temperature cluster center and the abnormal voltage cluster center;

[0089] The distance between the normal class cluster center and the abnormal class cluster center includes: the normal-abnormal temperature distance between the normal temperature class cluster center and the abnormal temperature class cluster center, and the normal-abnormal voltage distance between the normal voltage class cluster center and the abnormal voltage class cluster center.

[0090] Optionally, performing a thermal runaway warning based on the distance includes: performing a thermal runaway warning when the normal-abnormal temperature distance and the normal-abnormal voltage distance trigger at least two of the following conditions:

[0091] The temperature normal-abnormal distance is greater than a first preset threshold;

[0092] The rising rate of the normal-abnormal temperature distance is greater than a first preset rising rate threshold;

[0093] The voltage normal-abnormal distance is greater than a second preset threshold;

[0094] The rising rate of the voltage normal-abnormal distance is greater than a second preset rising rate threshold.

[0095] Optionally, the real-time thermal performance data includes all real-time temperature data, real-time single cell voltage data and real-time current data about the battery pack, then:

[0096] Normal data includes normal temperature data, normal cell voltage data and normal real-time state of charge SOC data, and abnormal data includes abnormal temperature data, abnormal cell voltage data and abnormal SOC data;

[0097] The normal cluster centers include the normal temperature cluster center, the normal cell voltage cluster center, and the normal SOC cluster center; the abnormal cluster centers include the abnormal temperature cluster center, the abnormal cell voltage cluster center, and the abnormal SOC cluster center;

[0098] The distances between the normal class cluster centers and the abnormal class cluster centers include: the normal-abnormal temperature distance between the normal temperature class cluster centers and the abnormal temperature class cluster centers, the normal-abnormal voltage distance between the normal cell voltage class cluster centers and the abnormal cell voltage class cluster centers, and the normal-abnormal SOC distance between the normal SOC class cluster centers and the abnormal SOC class cluster centers.

[0099] Optionally, thermal runaway warning is performed based on distance, including:

[0100] Thermal runaway warning is issued when the temperature normal-abnormal distance, voltage normal-abnormal distance, and SOC normal-abnormal distance trigger at least two of the following conditions:

[0101] The temperature normal-abnormal distance is greater than a first preset threshold;

[0102] The rising rate of the normal-abnormal temperature distance is greater than a first preset rising rate threshold;

[0103] The voltage normal-abnormal distance is greater than a second preset threshold;

[0104] The rising rate of the voltage normal-abnormal distance is greater than a second preset rising rate threshold;

[0105] The SOC normal-abnormal distance is greater than a third preset threshold;

[0106] The rising rate of the SOC normal-abnormal distance is greater than a third preset rising rate threshold.

[0107] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0108] Figure 4 FIG. 7 is a block diagram of an electronic device 700 according to an exemplary embodiment. Figure 4 As shown, the electronic device 700 may include: a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input / output (I / O) interface 704, and a communication component 705.

[0109] The processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the above-mentioned battery thermal management method. The memory 702 is used to store various types of data to support the operation of the electronic device 700, and these data may include, for example, instructions for any application or method used to operate on the electronic device 700, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone, which is used to receive external audio signals. The received audio signal may be further stored in the memory 702 or sent through the communication component 705. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules, and the above-mentioned other interface modules may be keyboards, mice, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 705 may include: Wi-Fi module, Bluetooth module, NFC module.

[0110] In an exemplary embodiment, the electronic device 700 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned battery thermal management method.

[0111] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned battery thermal management method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 702 including program instructions, and the above-mentioned program instructions can be executed by the processor 701 of the electronic device 700 to complete the above-mentioned battery thermal management method.

[0112] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0113] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0114] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. A battery thermal management method, characterized in that: include: Obtain real-time thermal performance data of the battery pack; Clustering the real-time thermal performance data into normal data and abnormal data by using a clustering algorithm; Calculating a normal cluster center of the normal data and an abnormal cluster center of the abnormal data; Calculating the distance between the normal class cluster center and the abnormal class cluster center; Providing a thermal runaway warning based on the distance; Wherein, the real-time thermal performance data includes the real-time temperature data of all temperature sensors arranged in the battery pack and the real-time voltage data of all voltage sensors, then: the normal class data includes normal temperature class data and normal voltage class data, the abnormal class data includes abnormal temperature class data and abnormal voltage class data, the normal class cluster center includes normal temperature class cluster center and normal voltage class cluster center, the abnormal class cluster center includes abnormal temperature class cluster center and abnormal voltage class cluster center, the distance between the normal class cluster center and the abnormal class cluster center includes the normal-abnormal temperature distance between the normal temperature class cluster center and the abnormal temperature class cluster center and the normal-abnormal voltage distance between the normal voltage class cluster center and the abnormal voltage class cluster center; the thermal runaway warning based on the distance includes: thermal runaway warning when the normal-abnormal temperature distance and the normal-abnormal voltage distance trigger preset conditions; or Wherein, the real-time thermal performance data includes all real-time temperature data, real-time cell voltage data and real-time current data about the battery pack, then: the normal class data includes normal temperature class data, normal cell voltage class data and real-time state of charge SOC normal class data, the abnormal class data includes abnormal temperature class data, abnormal cell voltage class data and abnormal SOC class data, the normal class cluster center includes normal temperature class cluster center, normal cell voltage class cluster center and normal SOC class cluster center, the abnormal class cluster center includes abnormal temperature class cluster center, abnormal cell voltage class cluster center and abnormal SOC class cluster center, The distance between the normal class cluster center and the abnormal class cluster center includes the temperature normal-abnormal distance between the normal temperature class cluster center and the temperature abnormal class cluster center, the voltage normal-abnormal distance between the cell voltage normal class cluster center and the cell voltage abnormal class cluster center, and the SOC normal-abnormal distance between the SOC normal class cluster center and the SOC abnormal class cluster center; the thermal runaway warning based on the distance includes: performing a thermal runaway warning when the normal temperature distance, the normal voltage distance and the normal SOC distance trigger preset conditions.

2. The method according to claim 1, characterized in that The calculating of the normal class cluster center of the normal class data and the abnormal class cluster center of the abnormal class data includes: Initializing the normal class cluster center and the abnormal class cluster center; Calculating the distance between each of the real-time thermal performance data and the normal class cluster center and the abnormal class cluster center; Clustering each of the real-time thermal performance data into a cluster center that is closest to the real-time thermal performance data among the normal cluster center and the abnormal cluster center; Calculating the sum of squares of distances from each of the real-time thermal performance data to its cluster center; If the sum of squared distances is less than a set value, the clustering process ends; If the sum of the squares of the distances is greater than a set value, the centroid of the real-time thermal performance data set clustered to the same cluster center is calculated and clustering is restarted as a new cluster center.

3. The method according to claim 1, characterized in that The thermal runaway warning is performed when the temperature normal-abnormal distance and the voltage normal-abnormal distance trigger preset conditions, including: performing a thermal runaway warning when the temperature normal-abnormal distance and the voltage normal-abnormal distance trigger at least two of the following conditions: The normal-abnormal temperature distance is greater than a first preset threshold; The rising rate of the normal-abnormal temperature distance is greater than a first preset rising rate threshold; The voltage normal-abnormal distance is greater than a second preset threshold; The rising rate of the voltage normal-abnormal distance is greater than a second preset rising rate threshold.

4. The method according to claim 1, characterized in that: The thermal runaway warning is performed when the temperature normal-abnormal distance, the voltage normal-abnormal distance, and the SOC normal-abnormal distance trigger preset conditions, including: Thermal runaway warning is performed when the temperature normal-abnormal distance, the voltage normal-abnormal distance, and the SOC normal-abnormal distance trigger at least two of the following conditions: The normal-abnormal temperature distance is greater than a first preset threshold; The rising rate of the normal-abnormal temperature distance is greater than a first preset rising rate threshold; The voltage normal-abnormal distance is greater than a second preset threshold; The rising rate of the voltage normal-abnormal distance is greater than a second preset rising rate threshold; The SOC normal-abnormal distance is greater than a third preset threshold; The rising rate of the SOC normal-abnormal distance is greater than a third preset rising rate threshold.

5. A battery thermal management device, characterized in that: include: An acquisition module is used to obtain real-time thermal performance data of the battery pack; A clustering module, used for clustering the real-time thermal performance data into normal data and abnormal data by using a clustering algorithm; A first calculation module, used to calculate the normal class cluster center of the normal class data and the abnormal class cluster center of the abnormal class data; A second calculation module, used to calculate the distance between the normal class cluster center and the abnormal class cluster center; An early warning module, used for providing a thermal runaway early warning based on the distance; Wherein, the real-time thermal performance data includes the real-time temperature data of all temperature sensors arranged in the battery pack and the real-time voltage data of all voltage sensors, then: the normal class data includes normal temperature class data and normal voltage class data, the abnormal class data includes abnormal temperature class data and abnormal voltage class data, the normal class cluster center includes normal temperature class cluster center and normal voltage class cluster center, the abnormal class cluster center includes abnormal temperature class cluster center and abnormal voltage class cluster center, the distance between the normal class cluster center and the abnormal class cluster center includes the normal-abnormal temperature distance between the normal temperature class cluster center and the abnormal temperature class cluster center and the normal-abnormal voltage distance between the normal voltage class cluster center and the abnormal voltage class cluster center; the thermal runaway warning based on the distance includes: thermal runaway warning when the normal-abnormal temperature distance and the normal-abnormal voltage distance trigger preset conditions; or Wherein, the real-time thermal performance data includes all real-time temperature data, real-time cell voltage data and real-time current data about the battery pack, then: the normal class data includes normal temperature class data, normal cell voltage class data and real-time state of charge SOC normal class data, the abnormal class data includes abnormal temperature class data, abnormal cell voltage class data and abnormal SOC class data, the normal class cluster center includes normal temperature class cluster center, normal cell voltage class cluster center and normal SOC class cluster center, the abnormal class cluster center includes abnormal temperature class cluster center, abnormal cell voltage class cluster center and abnormal SOC class cluster center, The distance between the normal class cluster center and the abnormal class cluster center includes the temperature normal-abnormal distance between the normal temperature class cluster center and the temperature abnormal class cluster center, the voltage normal-abnormal distance between the cell voltage normal class cluster center and the cell voltage abnormal class cluster center, and the SOC normal-abnormal distance between the SOC normal class cluster center and the SOC abnormal class cluster center; the thermal runaway warning based on the distance includes: performing a thermal runaway warning when the normal temperature distance, the normal voltage distance and the normal SOC distance trigger preset conditions.

6. The device according to claim 5, characterized in that The thermal runaway warning is performed when the temperature normal-abnormal distance and the voltage normal-abnormal distance trigger preset conditions, including: performing a thermal runaway warning when the temperature normal-abnormal distance and the voltage normal-abnormal distance trigger at least two of the following conditions: The normal-abnormal temperature distance is greater than a first preset threshold; The rising rate of the normal-abnormal temperature distance is greater than a first preset rising rate threshold; The voltage normal-abnormal distance is greater than a second preset threshold; The rising rate of the voltage normal-abnormal distance is greater than a second preset rising rate threshold.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in any one of claims 1 to 4 are implemented.

8. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 4.

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