阅读说明：本技术 电池热管理方法、装置、介质和设备 (Battery thermal management method, device, medium and equipment ) 是由 尹永刚 冯天宇 邓林旺 王南 于 2020-05-29 设计创作，主要内容包括：本公开涉及一种电池热管理方法、装置、介质和设备,属于电池管理技术领域,能够快速且准确地对热事件进行响应,有效地进行热失控预警。一种电池热管理方法,包括：获取电池包的实时热性能数据；通过聚类算法,将所述实时热性能数据聚类为正常类数据和异常类数据；计算所述正常类数据的正常类聚类中心和所述异常类数据的异常类聚类中心；计算所述正常类聚类中心与所述异常类聚类中心之间的距离；基于所述距离进行热失控预警。(The disclosure relates to a battery thermal management method, a device, a medium and equipment, which belong to the technical field of battery management, can quickly and accurately respond to a thermal event and effectively perform thermal runaway early warning. A battery thermal management method, comprising: acquiring real-time thermal performance data of the 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 center of the normal class data and an abnormal class center of the abnormal class data; calculating a distance between the normal cluster center and the abnormal cluster center; and carrying out thermal runaway early warning based on the distance.)

1. A method of thermal management of a battery, comprising:

acquiring real-time thermal performance data of the 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 center of the normal class data and an abnormal class center of the abnormal class data;

calculating a distance between the normal cluster center and the abnormal cluster center;

and carrying out thermal runaway early warning based on the distance.

2. The method of claim 1, wherein the computing the normal class hub for the normal class data and the abnormal class hub for the abnormal class data comprises:

initializing the normal clustering center and the abnormal clustering center;

calculating the distance from each real-time thermal performance data to the normal clustering center and the abnormal clustering center;

clustering each real-time thermal performance data to a clustering center which is closest to the real-time thermal performance data in the normal clustering center and the abnormal clustering center;

calculating the sum of squares of the distances from each of the real-time thermal performance data to the cluster center thereof;

if the sum of squares of the distances is less than a set value, the clustering process is finished;

and if the sum of the squares of the distances is larger than a set value, calculating the mass center of the real-time thermal performance data set clustered to the same clustering center, and restarting clustering as a new clustering center.

3. The method of claim 1 or 2, wherein the real-time thermal performance data comprises real-time temperature data for all temperature sensors and real-time voltage data for all voltage sensors disposed within the battery pack, then:

the normal class data comprises temperature normal class data and voltage normal class data, and the abnormal class data comprises temperature abnormal class data and voltage abnormal class data;

the normal class centers comprise normal temperature class centers and normal voltage class centers, and the abnormal class centers comprise abnormal temperature class centers and abnormal voltage class centers;

the distance between the normal class center and the abnormal class center includes: a temperature normal-anomaly distance between the temperature normal class center and the temperature anomaly class center, and a voltage normal-anomaly distance between the voltage normal class center and the voltage anomaly class center.

4. The method of claim 3, wherein the performing the thermal runaway warning based on the distance comprises: 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 temperature normality-anomaly distance is greater than a first preset threshold;

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

the voltage normal-abnormal distance is greater than a second preset threshold;

the voltage normal-abnormal distance has a rise rate greater than a second preset rise rate threshold.

5. The method of claim 1 or 2, wherein the real-time thermal performance data comprises all real-time temperature data, real-time cell voltage data, and real-time current data for the battery pack, then:

the normal class data comprises normal temperature class data, normal monomer voltage class data and real-time SOC normal class data, and the abnormal class data comprises abnormal temperature class data, abnormal monomer voltage class data and abnormal SOC class data;

the normal class centers comprise a temperature normal class center, a monomer voltage normal class center and an SOC normal class center, and the abnormal class centers comprise a temperature abnormal class center, a monomer voltage abnormal class center and an SOC abnormal class center;

the distance between the normal class center and the abnormal class center includes: a temperature normal-abnormal distance between the temperature normal class center and the temperature abnormal class center, a voltage normal-abnormal distance between the cell voltage normal class center and the cell voltage abnormal class center, and a SOC normal-abnormal distance between the SOC normal class center and the SOC abnormal class center.

6. The method of claim 5, wherein the performing the thermal runaway warning based on the distance comprises:

performing thermal runaway early warning 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 temperature normality-anomaly distance is greater than a first preset threshold;

the rising rate of the temperature normal-abnormal 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 normality-anomaly 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.

7. A battery thermal management device, comprising:

the acquisition module is used for acquiring real-time thermal performance data of the battery pack;

the clustering module is used for clustering the real-time thermal performance data into normal class data and abnormal class data through a clustering algorithm;

the first calculation module is used for calculating a normal clustering center of the normal class data and an abnormal clustering center of the abnormal class data;

the second calculation module is used for calculating the distance between the normal clustering center and the abnormal clustering center;

and the early warning module is used for carrying out thermal runaway early warning based on the distance.

8. The apparatus of claim 7, wherein the real-time thermal performance data comprises real-time temperature data for all temperature sensors and real-time voltage data for all voltage sensors disposed within the battery pack, then:

the normal class data comprises temperature normal class data and voltage normal class data, and the abnormal class data comprises temperature abnormal class data and voltage abnormal class data;

the normal class centers comprise normal temperature class centers and normal voltage class centers, and the abnormal class centers comprise abnormal temperature class centers and abnormal voltage class centers;

the distance between the normal class center and the abnormal class center includes: a temperature normal-abnormal distance between the temperature normal class center and the temperature abnormal class center, and a voltage normal-abnormal distance between the voltage normal class center and the voltage abnormal class center;

then, the performing thermal runaway early warning based on the distance includes: 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 temperature normality-anomaly distance is greater than a first preset threshold;

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

the voltage normal-abnormal distance is greater than a second preset threshold;

the voltage normal-abnormal distance has a rise rate greater than a second preset rise rate threshold.

9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.

10. An electronic device, comprising:

a memory having a computer program stored thereon;

a processor for executing the computer program in the memory to carry out the steps of the method of any one of claims 1 to 6.

Technical Field

The present disclosure relates to the field of battery management technologies, and in particular, to a battery thermal management method, apparatus, medium, and device.

Background

In order to solve the problem of thermal runaway of the battery, a plurality of temperature sensors and voltage sensors are generally configured in the battery pack, the temperature and the voltage of each module or each cell are collected, and the thermal safety state of the battery pack is monitored in real time by monitoring the temperature value of each temperature sensor and the voltage value of each voltage sensor. However, this method has a slow response speed to thermal events and a low accuracy.

Disclosure of Invention

The purpose of the disclosure is to provide a battery thermal management method, device, medium and equipment, which can quickly and accurately respond to a thermal event and effectively perform thermal runaway early warning.

According to a first embodiment of the present disclosure, there is provided a battery thermal management method, including: acquiring real-time thermal performance data of the 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 center of the normal class data and an abnormal class center of the abnormal class data; calculating a distance between the normal cluster center and the abnormal cluster center; and carrying out thermal runaway early warning based on the distance.

Optionally, the calculating a normal cluster center of the normal class data and an abnormal cluster center of the abnormal class data includes: initializing the normal clustering center and the abnormal clustering center; calculating the distance from each real-time thermal performance data to the normal clustering center and the abnormal clustering center; clustering each real-time thermal performance data to a clustering center which is closest to the real-time thermal performance data in the normal clustering center and the abnormal clustering center; calculating the sum of squares of the distances from each of the real-time thermal performance data to the cluster center thereof; if the sum of squares of the distances is less than a set value, the clustering process is finished; and if the sum of the squares of the distances is larger than a set value, calculating the mass center of the real-time thermal performance data set clustered to the same clustering center, and restarting clustering as a new clustering center.

Optionally, the real-time thermal performance data includes real-time temperature data for all temperature sensors and real-time voltage data for all voltage sensors disposed within the battery pack; then:

the normal class data comprises temperature normal class data and voltage normal class data, and the abnormal class data comprises temperature abnormal class data and voltage abnormal class data;

the normal class centers comprise normal temperature class centers and normal voltage class centers, and the abnormal class centers comprise abnormal temperature class centers and abnormal voltage class centers;

the distance between the normal class center and the abnormal class center includes: a temperature normal-anomaly distance between the temperature normal class center and the temperature anomaly class center, and a voltage normal-anomaly distance between the voltage normal class center and the voltage anomaly class center.

Optionally, the performing thermal runaway pre-warning based on the distance includes: 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 temperature normality-anomaly distance is greater than a first preset threshold;

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

the voltage normal-abnormal distance is greater than a second preset threshold;

the voltage normal-abnormal distance has a rise rate greater than a second preset rise rate threshold.

Optionally, the real-time thermal performance data includes all real-time temperature data, real-time cell voltage data, and real-time current data for the battery pack, then:

the normal class data comprises normal temperature class data, normal monomer voltage class data and real-time SOC normal class data, and the abnormal class data comprises abnormal temperature class data, abnormal monomer voltage class data and abnormal SOC class data;

the normal class centers comprise a temperature normal class center, a monomer voltage normal class center and an SOC normal class center, and the abnormal class centers comprise a temperature abnormal class center, a monomer voltage abnormal class center and an SOC abnormal class center;

the distance between the normal class center and the abnormal class center includes: a temperature normal-abnormal distance between the temperature normal class center and the temperature abnormal class center, a voltage normal-abnormal distance between the cell voltage normal class center and the cell voltage abnormal class center, and a SOC normal-abnormal distance between the SOC normal class center and the SOC abnormal class center.

Optionally, the performing thermal runaway pre-warning based on the distance includes:

performing thermal runaway early warning 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 temperature normality-anomaly distance is greater than a first preset threshold;

the rising rate of the temperature normal-abnormal 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 normality-anomaly 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.

According to a second embodiment of the present disclosure, there is provided a battery thermal management apparatus including: the acquisition module is used for acquiring real-time thermal performance data of the battery pack; the clustering module is used for clustering the real-time thermal performance data into normal class data and abnormal class data through a clustering algorithm; the first calculation module is used for calculating a normal clustering center of the normal class data and an abnormal clustering center of the abnormal class data; the second calculation module is used for calculating the distance between the normal clustering center and the abnormal clustering center; and the early warning module is used for carrying out thermal runaway early warning based on the distance.

Optionally, the first computing module is configured to: initializing the normal clustering center and the abnormal clustering center; calculating the distance from each real-time thermal performance data to the normal clustering center and the abnormal clustering center; clustering each real-time thermal performance data to a clustering center which is closest to the real-time thermal performance data in the normal clustering center and the abnormal clustering center; calculating the sum of squares of the distances from each of the real-time thermal performance data to the cluster center thereof; if the sum of squares of the distances is less than a set value, the clustering process is finished; and if the sum of the squares of the distances is larger than a set value, calculating the mass center of the real-time thermal performance data set clustered to the same clustering center, and restarting clustering as a new clustering center.

Optionally, the real-time thermal performance data includes real-time temperature data for all temperature sensors and real-time voltage data for all voltage sensors disposed within the battery pack; then:

the normal class data comprises temperature normal class data and voltage normal class data, and the abnormal class data comprises temperature abnormal class data and voltage abnormal class data;

the normal class centers comprise normal temperature class centers and normal voltage class centers, and the abnormal class centers comprise abnormal temperature class centers and abnormal voltage class centers;

the distance between the normal class center and the abnormal class center includes: a temperature normal-anomaly distance between the temperature normal class center and the temperature anomaly class center, and a voltage normal-anomaly distance between the voltage normal class center and the voltage anomaly class center.

Optionally, the performing thermal runaway pre-warning based on the distance includes: 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 temperature normality-anomaly distance is greater than a first preset threshold;

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

the voltage normal-abnormal distance is greater than a second preset threshold;

the voltage normal-abnormal distance has a rise rate greater than a second preset rise rate threshold.

Optionally, the real-time thermal performance data includes all real-time temperature data, real-time cell voltage data, and real-time current data for the battery pack, then:

the normal class data comprises normal temperature class data, normal monomer voltage class data and real-time SOC normal class data, and the abnormal class data comprises abnormal temperature class data, abnormal monomer voltage class data and abnormal SOC class data;

the normal class centers comprise a temperature normal class center, a monomer voltage normal class center and an SOC normal class center, and the abnormal class centers comprise a temperature abnormal class center, a monomer voltage abnormal class center and an SOC abnormal class center;

the distance between the normal class center and the abnormal class center includes: a temperature normal-abnormal distance between the temperature normal class center and the temperature abnormal class center, a voltage normal-abnormal distance between the cell voltage normal class center and the cell voltage abnormal class center, and a SOC normal-abnormal distance between the SOC normal class center and the SOC abnormal class center.

Optionally, the performing thermal runaway pre-warning based on the distance includes:

performing thermal runaway early warning 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 temperature normality-anomaly distance is greater than a first preset threshold;

the rising rate of the temperature normal-abnormal 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 normality-anomaly 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.

According to a third embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to the first embodiment of the present disclosure.

According to a fourth embodiment of the present disclosure, there is provided an electronic apparatus including: a memory having a computer program stored thereon; a processor for executing the computer program in the memory to carry out the steps of the method according to the first embodiment of the disclosure.

By adopting the technical scheme, the clustering algorithm is adopted to cluster the thermal performance data, each thermal performance data is clustered into two types of data, namely normal data, abnormal data and the like, and then the distance between the clustering centers of the normal data and the abnormal data is compared to judge the risk of thermal runaway.

Additional features and advantages of the disclosure will be set forth in the detailed description which follows.

Drawings

The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:

fig. 1 is a flow chart of a battery thermal management method according to one embodiment of the present disclosure.

Fig. 2 is a schematic diagram of a cluster center calculation process.

Fig. 3 is a schematic block diagram of a battery thermal management apparatus according to one embodiment of the present disclosure.

FIG. 4 is a block diagram illustrating an electronic device in accordance with an example embodiment.

Detailed Description

The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.

Fig. 1 is a flow chart of a battery thermal management method according to one embodiment of the present disclosure. As shown in fig. 1, the method includes the following steps S11 to S15.

In step S11, real-time thermal performance data for the battery pack is obtained.

Where real-time thermal performance data may be obtained directly from all sensing devices (e.g., temperature sensors, voltage sensors, etc.) disposed within the battery pack, or may be obtained from, for example, a battery management system.

In other embodiments, the real-time thermal performance data may include real-time temperature data for all of the temperature sensors disposed within the battery pack and real-time voltage data for all of the voltage sensors disposed within the battery pack.

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 for the battery pack.

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

The clustering algorithm can be a K-means algorithm or other clustering algorithms, and the specific form of the clustering algorithm is not limited in the disclosure as long as the clustering operation of data can be realized.

For example, assuming that there are N real-time temperature data currently, N real-time temperature data of the N real-time temperature data are clustered as normal class data and the remaining N-N real-time temperature data are clustered as abnormal class data by a clustering algorithm.

Under the condition that the real-time thermal performance data comprises 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 through a clustering algorithm, and the real-time voltage data can be clustered into normal voltage data and abnormal voltage data.

Under the condition that the real-time thermal performance data comprises 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 class data and abnormal-temperature class data, the real-time cell voltage data can be clustered into normal-cell-voltage class data and abnormal-cell-voltage class data, and the real-time SOC data can be clustered into normal-SOC class data and abnormal-SOC class data. The SOC of the cell may be calculated by an ampere-hour integration method, an Open Circuit Voltage (OCV) curve lookup method, or the like.

In step S13, a normal class center of the normal class data and an abnormal class center of the abnormal class data are calculated.

In one embodiment, the normal cluster center and the abnormal cluster center may be initialized first, that is, the normal cluster center and the abnormal cluster center are given corresponding initial values; then, calculating the distance from each real-time thermal performance data to the normal clustering center and the abnormal clustering center; then, clustering each real-time thermal performance data to a clustering center which is closest to the real-time thermal performance data in the normal clustering center and the abnormal clustering center; then, calculating the distance square sum of each real-time thermal performance data to the clustering center thereof; and then, if the sum of the squares of the distances is smaller than a set value, finishing the clustering process, and if the sum of the squares of the distances is larger than the set value, calculating the mass center of the real-time thermal performance data set clustered to the same clustering center, and restarting clustering as a new clustering center.

Fig. 2 is a schematic diagram of a cluster center calculation process. A1 and A2 are abnormal class data, B1 and B2 are normal class data, and a clustering center C1 of the abnormal class data and a clustering center C2 of the normal class data can be calculated at each moment.

In the case where the real-time thermal performance data includes real-time temperature data and real-time voltage data, a temperature normal class center T _ C2 of the temperature normal class data, a temperature abnormal class center T _ C1 of the temperature abnormal class data, a voltage normal class center V _ C2 of the voltage normal class data, and a voltage abnormal class center V _ C1 of the voltage abnormal class data are calculated, and the calculation processes of these cluster centers are calculated as described above.

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 regarding the battery pack, a temperature normal class center of temperature normal class data, a temperature abnormal class center of temperature abnormal class data, a cell voltage normal class center of cell voltage normal class data, a cell voltage abnormal class center of cell voltage abnormal class data, an SOC normal class center of SOC normal class data, and an SOC abnormal class center of SOC abnormal class data are calculated. The calculation of these cluster centers is performed as described above.

In step S14, the distance between the normal and abnormal cluster centers is calculated.

Under the condition that the real-time thermal performance data comprises real-time temperature data and real-time voltage data, calculating the distance between the temperature normal class center and the temperature abnormal class center to obtain a temperature normal-abnormal distance, and calculating the distance between the voltage normal class center and the voltage abnormal class center to obtain a voltage normal-abnormal distance.

Under the condition that the real-time thermal performance data comprises all real-time temperature data, real-time cell voltage data and real-time current data about the battery pack, calculating the distance between the normal temperature class center and the abnormal temperature class center to obtain a normal-abnormal temperature distance, calculating the distance between the normal cell voltage class center and the abnormal cell voltage class center to obtain a normal-abnormal voltage distance, and calculating the distance between the normal SOC class center and the abnormal SOC class center to obtain a normal-abnormal SOC distance.

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

Under the condition that the real-time thermal performance data comprises real-time temperature data and real-time voltage data, thermal runaway early warning can be performed under the condition that at least two of the following conditions are triggered by the temperature normal-abnormal distance and the voltage normal-abnormal distance: (1) the temperature normality-anomaly distance is greater than a first preset threshold; (2) the rising rate of the temperature normal-abnormal distance is larger than a first preset rising rate threshold value; (3) the voltage normal-abnormal distance is greater than a second preset threshold; (4) the rate of rise of the voltage normal-abnormal distance is greater than a second preset rate of rise threshold. Further, by the combination of the logical and or of the above 4 conditions, the internal short warning can be performed.

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 regarding the battery pack, the thermal runaway pre-warning may be performed in a case where the temperature normal-abnormal distance, the voltage normal-abnormal distance, and the SOC normal-abnormal distance trigger at least two of the following conditions: (1) the temperature normality-anomaly distance is greater than a first preset threshold; (2) the rising rate of the temperature normal-abnormal distance is larger than a first preset rising rate threshold value; (3) the voltage normal-abnormal distance is greater than a second preset threshold; (4) the rising rate of the voltage normal-abnormal distance is greater than a second preset rising rate threshold value; (5) the SOC normality-anomaly distance is greater than a third preset threshold; (6) the rate of rise of the SOC normal-to-abnormal distance is greater than a third preset rate of rise threshold.

In the present disclosure, the rate of rise of the temperature normality-anomaly distance refers to the rate of rise of the temperature normality-anomaly distance per unit time; the rising rate of the voltage normal-abnormal distance refers to a rising speed of the voltage normal-abnormal distance per unit time; the rising rate of the SOC normal-abnormal distance refers to a rising speed of the SOC normal-abnormal distance per unit time.

By adopting the technical scheme, the clustering algorithm is adopted to cluster the thermal performance data, each thermal performance data is clustered into two types of data, namely normal data, abnormal data and the like, and then the distance between the clustering centers of the normal data and the abnormal data is compared to judge the risk of thermal runaway.

Fig. 3 is a schematic block diagram of a battery thermal management apparatus according to one embodiment of the present disclosure. As shown in fig. 3, the apparatus includes: an obtaining module 31, configured to obtain real-time thermal performance data of the battery pack; the clustering module 32 is used for clustering the real-time thermal performance data into normal class data and abnormal class data through a clustering algorithm; the first calculation module 33 is configured to calculate a normal clustering center of the normal class data and an abnormal clustering center of the abnormal class data; a second calculating module 34, configured to calculate a distance between the normal clustering center and the abnormal clustering center; and the early warning module 35 is used for carrying out thermal runaway early warning based on the distance.

By adopting the technical scheme, the clustering algorithm is adopted to cluster the thermal performance data, each thermal performance data is clustered into two types of data, namely normal data, abnormal data and the like, and then the distance between the clustering centers of the normal data and the abnormal data is compared to judge the risk of thermal runaway.

Optionally, the first calculation module 33 is configured to: initializing a normal clustering center and an abnormal clustering center; calculating the distance from each real-time thermal performance data to the normal cluster center and the abnormal cluster center; clustering each real-time thermal performance data to a clustering center which is closest to the real-time thermal performance data in a normal clustering center and an abnormal clustering center; calculating the sum of squares of the distances from each real-time thermal performance data to the clustering center of the real-time thermal performance data; if the sum of the squares of the distances is less than a set value, the clustering process is ended; and if the sum of the squares of the distances is larger than a set value, calculating the mass center of the real-time thermal performance data set clustered to the same clustering center, and restarting clustering as a new clustering center.

Optionally, the real-time thermal performance data includes real-time temperature data of all temperature sensors and real-time voltage data of all voltage sensors disposed within the battery pack; then:

the normal class data comprises temperature normal class data and voltage normal class data, and the abnormal class data comprises temperature abnormal class data and voltage abnormal class data;

the normal class center comprises a temperature normal class center and a voltage normal class center, and the abnormal class center comprises a temperature abnormal class center and a voltage abnormal class center;

the distance between the normal cluster center and the abnormal cluster center includes: a temperature normal-abnormal distance between the temperature normal class center and the temperature abnormal class center, and a voltage normal-abnormal distance between the voltage normal class center and the voltage abnormal class center.

Optionally, the warning of thermal runaway is performed based on the distance, including: performing thermal runaway warning when the temperature normal-abnormal distance and the voltage normal-abnormal distance trigger at least two of the following conditions:

the temperature normality-anomaly distance is greater than a first preset threshold;

the rising rate of the temperature normal-abnormal distance is larger than a first preset rising rate threshold value;

the voltage normal-abnormal distance is greater than a second preset threshold;

the rate of rise of the voltage normal-abnormal distance is greater than a second preset rate of rise threshold.

Optionally, the real-time thermal performance data includes all real-time temperature data, real-time cell voltage data, and real-time current data for the battery pack, then:

the normal class data comprises normal temperature class data, normal monomer voltage class data and real-time SOC normal class data, and the abnormal class data comprises abnormal temperature class data, abnormal monomer voltage class data and abnormal SOC class data;

the normal class center comprises a normal temperature class center, a normal monomer voltage class center and a normal SOC class center, and the abnormal class center comprises an abnormal temperature class center, an abnormal monomer voltage class center and an abnormal SOC class center;

the distance between the normal cluster center and the abnormal cluster center includes: a temperature normal-abnormal distance between the temperature normal class center and the temperature abnormal class center, a voltage normal-abnormal distance between the cell voltage normal class center and the cell voltage abnormal class center, and a SOC normal-abnormal distance between the SOC normal class center and the SOC abnormal class center.

Optionally, the warning of thermal runaway is performed based on the distance, including:

performing thermal runaway early warning under the condition that 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 temperature normality-anomaly distance is greater than a first preset threshold;

the rising rate of the temperature normal-abnormal distance is larger than a first preset rising rate threshold value;

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 value;

the SOC normality-anomaly distance is greater than a third preset threshold;

the rate of rise of the SOC normal-to-abnormal distance is greater than a third preset rate of rise threshold.

With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.

Fig. 4 is a block diagram illustrating an electronic device 700 according to an example embodiment. As shown in fig. 4, 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.

The processor 701 is configured to control the overall operation of the electronic device 700, so as to complete all or part of the steps in the battery thermal management method. The memory 702 is used to store various types of data to support operation at the electronic device 700, such as instructions for any application or method operating on the electronic device 700 and application-related data, such as contact data, transmitted and received messages, pictures, audio, video, and the like. The Memory 702 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia components 703 may include screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 702 or transmitted through the communication component 705. The audio assembly 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, such as a keyboard, mouse, 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 that the corresponding Communication component 705 may include: Wi-Fi module, bluetooth module, NFC module.

In an exemplary embodiment, the electronic Device 700 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above-described battery thermal management method.

In another exemplary embodiment, a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the battery thermal management method described above is also provided. For example, the computer readable storage medium may be the memory 702 described above that includes program instructions that are executable by the processor 701 of the electronic device 700 to perform the battery thermal management method described above.

The preferred embodiments of the present disclosure are described in detail with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.

It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, various possible combinations will not be separately described in this disclosure.

In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.