Battery pack temperature sensor detection method and device, medium and program product

Abstract

The embodiment of the invention relates to the technical field of battery detection, and discloses a battery pack temperature sensor detection method and device, a medium and a program product. The battery pack temperature sensor detection method comprises the steps that battery cell temperature data collected by a plurality of temperature sensors in a battery pack are acquired, and the battery cell temperature data comprise data of a plurality of time nodes in a preset time period; identifying the battery cell temperature data to determine a time node with abnormal data and determine a temperature sensor with abnormal data; and for each temperature sensor with the abnormal data, determining the abnormal condition of the temperature sensor according to the time node with the abnormal data. The method has the beneficial effects that the abnormity of the temperature sensor can be accurately judged and identified, so that the safety and the reliability of the operation of the battery pack are improved.

Description

Technical Field

[0001] This application relates to the field of battery testing technology, and in particular to a method, device, medium, and program product for detecting a battery pack temperature sensor. Background Technology

[0002] During long-term operation, temperature sensors in existing battery packs may exhibit abnormalities such as distorted temperature data (too high / too low). Since battery pack thermal management and safety protection strategies heavily rely on sensor temperature information, when a single or partial temperature sensor malfunctions, the system struggles to distinguish between sensor failure and actual temperature rise in a timely and accurate manner. This can lead to safety hazards and other misjudgments and false protections. For example, abnormal temperature readings might be identified as sensor failures, masking the true overheating risk of individual battery cells or modules, delaying over-temperature protection actions, or even triggering thermal runaway. Alternatively, the system might misinterpret sensor anomalies as cell temperature rises, triggering unnecessary power derating or shutdowns, reducing the availability and reliability of the energy storage system. Currently, the identification of temperature sensor anomalies mainly relies on manual inspection or single-point threshold judgment, which is insufficient for rapid and accurate anomaly localization under complex operating conditions with multiple sensors monitoring in parallel.

[0003] In related technologies, the identification of temperature sensor anomalies mainly relies on manual investigation or single-point threshold judgment, which makes it difficult to achieve rapid and accurate anomaly location in complex working conditions with multiple sensors monitoring in parallel. Summary of the Invention

[0004] The purpose of this application is to provide a method, device, medium, and program product for detecting a battery pack temperature sensor, which can accurately identify and judge temperature sensor abnormalities, thereby improving the safety and reliability of battery pack operation.

[0005] This application proposes a method for detecting a battery pack temperature sensor, comprising: acquiring cell temperature data collected by multiple temperature sensors within the battery pack, wherein the cell temperature data includes data from multiple time points within a preset time period; identifying the cell temperature data to determine the time points where abnormal data exists, and identifying the temperature sensors where the abnormal data exists; and for each temperature sensor where the abnormal data exists, determining the abnormal condition of the temperature sensor based on the time points where the abnormal data exists.

[0006] This application also proposes an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method as described above.

[0007] This application also proposes a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the method described above.

[0008] This application also proposes a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described above.

[0009] The battery pack temperature sensor detection method in this embodiment collects data from the temperature sensors in the battery pack at multiple time points within a preset time period. For the collected data, abnormal data is identified, and temperature sensors that may be abnormal are located. Subsequently, based on each temperature sensor with abnormal data, the abnormal condition of the temperature sensor is determined by the time point where the abnormal data is found, rather than making isolated judgments based solely on the collected temperature data. This enables accurate identification and judgment of temperature sensor abnormalities, thereby improving the safety and reliability of battery pack operation. Attached Figure Description

[0010] Figure 1 A schematic flowchart of a battery pack temperature sensor detection method provided in an embodiment of this application. Figure 1 ; Figure 2 A schematic flowchart of a battery pack temperature sensor detection method provided in an embodiment of this application. Figure 2 ; Figure 3 A schematic flowchart of a battery pack temperature sensor detection method provided in an embodiment of this application. Figure 3 ; Figure 4 A schematic flowchart of a battery pack temperature sensor detection method provided in an embodiment of this application. Figure 4 ; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the various embodiments of this application to help readers better understand this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various changes and modifications based on the following embodiments. The division of the various embodiments below is for the convenience of description and should not constitute any limitation on the specific implementation of this application. The various embodiments can be combined with and referenced by each other without contradiction.

[0012] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0013] In related technologies, when detecting the temperature of a battery pack, if a single or partial temperature sensor malfunctions, the system struggles to distinguish between sensor failure and actual temperature rise in a timely and accurate manner. This can lead to safety hazards and other misjudgments and false protections. For example, abnormal temperature readings may be identified as sensor failures, masking the true overheating risk of individual battery cells or modules, delaying over-temperature protection actions, or even triggering thermal runaway. Alternatively, the system may misinterpret sensor anomalies as cell temperature rises, triggering unnecessary power derating or shutdowns, reducing the availability and reliability of the energy storage system. Currently, the identification of temperature sensor anomalies mainly relies on manual inspection or single-point threshold judgment, which is insufficient for rapid and accurate anomaly localization under complex operating conditions with multiple sensors monitoring in parallel.

[0014] In view of this, embodiments of this application propose a method, device, medium, and program product for detecting battery pack temperature sensors. The method for detecting battery pack temperature sensors includes: acquiring cell temperature data collected by multiple temperature sensors within the battery pack, wherein the cell temperature data includes data from multiple time points within a preset time period; identifying the cell temperature data to determine the time points where abnormal data exists, and identifying the temperature sensors where the abnormal data exists; and for each temperature sensor with abnormal data, determining the abnormal condition of the temperature sensor based on the time point where the abnormal data exists. The battery pack temperature sensor detection method in this application collects data from temperature sensors in the battery pack at multiple time points within a preset time period, identifies abnormal data in the collected data, and locates potentially abnormal temperature sensors. Subsequently, based on each temperature sensor with abnormal data, the abnormal condition of the temperature sensor is determined by the time point where the abnormal data exists, rather than making isolated judgments based solely on the collected temperature data. This allows for accurate identification and judgment of temperature sensor abnormalities, thereby improving the safety and reliability of battery pack operation. The following is a detailed description of the implementation details of the battery pack temperature sensor detection method according to the embodiments of this application. The following implementation details are provided for ease of understanding only and are not necessary for implementing this solution.

[0015] Reference Figure 1 As shown, Figure 1This is a schematic flowchart illustrating a method for detecting a battery pack temperature sensor according to an embodiment of this application. This application provides a method for detecting a battery pack temperature sensor, comprising the following steps: Step 100: Obtain cell temperature data collected by multiple temperature sensors within the battery pack, wherein the cell temperature data includes data from multiple time points within a preset time period.

[0016] The battery pack temperature sensor detection method in this application can be applied to a battery management system. A temperature sensor can be set for each cell in the battery pack to detect the temperature. The system can sequentially acquire cell temperature data fed back by the temperature sensors at multiple time points within a preset time period. Under normal circumstances, these cell temperature data reflect the temperature of the cells at each time point.

[0017] The preset time period and time nodes can be set according to actual needs. For example, if the preset time period is 00:00:00 to 23:59:59 (24h) and the data acquisition frequency is 1Hz, then the time nodes in this application are every second within this 24h, resulting in 86,400 time nodes, which is equivalent to 86,400 frames of temperature data. If there are 28 temperature sensors in the battery pack, then each time node contains 28 cell temperature data. If the data acquisition frequency is 2Hz, then the time nodes in this application are one node every second within this 24h. Other formats will not be elaborated here.

[0018] This data acquisition method based on time series and temperature sensor clusters provides rich data dimensions and contextual information for subsequent analysis, avoids the randomness of single-point data judgment, and the data type is relatively simple, avoiding excessive computational complexity.

[0019] Step 200: Identify the cell temperature data to determine the time point where abnormal data exists, and identify the temperature sensor where the abnormal data exists.

[0020] In one embodiment, the cell temperature data at all time points can be sorted, and then the cell temperature data of all temperature sensors at each sampling time point can be checked one by one, marking the abnormal data and the temperature sensors containing abnormal data. This step does not simply identify the abnormality of a certain temperature sensor data and determine that the temperature sensor is faulty, but rather accurately locates at which time point and which temperature sensor the abnormal data occurred, providing a basis for subsequent fault mode analysis.

[0021] Identifying abnormal data in cell temperature data can be based on comparing the difference and variance of the mean and median, or on comparing temperature comparison values. For example, the system will check each sampling time point one by one and mark the abnormal values ​​that deviate significantly from the median of all temperature sensors and their corresponding sensor numbers.

[0022] Step 300: For each temperature sensor with abnormal data, determine the abnormal condition of the temperature sensor based on the time node where the abnormal data exists.

[0023] For each temperature sensor, simply identifying abnormal data to determine if the sensor is faulty is insufficient to guarantee detection accuracy and to accurately distinguish the specific type of abnormality. For example, a normal temperature sensor may occasionally generate abnormal data due to transient interference, but a truly faulty temperature sensor may exhibit persistent, systematic, or regular abnormal data. Therefore, in this embodiment, for each temperature sensor potentially identified as faulty in step 200, all time points showing abnormal data are determined. Subsequent analysis of these time points can then be used to assess the sensor's abnormality. For instance, the sensor's abnormality can be determined based on the total number of abnormal data time points, the proportion of abnormal data time points to the total number of time points, the continuity of abnormal data time points, and the concentration / dispersion of abnormal data time points within a preset time period.

[0024] In one specific embodiment, for each temperature sensor, by counting the number of abnormal time points and comparing it with the total number of time points, it is possible to effectively distinguish between transient interference and permanent failure. For example, if a temperature sensor has abnormal data at more than a preset percentage of time points, then the probability of the temperature sensor experiencing a hard failure is much higher than if it is only subject to temporary interference.

[0025] The battery pack temperature sensor detection method in this embodiment collects data from the temperature sensors in the battery pack at multiple time points within a preset time period. For the collected data, abnormal data is identified, and temperature sensors that may be abnormal are located. Subsequently, based on each temperature sensor with abnormal data, the abnormal condition of the temperature sensor is determined by the time point where the abnormal data is found, rather than making isolated judgments based solely on the collected temperature data. This enables accurate identification and judgment of temperature sensor abnormalities, thereby improving the safety and reliability of battery pack operation.

[0026] Reference Figure 2As shown, in an optional embodiment of this application, determining the abnormal condition of each temperature sensor based on the time point at which the abnormal data exists includes: Step 301: For each temperature sensor with abnormal data, determine the number of time nodes belonging to the same type of abnormality among the time nodes with abnormal data. Step 302: Determine the abnormality of the temperature sensor based on the number of time points of the same type of abnormality.

[0027] In actual battery pack operation, temperature sensors exhibit different failure modes, resulting in drastically different abnormal data characteristics. This application introduces a classification concept; specifically, abnormal data can be categorized based on its identification, thereby distinguishing these different failure modes.

[0028] In practice, an anomaly type label, such as "high temperature anomaly" or "low temperature anomaly," can be assigned to each anomaly data point simultaneously or after its identification. It's important to understand that this label does not directly determine whether the temperature sensor is considered abnormal at this stage. Subsequently, in step 301, for each temperature sensor marked with anomaly data, the number of time points showing high temperature anomalies and low temperature anomalies can be counted. The specific anomaly of the temperature sensor can then be determined based on the number of time points showing the same type of anomaly.

[0029] Reference Figure 3 As shown, the battery pack temperature sensor detection method in this embodiment can not only determine whether the temperature sensor is abnormal, but also determine how it is abnormal, thereby greatly improving the accuracy of fault diagnosis and the targeted nature of subsequent risk management.

[0030] In an optional embodiment of this application, determining the abnormality of the temperature sensor based on the number of time points of the same type of abnormality includes: Step 312: Determine the percentage of abnormal nodes based on the number of time nodes of the same type of abnormality and the total number of time nodes; Step 322: Determine the abnormality of the temperature sensor based on the percentage of abnormal nodes.

[0031] In this embodiment of the application, the absolute number of time points of the same type of anomaly is transformed into a relative proportion, thereby unifying the judgment criteria and eliminating the interference of analysis duration and sampling frequency.

[0032] For high-temperature and low-temperature anomalies, the number of time points for high-temperature anomalies and the number of time points for low-temperature anomalies can be determined separately. The ratio of these anomalous time points to the total number of time points represents the percentage of anomalous time points for high-temperature anomalies and the percentage of anomalous time points for low-temperature anomalies, respectively. It can be understood that for a temperature sensor, if an anomaly occurs, typically only time points with high-temperature anomaly data will appear within a preset time period.

[0033] The percentage of identified abnormal nodes can be compared with a preset threshold to determine the anomaly. This preset threshold can be set and adjusted based on historical data, experimental verification, and requirements for the system's false alarm rate and false negative rate.

[0034] In an optional embodiment of this application, determining the abnormality of the temperature sensor based on the percentage of abnormal nodes includes: When the proportion of abnormal nodes is greater than the preset proportion of nodes, it is determined whether the temperature sensor corresponding to the abnormal data has regional characteristics. If not, then the temperature sensor is determined to have temperature distortion.

[0035] In this embodiment, a preset node ratio is compared with the abnormal node ratio to determine whether the abnormality of the temperature sensor exceeds expectations. If it does, the system further determines whether the temperature sensor corresponding to the abnormal data has regional characteristics. For example, based on this temperature sensor and other temperature sensors, methods such as neighborhood statistics, connected component analysis, and convolution operations are used to determine whether there are regional characteristics among all abnormal temperature sensors. If the temperature sensor with abnormal data does not have the aforementioned regional characteristics compared to other temperature sensors with abnormal data, it indicates that the temperature sensor is experiencing temperature distortion. If regional characteristics exist, it may be other types of anomalies, such as data anomalies, etc.

[0036] In an optional embodiment of this application, the preset node ratio includes a first preset ratio and a second preset ratio, and the step of determining the abnormality of the temperature sensor based on the abnormal node ratio includes: For abnormal data of high temperature, if the percentage of abnormal nodes is greater than a first preset percentage and the temperature sensor corresponding to the abnormal data does not have regional characteristics, then the temperature sensor is determined to have a first type of high temperature anomaly.

[0037] For abnormal data of low temperature, if the percentage of abnormal nodes is greater than a second preset percentage and the temperature sensor corresponding to the abnormal data does not have regional characteristics, then the temperature sensor is determined to have a first type of low temperature anomaly.

[0038] For a temperature sensor that generates temperature distortion, it may typically exhibit high temperature anomalies or low temperature anomalies. This application method sets two judgment thresholds for these temperature distortions, namely, a preset node proportion, which includes a first preset proportion and a second preset proportion. The two proportions can be set to be the same or different according to the actual situation. Specifically, when the proportion of high temperature anomalies of a certain temperature sensor exceeds the first preset proportion, it is determined that the temperature sensor has a first type of high temperature anomaly; when the proportion of low temperature anomalies exceeds the second preset proportion, it is determined that a first type of low temperature anomaly has occurred.

[0039] Therefore, the battery pack temperature sensor detection method in this application embodiment, from identifying abnormal data, to determining the proportion of abnormal data time nodes, and locating the specific fault type, enables operators to more accurately detect abnormalities in the temperature sensor, rather than relying entirely on the temperature sensor readings for judgment, thereby achieving precise fault location and greatly improving the efficiency of fault handling and the accuracy of system safety monitoring.

[0040] For abnormal data of high temperature, if the percentage of abnormal nodes is less than or equal to a first preset percentage, or if the temperature sensor corresponding to the abnormal data has regional characteristics, then the temperature sensor is determined to have a high-temperature anomaly / high-temperature jump / data anomaly. For abnormal data of low temperature, if the percentage of abnormal nodes is less than or equal to a second preset percentage, or if the temperature sensor corresponding to the abnormal data has regional characteristics, then the temperature sensor is determined to have a low-temperature anomaly / low-temperature jump / data anomaly. Further determinations can be made for these anomalies.

[0041] Reference Figure 4 As shown, in an optional embodiment of this application, identifying the cell temperature data to determine the time point where abnormal data exists includes: Step 201: For each time point, determine the median temperature of all the cell temperature data, and compare the median temperature with each of the cell temperature data. Step 202: Determine whether the cell temperature data is abnormal based on the comparison.

[0042] Typically, the temperature difference among all cells in a battery pack at the same point in time is not significant. Therefore, in this embodiment, when identifying cell temperature data, the operation can be performed independently for each time point. Specifically, the readings of all temperature sensors at that time point are collected, and then the median of these readings is calculated. It is understood that even if there are a few severely abnormal data points, they will not significantly affect the median value. Therefore, the median can serve as an ideal reference benchmark. Subsequently, the readings of each temperature sensor at that time point are compared with the median temperature to determine whether the cell temperature data is abnormal based on the comparison.

[0043] For example, in a battery pack with multiple temperature sensors, even if one or two temperature sensors malfunction and output extremely high or low values, the median temperature can still accurately reflect the overall temperature level of the battery pack at that moment. By comparing the cell temperature data from all temperature sensors with this median temperature, it is possible to accurately identify those one or two temperature sensors with significantly different readings, which can then be marked as abnormal data.

[0044] In an optional embodiment of this application, determining whether the cell temperature data is abnormal based on the comparison includes: When the difference between the cell temperature data and the median is greater than a first temperature difference threshold, the cell temperature data is determined to be abnormal data; and / or When the difference between the cell temperature data and the median is less than the second temperature difference threshold, the cell temperature data is determined to be abnormal data.

[0045] In this embodiment, the difference between the data from each temperature sensor and the current median can be calculated. Then, this difference is compared with a preset threshold. The set temperature difference threshold provides an objective physical standard for judging anomalies, and the cell temperature threshold can be calibrated based on the temperature uniformity of the battery pack during normal operation.

[0046] In one specific embodiment, if the difference is greater than a first temperature difference threshold, the data is determined to be high-temperature abnormal data.

[0047] If the difference is less than the second temperature difference threshold, the data is determined to be low-temperature abnormal data.

[0048] The above can be used to set both high and low thresholds to form a reasonable temperature range, and data outside the range will be considered abnormal; or only one cell temperature threshold can be set for judgment according to actual needs.

[0049] In this embodiment, the median temperature is used for anomaly data identification, which has strong anti-interference capability and robustness. Compared with using the arithmetic mean as a benchmark, the median can effectively avoid the problem of a few faulty sensors affecting the reference benchmark. It is suitable for scenarios such as battery packs with a large number of temperature sensors and relatively uniform temperature distribution under normal conditions, ensuring the accuracy of the anomaly identification process and providing high-quality and highly reliable input for subsequent fault statistics and judgment.

[0050] In an optional embodiment of this application, before the steps of identifying the cell temperature data to determine the time point where abnormal data exists, and determining the temperature sensor where the abnormal data exists, the method further includes: Identify invalid data in the cell temperature data to filter out data at time points with invalid cell temperature data; Determine the percentage of valid nodes with valid cell temperature data for each time point. Specifically, when the proportion of effective nodes is greater than or equal to a third preset proportion, the steps of identifying the cell temperature data to determine the time node where abnormal data exists, and identifying the temperature sensor where the abnormal data exists are executed.

[0051] When the proportion of effective nodes is less than the third preset proportion, the step of obtaining cell temperature data collected by multiple temperature sensors in the battery pack can be repeated.

[0052] In this embodiment of the application, before the actual identification of cell temperature data, a data preprocessing and quality control process is introduced to improve the accuracy of subsequent analysis by exposing invalid data in the original cell temperature data.

[0053] Invalid data may include data loss caused during communication, values ​​exceeding the temperature sensor's range, and signal jumps. In one specific embodiment, data whose temperature values ​​are no longer within the valid range are removed; the corresponding time points of these data are considered invalid. After filtering out these data, the system calculates the percentage of valid time points, i.e., the proportion of valid time points to the total number of time points. Only when this percentage is higher than or equal to a third preset percentage (e.g., 99%) will subsequent steps 200 and 300 be triggered.

[0054] If the percentage of valid data is too low, it indicates that the data quality is unreliable within the current analysis period, possibly due to severe communication problems or large-scale transient interference. In this case, forcibly performing anomaly detection is likely to yield unreliable conclusions. Therefore, when the percentage of valid nodes is less than the third preset percentage, the step of acquiring cell temperature data collected by multiple temperature sensors within the battery pack is repeated until the subsequently detected data meets the analysis requirements.

[0055] The battery pack temperature sensor detection method in this application embodiment prevents the system from making incorrect fault diagnoses when the data is incomplete or of extremely poor quality by conducting a preliminary data validity check. This ensures that the detection method operates only when the data quality is high enough to support reliable diagnosis, thereby guaranteeing the authority and credibility of its output results.

[0056] In a complete embodiment of this application, the detection process includes the following steps, with each second (one frame) within a preset time period as the time node: First, when performing temperature sensor detection, acquire the temperature data of all cells in a certain battery pack (the cell temperature data collected by the temperature sensor), with a time window (preset time period) of N hours, such as 24 hours; Remove data with temperature values ​​that are outside the valid range. For example, identify and filter out temperature values ​​that are significantly too high or too low. This means that for time points where such temperature values ​​exist, all data for that time point will be deleted. The total number of frames D of all valid data within the statistical time window. all ; Determine if the data integrity rate (percentage of valid nodes) is greater than or equal to A%. If not, the data integrity rate is too low, the data segment is unusable, and data needs to be acquired again and the data segment selected. If yes, all cell temperature data can be sorted by time for subsequent identification and analysis. Calculate the median T of the temperature data of all cells in the battery pack for each frame. med (Median temperature); Calculate the temperature difference ΔT between the median and the temperature data of all cells in the battery pack for each frame. i =T i -T med , where i is the temperature sensor number; For ΔT i ≥B℃ (first temperature difference threshold) or ΔT i Data frames ≤ C℃ (second temperature difference threshold) are tagged with an anomaly label, and the number i of the abnormal cell temperature sensor is recorded; where ΔT i ≥B℃ is labeled as a high temperature anomaly, ΔT i ≤C℃ should be labeled as low temperature anomaly; For each high-temperature abnormal cell, extract its corresponding high-temperature abnormality tag data frame from the temperature sensor i, and count the total number of frames D. i，H (Number of time points for high-temperature anomalies); For each low-temperature anomaly cell, extract its corresponding low-temperature anomaly tag data frame from temperature sensor i, and count the total number of frames D. i，L(Number of time points for low-temperature anomalies).

[0057] Calculate the proportion of high-temperature abnormal data frames (proportion of high-temperature abnormal nodes) R i，H =D i，H / D all (Total number of time nodes); Calculate the proportion of low-temperature abnormal data frames (proportion of high-temperature abnormal nodes) R i，L =D i，L / D all ; When R i，H >θ H If R is abnormally high, it indicates that the temperature sensor is experiencing a high-temperature anomaly; otherwise, it could be another type of high-temperature anomaly / high-temperature jump / data anomaly. i，L >θ L If the reading is positive, it indicates that the temperature sensor has a low-temperature anomaly; otherwise, it may be another type of low-temperature anomaly / low-temperature jump / data anomaly.

[0058] In a specific embodiment of this application, for a certain energy storage system, the battery pack structure has X built-in temperature sensors, the data acquisition frequency is 1Hz, the preset time period is set to 48h, and the data of battery pack A from 00:00:00 to 23:59:59 is acquired, and the effective range of the temperature value is set to T. min -T max Data points outside the specified range are filtered out, leaving L frames. All cell temperature Ti (i is an integer from 1 to X) data are sorted by timestamp, and for each time point, the median T of Ti is calculated. med and the corresponding ΔT i =T i -T med Set the first temperature difference threshold for high-temperature anomalies to Z℃, and filter out abnormal frames with ΔT6 ≥ Z℃; count the total number D high-temperature anomaly frames with ΔT6 ≥ Z℃ within a preset time period. 6，H =M; Calculate the proportion R of high-temperature anomaly data frames. 6，H =D 6，H / D all That is, M / L = R%, exceeding the set abnormal frame percentage threshold (first preset percentage) δ%. Based on all temperature sensors with abnormal data, neighborhood statistics, connected component analysis, convolution operations, etc., are used to determine whether there are regional characteristics among all abnormal temperature sensors. When none of them meet the regional characteristics, it is determined that temperature sensor No. 6 of battery pack A has a first-type high-temperature anomaly. Resistance measurement can be performed for verification as needed.

[0059] In another specific embodiment of this application, for a certain energy storage system, the battery pack structure has X built-in temperature sensors, the data acquisition frequency is 1Hz, the preset time period is set to 6h, and the data of battery pack B from 08:00:00 to 13:59:59 is acquired, and the effective range of the temperature value is set to T. min -T max Data points outside the specified range are filtered out, leaving L frames. All cell temperature Ti (i is an integer from 1 to X) data are sorted by timestamp, and for each time point, the median T of Ti is calculated. med and the corresponding ΔT i =T i -T med The temperature difference threshold for low-temperature anomalies was set to H℃, and ΔT was found during screening. 12 There are abnormal frames with temperatures ≤H℃; statistics are compiled within a preset time period, ΔT 12 Total number of frames D for low temperature anomalies ≤-6 12，L =J; Calculate the proportion R of low-temperature anomaly data frames. 12，L =D 12，L / D all That is, J / L=K%, exceeding the abnormal frame percentage threshold (second preset percentage) γ%. Based on all temperature sensors with abnormal data, neighborhood statistics, connected component analysis, convolution operation and other methods are used to determine whether there are regional characteristics among all abnormal temperature sensors. When none of them meet the regional characteristics, it is determined that temperature sensor No. 12 of battery pack B has a low temperature abnormality. Resistance measurement can be performed as needed for subsequent verification.

[0060] For scenarios involving a relatively long preset time period and a large amount of data, the method described in this application embodiment can be configured in the cloud and run based on cloud-based algorithms. For scenarios involving a relatively short time period and a relatively small amount of data, the method described in this application embodiment can be configured on the website and run based on website-based algorithms, or it can be run based on the cloud and corresponding algorithms.

[0061] Figure 5 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Figure 5 As shown, it includes at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method described in the above method embodiments.

[0062] The memory and processor can be connected via a bus, which can include any number or type of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0063] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. The memory can also be used to store data used by the processor during operation.

[0064] Another embodiment of this application relates to a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the method described in the above-described method embodiments.

[0065] Another embodiment of this application relates to a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0066] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0067] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing this application, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of this application.

Claims

1. A method for detecting the temperature of a battery pack using a temperature sensor, characterized in that, include: The battery cell temperature data is acquired from multiple temperature sensors within the battery pack, wherein the battery cell temperature data includes data from multiple time points within a preset time period; The cell temperature data is identified to determine the time point where abnormal data exists, and to identify the temperature sensor where the abnormal data exists; For each temperature sensor that has the abnormal data, the abnormal condition of the temperature sensor is determined based on the time point at which the abnormal data is found.

2. The detection method of the battery pack temperature sensor according to claim 1, characterized in that, For each temperature sensor exhibiting abnormal data, determining the abnormal condition of the temperature sensor based on the time point at which the abnormal data was found includes: For each temperature sensor that has the abnormal data, determine the number of time nodes that belong to the same type of abnormality among the time nodes where the abnormal data exists; The abnormality of the temperature sensor is determined based on the number of time points of the same type of abnormality.

3. The detection method of the battery pack temperature sensor according to claim 2, characterized in that, The step of determining the abnormality of the temperature sensor based on the number of time points of the same type of abnormality includes: The percentage of abnormal nodes is determined based on the number of time points of the same type of abnormality and the total number of time points. The abnormality of the temperature sensor is determined based on the percentage of abnormal nodes.

4. The detection method of the battery pack temperature sensor according to claim 3, characterized in that, The step of determining the abnormality of the temperature sensor based on the proportion of abnormal nodes includes: When the proportion of abnormal nodes is greater than the preset proportion of nodes, it is determined whether the temperature sensor corresponding to the abnormal data has regional characteristics. If not, then the temperature sensor is determined to have temperature distortion.

5. The detection method of the battery pack temperature sensor according to any one of claims 1-4, characterized in that, The process of identifying the cell temperature data to determine the time points where abnormal data exists includes: For each time point, the median temperature of all the cell temperature data is determined, and the median temperature is compared with each cell temperature data. Based on the comparison, determine whether the cell temperature data is abnormal.

6. The detection method of the battery pack temperature sensor according to claim 5, characterized in that, The step of determining whether the cell temperature data is abnormal based on the comparison includes: When the difference between the cell temperature data and the median is greater than a first temperature difference threshold, the cell temperature data is determined to be abnormal data; and / or When the difference between the cell temperature data and the median is less than the second temperature difference threshold, the cell temperature data is determined to be abnormal data.

7. The method for detecting the battery pack temperature sensor according to any one of claims 1-4 and 6, characterized in that, Before the steps of identifying the cell temperature data to determine the time point where abnormal data exists, and identifying the temperature sensor where the abnormal data exists, the method further includes: Identify invalid data in the cell temperature data to filter out data at time points with invalid cell temperature data; Determine the percentage of valid nodes with valid cell temperature data for each time point. Specifically, when the proportion of effective nodes is greater than or equal to a third preset proportion, the steps of identifying the cell temperature data to determine the time node where abnormal data exists, and identifying the temperature sensor where the abnormal data exists are executed.

8. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method of any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 7.

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