Abnormality identification method and device for battery sampling, medium and electronic equipment

Abstract

The invention belongs to the technical field of electric vehicles, and relates to a battery sampling abnormity identification method and device, a medium and electronic equipment. The method comprises the following steps: acquiring temperature sampling data of a battery, determining a temperature difference sequence duration according to the temperature sampling data, and sampling the battery through a temperature sensor; determining an outlier sensor in the temperature sensors based on the time length of the temperature difference sequence, and determining a proportion ratio of the outlier sensor; and determining an abnormal identification result of the outlier sensor according to the proportion ratio. According to the anomaly identification method provided by the invention, a large amount of failure sample data does not need to be acquired, model training does not need to be carried out, the potential temperature sampling failure mode without obvious representation can be specifically identified, and data support and theoretical basis are provided for timely and effective anomaly early warning; and the response time of spare parts and after-sales processing in advance is reserved, so that the bad influence on the use of the user is avoided.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of electric vehicles, and in particular relates to a battery sampling anomaly recognition method, a battery sampling anomaly recognition device, a computer-readable storage medium, and an electronic device. Background Art

[0002] With the widespread promotion of battery applications such as electric vehicles and energy storage systems, the importance of battery management systems (BMS) has become increasingly prominent. Among them, the identification of abnormal battery temperature sampling has become an important issue in battery management systems.

[0003] The commonly used method for identifying temperature sampling anomalies is to judge through threshold and rate methods. However, this method can only identify some relatively obvious abnormal problems, such as temperature jumps caused by open circuit, short circuit, water ingress, etc. of temperature sampling. Some potential failure modes, such as abnormalities such as poor thermal conductivity of NTC (Negative Temperature Coefficient Thermistor) caused by assembly or aging, cannot be identified. Summary of the invention

[0004] In order to overcome the problems existing in the related art, the present disclosure provides a battery sampling abnormality identification method, a battery sampling abnormality identification device, a computer-readable storage medium and an electronic device.

[0005] According to a first aspect of an embodiment of the present disclosure, a method for identifying abnormalities in battery sampling is provided, comprising: Acquire temperature sampling data of a battery, and determine a temperature difference sequence duration according to the temperature sampling data, wherein the battery is sampled by a temperature sensor; Based on the duration of the temperature difference sequence, determining an outlier sensor among the temperature sensors, and determining a ratio of the outlier sensor; The abnormality identification result of the outlier sensor is determined according to the proportion ratio.

[0006] Optionally, the temperature sampling data includes first temperature data, average temperature data and second temperature data. Determining the temperature difference sequence duration according to the temperature sampling data includes: determining a first temperature difference according to the first temperature data and the second temperature data, and determining a second temperature difference according to the average temperature data and the first temperature data; The duration during which the first temperature difference is greater than a first threshold and the second temperature difference is greater than a second threshold is counted, and the duration during which the temperature difference is greater than a third threshold is determined as the temperature difference sequence duration.

[0007] Optionally, the temperature sampling data includes first temperature data, average temperature data and second temperature data. Determining the temperature difference sequence duration according to the temperature sampling data includes: determining a first temperature difference according to the first temperature data and the second temperature data, and determining a third temperature difference according to the average temperature data and the second temperature data; The duration during which the first temperature difference is greater than a first threshold and the third temperature difference is greater than a second threshold is counted, and the duration during which the temperature difference is greater than the third threshold is determined as the temperature difference sequence duration.

[0008] Optionally, the determining of the outlier sensor in the temperature sensor based on the duration of the temperature difference sequence includes: Determine a quantile value according to the temperature sampling data, and determine a quantile range of the temperature sampling data according to the quantile value; Determine the outlier of the temperature sensor according to the temperature sampling data, the quantile value and the quantile range, and count the first number of the outlier within the duration of the temperature difference sequence; When the first number is greater than a fourth threshold, it is determined that the temperature sensor is an outlier sensor.

[0009] Optionally, determining the outlier of the temperature sensor according to the temperature sampling data, the quantile value and the quantile range includes: Calculating the quantile value and the quantile range to obtain a fifth threshold; When the temperature sampling data is less than the fifth threshold, the sampling point of the temperature sampling data is determined to be an outlier of the temperature sensor.

[0010] Optionally, determining the outlier of the temperature sensor according to the temperature sampling data, the quantile value and the quantile range includes: Calculating the quantile value and the quantile range to obtain a sixth threshold value; When the temperature sampling data is greater than the sixth threshold, the sampling point of the temperature sampling data is determined to be an outlier of the temperature sensor.

[0011] Optionally, determining the proportion of the outlier sensors includes: Counting the number of outliers of the outlier sensors; When the number of outliers is less than or equal to a seventh threshold, counting the number of sampling points of the outlier sensor within the duration of the temperature difference sequence and a second number corresponding to the outlier point; The proportion of the outlier sensors is determined according to the number of sampling points and the second number.

[0012] Optionally, determining the abnormality identification result of the outlier sensor according to the proportion ratio includes: When the proportion ratio is greater than an eighth threshold, it is determined that the abnormal identification result of the outlier sensor is a sampling abnormality, and an abnormal warning is issued to the outlier sensor according to the abnormal identification result.

[0013] According to a second aspect of an embodiment of the present disclosure, a battery sampling abnormality identification device is provided, comprising: A data acquisition module is configured to acquire temperature sampling data of a battery and determine a temperature difference sequence duration according to the temperature sampling data, wherein the battery is sampled by a temperature sensor; an outlier determination module, configured to determine outlier sensors among the temperature sensors based on the duration of the temperature difference sequence, and determine a proportion ratio of the outlier sensors; The abnormality identification module is configured to determine an abnormality identification result of the outlier sensor according to the proportion ratio.

[0014] According to a third aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored. When the program instructions are executed by a processor, the steps of any one of the battery sampling abnormality identification methods provided in the first aspect of the present disclosure are implemented.

[0015] According to a fourth aspect of an embodiment of the present disclosure, there is provided an electronic device, including: processor; a memory for storing processor-executable instructions; The processor is configured to: execute the executable instructions to implement any one of the steps of the battery sampling abnormality identification method provided in the first aspect of the present disclosure.

[0016] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects: In the method and device provided by the exemplary embodiments of the present disclosure, when the duration of the temperature difference sequence is determined based on the temperature sampling data, the outlier sensor can be determined in the temperature sensor, and the corresponding abnormal recognition result can be determined by the proportion ratio of the outlier sensor, thereby providing an abnormal recognition method that does not require the acquisition of a large amount of failure sample data and does not require model training. It can specifically identify potential temperature sampling failure modes that have no obvious characteristics, and provide data support and theoretical basis for timely and effective abnormal warnings, reserving response time for spare parts and after-sales processing in advance to avoid adverse effects on user use.

[0017] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0019] Figure 1 A schematic diagram of a process flow of a battery sampling anomaly identification method in an exemplary embodiment of the present disclosure is schematically shown; Figure 2 The flowchart of the method for determining the duration of a temperature difference sequence in an exemplary embodiment of the present disclosure is schematically shown; Figure 3 A schematic diagram of a flow chart of a method for determining an outlier sensor in an exemplary embodiment of the present disclosure is schematically shown; Figure 4 A schematic diagram of a flow chart of a method for determining outliers of a temperature sensor in an exemplary embodiment of the present disclosure is schematically shown; Figure 5 A schematic flowchart of another method for determining outliers of a temperature sensor in an exemplary embodiment of the present disclosure is schematically shown; Figure 6 A schematic diagram of a flow chart of a method for determining a proportion ratio value in an exemplary embodiment of the present disclosure is schematically shown; Figure 7 A schematic diagram showing a framework structure of a battery sampling anomaly identification method in an application scenario in an exemplary embodiment of the present disclosure; Figure 8 A schematic diagram schematically illustrates a flow chart of a method for identifying abnormality in battery sampling in an application scenario in an exemplary embodiment of the present disclosure; Fig. 9 A schematic diagram schematically illustrates the effect of the abnormality identification method of battery sampling in an application scenario in an exemplary embodiment of the present disclosure; Fig.10 A schematic diagram schematically shows the structure of a battery sampling abnormality identification device in an exemplary embodiment of the present disclosure; Fig.11 A schematic diagram schematically shows the structure of another abnormality identification device for battery sampling in an exemplary embodiment of the present disclosure; Fig.12 A schematic diagram of the structure of another abnormality identification device for battery sampling in an exemplary embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0020] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0021] It should be noted that all actions of acquiring signals, information or data in the present disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the device is located and with the authorization given by the owner of the corresponding device.

[0022] With the widespread promotion of battery applications such as electric vehicles and energy storage systems, the importance of battery management systems has become increasingly prominent. Battery management systems need to monitor and control battery status in real time. Among them, battery temperature is an important monitoring indicator.

[0023] However, since battery temperature is affected by many factors, such as ambient temperature, battery charge and discharge status, battery internal structure, aging status, etc., battery temperature sampling data may be abnormal. If these abnormal data are not identified and processed in time, it may have a negative impact on the performance and safety of the battery management system, such as failure to perform safety protection actions or thermal management errors.

[0024] Therefore, the identification of abnormal battery temperature sampling becomes an important issue in battery management systems.

[0025] In electric vehicles, multiple temperature sampling NTCs are usually used to monitor the temperature of the entire battery pack. Temperature sampling NTC refers to a temperature sensor used in the battery pack. It is a negative temperature coefficient thermistor, that is, the resistance value of NTC decreases as the temperature increases. In the battery pack, NTC is usually placed on the surface or inside of the battery module or battery cell to measure the temperature of the battery.

[0026] The commonly used method for identifying temperature sampling anomalies is to judge through threshold and rate methods. For example, when the temperature value exceeds or falls below a certain threshold, or the temperature difference between different temperature sampling points is greater than a certain threshold, or the temperature rise rate exceeds a certain threshold, an alarm is issued, and then further manual review is performed to confirm whether it is a sampling problem or the actual temperature situation.

[0027] However, this method can only identify some relatively obvious abnormal problems, such as temperature jumps caused by open circuit, short circuit, water ingress, etc., as well as over-range, abnormal rate, etc. As for some potential failure modes, such as poor NTC thermal conductivity due to assembly or aging, these failure modes usually do not have obvious phenomena such as over-range, large temperature difference, abnormal temperature rise rate, etc., so they cannot be identified.

[0028] In the related art, a method for handling abnormal battery temperature acquisition is provided. The method determines whether the temperature sampling is abnormal and adopts different power limiting strategies. The abnormal temperature is determined by whether the temperature exceeds the threshold (sampling disconnection).

[0029] However, this solution can only identify obvious temperature anomalies, such as temperature jumps caused by open circuit, short circuit, water ingress, etc., as well as over-range, rate anomalies, etc. It cannot identify some potential failure modes, such as poor NTC thermal conductivity due to assembly or aging.

[0030] In addition, a temperature sampling anomaly identification method is also provided. This method uses a machine learning method to identify battery temperature anomalies. It is necessary to train on abnormal samples to obtain a logistic regression equation, and then apply the logistic regression equation to new samples to identify whether there is an anomaly.

[0031] This solution requires a large number of failure samples for training. Often, due to the lack of failure samples in the early stage of vehicle launch, this method cannot achieve the expected effect.

[0032] In view of the problems existing in the related art, the present disclosure provides a method for identifying abnormality of battery sampling. Figure 1 is a flow chart of a method for identifying abnormality in battery sampling according to an exemplary embodiment. Figure 1 As shown, the method may at least include the following steps: Step S110: Acquire temperature sampling data of the battery, and determine the duration of the temperature difference sequence according to the temperature sampling data, wherein the battery is sampled by a temperature sensor.

[0033] Step S120: Based on the duration of the temperature difference sequence, outlier sensors are determined in the temperature sensors, and a ratio of the outlier sensors is determined.

[0034] Step S130: Determine the abnormal identification result of the outlier sensor according to the proportion ratio.

[0035] In an exemplary embodiment of the present disclosure, when the duration of the temperature difference sequence is determined based on the temperature sampling data, outlier sensors can be determined among the temperature sensors, and the corresponding abnormality identification results can be determined by the proportion ratio of the outlier sensors. This provides an abnormality identification method that does not require the acquisition of a large amount of failure sample data and does not require model training. It can specifically identify potential temperature sampling failure modes that are not obviously characterized, and provide data support and theoretical basis for timely and effective abnormality warnings, reserving response time for spare parts and after-sales processing in advance to avoid adverse effects on user use.

[0036] The following is a detailed description of each step of the abnormality identification method for battery sampling.

[0037] In step S110, temperature sampling data of the battery is acquired, and the duration of the temperature difference sequence is determined according to the temperature sampling data, wherein the battery is sampled by a temperature sensor.

[0038] In an exemplary embodiment of the present disclosure, the temperature sensor may be a sensor that performs sampling by contacting the battery, such as an NTC, a thermocouple sensor, a semiconductor-based sensor, and other temperature sensors, which is not particularly limited in the present exemplary embodiment.

[0039] For example, multiple NTCs are usually deployed in the battery as temperature sampling points to detect the battery's temperature data in real time. The battery management system can record all NTC sampled temperature data and upload the temperature data to the cloud.

[0040] Furthermore, the cloud selects the temperature data of the vehicle battery in the past N days, such as 1 day or 3 days, as the temperature sampling data, wherein N can also be adjusted according to actual conditions and needs, and this exemplary embodiment does not make any special limitation on this.

[0041] In an optional embodiment, the temperature sampling data includes first temperature data, average temperature data and second temperature data. Figure 2 A flow chart of a method for determining the duration of a temperature difference sequence is shown, such as Figure 2 As shown, the method may at least include the following steps: in step S210, a first temperature difference is determined according to the first temperature data and the second temperature data, and a second temperature difference is determined according to the average temperature data and the first temperature data.

[0042] Among them, the first temperature data can be the minimum temperature sampling data of all NTCs of the vehicle at a certain moment, the second temperature data can be the maximum temperature sampling data of all NTCs of the vehicle at a certain moment, and the average temperature data can be the average temperature sampling data of all NTCs of the vehicle at a certain moment.

[0043] According to difftemp1=temp_max- temp_min, the temperature difference between the maximum temperature sampling data and the minimum temperature sampling data of the vehicle at each moment can be calculated as the first temperature difference; according to difftemp2=temp_avg-temp_min, the temperature difference between the average temperature sampling data and the minimum temperature sampling data of the vehicle at each moment can be calculated as the second temperature difference. Among them, temp_max is the second temperature data, that is, the maximum temperature sampling data of all NTCs of the vehicle at a certain moment; temp_min is the first temperature data, that is, the minimum temperature sampling data of all NTCs of the vehicle at a certain moment; temp_avg is the average temperature data, that is, the average temperature sampling data of all NTCs of the vehicle at a certain moment.

[0044] In step S220, the duration that the first temperature difference is greater than the first threshold and the second temperature difference is greater than the second threshold is counted, and the duration greater than the third threshold is determined as the temperature difference sequence duration.

[0045] For any vehicle, when the first temperature difference difftemp1>dt1, and the second temperature difference difftemp2>dt2, and the duration of this condition>t, the duration can be determined as the temperature difference sequence duration. Wherein, dt1 represents the first threshold, dt2 represents the second threshold, and t represents the third threshold. dt1, dt2 and t can be determined according to the specific temperature field distribution of the vehicle model, and this exemplary embodiment does not make special restrictions on this. Generally, dt1 can be determined as 6°C, dt2 can be determined as 3°C, and t can be determined as 30min.

[0046] It is worth noting that when the vehicle does not meet any of the conditions that the first temperature difference is greater than the first threshold, the second temperature difference is greater than the duration of the second threshold, and the duration is greater than the third threshold, it indicates that there is no abnormality in the vehicle and there is no need to determine the duration of the temperature difference sequence.

[0047] In step S230, a first temperature difference is determined according to the first temperature data and the second temperature data, and a third temperature difference is determined according to the average temperature data and the second temperature data.

[0048] Among them, the first temperature data can be the minimum temperature sampling data of all NTCs of the vehicle at a certain moment, the second temperature data can be the maximum temperature sampling data of all NTCs of the vehicle at a certain moment, and the average temperature data can be the average temperature sampling data of all NTCs of the vehicle at a certain moment.

[0049] According to difftemp1=temp_max-temp_min, the temperature difference between the maximum temperature sampling data and the minimum temperature sampling data of the vehicle at each moment can be calculated as the first temperature difference; according to difftemp3=temp_max-temp_avg, the temperature difference between the maximum temperature sampling data and the average temperature sampling data of the vehicle at each moment can be calculated as the third temperature difference. Among them, temp_max is the second temperature data, that is, the maximum temperature sampling data of all NTCs of the vehicle at a certain moment; temp_min is the first temperature data, that is, the minimum temperature sampling data of all NTCs of the vehicle at a certain moment; temp_avg is the average temperature data, that is, the average temperature sampling data of all NTCs of the vehicle at a certain moment.

[0050] In step S240, the duration during which the first temperature difference is greater than the first threshold and the third temperature difference is greater than the second threshold is counted, and the duration greater than the third threshold is determined as the temperature difference sequence duration.

[0051] For any vehicle, when the first temperature difference difftemp1>dt1, and the third temperature difference difftemp3>dt2, and the duration of this condition>t, the duration can be determined as the temperature difference sequence duration. Wherein, dt1 represents the first threshold, dt2 represents the second threshold, and t represents the third threshold. dt1, dt2 and t can be determined according to the specific temperature field distribution of the vehicle model, and this exemplary embodiment does not make special restrictions on this. Generally, dt1 can be determined as 6°C, dt2 can be determined as 3°C, and t can be determined as 30min.

[0052] It is worth noting that when the vehicle does not meet any of the conditions that the first temperature difference is greater than the first threshold, the duration of the third temperature difference is greater than the second threshold, and the duration is greater than the third threshold, it indicates that there is no abnormality in the vehicle and there is no need to determine the duration of the temperature difference sequence.

[0053] In step S120 , outlier sensors are identified among the temperature sensors based on the duration of the temperature difference sequence, and a ratio of the outlier sensors is determined.

[0054] In an exemplary embodiment of the present disclosure, after determining the duration of the temperature difference sequence, outlier sensors may be further determined among the temperature sensors.

[0055] In an alternative embodiment, Figure 3 A flow chart of a method for determining an outlier sensor is shown, Figure 3 As shown, the method may at least include the following steps: In step S310, a quantile value is determined according to the temperature sampling data, and a quantile range of the temperature sampling data is determined according to the quantile value.

[0056] Among them, the quantile values include the upper quartile value (Q3) and the lower quartile value (Q1). Specifically, after arranging the temperature sampling data in ascending order, the value at the 75% position is used as the upper quartile value, and the value at the 25% position is used as the lower quartile value.

[0057] After determining the quantile values, the interquartile range IQR can be calculated in the way of IQR = Q3 - Q1.

[0058] In step S320, based on the temperature sampling data, the quantile values, and the interquartile range, the outliers of the temperature sensor are determined, and the first number of the outliers is statistically counted within the duration of the temperature difference sequence.

[0059] In an alternative embodiment, Figure 4 shows a schematic flowchart of a method for determining the outliers of a temperature sensor, as Figure 4 shown. The method may at least include the following steps: In step S410, the fifth threshold is calculated from the quantile values and the interquartile range.

[0060] Specifically, the fifth threshold is calculated in the way of Q1 - 1.5 * IQR using the lower quartile value and the interquartile range.

[0061] In step S420, when the temperature sampling data is less than the fifth threshold, the sampling point of the temperature sampling data is determined as the outlier of the temperature sensor.

[0062] When the temperature sampling data of any NTC satisfies t < Q1 - 1.5 * IQR, the sampling point of the temperature sampling data is determined as an outlier of the temperature sensor.

[0063] In an alternative embodiment, Figure 5 shows a schematic flowchart of another method for determining the outliers of a temperature sensor, as Figure 5 shown. The method may at least include the following steps: In step S510, the sixth threshold is calculated from the quantile values and the interquartile range.

[0064] Specifically, the sixth threshold is calculated in the way of Q3 + 1.5 * IQR using the upper quartile value and the interquartile range.

[0065] In step S520, when the temperature sampling data is greater than the sixth threshold, the sampling point of the temperature sampling data is determined as the outlier of the temperature sensor.

[0066] When the temperature sampling data of any NTC satisfies t > Q3 + 1.5 * IQR, the sampling point of the temperature sampling data is determined as an outlier of the temperature sensor.

[0067] Furthermore, the number of outliers of all temperature sensors within the duration of the temperature difference sequence is summarized as the first number.

[0068] In step S330 , when the first number is greater than a fourth threshold, it is determined that the temperature sensor is an outlier sensor.

[0069] After the first number is counted, the first number can be compared with the corresponding fourth threshold value. The fourth threshold value can be obtained by the total number of points sampled in the temperature difference sequence duration*0.9. The value of 0.9 can be set according to actual conditions, and this exemplary embodiment does not make any special limitation on this.

[0070] When the comparison result is that the first number is greater than the fourth threshold, the temperature sensor may be determined as an outlier sensor.

[0071] In an alternative embodiment, Figure 6 A flow chart of a method for determining a proportion ratio is shown, such as Figure 6 As shown, the method may at least include the following steps: In step S610, the number of outliers of the outlier sensors is counted.

[0072] After the outlier sensors are determined, the number of outlier sensors may be used as the number of outliers, and recorded as k.

[0073] In step S620, when the number of outliers is less than or equal to the seventh threshold, the number of sampling points of the outlier sensor within the duration of the temperature difference sequence and the second number corresponding to the outlier point are counted.

[0074] After determining the number of outliers k, the number of outliers can be compared with the corresponding seventh threshold. The seventh threshold is selected based on the total number of NTCs in the actual battery pack, and is generally not greater than the total number of NTCs / 10, indicating that the number of failed NTCs is usually within a certain range. Therefore, the seventh threshold can be set to the total number of NTCs / 10, or can be set and adjusted according to actual conditions, and this exemplary embodiment does not make any special restrictions on this.

[0075] When the comparison result is that the number of outliers is less than or equal to the seventh threshold, the number of sampling points of the outlier sensor sum(T) within the temperature difference sequence duration and the second number sum(Ti) corresponding to the outlier point are counted.

[0076] Among them, sum(T) is the cumulative sum of all time segments that meet the previous conditions, that is, the total number of points sampled within the temperature difference sequence duration; sum(Ti) is the cumulative sum of time segments in which the i-th NTC is the lowest temperature monomer in all time segments that meet the previous conditions in one case, such as battery heating, that is, on the basis of the first number, the number of time segments that also meet the condition that the outlier NTC is the lowest temperature monomer, or in another case, such as battery cooling, the cumulative sum of time segments in which the i-th NTC is the highest temperature monomer in all time segments that meet the previous conditions, that is, on the basis of the first number, the number of time segments that also meet the condition that the outlier NTC is the highest temperature monomer.

[0077] In step S630, the proportion of outlier sensors is determined according to the number of sampling points and the second number.

[0078] After determining the number of sampling points and the second number, you can follow Calculate the lowest sequence number ratio K by i As the ratio of the i-th NTC.

[0079] In step S130, the abnormality identification result of the outlier sensor is determined according to the proportion ratio.

[0080] In an exemplary embodiment of the present disclosure, after the proportion ratio value is determined, the abnormality identification result of the outlier sensor may be determined according to the proportion ratio value.

[0081] In an optional embodiment, when the proportion ratio is greater than an eighth threshold, it is determined that the abnormal identification result of the outlier sensor is a sampling abnormality, and an abnormal warning is issued for the outlier sensor according to the abnormal identification result.

[0082] After determining the proportion ratio, the proportion ratio can be compared with the corresponding eighth threshold value. The eighth threshold value is denoted as R, which is generally related to the number of NTC outliers and can be set according to actual conditions. For example, the eighth threshold value can be set to 90% / k, where k is the number of NTC outliers.

[0083] When the comparison result is that the proportion ratio is greater than the eighth threshold, it indicates that the outlier sensor is identified as abnormal, and the number of the NTC can be recorded at the same time to issue an abnormal warning.

[0084] The abnormality identification method of battery sampling in the embodiment of the present disclosure is described in detail below in conjunction with an application scenario.

[0085] Figure 7 The framework structure diagram of the abnormality identification method of battery sampling in the application scenario is shown as follows: Figure 7 As shown, in part of the cloud-based electric vehicle high-voltage system fault warning function, the warning object is the battery temperature acquisition module.

[0086] Among them, the battery provides energy input for the entire external load to provide the power performance of the whole vehicle. Multiple temperature sampling points are usually deployed in the battery. The battery management system can detect the temperature value of the battery in real time through the temperature acquisition and processing module, and forward it to the Internet of Vehicles system through the vehicle controller, and finally upload it to the cloud through the network protocol. The cloud can calculate the collected data and generate early warning information to be passed to the after-sales department for processing.

[0087] Figure 8 A flow chart of an abnormality identification method for battery sampling in an application scenario is shown, such as Figure 8 As shown, in step S801, the BMS records all NTC sampled temperature data and uploads them to the cloud for storage.

[0088] The battery management system can record all temperature data sampled by the NTC and upload the temperature data to the cloud.

[0089] In step S802 , the cloud selects the temperature data of all vehicles in the past N days.

[0090] The cloud selects the temperature data of the vehicle battery in the past N days, such as 1 day or 3 days, as the temperature sampling data. Wherein, N can also be adjusted according to actual conditions and needs, and this exemplary embodiment does not make any special limitation on this.

[0091] In step S803, temperature difference series data is calculated for each vehicle.

[0092] In the case of battery heating, some NTCs have low temperatures due to abnormal heat transfer, outliers from other normal NTCs.

[0093] Therefore, when calculating the temperature difference series data, according to difftemp1=temp_max-temp_min, the temperature difference between the maximum temperature sampling data and the minimum temperature sampling data of the vehicle can be calculated as the first temperature difference at each moment; according to difftemp2=temp_avg-temp_min, the temperature difference between the average temperature sampling data and the minimum temperature sampling data of the vehicle can be calculated as the second temperature difference at each moment.

[0094] When the battery is cooled, some NTCs have high temperatures due to abnormal heat transfer, outliers from other normal NTCs.

[0095] Therefore, when calculating the temperature difference series data, according to difftemp1 = temp_max - temp_min, the temperature difference between the maximum temperature sampling data and the minimum temperature sampling data of the vehicle can be calculated at each moment as the first temperature difference; according to difftemp3 = temp_max - temp_avg, the temperature difference between the maximum temperature sampling data and the average temperature sampling data of the vehicle can be calculated at each moment as the third temperature difference.

[0096] In step S804, determine the temperature difference series data that meet the conditions.

[0097] In the case of battery heating, when the first temperature difference difftemp1 > dt1, the second temperature difference difftemp2 > dt2, and the duration of this condition > t, the duration of this persistence can be determined as the temperature difference sequence duration.

[0098] In the case of battery cooling, when the first temperature difference difftemp1 > dt1, the third temperature difference difftemp3 > dt2, and the duration of this condition > t, the duration of this persistence can be determined as the temperature difference sequence duration.

[0099] In step S805, calculate the number of temperature outliers in these segments.

[0100] After determining the temperature difference sequence duration, the outlier resistor can be further determined in the thermistor.

[0101] Specifically, the judgment method of IQR (interquartile range) can be adopted. Determine the quantile value according to the temperature sampling data, and determine the interquartile range of the temperature sampling data according to the quantile value. Among them, the quantile value includes the upper 1 / 4 quantile value (Q3) and the lower 1 / 4 quantile value (Q1).

[0102] In the case of battery heating, calculate the fifth threshold value in the way of Q1 - 1.5 * IQR with the lower 1 / 4 quantile value and the interquartile range. When the temperature sampling data of any NTC satisfies t < Q1 - 1.5 * IQR, determine the sampling point of this temperature sampling data as an outlier point of the thermistor.

[0103] In the case of battery cooling, calculate the sixth threshold value in the way of Q3 + 1.5 * IQR with the upper 1 / 4 quantile value and the interquartile range. When the temperature sampling data of any NTC satisfies t > Q3 + 1.5 * IQR, determine the sampling point of this temperature sampling data as an outlier point of the thermistor.

[0104] Further, the number of outliers of all thermistors within the temperature difference sequence duration is summarized as the first number. When the first number is greater than a fourth threshold, the thermistor is determined to be an outlier resistor.

[0105] After the outlier resistors are determined, the number of outliers may be taken as the number of outliers and recorded as k.

[0106] In step S806, it is determined whether the number of temperature outliers is less than or equal to N.

[0107] After determining the number of outliers k, the number of outliers may be compared with the corresponding seventh threshold to determine whether the number of outliers is less than or equal to the seventh threshold N.

[0108] In step S807, the sequence number ratio is calculated.

[0109] When the comparison result is that the number of outliers is less than or equal to the seventh threshold, the number of sampling points of outlier resistors sum(T) within the temperature difference sequence duration and a second number sum(Ti) corresponding to the outliers are counted.

[0110] Among them, sum(T) is the cumulative sum of all time segments that meet the previous conditions, that is, the total number of points sampled within the temperature difference sequence duration; sum(Ti) is the cumulative sum of time segments in which the i-th NTC is the lowest temperature monomer in all time segments that meet the previous conditions in one case, such as battery heating, that is, on the basis of the first number, the number of time segments that also meet the condition that the outlier NTC is the lowest temperature monomer, or in another case, such as battery cooling, the cumulative sum of time segments in which the i-th NTC is the highest temperature monomer in all time segments that meet the previous conditions, that is, on the basis of the first number, the number of time segments that also meet the condition that the outlier NTC is the highest temperature monomer.

[0111] After determining the number of sampling points and the second number, you can follow Calculate the lowest sequence number ratio K by i As the ratio of the i-th NTC.

[0112] In step S808, it is determined whether the sequence number ratio>R.

[0113] After the proportion ratio value is determined, the proportion ratio value may be compared with the corresponding eighth threshold R.

[0114] In step S809, an early warning is issued to the vehicle.

[0115] When the comparison result is that the proportion value is greater than the eighth threshold value, it indicates that the outlier resistor is identified as abnormal, and the number of the NTC can be recorded at the same time to issue an abnormal warning.

[0116] In step S810, no warning is required.

[0117] When the vehicle does not meet any of the conditions that the first temperature difference is greater than the first threshold, the duration of the third temperature difference is greater than the second threshold, and the duration is greater than the third threshold, it indicates that there is no abnormality in the vehicle.

[0118] When the number of outliers is greater than the seventh threshold, it indicates that the vehicle has no abnormality.

[0119] When the comparison result is that the proportion ratio is less than or equal to the eighth threshold, it indicates that there is no abnormality with the vehicle and no warning is needed.

[0120] In step S811, T=T+1, the sliding window is used to perform early warning calculation every day.

[0121] Steps S802 to S809 or step S810 may be repeated every day to identify and warn whether the battery sampling is abnormal.

[0122] Fig. 9 The schematic diagram shows the effect of the abnormal identification method of battery sampling in the application scenario, such as Fig. 9 As shown in the figure, due to abnormal assembly of thermal conductive adhesive, the No. 11 single NTC has been in a lower state than other NTCs when the thermal management is turned on (cooling), but the overall temperature difference is not large (<10 degrees), and the temperature does not exceed the range.

[0123] Therefore, the abnormality cannot be identified by traditional methods. The abnormality identification method of battery sampling in this application scenario can accurately identify the abnormal cell (the minimum serial number ratio is 100%).

[0124] In an exemplary embodiment of the present disclosure, when the duration of the temperature difference sequence is determined based on the temperature sampling data, the outlier sensor can be determined in the temperature sensor, and the corresponding abnormal identification result can be determined by the proportion of the outlier sensor, providing an abnormal identification method that does not require the acquisition of a large amount of failure sample data and does not require model training, and can specifically identify potential temperature sampling failure modes that are not obviously characterized. In addition, it can effectively provide early warning (especially for those caused by aging), and the advance time is generally at the monthly / weekly level, providing data support and theoretical basis for timely and effective abnormal warnings, and reserving response time for spare parts and after-sales processing in advance to avoid adverse effects on user use.

[0125] In addition, in an exemplary embodiment of the present disclosure, a battery sampling abnormality identification device is also provided. Fig.10 The schematic diagram of the structure of the abnormality identification device for battery sampling is shown as follows: Fig.10As shown, the abnormality identification device 1000 for battery sampling may include: a data acquisition module 1010, an outlier determination module 1020 and an abnormality identification module 1030. Among them: The data acquisition module 1010 is configured to acquire temperature sampling data of the battery and determine the duration of the temperature difference sequence according to the temperature sampling data, wherein the battery is sampled by a temperature sensor; An outlier determination module 1020 is configured to determine outlier sensors among the temperature sensors based on the duration of the temperature difference sequence, and determine a proportion of the outlier sensors; The abnormality identification module 1030 is configured to determine an abnormality identification result of the outlier sensor according to the proportion ratio.

[0126] In some embodiments of the present disclosure, the temperature sampling data includes first temperature data, average temperature data, and second temperature data. The data acquisition module 1010 is configured to: determining a first temperature difference according to the first temperature data and the second temperature data, and determining a second temperature difference according to the average temperature data and the first temperature data; The duration during which the first temperature difference is greater than a first threshold and the second temperature difference is greater than a second threshold is counted, and the duration during which the temperature difference is greater than a third threshold is determined as the temperature difference sequence duration.

[0127] In some embodiments of the present disclosure, the temperature sampling data includes first temperature data, average temperature data, and second temperature data. The data acquisition module 1010 is configured to: determining a first temperature difference according to the first temperature data and the second temperature data, and determining a third temperature difference according to the average temperature data and the second temperature data; The duration during which the first temperature difference is greater than a first threshold and the third temperature difference is greater than a second threshold is counted, and the duration during which the temperature difference is greater than the third threshold is determined as the temperature difference sequence duration.

[0128] In some embodiments of the present disclosure, the outlier determination module 1020 is configured to: Determine a quantile value according to the temperature sampling data, and determine a quantile range of the temperature sampling data according to the quantile value; Determine the outlier of the temperature sensor according to the temperature sampling data, the quantile value and the quantile range, and count the first number of the outlier within the duration of the temperature difference sequence; When the first number is greater than a fourth threshold, it is determined that the temperature sensor is an outlier sensor.

[0129] In some embodiments of the present disclosure, the outlier determination module 1020 is configured to: Calculating the quantile value and the quantile range to obtain a fifth threshold; When the temperature sampling data is less than the fifth threshold, the sampling point of the temperature sampling data is determined to be an outlier of the temperature sensor.

[0130] In some embodiments of the present disclosure, the outlier determination module 1020 is configured to: Calculating the quantile value and the quantile range to obtain a sixth threshold value; When the temperature sampling data is greater than the sixth threshold, the sampling point of the temperature sampling data is determined to be an outlier of the temperature sensor.

[0131] In some embodiments of the present disclosure, the outlier determination module 1020 is configured to: Counting the number of outliers of the outlier sensors; When the number of outliers is less than or equal to a seventh threshold, counting the number of sampling points of the outlier sensor within the duration of the temperature difference sequence and a second number corresponding to the outlier point; The proportion of the outlier sensors is determined according to the number of sampling points and the second number.

[0132] In some embodiments of the present disclosure, the abnormality identification module 1030 is configured to: When the proportion ratio is greater than an eighth threshold, it is determined that the abnormal identification result of the outlier sensor is a sampling abnormality, and an abnormal warning is issued to the outlier sensor according to the abnormal identification result.

[0133] 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.

[0134] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon, and when the program instructions are executed by a processor, the steps of the abnormality identification method for battery sampling provided by the present disclosure are implemented.

[0135] Fig.11 1 is a block diagram of another abnormality identification device 1100 for battery sampling according to an exemplary embodiment. For example, the device 1100 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0136] Reference Fig.11, the device 1100 may include one or more of the following components: a processing component 1102 , a memory 1104 , a power component 1106 , a multimedia component 1108 , an audio component 1110 , an input / output interface 1112 , a sensor component 1114 , and a communication component 1116 .

[0137] The processing component 1102 generally controls the overall operation of the device 1100, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 1102 may include one or more processors 1120 to execute instructions to complete all or part of the steps of the above method. In addition, the processing component 1102 may include one or more modules to facilitate the interaction between the processing component 1102 and other components. For example, the processing component 1102 may include a multimedia module to facilitate the interaction between the multimedia component 1108 and the processing component 1102.

[0138] The memory 1104 is configured to store various types of data to support the operation of the device 1100. Examples of such data include instructions for any application or method operating on the device 1100, contact data, phone book data, messages, pictures, videos, etc. The memory 1104 can be implemented by any type of volatile or non-volatile storage device or a 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.

[0139] The power supply component 1106 provides power to the various components of the device 1100. The power supply component 1106 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device 1100.

[0140] The multimedia component 1108 includes a screen that provides an output interface between the device 1100 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 1108 includes a front camera and / or a rear camera. When the device 1100 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0141] The audio component 1110 is configured to output and / or input audio signals. For example, the audio component 1110 includes a microphone (MIC), and when the device 1100 is in an operation mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 1104 or sent via the communication component 1116. In some embodiments, the audio component 1110 also includes a speaker for outputting audio signals.

[0142] The input / output interface 1112 provides an interface between the processing component 1102 and the peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0143] The sensor assembly 1114 includes one or more sensors for providing various aspects of the status assessment of the device 1100. For example, the sensor assembly 1114 can detect the open / closed state of the device 1100, the relative positioning of components, such as the display and keypad of the device 1100, the sensor assembly 1114 can also detect the position change of the device 1100 or a component of the device 1100, the presence or absence of user contact with the device 1100, the orientation or acceleration / deceleration of the device 1100, and the temperature change of the device 1100. The sensor assembly 1114 can include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 1114 can also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 1114 can also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0144] The communication component 1116 is configured to facilitate wired or wireless communication between the device 1100 and other devices. The device 1100 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 1116 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 1116 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0145] In an exemplary embodiment, the apparatus 1100 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 to perform the above methods.

[0146] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 1104 including instructions, and the instructions can be executed by a processor 1120 of the device 1100 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0147] In addition to being an independent electronic device, the above-mentioned device can also be a part of an independent electronic device. For example, in one embodiment, the device can be an integrated circuit (IC) or a chip, wherein the integrated circuit can be an IC or a collection of multiple ICs; the chip can include but is not limited to the following types: GPU (Graphics Processing Unit), CPU (Central Processing Unit), FPGA (Field Programmable Gate Array), DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), SOC (System on Chip, SoC, system on chip or system-level chip), etc. The above-mentioned integrated circuit or chip can be used to execute executable instructions (or codes) to implement the above-mentioned abnormality identification method of battery sampling. The executable instructions can be stored in the integrated circuit or chip, or can be obtained from other devices or equipment, for example, the integrated circuit or chip includes a processor, a memory, and an interface for communicating with other devices. The executable instruction may be stored in the memory, and when the executable instruction is executed by the processor, the above-mentioned battery sampling abnormality identification method is implemented; alternatively, the integrated circuit or chip may receive the executable instruction through the interface and transmit it to the processor for execution, so as to implement the above-mentioned method.

[0148] In another exemplary embodiment, a computer program product is also provided. The computer program product includes a computer program executable by a programmable device. The computer program has a code portion for executing the above-mentioned abnormality identification method for battery sampling when executed by the programmable device.

[0149] Fig.12 FIG. 1 is a block diagram of another abnormality identification device 1200 for battery sampling according to an exemplary embodiment. For example, the device 1200 may be provided as a server. Fig.12 The device 1200 includes a processing component 1222, which further includes one or more processors, and a memory resource represented by a memory 1232 for storing instructions executable by the processing component 1222, such as an application. The application stored in the memory 1232 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1222 is configured to execute instructions to perform the above-mentioned battery sampling abnormality identification method.

[0150] The device 1200 may also include a power supply component 1226 configured to perform power management of the device 1200, a wired or wireless network interface 1250 configured to connect the device 1200 to a network, and an input / output interface 1258. The device 1200 may operate based on an operating system stored in the memory 1232.

[0151] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the present disclosure. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0152] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A battery sampling anomaly identification method, characterized in that: include: Acquire temperature sampling data of a battery, and determine a temperature difference sequence duration according to the temperature sampling data, wherein the battery is sampled by a temperature sensor; Based on the duration of the temperature difference sequence, determining an outlier sensor among the temperature sensors, and determining a proportion ratio of the outlier sensor; The abnormality identification result of the outlier sensor is determined according to the proportion ratio.

2. The battery sampling anomaly identification method according to claim 1, characterized in that: The temperature sampling data includes first temperature data, average temperature data and second temperature data. Determining the temperature difference sequence duration according to the temperature sampling data includes: determining a first temperature difference according to the first temperature data and the second temperature data, and determining a second temperature difference according to the average temperature data and the first temperature data; Counting the duration that the first temperature difference is greater than a first threshold and the second temperature difference is greater than a second threshold, and determining the duration greater than a third threshold as the temperature difference sequence duration; or determining a first temperature difference according to the first temperature data and the second temperature data, and determining a third temperature difference according to the average temperature data and the second temperature data; The duration during which the first temperature difference is greater than a first threshold and the third temperature difference is greater than a second threshold is counted, and the duration during which the temperature difference is greater than the third threshold is determined as the temperature difference sequence duration.

3. The battery sampling abnormality identification method according to claim 1, characterized in that: The step of determining an outlier sensor in the temperature sensor based on the duration of the temperature difference sequence includes: Determine a quantile value according to the temperature sampling data, and determine a quantile range of the temperature sampling data according to the quantile value; Determine the outlier of the temperature sensor according to the temperature sampling data, the quantile value and the quantile range, and count the first number of the outlier within the duration of the temperature difference sequence; When the first number is greater than a fourth threshold, it is determined that the temperature sensor is an outlier sensor.

4. The battery sampling anomaly identification method according to claim 3, characterized in that: The determining of the outlier point of the temperature sensor according to the temperature sampling data, the quantile value and the quantile range includes: Calculating the quantile value and the quantile range to obtain a fifth threshold; When the temperature sampling data is less than the fifth threshold, the sampling point of the temperature sampling data is determined to be an outlier of the temperature sensor.

5. The battery sampling anomaly identification method according to claim 3, characterized in that: The determining of the outlier point of the temperature sensor according to the temperature sampling data, the quantile value and the quantile range includes: Calculating the quantile value and the quantile range to obtain a sixth threshold; When the temperature sampling data is greater than the sixth threshold, the sampling point of the temperature sampling data is determined to be an outlier of the temperature sensor.

6. The battery sampling anomaly identification method according to claim 3, characterized in that: The determining the proportion of the outlier sensors includes: Counting the number of outliers of the outlier sensors; When the number of outliers is less than or equal to a seventh threshold, counting the number of sampling points of the outlier sensor within the duration of the temperature difference sequence and a second number corresponding to the outlier point; The proportion of the outlier sensors is determined according to the number of sampling points and the second number.

7. The battery sampling anomaly identification method according to claim 1, characterized in that: The determining the abnormality identification result of the outlier sensor according to the proportion ratio includes: When the proportion ratio is greater than an eighth threshold, it is determined that the abnormal identification result of the outlier sensor is a sampling abnormality, and an abnormal warning is issued to the outlier sensor according to the abnormal identification result.

8. A battery sampling abnormality identification device, characterized in that: include: A data acquisition module is configured to acquire temperature sampling data of a battery and determine a temperature difference sequence duration according to the temperature sampling data, wherein the battery is sampled by a temperature sensor; an outlier determination module, configured to determine outlier sensors among the temperature sensors based on the duration of the temperature difference sequence, and determine a proportion ratio of the outlier sensors; The abnormality identification module is configured to determine an abnormality identification result of the outlier sensor according to the proportion ratio.

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

10. 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 7.

Images