Battery NTC anomaly detection method and device, vehicle, electronic equipment and storage medium

Abstract

The invention discloses a battery NTC anomaly detection method and device, a vehicle, electronic equipment and a storage medium, and the method comprises the steps: obtaining NTC temperature data of a power battery pack, the NTC temperature data comprising a temperature sequence of each NTC in a target sampling period; calculating a temperature change rate set of each NTC in the target sampling period by using the NTC temperature data; performing uncertainty statistics based on each temperature change rate set to obtain statistical data corresponding to the temperature change rate set; and if target statistical data greater than an abnormal threshold exists in the plurality of statistical data, determining the NTC corresponding to the target statistical data as an abnormal NTC. According to the method, after the temperature change rate set of the single NTC is obtained through calculation according to the NTC temperature data, the statistical data of the uncertainty statistics corresponding to the temperature change rate set is calculated, NTC temperature values do not need to be grouped, the abnormal recognition result of the statistical data of the NTC uncertainty statistics is used for representing the detection result of the NTC abnormality, and the accuracy of battery NTC abnormality detection is improved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of vehicles, and particularly relates to a battery NTC abnormality detection method and device, a vehicle, an electronic device and a storage medium. BACKGROUND

[0002] In a vehicle battery, a negative temperature coefficient thermistor (NTC) is a kind of sensor resistor whose resistance value decreases with temperature increase. It is usually placed inside the battery to monitor the working state of the battery. Since the resistance value of the NTC is negatively related to the temperature, when there is a virtual connection abnormality in the NTC in the battery pack, the NTC with the virtual connection abnormality will show a large internal resistance and a small temperature. When there is a resistance value abnormality in the NTC, the NTC with the resistance value abnormality will show an abnormally high temperature compared with other normal NTCs in the battery pack. When any NTC in the battery pack has a temperature abnormality, it will cause a temperature abnormality of the battery pack, thereby reducing the working performance of the battery pack and even causing a safety hazard.

[0003] In order to solve the above problem, the NTC abnormality detection method adopted is to report the temperature values of all NTCs in the battery pack in real time, divide all NTCs into several groups according to the temperature value changes of the NTCs under different working conditions, and the temperature difference between different groups is relatively large, and the temperature difference between the maximum temperature value and the minimum temperature value in the same group is relatively small. According to the temperature difference between the maximum temperature value and the minimum temperature value in the same group and the temperature of other NTCs in the group, the NTC with the abnormality is detected. Although the above method can detect the abnormal NTC to some extent, the NTC abnormality detection method depends on the accurate division of the NTC groups, and there are many false negatives or false positives, resulting in a low accuracy of NTC abnormality detection. SUMMARY

[0004] The present disclosure provides a battery NTC abnormality detection method and device, a vehicle, an electronic device and a storage medium. The main purpose is to solve the problem that the existing NTC abnormality detection method depends on the accurate division of the NTC groups, and there are many false negatives or false positives, resulting in a low accuracy of NTC abnormality detection.

[0005] According to a first aspect of the present disclosure, a battery NTC abnormality detection method is provided, comprising:

[0006] obtaining NTC temperature data of a power battery pack, the NTC temperature data comprising a temperature sequence of each NTC in a target sampling period;

[0007] calculating a temperature change rate set of each NTC in the target sampling period using the NTC temperature data;

[0008] statistical data corresponding to each of the temperature change rate sets is obtained based on uncertainty statistics of each of the temperature change rate sets;

[0009] If there is target statistical data greater than an abnormal threshold in the plurality of statistical data, an NTC corresponding to the target statistical data is determined as an abnormal NTC.

[0010] In some embodiments, the statistical data corresponding to each of the temperature change rate sets is obtained based on uncertainty statistics of each of the temperature change rate sets, including:

[0011] temperature information entropy corresponding to each of the temperature change rate sets is calculated;

[0012] The abnormal NTC is a virtual connection NTC.

[0013] In some embodiments, the statistical data corresponding to each of the temperature change rate sets is obtained based on uncertainty statistics of each of the temperature change rate sets, including:

[0014] For each of the temperature change rate sets, the frequency of a target temperature change rate in the temperature change rate set is determined; the target temperature change rate is a temperature change rate with the same value in the temperature change rate set;

[0015] For each of the target temperature change rates, information amount calculation is performed on the frequency of the target temperature change rate to obtain a target information amount corresponding to the target temperature change rate;

[0016] All target information amounts corresponding to each of the temperature change rate sets are respectively accumulated to obtain a sub-temperature information entropy corresponding to each of the temperature change rate sets;

[0017] The sub-temperature information entropies corresponding to all temperature change rate sets are combined to obtain the temperature information entropy.

[0018] In some embodiments, the information amount calculation on the frequency of the target temperature change rate to obtain the target information amount corresponding to the target temperature change rate includes:

[0019] The frequency of the target temperature change rate is subjected to logarithm calculation to obtain a logarithmic value of the frequency of the target temperature change rate;

[0020] The negative number of the frequency of the target temperature change rate is multiplied by the logarithmic value to obtain the target information amount corresponding to the target temperature change rate.

[0021] In some embodiments, the calculating the temperature change rate set of each NTC in the target sampling period using the NTC temperature data comprises:

[0022] calculating a first temperature difference and a time difference between two adjacent temperatures in the temperature sequence;

[0023] calculating a ratio of the first temperature difference and the time difference corresponding to each pair of two adjacent temperatures, to obtain the temperature change rate set of each NTC in the target sampling period.

[0024] In some embodiments, if there is a target statistical data greater than an abnormal threshold in a plurality of statistical data, the NTC corresponding to the target statistical data is determined as an abnormal NTC, comprising:

[0025] In the case that there is an outlier statistical data in a plurality of statistical data, if there is the target statistical data in the outlier statistical data, the NTC corresponding to the target statistical data is determined as an abnormal NTC.

[0026] In some embodiments, after the uncertainty statistics based on each temperature change rate set is performed to obtain the statistical data corresponding to the temperature change rate set, the method further comprises:

[0027] determining the abnormal threshold based on the box plot corresponding to each statistical data, wherein the abnormal entropy threshold is related to the upper quartile and the lower quartile of the box plot;

[0028] analyzing the box plot corresponding to each statistical data to determine whether there is an outlier statistical data in a plurality of statistical data.

[0029] According to a second aspect of the present disclosure, a device for battery NTC anomaly detection is provided, comprising:

[0030] an acquisition unit configured to acquire NTC temperature data of a power battery pack, wherein the NTC temperature data comprises a temperature sequence of each NTC in a target sampling period;

[0031] a first calculation unit configured to calculate a temperature change rate set of each NTC in the target sampling period using the NTC temperature data;

[0032] a second calculation unit configured to perform uncertainty statistics based on each temperature change rate set to obtain statistical data corresponding to the temperature change rate set;

[0033] a first determination unit configured to determine an NTC corresponding to a target statistical data as an abnormal NTC if there is the target statistical data greater than an abnormal threshold in a plurality of statistical data.

[0034] In some embodiments, the second computing unit is further configured to calculate a temperature information entropy corresponding to each of the temperature rate sets;

[0035] The abnormal NTC is a virtual connection NTC.

[0036] In some embodiments, the second computing unit comprises:

[0037] A determining module configured to determine, for each of the temperature rate sets, a frequency of a target temperature rate in the temperature rate set; the target temperature rate is a temperature rate with the same value in the temperature rate set;

[0038] A first computing module configured to perform information amount calculation on the frequency of the target temperature rate to obtain a target information amount corresponding to the target temperature rate, for each of the target temperature rates.

[0039] A second computing module configured to perform accumulation calculation on all target information amounts corresponding to each of the temperature rate sets respectively to obtain a sub-temperature information entropy corresponding to each of the temperature rate sets.

[0040] A combining module configured to combine the sub-temperature information entropies corresponding to all temperature rate sets to obtain the temperature information entropy.

[0041] In some embodiments, the first computing module is further configured to:

[0042] perform logarithm calculation on the frequency of the target temperature rate to obtain a logarithm value of the frequency of the target temperature rate;

[0043] perform product calculation on the negative of the frequency of the target temperature rate and the logarithm value to obtain the target information amount corresponding to the target temperature rate.

[0044] In some embodiments, the first computing unit comprises:

[0045] A first computing module configured to calculate a first temperature difference value and a time difference value between two adjacent temperatures in the temperature sequence;

[0046] A second computing module configured to calculate a ratio of the first temperature difference value to the time difference value corresponding to each group of two adjacent temperatures to obtain a temperature rate set of each NTC in the target sampling period.

[0047] In some embodiments, the first determining unit is further configured to, in a case where there is an outlier statistical data in the plurality of statistical data, determine an NTC corresponding to the target statistical data as an abnormal NTC if there is the target statistical data in the outlier statistical data.

[0048] In some embodiments, the apparatus further comprises:

[0049] a second determining unit, configured to determine the abnormal threshold based on the box plot corresponding to each statistical data after the second calculating unit performs uncertainty statistics based on each of the temperature change rate sets to obtain statistical data corresponding to the temperature change rate sets, wherein the abnormal entropy threshold is related to the upper quartile and the lower quartile of the box plot;

[0050] an analyzing unit, configured to analyze based on the box plot corresponding to each statistical data to determine whether the outlier statistical data exists in the plurality of statistical data.

[0051] According to a third aspect of the present disclosure, a vehicle is provided, comprising:

[0052] at least one battery pack, wherein at least two thermistors are arranged in the battery pack;

[0053] at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the method for battery NTC anomaly detection according to any one of the first aspect.

[0054] According to a fourth aspect of the present disclosure, an electronic device is provided, comprising:

[0055] at least one processor; and

[0056] a memory connected with the at least one processor in communication; wherein,

[0057] the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to the first aspect.

[0058] According to a fifth aspect of the present disclosure, a non-transitory computer readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method according to the first aspect.

[0059] According to a sixth aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method according to the first aspect.

[0060] The battery NTC abnormality detection method and device, vehicle, electronic device and storage medium provided by the present disclosure obtain NTC temperature data of a power battery pack, the NTC temperature data including a temperature sequence of each NTC in a target sampling period; calculate a temperature change rate set of each NTC in the target sampling period using the NTC temperature data; perform uncertainty statistics based on each temperature change rate set to obtain statistical data corresponding to the temperature change rate set; and if there is a target statistical data greater than an abnormal threshold in a plurality of statistical data, determine the NTC corresponding to the target statistical data as an abnormal NTC. The NTC abnormality detection method of the present disclosure calculates the temperature change rate set of a single NTC according to the NTC temperature data, and then calculates the statistical data of the uncertainty statistics corresponding to the temperature change rate set, without grouping processing of the NTC temperature values. The abnormality recognition result of the statistical data of the uncertainty statistics is used to represent the detection result of the NTC abnormality, thereby improving the accuracy of the battery NTC abnormality detection.

[0061] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0062] The accompanying drawings are used to better understand the present scheme and do not limit the present disclosure. Among them:

[0063] Figure 1 A flowchart of a battery NTC abnormality detection method provided by an embodiment of the present disclosure;

[0064] Figure 2 A temperature information entropy line graph of an abnormal NTC provided by an embodiment of the present disclosure;

[0065] Figure 3 A flowchart of another battery NTC abnormality detection method provided by an embodiment of the present disclosure;

[0066] Figure 4 A flowchart of another battery NTC abnormality detection method provided by an embodiment of the present disclosure;

[0067] Figure 5 A flowchart of another battery NTC abnormality detection method provided by an embodiment of the present disclosure;

[0068] Figure 6 A temperature distribution line graph of an abnormal NTC provided by an embodiment of the present disclosure;

[0069] Figure 7 A schematic diagram of a box plot provided by an embodiment of the present disclosure;

[0070] Figure 8 A structural schematic diagram of a battery NTC abnormality detection device provided by an embodiment of the present disclosure is shown in FIG. 1.

[0071] Figure 9 A structural schematic diagram of another battery NTC abnormality detection device provided by an embodiment of the present disclosure is shown in FIG. 2.

[0072] Figure 10 A schematic block diagram of an example electronic device 600 provided by an embodiment of the present disclosure is shown in FIG. 3. DETAILED DESCRIPTION

[0073] Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, which include various details of the embodiments of the present disclosure to assist in understanding, which should be considered in a descriptive sense only. Thus, it will be apparent to one of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Also, for the sake of brevity and clarity, descriptions of well-known functions and constructions are omitted from the following description.

[0074] A battery NTC abnormality detection method, device, electronic device, and storage medium of an embodiment of the present disclosure are described below with reference to the accompanying drawings.

[0075] Figure 1 A flowchart of a battery NTC abnormality detection method provided by an embodiment of the present disclosure is shown in FIG. 4. The method is applied in a vehicle, and the control method can be executed by a control device or equipment for information prompting, which can be configured in a server, a processor, or a master control chip, and can be disposed, for example, on a vehicle machine side, a power battery controller side, etc. The method includes the following steps:

[0076] In step 101, NTC temperature data of a power battery pack is acquired, and the NTC temperature data includes a temperature sequence of each NTC in a target sampling period.

[0077] In some embodiments provided by the present disclosure, the power battery pack includes a plurality of battery monomers or battery modules, and each battery monomer or battery module carries one or more NTCs. Therefore, the power battery pack includes a plurality of NTCs.

[0078] In an embodiment provided by the present disclosure, the target sampling period is an empirical value, which can be flexibly adjusted according to actual conditions. For example, in order to facilitate subsequent calculation, the target sampling period can be set to 1 second, or in order to reduce the occupation of computing resources, the target sampling period can also be set to 1 minute or 2 minutes. The specific target sampling period is not limited in the embodiments of the present disclosure.

[0079] In actual applications, the temperature of all NTCs of the power battery pack at all running time points, including but not limited to power-on, charging or driving of the power battery pack, will change with the change of running time. In order to calculate the temperature change rate of the NTC, the temperature change of the NTC in the target sampling period needs to be recorded to obtain the temperature sequence. The target sampling period in the embodiments of the present application can be a sampling period in any scenario of power-on, charging and driving, which is not limited in the embodiments of the present application.

[0080] For example, assuming that the target sampling period is 1 minute, the temperature sequence can be 25℃, 27℃, 28℃, 30℃, 31℃ and 33℃, wherein each temperature is separated by 10 seconds.

[0081] In step 102, the temperature change rate set of each NTC in the target sampling period is calculated by using the NTC temperature data.

[0082] Based on the NTC temperature data, the change rate of each NTC is recorded, and the trend change of the NTC caused by the working condition environment or the battery thermal management strategy of the thermistor is removed. In one embodiment provided in the present disclosure, the temperature change rate set is the set of temperature changes of each NTC in each sampling period.

[0083] The temperature change rate can be the trend change amount of each NTC temperature data caused by the working condition environment or the battery thermal management strategy of the corresponding thermistor. The removal of the temperature change rate can be achieved by statistical processing of the NTC temperature data. Since the running temperature of each thermistor NTC temperature data at different time points in a period of time changes due to the influence of the working condition environment or the battery thermal management strategy, the change amount can be approximately the same. The change amount of the two temperature data can be removed by subtracting the running temperature of one thermistor NTC temperature data at different time points, so that the same change amount in the two temperature data can be removed. It can be understood that the closer the two time points are, the more accurate the removed temperature change rate will be. On this basis, the removal method of the temperature change rate can include but is not limited to determining the growth rate of the running temperature data, determining the difference between the running temperature data and the median value or the average value, etc.

[0084] In the embodiments of the present application, for the NTC temperature data of each thermistor, the NTC temperature data can be processed to remove the temperature change rate, so as to eliminate the temperature change rate caused by the working condition environment or the battery thermal management strategy in the NTC temperature data. The processing can be realized by determining the change speed of the NTC temperature data or determining the difference between each temperature data in the NTC temperature data and the median value of the NTC temperature data. It can be understood that the above-mentioned processing mode of removing the temperature change rate is only an example and is not limited, for example, the NTC temperature data can also be processed to remove the temperature change rate by a pre-trained neural network model.

[0085] For example, after the power battery is powered on, the NTC temperature data is collected. Assuming that the power-on time of a certain NTC is 10 minutes and the target sampling period is set to 1 minute, the NTC can be divided into 10 target sampling periods. If one temperature change rate is calculated in one target sampling period, there are 10 temperature change rates in the temperature change rate set of the NTC. It should be noted that the above-mentioned power-on time and target sampling period are only examples and are not specific limitations of the power-on time and target sampling period.

[0086] In step 103, the uncertainty statistics are performed based on each temperature change rate set to obtain statistical data corresponding to the temperature change rate set.

[0087] In some embodiments provided by the present disclosure, the information entropy is an index for measuring uncertainty, that is, the probability of occurrence of a discrete random event. In simple terms, the more chaotic the situation is, the greater the information entropy is, and vice versa. In the embodiments of the present disclosure, the statistical data of the temperature change rate set corresponding to the uncertainty statistics is used to determine whether an abnormality occurs in the temperature change rate set, so as to determine whether the NTC is abnormal.

[0088] The uncertainty statistics can be a processing of statistical processing of the uncertainty in the temperature change rate set. The uncertainty statistics can generate an uncertainty value of the temperature change rate set, which can represent the uncertainty degree of the temperature change rate set. The higher the uncertainty degree in the temperature change rate set is, the higher the probability of occurrence of an abnormality in the temperature change rate set is. The uncertainty statistics can be realized by standard deviation probability statistics, upper quartile probability statistics, information entropy statistics, etc. The statistical data can be an uncertainty value generated by the uncertainty statistics of the temperature change rate set. The statistical data can be generated based on the way of the uncertainty statistics of the temperature change rate set, and can include but is not limited to a standard deviation value, an upper quartile probability value, an information entropy, etc.

[0089] In the embodiments of the present application, the uncertainty statistics can be performed for each temperature change rate set, the probability distribution statistics or information entropy calculation can be performed on the data value of the temperature change rate set, and the statistical results or information entropy results can be taken as the statistical data generated by the uncertainty statistics. The value of each operating temperature data can be determined to meet the specified probability distribution or the information entropy of the operating temperature data. It can be understood that the uncertainty statistics for each temperature change rate set can be performed by one or more of the statistical methods such as standard deviation probability statistics, upper quartile probability statistics, and information entropy statistics. For example, the statistical results of the standard deviation probability statistics, the upper quartile probability statistics, and the information entropy statistics can be determined for each operating temperature data, and the weighted sum of the statistical results can be taken as the uncertainty of the operating temperature data.

[0090] In step 104, if there is target statistical data greater than the abnormal threshold in the plurality of statistical data, the NTC corresponding to the target statistical data is determined as an abnormal NTC.

[0091] In one embodiment provided by the present disclosure, the abnormal threshold is an abnormal range or an abnormal value of the statistical data. For example, if the normal range of the statistical data is 0 to 0.10, the abnormal threshold is greater than 0.10. For example, if the value of the statistical data of a certain NTC is 0.72, the NTC corresponding to the statistical data is determined as an abnormal NTC. On the contrary, if the value of the statistical data of the NTC is 0.06, the NTC is normal.

[0092] In the embodiments of the present disclosure, the NTC abnormality described in the present application includes but is not limited to a virtual NTC and a resistance abnormality. The virtual NTC refers to a poor physical connection between the NTC and the corresponding battery monomer or battery module. The resistance abnormality corresponds to a temperature that is continuously higher than a preset abnormal duration and a continuously high temperature that exceeds a preset temperature abnormal threshold.

[0093] Due to the difference between the virtual NTC and the resistance abnormality, the way of setting the abnormal threshold is also different when determining whether it is abnormal. It should be noted that the normal range of 0 to 0.10 is only an example of the description. This description is not intended to limit the normal range to only 0 to 0.10. The normal range can also be 0 to 0.15, 0 to 0.09, etc. The specific normal range can be adjusted according to the application scenario. In addition, the preset temperature abnormal threshold and the preset abnormal duration are not limited.

[0094] As another implementation manner of the embodiment of the present application, the abnormal entropy value threshold is related to the standard deviation of the temperature information entropy. After the temperature information entropy corresponding to each temperature change rate set is calculated, the standard deviation σ of all temperature information entropies is calculated. When the abnormal entropy value threshold is set, the abnormal entropy value threshold can be set as 3σ or the like. Specifically, the embodiment of the present application does not limit the specific value of the abnormal entropy value threshold.

[0095] In order to better understand the abnormal NTC in the above embodiment, the present disclosure provides a broken line graph of the temperature information entropy of the abnormal NTC, as shown in the figure, the horizontal axis coordinate of the graph is the NTC number, and the vertical axis coordinate is the entropy value of the temperature information entropy of the NTC. In the graph, the normal entropy value threshold is 0.1, the entropy value of the NTC with the NTC number 10 obviously exceeds the normal entropy value threshold, and meets the abnormal entropy value threshold. Therefore, the NTC with the NTC number 10 can be determined as the abnormal NTC. Figure 2

[0096] The battery NTC abnormality detection method provided by the present disclosure obtains NTC temperature data of a power battery pack, the NTC temperature data including temperature sequences of each NTC in a target sampling period; calculates a temperature change rate set of each NTC in the target sampling period by using the NTC temperature data; performs uncertainty statistics based on each temperature change rate set to obtain statistical data corresponding to the temperature change rate set; and determines an NTC corresponding to a target statistical data greater than an abnormal threshold as an abnormal NTC if the target statistical data exists in a plurality of statistical data. The NTC abnormality detection method of the present disclosure calculates the temperature change rate set of a single NTC according to the NTC temperature data, and then calculates the statistical data of the uncertainty statistics corresponding to the temperature change rate set. The NTC temperature value does not need to be grouped and processed. The abnormality recognition result of the statistical data of the uncertainty statistics is used to represent the detection result of the NTC abnormality, thereby improving the accuracy of the battery NTC abnormality detection.

[0097] The above embodiment shows that the abnormal NTC includes a virtual connection NTC. Since the virtual connection is disordered, the detection is performed by using the uncertainty statistics. Therefore, when the uncertainty statistics is performed based on each temperature change rate set to obtain the statistical data corresponding to the temperature change rate set, the following manner can be used but is not limited to the following manner: calculating the temperature information entropy corresponding to each temperature change rate set, and the abnormal NTC being a virtual connection NTC. The virtual connection of the NTC is accurately recognized by using the temperature information entropy, so that the state of the NTC can be accurately monitored, and the abnormality can be identified as early as possible to avoid safety hazards.

[0098] In order to better understand the calculation of the temperature information entropy corresponding to each temperature change rate set, please refer to Figure 3 , Figure 3 ​Another flowchart of battery NTC abnormality detection provided by embodiments of the present disclosure is shown in FIG. 2B, which includes the following steps: Figure 3

[0099] In step 201, for each temperature rate set, the frequency of a target temperature rate in the temperature rate set is determined; the target temperature rate is a temperature rate with the same value in the temperature rate set.

[0100] In some embodiments provided by the present disclosure, there can be the same temperature rate in each temperature rate set of an NTC. For example, the temperature rate set of an NTC is {0.5, 0.3, 0.5, 0.4, 0.3, 0.2, 0.5, 0.6, 0.4, 0.2}, in which 0.5 appears 3 times, so the frequency of 0.5 is 0.3; 0.3 appears 2 times, so the frequency of 0.3 is 0.2; 0.4 appears 2 times, so the frequency of 0.4 is 0.2; 0.6 appears 1 time, so the frequency of 0.6 is 0.1; 0.2 appears 2 times, so the frequency of 0.2 is 0.2. The above example of the temperature rate set is only for understanding, and is not a limitation on the specific value. The embodiments of the present disclosure do not limit the value of the temperature rate set and the frequency of each value.

[0101] In step 202, for each target temperature rate, the frequency of the target temperature rate is calculated to obtain the target information amount corresponding to the target temperature rate.

[0102] In an embodiment provided by the present disclosure, when performing the information amount calculation, the frequency of the target temperature rate can be calculated by calling an information amount calculation algorithm, but is not limited thereto. The information amount calculation algorithm includes: first, the frequency of the target temperature rate is calculated to obtain the logarithm value of the frequency of the target temperature rate; then, the product of the negative of the frequency of the target temperature rate and the logarithm value is calculated to obtain the information amount corresponding to the target temperature rate. For better understanding of the information amount calculation algorithm, please refer to formula (1).

[0103] Information amount = -plog2p formula (1)

[0104] In formula (1), p is the frequency of the target temperature rate.

[0105] In step 203, the target information amount corresponding to each temperature rate set is calculated to obtain the sub-temperature information entropy corresponding to each temperature rate set.

[0106] ​The at least one target information quantity corresponding to each temperature rate set is calculated in sequence according to the calculation manner described in step 202, and in this step, the at least one target information quantity corresponding to each temperature rate set needs to be added and calculated to obtain the sub-temperature information entropy corresponding to each temperature rate set.

[0107] Step 204, combining the sub-temperature information entropy corresponding to all temperature rate sets to obtain the temperature information entropy.

[0108] By calculating the temperature information entropy of each NTC and further determining the abnormal NTC, the state of the NTC in the battery pack is accurately monitored, and the accuracy of the battery NTC abnormality detection is improved.

[0109] In order to better understand the calculation of the temperature rate set of each NTC in the target sampling period using the NTC temperature data in step 102, please refer to Figure 4 , Figure 4 Another flowchart of the battery NTC abnormality detection provided by the embodiment of the present disclosure is shown in FIG. 3, which comprises the following steps: Figure 4

[0110] Step 301, calculating the first temperature difference and the time difference between two adjacent temperatures in the temperature sequence.

[0111] In order to better understand the calculation process of the first temperature difference, taking the temperature sequence {25℃, 27℃, 28℃, 30℃, 31℃, 33℃, 35} as an example, the first temperature difference between two adjacent temperatures in the temperature sequence is {2℃, 1℃, 2℃, 1℃, 3℃, 2℃}. This method is not intended to limit the specific values of the temperature sequence.

[0112] In order to better understand the determination of the time corresponding to each temperature in the temperature sequence, please continue to refer to the temperature sequence {25℃, 27℃, 28℃, 30℃, 31℃, 33℃, 35} provided in the above embodiment, the cycle length of the sampling period corresponding to the temperature sequence is 1 minute, and the temperature is recorded every 10 seconds, i.e. the temperature at 0 second is 25℃, the temperature at 10 seconds is 27℃, the temperature at 20 seconds is 28℃, the temperature at 30 seconds is 30℃, the temperature at 40 seconds is 31℃, the temperature at 50 seconds is 33℃, and the temperature at 60 seconds is 35℃.

[0113] In order to better understand the time difference, please continue to refer to the above embodiment, the time difference between two adjacent times is 10 seconds, and the above description of the first temperature difference and the time difference between two adjacent temperatures is only exemplary, and the specific values are not limited in the embodiment. ​

[0114] Step 302, calculate the ratio of the first temperature difference value and the time difference value corresponding to each pair of adjacent temperatures in each group, to obtain a set of temperature change rates of each NTC in the target sampling period.

[0115] For a better understanding of the calculation process of the temperature change rate, please continue to refer to the example of step 301 described above. The first temperature difference value of the temperature sequence from 0s to 10s is 2℃, and the time difference value is 10s. Therefore, the corresponding temperature change rate is 0.2℃ / s. By combining all the temperature change rates in chronological order, the set of temperature change rates is obtained.

[0116] By calculating the set of temperature change rates of each NTC from the temperature data of all NTCs in the power battery pack, the influence of the vehicle thermal management strategy on the temperature change of the NTC is greatly reduced, thereby improving the accuracy of NTC abnormality detection.

[0117] For a better understanding of the battery NTC abnormality detection method provided by the present disclosure, please refer to Figure 5 , Figure 5 Another flowchart of the battery NTC abnormality detection method provided by an embodiment of the present disclosure is shown in Figure 5 The method comprises the following steps:

[0118] Step 401, determine whether the static duration of the power battery pack is greater than a preset static duration.

[0119] In one embodiment provided by the present disclosure, the static duration is an empirical value, which can be flexibly adjusted according to the working condition of the vehicle or the ambient temperature. For example, in winter, due to the low ambient temperature, the temperature of the battery pack decreases rapidly after the vehicle is powered off. At this time, the static duration can be set to be relatively short, for example, 5 minutes or 10 minutes, etc. Or, when the vehicle is in a heating working condition, the NTC temperature is high after the vehicle is powered off, and it cannot be restored to the ambient temperature within a short time. At this time, the static duration can be set to be relatively long, for example, 30 minutes or 40 minutes, etc. Specifically, the preset static duration is not limited in the present embodiment.

[0120] The purpose of determining whether the static duration of the power battery pack is greater than the preset static duration is to restore the NTC temperature to the ambient temperature and reduce the influence of the residual heat of the NTC on the battery NTC abnormality detection.

[0121] If the static duration is less than or equal to the preset static duration, step 405 is performed, and if the static duration is greater than the preset static duration, step 402 is performed.

[0122] At step 402, an initial temperature set corresponding to the time of power-on of all NTCs in the power battery pack is obtained, and the initial temperature set includes one initial temperature corresponding to each NTC.

[0123] At step 403, a temperature average of the initial temperature set is calculated, and a second temperature difference between each initial temperature in the initial temperature set is obtained to obtain a temperature difference set.

[0124] At step 404, if there is a target temperature difference in the temperature difference set that meets an abnormal temperature difference threshold, the NTC corresponding to the target temperature difference is determined as a resistance abnormal NTC.

[0125] In an embodiment provided by the present disclosure, the abnormal temperature difference threshold is an abnormal temperature difference range of the difference value. For example, if the temperature difference range of a normal NTC is 2-5°C, the temperature range exceeding the temperature range (2-5°C) is the abnormal temperature difference threshold.

[0126] In order to better understand the resistance abnormal NTC in the above embodiment, the present disclosure provides a line graph of temperature distribution of an abnormal NTC, as shown in FIG. 3. Figure 6 As shown in FIG. 3, the horizontal axis of the line graph is the NTC number, and the vertical axis is the temperature of the NTC. In the graph, the temperature average is 25°C, the normal temperature range is 2-5°C, the temperature of the NTC with the NTC number 9 is 35°C, the second temperature difference with the temperature average is 10°C, which obviously exceeds the normal temperature range and meets the abnormal temperature difference threshold. Therefore, the NTC with the NTC number 9 can be determined as the resistance abnormal NTC.

[0127] At step 405, the first temperature data of all NTCs in the power battery pack is obtained.

[0128] If the static duration is less than or equal to the preset static duration, the influence of residual heat on the NTC abnormality detection cannot be eliminated, and the detection of resistance abnormality of the NTC may have errors. Therefore, the first temperature data of all NTCs in the power battery pack is directly obtained to detect the virtual connection abnormality of the NTC, and the detection of the virtual connection abnormality is not affected by the residual heat.

[0129] In some embodiments, the embodiments of the present application also provide another implementation manner for determining the abnormal NTC, comprising: after the uncertainty statistics of each of the temperature change rate sets are obtained, the abnormal threshold is determined based on the box plot corresponding to each of the statistical data, the abnormal entropy threshold is related to the upper quartile and the lower quartile of the box plot, and the analysis is performed based on the box plot corresponding to each of the statistical data to determine whether there is the outlier statistical data in the plurality of statistical data.

[0130] In the case that there is the outlier statistical data in the plurality of statistical data, if there is the target statistical data in the outlier statistical data, the NTC corresponding to the target statistical data is determined as the abnormal NTC.

[0131] For better understanding of the outlier information entropy, please refer to Figure 7 This method uses the interquartile range (IQR) of the box plot to detect the abnormal NTC, and based on the upper and lower bounds in Figure 7 , the outlier information entropy can be identified.

[0132] The interquartile range (IQR) is the difference between the upper quartile and the lower quartile, and by taking 1.5 times of the IQR as the standard, the abnormal entropy threshold is defined: the points exceeding the upper quartile + 1.5 times of the IQR distance or the lower quartile - 1.5 times of the IQR distance are the abnormal NTC.

[0133] Compared with the 3σ principle, the box plot is drawn according to the actual data, and truly and intuitively shows the original appearance of the temperature information entropy distribution, and does not have any restrictive requirements on the temperature information entropy (the 3σ principle requires that the data should be subject to normal distribution or approximately subject to normal distribution), and the standard for judging the constant value is based on the quartiles and the interquartile range. The quartiles give some indication of the center, dispersion and shape of the temperature information entropy distribution, and have a certain robustness, i.e. 25% of the temperature information entropy can become arbitrarily far without greatly disturbing the quartiles, so the abnormal NTC usually cannot affect this standard. In view of this, the result of identifying the abnormal NTC by the box plot is relatively objective, and therefore has certain advantages in identifying the abnormal NTC.

[0134] The box plot provides a standard for identifying the abnormal NTC, i.e. the abnormal NTC is usually defined as the value less than QL-1.5IQR or greater than QU+1.5IQR. Wherein, QL is called the lower quartile, indicating that one quarter of the data values of the total temperature information entropy are less than it; QU is called the upper quartile, indicating that one quarter of the data values of the total temperature information entropy are greater than it; IQR is called the interquartile range, which is the difference between the upper quartile QU and the lower quartile QL, and half of the total temperature information entropy is contained therebetween.

[0135] In addition to the above embodiments described by the box type chart to identify outliers statistical data, can also be achieved by the following ways, including: can also be clustered for each statistical data, the distance between each statistical data and the cluster center can be used as the clustering result of the statistical data, the statistical data with a distance greater than the average distance or threshold can be used as the outlier statistical data, the clustering method of statistical data can include but not limited to k-means clustering, Gaussian mixture model clustering, etc. ; can also be classified by statistical data processing, the classification results after classification can be compared, and the statistical data different from other classification results can be used as the outlier statistical data, it can be understood that the classification processing can be realized by logistic regression classification algorithm, ridge regression classification algorithm, etc.

[0136] The specific numerical value of the abnormal entropy value threshold set by the embodiments of the present application is not limited.

[0137] In summary, the embodiments of the present disclosure can achieve the following effects:

[0138] 1. By calculating the temperature information entropy of each NTC, the virtual connected NTC is determined, the state of the NTC in the power battery pack is accurately monitored, and the accuracy of the battery NTC abnormality detection is improved.

[0139] 2. By calculating the temperature change rate set of each NTC from the temperature data of all NTCs in the power battery pack, the influence of the vehicle thermal management strategy on the temperature change of the NTC is greatly reduced, and the accuracy of the NTC abnormality detection is improved.

[0140] Corresponding to the above-mentioned battery NTC abnormality detection method, the present application also proposes a battery NTC abnormality detection device. Since the device embodiments of the present application correspond to the above-mentioned method embodiments, the details not disclosed in the device embodiments can be referred to the above-mentioned method embodiments, which will not be described in detail in the present application.

[0141] Figure 8 A structural schematic diagram of a battery NTC abnormality detection device provided by the embodiments of the present disclosure is shown in Figure 8 As shown in the figure, it includes:

[0142] The acquisition unit 51 is used for acquiring the NTC temperature data of the power battery pack, and the NTC temperature data includes the temperature sequence of each NTC in the target sampling period;

[0143] The first calculation unit 52 is used for calculating the temperature change rate set of each NTC in the target sampling period by using the NTC temperature data;

[0144] The second computing unit 53 is configured to perform uncertainty statistics based on each of the temperature change rate sets to obtain statistical data corresponding to the temperature change rate sets.

[0145] The first determining unit 54 is configured to determine the NTC corresponding to target statistical data greater than an abnormal threshold as an abnormal NTC if the target statistical data exists in the statistical data.

[0146] Further, in a possible implementation manner of the embodiment, as shown in Figure 9 The second computing unit 53 is further configured to calculate temperature information entropy corresponding to each of the temperature change rate sets.

[0147] The abnormal NTC is a virtual connection NTC.

[0148] Further, in a possible implementation manner of the embodiment, as shown in Figure 9 The second computing unit 53 comprises:

[0149] The determining module 531 is configured to determine, for each of the temperature change rate sets, a frequency of a target temperature change rate in the temperature change rate set; the target temperature change rate is a temperature change rate with the same value in the temperature change rate set.

[0150] The first computing module 532 is configured to perform information quantity calculation on the frequency of the target temperature change rate to obtain target information quantity corresponding to the target temperature change rate for each of the target temperature change rates.

[0151] The second computing module 533 is configured to perform accumulation calculation on all target information quantities corresponding to each of the temperature change rate sets respectively to obtain sub-temperature information entropy corresponding to each of the temperature change rate sets.

[0152] The combination module 534 is configured to combine the sub-temperature information entropy corresponding to all temperature change rate sets to obtain the temperature information entropy.

[0153] Further, in a possible implementation manner of the embodiment, as shown in Figure 9 The first computing module 532 is further configured to:

[0154] perform logarithm calculation on the frequency of the target temperature change rate to obtain a logarithm value of the frequency of the target temperature change rate;

[0155] perform product calculation on a negative number of the frequency of the target temperature change rate and the logarithm value to obtain the target information quantity corresponding to the target temperature change rate.

[0156] Further, in a possible implementation manner of the embodiment, as shown in Figure 9As shown, the first computing unit 52 comprises:

[0157] The first computing module 521 is configured to calculate a first temperature difference value and a time difference value between two adjacent temperatures in the temperature sequence.

[0158] The second computing module 522 is configured to calculate a ratio of the first temperature difference value and the time difference value corresponding to each group of two adjacent temperature pairs, to obtain a temperature change rate set of each NTC in the target sampling period.

[0159] Further, in one possible implementation manner of the embodiment, as shown in Figure 9 The first determining unit 54 is further configured to, in a case where there is an outlier statistical data in the plurality of statistical data, determine the NTC corresponding to the target statistical data as an abnormal NTC if there is the target statistical data in the outlier statistical data.

[0160] Further, in one possible implementation manner of the embodiment, as shown in Figure 9 The device further comprises:

[0161] The second determining unit 55 is configured to, after the second computing unit 53 performs uncertainty statistics based on each temperature change rate set to obtain statistical data corresponding to the temperature change rate set, determine the abnormal threshold based on a box plot corresponding to each statistical data, wherein the abnormal entropy threshold is related to the upper quartile and the lower quartile of the box plot.

[0162] The analysis unit 56 is configured to perform analysis based on the box plot corresponding to each statistical data to determine whether there is the outlier statistical data in the plurality of statistical data.

[0163] The battery NTC abnormality detection device provided by the disclosure obtains NTC temperature data of a power battery pack, wherein the NTC temperature data comprises a temperature sequence of each NTC in a target sampling period; calculates a temperature change rate set of each NTC in the target sampling period by using the NTC temperature data; performs uncertainty statistics based on each temperature change rate set to obtain statistical data corresponding to the temperature change rate set; and determines an NTC corresponding to target statistical data greater than an abnormal threshold as an abnormal NTC if there is the target statistical data in the plurality of statistical data. The NTC abnormality detection method provided by the disclosure calculates a temperature change rate set of a single NTC according to NTC temperature data, calculates statistical data of uncertainty statistics corresponding to the temperature change rate set, does not need to perform grouping processing on NTC temperature values, uses an abnormality recognition result of the statistical data of uncertainty statistics to represent a detection result of NTC abnormality, and improves the accuracy of battery NTC abnormality detection.

[0164] It should be noted that the foregoing description of the method embodiments also applies to the device according to this embodiment. Therefore, like reference numerals are used here to designate like elements illustrated in the preceding drawings.

[0165] According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a vehicle, a readable storage medium and a computer program product.

[0166] The vehicle provided by the embodiments of the present application comprises at least one battery pack, at least two thermistors are arranged in the battery pack.

[0167] The at least one processor and the memory connected with the at least one processor in communication, wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the method of battery NTC anomaly detection according to any of the embodiments.

[0168] Figure 10 A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present disclosure described and / or claimed in this document.

[0169] As shown in Figure 10 The device 600 includes a computing unit 601 that can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 602 or a computer program loaded into a RAM (Random Access Memory) 603 from a storage unit 608. Various programs and data required for the operation of the device 600 can also be stored in the RAM 603. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. An I / O (Input / Output) interface 605 is also connected to the bus 604.

[0170] A number of components in the device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a magnetic disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information / data with other devices through computer networks, such as the Internet, and / or various telecommunication networks.

[0171] The computing unit 601 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Unit), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 601 performs various methods and processes described above, such as the battery NTC abnormality detection method. For example, in some embodiments, the battery NTC abnormality detection method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded to the RAM 603 and executed by the computing unit 601, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, the computing unit 601 can be configured to perform the aforementioned battery NTC abnormality detection method by other any appropriate means, such as by means of firmware.

[0172] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a Field Programmable Gate Array (FPGA), an Application-Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a System on a Chip (SOC), a Complex Programmable Logic Device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0173] Program code to implement methods of the present disclosure is implemented in one or more computer programs, which are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0174] In the context of this disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include a linearly-programmed electronic storage, a portable computer diskette, a hard disk, RAM, ROM, EPROM (Electrically Programmable Read-Only-Memory), or flash memory, an optical fiber, a CD-ROM (Compact Disc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0175] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0176] The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a LAN (Local Area Network), a WAN (Wide Area Network), the Internet, and a blockchain network.

[0177] The computer system can include clients and servers. This relationship can be between a client and a server that are typically remote from each other and typically interact through a communication network. The relationship between client and server exists by virtue of computer programs running on the respective computer systems and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS (Virtual Private Server, or VPS for short) services. The server can also be a server of a distributed system, or a server combined with a blockchain.

[0178] It should be noted that artificial intelligence is a discipline that studies enabling computers to simulate some thinking processes and intelligent behaviors of humans (such as learning, reasoning, thinking, planning, etc.), both hardware and software technologies. Artificial intelligence hardware technology generally includes technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing, etc.; artificial intelligence software technology mainly includes computer vision technology, speech recognition technology, natural language processing technology, and machine learning / deep learning, big data processing technology, knowledge graph technology, etc. several major directions.

[0179] It should be understood that the various forms of the flow shown above can be used to reorder, add or delete steps. For example, each step described in the present disclosure can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, which is not limited herein.

[0180] The above detailed description does not constitute a limitation on the scope of protection of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present disclosure shall be included in the scope of protection of the present disclosure.

Claims

1. A method of battery NTC abnormality detection, the method comprising: The method comprises: acquiring NTC temperature data of a power battery pack, the NTC temperature data comprising a temperature sequence of each NTC in a target sampling period; calculating a temperature change rate set of each NTC in the target sampling period by using the NTC temperature data; performing uncertainty statistics based on each temperature change rate set to obtain statistical data corresponding to the temperature change rate set; if there is target statistical data greater than an abnormal threshold in a plurality of statistical data, determining the NTC corresponding to the target statistical data as an abnormal NTC.

2. The method of claim 1, wherein, The uncertainty statistics based on each temperature change rate set to obtain statistical data corresponding to the temperature change rate set comprises: calculating temperature information entropy corresponding to each temperature change rate set; wherein the abnormal NTC is a virtual connection NTC.

3. The method of claim 2, wherein, The uncertainty statistics based on each temperature change rate set to obtain statistical data corresponding to the temperature change rate set comprises: for each temperature change rate set, determining the frequency of a target temperature change rate in the temperature change rate set; the target temperature change rate is a temperature change rate with the same value in the temperature change rate set; for each target temperature change rate, performing information amount calculation on the frequency of the target temperature change rate to obtain a target information amount corresponding to the target temperature change rate; accumulatively calculating all target information amounts corresponding to each temperature change rate set to obtain a sub-temperature information entropy corresponding to each temperature change rate set; combining the sub-temperature information entropies corresponding to all temperature change rate sets to obtain the temperature information entropy.

4. The method of claim 3, wherein, The information amount calculation on the frequency of the target temperature change rate to obtain a target information amount corresponding to the target temperature change rate comprises: performing logarithm calculation on the frequency of the target temperature change rate to obtain a logarithmic value of the frequency of the target temperature change rate; performing product calculation on the negative of the frequency of the target temperature change rate and the logarithmic value to obtain the target information amount corresponding to the target temperature change rate.

5. The method of claim 1, wherein, The calculation of a temperature change rate set of each NTC in the target sampling period by using the NTC temperature data comprises: calculating a first temperature difference and a time difference between two adjacent temperatures in the temperature sequence; calculating the ratio of the first temperature difference and the time difference corresponding to each group of two adjacent temperatures to obtain the temperature change rate set of each NTC in the target sampling period.

6. The method of claim 1, wherein, If there is target statistical data greater than an abnormal threshold in a plurality of statistical data, determining the NTC corresponding to the target statistical data as an abnormal NTC, comprising: if there is the target statistical data in an outlier statistical data in the case that there is the outlier statistical data in a plurality of statistical data, determining the NTC corresponding to the target statistical data as an abnormal NTC.

7. The method of claim 6, wherein, After the uncertainty statistics based on each temperature change rate set to obtain statistical data corresponding to the temperature change rate set, the method further comprises: The abnormal threshold is determined based on a box plot corresponding to each statistical data, and the abnormal entropy value threshold is related to upper quartile and lower quartile of the box plot. The analysis is performed based on the box plot corresponding to each statistical data to determine whether the outlier statistical data exists in the plurality of statistical data.

8. A device for battery NTC abnormality detection, characterized by, The method comprises the following steps: An acquisition unit is configured to acquire NTC temperature data of a power battery pack, wherein the NTC temperature data comprises a temperature sequence of each NTC in a target sampling period; A first calculation unit is configured to calculate a temperature change rate set of each NTC in the target sampling period by using the NTC temperature data; A second calculation unit is configured to perform uncertainty statistics based on each temperature change rate set to obtain statistical data corresponding to the temperature change rate set; A first determination unit is configured to determine an NTC corresponding to a target statistical data greater than an abnormal threshold as an abnormal NTC if the target statistical data exists in the plurality of statistical data.

9. A vehicle characterized by comprising: The vehicle comprises: At least one battery pack, wherein at least two thermistors are arranged in the battery pack; At least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the method for battery NTC anomaly detection according to any one of claims 1-7.

10. An electronic device, comprising: The method comprises the following steps: At least one processor; and A memory connected with the at least one processor in communication; wherein The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to any one of claims 1-7.

11. A non-transitory computer-readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to enable the computer to execute the method according to any one of claims 1-7.

12. A computer program product, characterised in that, The computer program comprises computer instructions for enabling a processor to implement the method according to any one of claims 1-7 when the computer program is executed by the processor.

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