Method for detecting abnormal temperature sampling fluctuation

Abstract

The invention discloses a method for detecting temperature sampling fluctuation abnormity, which comprises the following steps of: calculating a difference value of adjacent points of single battery temperature data and a difference value of adjacent minutes of the single battery temperature data; comparing the temperature data with the difference value of the temperatures of the single batteries at the adjacent points and the difference value of the temperature data of the single batteries in adjacent minutes to judge the temperature data of the suspected abnormal single batteries, and calculating the variance of the temperature data of the suspected abnormal single batteries and the variance of the temperature data of the normal single batteries; the method comprises the following steps: setting a reference line to judge abnormal single battery temperature data, calculating a rising and falling index and an amplitude of the abnormal single battery temperature data, setting a boundary, comparing the rising and falling index and the amplitude of the abnormal single battery temperature data with the boundary, and judging whether temperature fluctuation abnormity occurs or not. And the temperature fluctuation abnormity is calibrated, so that the accuracy and the reliability of battery temperature sampling fluctuation abnormity detection are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature detection, and in particular to a method for detecting abnormal temperature sampling fluctuations. Background Art

[0002] Batteries are widely used in modern industry and technology, from electric vehicles to various portable electronic devices. Battery performance and safety are crucial, and temperature is one of the key factors affecting battery performance. For the battery itself, rising temperature increases its internal resistance, thereby affecting its efficiency and performance. Overheating can also cause electrolyte evaporation, accelerating unnecessary chemical reactions within the battery, leading to a decrease in overall performance and capacity. Unstable temperature can lead to unstable battery capacity and internal resistance, and also reduce charging efficiency. Therefore, accurately detecting abnormal fluctuations in battery temperature sampling is crucial to ensuring battery performance and safe operation.

[0003] At present, commonly used statistical analysis methods, such as mean, variance, standard deviation, etc., are mainly used to describe the overall distribution characteristics of data to indicate the size of data fluctuations. However, these traditional methods have significant limitations. On the one hand, they calculate the overall characteristics of the overall data. In actual application scenarios, when it is necessary to discover the fluctuations in part of the temperature curve, simply using statistical methods such as mean and variance is unable to accurately find the fluctuations and corresponding intervals. On the other hand, faced with complex actual working conditions and noise interference, these methods are difficult to effectively distinguish between normal fluctuations and abnormal fluctuations, resulting in low reliability of the detection results, which cannot meet the needs of accurate monitoring of battery temperature. Therefore, the development of a more accurate and reliable method for detecting abnormal fluctuations in battery temperature sampling has become an urgent problem to be solved.

[0004] The solution proposed by the present invention is to obtain the temperature data of the single cell battery in the battery module, calculate the difference between adjacent points of the single cell battery temperature data and the difference between adjacent one-minute values of the single cell battery temperature data, and preset a difference upper limit threshold. According to the preset difference upper limit threshold, the difference between the single cell battery temperatures at adjacent points and the difference between the single cell battery temperature data at adjacent one-minute values are compared and judged to obtain suspected abnormal single cell battery temperature data, calculate the variance of the suspected abnormal single cell battery temperature data and the variance of the single cell battery temperature data after removing the suspected abnormal single cell battery temperature data, set a baseline, make a judgment based on the baseline to obtain abnormal single cell battery temperature data, calculate the rise and fall index and amplitude of the abnormal single cell battery temperature data, set a boundary limit, compare the rise and fall index and amplitude of the abnormal single cell battery temperature data with the boundary data, judge whether temperature fluctuation abnormality occurs, and calibrate the temperature fluctuation abnormality, thereby improving the accuracy and reliability of battery temperature sampling fluctuation abnormality detection. Summary of the Invention

[0005] In order to solve the above-mentioned technical problems, the present invention provides a method for detecting abnormal temperature sampling fluctuations.

[0006] The technical solution of the present invention is implemented as follows: a method for detecting abnormal temperature sampling fluctuations, comprising:

[0007] S1. Obtaining temperature data of single cells in a battery module, and calculating differences between adjacent points of the temperature data of the single cells and differences between adjacent one-minute values of the temperature data of the single cells;

[0008] S2, presetting a difference upper limit threshold, comparing the difference between adjacent points of the single cell temperature data and the difference between adjacent one-minute single cell temperature data in step S1 with the difference upper limit threshold, and obtaining suspected abnormal single cell temperature data;

[0009] S3, calculating the variance of the suspected abnormal single cell temperature data in step S2 and the variance of the normal single cell temperature data excluding the suspected abnormal single cell temperature data, removing the first five values of the normal single cell temperature data with the largest variance and taking the sixth value to set a baseline, and performing judgment based on the baseline to obtain the abnormal single cell temperature data;

[0010] S4, calculating the rising and falling index and amplitude of the abnormal single cell temperature data in step S3, setting a boundary limit, and comparing the rising and falling index and amplitude of the abnormal single cell temperature data with the boundary limit to determine whether abnormal temperature fluctuation occurs;

[0011] S5. Calibrate the abnormal temperature fluctuation in step S4.

[0012] Furthermore, in step S1 , the temperature data of the single cells in the battery module are obtained, and the difference between adjacent points of the temperature data of the single cells and the difference between adjacent one-minute temperature data of the single cells are calculated.

[0013] Furthermore, in step S1, the specific steps are:

[0014] Using a temperature sensor in the battery module to collect temperature data of multiple single cells in different time periods, performing difference calculation on the temperature data of adjacent points of each single cell, and respectively calculating the temperature data of the previous minute and the temperature data of the next minute collected for the same single cell, and calculating the average value of the temperature data of the previous minute and the average value of the temperature data of the next minute for the same single cell, and subtracting the average value of the temperature data of the next minute from the average value of the temperature data of the previous minute to obtain the difference of the temperature data of the same single cell for adjacent one minute;

[0015] Furthermore, the difference between adjacent points of the single cell temperature data is calculated as:

[0016] D=T i+1,j -T i,j

[0017] Where D is the difference between the temperature data of the j-th single cell at the i+1th collection time and the temperature data at the i-th collection time, T i+1，j is the temperature data of the j-th single cell at the i+1th collection time, T i，j is the temperature data of the j-th single cell at the time of the i-th acquisition;

[0018] Furthermore, the difference between the temperature data of the single cell in adjacent one minute is calculated as:

[0019]

[0020] Among them, D' 1min The difference between the average temperature data of the j-th single cell obtained by collecting temperature data in the k+1th minute and the average temperature data of the temperature data collected in the kth minute in the adjacent one minute, is the average temperature data of the jth single cell in the k+1th minute, is the average temperature data of the jth single cell in the kth minute.

[0021] Furthermore, in step S2, a difference upper limit threshold is preset, and the difference between adjacent points of the single cell temperature data and the difference between adjacent one-minute single cell temperature data in step S1 are compared with the difference upper limit threshold to obtain suspected abnormal single cell temperature data.

[0022] Furthermore, in step S2, the specific steps are:

[0023] A preset upper limit threshold value of the difference is used, and the calculation result of the difference of the single cell temperature data of adjacent points of each single cell in step S1 and the difference result of the single cell temperature data in adjacent one minute are compared with the upper limit threshold value of the difference. When the calculation result of the difference of the single cell temperature data of two adjacent single cell is greater than the upper limit threshold value of the difference, it means that the single cell temperature data collected later of the two adjacent single cell temperature data corresponding to the current difference calculation result is suspected abnormal single cell temperature data; when the difference result of the single cell temperature data in adjacent one minute is greater than the upper limit threshold value of the difference, it means that the single cell temperature data collected in the next minute corresponding to the difference result of the single cell temperature data in adjacent one minute is suspected abnormal single cell temperature data.

[0024] Furthermore, in step S3, the variance of the suspected abnormal single cell temperature data in step S2 and the variance of the normal single cell temperature data excluding the suspected abnormal single cell temperature data are calculated, and a baseline is set by removing the first five values of the normal single cell temperature data with the largest variance and taking the sixth value. The abnormal single cell temperature data is obtained by judgment based on the baseline.

[0025] Furthermore, in step S3, the specific steps are:

[0026] The variance of the suspected abnormal single cell temperature data obtained in step S2 and the normal single cell temperature data excluding the suspected abnormal single cell temperature data are respectively calculated, and the variance results of the normal single cell temperature data are arranged in descending order, and the first five values with the largest variance of the normal single cell temperature data are removed, and the sixth value is taken as the baseline. The abnormal single cell temperature data is judged based on the comparison between the baseline and the variance of the suspected abnormal single cell temperature data. When the variance of the suspected abnormal single cell temperature data is greater than the baseline, the single cell temperature data corresponding to the variance of the suspected abnormal single cell temperature data is the abnormal single cell temperature data.

[0027] Furthermore, in step S4, the rising and falling index and amplitude of the abnormal single cell temperature data in step S3 are calculated, and a boundary limit is set. The rising and falling index and amplitude of the abnormal single cell temperature data are compared with the boundary limit to determine whether abnormal temperature fluctuation occurs;

[0028] Furthermore, in step S4, the specific steps are:

[0029] Obtaining the maximum and minimum values of abnormal single cell temperature data collected at the same time, calculating a rise and fall index of the abnormal single cell temperature data, and calculating an average of the abnormal single cell temperature data at the same time, and calculating the amplitude of the abnormal single cell temperature data by subtracting the average data of the abnormal single cell at the same time from the abnormal single cell temperature data at the same time;

[0030] Obtaining the maximum and minimum values of all battery cell temperature data excluding the abnormal battery cell temperature data at the same moment, determining upper and lower temperature limits based on the maximum and minimum values of the battery cell temperature data, respectively, calculating an upper limit and a lower limit of a rising and falling index based on the maximum and minimum values of the upper and lower temperature limits as rising and falling index limits, respectively, obtaining the upper and lower temperature mean data and the lower temperature mean data, and calculating an upper and lower amplitude limit as amplitude limits by subtracting the upper temperature mean data from the upper temperature data and subtracting the lower temperature mean data from the lower temperature data;

[0031] The rising / falling index limit and the amplitude limit are used as boundary limits, and compared with the rising / falling index and amplitude of the abnormal single-cell battery temperature data; if the rising / falling index or amplitude of the single-cell battery corresponding to the abnormal single-cell battery temperature data exceeds the boundary limits, the single-cell battery corresponding to the current abnormal single-cell battery temperature data has a temperature fluctuation abnormality;

[0032] Furthermore, the abnormal single cell temperature data rise and fall index is calculated as:

[0033]

[0034] Where I is the rise and fall index of abnormal single cell temperature data, T max is the maximum value of abnormal single cell temperature data, T min is the minimum value of abnormal single cell temperature data;

[0035] The amplitude of the abnormal single cell temperature data is calculated as:

[0036]

[0037] Where A is the amplitude of abnormal single cell temperature data, T i is the abnormal single cell temperature data, is the average temperature data of abnormal single cells;

[0038] Furthermore, the lifting index limit is calculated as:

[0039] Upper limit of lifting index:

[0040]

[0041] Among them, I ' is the upper limit of the lifting index, T m ' ax is the maximum value of the upper temperature limit data, T m ' in is the minimum value of the upper temperature limit data;

[0042] Lower limit of lift index:

[0043]

[0044] Among them, I ” is the lower limit of the lifting index, T m ” ax is the maximum value of the lower limit temperature data, T m ” in is the minimum value of the lower limit temperature data;

[0045] The amplitude limit is calculated as:

[0046] Amplitude upper limit:

[0047]

[0048] Among them, A ' is the upper limit of amplitude, T i ' is the upper temperature limit data, is the mean data of the upper temperature limit;

[0049] Amplitude lower limit:

[0050]

[0051] Among them, A ” is the lower limit of amplitude, T i ” is the lower limit of temperature data, is the lower limit mean value of temperature data.

[0052] Furthermore, in step S5, the temperature fluctuation anomaly in step S4 is calibrated;

[0053] Furthermore, in step S5, the specific steps are:

[0054] When it is determined that the temperature fluctuation of a single cell is abnormal, the temperature sensor is checked for faults. The temperature sensor data corresponding to the single cell with abnormal temperature fluctuation is compared with the data of other normally working temperature sensors in the same battery module to check whether the measurement accuracy of the sensor is within the allowable range. If it is determined that the sensor is faulty, the backup sensor is immediately started to collect data and the relevant information of the faulty sensor is recorded;

[0055] If the temperature sensor works normally, determine the time point corresponding to the abnormal temperature data and determine two normal temperature data before and after the time point, perform linear interpolation to calculate normal temperature data of the abnormal temperature data, and use the normal temperature data of the abnormal temperature data to replace the abnormal temperature data;

[0056] Furthermore, the linear difference is calculated as:

[0057]

[0058] Among them, T ”is the normal temperature data of the abnormal temperature data, T1 is the normal temperature data before the abnormal temperature data, T2 is the normal temperature data after the abnormal temperature data, t1 is the time point corresponding to the normal temperature data before the abnormal temperature data, t2 is the time point corresponding to the normal temperature data after the abnormal temperature data, and t0 is the time point corresponding to the abnormal temperature data.

[0059] Beneficial effects

[0060] The present invention addresses the shortcomings of insufficient accuracy and low reliability of battery temperature sampling fluctuation anomaly detection in the prior art. The present invention obtains temperature data of single cells in a battery module, calculates differences between adjacent points of the single cell temperature data and differences between adjacent one-minute values of the single cell temperature data, and presets a difference upper limit threshold. Based on the preset difference upper limit threshold, the differences between the single cell temperatures at adjacent points and the differences between the single cell temperature data within one minute are compared and judged to obtain suspected abnormal single cell temperature data. The variance of the suspected abnormal single cell temperature data and the variance of the single cell temperature data with the suspected abnormal single cell temperature data removed are calculated, and a baseline is set. Based on the baseline, judgment is made to obtain abnormal single cell temperature data. The rising and falling index and amplitude of the abnormal single cell temperature data are calculated, and a boundary limit is set. The rising and falling index and amplitude of the abnormal single cell temperature data are compared with the boundary data to determine whether temperature fluctuation anomaly occurs, and the temperature fluctuation anomaly is calibrated, thereby improving the accuracy and reliability of battery temperature sampling fluctuation anomaly detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a structural block diagram of a method for detecting abnormal temperature sampling fluctuations in an embodiment of the present invention;

[0062] Figure 2 The present invention is a flowchart of a method for detecting abnormal temperature sampling fluctuations according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0064] The preferred implementation methods of the present invention are described below with reference to the accompanying drawings. Those skilled in the art should understand that these implementation methods are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0065] See also Figure 1-Figure 2 As shown, a method for detecting abnormal temperature sampling fluctuation according to an embodiment of the present invention includes:

[0066] S1. Obtaining temperature data of single cells in a battery module, and calculating differences between adjacent points of the temperature data of the single cells and differences between adjacent one-minute values of the temperature data of the single cells;

[0067] S2, presetting a difference upper limit threshold, comparing the difference between adjacent points of the single cell temperature data and the difference between adjacent one-minute single cell temperature data in step S1 with the difference upper limit threshold, and obtaining suspected abnormal single cell temperature data;

[0068] S3, calculating the variance of the suspected abnormal single cell temperature data in step S2 and the variance of the normal single cell temperature data excluding the suspected abnormal single cell temperature data, removing the first five values of the normal single cell temperature data with the largest variance and taking the sixth value to set a baseline, and performing judgment based on the baseline to obtain the abnormal single cell temperature data;

[0069] S4, calculating the rising and falling index and amplitude of the abnormal single cell temperature data in step S3, setting a boundary limit, and comparing the rising and falling index and amplitude of the abnormal single cell temperature data with the boundary data to determine whether abnormal temperature fluctuation occurs;

[0070] S5. Calibrate the abnormal temperature fluctuation in step S4.

[0071] like Figure 2 As shown, in step S1, the temperature data of the single cells in the battery module are obtained, and the difference between adjacent points of the temperature data of the single cells and the difference between adjacent one-minute temperature data of the single cells are calculated.

[0072] Specifically, in this embodiment, the preset battery module includes 5 single cells. The temperature sensor in the battery module is used to collect the temperature data of the single cells every 10 seconds, and the collection time is 2 minutes. Such a collection frequency can obtain relatively dense temperature data and more accurately capture temperature changes. The temperature sensor model is LM35DZ integrated temperature sensor. The temperature data of adjacent points of each single cell are calculated by difference. This calculation method can accurately capture the instantaneous temperature change. The temperature difference of the adjacent points of the single cell is calculated as:

[0073] D=T i+1,j -T i,j

[0074] Where D is the difference between the temperature data of the j-th single cell at the i+1th collection time and the temperature data at the i-th collection time, T i+1，j is the temperature data of the j-th single cell at the i+1th collection time, T i，jis the temperature data of the j-th single cell at the time of the i-th acquisition;

[0075] Calculate the average temperature data of the previous minute and the average temperature data of the next minute for the same single cell, and subtract the average temperature data of the next minute from the average temperature data of the previous minute to obtain the difference in temperature data of the same single cell for adjacent one minute. The difference within one minute reflects the overall temperature change of the single cell in a short period of time. The difference in temperature data of the single cell for adjacent one minute is calculated as:

[0076]

[0077] Among them, D' 1min The difference between the average temperature data of the j-th single cell obtained by collecting temperature data in the k+1th minute and the average temperature data of the temperature data collected in the kth minute in the adjacent one minute, is the average temperature data of the jth single cell in the k+1th minute, is the average temperature data of the jth single cell in the kth minute.

[0078] like Figure 2 As shown, in step S2, a difference upper limit threshold is preset, and the difference between adjacent points of the single cell temperature data and the difference between adjacent one-minute single cell temperature data in step S1 are compared with the difference upper limit threshold to obtain suspected abnormal single cell temperature data.

[0079] Specifically, in this embodiment, a preset upper limit threshold of the difference is used to set a quantitative standard for determining whether the temperature fluctuation is abnormal. By presetting the upper limit threshold of the difference, data that may have abnormal fluctuations can be preliminarily screened out, thereby narrowing the scope of subsequent abnormal temperature fluctuation analysis.

[0080] The preset upper limit of the difference is 3°C. The difference calculation results of the temperature data of adjacent points of each single cell in step S1 and the difference results of the temperature data of the same single cell in the adjacent one minute are compared with the upper limit of the difference. For a single cell, the battery temperature data T1 collected for the first time, the battery temperature data T2 collected for the second time, the average temperature data T3 obtained from the temperature data collected within the first minute, and the average temperature data T4 obtained from the temperature data collected within the second minute are obtained. The difference results T2-T1 of the adjacent points of the single cell and the difference results of the adjacent one minute are calculated respectively. T4-T3, and compare the calculated result with the upper limit threshold of the difference. When the calculated difference result is greater than 3°C, it indicates that the battery temperature data T2 collected for the second time in the single cell is suspected abnormal single cell temperature data; when the difference result of the adjacent one minute is greater than 3°C, it indicates that the average temperature data T4 obtained from the temperature data collected within the second minute is suspected abnormal single cell temperature data. In the single cell temperature data collected in real time, these data exceeding the upper limit threshold of the difference are likely to represent abnormal temperature fluctuations. These data are listed as suspected abnormal single cell temperature data to improve the efficiency of abnormality detection.

[0081] like Figure 2 As shown, in step S3, the variance of the suspected abnormal single cell temperature data in step S2 and the variance of the normal single cell temperature data excluding the suspected abnormal single cell temperature data are calculated, and a baseline is set by removing the first five values with the largest variance of the normal single cell temperature data and taking the sixth value. The abnormal single cell temperature data is obtained by judgment based on the baseline.

[0082] Specifically, in this embodiment, variance calculation is performed on the suspected abnormal single cell temperature data obtained in step S2 and the normal single cell temperature data excluding the suspected abnormal single cell temperature data. Variance is a statistic that measures the degree of data dispersion. By calculating the variance, the degree of dispersion of the normal single cell temperature data and the difference in dispersion between the suspected abnormal single cell temperature data and the normal single cell temperature data can be understood. The variance of the normal single cell data reflects the fluctuation range of the battery temperature data under normal circumstances, while the variance of the suspected abnormal single cell temperature data can reflect the discrete characteristics of these suspected abnormal single cell temperature data.

[0083] The calculated variances of the normal single-cell battery temperature data are arranged in descending order, and the first five values with the largest variances of the normal single-cell battery temperature data are removed. The sixth value is taken as the baseline. Because the first few values with the largest variances may be outliers due to extreme conditions or noise interference, by removing extreme values and selecting a value that is relatively more representative of the dispersion of normal data as the baseline, a more robust standard can be established to judge subsequent data.

[0084] Abnormal single cell temperature data is determined based on a comparison between the baseline and the variance of the suspected abnormal single cell temperature data. When the variance of the suspected abnormal single cell temperature data is greater than the baseline, the suspected abnormal single cell temperature data is abnormal single cell temperature data. This is because when the variance of the suspected abnormal single cell temperature data is greater than the baseline, it indicates that the degree of dispersion of these data is significantly beyond the normal range, and is more likely to be caused by real abnormal fluctuations in battery temperature.

[0085] like Figure 2 As shown, in step S4, the rising and falling index and amplitude of the abnormal single cell temperature data in step S3 are calculated, and a boundary limit is set. The rising and falling index and amplitude of the abnormal single cell temperature data are compared with the boundary limit to determine whether abnormal temperature fluctuation occurs.

[0086] Specifically, in this embodiment, based on the abnormal single cell temperature data at a certain moment acquired in step S3, the temperature value with the largest value is found as the maximum value, and the temperature value with the smallest value is found as the minimum value from the abnormal single cell temperature data acquired at that moment, and the abnormal single cell rise and fall index at that moment is calculated as follows:

[0087]

[0088] Where I is the rise and fall index of abnormal single cell temperature data, T max is the maximum value of abnormal single cell temperature data, T min is the minimum value of abnormal single cell temperature data;

[0089] Calculate the average value of the abnormal single cell temperature data at the same time, and calculate the amplitude of the abnormal single cell temperature data by subtracting the average value of the abnormal single cell temperature data at the time from the abnormal single cell temperature data at the time, which is calculated as:

[0090]

[0091] Where A is the amplitude of abnormal single cell temperature data, T i is the abnormal single cell temperature data, is the average temperature data of abnormal single cells;

[0092] Obtain the maximum and minimum values of the temperature data of all single cells except the abnormal single cell temperature data at the same time, form upper temperature limit data based on the maximum value of the single cell temperature data of each single cell, and form lower temperature limit data based on the minimum value of the single cell temperature data of each single cell, then find the maximum and minimum values from the upper temperature limit data to calculate the upper limit of the rise and fall index, and find the maximum and minimum values from the lower temperature limit data to calculate the lower limit of the rise and fall index, which is calculated as:

[0093] Upper limit of lifting index:

[0094]

[0095] Among them, I ' is the upper limit of the lifting index, T' max is the maximum value of the upper temperature limit data, T' min is the minimum value of the upper temperature limit data;

[0096] Lower limit of lift index:

[0097]

[0098] Among them, I” is the lower limit of the lifting index, T” max The maximum value of the lower limit temperature data, T” min is the minimum value of the lower limit temperature data;

[0099] The upper temperature limit mean data and the lower temperature limit mean data are calculated respectively, and the upper amplitude limit is calculated by subtracting the upper temperature limit mean data from the upper temperature limit data, and the lower amplitude limit is calculated by subtracting the upper temperature limit mean data from the lower temperature limit data, and the calculation is:

[0100] Amplitude upper limit:

[0101]

[0102] Among them, A' is the upper limit of amplitude, T i ' is the upper temperature limit data, is the mean data of the upper temperature limit;

[0103] Amplitude lower limit:

[0104]

[0105] Among them, A ” is the lower limit of amplitude, T i ” is the lower limit of temperature data, is the lower limit mean data of temperature;

[0106] The upper and lower limits of the rise and fall index are used as the rise and fall index limits, the upper and lower limits of the amplitude are used as the amplitude limits, and the rise and fall index limits and the amplitude limits are used as the boundary limits. The boundary limits refer to the extreme range of battery temperature changes under normal circumstances.

[0107] The rising and falling indexes and amplitudes of the abnormal single-cell battery temperature data at the same moment are compared with the boundary limits at the same moment. If the rising and falling indexes and amplitudes of the single-cell battery corresponding to the abnormal single-cell battery temperature data exceed the boundary limit range, it is considered that the single-cell battery corresponding to the abnormal single-cell battery temperature data at the current moment has an abnormal temperature fluctuation. In this way, it is possible to more accurately determine whether the battery temperature fluctuation is an abnormal situation, avoid misjudging normal fluctuations as abnormalities, or miss real abnormal fluctuations, thereby improving the accuracy and reliability of temperature fluctuation anomaly detection.

[0108] like Figure 2 As shown, in step S5, the temperature fluctuation anomaly in step S4 is calibrated.

[0109] Specifically, in this embodiment, the temperature sensor data corresponding to the single cell experiencing abnormal temperature fluctuation is compared with the temperature sensor data of other normally functioning cells in the same battery module to check whether the temperature change trends measured by the other sensors differ significantly from those of the single cell experiencing abnormal temperature fluctuation within the same time period. The measurement accuracy of the sensor is also checked to see whether it is within the allowable range, which is set to ±0.5°C. If the accuracy exceeds the ±0.5°C range, it is determined that the sensor is faulty.

[0110] If a sensor failure is identified, the backup sensor is immediately activated for data collection, and relevant information of the faulty sensor is recorded, including the sensor number, the time of the failure, and the corresponding battery module number at the time of the failure, to facilitate subsequent repair or replacement of the sensor;

[0111] If the temperature sensor is working normally, determine the time point corresponding to the abnormal temperature data, and take the normal temperature data T1 before the abnormal temperature data and the time point corresponding to T1, and the normal temperature data T2 after the abnormal temperature data and the time point corresponding to T2, and calculate the normal temperature data of the abnormal temperature data by linear interpolation, which is calculated as:

[0112]

[0113] Among them, T ”is the normal temperature data of the abnormal temperature data, T1 is the normal temperature data before the abnormal temperature data, T2 is the normal temperature data after the abnormal temperature data, t1 is the time point corresponding to the normal temperature data before the abnormal temperature data, t2 is the time point corresponding to the normal temperature data after the abnormal temperature data, and t0 is the time point corresponding to the abnormal temperature data;

[0114] The calculation results are replaced with the corrected normal temperature data to complete the calibration of abnormal temperature fluctuations.

[0115] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0116] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for detecting abnormal temperature sampling fluctuations, characterized in that: The following steps are involved: S1. Obtaining temperature data of single cells in a battery module, and calculating differences between adjacent points of the temperature data of the single cells and differences between adjacent one-minute values of the temperature data of the single cells; S2, presetting a difference upper limit threshold, comparing the difference between adjacent points of the single cell temperature data and the difference between adjacent one-minute single cell temperature data in step S1 with the difference upper limit threshold, and obtaining suspected abnormal single cell temperature data; S3, calculating the variance of the suspected abnormal single cell temperature data in step S2 and the variance of the normal single cell temperature data excluding the suspected abnormal single cell temperature data, removing the first five values of the normal single cell temperature data with the largest variance and taking the sixth value to set a baseline, and performing judgment based on the baseline to obtain the abnormal single cell temperature data; S4, calculating the rising and falling index and amplitude of the abnormal single cell temperature data in step S3, setting a boundary limit, and comparing the rising and falling index and amplitude of the abnormal single cell temperature data with the boundary limit to determine whether abnormal temperature fluctuation occurs; S5. Calibrate the abnormal temperature fluctuation in step S4.

2. The method for detecting abnormal temperature sampling fluctuation according to claim 1, characterized in that: The difference between adjacent points of the single cell temperature data and the difference between adjacent one-minute single cell temperature data are calculated as follows: The difference calculation of adjacent points of the single cell temperature data is: D=T i+1,j -T i,j Where D is the difference between the temperature data of the j-th single cell at the i+1th collection time and the temperature data at the i-th collection time, T i+1，j is the temperature data of the j-th single cell at the i+1th collection time, T i，j is the temperature data of the j-th single cell at the time of the i-th acquisition; The difference between the temperature data of the single cell in adjacent one minute is calculated as follows: Among them, D′ 1min The difference between the average temperature data of the j-th single cell obtained by collecting temperature data in the k+1th minute and the average temperature data of the temperature data collected in the kth minute in the adjacent one minute, is the average temperature data of the jth single cell in the k+1th minute, is the average temperature data of the jth single cell in the kth minute.

3. The method for detecting abnormal temperature sampling fluctuation according to claim 1, characterized in that: The preset upper limit threshold of the difference is determined by comparing the difference between adjacent points of the single cell temperature data and the difference between adjacent one-minute single cell temperature data with the upper limit threshold, and the suspected abnormal single cell temperature data is obtained as follows: A preset upper limit threshold value of the difference is used, and the calculation result of the difference of the single cell temperature data of adjacent points of each single cell in step S1 and the difference result of the single cell temperature data in adjacent one minute are compared with the upper limit threshold value of the difference. When the calculation result of the difference of the single cell temperature data of two adjacent single cell is greater than the upper limit threshold value of the difference, it means that the single cell temperature data collected later of the two adjacent single cell temperature data corresponding to the current difference calculation result is suspected abnormal single cell temperature data; when the difference result of the single cell temperature data in adjacent one minute is greater than the upper limit threshold value of the difference, it means that the single cell temperature data collected in the next minute corresponding to the difference result of the single cell temperature data in adjacent one minute is suspected abnormal single cell temperature data.

4. The method for detecting abnormal temperature sampling fluctuation according to claim 1, characterized in that: The variance of the suspected abnormal single cell temperature data and the variance of the normal single cell temperature data excluding the suspected abnormal single cell temperature data are calculated, and the sixth value is taken to set a baseline by removing the first five values with the largest variance of the normal single cell temperature data. The abnormal single cell temperature data is determined according to the baseline to be: The variance of the suspected abnormal single cell temperature data and the normal single cell temperature data excluding the suspected abnormal single cell temperature data are respectively calculated, the variance results of the normal single cell temperature data are arranged in descending order, and the first five values of the normal single cell temperature data with the largest variance are removed, and the sixth value is taken as a baseline. The abnormal single cell temperature data is judged based on the baseline and the variance of the suspected abnormal single cell temperature data. When the variance of the suspected abnormal single cell temperature data is greater than the baseline, the single cell temperature data corresponding to the variance of the suspected abnormal single cell temperature data is the abnormal single cell temperature data.

5. The method for detecting abnormal temperature sampling fluctuation according to claim 1, characterized in that: The calculation of the rising and falling index and amplitude of the abnormal single cell temperature data, setting a boundary limit, and comparing the rising and falling index and amplitude of the abnormal single cell temperature data with the boundary limit to determine whether abnormal temperature fluctuation occurs are as follows: Obtaining the maximum and minimum values of abnormal single cell temperature data collected at the same time, calculating a rise and fall index of the abnormal single cell temperature data, and calculating an average of the abnormal single cell temperature data at the same time, and calculating the amplitude of the abnormal single cell temperature data by subtracting the average data of the abnormal single cell at the same time from the abnormal single cell temperature data at the same time; Obtaining the maximum and minimum values of all battery cell temperature data excluding the abnormal battery cell temperature data at the same moment, determining upper and lower temperature limits based on the maximum and minimum values of the battery cell temperature data, respectively, calculating an upper limit and a lower limit of a rising and falling index based on the maximum and minimum values of the upper and lower temperature limits as rising and falling index limits, respectively, obtaining the upper and lower temperature mean data and the lower temperature mean data, and calculating an upper and lower amplitude limit as amplitude limits by subtracting the upper temperature mean data from the upper temperature data and subtracting the lower temperature mean data from the lower temperature data; The rising and falling index limit and the amplitude limit are used as boundary limits and compared with the rising and falling index and amplitude of the abnormal single cell temperature data. If the rising and falling index or amplitude of the single cell corresponding to the abnormal single cell temperature data exceeds the boundary limits, the single cell corresponding to the current abnormal single cell temperature data has an abnormal temperature fluctuation.

6. The method for detecting abnormal temperature sampling fluctuation according to claim 5, characterized in that: The abnormal single cell rise and fall index and amplitude are calculated as follows: The abnormal single cell temperature data rise and fall index calculation: Where I is the rise and fall index of abnormal single cell temperature data, T max is the maximum value of abnormal single cell temperature data, T min is the minimum value of abnormal single cell temperature data; The amplitude calculation of the abnormal single cell temperature data is as follows: Where A is the amplitude of abnormal single cell temperature data, T i is the abnormal single cell temperature data, T i It is the average temperature data of abnormal single cells.

7. The method for detecting abnormal temperature sampling fluctuation according to claim 5, characterized in that: The lifting index limit and amplitude limit are calculated as: The lifting index limit calculation is: Upper limit of lifting index: Among them, I' is the upper limit of the lifting index, T' max is the maximum value of the upper temperature limit data, T′ min is the minimum value of the upper temperature limit data; Lower limit of lift index: Among them, I ” is the lower limit of the lifting index, T m ” ax is the maximum value of the lower limit temperature data, T m ” in is the minimum value of the lower limit temperature data; The amplitude limit is calculated as: Amplitude upper limit: Among them, A ' is the upper limit of amplitude, T i ' is the upper temperature limit data, is the mean data of the upper temperature limit; Amplitude lower limit: Among them, A ” is the lower limit of amplitude, T i ” is the lower limit of temperature data, is the lower limit mean value of temperature data.

8. The method for detecting abnormal temperature sampling fluctuation according to claim 1, characterized in that: The calibration of abnormal temperature fluctuation is as follows: When it is determined that the temperature fluctuation of a single cell is abnormal, the temperature sensor is checked for faults. The temperature sensor data corresponding to the single cell with abnormal temperature fluctuation is compared with the data of other normally working temperature sensors in the same battery module to check whether the measurement accuracy of the sensor is within the allowable range. If it is determined that the sensor is faulty, the backup sensor is immediately started to collect data and the relevant information of the faulty sensor is recorded; If the temperature sensor works normally, the time point corresponding to the abnormal temperature data is determined, and two normal temperature data before and after the time point are determined to perform linear interpolation to calculate normal temperature data of the abnormal temperature data, and the normal temperature data of the abnormal temperature data is used to replace the abnormal temperature data.

9. The method for detecting abnormal temperature sampling fluctuation according to claim 8, characterized in that: The linear difference is calculated as: Among them, T ” is the normal temperature data of the abnormal temperature data, T1 is the normal temperature data before the abnormal temperature data, T2 is the normal temperature data after the abnormal temperature data, t1 is the time point corresponding to the normal temperature data before the abnormal temperature data, t2 is the time point corresponding to the normal temperature data after the abnormal temperature data, and t0 is the time point corresponding to the abnormal temperature data.

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