Sampling wire harness detection method and system of power battery

Abstract

The invention relates to the technical field of data processing, in particular to a sampling wire harness detection method and system of a power battery. The method comprises the following steps: acquiring a voltage data sequence of each sampling wire harness at a preset initial sampling frequency; determining an instability index according to the fluctuation amplitude characteristic and the change rate characteristic of the voltage data sequence; if the instability index exceeds a preset adjustment threshold value, calculating an adjustment coefficient; adjusting the initial sampling frequency to a target sampling frequency according to the adjustment coefficient; collecting a test voltage data sequence at a target sampling frequency, and determining a sampling redundancy index according to the occurrence frequency of the repeated voltage value; if the sampling redundancy index is not within the preset threshold range, correcting the target sampling frequency to the optimal sampling frequency based on the sampling redundancy index; voltage data of all the sampling wire harnesses are collected at the optimal sampling frequency, and then the abnormal probability is determined; and if the abnormal probability exceeds a preset abnormal threshold value, determining that the sampling wire harness is abnormal, thereby improving the accuracy of the sampling wire harness detection result.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and specifically to a method and system for detecting sampling harnesses of power batteries. Background Art

[0002] As the signal transmission channel of the battery management system, the sampling harness of the power battery is responsible for transmitting key parameters such as voltage and temperature in real time. It directly affects the accuracy of judging safety risks such as overcharging, over-discharging, and even thermal runaway. If the signal is abnormal due to insulation failure, poor contact, or mechanical damage, it may cause the BMS to make a misjudgment. Therefore, strict testing of the sampling harness can identify potential harness hazards in advance and reduce the risk of accidents.

[0003] Currently, in the existing technology, when testing the sampling harness of a power battery, a fixed sampling frequency is usually used to collect the voltage signal of the sampling harness to detect faults. However, this method cannot respond in time to sudden faults in the sampling harness. For example, transient voltage fluctuations caused by poor contact cannot be captured in time. Also, if the collected signal changes rapidly, the sampling frequency interval is too large, which will lead to the loss of key features and ultimately affect the authenticity of the sampling harness test results. Summary of the Invention

[0004] To address the technical problem of critical data loss caused by inappropriate sampling frequencies, which affects the accuracy of sampling harness detection results, this invention provides a sampling harness detection method and system for power batteries. The specific technical solution adopted is as follows: This invention proposes a method for detecting the sampling harness of a power battery, the method comprising: The voltage data sequence of each sampling line bundle is collected in real time at a preset initial sampling frequency. The voltage data sequence consists of voltage values ​​arranged in chronological order. For each sampling harness, the instability index of the sampling harness is determined based on the fluctuation amplitude and rate of change characteristics of the voltage data sequence. If the instability index exceeds the preset adjustment threshold, an adjustment coefficient for adjusting the sampling frequency is determined based on the absolute difference between the instability index and adjacent voltage values; the initial sampling frequency is adjusted to the target sampling frequency based on the adjustment coefficient and the preset upper limit of the sampling frequency. The test voltage data sequence is acquired at the target sampling frequency. Based on the frequency of repeated voltage values ​​in the test voltage data sequence, a sampling redundancy index is determined to verify the effectiveness of the target sampling frequency. If the sampling redundancy index is not within the preset threshold range, the target sampling frequency is corrected to the optimal sampling frequency based on the sampling redundancy index. Voltage data of all sampling harnesses are collected synchronously at the optimal sampling frequency. Based on the differences in voltage values ​​at the same time among the voltage data of all sampling harnesses, the probability of anomalies is determined. If the probability of anomalies exceeds the preset anomaly threshold, the sampling harness is determined to be abnormal.

[0005] Furthermore, the process of determining instability indicators includes: Identify steady-state transition inflection points in a voltage data sequence, where a steady-state transition inflection point indicates the first data point from the first data point onwards where a preset number of consecutive voltage values ​​change from meeting steady-state conditions to not meeting steady-state conditions; Extract the unsteady-state voltage data from the steady-state transition point to the current moment from the voltage data sequence; Based on the differences between voltage values ​​in the unsteady-state voltage data, determine the variation index of the sampling harness; Obtain the duration from the steady-state transition inflection point to the current moment; The instability index is obtained by multiplying the voltage value and change index of the sampling harness at the current moment with the duration and then normalizing the result.

[0006] Furthermore, the process of determining the change indicators includes: Obtain the maximum and minimum voltage values ​​from the voltage data in the unsteady-state segment; Calculate the difference between the maximum voltage value and the minimum voltage value, and use it as the first difference value; Calculate the absolute difference between each pair of adjacent voltage values ​​in the unsteady-state voltage data, and use it as the second difference; Calculate the arithmetic mean of all second differences to obtain the first mean; Calculate the product of the first difference and the first average value, and use it as the indicator of change.

[0007] Furthermore, the process of determining the adjustment coefficients includes: Calculate the absolute difference between voltage values ​​of adjacent data points in the voltage data sequence as the third difference; for each third difference, divide the third difference by the time interval between the corresponding adjacent data points to obtain the rate of change; The average rate of change is obtained by averaging all the rates of change. The adjustment coefficient is obtained by multiplying the average rate of change by the instability index and then normalizing the result.

[0008] Furthermore, based on the adjustment coefficient and the preset upper limit of the sampling frequency, the initial sampling frequency is adjusted to the target sampling frequency, including: The target sampling frequency is obtained by linear interpolation calculation based on the difference between the initial sampling frequency and the preset upper limit of the sampling frequency, by adjusting the coefficients.

[0009] Furthermore, the process of determining the sampling redundancy index includes: Calculate the frequency ratio of repeated voltage value data points in the test voltage data sequence to the total frequency ratio of all data points; calculate the proportion of the sum of repeated voltage values ​​in the test voltage data sequence to the total sum of all voltage values. The product of the frequency ratio and the ratio value is calculated and used as a sampling redundancy index.

[0010] Furthermore, a method for detecting the sampling harness of a power battery also includes: If the sampling redundancy index is within the preset threshold range, the target sampling frequency will be directly used as the optimal sampling frequency.

[0011] Furthermore, the sampling redundancy index being outside the preset threshold range is divided into two cases: the first case is that the sampling redundancy index is higher than the upper limit of the preset threshold range, and the second case is that the sampling redundancy index is lower than the lower limit of the preset threshold range; the step of correcting the target sampling frequency to the optimal sampling frequency based on the sampling redundancy index if the sampling redundancy index is outside the preset threshold range includes: If the sampling redundancy index is not within the preset threshold range and falls under the first case, the target sampling frequency is corrected to the optimal sampling frequency based on the sampling redundancy index and through the preset first frequency correction function. If the sampling redundancy index is not within the preset threshold range and falls under the second case, the target sampling frequency is corrected to the optimal sampling frequency based on the sampling redundancy index and through a preset second frequency correction function.

[0012] Furthermore, the anomaly probability determination process includes: The voltage reference value is obtained by calculating the arithmetic mean of the voltage values ​​at the same time in all sampled harnesses. The sum of the absolute differences between the voltage values ​​at the same time and the corresponding voltage reference values ​​in the voltage data of all sampled harnesses is normalized to obtain the anomaly probability.

[0013] A sampling harness detection system for a power battery includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of a sampling harness detection method for a power battery.

[0014] The present invention has the following beneficial effects: This invention analyzes the fluctuation amplitude and rate of change characteristics of voltage data sequences to determine instability indicators. These instability indicators facilitate dynamic adjustment of the sampling frequency, enabling accurate capture of instantaneous features in the voltage data and ensuring complete recording of transient signals. Secondly, to determine appropriate sampling frequencies for different stages of voltage signal acquisition, the instability indicators and differences between adjacent voltage values ​​are analyzed to determine adjustment coefficients for the sampling frequency. This allows for real-time response to changes in the sampling harness, enabling more effective sampling frequency detection. Furthermore, after determining the target sampling frequency, it's possible to verify whether using the target sampling frequency will generate redundant voltage data. The sampling redundancy index is used to investigate the presence of redundant data in the test voltage data. If redundancy exists, the target sampling frequency is promptly adjusted using the sampling redundancy index to obtain the optimal sampling frequency. Finally, the optimal sampling frequency reduces redundant data in the acquired voltage data, lowering power consumption. By comparing the voltage differences of multiple sampling harnesses at the same time using the voltage data acquired at the optimal sampling frequency, the anomaly probability is used to detect whether there are faults in the sampling harness, improving the accuracy of the sampling harness detection results. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart of a sampling harness detection method for a power battery according to an embodiment of the present invention; Figure 2 This is an example diagram of unsteady-state voltage data provided in one embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a sampling harness detection method for a power battery proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The following describes in detail, with reference to the accompanying drawings, a specific scheme for a sampling harness detection method for power batteries provided by the present invention.

[0020] Please see Figure 1 The diagram illustrates a flowchart of a sampling harness detection method for a power battery according to an embodiment of the present invention. The method includes: S101: At a preset initial sampling frequency, the voltage data sequence of each sampling harness is acquired in real time, wherein the voltage data sequence consists of voltage values ​​arranged in chronological order.

[0021] It should be noted that at each acquisition moment, each sampling harness corresponds to an independent voltage signal, i.e., a voltage value.

[0022] It should be noted that the specific value of the preset initial sampling frequency is determined according to actual needs, and this embodiment does not impose a specific limitation. For example, when the power battery is charging and discharging normally, the voltage fluctuation is relatively slow. In order to capture the steady-state fluctuation of the voltage signal, the preset initial sampling frequency can be set to 50Hz (50 voltage values ​​are collected per second).

[0023] It is important to understand that before acquiring the voltage data sequence of each sampling harness in real time, some preparatory work can be done to avoid interference from other external factors on the voltage signal. For example, cleaning the terminals of the sampling harness to ensure that their contact surfaces are free of oxide layers, fixing the position of the sampling harness to reduce contact noise introduced by mechanical vibration; selecting high-precision, wide-range voltage sensors, configuring multi-channel data acquisition cards, and deploying sampling points at key nodes of the power battery module (such as harness connector interfaces); and adding shielding layers to each sampling harness to reduce electromagnetic interference.

[0024] For example, for sampling harness k, with a sampling frequency of 50Hz, assuming that the voltage values ​​have been collected in real time for a period of time, the total number of time points in the voltage data sequence can be represented by t, where t is a positive integer. Then, the voltage data sequence of sampling harness k can be represented as follows: ,in, This represents the voltage value of sampling line bundle k acquired at the first acquisition time. This represents the voltage value of sampling line bundle k acquired at the second acquisition time. This represents the voltage value of the sampling bundle k acquired at the t-th acquisition time.

[0025] S102: For each sampling harness, determine the instability index of the sampling harness based on the fluctuation amplitude and rate of change characteristics of the voltage data sequence.

[0026] It is important to understand that if there are no abnormalities in the sampling harness, the collected voltage data should be relatively stable. Therefore, by analyzing the fluctuation range and changes in voltage values, we can initially analyze whether the voltage data sequence is stable, and then detect whether there are any abnormalities in the sampling harness.

[0027] In this embodiment, a steady-state transition inflection point is identified in the voltage data sequence. The steady-state transition inflection point indicates the first data point where a predetermined number of consecutive voltage values ​​change from meeting the steady-state condition to not meeting the steady-state condition, starting from the first data point. Non-steady-state voltage data from the steady-state transition inflection point to the current time is extracted from the voltage data sequence. Based on the differences between the voltage values ​​in the non-steady-state voltage data, a change index for the sampling harness is determined. The duration from the steady-state transition inflection point to the current time is obtained. The voltage value of the sampling harness at the current time, the change index, and the duration are multiplied and then normalized to obtain an instability index.

[0028] It should be noted that the steady-state condition includes voltage fluctuations that are less than the stability threshold.

[0029] It should be noted that the specific values ​​of the preset quantity and the stable threshold are determined according to actual needs, and this embodiment does not impose specific limitations. For example, it can be set that the fluctuation range of a consecutive preset number (value is 100) of voltage values ​​is less than the stable threshold (value is 10mV).

[0030] For example, taking sampling harness k as an example, the following is an example diagram of voltage data in the unsteady-state section. Figure 2 As shown, where, Figure 2 middle The indicated data points represent steady-state transition inflection points. express The corresponding acquisition time (the s-th acquisition time).

[0031] Non-steady-state voltage data refers to the data in the voltage data sequence from the steady-state transition point to the last data point.

[0032] For example, suppose This is represented as the voltage value at the steady-state transition point. The data acquisition time is represented as the steady-state transition inflection point; therefore, the voltage data in the unsteady-state segment can be represented as... ,but It can also be expressed as ,in, Corresponding to , and The recorded acquisition time and voltage value are the same. This represents the voltage value of sampling line bundle k collected at the first acquisition time in the unsteady-state voltage data; Corresponding to , and The recorded acquisition time and voltage value are the same. This represents the voltage data in the unsteady-state segment at the [number]th [period]. The voltage value of sampling harness k collected at each sampling time.

[0033] To accurately determine the change index, one possible implementation is to determine the change index based on the maximum voltage difference and the absolute difference between adjacent voltage values ​​in the non-steady-state voltage data.

[0034] As an example, the maximum and minimum voltage values ​​in the unsteady-state voltage data are obtained; the difference between the maximum and minimum voltage values ​​is calculated as the first difference; the absolute difference between each pair of adjacent voltage values ​​in the unsteady-state voltage data is calculated as the second difference; the arithmetic mean of all the second differences is calculated to obtain the first average value; the product of the first difference and the first average value is calculated as the change index.

[0035] The first difference indicates the maximum voltage difference in the unsteady-state voltage data.

[0036] The first average value is used to reflect the overall change in voltage values ​​in the unsteady-state voltage data.

[0037] It should be noted that for each voltage value in the non-steady-state voltage data... (i=2,3,…, The corresponding preceding voltage value is located in the unsteady-state voltage data. Previous and adjacent voltage values , where i is the index number of the voltage value in the unsteady-state voltage data. Calculation and absolute difference | - |, as the second difference.

[0038] It should be noted that "previous adjacent" refers only to the forward order of time sequence in the voltage data of the unsteady segment, and does not include cyclic tracing (e.g., not associating the last voltage value with the first voltage value in the voltage data of the unsteady segment).

[0039] Since a larger difference between the maximum and minimum voltage values ​​in the unsteady-state voltage data, and a larger difference between adjacent voltage values, indicates that the unsteady-state voltage data changes significantly and drastically, reflecting a high degree of instability in the voltage signal of the sampling harness during the period in which the unsteady-state voltage data was collected, the change index can be expressed by the following formula: in, This indicates the change index of the sampling harness k; This represents the first difference in the sampling bundle k; This represents the voltage value of sampling line bundle k collected at time i+1 in the unsteady-state voltage data; This represents the voltage value of sampling line bundle k collected at time i in the unsteady-state voltage data; This represents the total number of time points in the voltage data during the unsteady-state phase. ; This represents the maximum voltage value in the unsteady-state voltage data of sampling harness k; This represents the minimum voltage value in the unsteady-state voltage data of the sampling harness k; | represents taking the absolute value.

[0040] It should be noted that, under actual operating conditions, a sufficient amount of voltage data is needed to investigate whether the voltage signal of the sampling harness is stable. Therefore, although the voltage values ​​of the sampling harness are collected in real time during the acquisition of the voltage data sequence, a voltage data sequence containing a sufficient amount of voltage values ​​is determined only after a period of time (e.g., 1 minute) of voltage value collection. Consequently, the number of data points in the voltage data sequence will definitely exceed 1, and the total number of time points in the non-steady-state voltage data will also definitely exceed 1. It cannot be zero.

[0041] It is important to understand that although unstable voltage data may exist in the sampling harness voltage data, if the degree of instability is low, it may be due to interference factors (such as noise generated by electromagnetic interference). In this case, there is no need to adjust the sampling frequency. Therefore, in order to avoid interference factors affecting the judgment of whether the sampling frequency needs to be adjusted, and to detect any significant changes in the voltage data of the sampling harness in a timely manner, further analysis is needed to determine whether the sampling frequency needs to be adjusted to avoid missing key voltage data.

[0042] Since a larger change index reflects drastic fluctuations in the voltage data, and a longer interval between the current moment and the steady-state transition point, coupled with a larger voltage value collected at the current moment, indicates a more complex internal situation in the sampling harness and a more severe degree of voltage data instability, requiring adjustment of the initial sampling frequency at the current moment, the instability index can be expressed by the following formula: in, This represents the instability index of the sampling harness k at time t (i.e., the current time); This indicates the duration from the steady-state transition inflection point to the current moment; Indicates the change index; This represents the voltage value of sampling harness k at time t (i.e., the current time). This represents the normalization function.

[0043] The current moment refers to the last acquisition moment of the voltage data sequence.

[0044] S103: If the instability index exceeds the preset adjustment threshold, determine the adjustment coefficient for adjusting the sampling frequency based on the absolute difference between the instability index and adjacent voltage values; adjust the initial sampling frequency to the target sampling frequency based on the adjustment coefficient and the preset upper limit of the sampling frequency.

[0045] It should be noted that the preset adjustment threshold is determined according to actual needs, and this embodiment does not impose a specific limitation. For example, if the preset adjustment threshold is 0.4, it can be found that the voltage signal instability of the sampling harness can be clearly distinguished. When the instability index exceeds the preset adjustment threshold (0.4), the voltage signal instability of the sampling harness is more severe.

[0046] In this embodiment, the absolute difference between voltage values ​​of adjacent data points in the voltage data sequence is calculated as the third difference; for each third difference, the third difference is divided by the time interval between the corresponding adjacent data points to obtain the rate of change; all rates of change are arithmetically averaged to obtain the average rate of change; the average rate of change is multiplied by the instability index and then normalized to obtain the adjustment coefficient.

[0047] In this context, it can be understood that a data point in the voltage data sequence refers to the voltage measurement value obtained at a specific acquisition time when the voltage signal of the sampling harness is discretized. Each data point contains timestamp information and the corresponding voltage value.

[0048] It should be noted that for each voltage value in the voltage data sequence (j=2, 3, ..., ), whose corresponding preceding adjacent voltage value is located in the voltage data sequence. Previous and adjacent voltage values , where j is the index number of the voltage value in the voltage data sequence. Calculate and absolute difference | - |

[0049] Since a large difference between adjacent voltage values ​​in a voltage data sequence indicates that many voltage values ​​were missed during the time period required to acquire the voltage data sequence, and the time interval between data points in the voltage data sequence is too long, it is necessary to increase the sampling frequency. Furthermore, a higher instability index indicates a more severe instability in the voltage signal of the sampling harness, requiring an even higher sampling frequency to avoid failing to capture critical voltage signals. Therefore, the adjustment coefficient can be expressed by the following formula: in, This represents the adjustment factor of the sampling harness k at time t (i.e., the current time); This represents the instability index of the sampling harness k at time t (i.e., the current time); This represents the voltage value at time j in the voltage data sequence of sampled harness k; Indicates and The voltage value of the preceding adjacent voltage at time j+1; express and The time interval between; represents the normalization function; | represents taking the absolute value.

[0050] It should be noted that, under actual operating conditions, a sufficient amount of voltage data is needed to investigate the stability of the voltage signal of the sampling harness. Therefore, although the voltage values ​​of the sampling harness are collected in real time during the acquisition of the voltage data sequence, a voltage data sequence containing a sufficient amount of voltage values ​​is determined only after a period of time (e.g., 1 minute) of voltage value collection. Thus, the number of data points in the voltage data sequence will definitely exceed 1. The sampling time interval cannot be zero; and the time interval between adjacent data points is the sampling time interval. To investigate the changes in the voltage value of the sampling harness, the sampling time interval cannot be set to zero. It cannot be zero.

[0051] In this embodiment, the target sampling frequency is obtained by linear interpolation calculation through adjustment coefficients based on the difference between the initial sampling frequency and the preset upper limit of the sampling frequency.

[0052] It should be noted that the specific value of the upper limit of the preset sampling frequency is determined according to the actual situation, and this embodiment does not impose a specific limitation. For example, in order to avoid increasing the burden on the power battery management system, the upper limit of the preset sampling frequency is usually set to 500Hz.

[0053] It should be noted that linear interpolation is a commonly used mathematical method used to estimate the value of unknown data points by constructing a straight line given some known data points. It is a well-known technique in the art and will not be described in detail in this embodiment. For example, the target sampling frequency = initial sampling frequency + (preset upper limit of sampling frequency - initial sampling frequency) × adjustment coefficient.

[0054] It should be noted that linear interpolation calculations ensure a smooth transition in sampling frequency, avoiding data gaps caused by abrupt changes.

[0055] S104: Acquire test voltage data sequences at the target sampling frequency, and determine the sampling redundancy index used to verify the effectiveness of the target sampling frequency based on the frequency of repeated voltage values ​​in the test voltage data sequence; if the sampling redundancy index is not within the preset threshold range, correct the target sampling frequency to the optimal sampling frequency based on the sampling redundancy index.

[0056] It is important to understand that the most suitable sampling frequency should be able to collect a large amount of fluctuating data in the non-steady-state voltage data, and there should be no large-scale repetition of data in the non-steady-state voltage data. Therefore, once the target sampling frequency is determined, if the target sampling frequency is too high, more repetitive data will be generated; if the target sampling frequency is still too low, some feature data will still be lost. Therefore, further testing is needed to determine whether the target sampling frequency is appropriate.

[0057] It should be noted that, in order to ensure the reliability of the test voltage data sequence and to more accurately test whether the target sampling frequency is effective, that is, to determine whether the voltage data collected at the target sampling frequency can achieve the effect of neither missing key voltage signals nor having too much duplicate data, the test voltage data sequence can be analyzed after a period of acquisition time (e.g., 5 seconds).

[0058] In this embodiment, the frequency ratio of repeated voltage value data points in the test voltage data sequence to the total number of all data points is calculated; the ratio of the sum of repeated voltage values ​​in the test voltage data sequence to the sum of all voltage values ​​is calculated; and the product of the frequency ratio and the ratio is used as a sampling redundancy index.

[0059] Since a higher frequency ratio reflects a higher proportion of repeated voltage value data points in the test voltage data sequence, meaning a higher frequency of repeated voltage values, a larger proportion of repeated voltage values ​​in the test voltage data sequence, and a larger amount of repeated data, indicates more repeated data in the test voltage data sequence. Therefore, at the target sampling frequency, the redundancy of the collected voltage data is higher. Thus, the sampling redundancy index can be expressed by the following formula: in, The sampling redundancy index represents the sampling harness k. This indicates the number of repeated voltage value data points in the test voltage data sequence of the sampling harness k; This represents the total number of data points in the test voltage data sequence of the sampling harness k; This represents the sum of repeated voltage values ​​in the test voltage data sequence of the sampling harness k; This represents the sum of all voltage values ​​in the test voltage data sequence of the sampling harness k.

[0060] It should be noted that, under actual operating conditions, in order to ensure the reliability of the test voltage data sequence, the test voltage data sequence needs to contain an appropriate number of data points. and None of them can be zero.

[0061] It should be noted that, in order to eliminate the influence of differences in the units and ranges of values ​​of different features, it is necessary to adjust the sampling redundancy index, i.e. Normalization is performed.

[0062] It is important to understand that the optimal sampling frequency should ensure that all voltage data values ​​are collected while minimizing duplicate data to reduce system power consumption. Therefore, if the target sampling frequency is too high, the repetition rate of the collected voltage data will be too high, in which case the target sampling frequency needs to be reduced. If the target sampling frequency is too low, critical voltage data is likely to be missed, in which case the target sampling frequency needs to be increased.

[0063] It should be noted that there are two situations where the sampling redundancy index is not within the preset threshold range. The first situation is that the sampling redundancy index is higher than the upper limit of the preset threshold range, and the second situation is that the sampling redundancy index is lower than the lower limit of the preset threshold range.

[0064] In this embodiment, if the sampling redundancy index is not within the preset threshold range and belongs to the first case, the target sampling frequency is corrected to the optimal sampling frequency based on the sampling redundancy index and through a preset first frequency correction function; if the sampling redundancy index is not within the preset threshold range and belongs to the second case, the target sampling frequency is corrected to the optimal sampling frequency based on the sampling redundancy index and through a preset second frequency correction function.

[0065] Since the sampling redundancy index is higher than the upper limit of the preset threshold range, the target sampling frequency needs to be reduced. Therefore, in the first case, the optimal sampling frequency can be represented by the following preset first frequency correction function: Since the sampling redundancy index is lower than the upper limit of the preset threshold range, the target sampling frequency needs to be increased. Therefore, in the second case, the optimal sampling frequency can be represented by the following preset second frequency correction function: in, Indicates the target sampling frequency of sampling harness k; This indicates the optimal sampling frequency for sampling harness k; This represents the sampling redundancy index of sampling harness k.

[0066] It should be noted that if the sampling redundancy index is within the preset threshold range, the target sampling frequency will be directly used as the optimal sampling frequency.

[0067] It should be noted that the specific range of the preset threshold is determined based on the actual situation, and this embodiment does not impose a specific limitation. For example, the preset threshold range can be set to a value of [value missing]. It can be found in In At this time, the voltage data collected at the target sampling frequency will not have a high data repetition rate, nor will there be any omission of key voltage data.

[0068] It should be noted that the upper limit of the preset threshold range is used to indicate the maximum threshold within the preset threshold range (e.g., 0.3 in the preset threshold range); the lower limit of the preset threshold range is used to indicate the minimum threshold in the preset threshold range (e.g., 0.3). (0.1 in the middle).

[0069] S105: Synchronously collect voltage data of all sampling harnesses at the optimal sampling frequency, determine the anomaly probability based on the differences in voltage values ​​at the same time among the voltage data of all sampling harnesses, and determine the anomaly probability if the anomaly probability exceeds the preset anomaly threshold.

[0070] It should be noted that, since each sampling harness is adjusted independently, in order to ensure the uniformity of the collected voltage data and facilitate the determination of whether the sampling harness is abnormal, the average value of the optimal sampling frequency of all sampling harnesses can be taken as the unified optimal sampling frequency for synchronous acquisition, thus ensuring synchronization.

[0071] It is important to understand that the voltage data collected by each sampling harness is independent of each other. Under normal circumstances, the voltage data detected by multiple sampling harnesses at the same time will not differ much. If there is a large difference between the voltage data detected at the same time, it indicates that there is a problem with the sampling harness.

[0072] It should be noted that, in order to better analyze the differences between the voltage data detected by different sampling harnesses at the same time, voltage data curves can be constructed based on the collected voltage data of each sampling harness. Then, multiple voltage data curves can be plotted on the same coordinate system to quickly obtain the voltage values ​​of multiple sampling harnesses at the same time.

[0073] It should be noted that the specific method for constructing the voltage data curve is a well-known technique in the art, and will not be described in detail in this embodiment.

[0074] It should be noted that the voltage data curves of each sampling line bundle are displayed in a two-dimensional Cartesian coordinate system, where the horizontal axis represents time (in seconds) and the vertical axis represents voltage value (in volts). The voltage data curves of each sampling line bundle are distinguished by different line types and markers (for example, sampling line bundle 1 is represented by a solid line and sampling line bundle 2 by a dashed line). Representing multiple voltage data curves in the same two-dimensional Cartesian coordinate system is a technique well known to those skilled in the art, and will not be elaborated upon in this embodiment.

[0075] In this embodiment, the voltage values ​​at the same time in the voltage data of all sampled harnesses are calculated by arithmetic mean to obtain the voltage reference value; the sum of the absolute differences between the voltage values ​​at the same time in the voltage data of all sampled harnesses and the corresponding voltage reference value is normalized to obtain the anomaly probability.

[0076] It should be noted that, in order to avoid abnormal data interfering with the voltage reference value, the data with the largest voltage deviation can be removed from all voltage data before calculating the voltage reference value.

[0077] It should be noted that the correspondence between the voltage value and the voltage reference value is used to indicate the voltage value and the voltage reference value at the same time.

[0078] It is important to understand that if the voltage values ​​of multiple sampling harnesses differ significantly at the same time, then the overall voltage data of the multiple sampling harnesses will also differ significantly. This indicates that the voltage stability of each harness is poor and the deviation of the overall trend is also high, which means that the sampling harnesses are more likely to be abnormal, and the probability of abnormality is also higher.

[0079] It should be noted that the specific value of the preset abnormal threshold is determined according to actual needs, and this embodiment does not impose a specific limitation. For example, a preset abnormal threshold of 0.2 can effectively distinguish abnormal problems in the sampling harness.

[0080] It should be noted that if the probability of an anomaly is not greater than the preset anomaly threshold, the sampling harness is determined to be free of anomalies.

[0081] An embodiment of the present invention provides a sampling harness detection system for a power battery. The sampling harness detection system for a power battery includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of a sampling harness detection method for a power battery.

[0082] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0083] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for detecting sampling harnesses in a power battery, characterized in that, The method includes: The voltage data sequence of each sampling line bundle is collected in real time at a preset initial sampling frequency. The voltage data sequence consists of voltage values ​​arranged in chronological order. For each sampling harness, the instability index of the sampling harness is determined based on the fluctuation amplitude and rate of change characteristics of the voltage data sequence. If the instability index exceeds the preset adjustment threshold, an adjustment coefficient for adjusting the sampling frequency is determined based on the absolute difference between the instability index and adjacent voltage values; the initial sampling frequency is adjusted to the target sampling frequency based on the adjustment coefficient and the preset upper limit of the sampling frequency. The test voltage data sequence is acquired at the target sampling frequency. Based on the frequency of repeated voltage values ​​in the test voltage data sequence, a sampling redundancy index is determined to verify the effectiveness of the target sampling frequency. If the sampling redundancy index is not within the preset threshold range, the target sampling frequency is corrected to the optimal sampling frequency based on the sampling redundancy index. Voltage data of all sampling harnesses are collected synchronously at the optimal sampling frequency. Based on the differences in voltage values ​​at the same time among the voltage data of all sampling harnesses, the probability of anomalies is determined. If the probability of anomalies exceeds the preset anomaly threshold, the sampling harness is determined to be abnormal.

2. The method for detecting the sampling harness of a power battery according to claim 1, characterized in that, The process for determining the instability index includes: Identify steady-state transition inflection points in a voltage data sequence, where a steady-state transition inflection point indicates the first data point from the first data point onwards where a preset number of consecutive voltage values ​​change from meeting steady-state conditions to not meeting steady-state conditions; Extract the unsteady-state voltage data from the steady-state transition point to the current moment from the voltage data sequence; Based on the differences between voltage values ​​in the unsteady-state voltage data, determine the variation index of the sampling harness; Obtain the duration from the steady-state transition inflection point to the current moment; The instability index is obtained by multiplying the voltage value and change index of the sampling harness at the current moment with the duration and then normalizing the result.

3. The method for detecting the sampling harness of a power battery according to claim 2, characterized in that, The process of determining the change index includes: Obtain the maximum and minimum voltage values ​​from the voltage data in the unsteady-state segment; Calculate the difference between the maximum voltage value and the minimum voltage value, and use it as the first difference value; Calculate the absolute difference between each pair of adjacent voltage values ​​in the unsteady-state voltage data, and use it as the second difference; Calculate the arithmetic mean of all second differences to obtain the first mean; Calculate the product of the first difference and the first average value, and use it as the indicator of change.

4. The method for detecting the sampling harness of a power battery according to claim 1, characterized in that, The process of determining the adjustment coefficient includes: Calculate the absolute difference between voltage values ​​of adjacent data points in the voltage data sequence as the third difference; for each third difference, divide the third difference by the time interval between the corresponding adjacent data points to obtain the rate of change; The average rate of change is obtained by averaging all the rates of change. The adjustment coefficient is obtained by multiplying the average rate of change by the instability index and then normalizing the result.

5. The method for detecting the sampling harness of a power battery according to claim 4, characterized in that, The step of adjusting the initial sampling frequency to the target sampling frequency based on the adjustment coefficient and the preset upper limit of the sampling frequency includes: The target sampling frequency is obtained by linear interpolation calculation based on the difference between the initial sampling frequency and the preset upper limit of the sampling frequency, by adjusting the coefficients.

6. The method for detecting the sampling harness of a power battery according to claim 1, characterized in that, The process of determining the sampling redundancy index includes: Calculate the frequency ratio of repeated voltage value data points in the test voltage data sequence to the total frequency ratio of all data points; calculate the proportion of the sum of repeated voltage values ​​in the test voltage data sequence to the total sum of all voltage values. The product of the frequency ratio and the ratio value is calculated and used as a sampling redundancy index.

7. The method for detecting the sampling harness of a power battery according to claim 1, characterized in that, The method further includes: If the sampling redundancy index is within the preset threshold range, the target sampling frequency will be directly used as the optimal sampling frequency.

8. The method for detecting the sampling harness of a power battery according to claim 1, characterized in that, There are two situations when the sampling redundancy index is not within the preset threshold range. The first situation is that the sampling redundancy index is higher than the upper limit of the preset threshold range, and the second situation is that the sampling redundancy index is lower than the lower limit of the preset threshold range. If the sampling redundancy index is not within the preset threshold range, the target sampling frequency is corrected to the optimal sampling frequency based on the sampling redundancy index, including: If the sampling redundancy index is not within the preset threshold range and falls under the first case, the target sampling frequency is corrected to the optimal sampling frequency based on the sampling redundancy index and through the preset first frequency correction function. If the sampling redundancy index is not within the preset threshold range and falls under the second case, the target sampling frequency is corrected to the optimal sampling frequency based on the sampling redundancy index and through a preset second frequency correction function.

9. The method for detecting the sampling harness of a power battery according to claim 1, characterized in that, The anomaly probability determination process includes: The voltage reference value is obtained by calculating the arithmetic mean of the voltage values ​​at the same time in all sampled harnesses. The sum of the absolute differences between the voltage values ​​at the same time and the corresponding voltage reference values ​​in the voltage data of all sampled harnesses is normalized to obtain the anomaly probability.

10. A sampling harness detection system for a power battery, characterized in that, The system includes a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the method as described in any one of claims 1 to 9.

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