Battery connectivity validity diagnosis method and system based on three elements

Abstract

The application belongs to the technical field of battery detection, and provides a storage battery communication effectiveness diagnosis method and system based on three elements, which comprises the following steps: obtaining single battery temperature data, float charging current data and time stamp information of the storage battery in a float charging state; identifying the storage battery abnormity according to the single battery temperature data and the float charging current data respectively to obtain temperature abnormality identification results and current abnormality identification results; performing time correlation verification on the temperature abnormality identification results and the current abnormality identification results based on the time stamp information to obtain abnormality preliminary screening results; performing three-dimensional correlation diagnosis on the storage battery according to the abnormality preliminary screening results to determine abnormal data points, abnormal root causes and abnormal levels of the storage battery in the float charging state, and obtaining communication effectiveness diagnosis results. The scheme provided by the application can efficiently and accurately identify the communication abnormality of the storage battery, thereby improving the accuracy, efficiency and reliability of the communication effectiveness diagnosis link of the storage battery.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery detection, and in particular to a storage battery connectivity effectiveness diagnosis method and system based on three elements. BACKGROUND

[0002] In the field of modern power systems and uninterruptible power supplies, as a key energy storage device, the operation reliability of the storage battery in the floating state is directly related to the stable power supply capability of the system. Floating refers to the working mode of continuously charging the storage battery with a small current to compensate for self-discharge loss while maintaining a full charge standby state. During this process, connectivity faults such as poor internal connection and abnormal increase of contact resistance of the storage battery can cause local overheating, capacity degradation, and even system power failure risk, so it is of great significance to accurately diagnose the connectivity effectiveness of the storage battery in the floating state.

[0003] At present, the traditional storage battery connectivity effectiveness diagnosis scheme mainly has the following defects: first, the diagnosis means is single, and it mainly depends on manual inspection or simple voltage and current monitoring. For example, the battery state is determined by measuring the voltage of the single battery, which cannot effectively identify the hidden faults of the connection part, resulting in late fault discovery. Second, there is a lack of multi-parameter coupling analysis capability, and the existing scheme often analyzes temperature, current and other data in isolation, without considering the comprehensive influence of the coupling relationship between heat and electricity on the connectivity state of the storage battery. For example, simple temperature monitoring cannot distinguish between environmental temperature changes and temperature rise caused by connection faults, which is easy to cause misjudgment. Third, the fault evaluation lacks time dimension consideration, and the traditional method only makes judgments based on current measurement data, ignoring the cumulative effect of the duration of abnormal conditions on the damage degree of the storage battery, making it difficult to accurately evaluate the fault risk level and develop reasonable handling strategies.

[0004] It is not difficult to find that the traditional storage battery connectivity effectiveness diagnosis scheme has the technical problems of low diagnosis accuracy, low efficiency and insufficient reliability. SUMMARY

[0005] The present application provides a storage battery connectivity effectiveness diagnosis method and system based on three elements to solve the defects of low diagnosis accuracy, low efficiency and insufficient reliability of the traditional storage battery connectivity effectiveness diagnosis scheme.

[0006] In one aspect, the present application provides a storage battery connectivity effectiveness diagnosis method based on three elements, comprising:

[0007] obtaining single battery temperature data, floating current data and timestamp information of the storage battery in the floating state;

[0008] respectively according to the single battery temperature data and the floating current data, performing abnormality identification on the storage battery to obtain temperature abnormality identification results and current abnormality identification results;

[0009] Based on the timestamp information, the temperature anomaly identification result and the current anomaly identification result are time correlation verified to obtain an anomaly preliminary screening result.

[0010] According to the anomaly preliminary screening result, three-dimensional correlation diagnosis is performed on the storage battery to determine an anomaly data point, an anomaly root cause and an anomaly level of the storage battery in the floating state, and a connectivity effectiveness diagnosis result is obtained.

[0011] According to the present application, a three-element-based storage battery connectivity effectiveness diagnosis method is provided, and according to the single-body temperature data, anomaly identification is performed on the storage battery to obtain a temperature anomaly identification result, including:

[0012] The single-body temperature data is scanned through a first set-width sliding window;

[0013] First temperature anomaly data with temperature mutation occurring in the sliding window is determined, and second temperature anomaly data triggering temperature consistency early warning in the sliding window is determined;

[0014] The first temperature anomaly data and the second temperature anomaly data are taken as the temperature anomaly identification result.

[0015] According to the present application, a three-element-based storage battery connectivity effectiveness diagnosis method is provided, and according to the floating current data, anomaly identification is performed on the storage battery to obtain a current anomaly identification result, including:

[0016] Current average value and current standard deviation of the floating current data at different time points are respectively determined;

[0017] According to the current average value and the current standard deviation, current anomaly data satisfying a current anomaly judgment condition is determined to obtain the current anomaly identification result;

[0018] The current anomaly judgment condition includes that the current average value exceeds a preset value interval and lasts for a first set time length, and / or the number of times that the current standard deviation exceeds a preset upper limit value within a second set time length is more than a set number of times.

[0019] According to the present application, a three-element-based storage battery connectivity effectiveness diagnosis method is provided, and based on the timestamp information, the temperature anomaly identification result and the current anomaly identification result are time correlation verified to obtain an anomaly preliminary screening result, including:

[0020] The timestamp information of any temperature anomaly data in the temperature anomaly identification result is subtracted from the timestamp information of any current anomaly data in the current anomaly identification result to obtain an anomaly data time difference;

[0021] If the abnormal data time difference is less than a preset time difference threshold, corresponding temperature abnormal data and current abnormal data are associated to obtain an abnormal association event;

[0022] According to all abnormal association events, an abnormal preliminary screening result is obtained.

[0023] According to the abnormal preliminary screening result, three-dimensional association diagnosis is performed on the storage battery to determine abnormal data points, abnormal root causes and abnormal levels of the storage battery in the floating state, and a connected effectiveness diagnosis result is obtained, including:

[0024] A thermoelectric coupling model is established, and in-depth abnormal detection is performed on the abnormal preliminary screening result according to the thermoelectric coupling model to determine abnormal data points and abnormal root causes of the storage battery in the floating state;

[0025] According to the abnormal data points and the abnormal root causes, a fault index of the storage battery in the floating state is determined.

[0026] According to a classification interval in which the fault index is located, an abnormal level of the storage battery in the floating state is determined.

[0027] According to the abnormal data points and the abnormal root causes, a fault index of the storage battery in the floating state is determined.

[0028] According to the abnormal data points and the abnormal root causes, a temperature abnormal level, a current abnormal level and a duration weight are determined;

[0029] The temperature abnormal level, the current abnormal level and the duration weight are subjected to weighted summation operation to calculate the fault index of the storage battery in the floating state.

[0030] According to the abnormal data points and the abnormal root causes, a fault index of the storage battery in the floating state is determined.

[0031] The single cell temperature data of the storage battery in the floating state within a preset time length before the current time is obtained to obtain historical temperature data;

[0032] The historical temperature data is subjected to standard deviation operation to obtain a historical temperature standard deviation;

[0033] If the single cell temperature data at the current time is more than a set multiple of the historical temperature standard deviation, it is determined that the single cell temperature data of the storage battery in the floating state is in a trend deterioration state.

[0034] According to the abnormal data points and the abnormal root causes, a fault index of the storage battery in the floating state is determined.

[0035] If the communication validity diagnosis result is a communication abnormality, a target timestamp information corresponding to a first temperature abnormality is determined from the temperature abnormality identification result;

[0036] Target float charging current data within a preset time period before and after the target timestamp information is extracted, and whether current fluctuation occurs at the first temperature abnormality is determined according to the target float charging current data;

[0037] If it is determined that current fluctuation occurs at the first temperature abnormality, a set number of battery monomers with the highest temperature in the monomer temperature data are taken as objects to be investigated, so as to investigate the objects to be investigated.

[0038] According to the three-element-based storage battery communication validity diagnosis method provided by the application, whether current fluctuation occurs at the first temperature abnormality is determined according to the target float charging current data, which comprises:

[0039] The current average value and the current standard deviation of the target float charging current data are determined respectively;

[0040] The current fluctuation coefficient of the target float charging current data is calculated according to the current average value and the current standard deviation of the target float charging current data;

[0041] If the current fluctuation coefficient is greater than a preset standard fluctuation coefficient, it is determined that current fluctuation occurs at the first temperature abnormality;

[0042] If the current fluctuation coefficient is less than the preset standard fluctuation coefficient, it is determined that current fluctuation does not occur at the first temperature abnormality.

[0043] On the other hand, the application also provides a three-element-based storage battery communication validity diagnosis system, which comprises:

[0044] An acquisition module is configured to acquire monomer temperature data, float charging current data and timestamp information of a storage battery in a float charging state;

[0045] An identification module is configured to identify the storage battery according to the monomer temperature data and the float charging current data respectively, to obtain temperature abnormality identification results and current abnormality identification results;

[0046] A verification module is configured to perform time correlation verification on the temperature abnormality identification results and the current abnormality identification results based on the timestamp information, to obtain abnormality preliminary screening results;

[0047] A diagnosis module is configured to perform three-dimensional correlation diagnosis on the storage battery according to the abnormality preliminary screening results, to determine abnormal data points, abnormal root causes and abnormal levels of the storage battery in the float charging state, and to obtain communication validity diagnosis results.

[0048] The application provides a three-element-based storage battery connectivity effectiveness diagnosis method and system, which obtains single battery temperature data, float charging current data and timestamp information of the storage battery in a float charging state; performs abnormality identification on the storage battery according to the single battery temperature data and the float charging current data respectively to obtain temperature abnormality identification results and current abnormality identification results; performs time correlation verification on the temperature abnormality identification results and the current abnormality identification results based on the timestamp information to obtain an abnormality preliminary screening result; performs three-dimensional correlation diagnosis on the storage battery according to the abnormality preliminary screening result to determine abnormal data points, abnormal root causes and abnormal levels of the storage battery in the float charging state, and obtain a connectivity effectiveness diagnosis result. Since the correlation influence of three elements of temperature, current and time is comprehensively considered in the diagnosis link, the connectivity abnormality of the storage battery can be efficiently and accurately identified, and therefore the accuracy, efficiency and reliability of the storage battery connectivity effectiveness diagnosis link are improved. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative effort.

[0050] Figure 1 is a flowchart of the three-element-based storage battery connectivity effectiveness diagnosis method provided by the embodiment of the application;

[0051] Figure 2 is a structural schematic diagram of the three-element-based storage battery connectivity effectiveness diagnosis system provided by the embodiment of the application;

[0052] Figure 3 is a structural schematic diagram of the electronic device provided by the embodiment of the application. DETAILED DESCRIPTION

[0053] In order to make the objectives, technical solutions and advantages of the application clearer, the technical solutions in the application will be described clearly and completely below with reference to the drawings in the application. Obviously, the described embodiments are some embodiments of the application, but not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative effort belong to the protection scope of the application.

[0054] The details of the three-element-based storage battery connectivity effectiveness diagnosis method and system provided by the embodiment of the application will be described below. Figures 1 to 3

[0055] Figure 1 ​is a flowchart of a three-element-based storage battery connectivity effectiveness diagnosis method provided by an embodiment of the present application.

[0056] As shown in Figure 1 The three-element-based storage battery connectivity effectiveness diagnosis method provided by the embodiment of the present application can be executed by a computer or a server with data transceiving and data processing capabilities, and the method mainly includes the following steps.

[0057] Step 110: Obtain the single-cell temperature data, the float charging current data and the timestamp information of the storage battery in the float charging state.

[0058] In actual application, a thermocouple temperature sensor can be pasted on the surface of each single cell of the storage battery, the precision of which can be set to ±0.5℃, and the response time of which is less than 10 seconds. Meanwhile, a Hall current sensor can be connected in series in the float charging circuit, the range of which is set to 0-10A, and the resolution of which is set to 0.01A. In addition, a data collector can be configured to synchronously record the single-cell temperature data, the float charging current data and the corresponding timestamp information, and the storage interval of which can be set to 1 minute.

[0059] Step 120: Perform abnormality identification on the storage battery according to the single-cell temperature data and the float charging current data respectively to obtain the temperature abnormality identification result and the current abnormality identification result.

[0060] In this embodiment, the temperature and the current of the storage battery in the float charging state can be preliminarily detected by temperature abnormality identification and current abnormality identification.

[0061] Step 130: Perform time correlation verification on the temperature abnormality identification result and the current abnormality identification result based on the timestamp information to obtain an abnormality preliminary screening result.

[0062] It can be understood that, by time correlation verification, the abnormality characteristics of the storage battery in the float charging state can be preliminarily screened, thereby providing effective data basis for subsequent further depth diagnosis, and the data processing amount of the abnormality diagnosis link can be reduced to some extent, thereby the diagnosis efficiency can be improved.

[0063] Step 140: Perform three-dimensional correlation diagnosis on the storage battery according to the abnormality preliminary screening result to determine the abnormal data point, the abnormal root cause and the abnormality level of the storage battery in the float charging state, and obtain a connectivity effectiveness diagnosis result.

[0064] The scheme provided by this embodiment can comprehensively diagnose the connectivity effectiveness of the storage battery in the float charging state based on the three elements of temperature, current and time, and can timely and accurately identify the connectivity abnormality, thereby improving the operation stability of the storage battery in the float charging state.

[0065] In an embodiment, the battery is subjected to abnormality identification according to the monomer temperature data to obtain temperature abnormality identification results, specifically including:

[0066] Firstly, the monomer temperature data is scanned by a first set width sliding window.

[0067] In this embodiment, the first set width can be set to 5 minutes, that is, the monomer temperature data is scanned by a 5-minute sliding window.

[0068] Then, the first temperature abnormality data of temperature mutation occurring in the sliding window is determined, and the second temperature abnormality data triggering temperature consistency warning in the sliding window is determined.

[0069] In practical application, the monomer temperature data at the initial time and the monomer temperature data at the end time in the sliding window can be subtracted to obtain the temperature difference value at both ends of the window, if the temperature difference value at both ends of the window is higher than the first set temperature threshold, such as higher than 5℃, it can be determined that temperature mutation occurs in the sliding window, and the first temperature abnormality data is obtained.

[0070] In practical application, in order to avoid misjudgment and exclude the influence of night environment temperature change on the first temperature abnormality identification data, the first set temperature threshold can be relaxed to 8℃ within the time period of 22:00-6:00 the next day.

[0071] In addition, the temperature maximum value and the temperature minimum value in all monomer temperature data in the sliding window can be subtracted to obtain the temperature limit difference value, if the temperature limit difference value exceeds the second set temperature threshold, such as more than 15℃, the temperature consistency warning will be triggered in the sliding window.

[0072] Finally, the first temperature abnormality data and the second temperature abnormality data are taken as the temperature abnormality identification results.

[0073] In this embodiment, two kinds of temperature abnormality data can be identified by two kinds of temperature abnormality identification strategies, and both of the two kinds of temperature abnormality data are taken as the temperature abnormality identification results for subsequent three-dimensional correlation analysis.

[0074] In an embodiment, the battery is subjected to abnormality identification according to the float current data to obtain current abnormality identification results, including:

[0075] Firstly, the current average value and the current standard deviation of the float current data at different times are determined respectively.

[0076] Then, the current abnormality data satisfying the current abnormality determination condition is determined according to the current average value and the current standard deviation, and the current abnormality identification results are obtained.

[0077] The current abnormality judgment condition includes that the current average value exceeds a preset numerical interval and lasts for a first set time length, and / or the number of times that the current standard deviation exceeds a preset upper limit value within a second set time length is more than a set number of times.

[0078] In this embodiment, the first set time length and the second set time length can both be set to 10 minutes, the set number of times can be set to 3 times, the current standard deviation refers to the absolute value of the standard deviation, which is a value greater than or equal to 0, the preset upper limit value of the current standard deviation can be set to 0.1, and the preset numerical interval can be reasonably set according to actual needs.

[0079] In an embodiment, based on the timestamp information, the temperature abnormality recognition result and the current abnormality recognition result are subjected to time correlation verification to obtain an abnormality preliminary screening result, including:

[0080] First, the timestamp information of any temperature abnormality data in the temperature abnormality recognition result is subtracted from the timestamp information of any current abnormality data in the current abnormality recognition result to obtain an abnormality data time difference.

[0081] Then, if the abnormality data time difference is less than a preset time difference threshold, the corresponding temperature abnormality data and current abnormality data are associated to obtain an abnormality association event.

[0082] In this embodiment, the preset time difference threshold can be set to 2 minutes, that is, when the current abnormality data time difference is less than 2 minutes, it can be considered that the temperature abnormality and the current abnormality at a certain time point coincide in time, and then an abnormality association event can be generated.

[0083] Finally, according to all the abnormality association events, an abnormality preliminary screening result is obtained.

[0084] In this embodiment, all the abnormality association events can be integrated as the abnormality preliminary screening result.

[0085] In an embodiment, according to the abnormality preliminary screening result, a three-dimensional association diagnosis is performed on the storage battery to determine abnormal data points, abnormal root causes and abnormal levels of the storage battery in the floating state, and a connectivity effectiveness diagnosis result is obtained, including:

[0086] First, a thermoelectric coupling model is established, and the abnormality preliminary screening result is subjected to in-depth abnormality detection according to the thermoelectric coupling model to determine abnormal data points and abnormal root causes of the storage battery in the floating state.

[0087] It can be understood that the thermoelectric coupling model can describe the functional relationship between the temperature rise and the floating current data, the contact resistance, and the abnormal duration.

[0088] In this embodiment, the thermoelectric coupling model can be represented as follows:

[0089] (1)

[0090] wherein, represents temperature rise, k represents thermal resistance coefficient, R represents contact resistance, represents abnormal duration, T0 represents ambient temperature.

[0091] On the one hand, the theoretical temperature rise can be calculated by the thermoelectric coupling model, and on the other hand, the actual temperature rise can be calculated by the single-body temperature data of each data point in the abnormal preliminary screening result. If the actual temperature rise corresponding to a data point is higher than a preset multiple of the theoretical temperature rise, and the floating current data at this time is higher than a preset current threshold, the data point can be regarded as an abnormal data point. In actual application, the preset multiple can be 1.5 times. In this case, the abnormal root cause is abnormal increase of contact resistance.

[0092] Secondly, the fault index of the storage battery in the floating state is determined according to the abnormal data point and the abnormal root cause.

[0093] In one specific implementation, the fault index of the storage battery in the floating state is determined according to the abnormal data point and the abnormal root cause, comprising:

[0094] Firstly, the temperature abnormality level, the current abnormality level and the duration weight are determined according to the abnormal data point and the abnormal root cause.

[0095] On the one hand, the temperature abnormality level can be determined comprehensively according to the temperature rise rate and the final temperature value of each abnormal point, combined with the abnormal root cause. For example, if the temperature rise rate is less than 0.5 degrees Celsius per minute, and the final temperature value is less than 35 degrees Celsius, the temperature abnormality level is first abnormality, which can be taken as 0.1; if the temperature rise rate is between 0.5 degrees Celsius per minute and 1.5 degrees Celsius per minute, or the final temperature value is between 35 degrees Celsius and 45 degrees Celsius, the temperature abnormality level is second abnormality, which can be taken as 0.3; if the temperature rise rate is higher than 1.5 degrees Celsius per minute, or the final temperature value is higher than 45 degrees Celsius, the temperature abnormality level is third abnormality, which can be taken as 0.6.

[0096] On the other hand, the current deviation value between the floating current data of each abnormal data point and the standard floating current corresponding to the charging duration can be calculated, and then the temperature abnormality level is determined according to the current deviation value. For example, if the current deviation value is within ±20%, the influence on the storage battery is small, and the current abnormality level is first abnormality, which can be taken as 0.05; if the current deviation value is between 20% and 50%, it will accelerate the aging of the storage battery, and the current abnormality level is second abnormality, which can be taken as 0.25; if the current deviation value is more than 50%, it may cause serious failure, and the current abnormality level is third abnormality, which can be taken as 0.7.

[0097] In another aspect, the duration weight can be determined according to the degree of influence of the length of the abnormal duration on the rate of loss of battery capacity, for example, if the abnormal duration is between 10 and 30 minutes, when the rate of loss of battery capacity is small, the influence on the battery performance is limited, and the duration weight can be set to 0.1; if the abnormal duration is between 31 and 60 minutes, when the rate of loss of battery capacity increases significantly, the influence on the battery life is greater, and the duration weight can be set to 0.3; if the abnormal duration is more than 61 minutes, when the loss of battery capacity is serious, which can lead to the battery being scrapped, and the duration weight can be set to 0.6.

[0098] Then, the temperature abnormality grade, the current abnormality grade, and the duration weight are subjected to a weighted summation operation, and the failure index of the battery in the floating state is calculated.

[0099] In this embodiment, the failure index of the battery in the floating state can be represented as follows:

[0100] (2)

[0101] wherein, represents the failure index at time t, represents the temperature abnormality grade, represents the current abnormality grade, represents the duration weight.

[0102] In a third step, according to the classification interval in which the failure index is located, the abnormality grade of the battery in the floating state is determined.

[0103] In this embodiment, if the failure index is less than 0.4, the abnormality grade is green, i.e. normal state; if the failure index is between 0.4 and 0.7, the abnormality grade is yellow, i.e. pre-warning state; if the failure index is higher than 0.7, the abnormality grade is red, i.e. failure state.

[0104] In actual application, if the abnormality grade is yellow and red, the connectedness validity diagnosis result is connectedness abnormality; if the abnormality grade is green, the connectedness validity diagnosis result is connectedness normality.

[0105] In some embodiments, in the process of diagnosing the connectedness validity of the battery in the floating state, the internal resistance data can also be introduced, which can be realized through the following process:

[0106] First, the actual internal resistance data of the battery in the floating state is obtained, and the initial internal resistance and the internal resistance standard range are determined.

[0107] Then, the actual internal resistance data is compared with the initial internal resistance and the internal resistance standard range respectively. Specifically, the actual internal resistance data can be subtracted from the initial internal resistance to obtain an internal resistance deviation value, and it is determined whether the actual internal resistance data is within the internal resistance standard range.

[0108] If the internal resistance deviation value exceeds the preset deviation threshold and the duration exceeds the set duration, and at the same time, the actual internal resistance data exceeds the internal resistance standard range, it can be determined that an internal resistance abnormality occurs.

[0109] Next, after determining that an internal resistance abnormality occurs, the actual internal resistance data is subtracted from the upper limit value or the lower limit value of the internal resistance standard range (specifically, the limit value closest to the actual internal resistance data), to obtain an internal resistance difference value. After normalizing the internal resistance deviation value, the duration and the internal resistance difference value, a weighted sum is performed to obtain an internal resistance abnormality level.

[0110] Finally, the internal resistance abnormality level is introduced into the fault index calculation scheme of the battery in the floating state. Specifically, on the basis of the weighted sum of the temperature abnormality level, the current abnormality level and the duration weight, the internal resistance abnormality level and the corresponding weight value are further added, so that a more accurate fault index is obtained.

[0111] In practical applications, after determining that an internal resistance abnormality occurs, an internal resistance trend analysis scheme can be further introduced. Specifically, the following can be performed:

[0112] First, the historical internal resistance data in a set period of time before the current time is input into a pre-trained internal resistance prediction model to obtain internal resistance prediction data in a future period of time output by the internal resistance prediction model. In this embodiment, the internal resistance prediction model can use previous historical internal resistance data as sample data, and the deep learning network model is trained and tested to obtain.

[0113] Then, according to the trend of the internal resistance prediction data, the internal resistance change trend in the future period of time is determined.

[0114] Finally, if the internal resistance change trend indicates that the internal resistance data gradually increases, which may indicate that the performance of the battery is gradually deteriorating, and the connectivity effectiveness of the floating state will gradually decrease. At this time, internal resistance abnormality warning information can be generated and sent out.

[0115] This embodiment introduces an internal resistance detection scheme, which can more comprehensively evaluate the connectivity effectiveness of the battery in the floating state on the basis of temperature, current and time, in combination with the internal resistance of the battery, thereby improving the diagnostic precision of the connectivity effectiveness of the battery.

[0116] In an embodiment, the above-mentioned battery connectivity effectiveness diagnosis method based on three elements can further include:

[0117] Firstly, the single cell temperature data of the storage battery in the floating state within a preset time period before the current time is obtained to obtain historical temperature data.

[0118] In this embodiment, the preset time period can be set to 7 days, that is, a sliding window of 7 days is taken to obtain the historical temperature data.

[0119] Then, the standard deviation operation is performed on the historical temperature data to obtain the historical temperature standard deviation.

[0120] Finally, if the single cell temperature data at the current time is more than a set multiple of the historical temperature standard deviation, it is determined that the single cell temperature data of the storage battery in the floating state is in a trend deterioration state.

[0121] In this embodiment, the set multiple can be 3 times, that is, if the single cell temperature data at the current time is more than 3 times the historical temperature standard deviation, it can be determined that the single cell temperature data of the storage battery in the floating state is in a trend deterioration state.

[0122] In actual application, the storage battery in the trend deterioration state can be abnormally warned to prompt the staff to timely troubleshoot the fault hidden danger.

[0123] In some embodiments, after obtaining the connectivity validity diagnosis result, the above-mentioned battery connectivity validity diagnosis method based on three elements can further include:

[0124] Firstly, if the connectivity validity diagnosis result is a connectivity abnormality, the target timestamp information corresponding to the first temperature abnormality is determined from the temperature abnormality identification result.

[0125] Then, the target floating charging current data within a preset time period before and after the target timestamp information is extracted, and whether current fluctuation occurs when the first temperature abnormality occurs is judged according to the target floating charging current data.

[0126] In this embodiment, the preset time period can be within 10 minutes before and after the target timestamp information, and the judgment of current fluctuation can be performed on the target floating charging current data within 10 minutes before the target timestamp information and the target floating charging current data within 10 minutes after the target timestamp information, respectively.

[0127] In one specific implementation, whether current fluctuation occurs when the first temperature abnormality occurs is judged according to the target floating charging current data, specifically including:

[0128] Firstly, the current average value and the current standard deviation of the target floating charging current data are determined, respectively.

[0129] Secondly, the current fluctuation coefficient of the target floating charging current data is calculated according to the current average value and the current standard deviation of the target floating charging current data.

[0130] In the embodiment, the current standard deviation of the target floating current data can be divided by the current average value to obtain a current fluctuation coefficient of the target floating current data.

[0131] In the third step, if the current fluctuation coefficient is greater than the preset standard fluctuation coefficient, it is determined that the current fluctuation occurs at the first temperature anomaly.

[0132] In actual application, the preset standard fluctuation coefficient can be set to 15%.

[0133] In the fourth step, if the current fluctuation coefficient is less than the preset standard fluctuation coefficient, it is determined that the current fluctuation does not occur at the first temperature anomaly.

[0134] Finally, if it is determined that the current fluctuation occurs at the first temperature anomaly, the battery cells with the highest temperature in the set number of battery cells in the single cell temperature data are taken as the objects to be investigated, so as to perform fault investigation on the objects to be investigated.

[0135] In the embodiment, the three battery cells with the highest temperature in the single cell temperature data can be taken as the objects to be investigated, and subsequent contact resistance segmentation test can be performed on the objects to be investigated, so as to further investigate the fault single cells preliminarily positioned to determine the specific fault position.

[0136] The three-element-based storage battery connection effectiveness diagnosis method provided in the embodiment can fuse three key elements of temperature, current and time, construct a thermal-electric coupling model and a time-weighted fault index calculation system, accurately identify hidden connection effectiveness abnormal conditions such as loose connection of the storage battery and abnormal contact resistance, and make the diagnosis more targeted through abnormal grading, thereby improving the diagnosis accuracy and efficiency of the connection effectiveness of the storage battery.

[0137] Based on the same overall inventive concept, the present application also protects a three-element-based storage battery connection effectiveness diagnosis system. The three-element-based storage battery connection effectiveness diagnosis system provided in the present application is described as follows, and the three-element-based storage battery connection effectiveness diagnosis system described below can be correspondingly referred to the three-element-based storage battery connection effectiveness diagnosis method described above.

[0138] As shown in Figure 2 The three-element-based storage battery connection effectiveness diagnosis system provided in the embodiment of the present application specifically includes:

[0139] The acquisition module 210 is configured to acquire single cell temperature data, floating current data and time stamp information of the storage battery in a floating state.

[0140] The identification module 220 is used to identify anomalies in the battery based on individual cell temperature data and float charge current data, respectively, and obtain temperature anomaly identification results and current anomaly identification results.

[0141] The verification module 230 is used to perform time correlation verification on the temperature anomaly identification results and current anomaly identification results based on timestamp information to obtain the initial screening results of anomalies.

[0142] The diagnostic module 240 is used to perform three-dimensional correlation diagnosis on the battery based on the initial screening results of anomalies, determine the abnormal data points, root causes and levels of anomalies in the battery under float charging state, and obtain the connectivity effectiveness diagnosis results.

[0143] Regarding the system in the above embodiments, the specific ways in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated further here.

[0144] Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention.

[0145] like Figure 3 As shown, the electronic device may include a processor 310, a communication interface 320, a memory 330, and a communication bus 340. The processor 310, communication interface 320, and memory 330 communicate with each other via the communication bus 340. The processor 310 can call logical instructions in the memory 330 to execute a three-factor-based battery connectivity effectiveness diagnosis method. This method includes: acquiring individual cell temperature data, float charge current data, and timestamp information of the battery in float charge mode; identifying anomalies in the battery based on the individual cell temperature data and float charge current data, respectively, to obtain temperature anomaly identification results and current anomaly identification results; performing time correlation verification on the temperature anomaly identification results and current anomaly identification results based on the timestamp information to obtain preliminary anomaly screening results; and performing three-dimensional correlation diagnosis on the battery based on the preliminary anomaly screening results to determine the abnormal data points, root causes, and anomaly levels of the battery in float charge mode, thereby obtaining connectivity effectiveness diagnosis results.

[0146] In addition, the logic instructions in the memory 330 described above can be implemented in the form of a software function unit and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for making a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0147] In another aspect, the present application also provides a computer program product, which comprises a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program can be executed by a processor to enable a computer to execute a three-element-based storage battery connectivity effectiveness diagnosis method, which comprises: acquiring single cell temperature data, float charging current data and timestamp information of the storage battery in a float charging state; performing abnormality identification on the storage battery according to the single cell temperature data and the float charging current data respectively to obtain temperature abnormality identification results and current abnormality identification results; performing time correlation verification on the temperature abnormality identification results and the current abnormality identification results based on the timestamp information to obtain an abnormality preliminary screening result; performing three-dimensional correlation diagnosis on the storage battery according to the abnormality preliminary screening result to determine abnormal data points, abnormal root causes and abnormal levels of the storage battery in the float charging state, and obtaining a connectivity effectiveness diagnosis result.

[0148] In another aspect, the present application also provides a computer program product, which comprises a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program can be executed by a processor to enable a computer to execute a three-element-based storage battery connectivity effectiveness diagnosis method, which comprises: acquiring single cell temperature data, float charging current data and timestamp information of the storage battery in a float charging state; performing abnormality identification on the storage battery according to the single cell temperature data and the float charging current data respectively to obtain temperature abnormality identification results and current abnormality identification results; performing time correlation verification on the temperature abnormality identification results and the current abnormality identification results based on the timestamp information to obtain an abnormality preliminary screening result; performing three-dimensional correlation diagnosis on the storage battery according to the abnormality preliminary screening result to determine abnormal data points, abnormal root causes and abnormal levels of the storage battery in the float charging state, and obtaining a connectivity effectiveness diagnosis result.

[0149] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected to achieve the purposes of the embodiments according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0150] Through the description of the above embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software and the necessary general hardware platform, and of course can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0151] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A three-element-based diagnosis method for battery connectivity effectiveness, characterized by, The method comprises the following steps: acquiring single-cell temperature data, float charging current data and timestamp information of the storage battery in a float charging state; performing abnormality identification on the storage battery according to the single-cell temperature data and the float charging current data respectively to obtain temperature abnormality identification results and current abnormality identification results; performing time correlation verification on the temperature abnormality identification results and the current abnormality identification results based on the timestamp information to obtain abnormality preliminary screening results; performing three-dimensional correlation diagnosis on the storage battery according to the abnormality preliminary screening results to determine abnormal data points, abnormal root causes and abnormal levels of the storage battery in the float charging state, and obtaining connectivity effectiveness diagnosis results.

2. The three-element-based battery connectivity validity diagnostic method of claim 1, wherein, performing abnormality identification on the storage battery according to the single-cell temperature data to obtain temperature abnormality identification results, which comprises the following steps: scanning the single-cell temperature data through a first sliding window with a set width; determining first temperature abnormal data with temperature mutation in the sliding window and second temperature abnormal data triggering temperature consistency early warning in the sliding window; taking the first temperature abnormal data and the second temperature abnormal data as the temperature abnormality identification results.

3. The three-element based battery connectivity validity diagnostic method of claim 1, wherein, performing abnormality identification on the storage battery according to the float charging current data to obtain current abnormality identification results, which comprises the following steps: determining current average values and current standard deviations of the float charging current data at different time points respectively; determining current abnormal data satisfying current abnormality determination conditions according to the current average values and the current standard deviations to obtain the current abnormality identification results; wherein the current abnormality determination conditions include that the current average values exceed a preset value range and last for a first set time length, and / or the number of times that the current standard deviations exceed a preset upper limit value within a second set time length is more than a set number of times.

4. The three-element based battery connectivity validity diagnostic method of claim 1, wherein, performing time correlation verification on the temperature abnormality identification results and the current abnormality identification results based on the timestamp information to obtain abnormality preliminary screening results, which comprises the following steps: determining abnormal data time differences by subtracting the timestamp information of any temperature abnormal data in the temperature abnormality identification results from the timestamp information of any current abnormal data in the current abnormality identification results; if the abnormal data time differences are less than a preset time difference threshold, associating the corresponding temperature abnormal data and current abnormal data to obtain abnormal association events; obtaining the abnormality preliminary screening results according to all the abnormal association events.

5. The three-element based battery connectivity validity diagnostic method of claim 1, wherein, performing three-dimensional correlation diagnosis on the storage battery according to the abnormality preliminary screening results to determine abnormal data points, abnormal root causes and abnormal levels of the storage battery in the float charging state, and obtaining connectivity effectiveness diagnosis results, which comprises the following steps: establishing a thermoelectric coupling model, performing in-depth abnormality detection on the abnormality preliminary screening results according to the thermoelectric coupling model to determine abnormal data points and abnormal root causes of the storage battery in the float charging state; determining a fault index of the storage battery in the float charging state according to the abnormal data points and the abnormal root causes; determining an abnormal level of the storage battery in the float charging state according to a classification interval in which the fault index is located.

6. The three-element based battery connectivity validity diagnostic method of claim 5, wherein, determining a fault index of the storage battery in the float charging state according to the abnormal data points and the abnormal root causes, which comprises the following steps: According to the abnormal data point and the abnormal root cause, a temperature abnormality level, a current abnormality level, and a duration weight are determined; The temperature abnormality level, the current abnormality level, and the duration weight are subjected to a weighted summation operation, and a fault index of the storage battery in the floating state is calculated.

7. The three-element based battery connectivity validity diagnostic method of claim 1, wherein, The method further comprises: obtaining single cell temperature data of the storage battery in the floating state within a preset time length before the current time, to obtain historical temperature data; performing a standard deviation operation on the historical temperature data, to obtain a historical temperature standard deviation; if the single cell temperature data at the current time is more than a set multiple of the historical temperature standard deviation, it is determined that the single cell temperature data of the storage battery in the floating state is in a trend deterioration state.

8. The three-element based battery connectivity validity diagnostic method of claim 1, wherein, After obtaining the connectivity validity diagnosis result, the method further comprises: if the connectivity validity diagnosis result is a connectivity abnormality, determining target timestamp information corresponding to a first temperature abnormality from the temperature abnormality identification result; extracting target floating charging current data within a preset time period before and after the target timestamp information, and determining whether current fluctuation occurs at the first temperature abnormality according to the target floating charging current data; if it is determined that current fluctuation occurs at the first temperature abnormality, a set number of battery cells with the highest temperature in the single cell temperature data are taken as objects to be investigated, so as to perform fault investigation on the objects to be investigated.

9. The three-element based battery connectivity validity diagnostic method of claim 8, wherein, According to the target floating charging current data, determining whether current fluctuation occurs at the first temperature abnormality comprises: determining a current average value and a current standard deviation of the target floating charging current data, respectively; calculating a current fluctuation coefficient of the target floating charging current data according to the current average value and the current standard deviation of the target floating charging current data; if the current fluctuation coefficient is more than a preset standard fluctuation coefficient, it is determined that current fluctuation occurs at the first temperature abnormality; if the current fluctuation coefficient is less than the preset standard fluctuation coefficient, it is determined that current fluctuation does not occur at the first temperature abnormality.

10. A three-element-based battery connectivity validity diagnostic system, comprising: comprises: an acquisition module configured to acquire single cell temperature data, floating charging current data, and timestamp information of a storage battery in a floating state; an identification module configured to perform abnormality identification on the storage battery according to the single cell temperature data and the floating charging current data, respectively, to obtain temperature abnormality identification results and current abnormality identification results; a verification module configured to perform time correlation verification on the temperature abnormality identification results and the current abnormality identification results based on the timestamp information, to obtain abnormality preliminary screening results; a diagnosis module configured to perform three-dimensional correlation diagnosis on the storage battery according to the abnormality preliminary screening results, to determine abnormal data points, abnormal root causes, and abnormality levels of the storage battery in the floating state, and to obtain connectivity validity diagnosis results.

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