Master control type intelligent equalization and fault diagnosis system for intelligent mobile power supply

Abstract

The invention discloses a master control type intelligent equalization and fault diagnosis system for an intelligent mobile power supply, and relates to the technical field of mobile power supply fault diagnosis, and the system comprises a first voltage threshold and a second voltage threshold based on the normal charging of a first number of single batteries under the same SOC; acquiring a first voltage analysis function based on the first voltage threshold; acquiring a second voltage analysis function based on the second voltage threshold; acquiring a real-time coordinate point based on the real-time SOC and the real-time voltage; judging whether the mobile power supply is normal based on the real-time coordinate point, the first voltage analysis function and the second voltage analysis function; acquiring a real-time trend function based on the real-time coordinate points; the mobile power supply fault diagnosis method and device are used for solving the problems that in an existing mobile power supply fault diagnosis technology, an appropriate abnormal threshold value is not set during charging, and abnormal early warning is carried out on the future based on the abnormal threshold value; therefore, the problem of inaccurate and untimely abnormal judgment during charging is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mobile power supply fault diagnosis, in particular to a main control type intelligent balancing and fault diagnosis system for intelligent mobile power supply. BACKGROUND

[0002] With the popularity of portable electronic devices and the rapid development of electric vehicles, intelligent mobile power supply has become a core energy storage unit supporting the efficient operation of modern society. However, the traditional mobile power supply management system has technical bottlenecks, such as insufficient battery pack consistency management and weak fault diagnosis capability, which leads to shortened battery life, prominent safety hazards, and severely restricts the reliability and economy of mobile power supply. The failure rate of mobile power supply is high during charging, so it is necessary to diagnose the fault of mobile power supply during charging.

[0003] First, the existing mobile power supply fault diagnosis technology often analyzes the mobile power supply as a whole, the data is relatively single, the quantity is insufficient, and the data processing is not comprehensive, which leads to long early warning time or low accuracy. Second, the traditional threshold method is not intelligent enough. When there is an internal short circuit fault in the battery, it is difficult to be detected in the early detection process, and the fault condition is hidden. It is necessary to improve the threshold detection method to improve the early warning accuracy. For example, in the patent application with the application publication number CN109866650A, a power supply management system for electric vehicles is disclosed. This scheme fails to set appropriate abnormal threshold values and fails to make abnormal charging early warning based on appropriate abnormal threshold values, causing safety hazards. The existing mobile power supply fault diagnosis technology fails to set appropriate abnormal threshold values and make abnormal early warning based on abnormal threshold values during charging, resulting in inaccurate and untimely abnormal judgment during charging. SUMMARY

[0004] The present application aims to at least solve one of the technical problems in the prior art. By obtaining a first voltage threshold value and a second voltage threshold value based on the normal charging of a first number of block single batteries at the same SOC; obtaining a first voltage analysis function based on the first voltage threshold value; obtaining a second voltage analysis function based on the second voltage threshold value; obtaining a real-time coordinate point based on the real-time SOC and the real-time voltage; determining whether the mobile power supply is normal based on the real-time coordinate point, the first voltage analysis function and the second voltage analysis function; obtaining a real-time trend function based on the real-time coordinate point; and determining whether an abnormality will occur in the future based on the real-time trend function, the first voltage function and the second voltage function, the present application solves the problem of inaccurate and untimely abnormal judgment during charging in the existing mobile power supply fault diagnosis technology, which fails to set appropriate abnormal threshold values and make abnormal early warning based on abnormal threshold values.

[0005] To achieve the above object, the application provides a main control type intelligent equalization and fault diagnosis system for intelligent mobile power supply, comprising a threshold acquisition module, a first function acquisition module, a second function acquisition module, a real-time data acquisition module, a real-time coordinate point acquisition module, a real-time abnormality judgment module, a real-time trend function acquisition module and a future abnormality judgment module.

[0006] The threshold acquisition module is configured to acquire a first voltage threshold and a second voltage threshold based on the first number of single batteries under the same SOC when normally charged.

[0007] The first function acquisition module is configured to acquire a first voltage analysis function based on the first voltage threshold.

[0008] The second function acquisition module is configured to acquire a second voltage analysis function based on the second voltage threshold.

[0009] The real-time data acquisition module is configured to acquire SOC data and voltage data of each single battery in the intelligent mobile power supply when charged in real time, and mark them as real-time SOC and real-time voltage respectively.

[0010] The real-time coordinate point acquisition module is configured to acquire real-time coordinate points based on the real-time SOC and the real-time voltage.

[0011] The real-time abnormality judgment module is configured to judge whether the mobile power supply is normal based on the real-time coordinate points, the first voltage analysis function and the second voltage analysis function.

[0012] The real-time trend function acquisition module is configured to acquire a real-time trend function based on the real-time coordinate points.

[0013] The future abnormality judgment module is configured to judge whether an abnormality will occur in the future based on the real-time trend function, the first voltage function and the second voltage function.

[0014] Further, the threshold acquisition module is configured with a segmentation interval strategy, and the segmentation interval strategy comprises:

[0015] The voltage of the first number of single batteries under the same SOC when normally charged is marked as historical normal voltage.

[0016] The range of the historical normal voltage is acquired, and the range of the historical normal voltage is divided into a second number of equal interval intervals, which are marked as normal voltage segmentation intervals.

[0017] The frequency of the historical normal voltage in each normal voltage segmentation interval is acquired, which is marked as normal voltage segmentation frequency.

[0018] Further, the threshold acquisition module is configured with a frequency threshold acquisition strategy, and the frequency threshold acquisition strategy comprises:

[0019] The segmentation frequency threshold is calculated as Py=k×(T÷S), wherein Py is the segmentation frequency threshold, k is the reflectivity frequency ratio, T is the sum of all normal voltage segmentation frequencies, and S is the second quantity;

[0020] The normal voltage segmentation frequency less than the segmentation frequency threshold is marked as an abnormal segmentation frequency.

[0021] Further, the threshold obtaining module is configured with a threshold obtaining strategy, and the threshold obtaining strategy comprises:

[0022] The minimum value of each normal voltage segmentation interval is obtained and marked as a first interval voltage;

[0023] The maximum value of each normal voltage segmentation interval is obtained and marked as a second interval voltage;

[0024] The normal voltage segmentation frequencies are sorted from left to right according to the corresponding first interval voltages from small to large;

[0025] It is judged whether the leftmost normal voltage segmentation frequency is an abnormal segmentation frequency, if not, the judgment is directly stopped, if yes, the abnormal segmentation frequency is deleted, and then it is judged whether the leftmost normal voltage segmentation frequency is an abnormal segmentation frequency, if yes, the deletion and judgment are continued, and the process is stopped until the leftmost normal voltage segmentation frequency is not an abnormal segmentation frequency; after the process is stopped, the first interval voltage of the normal voltage segmentation interval corresponding to the leftmost normal voltage segmentation frequency is obtained and marked as a first voltage threshold;

[0026] It is judged whether the rightmost normal voltage segmentation frequency is an abnormal segmentation frequency, if not, the judgment is directly stopped, if yes, the abnormal segmentation frequency is deleted, and then it is judged whether the rightmost normal voltage segmentation frequency is an abnormal segmentation frequency, if yes, the deletion and judgment are continued, and the process is stopped until the rightmost normal voltage segmentation frequency is not an abnormal segmentation frequency; after the process is stopped, the second interval voltage of the normal voltage segmentation interval corresponding to the rightmost normal voltage segmentation frequency is obtained and marked as a second voltage threshold.

[0027] Further, the first function obtaining module is configured with a first voltage analysis function obtaining strategy, and the first voltage analysis function obtaining strategy comprises:

[0028] Different SOCs are obtained at uniform intervals within the range of the SOC and marked as historical SOCs; the first voltage threshold under different historical SOCs is obtained;

[0029] A plane rectangular coordinate system is established with the historical SOCs as the horizontal coordinates and the first voltage thresholds as the vertical coordinates, and marked as a first analysis coordinate system;

[0030] A coordinate point is constructed, and is marked as a first coordinate point; wherein the abscissa of the first coordinate point is the historical SOC, and the ordinate of the first coordinate point is the first voltage threshold corresponding to the historical SOC;

[0031] All the first coordinate points are plotted in the first analysis coordinate system to obtain a scatter plot, which is marked as a first voltage analysis scatter plot;

[0032] The first voltage analysis scatter plot is subjected to function fitting to obtain a function, which is marked as a first voltage analysis function.

[0033] Further, the second function acquisition module is configured with a second voltage analysis function acquisition strategy, and the second voltage analysis function acquisition strategy comprises:

[0034] The second voltage threshold under different historical SOC conditions is acquired;

[0035] A plane rectangular coordinate system is established with the historical SOC as the abscissa and the second voltage threshold as the ordinate, and is marked as a second analysis coordinate system;

[0036] A coordinate point is constructed, and is marked as a second coordinate point; wherein the abscissa of the second coordinate point is the historical SOC, and the ordinate of the second coordinate point is the second voltage threshold corresponding to the historical SOC;

[0037] All the second coordinate points are plotted in the second analysis coordinate system to obtain a scatter plot, which is marked as a second voltage analysis scatter plot;

[0038] The second voltage analysis scatter plot is subjected to function fitting to obtain a function, which is marked as a second voltage analysis function.

[0039] Further, the real-time coordinate point acquisition module is configured with a real-time coordinate point acquisition strategy, and the real-time coordinate point acquisition strategy comprises:

[0040] A coordinate point is constructed, and is marked as a real-time coordinate point; wherein the abscissa of the real-time coordinate point is the real-time SOC, and the ordinate of the real-time coordinate point is the real-time voltage corresponding to the real-time SOC;

[0041] The real-time coordinate point is plotted in the first analysis coordinate system and the second analysis coordinate system, respectively; and all the plotted real-time coordinate points are retained.

[0042] Further, the real-time abnormality judgment is configured with a real-time abnormality judgment strategy, and the real-time abnormality judgment strategy comprises:

[0043] It is judged whether the ordinate of the real-time coordinate point is between the first voltage analysis function and the second voltage analysis function, if not, it indicates that the mobile power supply is abnormal, and if yes, it indicates that the mobile power supply is normal at this time.

[0044] Further, the real-time trend function acquisition module is configured with a real-time trend function acquisition strategy, and the real-time trend function acquisition strategy comprises:

[0045] If the mobile power supply is normal at this time, the real-time coordinate point at this time is marked as a present coordinate point, and the present coordinate point and the third number of real-time coordinate points before are acquired and marked as trend coordinate points;

[0046] Linear fitting is performed on all the trend coordinate points to obtain a function, which is marked as a real-time trend function.

[0047] Further, the future abnormality judgment is configured with a future abnormality judgment strategy, and the future abnormality judgment strategy comprises:

[0048] The abscissa of the present coordinate point is acquired and marked as a first present abscissa;

[0049] The first present abscissa is added to the first value to obtain a second present abscissa;

[0050] It is judged whether the real-time trend function between the first present abscissa and the second present abscissa has a cross point with the first voltage analysis function or the second voltage analysis function; if yes, a future mobile power supply abnormality signal is generated; and if no, a future mobile power supply normality signal is generated.

[0051] The present application has the following advantages: the present application is based on the first voltage threshold and the second voltage threshold when the first number of block single batteries are normally charged under the same SOC; the first voltage analysis function is acquired based on the first voltage threshold; the second voltage analysis function is acquired based on the second voltage threshold; the real-time coordinate point is acquired based on the real-time SOC and the real-time voltage; it is judged whether the mobile power supply is normal based on the real-time coordinate point, the first voltage analysis function and the second voltage analysis function; the real-time trend function is acquired based on the real-time coordinate point; and it is judged whether the future will appear abnormal based on the real-time trend function, the first voltage function and the second voltage function, which has the advantages that appropriate abnormality threshold is set, abnormality early warning is performed on the future based on the abnormality threshold, the accuracy of abnormality judgment is improved, and the timeliness of abnormality judgment is improved.

[0052] The present application acquires the real-time trend function based on the real-time coordinate point, which has the advantage that abnormality early warning is performed on the future based on the abnormality threshold, and the timeliness of abnormality judgment is improved. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 It is a principle block diagram of the system of the present application;

[0054] Figure 2 It is a schematic diagram of the first voltage analysis function of the present application;

[0055] Figure 3A schematic diagram of a second voltage analysis function of the present application;

[0056] Figure 4 A schematic diagram of a real-time trend function and a first voltage analysis function position relationship of the present application;

[0057] Figure 5 A schematic diagram of a real-time trend function and a second voltage analysis function position relationship of the present application. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0059] Embodiment 1, please refer to Figure 1 As shown in the figure, the present application provides a master intelligent equalization and fault diagnosis system for an intelligent mobile power supply, which comprises a threshold acquisition module, a first function acquisition module, a second function acquisition module, a real-time data acquisition module, a real-time coordinate point acquisition module, a real-time anomaly judgment module, a real-time trend function acquisition module and a future anomaly judgment module.

[0060] The threshold acquisition module is configured to acquire a first voltage threshold and a second voltage threshold based on normal charging of the first number of single batteries at the same SOC.

[0061] The threshold acquisition module is configured with a segmentation interval strategy, and the segmentation interval strategy comprises:

[0062] The voltage of the first number of single batteries at the same SOC and under normal charging is marked as a historical normal voltage. The mobile power supply contains multiple single batteries, and each single battery is analyzed to make the analysis data more and accurately find out the single battery with a fault. The first number of historical normal voltages is used to acquire the distribution of normal voltages, so the more the first data is set, the better, for example, the first number is 200.

[0063] The range of the historical normal voltage is divided into a second number of equal interval intervals, which are marked as normal voltage segmentation intervals. The second number of normal voltage segmentation intervals is used to observe the distribution of the historical normal voltage, so the second number should not be too small, for example, the second number is set to 10.

[0064] The frequency of the historical normal voltage in each normal voltage segmentation interval is acquired, which is marked as a normal voltage segmentation frequency.

[0065] In practical applications, for example, the range of historical normal voltage at SOC of 50% is 3.52V to 3.72V; dividing 3.52V to 3.72V into the 10th normal voltage division interval is respectively: 3.52V to 3.54V, 3.54V to 3.56V, …, 3.68V to 3.70V, 3.70V to 3.72V; and the corresponding normal voltage division frequency is respectively: 1, 3, …, 4, 1.

[0066] The threshold acquisition module is configured with a frequency threshold acquisition strategy, and the frequency threshold acquisition strategy includes:

[0067] The division frequency threshold Py is calculated as: Py=k×(T÷S); wherein Py is the division frequency threshold, k is the reflectivity frequency ratio, T is the sum of all normal voltage division frequencies, and S is the second number; the division frequency threshold is acquired in order to screen out smaller normal voltage division frequencies, and therefore the set value of k is small, for example, k is 0.1;

[0068] The normal voltage division frequency smaller than the division frequency threshold is marked as an abnormal division frequency;

[0069] In practical applications, the sum of all normal voltage division frequencies is 200, the division frequency threshold Py is calculated as: Py=0.1×(200÷10)=2; and the normal voltage division frequency smaller than 2 is marked as an abnormal division frequency.

[0070] The threshold acquisition module is configured with a threshold acquisition strategy, and the threshold acquisition strategy includes:

[0071] The minimum value of each normal voltage division interval is acquired and marked as a first interval voltage;

[0072] The maximum value of each normal voltage division interval is acquired and marked as a second interval voltage;

[0073] The normal voltage division frequencies are sorted from left to right in ascending order of the corresponding first interval voltage;

[0074] It is judged whether the leftmost normal voltage division frequency is an abnormal division frequency, if not, the judgment is directly stopped; if yes, the abnormal division frequency is deleted, and then it is judged whether the leftmost normal voltage division frequency is an abnormal division frequency, if yes, the deletion and judgment are continued, until the leftmost normal voltage division frequency is not an abnormal division frequency, and then the judgment is stopped; after the judgment is stopped, the first interval voltage of the normal voltage division interval corresponding to the leftmost normal voltage division frequency is acquired and marked as a first voltage threshold; the first voltage threshold is acquired in order to exclude the occurrence of an individual abnormal small historical normal voltage, and thus an accurate historical normal voltage range is acquired;

[0075] Determine if the rightmost normal voltage segmentation frequency is an abnormal segmentation frequency. If not, stop the determination. If it is, delete this abnormal segmentation frequency, and then continue determining if the rightmost normal voltage segmentation frequency is an abnormal segmentation frequency. If so, continue deleting and determining until the rightmost normal voltage segmentation frequency is no longer an abnormal segmentation frequency. After stopping, obtain the second interval voltage of the normal voltage segmentation interval corresponding to the rightmost normal voltage segmentation frequency, and mark it as the second voltage threshold. Obtaining the second voltage threshold can exclude historical normal voltages that are abnormally high, thereby obtaining an accurate historical normal voltage range. The historical normal voltage range can more accurately determine whether a fault has occurred.

[0076] In practical applications, the normal voltage segmentation frequencies are sorted from left to right according to the corresponding first interval voltage from smallest to largest as follows: 1, 3, ..., 4, 1; after deleting the abnormal segmentation frequencies, the normal voltage segmentation frequencies are sorted from left to right as follows: 3, ..., 4; the first interval voltage of the normal voltage segmentation interval corresponding to the leftmost normal voltage segmentation frequency 3 is 3.54V, so the first voltage threshold is 3.54V; the second interval voltage of the normal voltage segmentation interval corresponding to the rightmost normal voltage segmentation frequency 4 is 3.70V, so the second voltage threshold is 3.70V.

[0077] The first function acquisition module is used to acquire a first voltage analysis function based on a first voltage threshold.

[0078] The first function acquisition module is configured with a first voltage analysis function acquisition strategy, which includes:

[0079] Within the range of SOC, different SOCs are obtained at uniform intervals and marked as historical SOCs; the first voltage threshold under different historical SOC conditions is obtained; the range of SOC is 0% to 100%; the uniform interval is, for example, every 5%, and the smaller the interval, the better. Here, it is set to 5% for ease of calculation.

[0080] A Cartesian coordinate system is established with historical SOC as the x-axis and the first voltage threshold as the y-axis, and this system is marked as the first analysis coordinate system.

[0081] Construct a coordinate point and mark it as the first coordinate point; where the x-coordinate of the first coordinate point is the historical SOC and the y-coordinate of the first coordinate point is the first voltage threshold corresponding to the historical SOC;

[0082] Plot all the first coordinate points in the first analysis coordinate system to obtain a scatter plot, and mark it as the first voltage analysis scatter plot;

[0083] The first voltage analysis scatter plot is subjected to function fitting to obtain a function, which is marked as a first voltage analysis function; the first voltage analysis function is a function fitted from the minimum value of the historical normal voltage under each historical SOC, that is, the normal real-time coordinate point cannot be below the first voltage analysis function, and the first voltage threshold obtained through screening makes the first voltage analysis function more accurate, that is, the judgment of the charging abnormality is more accurate;

[0084] In practical applications, please refer to Figure 2 The first voltage analysis function is drawn as shown in the schematic diagram.

[0085] The second function acquisition module is configured with a second voltage analysis function acquisition strategy, and the second voltage analysis function acquisition strategy comprises:

[0086] The second function acquisition module is configured with a second voltage analysis function acquisition strategy, and the second voltage analysis function acquisition strategy comprises:

[0087] The second voltage threshold under different historical SOC conditions is obtained;

[0088] A plane rectangular coordinate system is established with the historical SOC as the horizontal coordinate and the second voltage threshold as the vertical coordinate, which is marked as a second analysis coordinate system;

[0089] A coordinate point is constructed, which is marked as a second coordinate point; wherein the horizontal coordinate of the second coordinate point is the historical SOC, and the vertical coordinate of the second coordinate point is the second voltage threshold corresponding to the historical SOC;

[0090] All second coordinate points are drawn in the second analysis coordinate system to obtain a scatter plot, which is marked as a second voltage analysis scatter plot;

[0091] The second voltage analysis scatter plot is subjected to function fitting to obtain a function, which is marked as a second voltage analysis function; the second voltage analysis function is a function fitted from the maximum value of the historical normal voltage under each historical SOC, that is, the normal real-time coordinate point cannot be above the first voltage analysis function, and the second voltage threshold obtained through screening makes the second voltage analysis function more accurate, that is, the judgment of the charging abnormality is more accurate;

[0092] In practical applications, please refer to Figure 3 The second voltage analysis function is drawn as shown in the schematic diagram.

[0093] The real-time data acquisition module is configured to acquire the SOC data and the voltage data of each single battery in the intelligent mobile power supply in real time, which are marked as real-time SOC and real-time voltage respectively.

[0094] The real-time coordinate point acquisition module is configured to acquire the real-time coordinate point based on the real-time SOC and the real-time voltage;

[0095] The real-time coordinate point acquisition module is configured with a real-time coordinate point acquisition strategy, and the real-time coordinate point acquisition strategy comprises:

[0096] A coordinate point is constructed and marked as a real-time coordinate point; wherein the abscissa of the real-time coordinate point is a real-time SOC, and the ordinate of the real-time coordinate point is a real-time voltage corresponding to the real-time SOC; the real-time coordinate point acquisition interval cannot be too large, for example, the real-time coordinate point is acquired every 2% of the real-time SOC interval, and the interval here can also be set smaller;

[0097] The real-time coordinate points are plotted in the first analysis coordinate system and the second analysis coordinate system respectively; and all previously plotted real-time coordinate points are retained; here, the real-time coordinate points represent real-time charging data, and based on the real-time coordinate points, it can be analyzed whether the charging of the power supply is abnormal, and all previously plotted real-time coordinate points are retained in order to predict whether the subsequent charging data is abnormal.

[0098] The real-time abnormality judgment module is used to judge whether the power supply is normal based on the real-time coordinate points, the first voltage analysis function and the second voltage analysis function;

[0099] The real-time abnormality judgment module is configured with a real-time abnormality judgment strategy, and the real-time abnormality judgment strategy comprises:

[0100] It is judged whether the ordinate of the real-time coordinate point is between the first voltage analysis function and the second voltage analysis function, if not, it indicates that the power supply is abnormal, if yes, it indicates that the power supply is normal at this time; because the first voltage analysis function is the minimum voltage function when charging normally, and the second voltage analysis function is the maximum voltage function when charging normally; therefore, if the real-time coordinate point is between them, it indicates that the charging voltage is normal; if it is not within the range, it indicates that the charging voltage is abnormal;

[0101] In practical application, please refer to Figure 4 and Figure 5 It is shown that the ordinate of the real-time coordinate point is between the first voltage analysis function and the second voltage analysis function, indicating that the power supply is normal at this time.

[0102] The real-time trend function acquisition module is used to acquire a real-time trend function based on the real-time coordinate points;

[0103] The real-time trend function acquisition module is configured with a real-time trend function acquisition strategy, and the real-time trend function acquisition strategy comprises:

[0104] If the mobile power supply is normal at this time, the real-time coordinate point at this time is marked as a present coordinate point, and the present coordinate point and the third number of real-time coordinate points before are obtained and marked as a walking coordinate point; the third number of real-time coordinate points are obtained to predict whether an abnormal charging voltage will occur in the future; because if an abnormal charging occurs, the real-time voltage will rise or fall, so a change trend will occur, and whether this trend occurs can be judged based on the walking coordinate point; in order to briefly predict whether the voltage in the future is normal, the third number cannot be too large, for example, the third number is 5;

[0105] All walking coordinate points are linearly fitted to obtain a function, which is marked as a real-time walking function; the real-time walking function is a change trend of real-time data.

[0106] In actual application, please refer to Figure 4 and Figure 5 , the obtained real-time walking function.

[0107] The future abnormality judgment module is configured to judge whether an abnormality will occur in the future based on the real-time walking function, the first voltage function, and the second voltage function.

[0108] The future abnormality judgment is configured with a future abnormality judgment strategy, and the future abnormality judgment strategy includes:

[0109] The abscissa of the present coordinate point is obtained and marked as a first present abscissa; the first present abscissa is the value of the abscissa of the real-time SOC;

[0110] The first present abscissa is added to the first value to obtain a second present abscissa; the second present abscissa is the value of the abscissa of the future predicted SOC; the first value should not be too large, and a too large setting will make the subsequent prediction result inaccurate, for example, the first value is 2%;

[0111] It is judged whether the real-time walking function between the first present abscissa and the second present abscissa has a cross point with the first voltage analysis function or the second voltage analysis function; if so, a future mobile power supply abnormality signal is generated; if not, a future mobile power supply normal signal is generated; the real-time walking function is a change trend of real-time data, and the value of future data can be briefly predicted based on the change trend;

[0112] In actual application, please refer to Figure 4 and Figure 5 , the first present abscissa is 60%; when the first value is 2%, the second present abscissa is 62%; it is judged that the real-time walking function between 60% and 62% has no cross point with the first voltage analysis function or the second voltage analysis function, a future mobile power supply normal signal, i.e., a normal voltage, is generated.

[0113] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, a system or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer readable storage media having computer readable program code embodied in the medium. The computer readable storage medium can be any available medium or combination thereof that is accessible by a general purpose or special purpose computer including, but not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read only memory (EEPROM), erasable programmable read only memory (EPROM), programmable read only memory (PROM), magnetic or optical memory, flash memory, magnetic disk or optical disk. The computer readable program code can include any suitable set of instructions directly or indirectly executable by such a computer, including, but not limited to, those stored in the memory of the computer whether permanent or temporary. The computer readable program code can also be downloaded into the computer or other programmable data processing apparatus from computer readable storage medium or to a computer or other programmable data processing apparatus for implementation. The computer readable program code can cause a series of operations to be performed on the computer or other programmable apparatus, either directly or after appropriate instruction conversion, which then produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowcharts and / or block diagrams. Figure 1 The functions noted in the block or blocks can be implemented as Figure 1 one or more functions or operations specified in the flowchart or block diagram.

[0114] In the embodiments provided in the present application, it should be understood that the disclosed device and method can be implemented by other means. The device embodiments described above are only illustrative, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, and for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some communication interfaces, devices or units, which can be electrical, mechanical or other forms.

Claims

1. A main-controlled intelligent balancing and fault diagnosis system for smart mobile power banks, characterized in that: include: The module includes a threshold acquisition module, a first function acquisition module, a second function acquisition module, a real-time data acquisition module, a real-time coordinate point acquisition module, a real-time anomaly judgment module, a real-time trend function acquisition module, and a future anomaly judgment module. The threshold acquisition module is used to obtain a first voltage threshold and a second voltage threshold based on a first number of single-cell batteries under the same SOC when they are normally charged. The first function acquisition module is used to acquire a first voltage analysis function based on a first voltage threshold; The second function acquisition module is used to acquire a second voltage analysis function based on a second voltage threshold; The real-time data acquisition module is used to acquire the SOC data and voltage data of each individual battery in the smart mobile power supply during charging in real time, and mark them as real-time SOC and real-time voltage respectively. The real-time coordinate point acquisition module is used to acquire real-time coordinate points based on real-time SOC and real-time voltage. The real-time anomaly detection module is used to determine whether the power bank is normal based on real-time coordinate points, a first voltage analysis function, and a second voltage analysis function. The real-time direction function acquisition module is used to acquire the real-time direction function based on real-time coordinate points; The future anomaly judgment module is used to determine whether an anomaly will occur in the future based on the real-time trend function, the first voltage function, and the second voltage function.

2. The intelligent balancing and fault diagnosis system for a smart mobile power bank according to claim 1, characterized in that, The threshold acquisition module is configured with a segmentation interval strategy, which includes: The voltage of the first number of individual cells under any same SOC when they are normally charged is marked as the historical normal voltage. Obtain the range of historical normal voltages; divide the range of historical normal voltages into a second number of equally spaced intervals, and mark them as normal voltage intervals; Obtain the frequency of historical normal voltage within each normal voltage segmentation interval and mark it as the normal voltage segmentation frequency.

3. The intelligent balancing and fault diagnosis system for smart mobile power banks according to claim 2, characterized in that, The threshold acquisition module is configured with a frequency threshold acquisition strategy, which includes: The segmentation frequency threshold is calculated as: Py = k × (T ÷ S); where Py is the segmentation frequency threshold, k is the reflectivity frequency ratio, T is the sum of all normal voltage segmentation frequencies, and S is the second quantity; Normal voltage segmentation frequencies that are less than the segmentation frequency threshold are marked as abnormal segmentation frequencies.

4. The intelligent balancing and fault diagnosis system for smart mobile power banks according to claim 3, characterized in that, The threshold acquisition module is configured with a threshold acquisition strategy, which includes: Obtain the minimum value of each normal voltage segmentation interval and mark it as the voltage of the first interval; Obtain the maximum value of each normal voltage segmentation interval and mark it as the second interval voltage; The normal voltage segmentation frequency is sorted from left to right according to the corresponding first interval voltage from smallest to largest. Determine whether the leftmost normal voltage segmentation frequency is an abnormal segmentation frequency. If not, stop the determination directly. If so, delete this abnormal segmentation frequency, and then continue to determine whether the leftmost normal voltage segmentation frequency is an abnormal segmentation frequency. If so, continue deleting and determining until the leftmost normal voltage segmentation frequency is no longer an abnormal segmentation frequency. After stopping, obtain the first interval voltage of the normal voltage segmentation interval corresponding to the leftmost normal voltage segmentation frequency and mark it as the first voltage threshold. Determine whether the rightmost normal voltage segmentation frequency is an abnormal segmentation frequency. If not, stop the determination directly. If so, delete this abnormal segmentation frequency, and then continue to determine whether the rightmost normal voltage segmentation frequency is an abnormal segmentation frequency. If so, continue deleting and determining until the rightmost normal voltage segmentation frequency is no longer an abnormal segmentation frequency. After stopping, obtain the second interval voltage of the normal voltage segmentation interval corresponding to the rightmost normal voltage segmentation frequency and mark it as the second voltage threshold.

5. The intelligent balancing and fault diagnosis system for a smart mobile power bank according to claim 4, characterized in that, The first function acquisition module is configured with a first voltage analysis function acquisition strategy, which includes: Within the range of SOC, different SOCs are obtained at uniform intervals and marked as historical SOCs; the first voltage threshold under different historical SOC conditions is obtained. A Cartesian coordinate system is established with historical SOC as the x-axis and the first voltage threshold as the y-axis, and this system is marked as the first analysis coordinate system. Construct a coordinate point and mark it as the first coordinate point; where the x-coordinate of the first coordinate point is the historical SOC and the y-coordinate of the first coordinate point is the first voltage threshold corresponding to the historical SOC; Plot all the first coordinate points in the first analysis coordinate system to obtain a scatter plot, and mark it as the first voltage analysis scatter plot; The function obtained by fitting the scatter plot of the first voltage analysis is labeled as the first voltage analysis function.

6. The intelligent balancing and fault diagnosis system for a smart mobile power bank according to claim 5, characterized in that, The second function acquisition module is configured with a second voltage analysis function acquisition strategy, which includes: Obtain the second voltage threshold under different historical SOC conditions; A Cartesian coordinate system is established with historical SOC as the x-axis and the second voltage threshold as the y-axis, and this system is labeled as the second analysis coordinate system. Construct a coordinate point and mark it as the second coordinate point; where the x-coordinate of the second coordinate point is the historical SOC, and the y-coordinate of the second coordinate point is the second voltage threshold corresponding to the historical SOC; Plot all the second coordinate points in the second analysis coordinate system to obtain a scatter plot, and label it as the second voltage analysis scatter plot; The function obtained by fitting the scatter plot of the second voltage analysis is labeled as the second voltage analysis function.

7. The intelligent balancing and fault diagnosis system for a smart mobile power bank according to claim 6, characterized in that, The real-time coordinate point acquisition module is configured with a real-time coordinate point acquisition strategy, which includes: Construct coordinate points and mark them as real-time coordinate points; where the x-coordinate of the real-time coordinate point is the real-time SOC, and the y-coordinate of the real-time coordinate point is the real-time voltage corresponding to the real-time SOC; Plot the real-time coordinate points in the first and second analysis coordinate systems respectively; and retain all previously plotted real-time coordinate points.

8. The intelligent balancing and fault diagnosis system for a smart mobile power bank according to claim 7, characterized in that, The real-time anomaly detection configuration includes a real-time anomaly detection strategy, which comprises: Determine whether the ordinate of the real-time coordinate point is between the first voltage analysis function and the second voltage analysis function. If it is not, it indicates that the power bank is abnormal; if it is, it indicates that the power bank is normal at this time.

9. The intelligent balancing and fault diagnosis system for a smart mobile power bank according to claim 8, characterized in that, The real-time trajectory function acquisition module is configured with a real-time trajectory function acquisition strategy, which includes: If the power bank is working properly, the current real-time coordinate point is marked as the current coordinate point. The current coordinate point and the previous third number of real-time coordinate points are obtained and marked as the direction coordinate points. A function is obtained by linearly fitting all the direction coordinate points, and it is labeled as the real-time direction function.

10. The intelligent balancing and fault diagnosis system for a smart mobile power bank according to claim 9, characterized in that, The future anomaly detection configuration includes a future anomaly detection strategy, which comprises: Get the x-coordinate of the current coordinate point and mark it as the first current x-coordinate; Get the second current x-coordinate by adding the first current x-coordinate to the first current x-coordinate; Determine whether the real-time trend function between the first and second current horizontal coordinates intersects with either the first or second voltage analysis function; if so, generate a future mobile power bank abnormal signal; if not, generate a future mobile power bank normal signal.

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