Battery pack fault early warning monitoring analysis method and system

Abstract

The invention discloses a battery pack fault early warning monitoring analysis method and system, relates to the technical field of battery detection, and ensures that the running state of a battery pack is tracked and managed in real time by collecting the working running data of the battery pack in real time, generating a feature vector FV1 and transmitting the feature vector FV1 to a monitoring analysis platform. And secondly, through overlap analysis of the feature vector FV1 and historical data, the system can calculate the health state index HS of the battery pack, obtain the current health state of the battery pack and ensure timely early warning of potential faults, and through secondary analysis of the anomaly degree DYZ and in combination with cumulative effects of different types of anomalies, an anomaly degree evaluation index SE is obtained, so that the safety of the battery pack is improved. And a targeted abnormity regulation and control strategy is generated to ensure that proper measures are taken under different abnormity situations. The regulation and control measures are executed through remote control, abnormity is prompted to the host in time, and instantaneity and effectiveness of a regulation and control strategy are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of battery detection, and in particular to a battery pack fault early warning monitoring and analysis method and system. Background Art

[0002] In modern industry and life, intelligent equipment and Internet of Things technologies are gradually being integrated into various fields, especially in the use scenarios of power tools, where technological innovation of battery packs is particularly critical. As a core component for providing energy, battery packs play an important role in portable devices such as power tools. In recent years, with the development of technology, battery packs not only need to have charging and power supply functions, but also require intelligent monitoring and remote operation to adapt to more complex use environments and diversified needs. Especially in high-frequency use scenarios such as automobile maintenance and construction, the demand for remote monitoring of smart battery packs has gradually become prominent.

[0003] However, existing battery pack monitoring systems often lack sufficient intelligence and remote management capabilities. Although traditional battery packs can provide basic charging and discharging functions, their monitoring of working status, charging status and abnormal conditions often relies on manual or on-site operations, making it difficult to achieve real-time and accurate early warnings. In addition, these systems are usually unable to record detailed historical data, such as important parameters such as the battery pack's cycle life, temperature changes, and single-cell voltage balance. This not only results in the battery pack being unable to be detected and processed in a timely manner when problems occur, but also makes after-sales maintenance and fault diagnosis more difficult, which in turn affects the service life and safety of the equipment. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a battery pack fault early warning monitoring and analysis method and system, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A battery pack fault early warning monitoring and analysis method comprises the following steps: S1, collect the working data of the battery pack in real time to form a feature vector FV1 of the battery pack, and transmit the feature vector FV1 to the monitoring and analysis platform through the Internet; S2. After overlapping the received feature vector FV1 with the historical data, the fluctuation state and fluctuation trend are analyzed to obtain the health status index HS of the health status of the battery pack, and match it with the preset battery pack operation status assessment threshold YZ to obtain the current battery pack operation status assessment result; S3. Perform secondary analysis on the abnormality degree DYZ of the battery pack and the cumulative effects of different types of abnormalities of the battery pack according to the operation status evaluation results, obtain the abnormality degree evaluation index SE, and match it with the preset fault risk assessment threshold GZ to obtain the abnormality control strategy solution; S4. Generate the current battery pack abnormal control measures according to the abnormal control strategy plan, and execute them through remote control and the host prompts the abnormality; S5. According to the execution of the abnormal control strategy program content, the working status and operation data of the battery pack are collected again after a fixed period, and the feature vector FV2 is generated. It is compared with the feature vector FV1 to obtain the effectiveness evaluation result of the execution of the abnormal control strategy program content.

[0006] Preferably, said S1 includes S11 and S12; S11, using the voltage sensor, current sensor and temperature sensor integrated inside the battery pack to collect the working status and operation data of the battery pack in real time, including the instantaneous voltage V, instantaneous current I and instantaneous temperature T, and record the number of charge and discharge cycles N of the battery pack, and then analyze the voltage balance of a single battery to obtain the battery balance state Be, forming the working operation data of the battery pack; S12. Generate feature vectors for the collected battery pack operating data, mark the collection time t, form a feature vector FV1={V(t), I(t), T(t), N, Be}, and upload it to the monitoring and analysis platform and the user-end platform through an Internet upload channel established through Wifi and 4G Internet of Things.

[0007] Preferably, S2 includes S21 and S22; S21, after overlapping the received feature vector FV1 with the historical data, by analyzing the fluctuation state and fluctuation trend, wherein the fluctuation state is obtained by overlapping the feature vector FV1 with the historical data and analyzing the difference fluctuation between each vector and the average value in the historical data, and the fluctuation trend is obtained by overlapping the feature vector FV1 with the historical data and analyzing the cumulative difference between each vector and the average value in the historical data within a fixed period, so as to obtain the health status index HS of the health status of the battery pack; The historical data includes the historical voltage Vhist, the historical current Ihist, the historical temperature Thist and the historical equilibrium average value Behist; The health status index HS is obtained by the following calculation formula: ; In the formula, △V (t) represents the instantaneous voltage deviation between the instantaneous voltage V at time t and the historical voltage Vhist, specifically indicating the abnormal fluctuation state of the instantaneous voltage, Vnom represents the cumulative value of the voltage fluctuation trend, specifically reflecting the voltage fluctuation trend through the cumulative difference between the voltage and its historical average value in a fixed period, △I (t) represents the instantaneous current deviation between the instantaneous current I at time t and the historical voltage Ihist, specifically used to measure the fluctuation amplitude of the current current. When it becomes larger, it indicates that the current has fluctuated significantly and there may be short-term abnormalities, Inom represents the cumulative value of the current fluctuation trend, △T (t) represents the instantaneous temperature T at time t and the historical temperature Thi st, is specifically used to measure the current temperature fluctuation state. When it becomes larger, it indicates that the temperature fluctuation amplitude is large, which may cause the risk of overheating or overcooling of the battery pack. Tnom represents the cumulative value of the temperature fluctuation trend. △Be(t) represents the instantaneous deviation of the balanced voltage between the battery balanced state Be at time t and the historical balanced average value Behist, which is specifically used to measure the internal balanced state of the battery pack. A large fluctuation in the battery balanced voltage may indicate that the voltage difference of a single battery cell inside the battery pack is large, affecting the overall performance of the battery. Benom represents the cumulative value of the balanced voltage fluctuation trend, which specifically indicates the cumulative difference in the balanced voltage over a period of time, reflecting the fluctuation trend of the balanced voltage. Preferably, S22, by matching the health status index HS with the preset battery pack operating status evaluation threshold YZ, obtain the current battery pack operating status evaluation result; The running status evaluation result is obtained by the following calculation formula: When the health status index HS is less than the battery pack operation status assessment threshold YZ, the current battery pack operation status assessment result is obtained as a qualified state; When the health status index HS is greater than or equal to the battery pack operating status assessment threshold YZ, the current battery pack operating status assessment result is obtained as an unqualified state, and the battery pack abnormality degree DYZ is marked as 1; When the health status index HS ≥ twice the battery pack operation status assessment threshold YZ, the current battery pack operation status assessment result is obtained as a warning state, and the battery pack abnormality degree DYZ is marked as 2.

[0008] Preferably, said S3 includes S31 and S32; S31, performing secondary analysis based on the operation status evaluation result, including the cumulative effect of the abnormality degree DYZ of the battery pack and different types of abnormalities of the battery pack, wherein the secondary analysis is triggered when the operation status evaluation result is an unqualified state and a warning state, and the secondary analysis includes marking and analyzing the feature vector FV1 of the battery pack operation data within a fixed period to obtain the abnormality degree evaluation index SE; The different types of battery pack anomalies include instantaneous voltage V, instantaneous current I, instantaneous temperature T and battery balance state Be anomalies; The abnormality evaluation index SE is obtained by the following calculation formula: ; In the formula, n represents the total number of samples in a fixed period, i represents the i-th sampling, specifically represents the feature vector FV1 obtained by the i-th sampling, △Vi, △Ii, △Ti and △Bei represent the voltage instantaneous deviation, current instantaneous deviation, temperature instantaneous deviation and balanced voltage instantaneous deviation obtained by the i-th sampling respectively.

[0009] Preferably, S32, matching the abnormality evaluation index SE with a preset fault risk evaluation threshold GZ, evaluating the abnormality of the battery pack, and obtaining an abnormality control strategy solution; The abnormal control strategy scheme is obtained by the following matching method: When the abnormality evaluation index SE is less than the fault risk evaluation threshold GZ, the secondary analysis of the current battery pack is obtained to show that the abnormality is risk-free, and the prompt restriction and power-off restriction of the battery pack are lifted; When the abnormality evaluation index SE ≥ the fault risk evaluation threshold GZ, the secondary analysis of the current battery pack is abnormal and there is a risk, and a prompt is given to limit the output power and charge and discharge rate of the battery pack, including notifying and prompting users and relevant managers through the monitoring platform and mobile devices; When the abnormality assessment index SE ≥ twice the fault risk assessment threshold GZ, the current battery pack secondary analysis abnormality is obtained and there is a warning risk. The battery pack is disconnected from the power tool after a fixed time, and the discharge operation of the battery pack is synchronously adjusted and stopped after a fixed time. This includes notifying and prompting users and relevant managers through the monitoring platform and mobile devices, and recording and storing the current abnormal status log information.

[0010] Preferably, the current battery pack abnormal control measures are generated according to the abnormal regulation strategy solution content, including generating control measures instructions for reducing power, extending charging and discharging time, switching battery pack power consumption mode, stopping battery pack connection and stopping battery pack discharge, and are executed by remote control and the host prompts the abnormality, including using WiFi and 4G network remote communication technology to transmit the generated power control measures instructions to the battery pack host for execution and the mobile terminal for prompting.

[0011] Preferably, said S5 includes S51 and S52; S51, according to the execution of the abnormal control strategy plan, including the execution of control measures instructions and prompt notifications, the working status and operation data of the battery pack are recollected after a fixed period, a feature vector FV2 is generated, and compared with the feature vector FV1 to obtain a difference index △FV; The difference index ΔFV is obtained by the following calculation formula: ; In the formula, k represents the correction coefficient, N2 and N1 represent the number of charge and discharge cycles N in the feature vector FV2 and the number of charge and discharge cycles N in the feature vector FV1 respectively.

[0012] Preferably, S52, judging the execution effect of the abnormal control strategy solution content according to the obtained difference index ΔFV, and obtaining the effectiveness evaluation result of the execution of the abnormal control strategy solution content; The effectiveness evaluation results are obtained by the following comparison method: When the difference index △FV is less than 1, the evaluation result of the effectiveness of the execution of the abnormal control strategy solution content is valid; When the difference index △FV≥1, the evaluation result of the effectiveness of the execution of the abnormal control strategy solution content is invalid.

[0013] A battery pack fault early warning monitoring and analysis system, comprising a data acquisition module, a data analysis module, a state evaluation module, a measure generation module and an execution evaluation module; The data acquisition module collects the working data of the battery pack in real time to form a feature vector FV1 of the battery pack, and transmits the feature vector FV1 to the monitoring and analysis platform via the Internet; After overlapping the received feature vector FV1 with the historical data, the data analysis module analyzes the fluctuation state and fluctuation trend, obtains the health status index HS of the battery pack health status, and matches it with the preset battery pack operation status assessment threshold YZ to obtain the current battery pack operation status assessment result; The state assessment module performs secondary analysis on the abnormality degree DYZ of the battery pack and the cumulative effects of different types of abnormalities of the battery pack according to the operation state assessment result, obtains the abnormality degree assessment index SE, and matches it with the preset fault risk assessment threshold GZ to obtain the abnormality control strategy solution; The measure generation module generates the current battery pack abnormality control measures according to the abnormality control strategy solution content, and executes them through remote control and the host prompts the abnormality; The execution evaluation module re-collects the working status and operation data of the battery pack after a fixed period according to the execution of the abnormal control strategy solution content, generates a feature vector FV2, and compares it with the feature vector FV1 to obtain the effectiveness evaluation result of the execution of the abnormal control strategy solution content.

[0014] The present invention provides a battery pack fault early warning monitoring and analysis method and system, which has the following beneficial effects: (1) Through steps S1 to S5, the working data of the battery pack is collected in real time, the feature vector FV1 is generated, and transmitted to the monitoring and analysis platform, ensuring the real-time tracking and management of the operating status of the battery pack. Secondly, through the overlapping analysis of the feature vector FV1 and the historical data, the system can calculate the health status index HS of the battery pack, obtain the current health status of the battery pack, ensure timely warning of potential faults, and obtain the abnormality evaluation index SE through secondary analysis of the abnormality degree DYZ, combined with the cumulative effect of different types of abnormalities, and generate targeted abnormal control strategies to ensure that appropriate measures are taken in different abnormal situations. These control measures are executed through remote control, and the host is prompted with abnormalities in a timely manner, ensuring the timeliness and effectiveness of the control strategy. After executing the control measures, the system evaluates the actual effect of the control measures through re-collection within a fixed period, solving the problems of lack of real-time warning, insufficient analysis of the cumulative effect of abnormalities, and inability to effectively execute the control strategy in traditional systems. Through the accurate evaluation of the health status and abnormality degree of the battery pack, combined with the remote execution of control measures and effectiveness evaluation, it is ensured that the battery pack can be handled in time when problems occur, extending the service life of the battery pack and improving safety and reliability.

[0015] (2) After a preliminary evaluation of the operating status of the battery pack in real time, the method can trigger secondary analysis in the unqualified state and the warning state, further explore the cumulative effects of different types of abnormalities in the battery pack, and obtain the abnormality evaluation index SE through the analysis of the feature vector FV1. This multi-level analysis mechanism not only identifies the abnormalities of instantaneous voltage V, instantaneous current I, instantaneous temperature T and balanced voltage Be, but also can quantitatively evaluate the cumulative effects of these parameters, ensuring the accurate identification of the long-term operation risks of the battery pack, and automatically generating abnormal regulation strategy solutions. For example, reducing the power output of the battery pack and limiting the charge and discharge rate, and even automatically disconnecting the battery pack from the power tool in severe cases, this remote control and regulation function, combined with the execution capabilities of WiFi and 4G networks, ensures that risks can be responded to immediately and real-time warnings can be provided to users and managers.

[0016] (3) Through the calculation and evaluation of the difference index △FV, the execution effect of the abnormal control strategy is accurately judged. After the control measures are executed, the working state of the battery pack is collected again and a new feature vector FV2 is generated. It is compared with the initial feature vector FV1, and the influence of the number of charge and discharge cycles N is calculated. This can effectively reflect the role of the control measures in the long-term operation of the battery pack, and can directly judge the effectiveness of the control measures based on the comparison results. This evaluation mechanism ensures that the actual effect of the control measures after execution can be quantified, and the system can further optimize the operating state of the battery pack through real-time feedback, effectively avoiding the problem of difficulty in evaluating the execution effect of control measures in traditional systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of the steps of a battery pack fault early warning monitoring and analysis method of the present invention; Figure 2 The present invention is a schematic diagram of a battery pack fault early warning monitoring and analysis system. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0019] Example 1

[0020] The present invention provides a battery pack fault early warning monitoring and analysis method, please refer to Figure 1 , including the following steps: S1, collect the working data of the battery pack in real time to form a feature vector FV1 of the battery pack, and transmit the feature vector FV1 to the monitoring and analysis platform through the Internet; S2. After overlapping the received feature vector FV1 with the historical data, the fluctuation state and fluctuation trend are analyzed to obtain the health status index HS of the health status of the battery pack, and match it with the preset battery pack operation status assessment threshold YZ to obtain the current battery pack operation status assessment result; S3. Perform secondary analysis on the abnormality degree DYZ of the battery pack and the cumulative effects of different types of abnormalities of the battery pack according to the operation status evaluation results, obtain the abnormality degree evaluation index SE, and match it with the preset fault risk assessment threshold GZ to obtain the abnormality control strategy solution; S4. Generate the current battery pack abnormal control measures according to the abnormal control strategy plan, and execute them through remote control and the host prompts the abnormality; S5. According to the execution of the abnormal control strategy program content, the working status and operation data of the battery pack are collected again after a fixed period, and the feature vector FV2 is generated. It is compared with the feature vector FV1 to obtain the effectiveness evaluation result of the execution of the abnormal control strategy program content.

[0021] In this embodiment, through steps S1 to S5, the working data of the battery pack is collected in real time, the feature vector FV1 is generated, and transmitted to the monitoring and analysis platform, ensuring the real-time tracking and management of the operating status of the battery pack. Secondly, through the overlapping analysis of the feature vector FV1 and the historical data, the system can calculate the health status index HS of the battery pack, obtain the current health status of the battery pack, ensure timely warning of potential faults, and obtain the abnormality evaluation index SE through secondary analysis of the abnormality degree DYZ, combined with the cumulative effect of different types of abnormalities, and generate targeted abnormal regulation strategies to ensure that appropriate measures are taken in different abnormal situations. These control measures are executed through remote control, and the host is prompted with abnormalities in a timely manner, which ensures the immediacy and effectiveness of the control strategy. After executing the control measures, the system evaluates the actual effect of the control measures through re-collection within a fixed period, solving the problems of lack of real-time warning, insufficient analysis of the cumulative effect of abnormalities, and inability to effectively execute the control strategy in the traditional system. Through the accurate evaluation of the health status and abnormality of the battery pack, combined with the remote execution of control measures and effectiveness evaluation, it is ensured that the battery pack can be handled in time when problems occur, extending the service life of the battery pack and improving safety and reliability.

[0022] Example 2

[0023] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: the S1 includes S11 and S12; S11, using the voltage sensor, current sensor and temperature sensor integrated inside the battery pack to collect the working status and operation data of the battery pack in real time, including the instantaneous voltage V, instantaneous current I and instantaneous temperature T, and record the number of charge and discharge cycles N of the battery pack, and then analyze the voltage balance of a single battery to obtain the battery balance state Be, forming the working operation data of the battery pack; S12. Generate feature vectors for the collected battery pack operating data, mark the collection time t, form a feature vector FV1={V(t), I(t), T(t), N, Be}, and upload it to the monitoring and analysis platform and the user-end platform through an Internet upload channel established through Wifi and 4G Internet of Things.

[0024] The S2 includes S21 and S22; S21, after overlapping the received feature vector FV1 with the historical data, by analyzing the fluctuation state and fluctuation trend, wherein the fluctuation state is obtained by overlapping the feature vector FV1 with the historical data and analyzing the difference fluctuation between each vector and the average value in the historical data, and the fluctuation trend is obtained by overlapping the feature vector FV1 with the historical data and analyzing the cumulative difference between each vector and the average value in the historical data within a fixed period, so as to obtain the health status index HS of the health status of the battery pack; The historical data includes the historical voltage Vhist, the historical current Ihist, the historical temperature Thist and the historical equilibrium average value Behist; The health status index HS is obtained by the following calculation formula: ; In the formula, △V (t) represents the instantaneous voltage deviation between the instantaneous voltage V at time t and the historical voltage Vhist, specifically indicating the abnormal fluctuation state of the instantaneous voltage, Vnom represents the cumulative value of the voltage fluctuation trend, specifically reflecting the voltage fluctuation trend through the cumulative difference between the voltage and its historical average value in a fixed period, △I (t) represents the instantaneous current deviation between the instantaneous current I at time t and the historical voltage Ihist, specifically used to measure the fluctuation amplitude of the current current. When it becomes larger, it indicates that the current has fluctuated significantly and there may be short-term abnormalities, Inom represents the cumulative value of the current fluctuation trend, △T (t) represents the instantaneous temperature T at time t and the historical temperature Thi st, which is specifically used to measure the current temperature fluctuation state. When it becomes larger, it indicates that the temperature fluctuation range is large, which may cause the risk of overheating or overcooling of the battery pack. Tnom represents the cumulative value of temperature fluctuation trend. △Be(t) represents the instantaneous deviation of the balanced voltage between the battery balanced state Be at time t and the historical balanced average value Behist, which is specifically used to measure the internal balanced state of the battery pack. A large fluctuation in the battery balanced voltage may indicate that the voltage difference of a single battery in the battery pack is large, affecting the overall performance of the battery. Benom represents the cumulative value of the balanced voltage fluctuation trend, which specifically indicates the cumulative difference in the balanced voltage over a period of time, reflecting the fluctuation trend of the balanced voltage.

[0025] S22, obtaining an evaluation result of the current battery pack operation state by matching the health state index HS with a preset battery pack operation state evaluation threshold YZ; The running status evaluation result is obtained by the following calculation formula: When the health status index HS is less than the battery pack operation status assessment threshold YZ, the current battery pack operation status assessment result is obtained as a qualified state; When the health status index HS is greater than or equal to the battery pack operating status assessment threshold YZ, the current battery pack operating status assessment result is obtained as an unqualified state, and the battery pack abnormality degree DYZ is marked as 1; When the health status index HS ≥ twice the battery pack operation status assessment threshold YZ, the current battery pack operation status assessment result is obtained as a warning state, and the battery pack abnormality degree DYZ is marked as 2.

[0026] In this embodiment, the accuracy of abnormal warning and fault diagnosis is ensured by comprehensively comparing and accumulating the real-time data collection of the battery pack operation status with the historical data. First, the system collects key parameters including instantaneous voltage V(t), instantaneous current I(t), instantaneous temperature T(t), battery equilibrium state Be(t) and battery charge and discharge cycle number N through integrated voltage sensors, current sensors and temperature sensors, generates a feature vector FV1, and transmits it to the monitoring and analysis platform in real time. By overlapping the feature vector FV1 with the historical voltage Vhist, historical current Ihist, historical temperature Thist and historical equilibrium average value Behist, the system can not only analyze the instantaneous fluctuation state of the battery pack, but also accurately measure the long-term changes of the battery pack in a fixed period through the cumulative calculation of the fluctuation trend. Through this analysis, the system can calculate the health status index HS of the battery pack and match it with the battery pack operation status evaluation threshold YZ, ensuring that not only abnormal fluctuations in the short term can be identified, but also the health status of the battery pack can be monitored for a long time based on the cumulative effect of fluctuations, which greatly improves the accuracy and sensitivity of the battery pack fault warning and prevents potential problems from evolving into major faults during accumulation.

[0027] Example 3

[0028] This embodiment is explained in Example 2. Please refer to Figure 1 , specifically: S3 includes S31 and S32; S31, performing secondary analysis based on the operation status evaluation result, including the cumulative effect of the abnormality degree DYZ of the battery pack and different types of abnormalities of the battery pack, wherein the secondary analysis is triggered when the operation status evaluation result is an unqualified state and a warning state, and the secondary analysis includes marking and analyzing the feature vector FV1 of the battery pack operation data within a fixed period to obtain the abnormality degree evaluation index SE; The different types of battery pack anomalies include instantaneous voltage V, instantaneous current I, instantaneous temperature T and battery balance state Be anomalies; The abnormality evaluation index SE is obtained by the following calculation formula: ; In the formula, n represents the total number of samples in a fixed period, i represents the i-th sampling, specifically represents the feature vector FV1 obtained by the i-th sampling, △Vi, △Ii, △Ti and △Bei represent the voltage instantaneous deviation, current instantaneous deviation, temperature instantaneous deviation and balanced voltage instantaneous deviation obtained by the i-th sampling respectively.

[0029] S32, matching the abnormality evaluation index SE with the preset fault risk evaluation threshold GZ, evaluating the abnormality of the battery pack, and obtaining an abnormality control strategy solution; The abnormal control strategy scheme is obtained by the following matching method: When the abnormality evaluation index SE is less than the fault risk evaluation threshold GZ, the secondary analysis of the current battery pack is obtained to show that the abnormality is risk-free, and the prompt restriction and power-off restriction of the battery pack are lifted; When the abnormality evaluation index SE ≥ the fault risk evaluation threshold GZ, the secondary analysis of the current battery pack is abnormal and there is a risk, and a prompt is given to limit the output power and charge and discharge rate of the battery pack, including notifying and prompting users and relevant managers through the monitoring platform and mobile devices; When the abnormality assessment index SE ≥ twice the fault risk assessment threshold GZ, the current battery pack secondary analysis abnormality is obtained and there is a warning risk. The battery pack is disconnected from the power tool after a fixed time, and the discharge operation of the battery pack is synchronously adjusted and stopped after a fixed time. This includes notifying and prompting users and relevant managers through the monitoring platform and mobile devices, and recording and storing the current abnormal status log information.

[0030] Generate abnormal control measures for the current battery pack based on the abnormal regulation strategy solution, including generating control measures instructions for reducing power, extending charge and discharge time, switching battery pack power consumption mode, stopping battery pack connection and stopping battery pack discharge, and execute them through remote control and prompt the host for abnormalities, including using WiFi and 4G network remote communication technology to transmit the generated power control measures instructions to the battery pack host for execution and prompt the mobile terminal.

[0031] In this embodiment, after a preliminary evaluation of the operating status of the battery pack in real time, the method can trigger a secondary analysis in the unqualified state and the warning state, further explore the cumulative effects of different types of abnormalities in the battery pack, and obtain the abnormality evaluation index SE through the analysis of the characteristic vector FV1. This multi-level analysis mechanism not only identifies the abnormalities of the instantaneous voltage V, instantaneous current I, instantaneous temperature T and balanced voltage Be, but also can quantitatively evaluate the cumulative effects of these parameters, ensuring the accurate identification of the long-term operation risks of the battery pack, and automatically generating abnormal control strategy solutions. For example, reducing the power output of the battery pack and limiting the charge and discharge rate, and even automatically disconnecting the battery pack from the power tool in severe cases, this remote control and regulation function, combined with the execution capabilities of WiFi and 4G networks, ensures that risks can be responded to immediately and real-time warnings can be provided to users and managers.

[0032] Example 4

[0033] This embodiment is explained in Example 3, please refer to Figure 1 , specifically: the S5 includes S51 and S52; S51, according to the execution of the abnormal control strategy plan, including the execution of control measures instructions and prompt notifications, the working status and operation data of the battery pack are recollected after a fixed period, a feature vector FV2 is generated, and compared with the feature vector FV1 to obtain a difference index △FV; The difference index ΔFV is obtained by the following calculation formula: ; In the formula, k represents the correction coefficient, N2 and N1 represent the number of charge and discharge cycles N in the feature vector FV2 and the number of charge and discharge cycles N in the feature vector FV1 respectively.

[0034] S52, judging the execution effect of the abnormal control strategy solution content according to the obtained difference index △FV, and obtaining the effectiveness evaluation result of the execution of the abnormal control strategy solution content; The effectiveness evaluation results are obtained by the following comparison method: When the difference index △FV is less than 1, the evaluation result of the effectiveness of the execution of the abnormal control strategy solution content is valid; When the difference index △FV≥1, the evaluation result of the effectiveness of the execution of the abnormal control strategy solution content is invalid.

[0035] In this embodiment, the calculation and evaluation of the difference index △FV achieves an accurate judgment of the execution effect of the abnormal control strategy solution. After the control measures are executed, the working state of the battery pack is re-collected and a new feature vector FV2 is generated, which is compared with the initial feature vector FV1, and the influence of the number of charge and discharge cycles N is calculated, which can effectively reflect the role of the control measures in the long-term operation of the battery pack, and can directly judge the effectiveness of the control measures based on the comparison results. This evaluation mechanism ensures that the actual effect of the control measures after execution can be quantified, and the system can further optimize the operating state of the battery pack through real-time feedback, effectively avoiding the problem of difficulty in evaluating the execution effect of control measures in traditional systems.

[0036] Example 5

[0037] A battery pack fault warning monitoring and analysis system, please refer to Figure 2 ,Specifically: including data acquisition module, data analysis module, status assessment module, measure generation module and execution assessment module; The data acquisition module collects the working data of the battery pack in real time to form a feature vector FV1 of the battery pack, and transmits the feature vector FV1 to the monitoring and analysis platform via the Internet; After overlapping the received feature vector FV1 with the historical data, the data analysis module analyzes the fluctuation state and fluctuation trend, obtains the health status index HS of the battery pack health status, and matches it with the preset battery pack operation status assessment threshold YZ to obtain the current battery pack operation status assessment result; The state assessment module performs secondary analysis on the abnormality degree DYZ of the battery pack and the cumulative effects of different types of abnormalities of the battery pack according to the operation state assessment result, obtains the abnormality degree assessment index SE, and matches it with the preset fault risk assessment threshold GZ to obtain the abnormality control strategy solution; The measure generation module generates the current battery pack abnormality control measures according to the abnormality control strategy solution content, and executes them through remote control and the host prompts the abnormality; The execution evaluation module re-collects the working status and operation data of the battery pack after a fixed period according to the execution of the abnormal control strategy solution content, generates a feature vector FV2, and compares it with the feature vector FV1 to obtain the effectiveness evaluation result of the execution of the abnormal control strategy solution content.

[0038] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A battery pack fault early warning monitoring and analysis method, characterized in that: The following steps are involved: S1, collect the working data of the battery pack in real time to form a feature vector FV1 of the battery pack, and transmit the feature vector FV1 to the monitoring and analysis platform through the Internet; S2. After overlapping the received feature vector FV1 with the historical data, the fluctuation state and fluctuation trend are analyzed to obtain the health status index HS of the health status of the battery pack, and match it with the preset battery pack operation status assessment threshold YZ to obtain the current battery pack operation status assessment result; S3. Perform secondary analysis on the abnormality degree DYZ of the battery pack and the cumulative effects of different types of abnormalities of the battery pack according to the operation status evaluation results, obtain the abnormality degree evaluation index SE, and match it with the preset fault risk assessment threshold GZ to obtain the abnormality control strategy solution; S4. Generate the current battery pack abnormal control measures according to the abnormal control strategy plan, and execute them through remote control and the host prompts the abnormality; S5. According to the execution of the abnormal control strategy program content, the working status and operation data of the battery pack are collected again after a fixed period, and the feature vector FV2 is generated. It is compared with the feature vector FV1 to obtain the effectiveness evaluation result of the execution of the abnormal control strategy program content.

2. A battery pack fault early warning monitoring and analysis method according to claim 1, characterized in that: Said S1 includes S11 and S12; S11, using the voltage sensor, current sensor and temperature sensor integrated inside the battery pack to collect the working status and operation data of the battery pack in real time, including the instantaneous voltage V, instantaneous current I and instantaneous temperature T, and record the number of charge and discharge cycles N of the battery pack, and then analyze the voltage balance of a single battery to obtain the battery balance state Be, forming the working operation data of the battery pack; S12, generating a feature vector for the collected battery pack operation data and marking the collection time t, The feature vector FV1 = {V(t), I(t), T(t), N, Be} is formed and uploaded to the monitoring and analysis platform and the user-end platform through an Internet upload channel established through Wifi and 4G Internet of Things.

3. A battery pack fault early warning monitoring and analysis method according to claim 2, characterized in that: The S2 includes S21 and S22; S21, after overlapping the received feature vector FV1 with the historical data, by analyzing the fluctuation state and fluctuation trend, wherein the fluctuation state is obtained by overlapping the feature vector FV1 with the historical data and analyzing the difference fluctuation between each vector and the average value in the historical data, and the fluctuation trend is obtained by overlapping the feature vector FV1 with the historical data and analyzing the cumulative difference between each vector and the average value in the historical data within a fixed period, so as to obtain the health status index HS of the health status of the battery pack; The historical data includes the historical voltage Vhist, the historical current Ihist, the historical temperature Thist and the historical equilibrium average value Behist; The health status index HS is obtained by the following calculation formula: ; In the formula, △V(t) represents the instantaneous voltage deviation between the instantaneous voltage V at time t and the historical voltage Vhist, Vnom represents the cumulative value of the voltage fluctuation trend, △I(t) represents the instantaneous current deviation between the instantaneous current I at time t and the historical voltage Ihist, Inom represents the cumulative value of the current fluctuation trend, △T(t) represents the instantaneous temperature deviation between the instantaneous temperature T at time t and the historical temperature Thist, Tnom represents the cumulative value of the temperature fluctuation trend, △Be(t) represents the instantaneous deviation of the balanced voltage between the battery balanced state Be at time t and the historical balanced average value Behist, and Benom represents the cumulative value of the balanced voltage fluctuation trend.

4. A battery pack fault early warning monitoring and analysis method according to claim 3, characterized in that: S22, obtaining an evaluation result of the current battery pack operation state by matching the health state index HS with a preset battery pack operation state evaluation threshold YZ; The running status evaluation result is obtained by the following calculation formula: When the health status index HS is less than the battery pack operation status assessment threshold YZ, the current battery pack operation status assessment result is obtained as a qualified state; When the health status index HS ≥ the battery pack operation status assessment threshold YZ, the current battery pack operation status assessment result is obtained as an unqualified state, and the battery pack abnormality degree DYZ is marked as 1; When the health status index HS ≥ twice the battery pack operation status assessment threshold YZ, the current battery pack operation status assessment result is obtained as a warning state, and the battery pack abnormality degree DYZ is marked as 2.

5. A battery pack fault early warning monitoring and analysis method according to claim 4, characterized in that: The S3 includes S31 and S32; S31, performing secondary analysis based on the operation status evaluation result, including the cumulative effect of the abnormality degree DYZ of the battery pack and different types of abnormalities of the battery pack, wherein the secondary analysis is triggered when the operation status evaluation result is an unqualified state and a warning state, and the secondary analysis includes marking and analyzing the feature vector FV1 of the battery pack operation data within a fixed period to obtain the abnormality degree evaluation index SE; The different types of battery pack anomalies include instantaneous voltage V, instantaneous current I, instantaneous temperature T and battery balance state Be anomalies; The abnormality evaluation index SE is obtained by the following calculation formula: ; In the formula, n represents the total number of samples in a fixed period, i represents the i-th sampling, specifically represents the feature vector FV1 obtained by the i-th sampling, △Vi, △Ii, △Ti and △Bei represent the voltage instantaneous deviation, current instantaneous deviation, temperature instantaneous deviation and balanced voltage instantaneous deviation obtained by the i-th sampling respectively.

6. A battery pack fault early warning monitoring and analysis method according to claim 5, characterized in that: S32, matching the abnormality evaluation index SE with the preset fault risk evaluation threshold GZ, evaluating the abnormality of the battery pack, and obtaining an abnormality control strategy solution; The abnormal control strategy scheme is obtained by the following matching method: When the abnormality evaluation index SE is less than the fault risk evaluation threshold GZ, the secondary analysis of the current battery pack is obtained to show that the abnormality is risk-free, and the prompt restriction and power-off restriction of the battery pack are lifted; When the abnormality evaluation index SE ≥ the fault risk evaluation threshold GZ, the secondary analysis of the current battery pack is abnormal and there is a risk, and a prompt is given to limit the output power and charge and discharge rate of the battery pack, including notifying and prompting users and relevant managers through the monitoring platform and mobile devices; When the abnormality assessment index SE ≥ twice the fault risk assessment threshold GZ, the current battery pack secondary analysis abnormality is obtained and there is a warning risk. The battery pack is disconnected from the power tool after a fixed time, and the discharge operation of the battery pack is synchronously adjusted and stopped after a fixed time. This includes notifying and prompting users and relevant managers through the monitoring platform and mobile devices, and recording and storing the current abnormal status log information.

7. A battery pack fault early warning monitoring and analysis method according to claim 6, characterized in that: in, Generate abnormal control measures for the current battery pack based on the abnormal regulation strategy solution, including generating control measures instructions for reducing power, extending charge and discharge time, switching battery pack power consumption mode, stopping battery pack connection and stopping battery pack discharge, and execute them through remote control and prompt the host for abnormalities, including using WiFi and 4G network remote communication technology to transmit the generated power control measures instructions to the battery pack host for execution and prompt the mobile terminal.

8. A battery pack fault early warning monitoring and analysis method according to claim 7, characterized in that: The S5 includes S51 and S52; S51, according to the execution of the abnormal control strategy plan, including the execution of control measures instructions and prompt notifications, the working status and operation data of the battery pack are recollected after a fixed period, a feature vector FV2 is generated, and compared with the feature vector FV1 to obtain a difference index △FV; The difference index ΔFV is obtained by the following calculation formula: ; In the formula, k represents the correction coefficient, N2 and N1 represent the number of charge and discharge cycles N in the feature vector FV2 and the number of charge and discharge cycles N in the feature vector FV1 respectively.

9. A battery pack fault early warning monitoring and analysis method according to claim 8, characterized in that: S52, judging the execution effect of the abnormal control strategy solution content according to the obtained difference index △FV, and obtaining the effectiveness evaluation result of the execution of the abnormal control strategy solution content; The effectiveness evaluation results are obtained by the following comparison method: When the difference index △FV is less than 1, the evaluation result of the effectiveness of the execution of the abnormal control strategy solution content is valid; When the difference index △FV≥1, the evaluation result of the effectiveness of the execution of the abnormal control strategy solution content is invalid.

10. A battery pack fault early warning monitoring and analysis system, comprising a battery pack fault early warning monitoring and analysis method according to any one of claims 1 to 9, characterized in that: It includes data collection module, data analysis module, status assessment module, measure generation module and execution assessment module; The data acquisition module collects the working data of the battery pack in real time to form a feature vector FV1 of the battery pack, and transmits the feature vector FV1 to the monitoring and analysis platform via the Internet; After overlapping the received feature vector FV1 with the historical data, the data analysis module analyzes the fluctuation state and fluctuation trend, obtains the health status index HS of the battery pack health status, and matches it with the preset battery pack operation status assessment threshold YZ to obtain the current battery pack operation status assessment result; The state assessment module performs secondary analysis on the abnormality degree DYZ of the battery pack and the cumulative effects of different types of abnormalities of the battery pack according to the operation state assessment result, obtains the abnormality degree assessment index SE, and matches it with the preset fault risk assessment threshold GZ to obtain the abnormality control strategy solution; The measure generation module generates the current battery pack abnormality control measures according to the abnormality control strategy solution content, and executes them through remote control and the host prompts the abnormality; The execution evaluation module re-collects the working status and operation data of the battery pack after a fixed period according to the execution of the abnormal control strategy solution content, generates a feature vector FV2, and compares it with the feature vector FV1 to obtain the effectiveness evaluation result of the execution of the abnormal control strategy solution content.

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