State monitoring and fault early warning system of energy storage equipment

Abstract

The invention relates to the technical field of equipment monitoring and early warning, in particular to a state monitoring and fault early warning system of energy storage equipment, which comprises a temperature monitoring module, an electrochemical monitoring module, a state evaluation module and an early warning and maintenance module. According to the method, continuous data recording is carried out by arranging the multi-point temperature sensors, temperature changes at key moments can be captured and analyzed in time, real-time temperature monitoring data and voltage and current monitoring are integrated, the analysis depth of electrochemical behaviors is improved, the equipment performance and abnormal reaction in the differential charging and discharging stage can be accurately recognized, and the detection accuracy is improved. Future performance degradation of the equipment is predicted, potential fault points are accurately pointed out, the maintenance decision process is optimized, real-time performance monitoring and dynamic maintenance guidance are synchronously carried out, the equipment fault probability is remarkably reduced, the reliability and economic benefits of the energy storage system are improved, and the operation safety and efficiency of the energy storage equipment are fundamentally enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment monitoring and early warning, and in particular to a state monitoring and fault early warning system for energy storage equipment. Background Art

[0002] The field of equipment monitoring and early warning technology covers systems that use advanced sensor technology, data processing software, and network communication technology to monitor the operating status of equipment in real time and issue early warning signals in a timely manner when abnormal situations occur. It can effectively predict equipment failures, optimize maintenance plans, and reduce downtime, thereby improving equipment efficiency and safety. It is usually used in many important industries such as industry, transportation, and energy, including monitoring of large-scale energy storage systems, production line machinery, and transportation vehicles. Through real-time data analysis and historical data comparison, the monitoring system can identify potential risk points and issue alarms to operators or automatic control systems in a timely manner to take corresponding preventive or repair measures.

[0003] Among them, the state monitoring and fault warning system of energy storage equipment is a monitoring system specifically for power storage equipment such as batteries or supercapacitors. Its main purpose is to ensure that the energy storage equipment can operate in a safe and optimal working state. By monitoring the key parameters of the equipment such as voltage, current, temperature, etc., any signs that may cause equipment performance degradation or failure can be discovered in time, and preventive measures can be taken before an accident occurs. This is crucial to maintaining the continuity and reliability of the energy system, especially in modern energy networks that rely on renewable energy and efficient energy storage solutions.

[0004] Although existing technologies include a wide range of equipment monitoring and early warning systems, they often lack accuracy and timeliness. In particular, in terms of temperature monitoring, current systems often lack real-time or multi-point monitoring capabilities, which limits the comprehensive capture of temperature anomalies of equipment at critical operating moments and increases the risk of outdated reactions. Electrochemical behavior analysis often fails to integrate multi-parameter data in real time, resulting in subtle performance changes during the charging and discharging process being difficult to accurately capture, affecting the accuracy and timeliness of fault prediction. These technical limitations make it difficult to optimize maintenance plans, increase maintenance costs and equipment downtime, especially in modern energy networks that rely on efficient energy storage solutions. The monitoring system's potential to optimize equipment safety and efficiency has not been fully utilized, posing a potential threat to the continuity and reliability of the energy system. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a state monitoring and fault warning system for energy storage equipment.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: the state monitoring and fault warning system of the energy storage device includes:

[0007] The temperature monitoring module deploys multi-point temperature sensors at key parts of the energy storage device, continuously records temperature data, monitors the temperature fluctuations of the energy storage device during the charging and discharging processes, conducts temperature trend analysis based on the monitoring results of the temperature fluctuations, and records the temperature changes at key time points to obtain real-time temperature monitoring data;

[0008] The electrochemistry monitoring module uses the real-time temperature monitoring data, simultaneously collects the voltage and current data of the energy storage device during operation, analyzes the electrochemistry behavior of the energy storage device, reveals the performance of the energy storage device in different charging and discharging stages according to the analysis results of the electrochemistry behavior, identifies abnormal electrochemical reactions, and outputs an electrochemistry stability report;

[0009] The state assessment module evaluates the current health status of the energy storage device according to the electrochemistry stability report, comprehensively considering the usage frequency and cycle times of the energy storage device, synchronously checks the key performance indicators of the energy storage device according to the evaluation results of the current health status, predicts the future performance degradation of the energy storage device, identifies potential fault points, and obtains the device health assessment and prediction results;

[0010] The warning and maintenance module, based on the device health assessment and prediction results, monitors the performance and operating conditions of the energy storage device in real time, analyzes the potential fault trends of the energy storage device, and guides the maintenance operations to generate fault handling and warning information.

[0011] As a further solution of the present invention, the monitoring steps of the temperature fluctuation are as follows:

[0012] Select the key parts of the battery unit and control system of the energy storage device, determine the thermal distribution characteristics of the key parts through thermal induction scanning, evaluate the heat-bearing capacity of each part, determine the deployment positions of the multi-point temperature sensors, and generate a list of deployment position coordinates;

[0013] According to the list of deployment position coordinates, position and install the sensors in sequence, perform on-site tests, verify the response speed and data accuracy of the sensors, and obtain sensor installation configuration verification information;

[0014] According to the sensor installation configuration verification information, adjust the time interval of data recording in combination with the charging and discharging cycles of the energy storage device, monitor the temperature fluctuations of the energy storage device in different operation stages in real time, record the key temperature change points, and generate a temperature fluctuation monitoring report.

[0015] As a further solution of the present invention, the obtaining steps of the real-time temperature monitoring data are as follows:

[0016] Based on the temperature fluctuation monitoring report, perform time series analysis, identify the periodic patterns of the temperature fluctuations, mark the temperature peaks and valleys during the charging and discharging processes, and generate a preliminary trend analysis report;

[0017] Based on the preliminary trend analysis report, set a monitoring threshold, identify temperature data beyond the normal fluctuation range and record it, determine the abnormal temperature frequency and duration, and generate a record of temperature changes at key time points;

[0018] Integrate the record of temperature changes at the key time points, perform data visualization processing, construct a temperature trend graph and a hot spot distribution graph, reveal the dynamic changes of temperature and highlight the key change points, and generate real-time temperature monitoring data.

[0019] As a further solution of the present invention, the analysis steps of the electrochemical behavior are as follows:

[0020] Deploy voltage sensors and current sensors on the energy storage device, synchronously collect voltage data V(t) and current data I(t) in real time, and combine the real-time temperature monitoring data to generate an electrochemical parameter data set;

[0021] Based on the electrochemical parameter data set, use the formula:

[0022]

[0023] Calculate the internal resistance R to generate a data set of internal resistance at time points, where V(t) represents real-time voltage data, I(t) represents real-time current data, and represent the change rates of voltage and current;

[0024] Analyze the data set of internal resistance at the time points, perform statistical analysis to determine the stability of the internal resistance, and use the formula:

[0025]

[0026] Calculate the coefficient of variation CV to obtain the analysis result of the electrochemical behavior, where σ represents the standard deviation of the internal resistance data and μ represents the mean value of the internal resistance data.

[0027] As a further solution of the present invention, the steps for obtaining the electrochemical stability report are as follows:

[0028] Based on the analysis result of the electrochemical behavior, by comparing the internal resistance change data and the voltage and current time series in different charge-discharge stages, analyze the performance of the energy storage device during the charge-discharge process, and generate a performance change analysis report;

[0029] According to the performance change analysis report, mark the abnormal voltage mutation and current instability phenomena, perform trend analysis and pattern recognition on the abnormal data, determine the abnormal reaction type and severity, and generate an abnormal electrochemical reaction identification report;

[0030] Based on the comprehensive analysis report of the above-mentioned performance changes and the abnormal electrochemical reaction identification report, record the electrochemical behavior of the energy storage device during all charge and discharge stages, analyze the actual impact of abnormal conditions on the performance of the energy storage device, and output an electrochemical stability report.

[0031] As a further aspect of the present invention, the evaluation step of the current health state of the device is as follows:

[0032] Based on the electrochemical stability report, extract the charge and discharge performance and internal resistance data of the energy storage device, analyze and determine the electrochemical baseline characteristics of the energy storage device, and generate electrochemical baseline characteristic data;

[0033] Analyze the electrochemical baseline characteristic data, combine the usage frequency f and the number of cycles n of the device, and use the formula:

[0034]

[0035] Calculate and output the device health state index H, where C current and C initial represent the current and initial charging capacities respectively;

[0036] Utilize the health state index, combine the historical maintenance records and abnormal reports of the energy storage device, compare the design life of the device and the expected performance degradation trend, evaluate and determine the health status of the energy storage device, and obtain the current health state evaluation result of the energy storage device.

[0037] As a further aspect of the present invention, the obtaining step of the device health assessment prediction result is as follows:

[0038] According to the current health state evaluation result of the energy storage device, conduct inspections on the key performance indicators of the device, including battery charging efficiency, energy density, power density, and internal resistance, compare with the performance standards, and generate key performance indicator data;

[0039] Utilize the key performance indicator data, combine the historical operation data of the device, identify potential performance degradation points, predict the future performance degradation trend of the energy storage device, draw a degradation curve, and generate a performance degradation prediction report;

[0040] Combine the performance degradation prediction report and the current health state evaluation result of the energy storage device, conduct electrochemical impedance spectroscopy and thermal imaging inspections, identify potential fault points, determine the fault components and types, and obtain the device health assessment prediction result.

[0041] As a further aspect of the present invention, the obtaining step of the fault handling and warning information is as follows:

[0042] Based on the predicted results of the device health assessment, the voltage, current, temperature, and charge-discharge state of the energy storage device are monitored in real time, key operating parameters are continuously collected, and real-time analysis is performed to generate a real-time performance monitoring report;

[0043] Based on the real-time performance monitoring report, the formula:

[0044]

[0045] is used to calculate the potential fault trend value T and generate a potential fault trend report, where p V represents the abnormal change value of voltage, p I represents the abnormal change value of current, p T represents the abnormal change value of temperature;

[0046] According to the potential fault trend report, key trend data is extracted, maintenance strategies and fault handling measures are designed, maintenance operations are formulated and executed, and fault handling and early warning information are generated.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0048] In the present invention, by deploying multi-point temperature sensors for continuous data recording, the temperature changes at critical moments can be captured and analyzed in a timely manner. Integrating real-time temperature monitoring data with voltage and current monitoring improves the depth of analysis of electrochemical behavior, enables accurate identification of the device performance and abnormal reactions in different charge-discharge stages, predicts the future performance degradation of the device and accurately points out potential fault points, optimizes the maintenance decision-making process, and synchronously conducts real-time performance monitoring and dynamic maintenance guidance, significantly reducing the probability of device failure, improving the reliability and economic benefits of the energy storage system, and fundamentally enhancing the operation safety and efficiency of the energy storage device. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is the system flow chart of the present invention;

[0050] Figure 2 is the monitoring flow chart of the temperature fluctuation of the present invention;

[0051] Figure 3 is the acquisition flow chart of the real-time temperature monitoring data of the present invention;

[0052] Figure 4 is the analysis flow chart of the electrochemical behavior of the present invention;

[0053] Figure 5 is the acquisition flow chart of the electrochemical stability report of the present invention;

[0054] Figure 6 is the evaluation flow chart of the current health state of the device of the present invention;

[0055] Figure 7 This is the flowchart for obtaining the prediction results of the equipment health assessment of the present invention;

[0056] Figure 8 This is the flowchart for obtaining the fault handling and early warning information of the present invention. Specific embodiments

[0057] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0058] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more unless otherwise specifically defined.

[0059] Please refer to Figure 1 , the state monitoring and fault early warning system for energy storage equipment includes:

[0060] The temperature monitoring module deploys multi-point temperature sensors at key parts of the energy storage equipment, continuously records temperature data, monitors the temperature fluctuations of the energy storage equipment during the charging and discharging processes, conducts temperature trend analysis based on the temperature fluctuation monitoring results, and records the temperature changes at key time points to obtain real-time temperature monitoring data;

[0061] The electrochemistry monitoring module uses the real-time temperature monitoring data, and at the same time collects the voltage and current data of the energy storage equipment during the operation process, analyzes the electrochemistry behavior of the energy storage equipment, reveals the performance of the energy storage equipment in different charging and discharging stages according to the electrochemistry behavior analysis results, identifies abnormal electrochemical reactions, and outputs an electrochemistry stability report;

[0062] The state evaluation module evaluates the current health state of the energy storage equipment according to the electrochemistry stability report, comprehensively considering the usage frequency and cycle times of the energy storage equipment. According to the current health state evaluation results, it synchronously checks the key performance indicators of the energy storage equipment, predicts the future performance degradation of the energy storage equipment, identifies potential fault points, and obtains the equipment health assessment prediction results;

[0063] Based on the prediction results of equipment health assessment, the warning and maintenance module monitors the performance and operating conditions of energy storage equipment in real time, analyzes the potential fault trends of energy storage equipment, and guides maintenance operations to generate fault handling and warning information.

[0064] The real-time temperature monitoring data includes cumulative hot spot distribution records, the number of temperature anomalies, and time series temperature graphs; the electrochemical stability report includes chemical reaction rates, records of changes in equipment internal resistance, and charge-discharge cycle efficiency; the prediction results of equipment health assessment include the remaining effective life of the equipment, the performance degradation rate, and the safety rating; the fault handling and warning information includes records of fault causes, warning signal types, and necessary maintenance measures.

[0065] Please refer to Figure 2 , and the monitoring steps for temperature fluctuations are as follows:

[0066] Select key parts of the battery unit and control system of the energy storage equipment, determine the thermal distribution characteristics of the key parts through thermal induction scanning, evaluate the heat-carrying capacity of each part, determine the deployment positions of multi-point temperature sensors, and generate a list of deployment position coordinates;

[0067] Based on the data of thermal induction scanning, first conduct a detailed analysis of the thermal distribution characteristics of each key part of the energy storage equipment. By analyzing the thermal images, determine the heat-carrying capacity and temperature change trend of each part, evaluate the sensitivity of the part, determine the optimal sensor deployment position, use high-precision thermal induction instruments to ensure the accuracy and repeatability of the data. Through precise measurement of hot spots, generate an accurate list of deployment position coordinates. These coordinate lists indicate the best positions where each sensor should be placed to ensure the comprehensiveness and effectiveness of the monitoring data. The sensor installation provides detailed technical support to ensure the scientific and practical nature of sensor deployment.

[0068] According to the list of deployment position coordinates, locate and install the sensors in sequence, perform on-site tests to verify the response speed and data accuracy of the sensors, and obtain sensor installation configuration verification information;

[0069] Use a standardized installation process to install temperature sensors. Each sensor position is accurately located according to the aforementioned coordinate list to ensure that the fixing and stability performance of each sensor meet the technical requirements. Through on-site installation tests, verify the response speed and data acquisition accuracy of each sensor, including multiple rounds of tests on the response time and stability of the sensors to ensure that each sensor can work normally under the set parameters. Conduct a preliminary analysis of the collected data to confirm that it meets the preset monitoring standards, obtain sensor installation and configuration verification information, which reflects the quality of sensor installation and the reliability of future operation, and provides guarantee for the data accuracy in actual operation.

[0070] According to the sensor installation configuration verification information, adjust the time interval of data recording in combination with the charge and discharge cycles of the energy storage device, monitor the temperature fluctuations of the energy storage device during different operation stages in real time, record the key temperature change points, and generate a temperature fluctuation monitoring report.

[0071] Using the already installed sensors, adjust the time interval of data recording according to the actual charge and discharge cycles of the energy storage device. For each stage of the device operation, monitor the temperature fluctuations, collect data in real time through the sensors, record every significant change in temperature, and use data analysis methods to analyze the patterns and abnormal points of temperature fluctuations. This process includes real-time data acquisition and immediate analysis to ensure the real-time update of monitoring data and accurately reflect the device status, thereby generating a detailed temperature fluctuation monitoring report that details the temperature changes of each part of the device during different charge and discharge cycles, providing data support and decision-making basis for device maintenance and optimization.

[0072] Please refer to Figure 3 , and the steps for obtaining real-time temperature monitoring data are as follows:

[0073] Based on the temperature fluctuation monitoring report, perform time series analysis, identify the periodic patterns of temperature fluctuations, mark the temperature peaks and valleys during the charge and discharge processes, and generate a preliminary trend analysis report.

[0074] First, organize and preprocess the data obtained from the multi-point temperature sensors, including removing outliers and filling in missing data points. Then, by calculating the moving average and standard deviation at each time point, identify the regularity and abnormal fluctuations of temperature changes. After that, apply the autoregressive moving average model to analyze the time series characteristics of temperature data, such as periodic fluctuations and trend changes. By comparing the temperature patterns in different time periods, accurately identify the key time points during the charging and discharging processes, which will be regarded as the observation focus in subsequent monitoring to promptly detect potential device failures or performance degradation.

[0075] Based on the preliminary trend analysis report, set the monitoring threshold, identify the temperature data beyond the normal fluctuation range and record it, determine the abnormal temperature frequency and duration, and generate a record of temperature changes at key time points.

[0076] Based on the temperature trend, focus on monitoring abnormal temperature fluctuations by setting specific thresholds. Use statistical methods, such as box plot analysis, to determine the normal fluctuation range and abnormal thresholds of temperature data. When the temperature exceeds these preset thresholds, automatically mark and record these events, and detail the timestamp and temperature value of each abnormal event. In addition, to ensure real-time update and accuracy of data, a data verification program is automatically executed every hour to quickly check and analyze the newly collected temperature data. The detailed records at key time points are not only used for daily performance monitoring but also provide a basis for equipment maintenance decisions.

[0077] Integrate the temperature change records at key time points, perform data visualization, construct temperature trend charts and hot spot distribution charts, reveal the dynamic changes of temperature and highlight key change points, and generate real-time temperature monitoring data;

[0078] The last step is to integrate the collected key temperature data and present it through data visualization techniques. First, use time series charts to show the overall trend of temperature, highlighting the temperature fluctuations at key time points. Emphasize these key points using color coding and markings to make them clearly visible in the chart. Then, use heat maps to show the temperature distribution of various parts of the device, especially the high-temperature regions at key time points. Through visualization methods, the report not only details the specific values of each monitoring but also intuitively shows the patterns and trends behind the data, providing direct and effective decision support for equipment operation and maintenance.

[0079] Please refer to Figure 4 , the analysis steps of the electrochemical behavior are as follows:

[0080] Deploy voltage sensors and current sensors on the energy storage device to synchronously collect voltage data V(t) and current data I(t) in real time, and combine with real-time temperature monitoring data to generate an electrochemical parameter data set;

[0081] To ensure the accuracy of the electrochemical performance analysis of energy storage devices, it is first necessary to deploy voltage and current sensors to ensure that the sensors cover all key parts of the device. The sensors will record voltage and current data in real time, and the data will be transmitted to the monitoring center in real time through a data acquisition system. At the same time, existing real-time temperature monitoring data is utilized. The data is sourced from temperature sensors in key hot spots of the device and can reflect the thermal state changes during the operation of the device. Next, these three types of data (voltage, current, temperature) are integrated into a unified data processing platform. This platform uses data synchronization technology to ensure the real-time update and accuracy of information. Through data integration, a comprehensive electrochemical parameter dataset can be obtained. This dataset will serve as the basis for subsequent analysis of electrochemical behavior. The generated electrochemical parameter dataset not only includes basic voltage and current information but also comprehensively considers the possible impact of temperature on device performance, providing a solid data foundation for further electrochemical behavior analysis.

[0082] Based on the electrochemical parameter dataset, the formula:

[0083]

[0084] is used to calculate the internal resistance R, generating an internal resistance dataset at time points. Here, V(t) represents real-time voltage data, I(t) represents real-time current data, and represent the change rates of voltage and current;

[0085] At a certain moment, it is set that V(t) = 12V and I(t) = 2A. Calculate the internal resistance R:

[0086]

[0087] The results show that the internal resistance has increased slightly at this time, indicating that there may be a slight performance decline or load change in the working state of the energy storage device at this moment.

[0088] Analyze the internal resistance dataset at time points, conduct statistical analysis to determine the stability of the internal resistance, and use the formula:

[0089]

[0090] to calculate the coefficient of variation CV, obtaining the results of electrochemical behavior analysis. Here, σ represents the standard deviation of the internal resistance data, and μ represents the mean value of the internal resistance data;

[0091] The internal resistance values in the internal resistance dataset are [5.8, 6.0, 6.1, 6.2, 5.9] ohms. Calculate the mean value μ and the standard deviation σ:

[0092]

[0093]

[0094] Then calculate the coefficient of variation CV:

[0095]

[0096] The result shows that the coefficient of variation of the internal resistance data is very low, indicating that the internal resistance is relatively stable, which means that during the current monitoring period, the electrochemical behavior of the energy storage device is relatively consistent without large fluctuations. It is an important indicator for judging the performance stability of the device and provides a strong basis for further analyzing the electrochemical behavior of the device.

[0097] Please refer to Figure 5 , and the steps to obtain the electrochemical stability report are as follows:

[0098] Based on the analysis results of the electrochemical behavior, by comparing the internal resistance change data and the voltage and current time series in different charge-discharge stages, analyze the performance of the energy storage device during the charge-discharge process and generate a performance change analysis report;

[0099] According to the analysis results of the electrochemical behavior, the performance of the device in different charge-discharge stages is determined. The process first involves the integrated analysis of the internal resistance change data, comparing the internal resistance data with the voltage and current data in time series, drawing charts through data processing software, observing the performance fluctuations in different stages, and marking the internal resistance mutation points with data. The stages with large fluctuations are then recorded in detail in the report for further analysis; the analysis report is automatically generated by an algorithm, summarizing all the data and providing charts and fluctuation analysis, enabling the intuitive display of the performance changes. The report not only points out the performance fluctuations in each stage but also provides detailed descriptions and inference bases for the corresponding data, thus supporting subsequent technical decisions and equipment optimization work.

[0100] According to the performance change analysis report, mark the abnormal voltage mutations and current instability phenomena, conduct trend analysis and pattern recognition on the abnormal data, establish the types and severities of abnormal reactions, and generate an abnormal electrochemical reaction identification report;

[0101] Continue to deeply analyze the abnormal electrochemical reactions marked in the stage performance report. Analyze the specific manifestations of the abnormal points through quantitative methods, collect relevant voltage, current, and internal resistance data, conduct trend analysis and abnormal pattern recognition. The key lies in identifying the abnormal fluctuations in the data and classifying the fluctuations, so as to determine the types and severities of abnormal reactions, automatically mark and classify the abnormal points, and thus generate a detailed abnormal identification report, which details the voltage, current, and internal resistance readings of each abnormal event, analyzes the conditions and possible causes of the abnormalities, and provides a scientific basis for correcting and optimizing the equipment.

[0102] Comprehensive performance change analysis report and abnormal electrochemical reaction identification report, record the electrochemical behavior of the energy storage device during all charge and discharge stages, analyze the actual impact of abnormal conditions on the performance of the energy storage device, and output an electrochemical stability report;

[0103] Integrate the analysis results of the first two steps to compile an electrochemical stability report. This report details the behavior of the device during each charge and discharge stage, compares and analyzes the performance differences between normal and abnormal conditions, and at the same time provides an assessment of the future operation risks of the device and maintenance suggestions; by integrating the aforementioned analysis results, the report proposes directions for optimizing the device performance, including recommended maintenance measures and performance monitoring points, to ensure the stability and efficiency of the device during future operation. The final report is presented in writing, detailing the analysis methods, data sources, as well as the conclusions and suggestions drawn, ensuring the operability and scientific nature of the report, and providing practical reference materials for the device management department.

[0104] Please refer to Figure 6 , the evaluation steps for the current health status of the device are as follows:

[0105] Based on the electrochemical stability report, extract the charge and discharge performance and internal resistance data of the energy storage device, analyze and determine the electrochemical baseline characteristics of the energy storage device, and generate electrochemical baseline characteristic data;

[0106] Using the charge and discharge performance and internal resistance data obtained from the electrochemical stability report, accurately determine the electrochemical baseline characteristics of the energy storage device. The process involves real-time monitoring of the device's performance under specific test conditions. The specific operation is to set up a battery test bench to conduct standard charge and discharge cycles on the energy storage device, while recording the changes in voltage and current. The data is collected in real-time through high-precision instruments such as voltage recorders and current sensors to ensure the high accuracy and reliability of the obtained data. Then, analyze the collected data to identify the changes in the battery's charging capacity and discharge rate. This analysis includes not only basic numerical calculations but also graphical displays of data trends. Finally, through these detailed analysis steps, convert the original electrochemical performance data into standardized electrochemical baseline characteristic data, providing a scientific basis for the future health status assessment and maintenance decision-making of the device, and generating electrochemical baseline characteristic data.

[0107] Analyze the electrochemical baseline characteristic data, combine the usage frequency f and cycle number n of the device, and use the formula:

[0108]

[0109] Calculate and output the device health status index H, where C current and C initial represent the current and initial charging capacities respectively;

[0110] Set specific values: Ccurrent = 800 Ah, C initial = 1000 Ah, f = 0.5 times per day, n = 300 times, the formula calculation is as follows:

[0111]

[0112] Calculate e -300.5 The value of, which is approximately 0, so:

[0113] H ≈ 0.8 × 0 = 0

[0114] The calculated result H is close to zero, indicating that the health state of the energy storage device has seriously decayed. The health state index approaching zero means that the actual capacity of the battery has decreased significantly compared to the initial capacity. The cumulative effect of the usage frequency and the number of cycles has led to significant performance degradation. It is necessary to conduct a detailed inspection of the battery pack and take recovery measures or replace the battery pack according to the actual situation to ensure the reliable operation and safety of the device.

[0115] Using the health state index, combined with the historical maintenance records and anomaly reports of the energy storage device, comparing the design life of the device and the expected performance degradation trend, evaluating and determining the health status of the energy storage device, and obtaining the current health state assessment result of the energy storage device;

[0116] Using the health state index and the data of its change with the device usage frequency and the number of cycles, comprehensively evaluating the health state of the energy storage device, including the analysis of maintenance records and anomaly reports. By comparing the health state index with the design life and performance degradation trend of the device, analyzing the maintenance activities and recorded anomalies in the device's history, clustering and regression analysis of the device's operation data during the process to identify possible performance degradation trends. At the same time, comparing the analysis results with the expected performance and safety standards of the device to determine whether it meets the expected operation standards. In addition, for any anomalies found, in-depth root cause analysis will be carried out to determine the priority and urgency of maintenance measures. The analysis results will ultimately be summarized into a detailed health state report, providing a guide for action to the device management team to ensure the reliability and safety of the device.

[0117] Please refer to Figure 7 , the steps to obtain the device health assessment prediction result are as follows:

[0118] According to the current health state assessment result of the energy storage device, conduct inspections on the key performance indicators of the device, including battery charging efficiency, energy density, power density, and internal resistance, compare with the performance standards, and generate key performance indicator data;

[0119] Based on the obtained current health status, key performance indicators of the energy storage device are inspected, involving the evaluation of the battery's charging efficiency, energy density, power density, and internal resistance. The specific operations include deploying multiple high-precision current and voltage sensors directly on the battery cells to capture the performance data of the battery in different charge and discharge cycles in real time. The output data of each sensor is automatically recorded by the data acquisition system and transmitted to the central monitoring system in real time. The central system is configured with a dynamic comparison algorithm to compare the collected performance data with the preset performance standards in real time and identify any changes beyond the normal range. When a performance deviation is detected, the system automatically triggers an alarm and notifies the technical team via email and dashboard for immediate inspection. In addition, the system also generates a real-time performance report, which details the status of each detection index.

[0120] Using the key performance indicator data and combining it with the device's historical operation data, potential performance degradation points are identified, the future performance degradation trend of the energy storage device is predicted, a degradation curve is plotted, and a performance degradation prediction report is generated.

[0121] Based on the key performance indicator data, a complex prediction model is used to estimate the future performance degradation of the energy storage device. First, in-depth analysis of the historical performance data is carried out, including the detailed records of each charge and discharge of the battery extracted from the device monitoring system, as well as the log data of environmental impact factors such as temperature and humidity. A machine learning-based prediction model is trained using the data, which can identify early signals of performance degradation under different operating conditions. In this way, the model not only predicts the future performance trend of the device but also points out potential degradation reasons. During this prediction process, data analysis tools such as time series analysis and regression analysis are used. These tools help to accurately simulate and predict the device performance under various operating conditions, and the prediction results are then used to guide the maintenance team to take targeted preventive measures.

[0122] Combining the performance degradation prediction report and the current health status assessment results of the energy storage device, electrochemical impedance spectroscopy and thermal imaging inspections are carried out to identify potential fault points, determine the faulty components and types, and obtain the device health assessment prediction results.

[0123] Comprehensively analyze the performance degradation prediction results and the current health status of the equipment. The use of techniques including electrochemical impedance spectroscopy analysis and thermal imaging can identify and locate in detail the specific causes and positions of battery failures. For example, electrochemical impedance spectroscopy analysis helps understand how the electrochemical reactions inside the battery change over time, while thermal imaging technology is used to detect performance degradation or failure risks caused by overheating in battery components. Through diagnostic data, it is possible to specifically point out which battery cells or connection parts may have potential hazards, and the analysis results are directly fed back to the maintenance team so that specific maintenance measures can be taken, such as replacing damaged battery cells or adjusting the configuration of the battery management system, thereby improving the overall reliability and performance of the equipment.

[0124] Please refer to Figure 8 , and the steps to obtain fault handling and warning information are as follows:

[0125] Based on the equipment health assessment prediction results, monitor in real time the voltage, current, temperature, and charge and discharge status of the energy storage equipment, continuously collect key operation parameters, and conduct real-time analysis to generate a real-time performance monitoring report;

[0126] First, based on the equipment health assessment prediction results, monitor in real time the key operation parameters of the energy storage equipment, which includes continuous monitoring of voltage, current, temperature, and charge and discharge status. Data is collected in real time through sensors installed on the equipment. The sensors can collect data hundreds of times per second to ensure the real-time nature and accuracy of the data. The monitoring system conducts real-time analysis on the collected data, including preliminary anomaly detection. For example, if the voltage suddenly exceeds the set safety threshold, the system will immediately mark this abnormal event. In addition, the system also evaluates the data quality and filters out incorrect data caused by sensor failures or external interferences. Through these steps, the system can generate a detailed real-time performance monitoring report, which not only records in detail the real-time readings of each parameter but also analyzes the data change trends and possible health risks, providing a scientific basis for equipment maintenance decisions.

[0127] Based on the real-time performance monitoring report, use the formula:

[0128]

[0129] Calculate the potential fault trend value T to generate a potential fault trend report, where p V represents the abnormal change value of voltage, p I represents the abnormal change value of current, p T represents the abnormal change value of temperature;

[0130] Continuously monitor voltage and current. When it is detected that the voltage or current exceeds the normal operating range by 10%, it is recorded as an anomaly, p V and p IRecord the degrees of these abnormal changes separately.

[0131] The temperature is continuously monitored by a sensor and compared with a preset threshold value to determine abnormalities, p T Record the abnormal values of the temperature.

[0132] At a specific time point, the abnormal value of the voltage of the device p V = 0.1, the abnormal value of the current p I = 0.2, the abnormal value of the temperature p T = 35. According to the formula, the calculation of the fault trend score T is as follows:

[0133] Calculate p V ·p I The product of:

[0134] p V ·p I = 0.1 × 0.2 = 0.02

[0135] Calculate

[0136]

[0137] Calculate p T +1:

[0138] p T +1 = 35 + 1 = 36

[0139] Substitute the above values into the formula T:

[0140]

[0141] The calculated result T = 0.003928 shows that, given the abnormal parameter values, the fault trend score is very low, which means the fault probability of the current device is low. This result indicates that the device is currently in a stable state without an immediate fault risk, which will help the maintenance team optimize the maintenance and overhaul plan, avoid unnecessary maintenance costs, and ensure the efficient operation of the device at the same time.

[0142] According to the potential fault trend report, extract key trend data, design maintenance strategies and fault handling measures, formulate and execute maintenance operations, and generate fault handling and warning information;

[0143] After extracting key data from the obtained potential fault trend report, the maintenance team will conduct a detailed analysis of this data to determine the specific maintenance requirements of the equipment. This analysis includes comparing the equipment's historical maintenance records with operation data, as well as evaluating the health status of key components. Based on this analysis, the team formulates targeted maintenance strategies, such as replacing overly worn components or updating outdated software systems. During the implementation process, the team adopts standardized operating procedures to ensure that each step complies with industry safety and quality standards. After performing the maintenance operations, the system will re-evaluate the performance of the equipment to ensure that all maintenance measures have achieved the expected effects. Finally, based on the performance of the maintained equipment, the system generates fault handling and warning information, which will detail the current status of the equipment and any issues that need attention, ensuring the continuous stability and safety of the equipment operation.

[0144] The above are only the preferred embodiments of the present invention, and there are no other forms of limitation to the present invention. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. The state monitoring and fault warning system of energy storage equipment is characterized by: The system comprises: The temperature monitoring module deploys multi-point temperature sensors at key locations of the energy storage device to continuously record temperature data and monitor temperature fluctuations during the charging and discharging process of the energy storage device. It performs temperature trend analysis based on the temperature fluctuation monitoring results and records temperature changes at key time points to obtain real-time temperature monitoring data. The electrochemical monitoring module utilizes the real-time temperature monitoring data and simultaneously collects the voltage and current data of the energy storage device during operation, analyzes the electrochemical behavior of the energy storage device, reveals the performance of the energy storage device in the differential charge and discharge stages according to the electrochemical behavior analysis results, identifies abnormal electrochemical reactions, and outputs an electrochemical stability report; The status assessment module evaluates the current health status of the energy storage device based on the electrochemical stability report and comprehensively considers the usage frequency and cycle number of the energy storage device. Based on the current health status assessment result, the key performance indicators of the energy storage device are simultaneously checked to predict the future performance degradation of the energy storage device, identify potential failure points, and obtain the equipment health assessment prediction result; The early warning and maintenance module monitors the performance and operating conditions of the energy storage equipment in real time based on the equipment health assessment prediction results, analyzes the potential failure trends of the energy storage equipment, guides maintenance operations, and generates fault handling and early warning information.

2. The state monitoring and fault warning system for energy storage equipment according to claim 1, characterized in that: The monitoring steps of the temperature fluctuation are: Select the battery cells and key parts of the control system of the energy storage equipment, determine the thermal distribution characteristics of the key parts through thermal induction scanning, evaluate the heat carrying capacity of each part, determine the deployment location of multi-point temperature sensors, and generate a deployment location coordinate list; According to the deployment location coordinate list, the sensors are positioned and installed in sequence, field tests are performed to verify the sensor's response speed and data accuracy, and sensor installation configuration verification information is obtained; According to the sensor installation configuration verification information, the time interval for data recording is adjusted in combination with the charge and discharge cycle of the energy storage device, the temperature fluctuation of the energy storage device in the differential operation stage is monitored in real time, the key temperature change points are recorded, and a temperature fluctuation monitoring report is generated.

3. The state monitoring and fault warning system for energy storage equipment according to claim 2 is characterized in that: The steps for obtaining the real-time temperature monitoring data are as follows: Based on the temperature fluctuation monitoring report, perform time series analysis to identify periodic patterns of temperature fluctuations, mark temperature peaks and troughs during charging and discharging, and generate a preliminary trend analysis report; Based on the preliminary trend analysis report, set monitoring thresholds, identify and record temperature data that exceeds the normal fluctuation range, determine the frequency and duration of abnormal temperatures, and generate temperature change records at key time points; The temperature change records at the key time points are integrated, and data visualization is performed to construct temperature trend graphs and hot spot distribution graphs to reveal the dynamic changes in temperature and highlight key change points, thereby generating real-time temperature monitoring data.

4. The state monitoring and fault warning system for energy storage equipment according to claim 3 is characterized in that: The analysis steps of the electrochemical behavior are: Deploy voltage sensors and current sensors on the energy storage device to synchronously collect voltage data V(t) and current data I(t) in real time, and generate an electrochemical parameter data set in combination with the real-time temperature monitoring data; Based on the electrochemical parameter data set, the formula is adopted: Calculate the internal resistance R and generate a time point internal resistance data set, where V(t) represents real-time voltage data and I(t) represents real-time current data. and Indicates the rate of change of voltage and current; The internal resistance data set at the time point is analyzed, and statistical analysis is performed to determine the stability of the internal resistance, using the formula: The coefficient of variation CV was calculated to obtain the electrochemical behavior analysis results, where σ represents the standard deviation of the internal resistance data and μ represents the mean of the internal resistance data.

5. The state monitoring and fault warning system for energy storage equipment according to claim 4, characterized in that: The steps for obtaining the electrochemical stability report are: Based on the electrochemical behavior analysis results, by comparing the internal resistance change data and the voltage and current time series in the differential charge and discharge stages, the performance of the energy storage device during the charge and discharge process is analyzed, and a performance change analysis report is generated; According to the performance change analysis report, abnormal voltage mutations and current instability phenomena are marked, trend analysis and pattern recognition are performed on abnormal data, the type and severity of abnormal reactions are determined, and an abnormal electrochemical reaction identification report is generated; The performance change analysis report and the abnormal electrochemical reaction identification report are combined to record the electrochemical behavior of the energy storage device in all charging and discharging stages, analyze the actual impact of abnormal conditions on the performance of the energy storage device, and output an electrochemical stability report.

6. The state monitoring and fault warning system for energy storage equipment according to claim 5, characterized in that: The steps for evaluating the current health status of the device are: Based on the electrochemical stability report, extracting the charge and discharge performance and internal resistance data of the energy storage device, analyzing and determining the electrochemical baseline characteristics of the energy storage device, and generating electrochemical baseline characteristic data; The electrochemical baseline characteristic data is analyzed, combined with the frequency of use f and the number of cycles n of the device, and the formula is used: Calculate and output the equipment health status index H, where C current and C initial Represent the current and initial charge capacities respectively; The health status index is used in combination with the historical maintenance records and abnormal reports of the energy storage equipment to compare the design life of the equipment with the expected performance degradation trend, evaluate and determine the health status of the energy storage equipment, and obtain the current health status evaluation result of the energy storage equipment.

7. The state monitoring and fault warning system for energy storage equipment according to claim 6, characterized in that: The steps for obtaining the equipment health assessment prediction result are as follows: According to the current health status assessment results of the energy storage device, check the key performance indicators of the device, including battery charging efficiency, energy density, power density and internal resistance, compare them with the performance standards, and generate key performance indicator data; Using the key performance indicator data, combined with the historical operation data of the equipment, potential performance degradation points are identified, the future performance degradation trend of the energy storage equipment is predicted, the degradation curve is drawn, and a performance degradation prediction report is generated; In combination with the performance degradation prediction report and the current health status assessment results of the energy storage device, electrochemical impedance spectroscopy and thermal imaging inspections are performed to identify potential fault points, locate faulty components and types, and obtain equipment health assessment prediction results.

8. The state monitoring and fault warning system for energy storage equipment according to claim 7, characterized in that: The steps of obtaining the fault handling and warning information are as follows: Based on the equipment health assessment prediction results, the voltage, current, temperature and charge and discharge status of the energy storage equipment are monitored in real time, key operating parameters are continuously collected, and real-time analysis is performed to generate a real-time performance monitoring report; Based on the real-time performance monitoring report, the formula is adopted: Calculate the potential failure trend value T and generate a potential failure trend report, where p V Indicates the abnormal change value of voltage, p I Indicates the abnormal change value of current, p T Indicates the abnormal change value of temperature; Based on the potential fault trend report, extract key trend data, design maintenance strategies and fault handling measures, formulate and execute maintenance operations, and generate fault handling and early warning information.

Images