Energy management system and method of hydrogen fuel cell module

Abstract

The invention relates to the technical field of battery energy management, in particular to an energy management system and method for a hydrogen fuel cell module, and the system comprises a monitoring collection module which collects and contrasts the readings of a sensor based on the voltage parameter, the current parameter and the temperature parameter of the hydrogen fuel cell module to obtain original monitoring data, and performing numerical comparison on the load demand of the power distribution network to generate supply and demand matching information. In the invention, the difference statistics and the difference operation improve the accuracy of energy distribution, so that the matching between the energy distribution and the actual load demand is more accurate, and the overall energy efficiency is improved while the energy waste is reduced. And frequency band extraction and rate recording can capture subtle changes output by the battery, and the response speed is increased. In addition, the analysis and adjustment of the phase feature group provide adaptive ability, and the stability and reliability of the load change of the power grid are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of battery energy management, and in particular to an energy management system and method for a hydrogen fuel cell module. Background Art

[0002] The energy management system of the hydrogen fuel cell module is a comprehensive energy monitoring and control system designed specifically for hydrogen fuel cells, aiming to maximize the performance and efficiency of the battery while ensuring its safe and stable operation. The system monitors key parameters of the hydrogen fuel cell such as voltage, current and temperature, and adjusts energy output in real time to adapt to different load requirements.

[0003] However, the existing technology has obvious shortcomings in dealing with dynamic load changes and adjusting energy output. Usually, the output strategy is fixed, which limits the flexibility in dealing with sudden high load demands. In the event of a high load emergency, the output may not be effectively adjusted due to insufficient response, resulting in energy supply shortages or over-discharge, affecting battery performance and life. Therefore, improvements are needed. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an energy management system and method for a hydrogen fuel cell module.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: An energy management system for a hydrogen fuel cell module comprises: The monitoring and acquisition module collects and compares sensor readings based on the voltage parameters, current parameters and temperature parameters of the hydrogen fuel cell module to obtain original monitoring data, compares the load demand of the distribution network, generates supply and demand matching information, and synchronizes, arranges the original monitoring data and the supply and demand matching information, performs time sequence arrangement and checks for abnormal values ​​to obtain a preliminary state sequence; The dynamic allocation module performs interval division, differential statistics and difference calculation on the current parameters, temperature parameters and voltage parameters based on the preliminary state sequence to obtain the energy allocation basis, performs correlation comparison, discharge path screening, multi-dimensional coordinate comparison and abnormal value screening on the energy allocation basis and the load demand of the distribution network to establish the discharge allocation value; The high-frequency control module extracts the frequency band and records the rate of the output power of the hydrogen fuel cell based on the discharge adjustment value, obtains the power segmentation data, compares the power segmentation data with the load change of the distribution network and measures the phase to obtain a phase feature group, performs frequency division screening and amplitude adjustment on the phase feature group, and establishes a high-frequency adjustment signal; The health management module continuously monitors the temperature parameters and output power of the hydrogen fuel cell, makes limit judgments and compares the safety thresholds based on the high-frequency adjustment signal, obtains safety monitoring information, performs state analysis and indicator comparison on the safety monitoring information and the remaining life parameters, and obtains life assessment data.

[0006] Preferably, the steps of obtaining the preliminary state sequence are: Receive the voltage parameters, current parameters and temperature parameters of the hydrogen fuel cell, perform data fusion with the supply and demand matching information, and arrange the collected data points in time sequence by timestamp to obtain a time-sequenced data set; Based on the time-sorted data set, by calculating the mean value and standard deviation of each parameter, outliers that are beyond the range of two times the standard deviation of the mean value are identified and eliminated to obtain an outlier-processed data set; Based on the data set after outlier processing, the maximum value, minimum value, mean value and coefficient of variation of each parameter are calculated to form a preliminary state sequence.

[0007] Preferably, the steps for obtaining the energy distribution basis are: Based on the preliminary state sequence, each parameter is divided into intervals, a threshold is set according to the historical data range of each parameter and the current operating environment, the parameter is divided into multiple working intervals, each interval represents a different operating state, and a parameter interval data set is generated; According to the parameter interval data set, the interval difference statistics are calculated, and the calculation formula is:

[0008] in, represents the value of the i-th parameter in the j-th interval, represents the average value of the i-th parameter in this interval, is the interval difference statistic of the i-th parameter, and N is the number of data points in each interval; Based on the interval difference statistics of each parameter, the sizes of the interval difference values ​​of each parameter are compared, the relative change rates and fluctuation characteristics between the parameters are analyzed, and the basis for energy distribution is formed.

[0009] Preferably, the steps of obtaining the discharge adjustment value are: Based on the energy allocation basis, a comparison is performed with the load demand of the distribution network. By analyzing the difference between energy output and demand in each period, the periods when the demand is exceeded and not met are identified to form a preliminary difference data set; Based on the preliminary difference data set, the overall matching degree between energy output and demand in each period is calculated, and the calculation formula is:

[0010] in, is the energy output in the i-th period, is the load demand in the i-th period, is the total number of periods evaluated, Indicates the overall matching degree; Based on the overall matching degree, discharge path screening and multi-dimensional coordinate comparison are performed, and the discharge strategy is optimized in combination with historical data. After screening out abnormal values, the discharge allocation value is formed.

[0011] Preferably, the steps of acquiring the phase feature group are: Based on the discharge allocation value, by setting the time window, measuring the power peak and valley in each window, and obtaining the power segmentation data; According to the power segment data, the data of load change of the power distribution network is compared, and each power segment is time synchronized to obtain an aligned comparison result; Based on the alignment comparison result, the phase measurement between power and load is performed, and the periodicity and consistency of the changes are quantified by analyzing the time offset and phase difference between the power change and the load change to form a phase feature group.

[0012] Preferably, the step of obtaining the high frequency adjustment signal is: Based on the phase feature group, frequency division screening is performed on the data to select the frequency interval with the maximum energy response to obtain the screened frequency data; According to the filtered frequency data, the adjusted total amplitude is calculated using the following formula:

[0013] in, represents the response amplitude of the ith frequency, is the maximum response amplitude among all frequencies, represents the corresponding time delay, is a decay factor, is the total frequency, is the total adjusted magnitude; Based on the adjusted total amplitude, the output power of the hydrogen fuel cell is optimized, the high-frequency signal is adjusted to match the real-time load demand of the power grid, and a high-frequency regulation signal is formed.

[0014] Preferably, the steps of obtaining the safety monitoring information are: Based on the high-frequency adjustment signal, the data points per minute are recorded in real time for the temperature parameters and output power of the hydrogen fuel cell, including the peak value, the valley value and the average value, to obtain real-time monitoring data; Using the real-time monitoring data, check whether the temperature and power exceed the predetermined safety threshold for each data point, mark the over-limit data, and record the timestamp and parameter value of the over-limit event to form a preliminary abnormal result; Based on the preliminary abnormal results, the marked out-of-limit events are analyzed to verify the severity and risk level of the out-of-limit events and form safety monitoring information.

[0015] Preferably, the steps of obtaining the life assessment data are: Based on the safety monitoring information, analyze the current state of the hydrogen fuel cell, record safety-related events and performance trends, including temperature exceeding standard events and power fluctuations, and generate state analysis results; According to the state analysis results, the remaining life percentage is calculated using the following formula:

[0016] in, represents the safety score of the ith safety monitoring event, is the weight factor of the ith safety monitoring event, is the total number of events, Indicates the estimated remaining life percentage; Based on the remaining life percentage, the overall health status and expected life of the hydrogen fuel cell module are evaluated to form life evaluation data.

[0017] The present invention provides an energy management method for a hydrogen fuel cell module, comprising the following steps: Based on the voltage parameters, current parameters and temperature parameters of the hydrogen fuel cell, the original monitoring data is collected from the sensor readings, and the values ​​are compared with the load demand of the distribution network to generate supply and demand matching information. The original monitoring data and the supply and demand matching information are synchronously merged, time-sequenced and checked for abnormal values ​​to obtain a preliminary state sequence; Based on the preliminary state sequence, the current parameters, temperature parameters and voltage parameters are divided into intervals, differential statistics and difference operations are performed to obtain the basis for energy allocation, and then correlated with the load demand of the distribution network. Through discharge path screening, multi-dimensional coordinate comparison and abnormal value screening, the discharge allocation value is established; Based on the discharge adjustment value, the frequency band of the output power of the hydrogen fuel cell is extracted and the rate is recorded to obtain the power segmentation data, which is compared with the load change of the distribution network and the phase is measured to obtain the phase feature group. The phase feature group is then frequency-filtered and amplitude-adjusted to establish a high-frequency adjustment signal. Based on high-frequency regulation signals, the temperature parameters and output power of hydrogen fuel cells are continuously monitored, limit judgment and safety threshold comparison are performed to obtain safety monitoring information, and the safety monitoring information is analyzed and compared with the remaining life parameters to obtain life assessment data; Based on the life assessment data, the health management of the hydrogen fuel cell module is performed, and the battery status is evaluated and monitored through battery temperature, output power and safety monitoring information.

[0018] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, differential statistics and difference operations improve the accuracy of energy distribution, making the match between energy distribution and actual load demand more accurate, reducing energy waste and improving overall energy efficiency. Frequency band extraction and rate recording can capture subtle changes in battery output and improve response speed. In addition, the analysis and adjustment of the phase feature group provides adaptive capabilities and enhances the stability and reliability of grid load changes. Comprehensive monitoring and safety threshold control checks enhance safety, and the introduction of life assessment makes maintenance decisions more scientific and extends the service life of equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 intended to limit the present invention.

[0021] See also Figure 1 The present invention provides a technical solution: an energy management system for a hydrogen fuel cell module comprises: The monitoring and acquisition module collects and compares the sensor readings based on the voltage parameters, current parameters and temperature parameters of the hydrogen fuel cell module to obtain the original monitoring data, compares the load demand of the distribution network, generates supply and demand matching information, synchronizes and merges the original monitoring data with the supply and demand matching information, arranges the time sequence and checks for abnormal values ​​to obtain a preliminary state sequence; The dynamic allocation module performs interval division, differential statistics and difference operations on the current parameters, temperature parameters and voltage parameters based on the preliminary state sequence to obtain the basis for energy allocation. It performs correlation comparison, discharge path screening, multi-dimensional coordinate comparison and abnormal value screening on the energy allocation basis and the load demand of the distribution network to establish the discharge allocation value. The high-frequency control module extracts the frequency band and records the rate of the output power of the hydrogen fuel cell based on the discharge adjustment value, obtains the power segmentation data, compares the power segmentation data with the load change of the distribution network and measures the phase to obtain the phase feature group, performs frequency division screening and amplitude adjustment on the phase feature group, and establishes a high-frequency adjustment signal; The health management module continuously monitors the temperature parameters and output power of the hydrogen fuel cell, makes limit judgments and compares safety thresholds based on high-frequency adjustment signals, obtains safety monitoring information, performs status analysis and indicator comparison on the safety monitoring information and remaining life parameters, and obtains life assessment data.

[0022] The steps to obtain the initial state sequence are: Receive the voltage parameters, current parameters and temperature parameters of the hydrogen fuel cell, perform data fusion with the supply and demand matching information, and arrange the collected data points in time sequence by timestamp to obtain a time-sequenced data set; Based on the time-sorted data set, by calculating the mean and standard deviation of each parameter, outliers that are beyond the range of two times the standard deviation of the mean are identified and eliminated to obtain a data set that has been processed for outliers; Based on the data set after outlier processing, the maximum, minimum, mean and coefficient of variation of each parameter are calculated to form a preliminary state sequence.

[0023] Specifically, after setting up sensors that can measure 0V to 24V voltage, 0A to 10A current, and 0℃ to 90℃ temperature range, the voltage, current and temperature information generated by the hydrogen fuel cell in different working stages are recorded respectively, and the corresponding processing is carried out with the supply and demand matching information of the distribution network. First, the acquisition frequency and synchronization reference time of each sensor are determined, and each record is compared with the previously established measurement range, such as comparing the temperature with the 0℃ to 90℃ range, the voltage with the 0V to 24V range, and the current with the 0A to 10A range. When the data is within the preset range, subsequent association is directly performed. When a data item exceeds any threshold, it is necessary to first determine the source of the threshold. For example, the threshold is established based on the combination of previous experimental results and industry experience, including additional safety Full margin. If it is found that it is not within the safe range, the parameter needs to be rechecked and the sensor needs to be reset or recalibrated if necessary. To ensure that the supply and demand matching information can be used for fusion, the time stamp of the supply and demand matching information needs to be sorted first. For example, the external load information is aligned as a time label in whole seconds or minutes, and then compared with the timestamp in the sensor data. During the comparison process, the supply and demand records of different time periods are interpolated or merged to form a consistent time series index. Then the voltage, current, and temperature parameters of the hydrogen fuel cell are associated with the aligned supply and demand matching information and merged into the same sequence structure. Then, all data points are reordered in order from the earliest to the latest according to the timestamp, and the time sequence information of each record is marked to finally obtain a time-sorted data set.

[0024] After obtaining the time-sorted data set, determine the parameters to be processed, including the voltage parameters, current parameters, and temperature parameters obtained previously, and compare the historical ranges of these parameters with the current measured value ranges. For example, the temperature corresponds to 0°C to 90°C, the current corresponds to 0A to 10A, and the voltage corresponds to 0V to 24V. When all data are within their respective ranges, the mean and standard deviation of each parameter are calculated. The mean value is obtained by dividing the sum of the samples in the same time window by the total number of samples, and the standard deviation can be obtained by summing the squares of the differences between each sample and the mean value and then dividing it by the total number of samples minus one. In order to distinguish For data points with obvious abnormal fluctuations, each data needs to be compared with the average value. If the deviation exceeds two standard deviations from the average value, it is considered to be beyond the empirical range. The "two standard deviations" threshold here is derived based on previous statistical results on the volatility of hydrogen fuel cell operating data, and conservative corrections are made with reference to equipment safety requirements. Therefore, when a data point is detected to exceed the set threshold, it is marked as abnormal. Then, these data points marked as abnormal are directly removed from the subsequent calculation links, retaining only unmarked data points. Finally, the overall data after removing the outliers is summarized again and recorded to obtain a data set that has been processed for outliers.

[0025] After obtaining the data set that has been processed for outliers, continue to perform segmented statistics on the retained voltage parameters, current parameters, and temperature parameters in order to calculate the maximum, minimum, mean, and coefficient of variation of each parameter. For the maximum and minimum values, each time interval can be screened one by one within the previous range to determine. For example, when processing temperature, all measured results within the range of 0°C to 90°C are retrieved one by one, and the highest and lowest values ​​that appear are selected. The coefficient of variation can be obtained by using the ratio of the standard deviation of the parameter to its mean value. If the standard deviation is And the average value is , then the coefficient of variation can be defined as , each symbol in this calculation formula is obtained under the statistical definition, where is the square root of the sum of the squares of the deviations of all sampling points from the mean of the parameter divided by the sampling amount minus one, and It is the total sampling divided by the sampling volume, and then combined with the statistical results of the three parameters of voltage, current and temperature, it can form the four important indicators of maximum value, minimum value, mean value and coefficient of variation of each parameter. Finally, the data of these indicators are summarized to get the preliminary state sequence.

[0026] The steps for obtaining the energy allocation basis are: Based on the preliminary state sequence, each parameter is divided into intervals, and thresholds are set according to the historical data range of each parameter and the current operating environment. The parameters are divided into multiple working intervals, each interval represents a different operating state, and a parameter interval data set is generated; According to the parameter interval data set, the interval difference statistics are calculated using the following formula:

[0027] in, represents the value of the i-th parameter in the j-th interval, represents the average value of the i-th parameter in this interval, is the interval difference statistic of the i-th parameter, and N is the number of data points in each interval; Based on the interval difference statistics of each parameter, the sizes of the interval difference values ​​of each parameter are compared, the relative change rates and fluctuation characteristics between the parameters are analyzed, and the basis for energy distribution is formed.

[0028] Specifically, based on the preliminary state sequence obtained previously, each parameter is divided into multiple working intervals. First, the historical data range of these parameters needs to be clarified. For example, the effective range of temperature is set to 0℃ to 90℃, the effective range of voltage is set to 0V to 24V, and the effective range of current is set to 0A to 10A. These ranges are obtained by statistically analyzing a large amount of data recorded during multiple startups and continuous operations of hydrogen fuel cells. Safety margins are added based on the minimum and maximum temperatures allowed by the comprehensive equipment, the upper and lower limits of voltage, and the current extremes. In addition, the upper and lower limits of each parameter must be confirmed in combination with current operating environment factors such as ambient temperature, air pressure, and load fluctuations. For example, the operating temperature of hydrogen fuel cells measured at high altitudes is often closer to Its maximum allowable temperature value, in order to prevent the parameter from exceeding the historical limit, will moderately lower the temperature threshold to 85℃ to avoid overheating. The working range determination method for current and voltage is similar to that for temperature. The upper limits are respectively corresponding to 9A and 23V as the actual measurement thresholds. Then, according to the acquisition time and value distribution law of the parameters, the parameters are divided into multiple working ranges. For example, the temperature can be divided into several ranges of 0℃ to 30℃, 30℃ to 60℃ and 60℃ to 85℃. The current ranges from 0A to 3A, 3A to 6A, 6A to 9A, and the voltage ranges from 0V to 8V, 8V to 16V, and 16V to 23V. The relevant measurement times and data fluctuation distribution in each range are recorded synchronously. Finally, the division results are unified and organized, and the upper and lower limits of the intervals and the corresponding interval numbers are marked to generate a parameter interval data set.

[0029] The symbolic parameters of the formula are introduced as follows: Indicates The parameters in The sampling value of an interval is the value obtained by recording the temperature, voltage or current in the corresponding interval after multiple detections. Indicates The average value of the parameter in the interval is obtained by summing all samples after multiple observations in the same interval and then dividing by the total number of samples. is the number of data points in the interval, obtained by counting the number of data points corresponding to all timestamps in the same interval.

[0030] Calculation process: Let the temperature parameter in a certain range The total number of observations The records are 25℃, 28℃, 30℃, 29℃, 27℃, then equal:

[0031] Then calculate :

[0032]

[0033]

[0034] Then put in the denominator , we can get:

[0035] Finally, we get the square root Right now:

[0036] The results show that the interval difference statistics can measure the fluctuation amplitude of the parameters within the interval. The larger the value, the more obvious the temperature fluctuation within the interval. For other parameters such as current or voltage, the same method can be used to perform interval difference calculation and obtain corresponding results.

[0037] Based on the interval difference statistics obtained above, it is necessary to further compare the difference values ​​of each parameter in each interval. Here, the temperature, voltage, and current in different intervals are collected. The results are listed one by one, such as the temperature in each interval , , , the voltage is obtained in each interval , , , the current in each interval is obtained , , In the comparison process, the differential statistics of the same type of parameters in all intervals need to be arranged in the same order. For example, the numerical values ​​of the temperature are checked one by one in the order from the first interval to the third interval, then to the three intervals of current, and the three intervals of voltage. Through numerical comparison, it can be found that the temperature differential value reaches 2.5 in the second interval, which has exceeded the preset maximum safety fluctuation threshold of 2.4. This threshold is obtained by averaging the temperature fluctuation records in multiple intervals during the long-term monitoring of the equipment and adding a safety factor of 0.2. When it is detected that the threshold is exceeded, it is necessary to re-determine whether an overheating problem has occurred and record the abnormal situation. The same treatment is performed on other parameters. When the differential value of voltage or current exceeds the reference threshold of 1.5 and 2.0, the corresponding interval number and measurement time will be recorded. Then, after the comparison of the differential values ​​of all intervals is completed, according to the fluctuation rate information of temperature, current and voltage in different intervals, their relative change characteristics are combined, and this information is summarized and the fluctuation characteristics of each interval are marked to form the basis for energy distribution.

[0038] The steps to obtain the discharge adjustment value are: Based on the energy allocation basis, a comparison is performed with the load demand of the distribution network. By analyzing the difference between energy output and demand in each period, the periods when the demand is exceeded and not met are identified to form a preliminary difference data set; Based on the preliminary difference data set, the overall matching degree between energy output and demand in each period is calculated. The calculation formula is:

[0039] in, is the energy output in the i-th period, is the load demand in the i-th period, is the total number of periods evaluated, Indicates the overall matching degree; Based on the overall matching degree, discharge path screening and multi-dimensional coordinate comparison are carried out, and the discharge strategy is optimized in combination with historical data. After screening out abnormal values, the discharge allocation value is formed.

[0040] Specifically, based on the energy allocation basis obtained above, the energy output data of the hydrogen fuel cell in each time period and the load demand data of the distribution network are obtained. First, the actual load demand value of each time period is retrieved in chronological order, and then the hydrogen fuel cell energy output value recorded in each time period is compared. Each record is compared with a predetermined numerical range. For example, the energy output value is set to a range of 0kWh to 10kWh, and the load demand value is set to a range of 0kWh to 12kWh. These ranges are obtained through multiple observations and empirical statistics. When it is detected that the energy output value is less than its lower limit or exceeds its upper limit, it is necessary to further determine whether the collection time is Whether there is misalignment or equipment failure. If it does deviate from the maximum allowable range, it will be marked as an abnormality and the data and time label will be recorded. When continuing to compare the data of the remaining time periods, if it is found that the output value is higher than the demand value of the corresponding time period, it will be marked as the demand exceeded time period, and these time periods will be concentrated in the "output surplus time period" list. Conversely, if the output value is lower than the demand value, it will be marked as the unsatisfied time period, and these time periods will be concentrated in the "output shortage time period" list. By summarizing the above two lists, it can be concluded whether there is a continuous surplus or shortage in adjacent time periods, and the detailed degree of difference will be listed for each time period, finally forming a preliminary difference data set.

[0041] The benefit of the formula is that by introducing This difference ratio combined with exponential mapping can perform a nonlinear transformation on the matching of energy output and demand, thereby avoiding the problem that simple linear comparison cannot reflect the characteristics of data distribution.

[0042] The acquisition steps are as follows: record the actual output energy of the hydrogen fuel cell in each period, read the readings with a sampling period of half an hour or one hour through the electric energy metering device installed on site, and count and store the output values ​​of each period collected to obtain ; The acquisition steps are as follows: by monitoring the real-time load demand of the distribution network in the same time interval, calculating the average load value and combining the peak and valley values ​​to correct and obtain a more accurate load demand value, forming ; The acquisition step is to count the total number of all valid time periods within the evaluation period of the day or week. For example, starting from 0:00 every day, one time period per hour, and ending at 24:00, 24 time periods can be obtained. For the whole week, more time periods can be obtained, and finally accumulated into .

[0043] Calculation process: Assume that the total number of evaluation periods is , the energy output was observed , , , load demand , ,

[0044] First calculate the first period :

[0045] The corresponding index ,therefore ; Then calculate the second period:

[0046] , ; Calculate the third period:

[0047] , ; Sum the three and divide by 3:

[0048] The result shows that the overall matching degree is approximately 0.5567. If the value is between 0.5 and 0.6, it means that there is still a certain gap between the output and demand, and a more sophisticated scheduling strategy is needed. When the value is higher than 0.8, it means that the output is more consistent with the demand.

[0049] Based on the overall matching degree obtained above, the discharge path is screened and compared with the multi-dimensional coordinates. It is necessary to sort out the load data of each time period in the historical records and the output value of the hydrogen fuel cell in the corresponding time period in advance. For example, each day is divided into 24 hourly time periods, and the load demand and output values ​​of each time period are recorded by mutual comparison. Then, in the process of comparing multiple time periods, some abnormal data exceeding the predetermined fluctuation threshold are identified. For example, the upper and lower limits of voltage are set to 0V to 24V. If the voltage in a certain time period exceeds 25V, it means that there may be collection errors in the measurement of the record, which needs to be marked and eliminated. The key data such as power output are checked in the same way. The threshold is set by statistically analyzing all the measurement data and taking into account the battery type and design parameters. After the abnormal records are screened out, the remaining multi-dimensional coordinate data is used to uniformly arrange the indicators such as current, voltage, temperature and load change. The horizontal axis represents the order of the time periods for the data of adjacent time periods, and the vertical axis represents the relative increase or decrease of each indicator to judge the change trend. If there is a serious deviation in any key time period, the reason for further verification of the time period will be marked in the record. Finally, the discharge law is compared with the historical data, and the energy output arrangement is corrected in combination with the temperature and pressure data collected in different seasons and operating conditions. The discharge allocation value is obtained through system summary.

[0050] The steps for obtaining the phase feature group are: Based on the discharge allocation value, by setting the time window, measuring the power peak and valley in each window, and obtaining the power segmentation data; According to the power segment data, it is compared with the load change data of the distribution network, and each power segment is time synchronized to obtain the aligned comparison result; Based on the alignment comparison results, the phase measurement between power and load is performed. By analyzing the time offset and phase difference between power change and load change, the periodicity and consistency of the change are quantified to form a phase feature group.

[0051] Specifically, based on the discharge allocation values ​​obtained previously, the entire process needs to be divided into several time windows according to the actual operating time, and then the power output data of the hydrogen fuel cell is recorded in each time window. In order to ensure data accuracy, a fixed time interval is usually set, such as 1 minute or 5 minutes, as the length of each window, and the output power generated by the hydrogen fuel cell at different time points is collected in the window. Then, the maximum and minimum values ​​are retrieved one by one from the collected output power data, and marked as power peaks and power troughs respectively. When the maximum or minimum values ​​in certain windows fluctuate extremely, it is also necessary to verify whether the threshold setting is reasonable. The threshold is generally obtained by collecting previous The operating data of multiple tests are determined with reference to the actual performance of hydrogen fuel cells under different load conditions. In order to maintain the accuracy of power measurement, the signal sampling frequency also needs to be set. For example, when monitoring the power output in the range of 0kW to 10kW, the excessive power is included in the abnormal range and recorded. According to the time position of the peak and trough in each recorded window, these segmented information are summarized. If it is found that the extreme value data in some windows exceeds the safety range, such as exceeding the preset upper limit of 10kW or being lower than 0kW, it is necessary to combine the previously statistical power monitoring upper limit data to determine whether there is an instrument error or fault. Finally, the peak and trough values ​​of each time window are arranged in order to obtain the power segmented data.

[0052] According to the power segmentation data obtained above, it is necessary to match it with the load change data of the distribution network one by one and perform time alignment. First, the load change data should be collected and ensured to have the same or convertible time stamp. For example, when the recording period is 1 minute, the average value or instantaneous value of the load change should be obtained within the period, and a timestamp consistent with the power segmentation data should be added. Then, the time fields in the two data sources are sorted. If there is a missing period or delayed record, it is necessary to determine whether to interpolate or replace it with the record of the previous moment, otherwise deviations may occur in the comparison process. For the load change reference range required in these steps, such as 0kW to 12kW, appropriate upper and lower limits can be determined based on historical operation statistics when setting the threshold. The peak and trough values ​​of each window power in the alignment process are numerically compared with the load changes in the corresponding period. If there is a power difference or load difference exceeding the upper limit, this period is marked as an abnormal period and the detailed time is recorded. If the comparison result is in a reasonable range, the original record is maintained. Finally, the alignment is completed and the comparison result is output. After the time sequence and numerical value are fully matched, these aligned data can be further summarized for subsequent measurement and analysis.

[0053] Based on the comparison results after alignment, the phase measurement between power and load is carried out. It is necessary to first extract the fluctuation period of power in each time window and the change period of load in the corresponding period, and offset and compare the two on the same time axis. In order to obtain the time offset of power and load, each record needs to be compared with the pre-established valid range, such as the power comparison range of 0kW to 10kW and the load comparison range of 0kW to 12kW, and the positions of the respective peak and valley points are found in a graphical manner or numerical sequence, and these peak and valley points are marked and aligned with each other, and then the time difference between the two is counted, and then the difference is quantified by calculating the phase difference. For example, the peak value in a certain period of time differs by 10 seconds, or the difference is 0.1 cycle when the frequency unit is millihertz. These difference values ​​can be converted into phase angles for recording. After completing the statistics of all time windows, the power and load phase differences of each window are grouped and sorted, and the periodicity and consistency indicators are calculated according to the fluctuation law. If it is found that the phase difference of some windows is too large, it is necessary to verify whether there is a delay configuration problem in the power output or load acquisition link. Finally, a phase feature group is formed based on the analysis results of periodicity and consistency.

[0054] The steps for obtaining the high-frequency adjustment signal are: Based on the phase feature group, frequency division screening is performed on the data to select the frequency interval with the maximum energy response to obtain the screened frequency data; According to the filtered frequency data, the adjusted total amplitude is calculated as follows:

[0055] in, represents the response amplitude of the ith frequency, is the maximum response amplitude among all frequencies, represents the corresponding time delay, is a decay factor, is the total frequency, is the total adjusted magnitude; Based on the adjusted total amplitude, the output power of the hydrogen fuel cell is optimized, the high-frequency signal is adjusted to match the real-time load demand of the power grid, and a high-frequency regulation signal is formed.

[0056] Specifically, based on the phase feature group obtained previously, the data is frequency-filtered. It is necessary to first check the power performance data for different frequency components in the existing records, and make a preliminary distinction between all frequency components in the range of 0 Hz to 50 Hz. For example, 0 Hz to 5 Hz is regarded as a low frequency band, 5 Hz to 20 Hz is regarded as a medium frequency band, and 20 Hz to 50 Hz is regarded as a high frequency band. The frequency range here is based on the power frequency range that may be generated by hydrogen fuel cells under different operating conditions. The changes in the output waveform of the hydrogen fuel cell are collected in multiple tests to determine the appropriate upper and lower limits, and then the power amplitude values ​​in each frequency band are checked. If it is found that some amplitude values ​​deviate too much from the range, it is necessary to first compare them with the pre-established threshold to determine whether it is a measurement error. For example, The amplitude threshold is defined in the range of 0 to 100 units. When the monitored amplitude value exceeds 120, it is marked as abnormal and the time tag of the measurement is recorded. After completing the statistics of all frequency bands, the frequency band with the highest energy response value is selected and marked as the maximum energy response frequency interval. The remaining frequency band data is retained in the database as a reference. If the maximum response frequency interval occurs between 15Hz and 22Hz, the amplitude value, phase characteristics and other information of the interval are summarized and combined with the load response records of the same time period for synchronous comparison. When there is a time delay of more than 0.5 seconds or a phase shift of more than 10°, it is necessary to reconfirm whether the signal acquisition frequency setting meets the current monitoring requirements. Finally, after comprehensive comparison, the frequency interval with maximum energy response is selected to obtain the filtered frequency data.

[0057] The benefit of the formula is that by introducing an attenuation factor into the amplitude ratio and taking time delay into account, the energy characteristics and relative response timing of different frequency components are taken into account, thus avoiding the problem that the timing difference cannot be reflected by using only amplitude comparison.

[0058] The acquisition steps are as follows: the amplitude value of the output frequency is collected point by point within the frequency interval of the maximum energy response obtained previously, the actual power waveform is discretely sampled by a high-precision measurement device, and each recorded amplitude value is accumulated into an array, and then the ; The acquisition step is to determine the highest amplitude value among all the collected frequency components, and record the highest amplitude value as ; The steps to obtain are as follows: The waveform of each frequency component is recorded, and the time when the main peak arrives is counted on the time axis. The time delay value is recorded after subtracting it from the unified reference time. ; The acquisition steps are as follows: by observing the attenuation phenomenon in different time periods, the energy change trend of the same frequency in different time windows is counted. After multiple comparisons, an attenuation factor range can be obtained, and then the typical value is taken according to the operating status of the equipment to cover the common frequency attenuation characteristics; It is the total number of frequencies identified in the selected frequency interval, determined by counting after scanning the waveform features one by one.

[0059] Calculation process: Assume , the amplitude data is collected , , ,in , the time delay is measured respectively , , , attenuation factor

[0060] Calculate first :

[0061]

[0062] Then calculate :

[0063]

[0064] Recalculate :

[0065]

[0066] Add the three together:

[0067] Finally, the square root:

[0068] The results show that after considering the attenuation factor and time delay, the total adjustment amplitude of the current frequency component is about 1.10. If the value is greater than 1.0, it means that the comprehensive strength is relatively higher. If it is lower than 0.5, it means that the amplitude response within the same range may be significantly weakened.

[0069] Based on the adjusted total amplitude, when optimizing the output power of the hydrogen fuel cell, it is necessary to check the power situation of each major time period. First, sort out the output load situation of the current time period from the statistical data, for example, compare the power value with the pre-established effective range, such as the output power comparison between 0kW and 10kW. If the power value is lower than 0kW or exceeds 10kW, the point is recorded as abnormal collection and the reason is indicated. Then, for the power value within the normal range, combined with the adjusted total amplitude that has been calculated, the amplitude setting in the high-frequency signal part is gradually corrected. For example, when the frequency component of a certain period reaches 40Hz and the measured delay time is 1.2 seconds, the amplitude of the component is reduced or increased accordingly according to the attenuation factor information obtained previously, and the different frequency components are proofread in sequence. If it is found that the power exceeds the empirical threshold in the high-frequency segment, such as exceeding 12kW, it is necessary to compare the actual equipment carrying capacity again and adjust the output convergence. Finally, all frequency adjustment operations are combined to establish a new high-frequency signal output configuration for the hydrogen fuel cell under the current load demand conditions, and then obtain a high-frequency adjustment signal.

[0070] The steps to obtain safety monitoring information are as follows: Based on the high-frequency regulation signal, the temperature parameters and output power of the hydrogen fuel cell are recorded in real time, including the peak value, valley value and average value, and the real-time monitoring data is obtained; Using real-time monitoring data, check whether the temperature and power exceed the preset safety thresholds for each data point, mark the over-limit data, and record the timestamp and parameter value of the over-limit event to form a preliminary abnormal result; Based on the preliminary abnormal results, the marked out-of-limit events are analyzed to verify the severity and risk level of the out-of-limit events and form safety monitoring information.

[0071] Specifically, based on the high-frequency adjustment signal, for the temperature parameters and output power of the hydrogen fuel cell, it is first necessary to select each minute as the recording period, and sample the temperature and power multiple times within the period. In order to make the data comparable, it is necessary to set the temperature comparison range of 0°C to 90°C and the output power comparison range of 0kW to 10kW in advance. These ranges are derived from the actual range statistics of the equipment in previous operations. Then, at the end of each minute, all the sampled values ​​in the period are retrieved one by one, and the peak, valley and average values ​​are identified respectively. These data are recorded line by line in chronological order. If a peak value is found to exceed the preset upper limit, such as the temperature exceeds 90°C or the output power exceeds 10kW, the sampling timestamp and the corresponding sensor need to be verified first to confirm Is there any sensor deviation or abnormal reading? If the reading is accurate after verification, its value will be marked as exceeding the acceptable range when recording. Otherwise, it will be marked as data abnormality and summarized separately. In order to prevent a large number of frequent temperature or power fluctuations from interfering with the next analysis, differential statistics are also required according to the number of collections at different measuring points during the final summary. For example, during a daily monitoring period, if the peak temperature of the hydrogen fuel cell reaches between 85°C and 90°C many times, it means that it is near the upper temperature limit. At this time, the data of adjacent time periods should continue to be recorded and check whether other environmental conditions have changed. Finally, the minute-level real-time monitoring data formed will be classified and sorted on a daily, hourly or minute-by-minute basis and saved in the database, and the correlation information of temperature, power and high-frequency signals will be combined to obtain real-time monitoring data.

[0072] Using the above real-time monitoring data, two key items, temperature and output power, are retrieved for each data point. First, each record is compared with the previously established safety threshold. For example, the temperature threshold is set within 90°C. This value is obtained by combining the heat resistance of the equipment material and the maximum safe temperature of the field test. Then the output power threshold is set within 10kW. This value is based on the optimal output range of the hydrogen fuel cell under different test loads and is corrected with reference to the operating conditions. Then, the peak, valley and average values ​​of temperature and power in the records are scanned one by one. If the peak or average value exceeds the above set threshold, the record is marked as "out-of-limit data" and its timestamp and corresponding temperature or power value are noted in the database for subsequent reference. When it is found that the valley value is also abnormal, such as the temperature valley value is lower than 0°C or the power valley value is less than 0kW, it is also judged and marked according to the over-limit logic to ensure that any data deviating from the acceptable range is individually identified. If the over-limit phenomenon occurs for multiple consecutive time periods, the cause will be further analyzed. Finally, all marked over-limit events are summarized and distinguished from normal records to form a preliminary abnormal result.

[0073] According to the preliminary abnormal results obtained above, it is necessary to perform subsequent analysis according to the timestamp and parameter value of each over-limit event. In order to determine the severity and risk level of each abnormal event, it is necessary to first check the deviation range of the temperature or power value involved in the event. For example, if the temperature peak in a record exceeds the 90℃ threshold by more than 2℃ and occurs three times in a row, it may be assessed as a high-risk event. If a record is only slightly higher than the upper threshold by 0.5℃ or the power exceeds 10kW by no more than 0.3kW, it is classified into a lower-risk group. In addition, it is necessary to refer to factors such as the continuous operation time of the equipment, ambient temperature and load conditions to confirm whether these events can be regarded as sporadic in the short term or whether there are signs of further escalation. If there are multiple high-temperature or high-power anomalies that occur in adjacent time periods, they are combined for event correlation analysis to find out whether they are common over-limits caused by the same fault cause. Finally, the event is divided into high risk or medium-low risk according to the risk level, and the relevant information is recorded. The result obtained after this procedure is the safety monitoring information.

[0074] The steps to obtain life assessment data are: Based on safety monitoring information, analyze the current status of the hydrogen fuel cell, record safety-related events and performance trends, including temperature overshoot events and power fluctuations, and generate status analysis results; According to the status analysis results, the remaining life percentage is calculated using the following formula:

[0075] in, represents the safety score of the ith safety monitoring event, is the weight factor of the ith safety monitoring event, is the total number of events, Indicates the estimated remaining life percentage; Based on the remaining life percentage, the overall health status and expected life of the hydrogen fuel cell module are evaluated to form life assessment data.

[0076] Specifically, based on the safety monitoring information obtained previously, to analyze the current status of the hydrogen fuel cell, it is necessary to first select those safety-related events and performance trends from the records, and focus on the two indicators of temperature and power. They should be compared with the previously cumulatively monitored temperature range of 0°C to 90°C and power range of 0kW to 10kW. If the temperature peak exceeds 90°C for many times or the power fluctuation soars in a short period of time, mark it in the record and attach a time tag. Then check whether these over-limit or high-fluctuation events show an increasing trend in the recent period. For example, count the number and severity of high temperatures in the last 24 hours and make a horizontal comparison with the same time period before. If the high temperature exceeding the standard increases too fast, it may be necessary to Pay attention to whether the cooling system or ambient temperature of the equipment is abnormal. In addition, check whether large load changes often occur from the perspective of power fluctuations, and check the power and load change data against each other. For example, when the power value approaches the upper limit of 10kW for several consecutive times, check whether there is a peak or abnormal difference in the distribution network demand in the same period. If the power fluctuation crosses peaks and troughs multiple times in 1 minute or less, it is necessary to check whether there are problems such as unstable hydrogen supply pressure or too dense sensor collection intervals. Record the occurrence time and detailed parameters of all the above abnormal or high-frequency events, and combine them with the two main phenomena of temperature exceeding the standard and drastic power fluctuations to form a comprehensive analysis of the safety of the equipment during this period, and finally generate a status analysis result.

[0077] The benefit of the formula is that by introducing In this way, abnormal events with lower safety scores are highlighted and weighted accumulation is performed in combination with weight factors, thereby avoiding the problem that simple average cannot reflect the impact of events at different risk levels.

[0078] The acquisition steps are as follows: for each over-limit event or safety monitoring event, score it according to its severity and duration and record it as a safety score from 0 to 100. For example, during the continuous operation of the equipment for 300 hours, each event of the temperature exceeding 90°C and the corresponding power fluctuation are comprehensively considered. If the temperature exceeds the range by a large amount and lasts for a long time, a lower safety score of 50 to 60 will be given. If it is only a slight over-limit, the score can be as high as 80 or above; The steps to obtain the data are to compile the frequency and potential impact of similar events in historical records into weight values. The weights are related to the foreseeable degree of damage caused by the event. The known failure rates of each type of event can be analyzed first, and then the industry equipment safety standards or on-site assessments can be used to correct the failure rates. ; The acquisition step is to summarize the total number of safety monitoring events registered during this period, including temperature exceeding the standard, power overload and other related anomalies, and accumulate each new event once.

[0079] Calculation process: Assume the total number of registered safety monitoring events , its safety score , , After multiple statistics, weight factors are assigned to events 1, 2, and 3 respectively. , ,

[0080] First calculate each :

[0081] Then square it and multiply it by the corresponding weight:

[0082]

[0083]

[0084] Adding these three together:

[0085] Then take the square root of the result:

[0086] Finally, we put the formula into :

[0087] The result shows that the current estimated remaining life percentage is about 76.51. If the value is close to or lower than 50, it is necessary to closely monitor the equipment condition and take preventive measures in time. If the value exceeds 80, it means that the overall condition is relatively good.

[0088] Based on the remaining life percentage obtained above, it is necessary to conduct a follow-up assessment of the overall health of the hydrogen fuel cell module, and comprehensively consider the fault conditions and maintenance records in multiple previous operation cycles during the assessment process. First, compare the temperature fluctuations and power changes in the recent period to confirm whether there are more high-temperature or abnormal events under high-load conditions, and list the occurrence time periods and durations of these events one by one. If it is found that most of the events are concentrated in certain specific time periods, it means that there may be external environmental interference or the equipment load configuration is too high, and it is necessary to pay extra attention to these time periods in the subsequent operation. On the other hand, if the records show that the temperature and power of the hydrogen fuel cell have not exceeded the preset safety range for several consecutive days, it means that the current state is relatively stable, but the remaining life percentage still needs to be periodically reviewed. By comparing the newly generated monitoring data with the previous cumulative records to identify trend changes, if the remaining life percentage shows a clear downward trend, the subsequent operation plan will be adjusted. Finally, the results of this assessment are compared with the health status of the previous cycle, and summarized in combination with the time points of maintenance and overhaul to form life assessment data.

[0089] The present invention provides an energy management method for a hydrogen fuel cell module, comprising the following steps: Based on the voltage parameters, current parameters and temperature parameters of the hydrogen fuel cell, the original monitoring data is collected from the sensor readings, and the values ​​are compared with the load demand of the distribution network to generate supply and demand matching information. The original monitoring data and the supply and demand matching information are synchronously merged, time-sequenced and checked for abnormal values ​​to obtain a preliminary state sequence; Based on the preliminary state sequence, the current parameters, temperature parameters and voltage parameters are divided into intervals, differential statistics and difference operations are performed to obtain the basis for energy allocation, and then correlated with the load demand of the distribution network. Through discharge path screening, multi-dimensional coordinate comparison and abnormal value screening, the discharge allocation value is established; Based on the discharge adjustment value, the frequency band of the output power of the hydrogen fuel cell is extracted and the rate is recorded to obtain the power segmentation data, which is compared with the load change of the distribution network and the phase is measured to obtain the phase feature group. The phase feature group is then frequency-filtered and amplitude-adjusted to establish a high-frequency adjustment signal. Based on high-frequency regulation signals, the temperature parameters and output power of hydrogen fuel cells are continuously monitored, limit judgment and safety threshold comparison are performed to obtain safety monitoring information, and the safety monitoring information is analyzed and compared with the remaining life parameters to obtain life assessment data; Based on the life assessment data, the health management of the hydrogen fuel cell module is performed, and the battery status is evaluated and monitored through battery temperature, output power and safety monitoring information.

Claims

1. An energy management system for a hydrogen fuel cell module, characterized in that: The system comprises: The monitoring and acquisition module collects and compares sensor readings based on the voltage parameters, current parameters and temperature parameters of the hydrogen fuel cell module to obtain original monitoring data, compares the load demand of the distribution network, generates supply and demand matching information, and synchronizes, arranges the original monitoring data and the supply and demand matching information, performs time sequence arrangement and checks for abnormal values ​​to obtain a preliminary state sequence; The dynamic allocation module performs interval division, differential statistics and difference calculation on the current parameters, temperature parameters and voltage parameters based on the preliminary state sequence to obtain the energy allocation basis, performs correlation comparison, discharge path screening, multi-dimensional coordinate comparison and abnormal value screening on the energy allocation basis and the load demand of the distribution network to establish the discharge allocation value; The high-frequency control module extracts the frequency band and records the rate of the output power of the hydrogen fuel cell based on the discharge adjustment value, obtains the power segmentation data, compares the power segmentation data with the load change of the distribution network and measures the phase to obtain a phase feature group, performs frequency division screening and amplitude adjustment on the phase feature group, and establishes a high-frequency adjustment signal; The health management module continuously monitors the temperature parameters and output power of the hydrogen fuel cell, makes limit judgments and compares the safety thresholds based on the high-frequency adjustment signal, obtains safety monitoring information, performs state analysis and indicator comparison on the safety monitoring information and the remaining life parameters, and obtains life assessment data.

2. The energy management system of the hydrogen fuel cell module according to claim 1, characterized in that: The steps for obtaining the preliminary state sequence are: Receive the voltage parameters, current parameters and temperature parameters of the hydrogen fuel cell, perform data fusion with the supply and demand matching information, and arrange the collected data points in time sequence by timestamp to obtain a time-sequenced data set; Based on the time-sorted data set, by calculating the mean value and standard deviation of each parameter, outliers that are beyond the range of two times the standard deviation of the mean value are identified and eliminated to obtain an outlier-processed data set; Based on the data set after outlier processing, the maximum value, minimum value, mean value and coefficient of variation of each parameter are calculated to form a preliminary state sequence.

3. The energy management system of the hydrogen fuel cell module according to claim 1, characterized in that: The steps for obtaining the energy allocation basis are: Based on the preliminary state sequence, each parameter is divided into intervals, a threshold is set according to the historical data range of each parameter and the current operating environment, the parameter is divided into multiple working intervals, each interval represents a different operating state, and a parameter interval data set is generated; According to the parameter interval data set, the interval difference statistics are calculated, and the calculation formula is: in, represents the value of the i-th parameter in the j-th interval, represents the average value of the i-th parameter in this interval, is the interval difference statistic of the i-th parameter, and N is the number of data points in each interval; Based on the interval difference statistics of each parameter, the sizes of the interval difference values ​​of each parameter are compared, the relative change rates and fluctuation characteristics between the parameters are analyzed, and the basis for energy distribution is formed.

4. The energy management system of the hydrogen fuel cell module according to claim 1, characterized in that: The steps for obtaining the discharge adjustment value are as follows: Based on the energy allocation basis, a comparison is performed with the load demand of the distribution network. By analyzing the difference between energy output and demand in each period, the periods when the demand is exceeded and not met are identified to form a preliminary difference data set; Based on the preliminary difference data set, the overall matching degree between energy output and demand in each period is calculated, and the calculation formula is: in, is the energy output in the i-th period, is the load demand in the i-th period, is the total number of periods evaluated, Indicates the overall matching degree; Based on the overall matching degree, discharge path screening and multi-dimensional coordinate comparison are performed, and the discharge strategy is optimized in combination with historical data. After screening out abnormal values, the discharge allocation value is formed.

5. The energy management system of the hydrogen fuel cell module according to claim 1, characterized in that: The steps of obtaining the phase feature group are: Based on the discharge allocation value, by setting the time window, measuring the power peak and valley in each window, and obtaining the power segmentation data; According to the power segment data, the data of load change of the power distribution network is compared, and each power segment is time synchronized to obtain an aligned comparison result; Based on the alignment comparison result, the phase measurement between power and load is performed, and the periodicity and consistency of the changes are quantified by analyzing the time offset and phase difference between the power change and the load change to form a phase feature group.

6. The energy management system of the hydrogen fuel cell module according to claim 1, characterized in that: The steps of obtaining the high frequency adjustment signal are as follows: Based on the phase feature group, frequency division screening is performed on the data to select the frequency interval with the maximum energy response to obtain the screened frequency data; According to the filtered frequency data, the adjusted total amplitude is calculated using the following formula: in, represents the response amplitude of the ith frequency, is the maximum response amplitude among all frequencies, represents the corresponding time delay, is a decay factor, is the total frequency, is the total adjusted magnitude; Based on the adjusted total amplitude, the output power of the hydrogen fuel cell is optimized, the high-frequency signal is adjusted to match the real-time load demand of the power grid, and a high-frequency regulation signal is formed.

7. The energy management system of the hydrogen fuel cell module according to claim 1, characterized in that: The steps for obtaining the safety monitoring information are as follows: Based on the high-frequency adjustment signal, the data points per minute are recorded in real time for the temperature parameters and output power of the hydrogen fuel cell, including the peak value, the valley value and the average value, to obtain real-time monitoring data; Using the real-time monitoring data, check whether the temperature and power exceed the predetermined safety threshold for each data point, mark the over-limit data, and record the timestamp and parameter value of the over-limit event to form a preliminary abnormal result; Based on the preliminary abnormal results, the marked out-of-limit events are analyzed to verify the severity and risk level of the out-of-limit events and form safety monitoring information.

8. The energy management system of the hydrogen fuel cell module according to claim 1, characterized in that: The steps for obtaining the life assessment data are as follows: Based on the safety monitoring information, analyze the current state of the hydrogen fuel cell, record safety-related events and performance trends, including temperature exceeding standard events and power fluctuations, and generate state analysis results; According to the state analysis results, the remaining life percentage is calculated using the following formula: in, represents the safety score of the ith safety monitoring event, is the weight factor of the ith safety monitoring event, is the total number of events, Indicates the estimated remaining life percentage; Based on the remaining life percentage, the overall health status and expected life of the hydrogen fuel cell module are evaluated to form life evaluation data.

9. A method for energy management of a hydrogen fuel cell module, characterized in that: The energy management system of the hydrogen fuel cell module according to any one of claims 1 to 8 comprises the following steps: Based on the voltage parameters, current parameters and temperature parameters of the hydrogen fuel cell, the original monitoring data is collected from the sensor readings, and the values ​​are compared with the load demand of the distribution network to generate supply and demand matching information. The original monitoring data and the supply and demand matching information are synchronously merged, time-sequenced and checked for abnormal values ​​to obtain a preliminary state sequence; Based on the preliminary state sequence, the current parameters, temperature parameters and voltage parameters are divided into intervals, differential statistics and difference operations are performed to obtain the basis for energy allocation, and then correlated with the load demand of the distribution network. Through discharge path screening, multi-dimensional coordinate comparison and abnormal value screening, the discharge allocation value is established; Based on the discharge adjustment value, the frequency band of the output power of the hydrogen fuel cell is extracted and the rate is recorded to obtain the power segmentation data, which is compared with the load change of the distribution network and the phase is measured to obtain the phase feature group. The phase feature group is then frequency-filtered and amplitude-adjusted to establish a high-frequency adjustment signal. Based on high-frequency regulation signals, the temperature parameters and output power of hydrogen fuel cells are continuously monitored, limit judgment and safety threshold comparison are performed to obtain safety monitoring information, and the safety monitoring information is analyzed and compared with the remaining life parameters to obtain life assessment data; Based on the life assessment data, the health management of the hydrogen fuel cell module is performed, and the battery status is evaluated and monitored through battery temperature, output power and safety monitoring information.

Images