An intelligent overload protection and fault prediction system suitable for mobile power supply

Abstract

The application relates to the technical field of mobile power supply monitoring, in particular to an intelligent overload protection and fault prediction system suitable for a mobile power supply, which comprises a real-time impedance monitoring module; during the use of the mobile power supply, the real-time impedance monitoring module continuously monitors the internal impedance of a battery and generates a real-time impedance analysis result; according to the real-time impedance analysis result, the charging current is adjusted, the real-time state of the battery is matched, and an impedance adjustment decision is generated. In the application, the internal impedance of the mobile power supply is continuously monitored and analyzed, the charging current can be dynamically adjusted, the real-time state of the battery can be accurately adapted, the safety of the charging process is improved, and the service life of the battery is prolonged; the voltage and current data are continuously compared with a preset normal range, and real-time detection of abnormal fluctuations is carried out, the response speed to the abnormal state of the battery is improved, and the safety risk in the operation process of the battery is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mobile power supply monitoring, and particularly relates to an intelligent overload protection and fault prediction system suitable for a mobile power supply. BACKGROUND

[0002] The intelligent overload protection and fault prediction system is suitable for a mobile power supply of a new energy vehicle, and automatically identifies an overload condition, intelligently protects, and predicts and diagnoses a possible fault in advance during operation, so as to reduce the occurrence of safety accidents and improve the operation safety and reliability of the mobile power supply of the new energy vehicle.

[0003] In actual operation, the existing technology only relies on voltage and current abnormality detection after a fault occurs to judge the overload protection and fault prediction, and it is difficult to realize real-time tracking of internal state changes of the battery, so that the monitoring means is single and the hysteresis is obvious. Due to the lack of attention to the real-time impedance state of the battery, abnormal signs are not found in time in the gradual degradation process of the battery, so that the early warning mechanism is passive and the prediction result accuracy is insufficient. Therefore, improvement is needed. SUMMARY

[0004] The purpose of the present application is to solve the shortcomings in the prior art and provide an intelligent overload protection and fault prediction system suitable for a mobile power supply.

[0005] In order to achieve the above purpose, the present application adopts the following technical scheme: an intelligent overload protection and fault prediction system suitable for a mobile power supply comprises:

[0006] A real-time impedance monitoring module continuously monitors the internal impedance of the battery during use of the mobile power supply, generates a real-time impedance analysis result, adjusts the charging current according to the real-time impedance analysis result, matches the real-time state of the battery, and generates an impedance adjustment decision;

[0007] A voltage and current abnormality detection module continuously monitors the voltage and current data of the battery, compares with a preset normal working range, generates a voltage and current monitoring result, judges whether voltage or current abnormal fluctuation is detected based on the voltage and current monitoring result, and immediately issues an abnormal warning and generates an abnormal state alarm if the abnormal fluctuation is detected;

[0008] A battery maintenance scheduling module calculates the charging and discharging efficiency of the battery according to the abnormal state alarm and the charging and discharging cycle of the battery, generates a battery health evaluation result, and obtains a maintenance and replacement schedule of the battery according to the battery health evaluation result, and generates a maintenance scheduling plan;

[0009] A fault prediction and alarm module combines the impedance adjustment decision and the maintenance scheduling plan, uses a statistical model to predict and estimate the battery fault, and generates a fault prediction analysis result.

[0010] Preferably, the step of obtaining the real-time impedance analysis result is: continuously collecting the internal impedance change of the battery under various use states, recording the impedance value of each collection, and obtaining the battery impedance raw data;

[0011] Based on the battery impedance raw data, a sliding window average is applied to calculate the average impedance value in each window, identify and eliminate data points exceeding twice the average impedance value, and generate the real-time impedance analysis result.

[0012] Preferably, the step of obtaining the impedance adjustment decision is: based on the real-time impedance analysis result, calculating the charging current, and the calculation formula is:

[0013]

[0014] Wherein, I new represents the adjusted charging current, I base represents the initial charging current, and α represents the adjustment sensitivity parameter, R current represents the current impedance value, represents the impedance change rate, T cell represents the battery temperature, and β and γ are adjustment parameters to reflect the influence of the impedance change rate and the temperature on the charging current, R base represents the reference impedance value;

[0015] Based on the charging current, the charging strategy is adjusted according to the state of the battery, and the impedance adjustment decision is formed and implemented.

[0016] Preferably, the step of obtaining the voltage and current monitoring result is: real-time recording the voltage and current data of the mobile power supply under various working states, forming the battery voltage and current raw data set;

[0017] Based on the battery voltage and current raw data set, by comparing each data point with the preset normal working voltage and current range, identifying and marking all abnormal points exceeding the normal working voltage and current range, and forming the voltage and current monitoring result.

[0018] Preferably, the step of obtaining the abnormal state alarm is: receiving the voltage and current monitoring result, analyzing the voltage and current data at all time points, extracting the measurement value at each time point, and calculating the voltage change, current change, battery temperature, battery load state, battery capacity remaining ratio and environment temperature at each time point, and establishing the voltage and current fluctuation data set after sorting;

[0019] Based on the voltage and current fluctuation data set, the abnormal fluctuation judgment value is calculated, and the calculation formula is:

[0020]

[0021] Where A is the abnormal fluctuation judgment value, Q i Let J be the voltage value measured in the i-th measurement. j Let j be the current value measured in the j-th measurement. The mean of the current measurements is given by T, where m and n are the number of voltage and current measurements, respectively. cell For battery temperature, T e Where L is the ambient temperature, C is the current state of battery load, and L is the current state of battery load. r Q represents the ratio of remaining battery capacity. max Q is the maximum voltage value within the sampling period. min Q is the minimum voltage value within the sampling period. i-1 This represents the voltage value measured in the (i-1)th time.

[0022] Based on the abnormal fluctuation judgment value, it is detected whether the set threshold is exceeded. If it is exceeded, an abnormal state alarm is triggered.

[0023] Preferably, the steps for obtaining the battery health assessment results are as follows: based on the abnormal state alarm and the battery charge and discharge cycle data, organize the charging amount, discharging amount, cycle duration and the number of associated abnormal state alarms in each cycle to obtain the battery charge and discharge cycle dataset.

[0024] Based on the battery charge / discharge cycle data set, the battery charge / discharge efficiency is calculated using the following formula:

[0025]

[0026] Where E is the battery charge / discharge efficiency, and D... k C represents the discharge amount in the k-th cycle. k Let N be the charge amount in the k-th cycle, p be the number of observation cycles, and N be the total charge amount. a T represents the number of abnormal alarms in the k-th period. avg T represents the average charge / discharge cycle duration. max The longest single charge-discharge cycle duration during the observation period;

[0027] Based on the battery charge and discharge efficiency, the battery's health status is assessed, and a battery health assessment result is generated.

[0028] Preferably, the step of obtaining the maintenance scheduling plan is as follows: based on the battery health assessment results, calculate the next maintenance replacement time, using the following formula:

[0029]

[0030] Among them, T next H is scheduled for the next maintenance and replacement time. max For battery design life, H current For the currently assessed remaining lifetime, Cinitial C is the initial capacity of the battery current E is the current capacity initial E is the initial charging efficiency of the battery current N is the current charging efficiency cycles N is the cumulative number of charge and discharge cycles of the battery

[0031] Based on the next maintenance replacement time, combined with the maintenance period and replacement strategy, a maintenance and replacement schedule is formulated, and a maintenance scheduling plan is generated.

[0032] Preferably, the fault prediction analysis result acquisition step is: based on the impedance adjustment decision and the maintenance scheduling plan, the estimated failure probability of the battery is estimated, and the calculation formula is:

[0033]

[0034] Wherein, F represents the failure probability, λ is the baseline failure rate fitted according to the failure data, Da is the number of days since the last maintenance, Ca is the number of charging times since the last maintenance, and Na is the number of discharging times since the last maintenance.

[0035] Based on the failure probability, the fault prediction analysis is carried out to guide the maintenance and replacement strategy, and the fault prediction analysis result is obtained.

[0036] Compared with the prior art, the advantages and positive effects of the present application are:

[0037] In the present application, by continuously monitoring and analyzing the internal impedance of the mobile power supply, the charging current can be dynamically adjusted to accurately adapt to the real-time state of the battery, improve the safety of the charging process and the service life of the battery; Based on the continuous comparison of voltage and current data with the preset normal range and the real-time detection of abnormal fluctuations, the response speed to abnormal state of the battery is improved, and the safety risk in the operation process of the battery is reduced; Combined with the health evaluation result calculated by the battery charge and discharge cycle, the maintenance and replacement schedule is formulated, so that the maintenance of the battery is more targeted, and the resource waste caused by untimely maintenance or excessive maintenance is reduced; The impedance adjustment decision and the maintenance plan are combined, the possible faults are predicted through statistical analysis, the active identification and early disposal of the safety hidden danger of the battery are realized, and the operation reliability and overall safety level of the mobile power supply are improved. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 The system flowchart of the present application. DETAILED DESCRIPTION

[0039] In order to make the objects, technical solutions and advantages of the present application clearer, the present application 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 application and should not be used to limit the present application.

[0040] Referring to Figure 1 The present application provides a technical solution: an intelligent overload protection and fault prediction system suitable for a mobile power supply, comprising:

[0041] A real-time impedance monitoring module continuously monitors the internal impedance of the battery during the use of the mobile power supply and generates a real-time impedance analysis result; according to the real-time impedance analysis result, the charging current is adjusted to match the real-time state of the battery, and an impedance adjustment decision is generated;

[0042] A voltage and current anomaly detection module continuously monitors the voltage and current data of the battery, compares with the preset normal working range, and generates a voltage and current monitoring result; based on the voltage and current monitoring result, it is judged whether voltage or current abnormal fluctuation is detected, and if detected, an abnormal warning is immediately issued, and an abnormal state alarm is generated;

[0043] A battery maintenance scheduling module calculates the charging and discharging efficiency of the battery according to the abnormal state alarm and the charging and discharging cycle of the battery, generates a battery health assessment result; according to the battery health assessment result, the maintenance and replacement schedule of the battery is obtained, and a maintenance scheduling plan is generated;

[0044] A fault prediction and alarm module combines the impedance adjustment decision and the maintenance scheduling plan, uses a statistical model to predict and estimate the battery faults that occur, and generates a fault prediction analysis result.

[0045] The acquisition step of the real-time impedance analysis result is: continuously collecting the internal impedance changes of the battery under various use states, recording the impedance value of each collection, and obtaining the battery impedance original data;

[0046] Based on the battery impedance original data, a sliding window average is applied to calculate the average impedance value in each window, identify and eliminate data points more than twice the average impedance value, and generate a real-time impedance analysis result.

[0047] Specifically, the internal impedance of the battery is continuously observed in different temperature ranges (e.g. 0°C to 90°C), different voltage ranges (e.g. 0V to 24V), and different load current ranges (e.g. 0A to 5A). The collection time and environmental conditions are identified according to the pre-set ranges of these usage states. The impedance values collected each time are recorded while labeling the measurement time, operating voltage, current size, and temperature information. To avoid missing any extreme state, an additional sampling frequency of 10 times per second is set and continuously observed. The observed impedance values are directly compared with the actual usage scenarios that may exist to confirm the integrity and consistency of the data. For example, when the temperature sampling value falls between 0°C and 90°C, the voltage value is within the range of 0V to 24V, and the current value is within the range of 0A to 5A, it is considered as a valid record. Otherwise, the corresponding collection results will be separately marked to facilitate further confirmation or troubleshooting in the subsequent process. This marking method is achieved by maintaining a judgment identification field in the database. The threshold and range come from the comprehensive analysis of the technical parameters provided by the battery supplier and the results of multiple experimental measurements, and the reliability is verified by actual examples. For example, in several tests, the impedance of the same type of battery was measured at 15°C, 25°C, and 40°C, and similar resistance ranges were obtained, thus determining that the above temperature and voltage current ranges can cover the main usage conditions. Each collection record is real-time counted into a data table to form serialized data, providing an index for subsequent processing and maintaining smooth timing. The battery impedance raw data is obtained.

[0048] Based on the previously obtained battery impedance raw data, each impedance value collected is processed using a sliding window average method with a known observation sequence length. The window size is determined from actual examples, with a window width of 10 being selected and can be adjusted according to different battery capacities and test requirements. In each window, all impedance values are added and divided by 10 to obtain the average impedance value, denoted as Then, the is calculated as a threshold The coefficient of 2 is selected from the statistical evaluation of the impedance distribution of the same type of battery in repeated experiments, combined with the actual measurement accuracy and error range. For example, if the average impedance value in a certain window is 0.05Ω, the threshold is 0.10Ω. When a data point exceeds 0.10Ω, it is identified as an abnormal value and marked as a data to be excluded. To illustrate the identification process of this abnormal value, the overall distribution of the same batch of data in other window segments is also compared to confirm that it is not caused by measurement equipment failure. This judgment process also refers to the previously set temperature, voltage, and current ranges, and combines the repeated comparison results of impedance measurement. Once it is confirmed that these data points exceeding the threshold are indeed deviating from the normal level, they are excluded. After completing the data scanning of all windows, the valid impedance mean sequence of each window is summarized, and the real-time impedance analysis result is generated.

[0049] The obtaining step of the impedance adjustment decision is: based on the real-time impedance analysis result, the charging current is calculated, and the calculation formula is:

[0050]

[0051] wherein, I new represents the adjusted charging current, I base represents the initial charging current, and a represents the adjustment sensitivity parameter, R current represents the current impedance value, represents the rate of change of impedance, T cell represents the battery temperature, and β and γ are adjustment parameters to reflect the influence of the rate of change of impedance and temperature on the charging current, R base represents the reference impedance value;

[0052] Based on the charging current, the charging strategy is adjusted in combination with the state of the battery, the impedance adjustment decision is formed and implemented.

[0053] Specifically, the formula has the beneficial effect that by introducing multiple parameters such as a, β, and γ in the denominator and comprehensively considering multiple monitoring results such as the current impedance value, the rate of change of impedance, and the temperature factor, the charging current can be dynamically adjusted more flexibly in actual operation, especially when the impedance deviates or the temperature is in different intervals, a more refined charging strategy can be formed, and a charging process that takes into account safety and efficiency in the overall system can be obtained.

[0054] I base The parameter obtaining step is: when the system is initially established, in order to obtain the reference charging current, charging experiments need to be performed on several batteries of the same model or same series, and the current values of the initial charging phase of the batteries are recorded, and the performance of each battery is continuously tracked within a period of time. The recorded initial charging current data is statistically analyzed according to different temperature intervals, for example, the temperature is compared with the interval of 0℃ to 90℃, if the initial charging current of most batteries is measured to be in the range of 2.0A to 2.3A at 25℃, then 2.0A can be established as the initial reference value of this model, and for other temperature intervals, the reference current corresponding to each temperature is collected and summarized in the same way, and finally the average value of the appropriate interval is taken to determine I base In a data of 5 repeated experiments, if 2.0A, 2.1A, 2.1A, 2.2A, and 2.0A are obtained, the average value 2.08A can be evaluated, and I base is rounded to 2.1A.

[0055] The step of obtaining the parameter a is: a is used to represent the adjustment sensitivity of the charging current with respect to the impedance and temperature changes, and needs to be quantified according to the amplitude of the influence of the impedance fluctuation and the temperature difference on the actual charging current after integrating the multi-day sampling data. The specific method is to record the current deviation caused by the impedance fluctuation in several days, and then compare it with the temperature range. For example, in a 30-day monitoring, the current drop caused by the impedance change from 0.05Ω to 0.06Ω is calculated, for example, from 2.1A to 1.95A, and the adjustment ratio is about Then cross-compare the fluctuation range of the temperature (for example, 25℃ to 40℃), and calculate the cumulative value according to the time series data, thereby generating a plurality of corresponding value pairs of the adjustment ratio and the temperature difference, and then obtaining a according to the regression method. The regression process is as follows: Where ΔI i is the current change amount at the i-th collection, I base,i is the reference current, ΔT i is the difference between the temperature and the reference temperature, and n is the number of observations. In an actual calculation, if a ≈ 0.28 is obtained, the average value 0.3 is taken according to the relatively stable stage of the monitoring data.

[0056] R current The step of obtaining the parameter is: the value of R current is obtained by measuring the impedance of the battery at the current time, the original data of the battery impedance obtained in the foregoing is time-synchronized and the latest effective impedance measurement value is extracted, it is assumed that the measurement value is collected and recorded at 9 o'clock in the morning, then it is directly included in the current impedance information, if the measured impedance is 0.06Ω at this time, it is recorded as R current = 0.06Ω.

[0057] The step of obtaining the parameter is: the parameter represents the change rate of the impedance, and needs to continuously monitor the impedance measurement values of the last few times, and then obtains the change rate through difference or slope calculation method. The specific steps are as follows: first, obtain the impedance values at intervals of 1 hour in a day Divide the difference between the adjacent two measurements by the measurement interval, for example, if the impedance measured at 10 o'clock is 0.055Ω and the impedance measured at 11 o'clock is 0.06Ω, then

[0058] T cell The step of obtaining the parameter is: T cell ​This is the current temperature of the battery, recorded by placing a temperature sensor inside the device or close to its surface. This location is chosen near the positive and negative terminals to reflect internal temperature rise. By continuously collecting 24-hour data and differentiating between high-temperature (above 40°C), normal-temperature (20°C to 40°C), and low-temperature (0°C to 20°C) monitoring results, the optimal temperature for the battery at any given moment can be determined. For example, if the temperature probe measures 25.5°C during this period, then the T... cell The value is set to 25.5.

[0059] The steps for obtaining the β parameter are as follows: β is used to reflect the weight of the impedance change rate on the current regulation part, in establishing... When considering the correlation between the actual current adjustment and the actual current adjustment, the factors within several measurement cycles can be calculated first. Increase the amount of current decrease generated and perform statistical analysis, then compare this decrease with... The difference between the two values ​​is used to obtain the β value through linear or nonlinear fitting. In the example, data from different time periods within two weeks might be collected first. With the corresponding current reduction ratio, through: M represents the number of samplings. This ratio is determined according to the distribution range of the relatively stable monitoring environment. If β = 1.2 is obtained in a certain calculation, it can be finally selected between 1.1 and 1.2 after multiple comparisons.

[0060] The steps for obtaining the γ parameter are as follows: γ is used to reflect the correction amount of temperature to current change. Similar to the method for obtaining β, it is necessary to conduct multiple analyses on the relationship between temperature range and actual charging current adjustment during system operation. First, extract the current data under each temperature range, and perform difference statistics with the current data at the reference temperature. Then, correlate these differences with the temperature deviation to obtain the mapping relationship. Specifically, the following can be used: P represents the number of monitoring days, I temp,p T represents the current value measured within the temperature range corresponding to day p. base You can take the temperature at which the battery model operates most frequently (e.g., 25℃), and select a relatively stable average value as γ after weighting the data from multiple days. If γ is 1.05 after multiple calculations, it can be set in the range of 1.0 to 1.1.

[0061] R base The steps to obtain the parameters are: R baseis the reference impedance value for comparison, which is obtained by collecting and statistically analyzing the impedance data of a plurality of batteries of the same model measured at normal temperature (fluctuating by 5℃ around 25℃), and taking the average of the remaining impedance values after removing extreme cases as the final reference impedance value. In a centralized measurement of 20 batteries, if most of the measured impedance values are between 0.04Ω and 0.06Ω, the middle value 0.05Ω can be selected as R base .

[0062] Calculation process:

[0063] In the case where the values of the first nine parameters have been obtained, the operation can be performed, assuming that the observation conditions at the current time are as follows:

[0064] I base = 2.1A, α = 0.3, R current = 0.06Ω, T cell = 25.5, β = 1.1, γ = 1.05, R base = 0.05Ω;

[0065] First, write the numerator part:

[0066] I base = 2.1A;

[0067] Then calculate the internal term in the denominator, let:

[0068]

[0069] Divide by R base again:

[0070]

[0071] Take the natural logarithm and take the absolute value:

[0072] |ln(536.81)|≈6.28;

[0073] Then multiply by α:

[0074]

[0075] Add 1 to it:

[0076] 1+1.884=2.884;

[0077] Finally, divide the numerator by this result: This result shows that when the system real-time monitors that the current impedance, temperature and other parameters are in the above state, the calculated I newabout 0.73A, which corresponds to the current relatively conservative charging current intensity, if the subsequent impedance or temperature changes, the same formula can be calculated again and obtain a new charging current value, so as to complete the subsequent adjustment of the charging strategy.

[0078] Based on the charging current value obtained and the actual charging and discharging records registered, the current charging current is compared with the activity state of the battery at different temperatures. First, set to read voltage, current and temperature data every 15 minutes within a day and mark the time stamp. In order to ensure data accuracy, interval judgment should be performed on all read values, such as temperature comparison 0℃ to 90℃ interval, voltage comparison 0V to 24V interval, current comparison 0A to 5A interval, and records exceeding the range are marked as data to be reviewed. Then, select the confirmed valid data to calculate the real-time charging rate and analyze the charging stage length. Pair each group of data with impedance measurement records and focus on whether impedance changes with temperature and current. Also, use the average impedance of the same period as a parameter to compare with the impedance analysis results obtained before. If there are continuous multiple impedance exceeding specified values, the value source needs to be checked and confirmed whether it reaches the warning threshold set in advance. These warning thresholds come from the comprehensive analysis of multiple sample test experience and multiple scene monitoring results. If the impedance is higher than 0.06Ω or the temperature is higher than 45℃, it will be listed as a higher risk range. Combined with the actual temperature rise speed, it is judged whether the charging needs to be terminated in time or enter the next safety review link. In order to complete the corresponding processing, more detailed classification standards need to be set in this range to determine the number of continuous abnormalities or the degree of abnormality. After completing all comparisons, the charging strategy adjustment behavior at the current time can be obtained, and the impedance adjustment decision is formed and implemented.

[0079] The acquisition step of voltage and current monitoring results is to record the voltage and current data of the mobile power supply in various working states in real time to form a battery voltage and current original data set.

[0080] Based on the battery voltage and current original data set, by comparing each data point with the preset normal working voltage and current range, all abnormal points exceeding the normal working voltage and current range are identified and marked to form the voltage and current monitoring results.

[0081] Specifically, according to the prepared operation data of the mobile power supply, the voltage and current data under different load modes are recorded, and the power supply is continuously monitored in standby, light load, full load and other states. Real-time sampling is obtained every 10 seconds. In order to ensure the accuracy of the data, it is necessary to check whether the temperature is between 0 DEG C and 90 DEG C, whether the voltage is between 0 V and 24 V, and whether the current is between 0 A and 5 A during sampling. The current timestamp and the corresponding working condition information are recorded at the same time. These judgment ranges come from repeated measurement and statistical induction of many mobile power supplies, and a threshold system suitable for this type of mobile power supply is formed after repeated tests under normal temperature, lower temperature and higher temperature environments. For example, the voltage of a certain 10000mAh capacity power supply generally maintains in the interval of 5V to 12V, and the current maintains in the interval of 1A to 3A, which indicates normality. When the environmental temperature changes or extreme conditions are encountered, the threshold range can be increased or decreased accordingly. In the reference interval obtained in this way, each item is compared. If the voltage or current value in a certain collection is not within the range of 0V to 24V or 0A to 5A, a mark is given through a specially set judgment field. If the interval is exceeded, such data is listed as observation value that needs to be investigated additionally. If the interval is not exceeded, it is considered as preliminary valid data. Subsequently, all preliminary valid data are uniformly summarized to obtain the battery voltage and current raw data set.

[0082] After obtaining the battery voltage and current raw data set summarized above, each record in it is compared with the previously set normal voltage range and normal current range. The voltage is compared with the interval of 0V to 24V, and the current is compared with the interval of 0A to 5A. Records that do not meet the conditions are marked one by one. These ranges come from the arrangement and statistics of many charging and discharging measured values. The reasons for abnormal marked values are divided into specific types such as excessive voltage, low voltage, excessive current or small current. If the voltage in a sample exceeds 24V, it is determined to be a voltage abnormal point and is marked. If the current in a sample is higher than 5A or lower than 0A, the same marking is performed. If the temperature information in the record exceeds 90 DEG C, it also needs to be separately marked as over-temperature data. Considering that the value may jump for a short time in some extreme cases, the observation of multiple over-limit in multiple data in continuous sampling is considered as long-term abnormality. Otherwise, it is only treated as a short-term deviation. All the marked data that are still determined to be over-limit after investigation are collected and stored in the abnormal list for subsequent investigation and detection. After the comparison and marking of all records are completed, the voltage and current monitoring results can be generated.

[0083] The acquisition steps of the abnormal state alarm are as follows: receiving the voltage and current monitoring results, analyzing the voltage and current data at all time points, extracting the measurement value at each time point, and calculating the voltage change, current change, battery temperature, battery load state, battery capacity remaining ratio and environmental temperature at each time point. After arrangement, the voltage and current fluctuation data set is established.

[0084] Based on the voltage and current fluctuation data set, the abnormal fluctuation determination value is calculated, and the calculation formula is:

[0085]

[0086] Wherein, A is the abnormal fluctuation determination value, Q i is the voltage value of the ith measurement, J j is the current value of the jth measurement, is the average value of the current measurement, m and n are the measurement times of voltage and current, T cell is the battery temperature, T e is the ambient temperature, L is the current load state of the battery, C r is the remaining capacity ratio of the battery, Q max is the maximum voltage value in the sampling period, Q min is the minimum voltage value in the sampling period, Q i-1 is the voltage value of the i-1th measurement;

[0087] Based on the abnormal fluctuation determination value, it is detected whether it exceeds the set threshold value, and if it exceeds, the abnormal state alarm is triggered.

[0088] Specifically, the voltage and current monitoring results obtained in the foregoing are received, the corresponding voltage and current measurement values are extracted for each time point and archived on the same time axis, the basic state of all time points is counted, and then the temperature parameters, the current load state of the battery, the remaining capacity ratio and other information in the time point are recorded. In order to ensure the comparability of the data, all parameters at the same time point are packaged for comparison and the data integrity is judged. If there is a missing measurement value or a period of broken measurement value, it is divided into an abnormal record list and arranged for subsequent review. The time points here can be collected in a fixed time interval manner, for example, recorded once every 30 seconds and marked the change of ambient temperature in the range of 0℃ to 90℃. Then, the voltage change amount at each time point is calculated according to the difference between the voltage value at the time point and the voltage value at the previous time point, and is summarized. All period data is processed and a voltage change sequence is formed. The same way is applied to the current dimension to get a current change amount sequence. After being uniformly arranged, the battery temperature, the battery load state, the battery capacity remaining ratio and the ambient temperature and other fields are added. If the voltage amplitude is greater than 24V or the current amplitude is greater than 5A is detected during this period, this record will be included in a separate high-order record queue. Combined with the known power demand range, subsequent analysis is carried out. When the induction of all period records is completed, the corresponding voltage and current fluctuation data set can be obtained.

[0089] The advantage of the formula is that by comprehensively superimposing factors such as voltage fluctuation, current deviation, temperature difference, load status, and remaining capacity ratio, it can reflect the influence of multiple factors on abnormal fluctuations in the same expression. Furthermore, by setting different correction elements in the denominator, different parameters can form a relatively reasonable balance during calculation. The entire formula helps to obtain more intuitive abnormal judgment values ​​in the monitoring process and takes into account multiple types of factors in the calculation.

[0090] Q i The steps to obtain the parameters are: Q i Q refers to the voltage value measured in the i-th time. This specific value is obtained by periodically sampling the voltage measuring device during the monitoring process. For example, if the voltage measurement value is 12.3V in the 12th record at a certain sampling time on a certain day, then Q can be... 12 It is recorded as 12.3V.

[0091] Q i-1 The steps to obtain the parameters are: Q i-1 This represents the voltage value measured in the (i-1)th time, located immediately next to Q in the data table. i Previously, it was used to calculate the difference between two measurements in order to accurately obtain Q. i-1 To ensure the voltage sequence is generated continuously and without omissions, and that the sampling frequency is constant, a cumulative count can be appended to each record. When calculating the voltage change, only the (i-1)th record needs to be indexed. For example, as mentioned above, the record preceding the 12th record is the 11th record. If the voltage in the 11th record is 11.5V, then Q... 11 That is equivalent to 11.5V.

[0092] J j The steps to obtain the parameters are: J j The current value measured in the j-th time needs to be recorded using the same periodic sampling mechanism. The sampling range can be set between 0A and 5A, and the sampling time and load condition should be noted in each record. To accurately reflect the changes in current under different conditions, the voltage sequence and its corresponding current sequence should be listed simultaneously, with both corresponding to the same time step. If the current measurement value is 2.3A in a certain record, then the j-th record can be located and J... j Write it as 2.3A.

[0093] The steps to obtain the parameters are as follows: This is the average of the current measurements. It requires summing the current records from all observation intervals and then dividing by the number of measurements. The number of measurements here only corresponds to the records included in the effective monitoring range within that time period. This can be achieved using an addition and division method, for example: Where n effis the current number of valid data points, assuming 180 current values are recorded in a 30-minute monitoring period and all fall in the 0A to 5A interval, then the sum of the 180 records is added and divided by 180 to obtain For example, the sum of all current records is 350A, then

[0094] The acquisition step of the m parameter is that m indicates the number of voltage measurements, which is the total number of valid records of voltage in a certain time period, for example, once every 10 seconds and lasting for 6 hours, then m = 6 hours x

[0095] (3600 seconds ÷ 10 seconds) = 2160.

[0096] The acquisition step of the n parameter is that n represents the number of current measurements, which is similar to m and also accumulates the number of current sampling times in the data table.

[0097] T cell The acquisition step of the parameter is that T cell represents the battery temperature, which needs to be monitored by a temperature collection device attached to the battery shell or inside, and the temperature value is collected synchronously at each time point, and the value range is usually based on 0℃ to 90℃, and when the temperature is monitored to be 32.5℃ at a certain moment, T cell is established as 32.5.

[0098] T e The acquisition step of the parameter is that T e represents the ambient temperature, which needs to be recorded by a special temperature measuring device set at an external environment location, generally keeping close to the space where the battery is located, and collected by the same timestamp and corresponding to T cell , if the current ambient temperature is 26℃, then T e = 26.

[0099] The acquisition step of the L parameter is that L represents the current load state of the battery, which is a numerical field used to describe the load level, which can be obtained by monitoring the real-time output power and dividing it according to the power interval, or linearly quantizing the output power in a certain range. For example, power between 0W and 10W corresponds to L = 0.2, power between 10W and 20W corresponds to L = 0.4, etc. After dividing the entire power interval into several segments, the actual measured power falls into which interval to determine L. Take a value process as an example, for example, when the output power is 12W, find the interval where the power is located and get L = 0.4.

[0100] C r The acquisition step of the parameter is that C rThe battery remaining capacity ratio needs to be obtained at the battery management level through capacity statistics. The capacity statistics can be calculated by combining the rated capacity at the factory and the current discharge metering. After determining the remaining capacity value, the ratio is obtained by dividing the remaining capacity by the rated capacity. For example, a 20000mAh battery has a remaining capacity of 14000mAh at the current time,

[0101] Q max The parameter acquisition step is: Q max is the maximum voltage value in the sampling period. All valid Q i values in this period need to be extracted and traversed in the same sequence to find the maximum value, which is recorded as Q max , for example, 300 voltage values are collected in a segment, and the highest voltage value is found to be 19.8V, then Q max = 19.8V.

[0102] Q min The parameter acquisition step is: Q min is the minimum voltage value in the sampling period, for example, the lowest voltage in the 300 voltage values in the same segment is 5.5V, then record Q min = 5.5V.

[0103] Calculation process:

[0104] After all parameters are obtained, they are brought into the above expression for step-by-step operation. The following example is given:

[0105] m = 10, n = 10, Q 1..10 = {12.2, 12.7, 13.0, 12.8, 12.9, 13.2, 13.3, 12.6, 12.4, 13.1};

[0106] Q max = 13.3, Q min = 12.2, T cell = 32.5, T e = 26, L = 0.4, C r = 0.7;

[0107] J 1..10 = {1.8, 1.9, 2.0, 2.1, 1.6, 1.7, 2.2, 1.9, 2.0, 2.1};

[0108] First calculate the voltage variation part:

[0109]

[0110] Because the first Q0 can be the same as the first one to avoid no previous value, the first item is 0, here the absolute value is taken after subtraction and accumulation, and the result is:

[0111] 0+0.5+0.3+0.2+0.1+0.3+0.1+0.7+0.2+0.7=2.9;

[0112]

[0113] Then calculate the current fluctuation part, first find

[0114]

[0115] Each item is calculated in turn and accumulated, and the total is about 0.51;

[0116]

[0117] Then the temperature difference part:

[0118]

[0119] Finally, the remaining capacity ratio part:

[0120]

[0121] Add the above four parts: A = 0.29 + 0.714 + 4.64 + 0.333 ≈ 5.977, which indicates that the integrated abnormal fluctuation judgment value of this period is about 5.977, this value can be used to judge whether the current fluctuation exceeds the warning line in a threshold system prepared in advance, when the threshold set in the monitoring is for example 5, then A>5 indicates higher than the threshold, so the abnormal state alarm is triggered, when A≤5 it is considered that the abnormal condition has not been reached, and thus the smooth monitoring is performed.

[0122] The acquisition steps of the battery health assessment result are: based on the abnormal state alarm and the battery charge and discharge cycle data, the charging capacity, the discharging capacity, the cycle duration and the associated abnormal state alarm times in each cycle are sorted to obtain a battery charge and discharge cycle data set;

[0123] Based on the battery charge and discharge cycle data set, the battery charge and discharge efficiency is calculated, and the calculation formula is:

[0124]

[0125] Wherein, E is the battery charge and discharge efficiency, D k is the discharge capacity of the kth cycle, C k is the charging capacity of the kth cycle, p is the observation period number, N a is the number of abnormal alarms in the kth cycle, Tavg is the average charge-discharge cycle time length, T max is the longest single charge-discharge cycle time length during the observation period;

[0126] Based on the battery charge-discharge efficiency, the health status of the battery is evaluated, and a battery health evaluation result is generated.

[0127] Specifically, according to the abnormal state alarm obtained in the foregoing and the battery charge-discharge cycle data, the charge-discharge period matching the current device is extracted cycle by cycle and the corresponding start and end time is recorded, and the charge amount data and the discharge amount data occurring in each cycle are summarized from these records, and the start time and the end time of each cycle are registered in the same index system and the cycle time length is calculated, if there are multiple abnormal state alarms for some cycles, the alarm number field is added to the corresponding cycle record, each cycle record is subjected to complete data comparison to confirm whether there is deviation or omission and is subjected to audit against the pre-established valid range, for example, the charging current is compared against the 0A to 5A interval, the voltage is compared against the 0V to 24V interval, and whether the temperature is within the 0℃ to 90℃ interval is combined to confirm whether the current cycle monitoring data is available, when any observation value exceeds the range, the corresponding mark is added to the cycle record and is arranged in time sequence, so that all cycles obtain charge amount, discharge amount and associated abnormal alarm number information, after all cycle data are checked, the sorted information is marked and summarized and arranged according to cycle number for subsequent retrieval, when all required entries are checked, the battery charge-discharge cycle data set is obtained.

[0128] The advantage of the formula is that by comprehensively considering multiple dimensions of elements such as the discharge amount, the charge amount, the abnormal alarm number and the cycle time length distribution of each cycle, a value that can evaluate the overall battery charge-discharge efficiency is calculated, and the correction factor of N a and is introduced in the denominator, so that high-frequency abnormalities or long-time charge-discharge states have a more intuitive impact on the efficiency evaluation.

[0129] D k The acquisition step of the parameter is: D k represents the discharge amount of the kth cycle, which needs to be queried from the battery charge-discharge cycle data set obtained in the foregoing for the actual discharge value corresponding to the cycle, for example, in a battery with a capacity of 10000mAh, a complete discharge process consumes 2800mAh, then D k = 2800, and after continuously collecting a number of cycles, D

[0130] C k The acquisition step of the parameter is: C k represents the charge amount of the kth cycle, and D kThe same is also read from the previously aggregated cycle data set, for example, if 3000 mAh is obtained in the same battery under the current cycle measurement, C k = 3000, and the cumulative data of several cycles can be obtained

[0131] The acquisition step of the p parameter is: p represents the number of observation cycles, which is obtained by counting all complete charging and discharging processes experienced by the battery within a certain time range. The statistical method is to number each valid cycle in the cycle table and perform time correlation. When the same battery is cycled multiple times, it may accumulate several cycles. Generally, p valid cycles can be recorded within one week or longer. Each cycle has information such as starting charging time, ending discharging time, and abnormal alarm times. Finally, the total number of statistics is recorded as p.

[0132] N a The acquisition step of the parameter is: N a represents the number of abnormal alarms in the kth cycle. When viewing cycle data, all alarms triggered in the cycle need to be counted. Alarm information can be associated with the abnormal state alarm obtained earlier. If a total of 3 alarms are recorded in the kth cycle, N a = 3.

[0133] T avg The acquisition step of the parameter is: T avg represents the average charging and discharging cycle length. The start and end time of each cycle needs to be recorded and the length of the cycle is calculated. Then, the length values of all cycles are added and divided by the number of cycles p. For example, there are p = 5 cycles, and the cycle lengths are 3.5 hours, 4 hours, 3.8 hours, 3.9 hours, and 4.1 hours. Then,

[0134] T max The acquisition step of the parameter is: T max represents the longest single charging and discharging cycle length during the observation period. It is found in the same batch of observation cycles that the longest one has a length of T max . It can be obtained by traversing the length of all cycles and comparing the maximum value. Still taking the case of p = 5 cycles as an example, the maximum of the above 5 cycle lengths is 4.1 hours, and T max = 4.1.

[0135] Calculation process:

[0136] An actual example is given below, let p = 3 and obtain from the cycle data set:

[0137] D1 = 2500 mAh, D2 = 2800 mAh, D3 = 2700 mAh;

[0138] C1 = 3000 mAh, C2 = 3100 mAh, C3 = 3050 mAh;

[0139] N a = 4, T avg = 3.5 hours, T max = 4 hours;

[0140] First, calculate the ratio of discharge capacity to charge capacity:

[0141]

[0142] Next, process

[0143]

[0144] Finally, process Multiply the three parts to get: E = 0.874 x 20 x 0.875 ≈ 15.313; This result shows that the battery's charge and discharge efficiency in this example is 15.313 over the three cycles. This is not a pure efficiency value in the sense of a percentage, but a comprehensive evaluation value defined by this formula. If the same calculation is performed for subsequent monitoring or other batteries, a horizontal comparison can be made. If the value is less than 10, it means that the efficiency is low or there are many abnormal alarms in this stage. If it exceeds 20, it means that the overall performance between periods is good or the charge and discharge matching is higher. Further evaluation of the battery health status can be made in combination with other data.

[0145] Based on the battery charge and discharge efficiency calculated above, the efficiency sequence is sorted and compared under the same observation environment. The data rows with large deviations are checked in detail, and the remaining efficiency values are segmented and correlated with the corresponding operating temperature, current, voltage, and load interval. If low efficiency or sudden high abnormal alarms occur in multiple observations, check whether the temperature interval is between 0°C and 90°C, the voltage interval is between 0V and 24V, and the current interval is between 0A and 5A to confirm whether there is an out-of-range operation. Then trace back the possible unstable charging rate points. If no significant abnormalities are found, merge the sample into the final health status score sequence. Here, the scoring calculation method needs to be statistically analyzed and verified with the previous efficiency results. Then add the corresponding score labels to each cycle and divide the health level. The overall evaluation data is compared with historical operation data to obtain the final battery health evaluation result.

[0146] The maintenance scheduling plan is obtained by: based on the battery health evaluation result, calculating the next maintenance replacement time, the calculation formula is:

[0147]

[0148] wherein, T next is the next maintenance replacement time, H max is the battery design life, H current is the current estimated remaining life, C initial is the battery initial capacity, C current is the current capacity, E initial is the battery initial charging efficiency, E current is the current charging efficiency, N cycles is the cumulative number of charge and discharge cycles of the battery.

[0149] Based on the next maintenance replacement time, combined with the maintenance period and replacement strategy, a maintenance and replacement schedule is formulated, and a maintenance scheduling plan is generated.

[0150] Specifically, the formula has the beneficial effect that by introducing the logarithmic relationship of the remaining life, the capacity ratio, the efficiency ratio, and the cumulative number of charge and discharge cycles in the product, the battery state information in different dimensions can be integrated in the same calculation framework, and the specific time of the next maintenance replacement can be further quantified according to the battery health evaluation result.

[0151] H max The acquisition step of the parameter is that: H max represents the battery design life, which needs to be read from the technical specifications or factory standards of the battery model and corrected combined with actual evaluation data to obtain a reference value that can be used within the monitoring period. If the design life is marked on the nameplate as 800 charge and discharge cycles or 2 years of service life, for example, the design life of a certain mobile power supply is about 1.5 years and is converted to 13140 hours, then H max = 13140.

[0152] H current The acquisition step of the parameter is that: H current is the current estimated remaining life, which needs to be detected for the battery multiple times, for example, the estimated remaining life is about 0.9 years, and it is recorded in H current , for example, 0.9 years corresponds to 7884 hours, then H current = 7884.

[0153] C initial The acquisition step of the parameter is that: C initial is the battery initial capacity, which is usually given by the manufacturer or the quality inspection link when it is just out of the factory, for example, a battery with a nominal capacity of 20000mAh can actually reach about 19500mAh in the initial factory detection.

[0154] C currentThe parameter acquisition step is: C current Representing the current capacity, the available capacity of the battery needs to be measured or estimated after the device has been used for a period of time. The measurement method includes recording the discharge amount under certain constant current or constant power discharge conditions and comparing it with the rated capacity. If it is detected after a period of use that the current available capacity is only 17000mAh, then C current = 17000.

[0155] E initial The parameter acquisition step is: E initial Indicates the initial charging efficiency of the battery. It needs to be combined with actual test data or previous multiple charging and discharging experiments on this type of battery to obtain a relatively stable efficiency value. Usually, the average of the measured results of multiple complete charging and discharging in the initial stage can be taken as E initial , for example, if the average charging and discharging efficiency is about 92% after continuously measuring 5 complete charging and discharging cycles and comparing the actual input and output capacity ratio, then E initial = 0.92.

[0156] E current The parameter acquisition step is: E current The current charging efficiency, similar to E initial obtained above, also needs to be obtained through real-time monitoring or the previous evaluation and calculation process. When the efficiency of the battery decreases after a certain period of use, for example, if the average efficiency of the last 10 charging and discharging cycles is only 0.87, then E current = 0.87 and record it.

[0157] N cycles The parameter acquisition step is: N cycles Used to represent the cumulative number of charging and discharging cycles of the battery. It needs to be accumulated for all complete charging and discharging cycles since the start of use. When discharging to the end of the power and charging to full power is considered as a complete cycle, it is accumulated one by one to obtain N cycles , if many cycles have been registered in the monitoring system and the statistical result is 415 times, then N cycles = 415.

[0158] Calculation process:

[0159] The following gives a measured example, the relevant indicators of the same battery monitored after about 8 months of use are brought into the calculation:

[0160] H max = 13140 hours, H current = 7884 hours, C initial = 19500mAh, C current = 17000mAh;

[0161] Einitial = 0.92, E current = 0.87, N cycles = 415;

[0162] First calculate

[0163]

[0164] Second calculate ln(1+N

[0165]

[0166]

[0167] Subsequently

[0168]

[0169] Second calculate ln(1+N cycles ):

[0170] 1 + 415 = 416, ln(416) ≈ 6.03;

[0171] Multiply the above four parts:

[0172] T next = 1.20 x 1.07 x 1.057 x 6.03;

[0173] T next ≈ 1.20 x 1.07 x 1.057 x 6.03 ≈ 8.12;

[0174] If all the quantities here are calculated in hours, then T next ≈ 8.12 hours. The result shows that the next maintenance replacement time obtained by the formula is about 8.12 hours, indicating that under the current monitoring conditions and the known degree of attenuation, the system can be further repaired or replaced after about 8 hours according to the preset maintenance rules. If the value is greater than 24, it may indicate that the system can be maintained for a longer period of time. If the calculated result is less than 1, it indicates that the system needs to be repaired or replaced soon. The subsequent maintenance period can be further split.

[0175] Based on the next maintenance replacement time obtained in the foregoing, the maintenance period is cross-referenced with the replacement strategy. First, adjacent maintenance periods are extracted according to the recorded maintenance period, and the remaining capacity and temperature and other key indicators of the current time period are marked on the same timeline. Then, it is checked whether there is a situation where the current is in the 0A-5A range, the voltage is in the 0V-24V range, and the temperature is in the 0℃-90℃ range. If it is found that the data falls outside the range multiple times, an emergency check step is added to the maintenance plan, and attention is paid to whether the cumulative charge and discharge times continue to grow to the set threshold when comparing multiple period records. These threshold values come from actual operation and maintenance statistics. For example, it is confirmed through years of tracking of the same type of power supply that when the cumulative charge and discharge times approach 500 or more, the probability of rapid capacity decay increases, so it is set as the preliminary threshold for this type of power supply. When the current cumulative number of times has exceeded the above threshold, a specific encryption observation scheme is determined. After completing all cross-referencing, the plan containing important time points and check contents is arranged into a schedule, and the check sequence in the next few days or weeks is arranged according to the strategy being executed. The time points recorded in the schedule are sequentially linked to the maintenance timeline of the system to avoid omissions or conflicts, thereby finally generating a maintenance scheduling plan.

[0176] The obtaining step of the failure prediction analysis result is: based on the impedance adjustment decision and the maintenance scheduling plan, estimating the failure probability of the battery, and the calculation formula is:

[0177]

[0178] Wherein, F represents the failure probability, λ is the baseline failure rate fitted according to the failure data, Da is the number of days since the last maintenance, Ca is the number of charging times since the last maintenance, and Na is the number of discharging times since the last maintenance.

[0179] Based on the failure probability, failure prediction analysis is performed to guide the maintenance and replacement strategy, and the failure prediction analysis result is obtained.

[0180] Specifically, the formula has the beneficial effect that by comprehensively considering the influence of the number of days and the number of charge and discharge times in the exponential term, and setting the two weight values of 0.7 and 0.3 to distinguish the influence of the number of days and the proportion of charge and discharge on the failure rate, both the daily time lapse and the use intensity (based on the number of charge and discharge times) can be considered in the same function form, and the baseline failure rate λ is used as the overall adjustment, so that the entire method can quantify the failure probability according to historical or current data.

[0181] The λ parameter acquisition step is: λ is the baseline failure rate, which is obtained by performing statistical fitting on the collected failure data, the specific method is to first group a large number of known failure cases according to the occurrence time or charge-discharge cycle, and then count the failure frequency in the record, and then use logarithmic linear regression or exponential distribution fitting to obtain the failure rate, in the example, the following formula can be used: Where f i is the cumulative frequency of failure in the ith batch, t i is the observation time or usage cycle number of the batch, and M is the number of batches included. After integrating these samples, a more stable λ can be obtained and recorded as the baseline failure rate. For example, after tracking and statistics of 100 batteries for 2 years, it is found that the average annual failure frequency of the sample population is 0.015 / hour, then λ = 0.015.

[0182] The Da parameter acquisition step is: Da represents the number of days since the last maintenance, which needs to be found in the maintenance record to calculate the exact date of the last maintenance and subtract the current date, which can be calculated by a time difference operation to obtain the complete number of days. If the last maintenance date is March 1, 2025, and the current date is March 15, 2025, then Da = 14 days.

[0183] The Ca parameter acquisition step is: Ca is the number of charges since the last maintenance, and each complete charging process from low to full state can be considered as an additional charge count. In the example, if 7 complete charging processes are observed in the past 14 days, then Ca = 7.

[0184] The Na parameter acquisition step is: Na represents the number of discharges since the last maintenance, which is similar to Ca and is also accumulated by counting the discharge processes. If there are 6 complete discharges in the past 14 days, then Na = 6.

[0185] The setting of the weights 0.7 and 0.3 is based on: In the formula, the former factor 0.7 multiplied by Da mainly represents the influence of time factor on failure rate, and the latter factor 0.3 multiplied by mainly measures the relative strength of charge-discharge behavior in the same time, and the sum is multiplied by λ to obtain the exponential term. The weights 0.7 and 0.3 need to be analyzed for sensitivity after sampling a large number of failure cases, and it is determined that the time lapse has a greater impact on the failure rate, so the value is 0.7, and the charge-discharge ratio is supplemented by 0.3. If different types or models of equipment are used, regression fitting can be done again to get slightly different weight values. In the example, 0.7 and 0.3 are still maintained to be compatible with the existing data system.

[0186] Calculation process:

[0187] An example of actual measurement is as follows:

[0188] λ = 0.015, Da = 14, Ca = 7, Na = 6;

[0189] First, calculate the product of 0.7 and Da:

[0190] 0.7 x 14 = 9.8;

[0191] Then, calculate the product of 0.3 and

[0192]

[0193] 0.3 x 1.167 ≈ 0.350;

[0194] Add them together:

[0195] 9.8 + 0.350 = 10.15;

[0196] Multiply by λ:

[0197]

[0198] Plug into the index:

[0199] e -0.152 ≈ 0.859;

[0200] F = 1 - 0.859 = 0.141;

[0201] This result shows that the current failure probability F is about 0.141, indicating that under the monitoring conditions at this time, the possibility of system failure is about 14.1%. If subsequent detection shows that increases or the time interval significantly lengthens, the calculated F will increase accordingly, and when this value exceeds 0.5 or higher, it often needs to be recorded in the high-risk list for timely follow-up and processing.

[0202] According to the failure probability obtained in the foregoing, the impedance adjustment decision for the battery is compared with the maintenance scheduling plan item by item to check whether the necessary monitoring and inspection have been completed in the current operation and maintenance period. First, all the time stamps in the past week are extracted from the full use record, and it is checked whether the current, voltage and temperature data are outside the pre-established 0A to 5A current range, 0V to 24V voltage range and 0℃ to 90℃ temperature range. When any monitoring record exceeds these ranges, a data entry that needs to be investigated additionally is formed. Then, all the abnormal entries in the past week are compared with the recent trend curve of the failure probability. If the abnormal entries obviously increase in the same day and the failure probability increases by a large margin, it is recorded as a notice event, and more intensive observation time points are added to the maintenance list. If no obvious out-of-range record is found in the comparison process and the failure probability is at a relatively low level, the original maintenance replacement strategy is retained, and the period is directly regarded as a normal observation period. After checking all the final records, a failure prediction analysis result is sorted out, and the next work plan is recorded.

Claims

1. A smart overload protection and failure prediction system suitable for mobile power supply, characterized in that, The system comprises: A real-time impedance monitoring module continuously monitors the internal impedance of the battery during the use of the mobile power supply, generates a real-time impedance analysis result, adjusts the charging current according to the real-time impedance analysis result, matches the real-time state of the battery, and generates an impedance adjustment decision; A voltage and current anomaly detection module continuously monitors the voltage and current data of the battery, compares the preset normal working range, generates a voltage and current monitoring result, judges whether voltage or current abnormal fluctuation is detected based on the voltage and current monitoring result, and immediately issues an abnormal warning if abnormal fluctuation is detected to generate an abnormal state alarm; A battery maintenance scheduling module calculates the charging and discharging efficiency of the battery according to the abnormal state alarm and the charging and discharging cycle of the battery, generates a battery health evaluation result, and obtains the maintenance and replacement schedule of the battery according to the battery health evaluation result to generate a maintenance scheduling plan; A fault prediction and alarm module combines the impedance adjustment decision and the maintenance scheduling plan, uses a statistical model to predict and estimate the battery faults that occur, and generates a fault prediction analysis result. The impedance adjustment decision is obtained by calculating the charging current based on the real-time impedance analysis result, and the calculation formula is: ； wherein, represents the adjusted charging current, represents the initial charging current, represents the adjustment sensitivity parameter, represents the current impedance value, represents the rate of change of impedance, represents the battery temperature, and is an adjustment parameter to reflect the effect of the rate of change of impedance and temperature on the charging current, represents the reference impedance value; Based on the adjusted charging current, the state of the battery is adjusted to form and implement the impedance adjustment decision.

2. The intelligent overload protection and failure prediction system suitable for mobile power supply as claimed in claim 1 wherein, The real-time impedance analysis result is obtained by continuously collecting the internal impedance changes of the battery under various use states, recording the impedance value of each collection, and obtaining the battery impedance raw data; Based on the battery impedance raw data, the average impedance value in each window is calculated by applying a sliding window average, data points exceeding twice the average impedance value are identified and removed, and a real-time impedance analysis result is generated.

3. The intelligent overload protection and failure prediction system suitable for mobile power supply as claimed in claim 1 wherein, The voltage and current monitoring result is obtained by real-time recording of the voltage and current data of the mobile power supply under various working states to form a battery voltage and current raw data set; Based on the battery voltage and current raw data set, all abnormal points exceeding the normal working voltage and current range are identified and marked by comparing each data point with the preset normal working voltage and current range to form a voltage and current monitoring result.

4. The intelligent overload protection and failure prediction system suitable for mobile power supply as claimed in claim 1 wherein, The abnormal state alarm is obtained by receiving the voltage and current monitoring result, analyzing the voltage and current data at all time points, extracting the measurement value at each time point, and calculating the voltage change, current change, battery temperature, battery load state, battery capacity remaining ratio, and environment temperature at each time point to establish a voltage and current fluctuation data set after sorting; Based on the voltage and current fluctuation data set, the abnormal fluctuation judgment value is calculated, and the calculation formula is: ； Where A is the abnormal fluctuation judgment value. Let be the voltage value measured in the i-th measurement. For the first The current value measured this time. The mean of the current measurements is given by T, where m and n are the number of voltage and current measurements, respectively. cell For battery temperature, T e Where L is the ambient temperature, C is the current state of battery load, and L is the current state of battery load. r This represents the percentage of remaining battery capacity. The maximum voltage value within the sampling period. The minimum voltage value within the sampling period. For the first The voltage value measured this time; Based on the abnormal fluctuation judgment value, it is detected whether the set threshold is exceeded, and if it is exceeded, the abnormal state alarm is triggered.

5. The intelligent overload protection and failure prediction system suitable for mobile power supply as claimed in claim 1 wherein, The battery health evaluation result is obtained by sorting the charging amount, discharging amount, cycle length, and associated abnormal state alarm times in each cycle based on the abnormal state alarm and the charging and discharging cycle data of the battery to obtain a battery charging and discharging cycle data set; Based on the battery charging and discharging cycle data set, the battery charging and discharging efficiency is calculated, and the calculation formula is: ； wherein E is the battery charge-discharge efficiency, D k is the discharge amount of the kth cycle, C k is the charge amount of the kth cycle, P is the number of observation cycles, N a is the number of abnormal alarms in the kth cycle, T avg is the average charge-discharge cycle time, T max is the longest single charge-discharge cycle time during the observation period; Based on the battery charging and discharging efficiency, the health state of the battery is evaluated, and a battery health evaluation result is generated.

6. The intelligent overload protection and failure prediction system suitable for mobile power supply as claimed in claim 1 wherein, The obtaining step of the maintenance scheduling plan is: based on the battery health evaluation result, the next maintenance replacement time is calculated, and the calculation formula is: ； where T next is the next maintenance replacement time, H max is the battery design life, H current is the current estimated remaining life, C initial is the battery initial capacity, C current is the current capacity, E initial is the battery initial charge efficiency, E current is the current charge efficiency, N cycles is the cumulative number of charge and discharge cycles for the battery; Based on the next maintenance replacement time, the maintenance and replacement schedule is formulated in combination with the maintenance period and the replacement strategy, and a maintenance scheduling plan is generated.

7. The intelligent overload protection and failure prediction system suitable for mobile power supply as claimed in claim 1 wherein, The obtaining step of the failure prediction analysis result is: based on the impedance adjustment decision and the maintenance scheduling plan, the estimated failure probability of the battery is estimated, and the calculation formula is: ； wherein F represents a failure probability, is a baseline failure rate fitted according to failure data, Da is the number of days since the last maintenance, Ca is the number of charging times since the last maintenance, and Na is the number of discharging times since the last maintenance. Based on the failure probability, failure prediction analysis is performed to guide the maintenance and replacement strategy, and a failure prediction analysis result is obtained.

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