Remote Monitoring and Analysis System for Rechargeable Mobile Power Stations

Abstract

The present invention relates to the technical field of battery management, specifically a remote monitoring and analysis system for a rechargeable mobile power station. The system includes a battery pack status monitoring module, a monitoring path adjustment module, a partition monitoring and energy scheduling module, an energy distribution module, and a charging efficiency optimization monitoring module. In the present invention, through refined battery-level monitoring and health status analysis, the real-time performance and accuracy of battery management are improved. The voltage, current, and SOC parameters of the battery are monitored in real time, the health status of the battery is comprehensively evaluated, the monitoring path and monitoring frequency are dynamically adjusted to ensure the maximization of resource allocation and efficiency, the energy distribution differences between battery packs are accurately identified, overcharging and over-discharging are avoided by adjusting the charge and discharge flow rates, the stability and lifespan of the battery are improved, the charging current distribution and charging plan are optimized to achieve efficient charging, the energy utilization efficiency is improved, the overall system response speed and accuracy are enhanced, and a more intelligent and personalized solution is provided to meet the requirements of different application scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and particularly to a remote monitoring and analysis system for a rechargeable mobile power station. Background Art

[0002] The technical field of battery management includes related technical contents for monitoring, controlling, and maintaining a battery system. The core contents of this technical field include the management and optimization of key parameters such as the charging state, discharging performance, remaining power, and service life of the battery. The technical field of battery management as a whole involves the detection, protection, and charge-discharge control of battery performance, and realizes the accurate assessment of the battery state through precise data collection and analysis. It is widely applied in fields such as portable devices, energy storage systems, and electric transportation tools. In order to meet the requirements for battery performance in different scenarios, battery management technology combines hardware and software collaborative design to achieve real-time monitoring and comprehensive management of the battery operating state.

[0003] Among them, the remote monitoring and analysis system for a rechargeable mobile power station refers to an integrated system for battery charging and monitoring equipment of a mobile power station, which mainly covers remote monitoring and data analysis functions for the battery charging state, power information, and health parameters. The system completes the collection and analysis of real-time data such as battery voltage, current, and temperature through a built-in battery state monitoring module, and realizes remote data transmission based on wireless communication technology. The system also includes an analysis algorithm based on a battery model for evaluating the battery health state and fault warning. The entire system relies on the combination of an embedded hardware platform and a communication interface to achieve unified scheduling of charging equipment and real-time monitoring of the battery state.

[0004] Existing technologies in battery management systems mostly rely on regular monitoring and preset algorithms for charging control, lacking dynamic adjustment for different battery levels and real-time battery health states. The evaluation of battery health in existing technologies is carried out through a single monitoring frequency and parameters, and fails to make timely adjustments according to the specific state of the battery. When the system faces different working states of the battery, it cannot accurately judge the battery state and give timely fault warnings, resulting in phenomena such as overcharging and over-discharging of the battery, and even affecting the stability and safety of the system. The battery energy distribution in existing technologies is relatively simple and cannot cope with the differences between batteries and diverse battery states, resulting in over-discharge or over-charging of some batteries, further affecting the battery service life and system performance. The charge-discharge process of the battery fails to adjust according to real-time data, causing some batteries to encounter high loads or ineffective charging, resulting in uneven energy utilization, thereby affecting the efficiency and stability of the entire battery system. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose a remote monitoring and analysis system for a rechargeable mobile power station.

[0006] To achieve the above object, the present invention adopts the following technical solutions: The rechargeable mobile power station remote monitoring and analysis system includes:

[0007] The battery pack status monitoring module monitors each level in real time based on the battery cell, battery module, and battery pack levels, extracts key parameters such as voltage, current, and SOC, identifies the battery health status, evaluates the stability of the battery pack, adjusts the monitoring priority and level monitoring indicators, and generates a level monitoring data set;

[0008] The monitoring path adjustment module dynamically excludes dead zones based on the level monitoring data set, adjusts the monitoring path according to the battery health status, analyzes and adjusts the monitoring frequency of different battery levels, and generates an optimized monitoring path result;

[0009] The partition monitoring and energy scheduling module analyzes the energy requirements of the battery partitions based on the optimized monitoring path result, calculates the energy distribution difference value between battery packs by monitoring the current and voltage fluctuations of the partition battery packs, adjusts the battery charge and discharge flow, eliminates overcharging and over-discharging phenomena, and generates an optimized energy scheduling result;

[0010] The energy distribution module identifies the charging requirements of each battery module based on the optimized energy scheduling result, adjusts the distribution of the charging current, reasonably distributes the charging flow, and generates a charging current distribution data set;

[0011] The charging efficiency optimization monitoring module adjusts the charging plan according to the real-time working environment of the battery based on the charging current distribution data set, analyzes the battery status in different charging scenarios, dynamically optimizes the charging parameters, and generates a mobile power station efficiency monitoring and analysis plan.

[0012] As a further solution of the present invention, the specific steps for obtaining the battery health status are as follows:

[0013] Based on the battery cell, battery module, and battery pack levels, capture real-time voltage, current, and SOC data, monitor the parameter changes of each level, and record the voltage, current, and SOC values of the battery levels to obtain the battery pack voltage, current, and SOC data;

[0014] Based on the battery pack voltage, current, and SOC data, perform time series alignment and calibration, eliminate data deviations caused by external environment changes and equipment errors, compare the data using timestamps, check the change trends of data in different time periods, and generate a battery pack data timeliness calibration result;

[0015] Based on the calibration result of the timeliness of the battery pack data, analyze the health status of the battery cells. By comparing the volatility of voltage and SOC, determine whether there are abnormal conditions in the battery. Based on the current change between each cell in the battery pack, evaluate the battery health level to obtain the health status of the battery pack.

[0016] As a further solution of the present invention, the step of obtaining the hierarchical monitoring data set is specifically as follows:

[0017] Evaluate the stability of the battery pack according to the battery health status, capture the state parameters of the battery pack, determine the health status of the battery monomers, and perform weighted processing through the working environment, temperature, and load factors of the battery pack to obtain the battery pack health status value;

[0018] Execute the analysis of the battery pack health status value, compare the battery pack health status with the original stability data to judge the change trend, and perform regression analysis on the current state using the original stability score. The formula is:

[0019] ;

[0020] Calculate the battery pack stability score;

[0021] Among them, represents the battery pack stability score, is the weight of the i-th battery monomer, is the health status of the i-th battery monomer, is the environmental temperature factor, is the influence coefficient of the original stability score, is the original stability score, is the total number of battery monomers;

[0022] According to the battery pack stability score, adjust the monitoring priority and hierarchical monitoring indicators, set the monitoring priority conditions, perform weighted allocation on the monitoring indicators, and use hierarchical monitoring for dynamic adjustment to generate a hierarchical monitoring data set.

[0023] As a further solution of the present invention, the step of obtaining the monitoring path optimization result is specifically as follows:

[0024] Based on the hierarchical monitoring data set, extract the health data of the battery monomers, including the temperature, charge state, discharge efficiency, and internal resistance parameters of the battery, combine with the working conditions of the battery pack for analysis, and perform preliminary monitoring on the battery monomers according to the battery health status to obtain the baseline monitoring frequency data;

[0025] Based on the baseline monitoring frequency data, analyze and adjust the differential monitoring frequencies of different battery cells at different levels, increasing the monitoring frequency for battery cells with high priority and decreasing the monitoring frequency for battery cells with low priority. Combining with the changing trend of the battery health state, use the formula:

[0026] ;

[0027] to obtain the adjusted monitoring frequency data set;

[0028] wherein, is the adjusted monitoring frequency data, is the preliminary monitoring frequency, is the health state of the i-th battery cell, is the average value of the battery pack health state, is the operating temperature of the i-th battery cell, is the average temperature of the battery pack, is the adjustment coefficient;

[0029] According to the adjusted monitoring frequency data set, by dynamically excluding the dead zones in the battery pack, adjust the monitoring path, eliminate the battery cells with good health state, analyze the battery cells with poor health state, and generate the optimized monitoring path result.

[0030] As a further solution of the present invention, the specific steps for obtaining the energy distribution difference value between battery packs are as follows:

[0031] Based on the optimized monitoring path result, by monitoring the fluctuations of the battery pack current and voltage, extract the real-time operation data of the battery partitions, analyze the current, voltage, and power output values of the partitioned battery packs, and combine the voltage and current data fluctuation trends between battery packs to identify the energy requirements of each partition, so as to obtain the battery partition energy requirement data;

[0032] According to the battery partition energy requirement data, perform energy distribution, combine the energy requirements of the battery packs, compare the current and voltage fluctuations between battery packs, and use the formula:

[0033] ;

[0034] to calculate the energy distribution difference value between battery packs;

[0035] wherein, represents the energy distribution difference value between battery packs, is the voltage of the k-th battery pack, is the current of the k-th battery pack, is the total power of the battery pack, represents the number of battery packs.

[0036] As a further solution of the present invention, the steps for obtaining the optimized energy scheduling result are specifically as follows:

[0037] Based on the energy distribution difference value between the battery packs, capture the charge and discharge data of the battery packs, monitor the current and voltage changes of the battery packs, record the power status of each battery pack in real time, evaluate the energy distribution difference, and obtain the battery pack energy difference data;

[0038] Based on the battery pack energy difference data, analyze the current demand of the battery packs, combine with the real-time load of the batteries, evaluate the charge and discharge requirements of each battery pack, analyze the operation range of adjusting the current by comparing the voltage and current changes of the differential battery packs, and obtain the adjustment range of the battery pack charge and discharge requirements;

[0039] Based on the adjustment range of the battery pack charge and discharge requirements, adjust the charge and discharge flow of the battery packs, adjust the charge and discharge current of each battery pack in real time according to the load and difference of the battery packs, eliminate the phenomenon of overcharging and over-discharging, and generate the optimized energy scheduling result.

[0040] As a further solution of the present invention, the steps for obtaining the charging current distribution data set are specifically as follows:

[0041] Based on the optimized energy scheduling result, extract the charging requirements of each battery module from the data of the battery partition, including the current demand of the battery module, the current charging status, the discharge efficiency, and the remaining power, compare and analyze the charging requirements of the battery modules, and determine the magnitude of the charging current required for each module to obtain the battery module charging requirement data;

[0042] Based on the battery module charging requirement data, perform the distribution of the charging current. Combine the charging requirements of each battery module and the load condition of the battery pack, and use the formula:

[0043] ;

[0044] Calculate the adjusted charging current;

[0045] Wherein, represents the adjusted charging current, is the initial charging current, is the charging requirement of the kth battery module, is the total charging capacity of the battery pack, is the remaining power of the battery module, is the average remaining power of the battery pack;

[0046] According to the adjusted charging current, perform a reasonable distribution of the charging current, increase the charging current for the battery modules with high charging requirements, and decrease the charging current for the battery modules with low charging requirements to generate the charging current distribution data set.

[0047] As a further solution of the present invention, the acquisition steps of the mobile power station efficiency monitoring and analysis solution are specifically as follows:

[0048] Based on the charging current distribution data set, extract the charging current, voltage, ambient temperature, and battery internal resistance parameters and classify them. Normalize the parameters according to the battery performance characteristic data, analyze the extreme value range within the parameter change interval, and establish a data set of the battery working environment and performance characteristics;

[0049] Based on the data set of the battery working environment and performance characteristics, extract the change range of parameters in different charging scenarios, compare them in combination with the battery performance characteristics in the scenarios, classify the parameter combination relationships in the scenarios, and construct a parameter mapping table for different charging scenarios;

[0050] Based on the parameter mapping table for different charging scenarios, adjust the real-time distribution plan of the charging current and voltage according to the parameter combination relationship in the scenario, monitor and optimize the battery performance data during the charging process, analyze the charging efficiency and the change of the battery state, and generate a mobile power station efficiency monitoring and analysis solution.

[0051] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0052] In the present invention, through refined battery-level monitoring and health status analysis, the real-time performance and accuracy of battery management are improved. The batteries at different levels are monitored in real time, combined with key parameters such as voltage, current, and SOC to ensure a comprehensive assessment of the battery health status. By dynamically adjusting the monitoring path, based on the actual health status of the battery, the monitoring frequency is optimized to ensure the reasonable allocation of resources and the maximization of efficiency. The energy distribution difference between battery packs is accurately identified. By adjusting the battery charge and discharge flow, overcharging and over-discharging phenomena are avoided, effectively improving the stability and lifespan of the battery pack. By reasonably allocating the charging current and optimizing the charging plan, efficient charging of the battery is achieved, and the overall energy utilization efficiency of the system is maximized. It not only improves the response speed and accuracy of the system, but also comprehensively improves the performance of the battery system by adjusting the monitoring strategy and optimizing the energy scheduling, providing a more intelligent and personalized solution to meet the requirements of different application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is the system flow chart of the present invention;

[0054] Figure 2 is the flow chart of the battery health status in the present invention;

[0055] Figure 3 is the flow chart of the hierarchical monitoring data set in the present invention;

[0056] Figure 4 It is a flowchart for optimizing the monitoring path in the present invention;

[0057] Figure 5 It is a flowchart for the energy distribution difference value between battery packs in the present invention;

[0058] Figure 6 It is a flowchart for the optimized energy scheduling result in the present invention;

[0059] Figure 7 It is a flowchart for the charging current distribution data set in the present invention;

[0060] Figure 8 It is a flowchart for the mobile power station efficiency monitoring and analysis solution in the present invention. Detailed implementation manners

[0061] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0062] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more unless otherwise specifically defined.

[0063] Please refer to Figure 1 , the rechargeable mobile power station remote monitoring and analysis system includes:

[0064] The battery pack status monitoring module monitors each level in real time based on the battery cell, battery module, and battery pack levels, extracts key parameters such as voltage, current, and SOC, identifies the battery health status, evaluates the battery pack stability, adjusts the monitoring priority and level monitoring indicators, and generates a level monitoring data set;

[0065] The monitoring path adjustment module dynamically excludes dead zones based on the level monitoring data set, adjusts the monitoring path according to the battery health status, analyzes and adjusts the monitoring frequency of different battery levels, and generates an optimized monitoring path result;

[0066] Based on the optimized monitoring path results, the partition monitoring and energy scheduling module analyzes the energy requirements of the battery partitions, calculates the energy distribution difference value between battery packs by monitoring the current and voltage fluctuations of the partition battery packs, adjusts the battery charge and discharge flow rates, eliminates overcharging and over-discharging phenomena, and generates optimized energy scheduling results;

[0067] Based on the optimized energy scheduling results, the energy distribution module identifies the charging requirements of each battery module, adjusts the distribution of the charging current, reasonably distributes the charging flow rate, and generates a charging current distribution data set;

[0068] Based on the charging current distribution data set, the charging efficiency optimization monitoring module adjusts the charging plan according to the real-time working environment of the battery, analyzes the battery status in different charging scenarios, dynamically optimizes the charging parameters, and generates a monitoring and analysis plan for the mobile power station efficiency.

[0069] The battery health status includes voltage, current, and SOC parameters. The hierarchical monitoring data set includes the monitoring data of battery cells, battery modules, and battery packs. The optimized monitoring path results include monitoring path adjustment, monitoring frequency adjustment, and dead zone elimination. The energy distribution difference value between battery packs includes current fluctuation, voltage fluctuation, and energy difference. The optimized energy scheduling results include charge and discharge flow rate adjustment and overcharging / over-discharging elimination. The charging current distribution data set includes the charging requirements and current distribution of each battery module. The monitoring and analysis plan for the mobile power station efficiency includes charging plan adjustment and charging parameter optimization.

[0070] Please refer to Figure 2 , and the specific steps for obtaining the battery health status are as follows:

[0071] Based on the battery cell, battery module, and battery pack levels, capture real-time voltage, current, and SOC data, monitor the parameter changes at each level, and record the voltage, current, and SOC values of the battery levels to obtain the battery pack voltage, current, and SOC data;

[0072] By setting the sampling frequency of each battery cell, battery voltage, current, and SOC (state of charge) data are obtained in real time to form data sets at each level. At the battery cell level, the data acquisition system regularly obtains the voltage, current, and SOC values of individual cells through sensors and transmits them to the module control unit. At the battery module level, by integrating the monitoring information of multiple individual cells, the overall voltage, current, and SOC of the module are calculated to further provide more macroscopic battery state data. After integrating the data of each battery module at the battery pack level, the battery pack voltage, current, and SOC data are obtained, thus providing data support for the management and monitoring of the battery pack. During the data acquisition process, a time synchronization mechanism is adopted to ensure that each data point accurately corresponds to the acquisition time and avoid inconsistencies caused by data acquisition delays at different levels. It is also necessary to monitor parameters such as the temperature and humidity of the external environment to prevent environmental factors from causing deviations in battery performance data and ensure the accuracy of the data.

[0073] Based on the battery pack voltage, current, and SOC data, perform time series alignment and calibration to eliminate data deviations caused by external environmental changes and equipment errors. Use timestamps to compare the data and check the change trends of data in the differential time period to generate the calibration result of the timeliness of the battery pack data.

[0074] Timestamp the data collected from each battery level (individual cell, battery module, battery pack) to ensure the synchronization of data from different sources and avoid analysis errors caused by inconsistent data acquisition times. Identify the deviations caused by environmental changes and equipment errors (such as sensor drift, temperature changes) between different data sources. Adopt the method of time series alignment to adjust the battery data at different time periods to a unified time scale. For the differences in data trends within different time periods, conduct a comparative analysis. Use smoothing processing based on a time window to eliminate short-term fluctuations and extract the trend changes of data in the long cycle, thereby eliminating the interference caused by equipment errors or external environmental impacts. Combine the known standard battery characteristics to perform data calibration and correct the data deviation through adjustment coefficients. Finally, output the calibration result of the timeliness of the battery pack data, providing more accurate time dimension support for the current health state of the battery pack in the battery and ensuring the reliability of the data in the subsequent analysis process.

[0075] Based on the calibration result of the timeliness of the battery pack data, analyze the health state of the battery cells. By comparing the volatility of voltage and SOC, determine whether there are abnormal conditions in the battery. Evaluate the battery health level based on the current changes between each unit in the battery pack to obtain the health state of the battery pack.

[0076] For the calibrated battery pack voltage, current, and SOC data, by comparing the voltage and SOC fluctuations of battery cells, modules, and the battery pack, evaluate whether there are any signs of battery degradation or anomalies. For battery cells with large voltage fluctuations, it is due to internal battery faults or aging, and such batteries will affect the performance of the entire battery pack, so they need to be closely monitored. For battery cells with large and unstable SOC fluctuations, it indicates a decrease in the charging efficiency of the battery or irreversible damage inside the battery, and further inspection and evaluation are necessary. It is necessary to monitor the current changes of each cell within the battery cell. A sharp fluctuation in current is a signal of internal short circuit, connection failure, or battery damage in the battery. Compare the current data of all battery cells to evaluate the overall health level of the battery pack, obtain the health status of the battery pack, and can also classify the health status of each battery cell, so as to provide a scientific basis for the maintenance and replacement of the battery pack. Identify potential problems in advance through an early warning system to avoid adverse effects on the operation of the entire power station caused by battery failures.

[0077] Please refer to Figure 3 , and the specific steps for obtaining the hierarchical monitoring data set are as follows:

[0078] Evaluate the stability of the battery pack based on the battery health status, capture the state parameters of the battery pack, determine the health status of the battery cells, and perform weighted processing through the working environment, temperature, and load factors of the battery pack to obtain the battery pack health status value;

[0079] First, it is necessary to obtain the health status of the battery by monitoring data such as the temperature, charge or discharge cycle, current, and voltage of the battery pack. When evaluating the health degree of battery cells, the parameters can be weighted and calculated to ensure that the changes in temperature and load are reasonably considered. The state of the battery can be obtained through simple calculations. For example, the estimation of the health value can be converted by combining the temperature and the load capacity of the battery. For instance, the battery will age faster at high temperatures, and an increase in current will also exacerbate internal losses. According to the indicators, obtain the quantitative value of the battery health status through experimental data, and finally obtain a total health score value based on the state of the battery cells. The weight values can be set to combine various monitoring indicators, and finally obtain the overall health status score of the battery pack. Based on the evaluation results, further provide the necessary input data for the battery pack stability score. The weight setting in this process should consider multiple factors such as ambient temperature, load, and usage time, and make dynamic adjustments according to different battery types and original data, and finally obtain the battery pack health status value.

[0080] Perform an analysis of the battery pack health status value, compare the battery pack health status with the original stability data, judge the change trend, and perform a regression analysis on the current state using the original stability score. Use the formula:

[0081] ;

[0082] Calculate the stability score of the battery pack;

[0083] Among them, represents the stability score of the battery pack, is the weight of the i-th battery cell, is the health state of the i-th battery cell, is the environmental temperature factor, is the influence coefficient of the original stability score, is the original stability score, is the total number of battery cells;

[0084] The benefit of the formula is that by introducing the weighted average of factors such as the health state of the battery cell and the environmental temperature, and combining with the original stability score, the current stability assessment is more accurate and dynamic, and can reflect the performance of the battery pack under different environmental conditions;

[0085] In this formula, represents the stability score of the battery pack, represents the weight of the i-th battery cell, represents the health state of the i-th battery, represents the environmental temperature factor, is the influence coefficient of the original stability score, represents the original stability score;

[0086] Laboratory test data is used to evaluate the state of health (SOH) of the battery and the original stability score ( ), mainly including charge and discharge cycle tests, capacity fade tests, internal resistance measurements, and environmental temperature impact tests. The charge and discharge cycle test is set at 500 cycles, with an initial capacity of 100% and a final capacity of 90%, a decay of 10%. The capacity fade test shows an initial capacity of 50 Ah, which drops to 45 Ah after 500 cycles, and the capacity retention rate is 90%, which is used to calculate the state of health . The internal resistance measurement results show that the initial internal resistance is 2.0 mΩ, which rises to 2.5 mΩ after 500 cycles, an increase of 25%, indicating battery aging. The environmental temperature impact test shows that under the conditions of 25 °C, 28 °C, and 30 °C, the battery state of health is 95%, 90%, and 85% respectively.

[0087] is the total number of battery cells, and three battery cells are set, ;

[0088] Set the state of health of the battery , , , and the value is the battery health value calculated from the experimental data;

[0089] Assume the ambient temperature , , , and the temperature value is obtained through a real-time monitoring device;

[0090] Set the weight , , , and the weight is based on a comprehensive consideration of the battery capacity and service life. Newer batteries have a higher weight;

[0091] Assume the original stability score , , and this coefficient is set based on the past performance of the battery pack;

[0092] By substituting the values, the formula calculation process is as follows:

[0093] ;

[0094] ;

[0095] ;

[0096] ;

[0097] The calculation results show that the stability score of the battery pack is 8.39, indicating that the overall operation of the battery is stable and the health status is good. A score higher than 8.0 indicates that the battery is still in an excellent state and does not require emergency maintenance. Future optimization can predict the change trend of the health status through regression analysis of the original data, and reduce the influence of the ambient temperature through temperature control management (controlled below 25°C). In addition, if the stability score continues to decline, preventive maintenance or battery replacement can be carried out in advance when it drops below 7.5. This calculation method combines laboratory data to make the stability assessment more accurate, which can be used to optimize the battery management strategy and improve the battery life and operation reliability.

[0098] According to the stability score of the battery pack, adjust the monitoring priority and hierarchical monitoring indicators, set the monitoring priority conditions, perform weighted allocation on the monitoring indicators, and use hierarchical monitoring for dynamic adjustment to generate a hierarchical monitoring data set;

[0099] First, it is necessary to determine the threshold of the battery pack stability score. If the stability score of the battery pack is lower than the set threshold, the monitoring frequency should be increased, and priority should be given to monitoring the battery cells with poor health status or high load. The monitoring indicators should be weighted according to the overall health status of the battery pack. If the battery pack stability score is high, the monitoring frequency can be reduced, and the focus can be on monitoring the performance of the key-node batteries. The original stability data can be used as a reference, and the original data can be obtained by comparing past monitoring records to further adjust the monitoring strategy to ensure that the key monitoring areas are always effectively concerned. Different sensors are used to obtain real-time data, and the monitoring priorities are dynamically adjusted based on the data feedback. For example, in extreme environments, the change in battery temperature will directly affect the assessment of the battery health status. Therefore, in scenarios with large temperature fluctuations, the priority of the temperature sensor data will be increased. Finally, through dynamic adjustment of the hierarchical monitoring model, a hierarchical monitoring data set is generated to ensure that the performance of each battery cell is fully concerned.

[0100] Please refer to Figure 4 , and the steps for obtaining the optimized monitoring path result are specifically as follows:

[0101] Based on the hierarchical monitoring data set, extract the health data of the battery cells, including the temperature, charge state, discharge efficiency, and internal resistance parameters of the battery. Analyze in combination with the working conditions of the battery pack, and conduct preliminary monitoring of the battery cells according to the battery health status to obtain the baseline monitoring frequency data;

[0102] First, it is necessary to obtain the health status of the battery by monitoring data such as the temperature, charge state, discharge efficiency, and internal resistance of the battery pack. The battery health value can be calculated based on the relationship between the internal resistance, temperature, and load of the battery. The increase in temperature will accelerate the aging of the battery, and the internal resistance of the battery gradually increases during use. Therefore, by real-time monitoring the parameters of the battery and comparing with the trend of the original data, the battery health status can be judged. Combining the health status of the battery cells, specific parameters (such as internal resistance, charge efficiency, etc.) are used to weight them, and combined with the working environment of the battery pack, the health value of each battery cell is calculated to obtain the baseline monitoring frequency data, and then the monitoring frequency of the battery is initially set. The battery cells with poor health status will be given a higher monitoring frequency, while the batteries with better health will be assigned a lower monitoring frequency to ensure the accuracy and efficiency of real-time monitoring.

[0103] According to the baseline monitoring frequency data, analyze and adjust the differentiated monitoring frequencies of the differentiated battery cells at different levels. Increase the monitoring frequency for the battery cells with high priority and decrease the monitoring frequency for the battery cells with low priority. Combining with the changing trend of the battery health status, use the formula:

[0104] ;

[0105] Obtain the adjusted monitoring frequency dataset;

[0106] Among them, is the adjusted monitoring frequency data, is the preliminary monitoring frequency, is the health state of the i-th battery cell, is the average value of the health state of the battery pack, is the operating temperature of the i-th battery cell, is the average temperature of the battery pack, is the adjustment coefficient;

[0107] The advantage of the formula is that by introducing the differences in the health states of the battery cells and the changes in the operating temperature, the adjustment of the monitoring frequency is optimized, enabling batteries with poor health conditions and large temperature fluctuations to obtain more monitoring resources to ensure real-time feedback of their health changes;

[0108] In this formula, is the adjusted monitoring frequency, is the preliminarily set monitoring frequency, is the health state of the i-th battery, is the average health state of the battery pack, is the temperature of the i-th battery, is the average temperature of the battery pack, is the adjustment coefficient used to reflect the impact of the health state difference on the monitoring frequency;

[0109] Assume that there are 3 battery cells in the battery pack, and the preliminary monitoring frequency is set to times per hour, and the battery health state , , , the ambient temperature , , , the average health state of the battery pack , the average temperature , the adjustment coefficient ;

[0110] Calculate the impact of the health state difference and temperature difference of each battery on the frequency:

[0111] ;

[0112] ;

[0113] ;

[0114] ;

[0115] ;

[0116] ;

[0117] Calculate the adjusted monitoring frequency:

[0118] ;

[0119] ;

[0120] ;

[0121] This result shows that after the monitoring frequency is adjusted, batteries with poor health status and higher temperatures receive more monitoring resources, while batteries with better health status and lower temperatures are moderately monitored, ensuring the reasonable allocation of monitoring resources.

[0122] According to the adjusted monitoring frequency dataset, by dynamically excluding the dead zones in the battery pack, adjusting the monitoring path, removing the battery cells with good health status, and analyzing the battery cells with poor health status, the optimized result of the monitoring path is generated;

[0123] First, it is necessary to exclude those battery cells with better health status according to the battery health status and temperature changes. The exclusion criterion can be set that battery cells with a health status higher than a certain preset threshold do not need to be monitored frequently. By the real-time feedback of the health status and working environment of each battery cell, the monitoring path is adjusted to ensure that those battery cells with poor health status and having a greater impact on the stability of the battery pack are preferentially monitored. When the temperature of the battery fluctuates abnormally, the decline rate of its health status will also accelerate. Therefore, it is necessary to increase frequent monitoring to obtain its change trend. Through the dynamic adjustment of the monitoring path, the optimized result of the monitoring path is finally generated. The optimized monitoring path will focus on the battery cells with poor health status and reduce the monitoring frequency of the battery cells with better health status.

[0124] Please refer to Figure 5 , and the specific steps for obtaining the difference value of energy distribution between battery packs are as follows:

[0125] Based on the optimized result of the monitoring path, by monitoring the fluctuations of the current and voltage of the battery pack, extracting the real-time operation data of the battery partition, analyzing the current, voltage, and power output values of the partition battery pack, and combining the voltage and current data fluctuation trends between the battery packs, the energy demand of each partition is identified to obtain the energy demand data of the battery partition;

[0126] First, by collecting the current and voltage data of each battery pack in real time, it is ensured that the operating status of the battery packs in each partition can be obtained in real time. Furthermore, the current and voltage fluctuations of the battery packs are analyzed to judge the current load situation of the battery packs. The voltage and current fluctuation values of the battery packs reflect their energy demand and output capacity. For example, when the voltage fluctuation of the battery in a certain partition is large, it indicates that the battery in that partition is facing a large load demand, and the current fluctuation value can further reveal the stability of the battery pack during discharge. Through the current and voltage data, the energy demand of each partition can be dynamically analyzed to obtain the energy demand data of the battery partitions, which serves as the basis for subsequent energy scheduling.

[0127] According to the energy demand data of the battery partitions, energy allocation is carried out. Combining the energy demand of the battery packs and comparing the current and voltage fluctuations between the battery packs, the formula is used:

[0128] ;

[0129] Calculate the energy allocation difference value between the battery packs;

[0130] Among them, represents the energy allocation difference value between the battery packs, is the voltage of the kth battery pack, is the current of the kth battery pack, is the total power of the battery packs, represents the number of battery packs;

[0131] The benefit of the formula is that by introducing the fluctuations of the battery pack voltage and current, the energy allocation difference between each battery pack can be accurately calculated, providing a scientific basis for subsequent energy scheduling;

[0132] In this formula, represents the energy allocation difference value between the battery packs, is the voltage of the kth battery pack, is the current of the kth battery pack, is the total power of the battery packs, is the number of battery packs;

[0133] Suppose the battery pack has 3 partitions, the voltage of battery pack 1 , the current , the voltage of battery pack 2 , the current , the voltage of battery pack 3 , the current , and the total power of the battery pack ;

[0134] Substitute into the formula for calculation:

[0135] ;

[0136] ;

[0137] The result shows that the energy distribution difference value between battery packs is 0.218, indicating that there are certain differences in the energy requirements of the battery packs among the three battery packs, providing key data for subsequent scheduling optimization.

[0138] Please refer to Figure 6 , and the specific steps for obtaining the optimized energy scheduling result are as follows:

[0139] Based on the energy distribution difference value between battery packs, capture the charge and discharge data of the battery packs, monitor the current and voltage changes of the battery packs, record the power status of each battery pack in real time, evaluate the energy distribution difference, and obtain the energy difference data of the battery packs;

[0140] The energy distribution difference within each battery pack will affect the overall operating efficiency and safety. Continuously monitor the charge and discharge process of the battery pack through the sensor system, collect the current and voltage data of the battery pack in real time, and calculate its power status. During the monitoring process, the voltage and current changes of each unit within the battery pack will be recorded, and the data will be transmitted to the remote monitoring system in real time for analysis. Compare the power status of different battery packs, monitor the current change trend during the charging process of each battery pack, and then calculate the energy difference between each battery pack. By comparing the voltage and current data changes of each battery pack at different time periods, identify the imbalance in energy distribution, help determine whether there is a large energy difference in each battery pack, ensure that the system can track the dynamic distribution of energy in real time, and provide corresponding warning signals to obtain the energy difference data of the battery packs, providing basic support for subsequent optimization of the charge and discharge strategy to ensure that the battery pack is always in the best working state.

[0141] Based on the energy difference data of the battery packs, analyze the current demand of the battery packs, combine with the real-time load of the battery, evaluate the charge and discharge requirements of each battery pack, analyze and adjust the operating range of the current by comparing the voltage and current changes of the differential battery packs, and obtain the adjusted range of the charge and discharge requirements of the battery packs;

[0142] The current demand of each battery pack is closely related to the load. When there is a large voltage difference among the battery cells inside the battery pack, it will lead to inconsistent current demands among the cells in the battery pack. By monitoring the current and voltage fluctuations of each battery pack during the charging and discharging processes and combining with the changing trend of the real-time load, the current demand of the battery pack is evaluated to ensure that each battery pack can operate within a safe range. During the adjustment process of the current demand of the battery pack, factors such as the load of the battery pack, temperature, and the power distribution of the battery cells need to be comprehensively considered to adjust the charging and discharging current range of the battery to meet the charging and discharging requirements of the battery pack. Among different battery packs, due to different battery performances and load conditions, the current demands will also be different. By comparing the voltage and current changes of the battery packs, the current adjustment range of each battery pack can be accurately analyzed, the load changes and charging and discharging requirements of each battery pack can be accurately judged, the charging and discharging demand adjustment range of the battery pack can be obtained, and the charging and discharging strategies of the battery pack can be flexibly adjusted based on real-time data to achieve the optimal allocation of the overall battery pack energy.

[0143] Based on the charging and discharging demand adjustment range of the battery pack, adjust the charging and discharging flow rate of the battery pack. According to the load and differences of the battery pack, adjust the charging and discharging current of each battery pack in real time to eliminate the phenomena of overcharging and over-discharging, and generate an optimized energy scheduling result;

[0144] In the charging and discharging scheduling of the battery pack, the real-time load and energy difference are the key factors for adjusting the current flow rate. By monitoring and analyzing the real-time load data of each battery pack, the current flow rate demand of each battery pack can be accurately judged. When the power of some battery packs is too low, the system will automatically increase the charging current to ensure the smooth progress of the charging process. On the contrary, when the power of the battery pack is too high, the system will reduce the charging current to avoid overcharging. Similarly, during the discharging process of the battery pack, when the power of the battery pack is too low, the system will reduce the discharging current to avoid over-discharging the battery and causing battery damage. Through a series of real-time monitoring and adjustments, the risks of overcharging and over-discharging can be automatically eliminated to ensure that the charging and discharging states of the battery pack are always within a safe range. According to the energy distribution differences of different battery packs, adjust the charging and discharging flow rates among the battery packs to optimize the energy scheduling scheme. Through this dynamic adjustment of the charging and discharging flow rate, an optimized energy scheduling result is generated to achieve the balanced and efficient operation of the entire battery pack, not only optimizing the battery usage efficiency, but also extending the service life of the battery pack and effectively reducing the loss of the battery pack.

[0145] Please refer to Figure 7 , the steps for obtaining the charging current distribution data set are specifically as follows:

[0146] Based on the optimized energy scheduling results, extract the charging requirements of each battery module from the data of battery partitions, including the current demand, current charging status, discharge efficiency, and remaining power of the battery module. Compare and analyze the charging requirements of the battery module to determine the magnitude of the charging current required for each module, and obtain the battery module charging requirement data;

[0147] First, obtain the charging requirement data of each battery module, including the remaining power, battery health status, and load conditions of each battery module. Obtain the data in real time through the monitoring system to analyze the charging requirements of each battery module. If the remaining power of a certain battery module is low and its health condition is good, its charging requirement will be high, while those battery modules with higher remaining power have lower charging requirements. Based on the charging requirements, adjust the distribution of the charging current to ensure a reasonable distribution of the charging flow, so that when charging the battery, the charging requirements of each battery module can be more effectively met, ensuring high efficiency and safety during the charging process. Finally, obtain the battery module charging requirement data to provide data support for subsequent energy scheduling and charging management.

[0148] Based on the battery module charging requirement data, allocate the charging current. Combine the charging requirements of each battery module and the load conditions of the battery pack, and use the formula:

[0149] ;

[0150] Calculate the adjusted charging current;

[0151] Among them, represents the adjusted charging current, is the initial charging current, is the charging requirement of the k-th battery module, is the total charging capacity of the battery pack, is the remaining power of the battery module, is the average remaining power of the battery pack;

[0152] The benefit of the formula is that by combining the charging requirements of the battery module and the proportion of the remaining power, the distribution of the charging current can be adjusted more precisely, so that each battery module can obtain an appropriate charging current according to actual needs;

[0153] In this formula, represents the adjusted charging current, is the initial charging current, is the charging requirement of the k-th battery module, is the total charging capacity of the battery pack, is the remaining power of the k-th battery module, is the average remaining power of the battery pack. By comparing the charging requirements of the battery modules with the remaining power, the distribution of the charging current can be dynamically adjusted, enabling the battery modules with higher charging requirements to obtain more current, while the battery modules with lower charging requirements receive less current distribution;

[0154] Assume the initial charging current , the total charging capacity of the battery pack , and set the remaining powers of the 3 battery modules in the battery pack to be respectively , , , and their charging requirements are respectively , , , while the average remaining power of the battery pack ;

[0155] Substitute into the formula for calculation:

[0156] ;

[0157] ;

[0158] ;

[0159] This result shows that by calculating the ratio of the charging requirement to the remaining power of each battery module, the adjusted charging current distribution is more reasonable, and the charging current can be reasonably distributed according to the actual requirements of each battery module, providing support for the subsequent optimization of charging management.

[0160] Based on the adjusted charging current, make a reasonable distribution of the charging current, increase the charging current for the battery modules with high charging requirements, while reduce the charging current for the battery modules with low charging requirements, and generate a charging current distribution data set;

[0161] When adjusting the distribution of the charging current, determine the charging current distribution strategy based on the charging requirements and the remaining power differences of each battery module. The charging current distribution will be adjusted according to the specific requirements of the battery modules to ensure efficient energy transfer to each battery module. The flow distribution during the charging process should give priority to meeting the battery modules with low power and poor health conditions, and use the actual monitoring data to dynamically adjust each battery module. When the remaining power of a battery module is low, its charging current will be correspondingly increased to accelerate its charging speed, while the battery module with a higher remaining power will have its charging current reduced accordingly. Finally, a charging current distribution data set is generated to ensure the efficiency and balance during the charging process of the battery pack.

[0162] Please refer to Figure 8 , and the specific steps for obtaining the mobile power station efficiency monitoring and analysis solution are as follows:

[0163] Based on the charging current distribution dataset, extract the charging current, voltage, ambient temperature, and battery internal resistance parameters and classify them. Normalize the parameters according to the battery performance characteristic data, analyze the extreme value ranges within the parameter change intervals, and establish a dataset of the battery working environment and performance characteristics.

[0164] First, collect the four main parameters of charging current, voltage, ambient temperature, and battery internal resistance, and analyze the value ranges of the parameters in different working environments using the original data to determine the change trends of each parameter under different conditions. For example, data on the battery internal resistance at different ambient temperatures can be sorted out, and the change intervals of the battery internal resistance at different temperatures can be calculated. By normalizing the parameters from different data sources, the fluctuations caused by environmental factors and measurement accuracy differences can be eliminated to ensure the consistency of the parameters. For the relationship between the battery internal resistance and voltage, further analyze the extreme value ranges of the battery performance during charging through data classification means, such as the situation where the voltage drops when the internal resistance increases, so as to provide data support for subsequent battery state monitoring. After this processing, establishing a dataset of the battery working environment and performance characteristics can help to deeply understand the performance of the battery in different working environments and provide a theoretical basis for formulating charging optimization strategies.

[0165] Based on the dataset of the battery working environment and performance characteristics, extract the change ranges of the parameters in different charging scenarios, compare them in combination with the battery performance characteristics in the scenarios, classify the parameter combination relationships in the scenarios, and construct a parameter mapping table for different charging scenarios.

[0166] First, it is necessary to identify the battery performance characteristics in different charging scenarios, including the effects of factors such as charging ambient temperature, battery charging cycle, charging current, and voltage changes on the battery state. For example, for the battery charging situation in low-temperature or high-temperature scenarios, it is necessary to compare the temperature intervals in the battery performance characteristic dataset with the changes in the battery internal resistance to identify the optimal ratio of the charging current and voltage of the battery at different temperatures. By classifying the combination relationships of the parameters in different scenarios, for example, in a typical low-temperature charging scenario, the battery voltage change range is more sensitive than that in the normal temperature scenario, and the change in the battery internal resistance will cause real-time adjustment of the charging current. During the comparison process, specific charging current and voltage adjustment strategies can be formulated according to the battery characteristics in different charging scenarios, and the parameters can be refined and classified according to the battery performance characteristics to ensure that the charging current and voltage distribution in each scenario can maximize the charging efficiency while ensuring the long-term health and stability of the battery, and construct a parameter mapping table for different charging scenarios.

[0167] Based on the differential charging scenario parameter mapping table, adjust the real-time distribution plan of charging current and voltage according to the parameter combination relationship in the scenario, monitor and optimize the battery performance data during the charging process, analyze the charging efficiency and battery state changes, and generate an efficiency monitoring and analysis scheme for the mobile power station;

[0168] First, according to the differential charging scenario parameter mapping table, adjust the real-time distribution of charging current and voltage in different scenarios. By real-time monitoring the battery performance data, especially parameters such as the charging state, temperature, and internal resistance of the battery, calculate the efficiency index under the current charging state, and dynamically adjust the change range of current and voltage in combination with the scenario mapping table. The monitoring of battery performance data includes a detailed analysis of multiple dimensions such as the change of battery internal resistance during the charging process, the fluctuation of charging voltage, and its impact on battery temperature. For example, when the charging current is too high, it causes abnormal increase in battery temperature and increase in battery internal resistance, affecting charging efficiency and battery health. Real-time adjust the distribution plan of charging current to reduce overcharging. Based on this real-time adjustment, combined with the state changes during the battery charging process, generate an efficiency monitoring and analysis scheme for the mobile power station to ensure that the charging process can be carried out efficiently and safely under different charging scenarios, and at the same time maintain a good expectation for the long-term service life of the battery.

[0169] The above is only a preferred embodiment of the present invention and does not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. The charging mobile power station remote monitoring and analysis system is characterized by: The system comprises: The battery pack status monitoring module performs real-time monitoring of each level based on the battery cell, battery module, and battery pack levels, extracts key parameters of voltage, current, and SOC, identifies battery health status, evaluates battery pack stability, adjusts monitoring priorities and hierarchical monitoring indicators, and generates hierarchical monitoring data sets; The monitoring path adjustment module dynamically excludes dead zones based on the hierarchical monitoring data set, adjusts the monitoring path according to the battery health status, analyzes and adjusts the monitoring frequency of the differentiated battery levels, and generates a monitoring path optimization result; The steps for obtaining the monitoring path optimization result are specifically as follows: Based on the hierarchical monitoring data set, extract the health data of the battery cells, including the temperature, charging state, discharge efficiency and internal resistance parameters of the battery, analyze them in combination with the working conditions of the battery pack, perform preliminary monitoring of the battery cells according to the battery health status, and obtain baseline monitoring frequency data; According to the baseline monitoring frequency data, the differentiated monitoring frequencies of differentiated battery cells at the level are analyzed and adjusted. The monitoring frequency of battery cells with high priority is increased, and the monitoring frequency of battery cells with low priority is reduced. Combined with the changing trend of battery health status, the formula is adopted: ； Obtain the adjusted monitoring frequency data set; in, To adjust the monitoring frequency data, For the initial monitoring frequency, is the health status of the ith battery cell, is the average value of the battery pack health status, is the operating temperature of the ith battery cell, is the average temperature of the battery pack, is the adjustment factor; According to the adjusted monitoring frequency data set, by dynamically eliminating dead zones in the battery pack, adjusting the monitoring path, eliminating battery cells in good health, analyzing battery cells in poor health, and generating a monitoring path optimization result; The partition monitoring and energy scheduling module analyzes the energy demand of the battery partition based on the monitoring path optimization result, calculates the energy distribution difference between the battery groups by monitoring the current and voltage fluctuations of the partition battery groups, adjusts the battery charge and discharge flow, eliminates overcharge and overdischarge, and generates an optimized energy scheduling result; The energy distribution module identifies the charging demand of each battery module based on the optimized energy scheduling result, adjusts the distribution of charging current, reasonably distributes the charging flow, and generates a charging current distribution data set; The charging efficiency optimization monitoring module adjusts the charging plan based on the charging current distribution data set according to the real-time working environment of the battery, analyzes the battery status of differentiated charging scenarios, dynamically optimizes charging parameters, and generates a mobile power station efficiency monitoring and analysis plan.

2. The charging mobile power station remote monitoring and analysis system according to claim 1 is characterized in that: The steps for obtaining the battery health status are specifically as follows: Based on the battery cell, battery module, and battery pack levels, capture real-time voltage, current, and SOC data, monitor parameter changes at each level, and record the voltage, current, and SOC values ​​at the battery level to obtain the battery pack voltage, current, and SOC data; Based on the battery pack voltage and current SOC data, timing alignment and calibration are performed to eliminate data deviations caused by external environment changes and equipment errors, and the data are compared using timestamps to check the change trend of data in differentiated time periods, thereby generating battery pack data timeliness calibration results; Based on the battery pack data timeliness calibration results, the health status of the battery cells is analyzed, and by comparing the fluctuations of voltage and SOC, it is determined whether the battery has abnormal conditions. Based on the current changes between each cell in the battery pack, the battery health level is evaluated to obtain the battery pack health status.

3. The charging mobile power station remote monitoring and analysis system according to claim 2 is characterized in that: The steps for obtaining the hierarchical monitoring data set are specifically as follows: Evaluate the stability of the battery pack according to the battery health status, capture the state parameters of the battery pack, determine the health status of the battery cells, and perform weighted processing through the working environment, temperature, and load factors of the battery pack to obtain the health status value of the battery pack; Perform the analysis of the battery health status value, compare the battery health status with the original stability data, determine the change trend, and use the original stability score to perform regression analysis on the current state, using the formula: ； The battery pack stability score is calculated; in, Represents the battery pack stability score, is the weight of the ith battery cell, is the health status of the ith battery cell, is the ambient temperature factor, is the influence coefficient of the original stability score, is the raw stability score, is the total number of battery cells; According to the battery pack stability score, the monitoring priority and hierarchical monitoring indicators are adjusted, the monitoring priority conditions are set, the monitoring indicators are weighted and allocated, and hierarchical monitoring is used for dynamic adjustment to generate a hierarchical monitoring data set.

4. The charging mobile power station remote monitoring and analysis system according to claim 1, characterized in that: The steps for obtaining the energy distribution difference value between the battery packs are specifically as follows: Based on the monitoring path optimization result, by monitoring the fluctuation of battery pack current and voltage, extracting the real-time operation data of the battery partition, analyzing the current, voltage and power output values ​​of the partitioned battery pack, combining the voltage and current data fluctuation trend between battery packs, identifying the energy demand of each partition, and obtaining the battery partition energy demand data; Energy distribution is performed based on the energy demand data of the battery partitions, combined with the energy demand of the battery pack, and comparing the current and voltage fluctuations between the battery packs, using the formula: ； Calculate the energy distribution difference between battery groups; in, Represents the difference in energy distribution between battery packs, is the voltage of the kth battery pack, is the current of the kth battery pack, is the total power of the battery pack, Indicates the number of battery packs.

5. The charging mobile power station remote monitoring and analysis system according to claim 4 is characterized in that: The steps for obtaining the optimized energy scheduling result are specifically as follows: Based on the energy distribution difference value between the battery packs, the charge and discharge data of the battery packs are captured, the current and voltage changes of the battery packs are monitored, the state of charge of each battery pack is recorded in real time, the energy distribution difference is evaluated, and the energy difference data of the battery packs is obtained; Based on the battery pack energy difference data, the current demand of the battery pack is analyzed, and the charge and discharge demand of each battery pack is evaluated in combination with the real-time load of the battery. By comparing the voltage and current changes of the differentiated battery packs, the operating range of the current adjustment is analyzed to obtain the battery pack charge and discharge demand adjustment range; Based on the battery pack charge and discharge demand adjustment range, the charge and discharge flow of the battery pack is adjusted. According to the load and difference of the battery pack, the charge and discharge current of each battery pack is adjusted in real time to eliminate the phenomenon of overcharge and overdischarge, and generate an optimized energy scheduling result.

6. The charging mobile power station remote monitoring and analysis system according to claim 5, characterized in that: The steps for acquiring the charging current distribution data set are specifically as follows: Based on the optimized energy scheduling result, the charging demand of each battery module is extracted from the battery partition data, including the current demand, current charging state, discharge efficiency and remaining power of the battery module, the charging demand of the battery module is compared and analyzed, the size of the charging current required for each module is determined, and the charging demand data of the battery module is obtained; Based on the battery module charging demand data, the charging current is distributed, and the charging demand of each battery module and the load of the battery pack are combined to adopt the formula: ； Calculate and obtain the adjusted charging current; in, represents the adjusted charging current, is the initial charging current, is the charging requirement of the kth battery module, is the total charging capacity of the battery pack, is the remaining power of the battery module, is the average remaining capacity of the battery pack; According to the adjusted charging current, the charging current is reasonably distributed, the charging current is increased for the battery modules with high charging demand, and the charging current is reduced for the battery modules with low charging demand, so as to generate a charging current distribution data set.

7. The charging mobile power station remote monitoring and analysis system according to claim 6, characterized in that: The steps for obtaining the mobile power station efficiency monitoring and analysis solution are specifically as follows: Based on the charging current distribution data set, the charging current, voltage, ambient temperature, and battery internal resistance parameters are extracted and classified, the parameters are normalized according to the battery performance characteristic data, the extreme value range within the parameter change interval is analyzed, and the battery working environment and performance characteristic data set is established; Based on the battery working environment and performance characteristic data set, extract the variation range of parameters in the differentiated charging scenario, compare them with the battery performance characteristics in the scenario, classify the parameter combination relationship under the scenario, and construct a differentiated charging scenario parameter mapping table; Based on the differentiated charging scenario parameter mapping table, the real-time distribution plan of charging current and voltage is adjusted according to the parameter combination relationship in the scenario, the battery performance data during the charging process is monitored and optimized, the charging efficiency and battery status changes are analyzed, and a mobile power station efficiency monitoring and analysis plan is generated.

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