Rechargeable mobile power station remote monitoring and analysis system

Abstract

The invention relates to the technical field of battery management, in particular to a charging type mobile power station remote monitoring and analysis system which comprises a battery pack state monitoring module, a monitoring path adjusting module, a partition monitoring and energy dispatching module, an energy distribution module and a charging efficiency optimization monitoring module. According to the invention, through refined battery level monitoring and health state 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 state of the battery is comprehensively evaluated, the monitoring path and the monitoring frequency are dynamically adjusted, resource allocation and efficiency maximization are ensured, the energy distribution difference between the battery packs is accurately identified, and the battery management efficiency is improved. By adjusting the charge and discharge flow, avoiding over-charge and over-discharge, improving the stability and service life of the battery and optimizing the charging current distribution and the charging plan, efficient charging is realized, the energy utilization efficiency is improved, the response speed and precision of the whole system are improved, a more intelligent and personalized solution is provided, and the requirements of different application scenes are met.

Description

Technical Field

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

[0002] The field of battery management technology includes relevant technical content for monitoring, controlling and maintaining battery systems. The core content of this technical field includes the management and optimization of key parameters such as battery charging status, discharge performance, remaining power, and service life. The field of battery management technology as a whole involves battery performance detection, protection, and charging and discharging control. It achieves accurate evaluation of battery status through precise data collection and analysis, and is widely used in portable devices, energy storage systems, electric vehicles and other fields. 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 battery operating status.

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

[0004] The existing technologies in battery management systems mostly rely on regular monitoring and preset algorithms for charging control, and lack dynamic adjustments for different battery levels and real-time battery health conditions. The existing technologies evaluate battery health through a single monitoring frequency and parameters, and fail to make timely adjustments based on the specific status of the battery. When the system faces different working conditions of the battery, it is unable to make accurate battery status judgments and timely fault warnings, resulting in battery overcharging, over-discharging, and other phenomena, and even affecting the stability and safety of the system. The existing technologies are relatively simple in battery energy distribution and are unable to cope with the differences and diverse battery conditions between batteries, resulting in over-discharging or over-charging of some batteries, further affecting the battery life and system performance. The battery charging and discharging process fails to be adjusted based on real-time data, causing some batteries to encounter high loads or invalid 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 shortcomings in the prior art and to propose a remote monitoring and analysis system for a charging mobile power station.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: The charging mobile power station remote monitoring and analysis 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 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.

[0007] As a further solution of the present invention, the step of obtaining the battery health status is 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.

[0008] As a further solution of the present invention, the steps of acquiring 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 battery pack health status value; 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.

[0009] As a further solution of the present invention, the step of obtaining the monitoring path optimization result is 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.

[0010] As a further solution of the present invention, the step of obtaining the energy distribution difference value between the battery packs is 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 energy distribution difference 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.

[0011] As a further solution of the present invention, 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.

[0012] As a further solution of the present invention, the step of acquiring the charging current distribution data set is specifically: 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.

[0013] As a further solution of the present invention, 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.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, the real-time and accuracy of battery management are improved through refined battery-level monitoring and health status analysis, and batteries at different levels are monitored in real time. Key parameters such as voltage, current, SOC, etc. are combined to ensure a comprehensive assessment of the battery health status. The monitoring path is dynamically adjusted and the monitoring frequency is optimized based on the actual health status of the battery to ensure the rational allocation of resources and maximized efficiency. The energy distribution differences between battery packs are accurately identified, and overcharging and over-discharging are avoided by adjusting the battery charge and discharge flow, effectively improving the stability and life of the battery pack. Efficient charging of the battery is achieved by reasonably allocating the charging current and optimizing the charging plan, and the energy utilization efficiency of the overall system is maximized. Not only the response speed and accuracy of the system are improved, but also the performance of the battery system is comprehensively improved by adjusting the monitoring strategy and optimizing the energy scheduling, providing a more intelligent and personalized solution to meet the needs of different application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a system flow chart of the present invention; Figure 2 A flow chart of the battery health status in the present invention; Figure 3 It is a flowchart of the middle-level monitoring data set of the present invention; Figure 4 A flow chart of monitoring path optimization results in the present invention; Figure 5 A flow chart showing the energy distribution difference between battery packs in the present invention; Figure 6 It is a flow chart of the optimized energy scheduling result in the present invention; Figure 7 A flow chart of charging current allocation data set in the present invention; Figure 8 It is a flow chart of the mobile power station efficiency monitoring and analysis solution in the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are 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 cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0018] See also Figure 1 , the charging mobile power station remote monitoring and analysis system includes: 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 differentiated battery levels, and generates monitoring path optimization results; The partition monitoring and energy scheduling module analyzes the energy demand of the battery partition based on the monitoring path optimization results. By monitoring the current and voltage fluctuations of the partition battery group, it calculates the energy distribution difference between the battery groups, adjusts the battery charge and discharge flow, eliminates overcharge and overdischarge, and generates optimized energy scheduling results. Based on the optimized energy scheduling results, the energy allocation module identifies the charging demand of each battery module, adjusts the distribution of charging current, reasonably allocates the charging flow, and generates a charging current distribution data set; The charging efficiency optimization monitoring module is based on the charging current distribution data set, adjusts the charging plan 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.

[0019] The battery health status includes voltage, current, and SOC parameters. The hierarchical monitoring data set includes monitoring data of battery cells, battery modules, and battery packs. The monitoring path optimization results include monitoring path adjustment, monitoring frequency adjustment, and dead zone elimination. The energy distribution difference values ​​between battery packs include current fluctuations, voltage fluctuations, and energy differences. The optimized energy scheduling results include charge and discharge flow adjustment and overcharge and over-discharge elimination. The charging current distribution data set includes the charging demand and current distribution of each battery module. The mobile power station efficiency monitoring and analysis solution includes charging plan adjustment and charging parameter optimization.

[0020] See also Figure 2 , the specific steps for obtaining the battery health status are: 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; By setting the sampling frequency of each battery cell, the 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 will regularly obtain the voltage, current and SOC values ​​of the single battery through sensors and transmit them to the module control unit. The battery module level integrates the monitoring information of multiple single batteries to calculate the overall voltage, current and SOC of the module, and further provide more macroscopic battery status data. After integrating the data of each battery module, the battery pack level obtains the battery pack voltage, current and SOC data, thereby providing data support for the management and monitoring of the battery pack. In the process of data collection, a time synchronization mechanism is used to ensure that each data point accurately corresponds to the collection time to avoid inconsistencies caused by data collection delays at different levels. It is also necessary to monitor parameters such as temperature and humidity in the external environment to prevent environmental factors from causing deviations in battery performance data and ensure data accuracy.

[0021] Based on the battery pack voltage, current and SOC data, timing alignment and calibration are performed to eliminate data deviations caused by external environment changes and equipment errors. The data is compared using timestamps to check the change trend of data in differentiated time periods, and the battery pack data timeliness calibration results are generated. The data collected from each battery level (cell, battery module, battery pack) is timestamped to ensure the synchronization of data from different sources, avoid analysis errors caused by inconsistent data collection time, identify deviations between different data sources caused by environmental changes and equipment errors (such as sensor drift and temperature changes), and use the time series alignment method to adjust the battery data of different time periods to a unified time scale. For the differences in data trends in different time periods, comparative analysis is performed, and time window-based smoothing is used to eliminate short-term fluctuations and extract trend changes in data over a long period, thereby eliminating interference caused by equipment errors or external environmental influences. Combined with known standard battery characteristics, data calibration is performed, and data deviations are corrected by adjusting coefficients. Finally, the battery pack data timeliness calibration results are output, which provides more accurate time dimension support for the current health status of the battery pack batteries and ensures the reliability of data in subsequent analysis processes.

[0022] Based on the battery pack data timeliness calibration results, analyze the health status of the battery cells, and determine whether the battery is abnormal by comparing the fluctuation of voltage and SOC. According to the current changes between each cell in the battery pack, evaluate the battery health level and obtain the battery pack health status; For the calibrated battery pack voltage, current and SOC data, by comparing the voltage and SOC fluctuations of battery cells, modules and battery packs, evaluate whether the battery has decay or abnormal phenomena. For battery cells with large voltage fluctuations, it is due to internal battery failure or aging. Such batteries will affect the performance of the entire battery pack, so they need to be paid special attention to. Battery cells with large and unstable SOC fluctuations indicate that the battery charging efficiency is reduced or there is irreversible damage inside the battery. Further inspection and evaluation are required. The current changes of each single cell in the battery cell need to be monitored. The violent fluctuation of current is a signal that the battery has an internal short circuit, connection failure or battery damage. The current data of all battery cells are compared to evaluate the overall health level of the battery pack and obtain the health status of the battery pack. The health status of each battery cell can also be graded, thereby providing a scientific basis for the maintenance and replacement of the battery pack. Potential problems can be identified in advance through the early warning system to avoid battery failures from having an adverse impact on the operation of the entire power station.

[0023] See also Figure 3 ,The specific steps for obtaining the hierarchical monitoring data set are: Evaluate the stability of the battery pack based on the battery health status, capture the status 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; First, it is necessary to obtain the health status of the battery by monitoring the temperature, charge or discharge cycle, current and voltage of the battery pack. When evaluating the health of the battery cell, the parameters can be weighted to ensure that the changes in temperature and load are reasonably considered. The battery status can be obtained through simple calculations. For example, the estimation of the health value can be converted by combining the temperature with the load capacity of the battery. For example, the battery will accelerate aging at high temperatures, and the increase in current will also increase internal losses. According to the indicators, the quantitative value of the battery health status is obtained through experimental data, and finally a total health score value based on the battery cell status is obtained. The weight value can be combined with various monitoring indicators by setting the weight value to finally obtain the overall health status score of the battery pack. Based on the evaluation results, the necessary input data is further provided for the stability score of the battery pack. The weight setting in this process should take into account multiple factors such as ambient temperature, load, and usage time, and dynamically adjust according to different battery types and original data to finally obtain the health status value of the battery pack.

[0024] Perform an analysis of the battery pack health status value, compare the battery pack health status with the original stability data, determine the change trend, and use the original stability score to perform a regression analysis on the current status 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; The benefit of the formula is that by introducing the weighted average of factors such as the health status of the battery cells and the ambient temperature, combined with the original stability score, the current stability assessment is made more accurate and dynamic, and can reflect the performance of the battery pack under different environmental conditions; In this formula, Indicates the stability score of the battery pack, represents the weight of the i-th battery cell, represents the health status of the ith battery, Indicates the ambient temperature factor, is the influence coefficient of the original stability score, represents the raw stability score; Laboratory test data is used to evaluate battery state of health (SOH) and raw stability score ( ), mainly including charge and discharge cycle test, capacity decay test, internal resistance measurement and ambient temperature impact test. The charge and discharge cycle test is set to 500 cycles, with an initial capacity of 100%, a final capacity of 90%, and a decay of 10%. The capacity decay test shows that the initial capacity is 50Ah, and after 500 cycles it drops to 45Ah, with a capacity retention rate of 90%, which is used to calculate the health status The internal resistance measurement results show that the initial internal resistance is 2.0mΩ, and it rises to 2.5mΩ after 500 cycles, an increase of 25%, indicating battery aging. The environmental temperature impact test shows that at 25℃, 28℃, and 30℃, the battery health status is 95%, 90%, and 85%, respectively.

[0025] is the total number of battery cells, assuming there are three battery cells, ; Set battery health status , , , the value is the battery health value calculated from experimental data; Assuming ambient temperature , , ,The temperature value is obtained through real-time monitoring equipment; Setting weights , , ,The weight is based on a combination of battery capacity and age, with newer batteries having higher weights; Assuming the original stability score , , this factor is set based on the battery pack’s past performance; By substituting the values, the calculation process of the formula is as follows: ; ; ; ; The calculation results show that the battery pack stability score is 8.39, indicating that the battery is running stably and in good health overall. A score above 8.0 indicates that the battery is still in good condition and does not require emergency maintenance. Future optimization can predict the trend of health status changes through regression analysis of raw data, and reduce the impact of ambient temperature through temperature control management (below 25°C). In addition, if the stability score continues to decrease, preventive maintenance or battery replacement can be performed in advance when it drops below 7.5. This calculation method combines laboratory data to make stability assessment more accurate, which can be used to optimize battery management strategies and improve battery life and operational reliability.

[0026] 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; 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 battery cells with poor health or high load, and 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, focusing on monitoring the performance of key node batteries. The original stability data can be used as a reference. The original data can be obtained by comparing past monitoring records, and the monitoring strategy can be further adjusted to ensure that key monitoring areas are always effectively paid attention to. Different sensors are used to obtain real-time data, and the monitoring priority is dynamically adjusted based on data feedback. For example, in extreme environments, changes in battery temperature will directly affect the assessment of battery health status. Therefore, in scenarios with large temperature fluctuations, the priority of temperature sensor data will be increased. Finally, the hierarchical monitoring model is dynamically adjusted to generate a hierarchical monitoring data set to ensure that the performance of each battery cell is given sufficient attention.

[0027] See also Figure 4 , the specific steps for obtaining the monitoring path optimization results are: Based on the hierarchical monitoring data set, the health data of the battery cells is extracted, including the battery temperature, charging state, discharge efficiency and internal resistance parameters. Combined with the working conditions of the battery pack, the data is analyzed, and the battery cells are preliminarily monitored according to the battery health status to obtain the baseline monitoring frequency data. First, it is necessary to obtain the health status of the battery by monitoring the temperature, charging status, discharge efficiency and internal resistance of the battery pack. The battery health value can be calculated through 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, the battery health status can be judged by real-time monitoring of the battery parameters and comparing them according to the trend of the original data. Combined with the health status of the battery cells, specific parameters (such as internal resistance, charging 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 preliminarily set. Batteries with poor health status will receive higher monitoring frequencies, and batteries with better health will be assigned lower monitoring frequencies to ensure the accuracy and efficiency of real-time monitoring.

[0028] According to the baseline monitoring frequency data, analyze and adjust the differentiated monitoring frequency of differentiated battery cells at the level, increase the monitoring frequency of high-priority battery cells, and reduce the monitoring frequency of low-priority battery cells. Combined with the changing trend of battery health status, the formula is used: ; 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; The formula is beneficial in that it optimizes the adjustment of monitoring frequency by introducing differences in the health status of battery cells and changes in operating temperature, so that batteries with poor health and large temperature fluctuations can obtain more monitoring resources to ensure real-time feedback of their health changes; In this formula, The frequency of monitoring after adjustment is: The monitoring frequency is initially set. is the health status of the ith battery, is the average health status of the battery pack, is the temperature of the ith battery, is the average temperature of the battery pack, is the adjustment coefficient, which is used to reflect the impact of differences in health status on the monitoring frequency; Assuming there are 3 battery cells in the battery pack, the initial monitoring frequency is set to times / hour, and battery health status , , , ambient temperature , , , average health status of the battery pack , average temperature , adjustment coefficient ; Calculate the impact of each battery's health status and temperature differences on the frequency: ; ; ; ; ; ; Calculate the adjusted monitoring frequency: ; ; ; The results show that after the monitoring frequency is adjusted, batteries with poor health and higher temperatures receive more monitoring resources, while batteries with better health and lower temperatures receive a moderate monitoring frequency, ensuring the rational allocation of monitoring resources.

[0029] According to the adjusted monitoring frequency data set, the dead zone in the battery pack is dynamically eliminated, the monitoring path is adjusted, the battery cells with good health status are eliminated, and the battery cells with poor health status are analyzed to generate the monitoring path optimization results; First, it is necessary to eliminate those batteries in good health based on the battery health status and temperature changes. The elimination standard can be set so that batteries with a health status higher than a preset threshold do not need to be monitored frequently. Through real-time feedback on the health status and working environment of each battery cell, the monitoring path is adjusted to ensure that those batteries with poor health and a greater impact on the stability of the battery pack are monitored first. When the battery temperature fluctuates abnormally, the rate of decline in its health status will also accelerate, so it is necessary to increase frequent monitoring to obtain its changing trend. Through dynamic adjustment of the monitoring path, the monitoring path optimization result is finally generated. The optimized monitoring path will focus on batteries with poor health and reduce the monitoring frequency of batteries with good health.

[0030] See also Figure 5 , the specific steps for obtaining the energy distribution difference value between battery packs are: Based on the monitoring path optimization results, by monitoring the fluctuation of battery pack current and voltage, extract the real-time operation data of 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 demand of each partition and obtain the battery partition energy demand data; First, by collecting the current and voltage data of each battery pack in real time, we can ensure that the operating status of the battery pack in each partition can be obtained in real time, and then analyze the current and voltage fluctuations of the battery pack to determine the current load of the battery pack. The voltage and current fluctuation values ​​of the battery pack reflect its energy demand and output capacity. For example, when the battery voltage of a partition fluctuates greatly, it means that the battery in the partition is facing a large load demand. 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 battery partition energy demand data as the basis for subsequent energy scheduling.

[0031] Energy distribution is performed based on the energy demand data of the battery partitions. Combined with the energy demand of the battery pack, the current and voltage fluctuations between the battery packs are compared, and the formula is used: ; Calculate the energy distribution difference between battery groups; in, Represents the energy distribution difference 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; The benefit of the formula is that by introducing the fluctuations of battery pack voltage and current, the energy distribution differences between battery packs can be accurately calculated, providing a scientific basis for subsequent energy scheduling; In this formula, Represents the energy distribution difference 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, is the number of battery packs; Assume that the battery pack has 3 partitions and the voltage of battery pack 1 is , current , the voltage of battery pack 2 , current , the voltage of battery pack 3 , current , and the total power of the battery pack ; Substitute into the formula to calculate: ; ; The result shows that the energy allocation difference between battery groups is 0.218, which means that the energy demand of the battery groups varies among the three battery groups, providing key data for subsequent scheduling optimization.

[0032] See also Figure 6 , the specific steps for obtaining the optimized energy scheduling results are: Based on the energy distribution difference between battery packs, the charging and discharging data of the battery packs are captured, the current and voltage changes of the battery packs are monitored, the power status 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; The difference in energy distribution within each battery pack will affect the overall operating efficiency and safety. The sensor system continuously monitors the charging and discharging process of the battery pack, collects the current and voltage data of the battery pack in real time, and calculates its charge state. During the monitoring process, the voltage and current changes of each unit in the battery pack will be recorded, and the data will be transmitted to the remote monitoring system in real time for analysis, and the charge states of different battery packs will be compared. The current change trend of each battery pack during the charging process is monitored, and then the energy difference between the battery packs is calculated. By comparing the changes in voltage and current data of each battery pack in different time periods, the imbalance in energy distribution is identified, which helps to determine whether there are large energy differences between the battery packs. Ensure that the system can track the dynamic distribution of energy in real time, and provide corresponding warning signals to obtain battery pack energy difference data, provide basic support for subsequent optimization of charging and discharging strategies, and ensure that the battery pack is always in the best working condition.

[0033] Based on the energy difference data of the battery packs, the current demand of the battery packs is analyzed. Combined with the real-time load of the battery, the charge and discharge demand of each battery pack is evaluated. By comparing the voltage and current changes of the differentiated battery packs, the operating range of the current adjustment is analyzed and the charge and discharge demand adjustment range of the battery pack is obtained. The current demand of each battery pack is closely related to the load. When the voltage difference of the battery cells inside the battery pack is large, the current demand between the cells in the battery pack will be inconsistent. By monitoring the current and voltage fluctuations of each battery pack during charging and discharging, combined with the change 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 current demand adjustment process of the battery pack, it is necessary to comprehensively consider factors such as the load, temperature and power distribution of the battery cells of the battery pack, and adjust the charge and discharge current range of the battery to meet the charging and discharging needs of the battery pack. Between different battery packs, the current demand will be different due to different battery performance and load conditions. By comparing the voltage and current changes of the battery pack, the current adjustment range of each battery pack can be accurately analyzed, the load changes and charge and discharge requirements of each battery pack can be accurately judged, the charge and discharge demand adjustment range of the battery pack can be obtained, and the charge and discharge strategy of the battery pack can be flexibly adjusted according to real-time data to achieve the optimal distribution of the overall battery pack energy.

[0034] Adjust the charge and discharge flow of the battery pack based on the adjustment range of the battery pack charge and discharge demand. Adjust the charge and discharge current of each battery pack in real time according to the load and difference of the battery pack to eliminate the phenomenon of overcharge and overdischarge, and generate optimized energy scheduling results. In the charge and discharge scheduling of battery packs, real-time load and energy differences are key factors in adjusting current flow. By monitoring and analyzing the real-time load data of each battery pack, the current flow demand of each battery pack can be accurately determined. When the power of some battery packs is too low, the system will automatically increase the charging current to ensure that the charging process is smooth. 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 discharge of the battery pack, when the power of the battery pack is too low, the system will reduce the discharge current to avoid excessive discharge of the battery and damage to the battery. Through a series of real-time monitoring and adjustments, the risk of overcharging and over-discharging can be automatically eliminated to ensure that the charge and discharge state of the battery pack is always within a safe range. According to the energy distribution differences of different battery packs, the charge and discharge flow between each battery pack is adjusted to optimize the energy scheduling plan. Through this dynamic charge and discharge flow adjustment, the optimized energy scheduling result is generated to achieve balanced and efficient operation of the entire battery pack, which not only optimizes the battery efficiency, but also extends the service life of the battery pack and effectively reduces the loss of the battery pack.

[0035] See also Figure 7 , the specific steps for obtaining the charging current distribution data set are: Based on the optimized energy scheduling results, the charging requirements of each battery module are extracted from the battery partition data, including the current requirement, current charging state, discharge efficiency and remaining power of the battery module. The charging requirements of the battery modules are compared and analyzed to determine the size of the charging current required for each module and obtain the battery module charging requirement data. First, the charging demand data of each battery module is obtained, including the remaining power of each battery module, battery health status and load conditions. The data is obtained in real time through the monitoring system to analyze the charging demand of each battery module. If the remaining power of a battery module is low and the health status is good, its charging demand will be higher, while those battery modules with higher remaining power have lower charging demand. Based on the charging demand, the distribution of charging current is adjusted to ensure that the charging flow is reasonably distributed so that when the battery is charging, the charging demand of each battery module can be more effectively met to ensure efficiency and safety during the charging process. Finally, the charging demand data of the battery module is obtained to provide data support for subsequent energy scheduling and charging management.

[0036] Based on the battery module charging demand data, the charging current is distributed, combined with the charging demand of each battery module and the load of the battery pack, using 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; The benefit of the formula is that, by combining the charging demand of the battery module and the proportion of the remaining power, the distribution of the charging current can be adjusted more accurately, so that each battery module can obtain the appropriate charging current according to the actual needs; In this formula, 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 kth battery module, The average remaining capacity of the battery pack. By comparing the charging demand of the battery module with the remaining capacity, the distribution of the charging current can be dynamically adjusted so that the battery module with higher charging demand can obtain more current, while the battery module with lower charging demand has less current distribution. Assuming the initial charging current , total charging capacity of the battery pack , and set the remaining power of the three battery modules of the battery pack to , , , and their charging requirements are , , , and the average remaining capacity of the battery pack ; Substitute into the formula to calculate: ; ; ; The results show that by calculating the ratio of the charging demand to the remaining power of each battery module, the adjusted charging current distribution is more reasonable, and the charging current can be reasonably allocated according to the actual needs of each battery module, providing support for subsequent charging management optimization.

[0037] According to the adjusted charging current, the charging current is reasonably distributed, the charging current is increased for the battery module with high charging demand, and the charging current is reduced for the battery module with low charging demand, and a charging current distribution data set is generated; When adjusting the distribution of charging current, the charging current distribution strategy is determined according to the charging demand and remaining power difference of each battery module. The charging current distribution will be adjusted based on the specific needs of the battery module to ensure that energy is efficiently transmitted to each battery module. The flow distribution during the charging process should give priority to battery modules with low power and poor health. Each battery module is dynamically adjusted using actual monitoring data. When the remaining power of a battery module is low, its charging current will increase accordingly, thereby accelerating its charging speed, while the charging current of a battery module with a higher remaining power will be reduced accordingly. Finally, a charging current distribution data set is generated to ensure high efficiency and balance during the charging process of the battery pack.

[0038] See also Figure 8 , the specific steps for obtaining the mobile power station efficiency monitoring and analysis solution are 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, and the extreme value range within the parameter change range is analyzed to establish the battery working environment and performance characteristic data set. First, the four main parameters of charging current, voltage, ambient temperature and battery internal resistance are collected, and the original data are used to analyze the value range of the parameters under different working environments to determine the changing trend of each parameter under different conditions. For example, data can be sorted for the battery internal resistance under different ambient temperatures, and the changing range of the battery internal resistance at different temperatures can be calculated. The parameters of different data sources are normalized to eliminate fluctuations caused by environmental factors and measurement accuracy differences, thereby ensuring the consistency of parameters. For the relationship between battery internal resistance and voltage, data classification is further used to analyze the extreme value range of battery performance during the charging process, such as the voltage drop when the internal resistance increases, to provide data support for subsequent battery status monitoring. After this processing, a battery working environment and performance characteristic data set is established, which can help to deeply understand the performance of the battery in different working environments and provide a theoretical basis for formulating charging optimization strategies.

[0039] Based on the battery working environment and performance characteristics data set, extract the parameter variation range in the differentiated charging scenarios, compare with the battery performance characteristics in the scenarios, classify the parameter combination relationship under the scenarios, and construct a differentiated charging scenario parameter mapping table; First, it is necessary to identify the battery performance characteristics of different charging scenarios, including the impact of factors such as charging environment temperature, battery charging cycle, charging current and voltage changes on the battery status. For example, for battery charging in low or high temperature scenarios, it is necessary to compare the temperature range in the battery performance characteristic data set with the change in battery internal resistance, identify the optimal ratio of battery charging current and voltage at different temperatures, and classify the combination relationship of parameters in different scenarios. For example, for a typical low-temperature charging scenario, the battery voltage change range is more sensitive than that of a normal temperature scenario, and the change in 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 of different charging scenarios, and the parameters can be finely 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 differentiated charging scenario parameter mapping table.

[0040] Based on the parameter mapping table of differentiated charging scenarios, the real-time allocation 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; First, according to the differentiated charging scenario parameter mapping table, the charging current and voltage are allocated and adjusted in real time in different scenarios. By real-time monitoring of battery performance data, especially parameters such as the battery's charging state, temperature and internal resistance, the efficiency index under the current charging state is calculated, and the range of current and voltage changes is dynamically adjusted in combination with the scenario mapping table. The monitoring of battery performance data includes detailed analysis of multiple dimensions such as the change in 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, the battery temperature rises abnormally, the battery internal resistance increases, and the charging efficiency and battery health are affected. The charging current allocation plan is adjusted in real time to reduce overcharging. Based on this real-time adjustment and combined with the state changes during the battery charging process, a mobile power station efficiency monitoring and analysis plan is generated to ensure that the charging process can be carried out efficiently and safely in different charging scenarios, while maintaining good expectations for the long-term service life of the battery.

[0041] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls 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 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 battery pack health status value; 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 3 is characterized in that: 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.

5. The charging mobile power station remote monitoring and analysis system according to claim 4 is 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 energy distribution difference 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.

6. The charging mobile power station remote monitoring and analysis system according to claim 5, 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.

7. The charging mobile power station remote monitoring and analysis system according to claim 6, 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.

8. The charging mobile power station remote monitoring and analysis system according to claim 7, 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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