A supercapacitor management system and method based on an adaptive optimization algorithm

Abstract

This invention discloses a supercapacitor management system and method based on an adaptive optimization algorithm, belonging to the field of capacitor management technology. By establishing a hierarchical optimized topology structure for the supercapacitor management system, the supercapacitor management objectives are prioritized. Based on the prioritization, the fusion confidence score between each pair of supercapacitor management objectives is calculated sequentially. Data fusion and retention are performed based on the fusion confidence score, with all retained synchronous sensing information used as the first fusion information. When all the first fusion information is finally merged into a single total information, the target fusion information is obtained and used as the control basis for supercapacitor optimized management. The charging and discharging strategy of the supercapacitor is optimized according to the adaptive optimization algorithm. This management method uses a hierarchical optimized topology structure to ensure data synchronization and consistency, dynamically optimizes the management objectives, and provides real-time feedback to adjust the charging and discharging strategy, effectively improving the energy efficiency and stability of the supercapacitor.

Description

Technical Field

[0001] This invention relates to the field of capacitor management technology, specifically to a supercapacitor management system and method based on an adaptive optimization algorithm. Background Technology

[0002] Supercapacitors are energy storage devices characterized by high power density, long lifespan, and rapid charging and discharging. Compared to traditional batteries, supercapacitors can provide high power output in a short time and play a key role in meeting the instantaneous energy needs of electrical equipment. They are widely used in electric vehicles, renewable energy systems, industrial automation, and portable electronic devices. However, the energy density of supercapacitors is lower than that of traditional batteries, and optimizing their charging and discharging process, extending their lifespan, and improving energy efficiency remain key research areas.

[0003] The existing technology has the following drawbacks:

[0004] Existing management systems fail to fully consider the dynamic changes of supercapacitors under different operating conditions, lack adaptive capabilities, and cannot dynamically adjust charging and discharging strategies according to environmental changes, load fluctuations, or system status. This can easily lead to problems such as overcharging, over-discharging, or energy waste. Furthermore, the topology and parameter settings are usually fixed, lacking flexibility and real-time adjustment capabilities, and cannot effectively cope with uncertainties in practical applications.

[0005] Based on this, the present invention proposes a supercapacitor management system and method with an adaptive optimization algorithm. It adopts a hierarchical optimization topology structure to ensure data synchronization and consistency, dynamically optimizes management objectives, and adjusts charging and discharging strategies in real time, which effectively improves the energy efficiency and stability of supercapacitors. Summary of the Invention

[0006] The purpose of this invention is to provide a supercapacitor management system and method with an adaptive optimization algorithm to address the shortcomings in the prior art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a supercapacitor management method based on an adaptive optimization algorithm, the management method comprising the following steps:

[0008] S1: Based on the sensing range and positional relationship of multiple sensors, establish a hierarchical optimized topology for the supercapacitor management system;

[0009] S2: Acquire the supercapacitor sensing data sensed by each sensor respectively;

[0010] S3: Group multiple supercapacitor sensing data according to a hierarchical optimized topology, obtain timestamps, and then perform synchronous processing on the supercapacitor sensing data to obtain synchronous sensing information.

[0011] S4: Based on the synchronous sensing information, sort the supercapacitor management targets according to priority;

[0012] S5: Calculate the fusion confidence between every two supercapacitor management targets based on the sorting results;

[0013] S6: Perform data fusion and retention based on fusion confidence;

[0014] S7: Use all retained synchronous sensing information as the first fusion information;

[0015] S8: When all the first fusion information is finally merged into a total information, the target fusion information is obtained, which is used as the control basis for supercapacitor optimization management, and the charging and discharging strategy of the supercapacitor is optimized according to the adaptive optimization algorithm.

[0016] In a preferred embodiment, step S4 involves prioritizing the supercapacitor management targets based on the synchronous sensing information, including the following steps:

[0017] Management objectives include charge / discharge control, voltage stability, temperature monitoring, health status monitoring, and power scheduling optimization;

[0018] The importance of each management objective is determined by multiple factors, including security, real-time requirements, and operational efficiency;

[0019] Based on the priority score of each management objective, the management objectives are sorted from highest to lowest priority score.

[0020] In a preferred embodiment, step S5 involves sequentially calculating the fusion confidence level between every two supercapacitor management targets, including the following steps:

[0021] By calculating the Pearson correlation coefficient between the perceived data of management objective A and management objective B, a preliminary fusion confidence level is obtained;

[0022] Based on the fusion weights of management objective A and management objective B, adjust the fusion confidence level to obtain the weighted fusion confidence level;

[0023] If the weighted fusion confidence score of management objective A and management objective B is greater than or equal to the confidence score threshold, then data fusion is performed. If the weighted fusion confidence score of management objective A and management objective B is lower than the confidence score threshold, it indicates that the perceived data of management objective A and management objective B are significantly different and need to be processed separately.

[0024] In a preferred embodiment, the weighted fusion confidence score is calculated as follows: In the formula, For the fusion weight of management objective A, For the fusion weight of management objective B, The Pearson correlation coefficient for the perceived data. For weighted fusion confidence.

[0025] In a preferred embodiment, step S6, which involves data fusion and retention based on fusion confidence, includes the following steps:

[0026] If the weighted fusion confidence score between two management objectives is greater than or equal to the confidence score threshold, the synchronously perceived information of the management objectives is fused and updated to the fused information;

[0027] If the weighted fusion confidence score between two management objectives is less than the confidence score threshold, the synchronous perception information of the two management objectives is retained.

[0028] After the data from the two management objectives is merged, the data for the lower-priority objective is deleted, while the data for the higher-priority objective is retained.

[0029] In a preferred embodiment, step S8, when all the first fusion information is finally merged into a total information, yields the target fusion information, including the following steps:

[0030] The retained synchronous sensing information is integrated into several sets of first fusion information. Based on the current optimization goals and data requirements, the several sets of first fusion information are merged into target fusion information.

[0031] The first fusion information of voltage control, temperature control, and charge / discharge management objectives is merged to form the total information that includes comprehensive information of voltage, current, and temperature, which is the target fusion information.

[0032] In a preferred embodiment, step S3 involves grouping multiple supercapacitor sensing data according to a hierarchical optimized topology and obtaining timestamps for synchronization, including the following steps:

[0033] In the topology, the measurement data from each sensor is divided into several groups, including:

[0034] Group 1: Voltage group, containing all voltage-related sensor data;

[0035] Group 2: Current group, including all current sensor data;

[0036] Group 3: Temperature Group, covering all temperature sensors;

[0037] Group 4: Health status group, which includes internal resistance and leakage current, and is used to reflect the health status of the capacitor;

[0038] Each time the sensor collects data, the main controller records the current timestamp and synchronizes the data based on the timestamp, so that all data are based on the same time window.

[0039] In a preferred embodiment, step S2 involves acquiring the supercapacitor sensing data sensed by each sensor, including the following steps:

[0040] A voltage sensor monitors the terminal voltage of the supercapacitor in real time, a current sensor measures the current during charging and discharging, and a temperature sensor monitors the capacitor temperature.

[0041] In a preferred embodiment, step S1 involves establishing a hierarchical optimized topology for the supercapacitor management system based on the sensing range and positional relationships of multiple sensors, including the following steps:

[0042] Based on the function of the sensor and the target being monitored, the sensors are divided into different levels;

[0043] The data collected by sensors at each level will undergo preliminary processing within their respective levels.

[0044] In highly dynamic environments, the topology is dynamically adjusted based on real-time monitoring data.

[0045] This application also provides a supercapacitor management system with an adaptive optimization algorithm, including a topology establishment module, a data grouping module, a data sorting and retention module, and an adaptive optimization module;

[0046] Topology establishment module: Based on the sensing range and positional relationship of multiple sensors, a hierarchical optimized topology of the supercapacitor management system is established;

[0047] Data grouping module: Acquires supercapacitor sensing data from each sensor, groups multiple supercapacitor sensing data according to a hierarchical optimized topology, and performs synchronization processing on the supercapacitor sensing data after obtaining the timestamp.

[0048] Data sorting and retention module: Based on the synchronous sensing information, the supercapacitor management targets are sorted according to priority. Based on the sorting results, the fusion confidence between each pair of supercapacitor management targets is calculated in turn. Data fusion and retention are performed based on the fusion confidence, and all retained synchronous sensing information is used as the first fusion information.

[0049] Adaptive optimization module: When all the first fusion information is finally merged into a total information, the target fusion information is obtained, which is used as the control basis for supercapacitor optimization management, and the charging and discharging strategy of the supercapacitor is optimized according to the adaptive optimization algorithm.

[0050] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0051] This invention establishes a hierarchical optimized topology for a supercapacitor management system. Multiple supercapacitor sensing data are grouped according to this topology. Based on synchronous sensing information, supercapacitor management targets are prioritized. The fusion confidence score between each pair of supercapacitor management targets is calculated sequentially based on the ranking. Data fusion and retention are then performed based on the fusion confidence score. All retained synchronous sensing information is used as the first fusion information. When all first fusion information is finally merged into a single total information, the target fusion information is obtained and used as the control basis for supercapacitor optimized management. An adaptive optimization algorithm is then used to optimize the supercapacitor's charging and discharging strategy. This management method uses a hierarchical optimized topology to ensure data synchronization and consistency, dynamically optimizes management targets, avoids fixed parameter settings and single control logic in traditional methods, and provides real-time feedback to adjust the charging and discharging strategy, effectively improving the energy efficiency and stability of the supercapacitor. Attached Figure Description

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0053] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Example: Please refer to Figure 1 As shown in this embodiment, a supercapacitor management method based on an adaptive optimization algorithm includes the following steps:

[0056] S1: Based on the sensing range and location relationship of multiple sensors, establish a hierarchical optimized topology for the supercapacitor management system.

[0057] Based on the sensing range and relative positions of multiple sensors (such as voltage sensors, current sensors, temperature sensors, etc.) within the system, a hierarchical optimized topology is established. This structure will help to rationally allocate sensor data and improve system efficiency.

[0058] S2: Acquire the supercapacitor-related data sensed by each sensor.

[0059] Real-time data (such as voltage, current, temperature, internal resistance, etc.) of the supercapacitor are acquired from various sensors. This data represents the current operating status and health condition of the supercapacitor.

[0060] S3: Group multiple supercapacitor sensing data according to a hierarchical optimized topology and obtain timestamps for synchronization.

[0061] Based on the hierarchical topology established in the previous step, the sensing information from multiple sensors is divided into several groups, with each group containing multiple sensing information pieces. To ensure data consistency and accuracy, the timestamps of each sensing information piece are obtained, and the data is synchronized to acquire synchronized sensing information.

[0062] S4: Based on the synchronous sensing information, prioritize the supercapacitor management objectives.

[0063] Based on the synchronized sensing information, the supercapacitor management objectives (such as charge and discharge control, voltage stability, temperature monitoring, etc.) are prioritized to ensure that the most critical objectives are addressed first.

[0064] S5: Calculate the fusion confidence level between every two supercapacitor management targets sequentially.

[0065] After sorting, the system calculates the fusion confidence score between every two targets. The fusion confidence score represents the consistency and reliability of the perceived data of the two targets in terms of control strategy.

[0066] S6: Perform data fusion and retention based on fusion confidence.

[0067] When the weighted fusion confidence between two targets exceeds a set threshold, the system will fuse the synchronous sensing information of the two targets and update the information with the fused data. The system will use the fused information as reference data for high-priority targets and delete the synchronous data of low-priority targets.

[0068] When the fusion confidence level is less than the threshold, the system will retain the synchronous perception information of the two targets to ensure that no data is lost.

[0069] S7: Use all retained synchronous sensing information as the first fusion information.

[0070] Each set of retained synchronous sensing information is used as the first fusion information.

[0071] S8: If the first fusion information contains only one element, obtain the target fusion information.

[0072] When all the initial fusion information is finally merged into a total information, the system obtains the target fusion information, which serves as the control basis for optimizing the supercapacitor management system. This information will be used to further optimize the charging and discharging strategy of the supercapacitor based on the adaptive optimization algorithm, and adjust energy management and performance scheduling.

[0073] This application establishes a hierarchical optimized topology for a supercapacitor management system. Multiple supercapacitor sensing data are grouped according to this topology. Based on synchronous sensing information, supercapacitor management targets are prioritized. The fusion confidence score between each pair of supercapacitor management targets is calculated sequentially based on the ranking. Data fusion and retention are then performed based on the fusion confidence score. All retained synchronous sensing information is used as the first fusion information. When all first fusion information is finally merged into a single total information, the target fusion information is obtained and used as the control basis for supercapacitor optimized management. An adaptive optimization algorithm is then used to optimize the supercapacitor's charging and discharging strategy. This management method uses a hierarchical optimized topology to ensure data synchronization and consistency, dynamically optimizes management targets, avoids fixed parameter settings and single control logic in traditional methods, and provides real-time feedback to adjust the charging and discharging strategy, effectively improving the energy efficiency and stability of the supercapacitor.

[0074] The supercapacitor management system with an adaptive optimization algorithm described in this embodiment includes a topology establishment module, a data grouping module, a data sorting and retention module, and an adaptive optimization module.

[0075] Topology establishment module: Based on the sensing range and positional relationship of multiple sensors, a hierarchical optimized topology of the supercapacitor management system is established, and the hierarchical optimized topology is sent to the data grouping module and the data sorting and retention module.

[0076] Data grouping module: acquires supercapacitor sensing data from each sensor, groups multiple supercapacitor sensing data according to a hierarchical optimized topology, and performs synchronization processing on the supercapacitor sensing data after obtaining timestamps. The synchronization processing results are sent to the data sorting and retention module.

[0077] Data sorting and retention module: Based on the synchronous sensing information, the supercapacitor management targets are sorted according to priority. Based on the sorting results, the fusion confidence between each pair of supercapacitor management targets is calculated in turn. Data fusion and retention are performed based on the fusion confidence. All retained synchronous sensing information is used as the first fusion information and sent to the adaptive optimization module.

[0078] Adaptive optimization module: When all the first fusion information is finally merged into a total information, the target fusion information is obtained, which is used as the control basis for supercapacitor optimization management, and the charging and discharging strategy of the supercapacitor is optimized according to the adaptive optimization algorithm.

[0079] S1: Based on the sensing range and location relationship of multiple sensors, establish a hierarchical optimized topology for the supercapacitor management system.

[0080] Based on the sensing range and relative positions of multiple sensors (such as voltage sensors, current sensors, temperature sensors, etc.) within the system, a hierarchical optimized topology is established. This structure will help to rationally allocate sensor data and improve system efficiency.

[0081] In supercapacitor management systems, sensors play a crucial role, with their sensing range and location directly impacting the accuracy and timeliness of data acquisition. To ensure the system can efficiently integrate data from different sensors, a reasonable hierarchical optimized topology must be established based on the sensors' operating environment and functional requirements. This structure not only optimizes information transmission efficiency but also enables effective resource allocation and data synchronization between different layers.

[0082] First, a supercapacitor management system typically includes multiple sensors, such as voltage sensors, current sensors, and temperature sensors, which are responsible for monitoring key parameters such as voltage, current, and temperature. The layout and location of these sensors should be determined based on their operating principles and the area to be sensed. For example, voltage and current sensors are usually placed at the input and output terminals of the supercapacitor to accurately measure the capacitor's charging and discharging state; temperature sensors should be placed in areas where overheating may occur, such as inside the capacitor or around the battery management system. Based on this, the hierarchical optimization topology can be designed from the following aspects:

[0083] First, based on their function and the targets they monitor, sensors are divided into different levels. For example, lower-level sensors are responsible for collecting basic physical quantity data (such as voltage and current), while higher-level sensors are responsible for integrating this data with the control system to provide a basis for decision-making. This hierarchical structure ensures the smooth transmission and management of data flow.

[0084] Data collected by sensors at each level undergoes preliminary processing within its respective level. Simple data processing and filtering algorithms, such as low-pass filters and mean filters, can remove noise from the data and ensure its accuracy. At higher levels, data from various sensors are aggregated and further analyzed to achieve overall system optimization.

[0085] In highly dynamic environments (such as applications with large load fluctuations and rapid temperature changes), the topology needs to be able to dynamically adjust based on real-time monitoring data. For example, when the temperature is too high or the current is overloaded, the system may need to readjust the sensor's operating mode or configuration to ensure stable system operation. This flexibility enhances the system's adaptability to environmental changes.

[0086] In a multi-level topology, data flow is a crucial component. The system needs a well-designed communication protocol to ensure stable and low-latency data transmission from lower-level sensors to higher-level control systems. For example, employing efficient network protocols (such as the CAN bus protocol) or data compression algorithms can reduce bandwidth consumption during data transmission and improve data transmission efficiency.

[0087] The specific processing logic includes:

[0088] Based on the monitoring range and accuracy requirements of each sensor, a corresponding sensing area is allocated. For example, a temperature sensor may need to be placed inside a supercapacitor, while voltage and current sensors can be placed at external connection points. This allocation can be based on actual measurement needs and physical limitations, ensuring that the measurement range of each sensor covers the most critical areas.

[0089] After initial processing of data collected by lower-level sensors, a more comprehensive mid-level dataset is synthesized through calculations, which is then processed by higher-level sensors for further decision-making. For example, if a current sensor detects an abnormal current, the system can calculate whether it is due to excessive load changes, excessive temperature, or capacitor failure, and select the optimal response strategy accordingly.

[0090] Based on real-time feedback data, the system should be able to dynamically adjust the operating modes of each sensor. For example, when the load changes abruptly, the supercapacitor management system may adjust the measurement accuracy of the temperature sensor or change the sensor's operating frequency according to real-time changes in current and voltage. This dynamic adjustment can optimize system response time and avoid excessive or unnecessary energy consumption.

[0091] By establishing this hierarchical optimized topology, the supercapacitor management system can efficiently integrate multi-sensor data, enabling real-time data processing and optimized control. This not only improves the system's response speed but also significantly enhances the stability and reliability of the supercapacitor under different operating conditions.

[0092] S2: Acquire the supercapacitor-related data sensed by each sensor.

[0093] Real-time data (such as voltage, current, temperature, internal resistance, etc.) of the supercapacitor are acquired from various sensors. This data represents the current operating status and health condition of the supercapacitor.

[0094] In a supercapacitor management system, real-time data acquisition is fundamental for system optimization and dynamic adjustment. The performance of a supercapacitor is closely related to its operating state. Multiple sensors monitor various physical quantities (such as voltage, current, temperature, and internal resistance), which effectively reflect the supercapacitor's working condition and health status. Therefore, accurately and in real-time acquiring data from each sensor and using it for subsequent analysis and decision-making is a crucial step in system design.

[0095] First, a supercapacitor management system typically consists of multiple sensors, each performing a specific monitoring task. For example, a voltage sensor monitors the supercapacitor's terminal voltage in real time, a current sensor measures the current during charging and discharging, a temperature sensor monitors the capacitor's temperature, and an internal resistance sensor assesses the capacitor's health. Furthermore, depending on the application scenario, other sensors may be added to further monitor environmental factors such as humidity and vibration. The data acquisition and processing flow is as follows:

[0096] A voltage sensor is installed at the port of the supercapacitor to monitor voltage changes across the capacitor in real time. By accurately measuring the voltage, the system can determine the supercapacitor's state of charge, remaining charge, and whether there is a risk of overcharging or over-discharging. Voltage data is typically acquired periodically at a certain sampling frequency (e.g., once per second) and converted into digital signals for processing.

[0097] Current sensors are typically connected to the charging and discharging circuit of a supercapacitor to monitor current fluctuations during the charging and discharging process. By collecting current data, the system can assess the charging and discharging speed and power requirements of the supercapacitor in real time. During system design, the accuracy and frequency of current sampling must be considered. Excessively high sampling frequencies may impose a computational burden, while excessively low frequencies may lead to untimely responses. Therefore, the current data acquisition frequency needs to be optimized based on load changes and response requirements.

[0098] Temperature sensors are typically placed inside supercapacitors or in critical locations, such as near the capacitor's electrodes. Temperature is a crucial factor affecting the performance and lifespan of supercapacitors; excessively high temperatures accelerate electrolyte aging and compromise capacitor stability. Temperature data collected by the sensors is periodically read and compared with set thresholds to determine whether the capacitor is operating within its safe temperature range.

[0099] Internal resistance sensors monitor changes in the internal resistance of supercapacitors. An increase in internal resistance usually indicates a decline in the capacitor's health, potentially affecting its charging and discharging efficiency and lifespan. By periodically measuring internal resistance, the system can promptly detect abnormal conditions in the supercapacitor. Internal resistance measurements are typically performed under specific conditions, such as during charging or in a static state, by measuring changes in current and voltage to estimate the internal resistance.

[0100] In practical applications, sensor data acquisition is not done independently; measurement data from each sensor needs to be processed synchronously to ensure the timeliness and accuracy of system analysis. To achieve this, the following processing logic is applied during data acquisition:

[0101] All sensor data acquisition is based on a unified time base, and the main controller triggers all sensors to collect data at the same time. This ensures that parameters (such as voltage, current, temperature, etc.) are accurately aligned during data analysis, thereby avoiding misjudgments caused by time delays or inconsistencies.

[0102] After acquiring the raw data, the main controller performs preliminary preprocessing, including noise filtering and data smoothing. Specific methods include using a low-pass filter to smooth voltage and current signals and eliminate high-frequency noise. Furthermore, temperature and internal resistance measurements are also calibrated to ensure accuracy and reliability.

[0103] Each batch of collected data is stored in the main controller's memory or external storage. During storage, the system timestamps each data point to track its timing during subsequent analysis. Data updates are performed at set intervals to ensure the system reflects the supercapacitor's operating status in real time. The system also sets thresholds to trigger an alarm mechanism when data measured by a sensor exceeds a preset range. For example, if a voltage sensor detects excessively low or high capacitor voltage, excessive current, or excessive temperature, the system immediately activates a protection mechanism, stops charging and discharging operations, and triggers an alarm to alert operators. Through this process, the supercapacitor management system can acquire data from various sensors in real time and ensure data accuracy through data synchronization, preprocessing, and correction. This real-time data provides a solid foundation for subsequent optimization decisions, charging and discharging strategy adjustments, and system health assessments.

[0104] S3: Group multiple supercapacitor sensing data according to a hierarchical optimized topology and obtain timestamps for synchronization.

[0105] Based on the hierarchical topology established in the previous step, the sensing information from multiple sensors is divided into several groups, with each group containing multiple sensing information pieces. To ensure data consistency and accuracy, the timestamps of each sensing information piece are obtained, and the data is synchronized.

[0106] In supercapacitor management systems, data synchronization is a crucial step in ensuring the consistency and reliability of sensor data. In the previous stage, an adaptive hierarchical topology was established to organize the sensor data. The goal of this step is to use this topology to rationally group data from different sensors and synchronize the data via timestamps, ensuring that all data accurately reflects the real-time status of the system in subsequent processing and optimization algorithms.

[0107] The location of each sensor within the topology determines its role and relationship in data processing. For example, voltage, current, and temperature sensors may belong to different levels, but they are all closely related to the same management objectives of the supercapacitor system. Based on the location and function of these sensors, the system will divide their measurement data into several groups:

[0108] Group 1: Voltage Group, containing all voltage-related sensor data (such as supercapacitor voltage, battery terminal voltage, etc.).

[0109] Group 2: Current group, including all current sensor data (such as charging current, discharging current, load current, etc.).

[0110] Group 3: Temperature Group, covering all temperature sensors (such as internal temperature of capacitors, ambient temperature, etc.).

[0111] Group 4: Health Status Group, which may include data reflecting the health status of the capacitor, such as internal resistance and leakage current.

[0112] Each data group is marked with the same timestamp, ensuring that all sensor data belong to the same time window during data processing. This data grouping helps the system perform different processing and analysis based on the data characteristics within each group.

[0113] To ensure the synchronization and consistency of data from multiple sensors, each data point must be accompanied by a precise timestamp. The timestamp is the basis for the system to perform unified time alignment of data from different sensors. For example, when a voltage sensor and a current sensor acquire voltage and current data respectively, these two data points need to be timestamped to ensure they were acquired within the same time period, thus enabling the data to be correlated and compared.

[0114] Each time a sensor acquires data, the main controller records a timestamp for the current system. The timestamp is typically generated by the system clock and is accurate to the millisecond level. All sensor timestamps are managed centrally by the system to ensure that their time records are synchronized.

[0115] Different sensors may have slight time differences due to hardware characteristics or data transmission delays. To eliminate this error, the system synchronizes data based on timestamps. For example, if the timestamps of voltage and current data differ by no more than a certain tolerance (e.g., 10 milliseconds), these two sets of data are considered synchronized and can be used for subsequent fusion and processing. If the time difference is too large, time interpolation or data compensation is required to ensure data consistency. The core algorithm for data synchronization typically employs the following steps:

[0116] For each set of sensor data, the data is aligned according to the timestamp. All data is based on a uniform time window, which may require time difference correction for each set of sensor data to ensure that the timestamps of each set of data are consistent.

[0117] If the time difference between two points in time is large, the system can use interpolation methods (such as linear interpolation or higher-order interpolation) to supplement the missing data. For example, between a current sensor and a voltage sensor, if the current data has a long delay, the missing data value can be calculated by interpolation based on the trend of current change.

[0118] In case of data loss or anomalies that may occur in certain sensors, the system will make inferences and corrections based on historical data or data from other sensors to ensure that the final dataset is complete and consistent.

[0119] Synchronized data is stored in a dedicated data cache or database, with each group of data arranged by timestamp and retaining the corresponding original timestamp. This grouping and synchronized storage method not only makes data management more efficient but also provides a stable data source for subsequent optimization algorithms. In practical applications, the system periodically cleans up expired data to ensure the real-time performance and efficiency of the data cache.

[0120] During synchronization, the system performs data verification to ensure the quality of each data point. If the synchronization error of a set of data is too large, or if there is a significant inconsistency with data from other sensors, the system will automatically perform calibration or mark it as abnormal data and isolate it. This step ensures that the data ultimately used by the system is reliable and efficient.

[0121] Example: Suppose that the voltage sensor, current sensor, and temperature sensor collect data at times T1, T2, and T3, respectively. If the time difference between T1, T2, and T3 is too large, the main controller will calculate the time difference of each set of data based on the timestamps of the sensors and perform interpolation or synchronization adjustment to ensure that these data correspond to the supercapacitor state at the same point in time, so that the data can be integrated to form a complete status report.

[0122] Through this process, the supercapacitor management system can ensure the consistency of data from multiple sensors over time, avoid data confusion at different points in time, and provide reliable data support for subsequent optimization and decision-making.

[0123] S4: Based on the synchronous sensing information, prioritize the supercapacitor management objectives.

[0124] Based on the synchronized sensing information, the supercapacitor management objectives (such as charge and discharge control, voltage stability, temperature monitoring, etc.) are prioritized to ensure that the most critical objectives are addressed first.

[0125] In supercapacitor management systems, prioritizing management objectives based on synchronous sensing information is a crucial step in ensuring efficient, stable, and safe system operation. Each management objective (such as charge / discharge control, voltage stability, and temperature monitoring) is essential for the normal operation of the system. However, due to limited system resources, it is impossible to process all objectives with equal priority simultaneously. Therefore, priorities must be dynamically adjusted based on factors such as the importance, real-time requirements, and system needs of the objectives to ensure that the most critical objectives are addressed first, preventing unnecessary delays or malfunctions in unforeseen circumstances.

[0126] It is necessary to clearly define the various management objectives in the supercapacitor system. Common management objectives include, but are not limited to:

[0127] Charge and discharge control: Ensure that the capacitor does not overcharge, over-discharge, or charge too quickly during charging and discharging.

[0128] Voltage stability: Maintaining a stable voltage across the capacitor to prevent damage or reduced efficiency caused by excessively high or low voltage.

[0129] Temperature monitoring: Monitors the temperature changes of the capacitor to ensure that the temperature is within the safe operating range and to prevent overheating from damaging the capacitor.

[0130] Health status monitoring: including internal resistance measurement, leakage current, etc., is used to determine whether the supercapacitor has a fault or performance degradation.

[0131] Power scheduling optimization: Optimize the power allocation of supercapacitors based on load changes and demand to ensure system energy efficiency.

[0132] The importance of various management objectives is usually determined by several factors, including:

[0133] System safety: Targets directly related to capacitor safety, such as voltage and temperature monitoring, typically have the highest priority. If the voltage is too high or too low, the capacitor may be damaged, and therefore must be addressed immediately.

[0134] Real-time requirements: Some objectives (such as charge and discharge control) require immediate adjustments based on real-time data and have high real-time requirements; while other objectives (such as health status monitoring) may have lower real-time requirements and can be processed with a slight delay.

[0135] System operating efficiency: Depending on load fluctuations and energy demand, power scheduling optimization may need to be prioritized to improve overall system efficiency.

[0136] Based on the priority score of each target, the system sorts the targets from highest to lowest priority. To ensure the real-time and dynamic nature of the sorting, the following algorithm logic can be used:

[0137] Each management objective is assigned a weight value based on different criteria. The weight value is evaluated based on the urgency, importance, and impact on the system of the objective.

[0138] The system monitors sensor data in real time and continuously adjusts the priority of various management objectives based on the latest sensing information. For example, if the battery voltage is close to its upper limit, the system will automatically increase the priority of the voltage stability objective to ensure that overcharging does not occur.

[0139] After all targets have had their weights defined, the system ranks them using a priority sorting algorithm. For each target i, its priority score P_i is calculated:

[0140] For each target, its importance score (I_i) is first calculated based on its current state and real-time data. This score can be obtained by measuring the deviation of sensor data from a set threshold.

[0141] Next, the system adjusts the weight (W_i) of the target based on its real-time or safety requirements. For example, the weight of voltage control increases when the voltage reaches a critical value, while the weight of health monitoring is relatively low.

[0142] Ultimately, the priority score P_i of the target is: P_i = I_i * W_i. The system will sort all targets according to P_i from high to low.

[0143] To eliminate the impact of varying degrees of fluctuation in detection parameters for different targets, we can standardize the fluctuation amplitude of each target's parameters to ensure that the impact of fluctuation on the weights is comparable. To this end, we can perform weighted adjustments based on the actual fluctuation range and safety threshold of the target's detection parameters.

[0144] The importance score (I_i) for each target is calculated based on the deviation of the monitored parameters from a set threshold, reflecting the target's impact on the system. This step is consistent with the previous one, for example:

[0145] Voltage control target: voltage deviation of 1V, I_i=1.

[0146] Temperature monitoring target: temperature deviation of 5℃, I_i=5.

[0147] Health status monitoring target: internal resistance deviation of 5Ω, I_i=5.

[0148] Different targets may have different fluctuation ranges and thresholds. For example, voltage control fluctuations may need to be accurate to 0.1V to cause safety issues, while temperature runaway may require fluctuations of 10°C to trigger an anomaly. To ensure that the impact of fluctuations on the weights of each target is consistent, we need to standardize the fluctuation amplitude.

[0149] Determine the standardization factor for each target. For example, set the maximum allowable fluctuation range for different targets according to system requirements (such as the maximum allowable fluctuation of voltage being ±1V, the maximum allowable fluctuation of temperature being ±5℃, etc.).

[0150] Calculate the standardized volatility: Divide the actual volatility of each target by its maximum permissible volatility range to obtain the standardized volatility. For example:

[0151] Voltage control target: Set the maximum allowable fluctuation to 1V, the actual fluctuation is 1V, and the standardized fluctuation amplitude is: 1 divided by 1 equals 1;

[0152] Temperature monitoring target: The maximum allowable fluctuation is set at 10℃, the actual fluctuation is 5℃, and the standardized fluctuation range is: 5 divided by 10 equals 0.5;

[0153] Health status monitoring target: Set the maximum allowable fluctuation to 10Ω, the actual fluctuation is 5Ω, and the standardized fluctuation amplitude is: 5 divided by 10 equals 0.5.

[0154] The evolution rate (R_i) of each target still reflects the rate of change of the monitored parameters for that target. The evolution rate of a target is calculated using standardized fluctuation amplitude and time. Assuming the rate of change for each target is known, we can adjust the rate using standardized fluctuation amplitude to ensure that targets with large fluctuation amplitudes are not ignored, as shown in the example below:

[0155] Voltage control target: voltage change rate of 0.5V / second, normalized fluctuation amplitude of 1, R_i=0.5.

[0156] Temperature monitoring target: the rate of temperature change is 1°C / second, the standardized fluctuation amplitude is 0.5, R_i=1*0.5=0.5.

[0157] Health status monitoring target: internal resistance change rate is 0.1Ω / second, the standardized fluctuation amplitude is 0.5, R_i=0.1*0.5=0.05.

[0158] The growth rates of all management objectives are summed to obtain a total growth rate of 1.05. The weight (W_i) of each objective is calculated based on its standardized fluctuation range and growth rate. The weight of an objective is equal to its growth rate divided by the total growth rate.

[0159] The target weight for voltage control is 0.5 divided by 1.05, which equals 0.476. The target weight for temperature monitoring is 0.5 divided by 1.05, which equals 0.476. The target weight for health status monitoring is 0.05 divided by 1.05, which equals 0.048.

[0160] The priority score (P_i) of each objective is the product of its importance score (I_i) and weight (W_i), therefore:

[0161] The priority score for voltage control target (P_i) is 1 multiplied by 0.47, which equals 0.476. The priority score for temperature monitoring target (P_i) is 5 multiplied by 0.476, which equals 2.38. The priority score for health status monitoring target (P_i) is 5 multiplied by 0.048, which equals 0.24.

[0162] Sort the data from highest to lowest according to the calculated priority score P_i. Sorting results:

[0163] Temperature monitoring target: P_i=2.38;

[0164] Voltage control target: P_i = 0.476;

[0165] Health status monitoring target: P_i=0.24.

[0166] The system needs to dynamically monitor the status changes of all targets and adjust their priorities in real time based on sensor data. For example, if the temperature is too high at a certain moment, the priority of the temperature monitoring target will increase, while the priority of charging / discharging control or other targets may decrease relatively. This ensures that the most urgent and important tasks are responded to and processed in the shortest possible time.

[0167] After sorting, the system will execute the targets sequentially according to priority, switching between different targets. To avoid wasting system resources and unnecessary conflicts, the system will check the current priority of a target when executing it. If a new target with a higher priority appears, it will immediately switch to that target. This strategy ensures that the system always prioritizes the most important tasks and reduces resource waste.

[0168] Example:

[0169] Suppose the system is currently charging, the current data is normal, but the temperature sensor detects that the capacitor temperature is approaching the set safety threshold, and the voltage data is also rising. If the temperature monitoring target has a higher priority in the priority ranking (for example, voltage and temperature monitoring are set to the highest priority), the system will prioritize temperature regulation to reduce the capacitor temperature and ensure that the capacitor does not overheat. Even if the charging control target has a relatively lower priority at this time, the system will temporarily postpone processing the charging process to address the temperature issue first.

[0170] Through the above steps, the system can dynamically adjust management strategies based on real-time data and the importance of each objective, ensuring that the supercapacitor is always in optimal operating condition and avoiding potential safety hazards or performance degradation.

[0171] S5: Calculate the fusion confidence level between every two supercapacitor management targets sequentially.

[0172] After sorting, the system calculates the fusion confidence score between every two targets. The fusion confidence score represents the consistency and reliability of the perceived data of the two targets in terms of control strategy.

[0173] In supercapacitor management systems, management objectives may exhibit correlations and mutual influences. Therefore, calculating the fusion confidence score between any two objectives is a crucial step. The fusion confidence score measures the consistency and reliability of the perceived data from two objectives in terms of control strategies, reflecting their effectiveness in collaborative optimization. By calculating the fusion confidence score, the system can determine whether data fusion is feasible between objectives or whether separate processing is necessary, thereby optimizing the overall management strategy.

[0174] The calculation of fusion confidence helps the system determine which objectives are well-coordinated in control strategies and which objectives may conflict during synchronous adjustments. In this way, the system can perform reasonable data fusion among multiple objectives, ensuring that the final optimized solution is both efficient and reliable. For example, when the data from voltage control and temperature monitoring objectives are highly consistent, their fusion confidence is high, and they can be merged into a unified adjustment strategy; conversely, if the data differ significantly, they may need to be processed separately to avoid negative impacts on the system. The fusion confidence calculation process is as follows:

[0175] Fusion confidence reflects the consistency of data relied upon for two management objectives during the control process. Suppose we have two objectives, objective A and objective B, each with different perceived data (such as voltage, current, temperature, etc.). The system calculates the similarity or consistency between these two objectives based on their perceived data to determine whether they can be effectively fused.

[0176] For each pair of targets A and B, the system first acquires their sensory data. Based on this data, their Pearson correlation coefficient is calculated, and this indicator can be evaluated using the following methods:

[0177] The linear relationship between target A and target B is quantified by calculating the Pearson correlation coefficient. A higher correlation coefficient indicates greater consistency between the perceived data of the two targets, and a higher fusion confidence level.

[0178] The Pearson correlation coefficient is an indicator that measures the strength of the linear relationship between two variables. Its calculation formula is as follows:

[0179] ,in: It is the Pearson correlation coefficient between target A and target B. and These are the first and second objectives of target A and target B, respectively. Sample data. and These are the average values ​​of target A and target B, respectively. , , where n is the total number of data samples.

[0180] For example, if the voltage control target and the current control target are highly correlated (e.g., the voltage changes accordingly when the current increases), then the fusion confidence level of the two will also be high.

[0181] Once the Pearson correlation coefficients of the data are obtained, the system will weight the consistency among different objectives. The fusion weights are typically set based on the importance, real-time nature, and impact on the system of the objective. For example, for voltage stability and temperature monitoring objectives, the voltage objective may have a higher fusion weight because voltage changes directly affect the safety and efficiency of the supercapacitor. Weighted fusion confidence calculation logic:

[0182] The correlation of each pair of targets is calculated to obtain the preliminary fusion confidence.

[0183] The fusion confidence score is adjusted based on the fusion weight of the objectives (e.g., objective importance or real-time requirements). For example, if objective A is a high-priority objective, the system will assign it a larger fusion weight, resulting in a higher fusion confidence score between objective A and other objectives. The calculation logic for the fusion weight is as follows: After obtaining the ranking results of all management objectives, the priority scores of all management objectives are summed to obtain the total score. The priority score of each management objective is divided by the total score to obtain its fusion weight. For example, the fusion weight of objective A is equal to its priority score divided by the total score. The weighted fusion confidence score formula is as follows: In the formula, For the fusion weight of management objective A, For the fusion weight of management objective B, The Pearson correlation coefficient for the perceived data. To weight the fusion confidence, this weighting method ensures that more important objectives have a greater influence in the fusion process.

[0184] Based on the calculated weighted fusion confidence score, the system can determine whether to fuse the data for two objectives. Typically, if the weighted fusion confidence score for the two management objectives is greater than or equal to a confidence threshold (e.g., a confidence threshold of 0.8), data fusion can be performed, merging the two objectives into a unified adjustment strategy. If the fusion confidence score is lower than the confidence threshold, it indicates that the perceived data for the two objectives differs significantly, and the system should process them separately to avoid potential control conflicts.

[0185] Suppose the system has two objectives—voltage control and temperature monitoring. Under high load operation, the data from the voltage and temperature sensors show a high correlation (i.e., as current increases, voltage and temperature change synchronously). Calculations show that the system achieves a high fusion confidence level for these two objectives, close to 0.9. This indicates good consistency in their sensing data, allowing them to be merged into a unified optimization strategy to ensure coordinated adjustment of voltage and temperature.

[0186] However, in another scenario, the data for voltage control and health status monitoring differ significantly (e.g., changes in voltage and internal resistance are not necessarily synchronized). In this case, their fusion confidence level is low, only 0.5, and the system will determine that fusion is not suitable, processing the two targets separately to avoid mutual interference.

[0187] By calculating the fusion confidence between any two objectives, the system can better manage multiple optimization objectives of supercapacitors. Objectives with high fusion confidence can be merged into a unified control strategy to ensure efficient and stable system operation; while objectives with low fusion confidence need to be handled separately to avoid potential control conflicts, thereby achieving more precise management and optimization.

[0188] S6: Perform data fusion and retention based on fusion confidence.

[0189] When the weighted fusion confidence score between two targets exceeds a set threshold, the system will fuse the synchronous sensing information of the two targets and update the data with the fused information. The system will use the fused information as reference data for high-priority targets and delete the synchronous data of low-priority targets. When the fusion confidence score is less than the threshold, the system will retain the synchronous sensing information of the two targets to ensure no data loss.

[0190] In supercapacitor management systems, data fusion is a crucial step in improving system efficiency and accuracy. By calculating the fusion confidence between any two targets, the system can decide whether to fuse the sensing data from multiple targets or retain their individual data. This step ensures that redundant data is minimized in the control strategy, while simultaneously improving the flexibility and accuracy of the system's response.

[0191] In the previous stage, the system assessed the consistency of the perceived data between each pair of targets by calculating the fusion confidence score. If the fusion confidence score between targets is greater than a preset threshold, it indicates that their data have high consistency in control strategies, and fusion processing can be considered. Conversely, if the fusion confidence score is lower than the threshold, it indicates that there are significant differences between the data of the two targets, and effective fusion may not be possible; in this case, the system retains the original data.

[0192] Typically, the system sets a threshold for fusion confidence (e.g., 0.8), meaning that the system will only perform data fusion operation when the fusion confidence is greater than 0.8. This threshold should be adjusted based on the system's operational requirements, the importance of the target, and data consistency requirements.

[0193] When the fusion confidence level exceeds a set threshold, the system will fuse the perceptual data of the two targets. The fusion process includes the following steps:

[0194] The system selects the higher-priority target from the two targets as the reference data after fusion. For example, if the fusion confidence of target A and target B is high, and the priority of target A is higher than that of target B, then the perceived data of target A will be used as the reference data after fusion.

[0195] During the fusion process, the system merges the synchronous sensing information of target A and target B. Specific methods may include weighted averaging of their voltage, current, and temperature data, or comprehensive analysis based on certain weighting rules, to ensure that the fused data is more accurate and conforms to the optimization strategy. The fused data will be updated in the data storage of target A and used as the basis for subsequent decision-making and control.

[0196] After data fusion is complete, the system will delete data from low-priority targets to avoid redundant information and ensure system efficiency. For example, if target B has a lower priority than target A, then after data fusion, data from target B will be cleared, and only data from target A will be retained.

[0197] When the fusion confidence between two targets is less than a set threshold, the system will not fuse their data, but will instead retain the synchronous sensing information of each target separately. The data retention process is as follows:

[0198] The system retains sensing data from both target A and target B and ensures that this data is not lost. For important parameters such as voltage, current, and temperature, the system continues to monitor and store this data for subsequent analysis and decision-making.

[0199] Even with low fusion confidence, the system still ensures that the perceived data of the two targets are synchronized and stored in the original timestamp order. This avoids erroneous judgments due to time differences or data loss, ensuring the data integrity of all targets.

[0200] During actual operation, the state of the supercapacitor may change, and the system will dynamically adjust the data fusion and retention strategy based on real-time sensing information. As sensor data is continuously updated, the fusion confidence level may also change. For example, when the ambient temperature rises sharply, the trends in current and voltage may change, causing discrepancies in data pairs that could have been fused. In this case, the system will reassess the fusion confidence level between the targets and decide whether to retain the data or re-fuse it based on the new data state.

[0201] The fused data is typically used for further decision-making and control processes. For example, if voltage and current control strategies have been fused, the system can adjust the supercapacitor's charging and discharging strategies based on the fused data. Temperature control strategies, if independent of voltage and current control (e.g., employing a separate temperature regulation mechanism), will have their data retained and processed separately. In this way, the system ensures coordinated operation between various objectives, maximizing the optimization of the supercapacitor's performance and efficiency.

[0202] Example:

[0203] Assuming the system is running, if the calculated fusion confidence score for the voltage control target and the current control target is 0.85, exceeding the preset threshold of 0.8, it indicates a high degree of consistency in the control strategy between the two targets. Therefore, the system will fuse the voltage control and current control data and update the voltage control target data. The current control target data will be deleted to avoid redundancy. However, if the fusion confidence score for the temperature control target and the current control target is only 0.6, below the threshold of 0.8, the system will retain the independent data for the current control target and the temperature control target to avoid impact and conflicts on the system.

[0204] By fusing and retaining target data based on fusion confidence levels, the system can dynamically adjust the processing method for target data, ensuring that the system always operates in an optimal state. Targets with high fusion confidence levels can be fused, improving system efficiency and response speed; targets with low fusion confidence levels are processed separately, ensuring system stability and data integrity. This strategy effectively avoids system resource waste and data redundancy, while improving the performance and reliability of the supercapacitor management system.

[0205] S7: Use all retained synchronous sensing information as the first fusion information.

[0206] Each set of retained synchronous sensing information is used as the first fusion information.

[0207] In a supercapacitor management system, the synchronous sensing information, after data fusion and retention, is a crucial foundation for further optimizing control strategies. The main task of this step is to integrate all retained synchronous sensing information into first-stage fused information for subsequent processing and optimization. Through this process, the system ensures the integrity and accuracy of the data, while providing reliable data support for subsequent control decisions.

[0208] In the previous step, the system calculated the fusion confidence score for each pair of target data and decided whether to fuse the data based on the result. When the fusion confidence score is low, the system retains the original synchronous sensing information of each target. At this time, the retained synchronous sensing information represents the original state of each target, including but not limited to key parameters such as voltage, current, temperature, and internal resistance. The synchronous sensing information of each target is timestamped to ensure that they are consistent in time, thereby accurately reflecting the actual state of the supercapacitor in subsequent processing.

[0209] The first set of fused information refers to the collection of each set of synchronized sensing information retained by the system after processing. Each set of data represents an independent state of a target, and after synchronization, they will be presented with a consistent time standard. In this stage, the system integrates all retained synchronized data to provide a unified basis for subsequent decision-making. For example, if the data of voltage control target and temperature monitoring target are not fused, the system will retain the data of these two targets as separate sets of synchronized sensing information, which will be stored and managed separately.

[0210] Using each set of retained synchronous sensing information as the first fusion information means that the system merges and stores each set of data. At this point, the system arranges all retained data in chronological order, ensuring data continuity and integrity. To ensure the efficiency and accuracy of data integration, the system typically employs the following methods: all data must be aligned to the same timestamp. Since different sensors may have slight time deviations, the system aligns them using timestamps to ensure effective data integration within the same time window. To reduce deviations caused by different data sources, the system can standardize each set of data, enabling effective comparison of parameters between different targets. For example, voltage and temperature data may have different units; the system converts them to a unified standard unit to ensure consistency in subsequent processing. Through these methods, the retained synchronous sensing information is integrated into the first fusion information according to the time sequence, facilitating subsequent unified optimization decisions and control strategy calculations.

[0211] The system can merge and store each set of data one by one based on timestamps. For example, by setting a time window T, the system will integrate all synchronous sensing data within that time window in chronological order. The merged data will form new state information, which can be used by subsequent control systems. The calculation logic of the integration process is as follows: First, the system removes invalid or abnormal data from the retained synchronous sensing data. For example, if a sensor malfunctions, causing its returned data to be highly abnormal, the system will automatically discard this data that does not conform to the reasonable range. The retained synchronous sensing information is sorted by timestamp from earliest to latest for subsequent processing. In the sorted data, the system merges the sensing information of each target one by one according to the timestamp. If the data of two targets have not been merged, their independent states are maintained; if their data have been merged, the system will use the merged data as new input.

[0212] The first fused information is a crucial foundation for subsequent control decisions and optimization algorithms. Through the integrated data, the system can monitor and adjust the supercapacitor in real time. For example, in later steps, the optimization algorithm will use this integrated data to adjust charging and discharging strategies, optimize power scheduling, and even predict the supercapacitor's remaining lifetime based on health monitoring information. The first fused information provides high-quality, synchronized input data for these decisions, ensuring the accuracy of the optimization process and the stability of the system.

[0213] As the system progresses, the supercapacitor's state changes, and new synchronous sensing information is continuously acquired and integrated into new primary fusion information. The system dynamically updates this information to ensure that real-time optimization decisions are always based on the latest data. For example, when key parameters such as temperature and internal resistance change, the system reassesses the supercapacitor's operating state based on the new data and adjusts the control strategy accordingly.

[0214] Example:

[0215] Assume that during system operation, data from the voltage control target and the temperature monitoring target are retained and synchronized. The sensing information for the voltage control target includes the voltage value and its changing trend, while the sensing information for the temperature monitoring target includes the temperature value and its changing trend. The system aligns these two sets of data according to their timestamps and merges them to form the first fused information. This information serves as input for subsequent decisions, helping the system adjust the charging current and temperature control strategies to ensure the capacitor operates safely and efficiently.

[0216] By using all retained synchronous sensing information as the first fusion information, the system can integrate data from multiple targets into consistent and usable state information, providing reliable data support for subsequent optimization algorithms. This process not only ensures data integrity and real-time performance but also provides a solid data foundation for the optimized management of supercapacitors, thereby improving the overall efficiency and stability of the system.

[0217] S8: If the first fusion information contains only one element, obtain the target fusion information.

[0218] When all the initial fusion information is finally merged into a total information, the system obtains the target fusion information, which serves as the control basis for optimizing the supercapacitor management system. This information will be used to further optimize the charging and discharging strategy of the supercapacitor based on the adaptive optimization algorithm, and adjust energy management and performance scheduling.

[0219] In a supercapacitor management system, the target fusion information is obtained when all the initial fusion information is finally combined into a single overall information. This process is a crucial step in system optimization, providing a comprehensive and optimized decision-making basis by integrating all sensing data and control objectives. The target fusion information serves as the core input for optimizing charging and discharging strategies, energy management, and performance scheduling, ensuring the supercapacitor operates at its optimal state under different load and environmental conditions.

[0220] In the previous step, the system has integrated the retained synchronous sensing information into several sets of first fusion information. After these first fusion information have been processed, the system will further merge this information into a unified, overall target fusion information based on the current optimization goals and data requirements.

[0221] The generation of target fusion information depends on merging data from various management targets. For example, if targets such as voltage control, temperature control, and charge / discharge management have each generated independent first fusion information, the system will merge these information to form a total information that includes comprehensive data such as voltage, current, and temperature.

[0222] Merging operations can be performed using methods such as weighted averaging, maximum value selection, and linear interpolation. The merged target information can more comprehensively reflect the overall operating status of the supercapacitor, facilitating unified optimization for subsequent decisions. For example, if temperature and voltage control targets are merged, the system will integrate these two factors to optimize voltage and temperature changes during the charging process.

[0223] The process of generating target fusion information is not just about merging multiple data sets together, but more importantly, about extracting useful feature information from them, which will directly affect subsequent optimization decisions.

[0224] The system will perform feature analysis on the merged target information to extract key operating characteristics of the capacitor, such as voltage fluctuation range, current change trend, and temperature change rate. These characteristics will serve as input parameters for subsequent decision-making, ensuring the accurate adjustment of the optimization strategy.

[0225] The system also performs state estimation based on target fusion information, assessing the current health status, charging efficiency, and energy storage status of the supercapacitor. This estimation result provides an accurate system model for the optimization algorithm, enabling subsequent performance scheduling and energy efficiency optimization. Ultimately, the target fusion information serves as the control basis for the supercapacitor management system, helping it execute precise charge / discharge control, energy management, and performance scheduling. For example:

[0226] By analyzing target fusion information, the system can optimize current, power, and voltage during charging and discharging in real time to ensure the capacitor reaches the target voltage in the shortest possible time while avoiding overcharging or over-discharging. The charge and current data in the target fusion information help the system adjust its energy allocation strategy, ensuring optimal efficiency in the supercapacitor's energy storage and release processes. Based on the fused target information, the system can dynamically schedule the supercapacitor's operating strategy under different loads and working environments, maximizing energy efficiency and extending its lifespan. For example, under conditions of significant load fluctuations, the system will use the target fusion information to adjust the supercapacitor's charging and discharging rate, thereby avoiding unnecessary energy waste.

[0227] Target fusion information, as the core basis for system optimization, directly affects the execution of the adaptive optimization algorithm. Through analysis of the target fusion information, the optimization algorithm can adjust its control strategy in real time, automatically responding to different operating conditions and environmental changes. Specifically, the algorithm dynamically adjusts the charging strategy based on the target fusion information, optimizing power scheduling and load balancing to ensure the system remains in optimal condition under various operating conditions. The adaptive optimization algorithm continuously monitors changes in the target fusion information and adjusts its control strategy accordingly. Whenever the system state changes, the target fusion information is promptly fed back to the optimization algorithm, guiding the system to adjust its operating mode. For example, when voltage and temperature exceed preset thresholds, the optimization algorithm immediately adjusts the charging rate to avoid overheating caused by excessively fast charging; when load demand increases, the system adjusts the charge-discharge balance based on the target fusion information to prevent system overload.

[0228] Example:

[0229] Assuming that at a certain moment, the voltage control target, current control target, and temperature control target each provide independent initial fusion information, the system will merge this information into a single target fusion information, which includes comprehensive data on multiple parameters such as voltage, current, and temperature. By analyzing this information, the system detects that the current voltage is high and the temperature is close to the safe upper limit. Therefore, the optimization algorithm will adjust the charging rate based on the target fusion information to reduce the rate of temperature rise, ensure stable capacitor charging, and avoid overcharging and overheating.

[0230] By merging all the initial fusion information into target fusion information, the system can provide comprehensive and unified control basis, providing accurate data support for subsequent adaptive optimization algorithms. Target fusion information not only effectively integrates the data of each target, but also provides a reliable decision-making basis for charging and discharging strategies, energy management, and performance scheduling, ensuring that the supercapacitor always maintains optimal operating conditions under varying operating conditions.

[0231] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0232] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A supercapacitor management method based on an adaptive optimization algorithm, characterized in that: The management method includes the following steps: S1: Based on the sensing range and positional relationship of multiple sensors, establish a hierarchical optimized topology for the supercapacitor management system, including the following steps: Based on the function of the sensor and the target being monitored, the sensors are divided into different levels; The data collected by sensors at each level will undergo preliminary processing within their respective levels. In highly dynamic environments, the topology is dynamically adjusted based on real-time monitoring data; S2: Acquire the supercapacitor sensing data sensed by each sensor respectively; S3: Group multiple supercapacitor sensing data according to a hierarchical optimized topology, obtain timestamps, and then perform synchronous processing on the supercapacitor sensing data to obtain synchronous sensing information. S4: Based on the synchronous sensing information, sort the supercapacitor management targets according to priority; S5: Calculate the fusion confidence between every two supercapacitor management targets based on the sorting results; The fusion confidence level between every two supercapacitor management objectives is calculated sequentially, including the following steps: By calculating the Pearson correlation coefficient between the perceived data of management objective A and management objective B, a preliminary fusion confidence level is obtained; Based on the fusion weights of management objective A and management objective B, adjust the fusion confidence level to obtain the weighted fusion confidence level; S6: Perform data fusion and retention based on fusion confidence; S7: Use all retained synchronous sensing information as the first fusion information; S8: When all the first fusion information is finally merged into a total information, the target fusion information is obtained, which is used as the control basis for supercapacitor optimization management, and the charging and discharging strategy of the supercapacitor is optimized according to the adaptive optimization algorithm.

2. The supercapacitor management method based on an adaptive optimization algorithm according to claim 1, characterized in that: In step S4, the supercapacitor management targets are prioritized based on the synchronous sensing information, including the following steps: Management objectives include charge / discharge control, voltage stability, temperature monitoring, health status monitoring, and power scheduling optimization; The importance of each management objective is determined by multiple factors, including security, real-time requirements, and operational efficiency; Based on the priority score of each management objective, the management objectives are sorted from highest to lowest priority score.

3. The supercapacitor management method based on an adaptive optimization algorithm according to claim 2, characterized in that: After obtaining the weighted fusion confidence score, if the weighted fusion confidence score of management target A and management target B is greater than or equal to the confidence score threshold, then data fusion is performed. If the weighted fusion confidence score of management target A and management target B is lower than the confidence score threshold, it indicates that the perceived data of management target A and management target B are significantly different and need to be processed separately.

4. The supercapacitor management method based on an adaptive optimization algorithm according to claim 3, characterized in that: The expression for calculating the weighted fusion confidence score is: In the formula, For the fusion weight of management objective A, For the fusion weight of management objective B, The Pearson correlation coefficient for the perceived data. For weighted fusion confidence.

5. The supercapacitor management method based on an adaptive optimization algorithm according to claim 4, characterized in that: In step S6, data fusion and retention are performed based on the fusion confidence level, including the following steps: If the weighted fusion confidence score between two management objectives is greater than or equal to the confidence score threshold, the synchronously perceived information of the management objectives is fused and updated to the fused information; If the weighted fusion confidence score between two management objectives is less than the confidence score threshold, the synchronous perception information of the two management objectives is retained. After the data from the two management objectives is merged, the data for the lower-priority objective is deleted, while the data for the higher-priority objective is retained.

6. The supercapacitor management method based on an adaptive optimization algorithm according to claim 5, characterized in that: In step S8, when all the first fusion information is finally merged into a total information, the target fusion information is obtained, including the following steps: The retained synchronous sensing information is integrated into several sets of first fusion information. Based on the current optimization goals and data requirements, the several sets of first fusion information are merged into target fusion information. The first fusion information of voltage control, temperature control, and charge / discharge management objectives is merged to form the total information that includes comprehensive information of voltage, current, and temperature, which is the target fusion information.

7. The supercapacitor management method based on an adaptive optimization algorithm according to claim 6, characterized in that: In step S3, multiple supercapacitor sensing data are grouped according to a hierarchical optimized topology and timestamps are obtained for synchronization, including the following steps: In the topology, the measurement data from each sensor is divided into several groups, including: Group 1: Voltage group, containing all voltage-related sensor data; Group 2: Current group, including all current sensor data; Group 3: Temperature Group, covering all temperature sensors; Group 4: Health status group, which includes internal resistance and leakage current, and is used to reflect the health status of the capacitor; Each time the sensor collects data, the main controller records the current timestamp and synchronizes the data based on the timestamp, so that all data are based on the same time window.

8. The supercapacitor management method based on an adaptive optimization algorithm according to claim 7, characterized in that: In step S2, the supercapacitor sensing data sensed by each sensor is acquired, including the following steps: A voltage sensor monitors the terminal voltage of the supercapacitor in real time, a current sensor measures the current during charging and discharging, and a temperature sensor monitors the capacitor temperature.

9. A supercapacitor management system based on an adaptive optimization algorithm, used to implement the management method according to any one of claims 1-8, characterized in that: It includes a topology building module, a data grouping module, a data sorting and retention module, and an adaptive optimization module; Topology establishment module: Based on the sensing range and positional relationship of multiple sensors, a hierarchical optimized topology of the supercapacitor management system is established; Data grouping module: Acquires supercapacitor sensing data from each sensor, groups multiple supercapacitor sensing data according to a hierarchical optimized topology, and performs synchronization processing on the supercapacitor sensing data after obtaining timestamps. Data sorting and retention module: Based on the synchronous sensing information, the supercapacitor management targets are sorted according to priority. Based on the sorting results, the fusion confidence between each pair of supercapacitor management targets is calculated in turn. Data fusion and retention are performed based on the fusion confidence, and all retained synchronous sensing information is used as the first fusion information. Adaptive optimization module: When all the first fusion information is finally merged into a total information, the target fusion information is obtained, which is used as the control basis for supercapacitor optimization management, and the charging and discharging strategy of the supercapacitor is optimized according to the adaptive optimization algorithm.

Images