Super capacitor management system and method of adaptive optimization algorithm

Abstract

The invention discloses a super capacitor management system and method based on an adaptive optimization algorithm, and relates to the technical field of capacitor management. A hierarchical optimization topological structure of the super capacitor management system is established, and super capacitor management targets are sorted according to priorities; and sequentially calculating the fusion confidence coefficient between every two super capacitor management targets based on the sorting result, carrying out data fusion and retention according to the fusion confidence coefficient, taking all the retained synchronous sensing information as first fusion information, obtaining target fusion information when all the first fusion information is finally merged into one piece of total information, and sending the target fusion information to the super capacitor management targets. The method is used as a control basis for optimization management of the super-capacitor, and optimizes a charging and discharging strategy of the super-capacitor according to an adaptive optimization algorithm. According to the management method, a hierarchical optimization topological structure is adopted to ensure the synchronism and consistency of data, a management target is dynamically optimized, a charging and discharging strategy is fed back and adjusted in real time, and the energy efficiency and stability of the super capacitor are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of capacitor management, and in particular to a supercapacitor management system and method based on an adaptive optimization algorithm. Background Art

[0002] Supercapacitor (also known as ultracapacitor, ultracapacitor) is an energy storage device with the characteristics of high power density, long life cycle, and fast charging and discharging. Compared with traditional batteries, supercapacitors provide high power output in a short period of time and play a key role in the instantaneous energy demand of power 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 during use, how to optimize their charging and discharging process, extend their service life, and improve energy efficiency are still research focuses.

[0003] The existing technology has the following defects:

[0004] Existing management systems fail to fully consider the dynamic changes of supercapacitors under different working conditions, lack adaptive capabilities, and are unable to 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. In addition, the topology and parameter settings are usually fixed, lacking flexibility and real-time adjustment capabilities, and cannot effectively deal with uncertainties in practical applications.

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

[0006] The purpose of the present invention is to provide a supercapacitor management system and method with an adaptive optimization algorithm to address the shortcomings of the background technology.

[0007] In order to achieve the above object, 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: Establish a hierarchical optimized topology of the supercapacitor management system based on the sensing range and position relationship of multiple sensors;

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

[0010] S3: Grouping multiple supercapacitor sensor data according to a hierarchical optimized topology structure, obtaining timestamps, and synchronously processing the supercapacitor sensor data to obtain synchronous sensing information;

[0011] S4: sorting the supercapacitor management targets according to priority based on the synchronous sensing information;

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

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

[0014] S7: taking all retained synchronous perception information as the first fusion information;

[0015] S8: When all the first fusion information is finally merged into a total information, target fusion information is obtained, which is used as a 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, in step S4, the supercapacitor management targets are sorted according to priority based on the synchronous sensing information, including the following steps:

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

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

[0019] According to the priority score of each management objective, the management objectives are sorted from high to low priority.

[0020] In a preferred embodiment, in step S5, the fusion confidence between each two supercapacitor management targets is calculated in sequence, including the following steps:

[0021] By calculating the Pearson correlation coefficient between the perception data of management target A and management target B, the preliminary fusion confidence is obtained;

[0022] According to the fusion weights of management target A and management target B, the fusion confidence is adjusted to obtain the weighted fusion confidence;

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

[0024] In a preferred embodiment, the calculation expression of the weighted fusion confidence is: , where To manage the fusion weight of target A, To manage the fusion weight of target B, is the Pearson correlation coefficient of the perception data, is the weighted fusion confidence.

[0025] In a preferred embodiment, in step S6, data fusion and retention are performed according to the fusion confidence, including the following steps:

[0026] If the weighted fusion confidence between the two management targets is greater than or equal to the confidence threshold, the synchronous perception information of the management targets is fused and updated to the fused information;

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

[0028] After the data fusion of the two management targets is completed, the data of the low-priority target is deleted and the data of the high-priority target is retained.

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

[0030] Integrate the retained synchronous perception information into several groups of first fusion information, and merge the several groups of first fusion information into target fusion information according to the current optimization goal and data requirements;

[0031] The first fusion information of the voltage control, temperature control, and charge and discharge management targets is merged to form total information including comprehensive information of voltage, current, and temperature, that is, target fusion information is obtained.

[0032] In a preferred embodiment, in step S3, the plurality of supercapacitor sensing data are grouped according to a hierarchical optimized topology structure, and timestamps are obtained for synchronization, including the following steps:

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

[0034] Group 1: Voltage group, including 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, including internal resistance and leakage current, which are used to reflect the health status of capacitors;

[0038] Every 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, in step S2, respectively acquiring the supercapacitor sensing data sensed by each sensor includes the following steps:

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

[0041] In a preferred embodiment, in step S1, a hierarchical optimized topology structure of a supercapacitor management system is established based on the sensing range and position relationship of multiple sensors, including the following steps:

[0042] Sensors are divided into different levels according to their functions and monitored targets;

[0043] The data collected by sensors at each level will be preliminarily processed within the level where it is located;

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

[0045] The present 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 building module: Based on the sensing range and position relationship of multiple sensors, a hierarchical optimized topology structure of the supercapacitor management system is established;

[0047] Data grouping module: acquires the supercapacitor sensor data sensed by each sensor, groups multiple supercapacitor sensor data according to a hierarchical optimized topology structure, obtains the timestamp, and performs synchronous processing on the supercapacitor sensor data;

[0048] Data sorting and retention module: Based on the synchronous perception information, the supercapacitor management targets are sorted according to priority. Based on the sorting results, the fusion confidence between each two supercapacitor management targets is calculated in sequence. Data is fused and retained based on the fusion confidence, and all retained synchronous perception 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 the supercapacitor optimization management, and the supercapacitor charging and discharging strategy is optimized according to the adaptive optimization algorithm.

[0050] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0051] The present invention establishes a hierarchical optimization topology structure of a supercapacitor management system, groups multiple supercapacitor sensor data according to the hierarchical optimization topology structure, sorts supercapacitor management targets according to priority based on synchronous perception information, calculates the fusion confidence between each two supercapacitor management targets based on the sorting results, fuses and retains data according to the fusion confidence, uses all retained synchronous perception information as the first fusion information, and when all the first fusion information are finally merged into a total information, obtains the target fusion information, which is used as the control basis for supercapacitor optimization management, and optimizes the supercapacitor charging and discharging strategy according to the adaptive optimization algorithm. The management method adopts a hierarchical optimization topology structure to ensure the synchronization and consistency of data, dynamically optimizes the management target, avoids the fixed parameter setting and single control logic in the traditional method, adjusts the charging and discharging strategy with real-time feedback, and effectively improves the energy efficiency and stability of the supercapacitor. BRIEF DESCRIPTION OF THE DRAWINGS

[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 Flowchart of the present invention. DETAILED DESCRIPTION

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0055] Example: See Figure 1 As shown, the supercapacitor management method of the adaptive optimization algorithm described in this embodiment includes the following steps:

[0056] S1: Establish a hierarchical optimized topology of the supercapacitor management system based on the sensing range and position relationship of multiple sensors

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

[0058] S2: Obtain supercapacitor related data sensed by each sensor

[0059] Get real-time data of the supercapacitor from different sensors (such as voltage, current, temperature, internal resistance, etc.). These data represent the current operating status and health of the supercapacitor.

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

[0061] Based on the hierarchical topology established in the previous step, the sensor information is divided into several groups, each containing multiple sensor information. To ensure data consistency and accuracy, the timestamp of each sensor information is obtained and the data is synchronized to obtain synchronized sensor information.

[0062] S4: Sort supercapacitor management targets by priority based on synchronous sensing information

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

[0064] S5: Calculate the fusion confidence between each two supercapacitor management targets in sequence

[0065] After sorting, the system calculates the fusion confidence between each two targets. The fusion confidence indicates the consistency and reliability of the perception data of the two targets in the control strategy.

[0066] S6: Data fusion and retention based on fusion confidence

[0067] When the weighted fusion confidence between two targets exceeds the set threshold, the system will fuse the synchronized perception information of the two targets and update it to the fused information. The system will use the fused information as reference data for the high-priority target and delete the synchronized data of the low-priority target.

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

[0069] S7: All retained synchronous perception information is used as the first fusion information

[0070] Each group of retained synchronous perception information is used as the first fusion information.

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

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

[0073] This application establishes a hierarchical optimization topology structure of a supercapacitor management system, groups multiple supercapacitor sensor data according to the hierarchical optimization topology structure, sorts supercapacitor management targets according to priority based on synchronous perception information, calculates the fusion confidence between each two supercapacitor management targets based on the sorting results, performs data fusion and retention based on the fusion confidence, and uses all retained synchronous perception information as the first fusion information. 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 optimizes the supercapacitor charging and discharging strategy based on the adaptive optimization algorithm. This management method uses a hierarchical optimization topology structure to ensure the synchronization and consistency of data, dynamically optimizes the management target, avoids the fixed parameter settings and single control logic in the traditional method, and adjusts the charging and discharging strategy with real-time feedback, effectively improving the energy efficiency and stability of the supercapacitor.

[0074] The supercapacitor management system of the 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 position relationship of multiple sensors, a hierarchical optimized topology structure of the supercapacitor management system is established, and the hierarchical optimized topology structure is sent to the data grouping module and the data sorting and retention module;

[0076] Data grouping module: obtains the supercapacitor sensor data sensed by each sensor respectively, groups multiple supercapacitor sensor data according to the hierarchical optimized topology structure, obtains the timestamp and performs synchronous processing on the supercapacitor sensor data, and sends the synchronous processing results to the data sorting and retention module;

[0077] Data sorting and retention module: Based on the synchronous perception information, the supercapacitor management targets are sorted according to priority. Based on the sorting results, the fusion confidence between each two supercapacitor management targets is calculated in sequence. Data is fused and retained according to the fusion confidence. All retained synchronous perception information is used as the first fusion information, and the first fusion information is 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 the supercapacitor optimization management, and the supercapacitor charging and discharging strategy is optimized according to the adaptive optimization algorithm.

[0079] S1: Establish a hierarchical optimized topology of the supercapacitor management system based on the sensing range and position relationship of multiple sensors

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

[0081] Sensors play a crucial role in supercapacitor management systems. Their sensing range and location directly impact the accuracy and timeliness of data collection. To ensure the system can efficiently integrate data from different sensors, a rational, hierarchical, and optimized topology must be established based on the sensors' operating environments and functional requirements. This structure not only optimizes information transmission efficiency but also enables effective resource allocation and data synchronization across all 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 position of these sensors should be determined based on their operating principles and the areas they need to sense. For example, voltage sensors and current sensors are usually placed at the input and output of the supercapacitor to accurately measure the charge and discharge status of the capacitor; temperature sensors should be placed in areas where overheating may occur, such as inside the capacitor or around the battery management system. On this basis, the hierarchical optimization topology can be designed from the following aspects:

[0083] First, sensors are divided into different hierarchies based on their function and the target they monitor. For example, low-level sensors collect basic physical data (such as voltage and current), while higher-level sensors integrate this data with the control system to provide a basis for decision-making. This hierarchical structure ensures smooth data flow and management.

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

[0085] In highly dynamic environments (such as those with large load fluctuations and rapid temperature changes), the topology must be able to dynamically adjust based on real-time monitoring data. For example, if 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 operation. This flexibility enhances the system's adaptability to environmental changes.

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

[0087] The specific processing logic includes:

[0088] Assign each sensor a specific sensing area based on its monitoring range and accuracy requirements. For example, a temperature sensor might need to be placed inside a supercapacitor, while voltage and current sensors can be placed on external connectors. This assignment can be based on actual measurement needs and physical limitations, ensuring that each sensor's measurement range covers the most critical areas.

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

[0090] Based on real-time feedback from the system, the system should be able to dynamically adjust the operating mode of each sensor. For example, when the load suddenly changes, the supercapacitor management system might adjust the temperature sensor's measurement accuracy or change the sensor's operating frequency based on the real-time changes in current and voltage. This dynamic adjustment optimizes system response time and avoids 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 greatly enhances the supercapacitor's stability and reliability under different operating conditions.

[0092] S2: Obtain supercapacitor related data sensed by each sensor

[0093] Get real-time data of the supercapacitor from different sensors (such as voltage, current, temperature, internal resistance, etc.). These data represent the current operating status and health of the supercapacitor.

[0094] In a supercapacitor management system, acquiring real-time data is fundamental to achieving system optimization and dynamic adjustments. Supercapacitor performance 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 operating condition and health. Therefore, accurately and in real time acquiring data from each sensor and using it for subsequent analysis and decision-making is a key step in system design.

[0095] First, a supercapacitor management system is usually composed of multiple sensors, each of which performs a specific monitoring task. For example, a voltage sensor monitors the terminal voltage of the supercapacitor in real time, a current sensor measures the current during charging and discharging, a temperature sensor monitors the capacitor temperature, and an internal resistance sensor evaluates the health of the capacitor. In addition, depending on the needs of the application scenario, other sensors may be equipped to further monitor environmental factors such as humidity and vibration. The data collection and processing process is as follows:

[0096] Voltage sensors are installed at the terminals of supercapacitors to monitor voltage changes across the capacitors in real time. By accurately measuring voltage, the system can determine the supercapacitor's charge state, remaining capacity, and whether there is a risk of overcharging or over-discharging. Voltage data is typically acquired at a regular sampling frequency (for example, once per second) and converted into a digital signal for processing.

[0097] Current sensors are typically connected to the supercapacitor's charge and discharge circuits to monitor current fluctuations during the charge and discharge process. By collecting current data, the system can assess the supercapacitor's charge and discharge speed and power requirements in real time. When designing the system, the accuracy and frequency of current sampling must be considered. Excessively high sampling frequencies can impose computational burdens, while excessively low frequencies can lead to delayed responses. Therefore, the current data collection frequency needs to be optimized based on load variations and response requirements.

[0098] Temperature sensors are typically placed inside supercapacitors or in key locations, such as near the electrodes. Temperature is a key factor affecting supercapacitor performance and lifespan. Excessively high temperatures accelerate electrolyte aging and affect capacitor stability. Temperature data collected by the temperature sensor is periodically read and compared with a set threshold to determine whether the capacitor is within a safe operating temperature range.

[0099] Internal resistance sensors monitor changes in the supercapacitor's internal resistance. An increase in internal resistance typically indicates a decline in the capacitor's health, potentially affecting its charge and discharge efficiency and service life. By regularly measuring internal resistance, the system can promptly detect abnormalities in the supercapacitor. Internal resistance measurements are typically performed under specific conditions, such as during charging or in a static state, to infer the internal resistance by measuring changes in current and voltage.

[0100] In practical applications, sensor data acquisition is not performed independently. The measurement data of 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 to the data acquisition process:

[0101] All sensor data is acquired based on a unified time base. The master controller triggers all sensors to collect data at the same time. This ensures that parameters (such as voltage, current, and temperature) are precisely aligned during data analysis, avoiding misjudgments due to delays or inconsistencies.

[0102] After acquiring the raw data, the main controller performs preliminary preprocessing, including noise filtering and data smoothing. Specifically, low-pass filters are used to smooth the voltage and current signals to eliminate high-frequency noise. Furthermore, temperature and internal resistance measurements are 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 piece of data so that the time information of each data point can be tracked during subsequent analysis. Data updates are performed according to a set cycle to ensure that the system can reflect the supercapacitor's operating status in real time. The system also sets thresholds to trigger an alarm mechanism when the data measured by a sensor exceeds the preset range. For example, if the voltage sensor detects that the capacitor voltage is too low or too high, the current is too high, or the temperature is too high, the system will immediately activate the protection mechanism, stop charging and discharging operations, and trigger an alarm to alert the operator. Through this process, the supercapacitor management system can obtain 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 sensor data according to a hierarchical optimized topology and obtain timestamps for synchronization

[0105] Based on the hierarchical topology established in the previous step, the sensor information is divided into several groups, each containing multiple sensor information. To ensure data consistency and accuracy, the timestamp of each sensor information is obtained and the data is synchronized.

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

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

[0108] Group 1: Voltage group, contains 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 capacitor internal temperature, ambient temperature, etc.).

[0111] Group 4: Health status group, which may contain 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 belongs 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 synchronization and consistency across multiple sets of sensor data, each piece of data must be accurately timestamped. Timestamping is fundamental to the system's ability to align data from different sensors. For example, when a voltage sensor and a current sensor acquire voltage and current data, respectively, these two data points require timestamping to ensure they were acquired within the same timeframe, enabling correlation and comparison between the data.

[0114] Each time a sensor collects data, the main controller records the current system timestamp. Timestamps are typically generated by the system clock and are accurate to the millisecond level. Timestamps for all sensors are centrally managed by the system to ensure they 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 timestamp difference between voltage and current data does not exceed a certain tolerance (such as 10 milliseconds), the 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 generally uses the following steps:

[0116] For each set of sensor data, align the data based on the timestamp. All data is based on a unified time window. It may be necessary to perform time difference correction on each set of sensor data to make the timestamps of each set of data consistent.

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

[0118] For possible data loss or anomalies of certain sensors, the system will make inferences and corrections based on historical data or data from other sensors to ensure that the final data set is complete and consistent.

[0119] Synchronized data is stored in a dedicated data cache or database. Each data set is arranged by timestamp, and the corresponding original timestamp is retained. 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 actual applications, the system periodically clears expired data to ensure the real-time and efficient operation of the data cache.

[0120] During the synchronization process, the system verifies the data to ensure the quality of each data point. If a set of data exhibits excessive synchronization errors or significant inconsistencies with data from other sensors, the system automatically calibrates it or marks it as an outlier and isolates it. This process ensures that the data ultimately used by the system is reliable and efficient.

[0121] For example, suppose 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 between each set of data based on the sensor's timestamp and perform interpolation or synchronization adjustments to ensure that the data corresponds to the supercapacitor status at the same point in time, thereby integrating the data to form a complete status report.

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

[0123] S4: Sort supercapacitor management targets by priority based on synchronous sensing information

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

[0125] In a supercapacitor management system, prioritizing management objectives based on synchronized sensing information is a key step in ensuring efficient, stable, and secure system operation. Each management objective (such as charge and discharge control, voltage stability, and temperature monitoring) is crucial to the proper functioning of the system. However, due to limited system resources, all objectives cannot be treated with equal priority simultaneously. Therefore, dynamic prioritization must be adjusted based on factors such as objective importance, real-time requirements, and system requirements to ensure that the most critical objectives are addressed first, thus avoiding unnecessary delays or failures in the event of an emergency.

[0126] It is necessary to clarify 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, overdischarge or charge too quickly during the charging and discharging process.

[0128] Voltage stability: Maintain the stability of the voltage across the capacitor to avoid excessively high or low voltage, which may cause damage to the capacitor or reduce working efficiency.

[0129] Temperature monitoring: Monitors the temperature changes of capacitors to ensure that the temperature is within a safe operating range and prevents capacitor damage caused by overheating.

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

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

[0132] The importance of each management objective is typically determined by a number of factors, including:

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

[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 operation efficiency: Depending on load fluctuations and energy demand, the goal of power dispatch optimization may need to be prioritized to improve the overall system efficiency.

[0136] Based on the priority score of each target, the system will sort the targets from high to low 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 based on different criteria. The weight is assessed based on the objective's urgency, importance, and impact on the system.

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

[0139] After all the goals have their weights defined, the system ranks them using a priority sorting algorithm. For each goal 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. The score can be obtained by measuring the deviation of sensor data from a set threshold.

[0141] The system then adjusts the weight of the target (W_i) based on the target's real-time requirements or safety requirements. For example, the weight of voltage control will increase when the voltage is at a critical value, while the weight of health monitoring will be relatively low.

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

[0143] To eliminate the impact of different target detection parameter fluctuations due to varying degrees of strictness, we can standardize the fluctuation range of each target's parameters to ensure that the impact of each target's fluctuation on the weight is comparable. To this end, we can adjust the weighting based on the actual fluctuation range of the target's detection parameters and the safety threshold.

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

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

[0146] Temperature monitoring target: temperature deviation is 5°C, I_i=5.

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

[0148] Different objectives may have different fluctuation ranges and thresholds. For example, voltage control fluctuations may require accuracy of 0.1V to cause safety issues, while temperature runaway may require a fluctuation of 10°C to cause an abnormality. To ensure that the fluctuations of each objective have a consistent impact on the weight, we need to standardize the fluctuation amplitude.

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

[0150] Calculate the normalized fluctuation range: Divide the actual fluctuation of each target by its maximum allowable fluctuation range to obtain the normalized fluctuation range, for example:

[0151] Voltage control target: The maximum allowable fluctuation is set to 1V. The actual fluctuation is 1V. The normalized fluctuation amplitude is: 1 divided by 1 equals 1;

[0152] Temperature monitoring target: The maximum allowable fluctuation is set to 10°C, the actual fluctuation is 5°C, and the normalized fluctuation range is: 5 divided by 10 equals 0.5;

[0153] Health status monitoring target: The maximum allowable fluctuation is set to 10Ω. The actual fluctuation is 5Ω. The normalized fluctuation amplitude is: 5 divided by 10 equals 0.5.

[0154] The development rate (R_i) of each target still reflects the speed of change of the monitoring parameters of the target. The target's development rate is calculated by the normalized fluctuation amplitude and time. Assuming that the change rate of each target is known, we can adjust it by the normalized fluctuation amplitude so that targets with large fluctuation amplitudes are not ignored. The example is as follows:

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

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

[0157] Health status monitoring target: The 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 development rates of all management objectives are added together to get a total development rate of 1.05. The weight of each objective (W_i) is calculated based on its normalized fluctuation range and development rate. The weight of an objective is equal to its development rate divided by the total development rate:

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

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

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

[0162] According to the calculated priority score P_i, sort from high to low. 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 target 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 charge and discharge control or other targets may decrease. This ensures that the most urgent and important tasks are responded to and processed in the shortest possible time.

[0167] Once sorting is complete, the system executes the goals in order of priority, switching between them. To avoid wasting system resources and unnecessary conflicts, the system checks the current priority of a goal while executing it. If a new, higher-priority goal emerges, it immediately switches to it. 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 and 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 sorting (for example, voltage and temperature monitoring are set as the highest priority), the system will prioritize temperature control 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 the charging process to prioritize resolving the temperature issue.

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

[0171] S5: Calculate the fusion confidence between each two supercapacitor management targets in sequence

[0172] After sorting, the system calculates the fusion confidence between each two targets. The fusion confidence indicates the consistency and reliability of the perception data of the two targets in the control strategy.

[0173] In a supercapacitor management system, management objectives may be correlated and mutually influential. Therefore, calculating the fusion confidence between each pair of objectives is a crucial step. This fusion confidence measures the consistency and reliability of the perception data from the two objectives in the control strategy, reflecting their effectiveness in collaborative optimization. By calculating the fusion confidence, the system can determine whether data fusion between the objectives is feasible or requires separate processing, thereby optimizing the overall management strategy.

[0174] Calculating fusion confidence can help the system determine which objectives have better coordination in control strategies and which objectives may conflict when adjusted synchronously. In this way, the system can reasonably fuse data between multiple objectives to ensure that the final optimization solution is both efficient and reliable. For example, when the data between the voltage control and temperature monitoring objectives are highly consistent, their fusion confidence is high and they can be combined into a unified adjustment strategy; conversely, if the data between the two is significantly different, they may need to be processed separately to avoid negative effects on the system. The fusion confidence calculation process is as follows:

[0175] Fusion confidence reflects the consistency of the data used by two management targets during the control process. Consider two targets, Target A and Target B, each with different sensory data (such as voltage, current, and temperature). The system calculates the similarity or consistency between the sensory data of the two targets to determine whether they can be effectively fused.

[0176] For each pair of target A and target B, the system first obtains their perception data. Based on this data, their Pearson correlation coefficient is calculated. This indicator can be evaluated by the following method:

[0177] The linear relationship between the two is quantified by calculating the Pearson correlation coefficient between the perception data of target A and target B. A higher correlation coefficient indicates more consistent perception data between the two and a higher fusion confidence.

[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: is the Pearson correlation coefficient between target A and target B. and are the first and second targets A and B respectively. Sample data. and are the average values ​​of target A and target B, namely: , , 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., when the current increases, the voltage changes accordingly), the confidence level of their fusion will also be high.

[0181] Once the Pearson correlation coefficient of the data is obtained, the system will weight the consistency between different targets. The fusion weight is usually set based on the importance, real-time nature, and impact of the target on the system. For example, for voltage stability and temperature monitoring targets, the voltage target may have a higher fusion weight because voltage changes directly affect the safety and efficiency of supercapacitors. Weighted fusion confidence calculation logic:

[0182] Calculate the correlation of each pair of targets and obtain the preliminary fusion confidence.

[0183] Adjust the fusion confidence based on the target's fusion weight (such as target importance or real-time nature). For example, if target A is a high-priority target, the system will assign it a greater fusion weight, making the fusion confidence of target A higher with other targets. The calculation logic of the fusion weight is: after obtaining the ranking results of all management targets, sum up the priority scores of all management targets to obtain the total score, divide the priority score of the management target by the total score to obtain the fusion weight of the management target, and include it. The fusion weight of target A is equal to the priority score of target A divided by the total score. Weighted fusion confidence formula, calculation expression: , where To manage the fusion weight of target A, To manage the fusion weight of target B, is the Pearson correlation coefficient of the perception data, This weighting method ensures that more important targets have greater influence in the fusion process.

[0184] Based on the calculated weighted fusion confidence, the system can determine whether to fuse the data of the two targets. Generally, if the weighted fusion confidence of the two management targets is greater than or equal to the confidence threshold (for example, the confidence threshold is 0.8), data fusion can be performed to combine the two targets into a unified adjustment strategy. If the fusion confidence is lower than the confidence threshold, it indicates that the perception data of the two targets is significantly different, and the system should process them separately to avoid potential control conflicts.

[0185] Consider a system with two objectives: voltage control and temperature monitoring. Under high-load conditions, 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 has a high confidence level for the fusion of these two objectives, approaching 0.9. This indicates a high degree of consistency in their sensed data. These two objectives can be combined into a unified optimization strategy to ensure coordinated adjustment of voltage and temperature.

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

[0187] By calculating the fusion confidence between each pair of objectives, the system can better manage the multiple optimization objectives of the supercapacitor. Objectives with high fusion confidence can be combined 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: Data fusion and retention based on fusion confidence

[0189] When the weighted fusion confidence between two targets exceeds a set threshold, the system fuses the synchronized perception information of the two targets and updates the information to the fused information. The system uses this fused information as reference data for the higher-priority target and deletes the synchronized data for the lower-priority target. When the fusion confidence falls below the threshold, the system retains the synchronized perception information of the two targets to ensure no data loss.

[0190] In supercapacitor management systems, data fusion is a key step in improving system efficiency and accuracy. By calculating the fusion confidence between each pair of targets, the system determines whether to fuse the perception data from multiple targets or retain each data separately. This step minimizes redundant data in the control strategy while improving the flexibility and accuracy of the system's response.

[0191] In the previous stage, the system evaluated the consistency of the perception data between each pair of targets by calculating the fusion confidence. If the fusion confidence between the targets is greater than a preset threshold, it indicates that their data are highly consistent in terms of control strategy and can be considered for fusion. Conversely, if the fusion confidence is lower than the threshold, it indicates that the data of the two targets are significantly different and may not be effectively fused. In this case, the system retains the original data.

[0192] Typically, the system sets a threshold for fusion confidence (e.g., 0.8). This means that the system will only perform data fusion 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 is greater than the set threshold, the system will fuse the perception data of the two targets. The fusion process includes the following steps:

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

[0195] During the fusion process, the system combines the synchronized sensing information from Target A and Target B. This can be achieved by taking a weighted average of their voltage, current, temperature, and other data, or by combining them according to certain weighting rules to ensure the fused data is more accurate and consistent with the optimization strategy. This fused data is then updated to Target A's data storage and serves as the basis for subsequent decision-making and control.

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

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

[0198] The system retains sensory data from 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 if the fusion confidence is low, the system will ensure that the perception data of the two targets are synchronized and stored in the order of the original timestamps. This can avoid misjudgments due to time differences or data loss, and ensure the data integrity of all targets.

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

[0201] The fused data is typically used for further decision-making and control. For example, if the voltage and current control strategies are fused, the system can adjust the supercapacitor's charging and discharging strategies based on this fused data. If the temperature control strategy is independent of voltage and current control (for example, using a separate temperature regulation mechanism), its data is retained and processed separately. This ensures that the various objectives work together to maximize the performance and efficiency of the supercapacitor.

[0202] Example:

[0203] Assume the system is running and the calculated fusion confidence score for the voltage and current control targets is 0.85, exceeding the preset threshold of 0.8. This indicates that the data for these two targets is highly consistent in terms of control strategy. Therefore, the system fuses the voltage and current control data and updates the voltage control target data. The current control target data is deleted to avoid redundancy. However, if the fusion confidence score for the temperature and current control targets is only 0.6, below the threshold of 0.8, the system retains the independent data for the current and temperature control targets to avoid system impact and conflict.

[0204] By fusing and retaining target data based on fusion confidence, the system dynamically adjusts its processing of target data to ensure optimal system operation. Targets with high fusion confidence can be fused, improving system efficiency and responsiveness; targets with low fusion confidence are processed separately to ensure system stability and data integrity. This strategy effectively avoids waste of system resources and data redundancy, while improving the performance and reliability of the supercapacitor management system.

[0205] S7: All retained synchronous perception information is used as the first fusion information

[0206] Each group of retained synchronous perception information is used as the first fusion information.

[0207] In a supercapacitor management system, synchronized sensing information, after data fusion and retention, is the key foundation for further optimizing control strategies. This step integrates all retained synchronized sensing information into a first fusion for subsequent processing and optimization. This process ensures data integrity and accuracy, providing reliable data support for subsequent control decisions.

[0208] In the previous step, the system calculated the fusion confidence level for each pair of target data and, based on the result, decided whether to fuse the data. If the fusion confidence level is low, the system retains the original synchronized sensing information for each target. This retained synchronized sensing information represents the original state of each target, including but not limited to key parameters such as voltage, current, temperature, and internal resistance. Each target's synchronized sensing information is timestamped to ensure temporal consistency, accurately reflecting the actual state of the supercapacitor during subsequent processing.

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

[0210] Treating each set of retained synchronized sensing information as the first fused information means the system merges and stores each data set. At this point, the system arranges all retained data in chronological order, ensuring data continuity and integrity. To ensure efficient and accurate data integration, the system typically employs the following approach: All data must be aligned to the same timestamp. Because different sensors may have slight time deviations, the system aligns data using timestamps to ensure that the data can be effectively integrated within the same time window. To reduce bias caused by different data sources, the system can standardize each data set to enable effective comparison of parameters across 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. In this way, the retained synchronized sensing information is integrated into the first fused information according to time sequence, facilitating the subsequent unified optimization decision-making and control strategy calculation.

[0211] The system can merge and store each set of data one by one based on the timestamp. For example, if a time window T is set, the system will integrate all the synchronous perception data within the time window in chronological order. The merged data will form new state information, which can be used by the subsequent control system. The calculation logic of the integration process: First, the system will remove invalid or abnormal data from the retained synchronous perception data. For example, if a sensor fails, causing the data it returns to be extremely abnormal, the system will automatically eliminate this data that does not fall within the reasonable range. The retained synchronous perception information is sorted from early to late by timestamp for subsequent processing. In the sorted data, the system merges the perception information of each target one by one according to the timestamp. If the data of two targets have not been fused, their respective independent states are maintained; if their data have been fused, the system will use the fused data as new input.

[0212] This first-stage fusion information forms a crucial foundation for subsequent control decisions and optimization algorithms. This integrated data enables the system to monitor and adjust the supercapacitor in real time. For example, in subsequent steps, the optimization algorithm will use this integrated data to adjust charging and discharging strategies, optimize power scheduling, and even predict the remaining life of the supercapacitor based on health monitoring information. This first-stage fusion 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 state of the supercapacitor changes, and new synchronized sensing information is continuously acquired and integrated into new first-stage 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 status 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 separately retained and synchronized. The voltage control target's sensing information includes voltage values ​​and their changing trends, while the temperature monitoring target's sensing information includes temperature values ​​and their changing trends. The system aligns these two sets of data based on timestamps and merges them to form the first fused information. This information serves as input for subsequent decision-making, helping the system adjust charging current and temperature control strategies to ensure safe and efficient capacitor operation.

[0216] By using all retained synchronized sensing information as the first fusion information, the system can integrate data from multiple targets into consistent, usable status 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, obtain the target fusion information

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

[0219] In the supercapacitor management system, when all the first-stage fusion information is finally combined into a single aggregated information, the system obtains the target fusion information. This process is a key step in system optimization. It integrates all sensor data and control objectives to provide a comprehensive, optimized decision-making basis. The target fusion information serves as the core input for optimizing the system's charging and discharging strategies, energy management, and performance scheduling, ensuring the supercapacitor's optimal operating state under different load and environmental conditions.

[0220] In the previous step, the system has integrated the retained synchronous perception information into several sets of first fusion information. Once 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 relies on combining data from various management targets. For example, if targets such as voltage control, temperature control, and charge and discharge management have each generated independent first-level fusion information, the system will merge this information to form a comprehensive information containing voltage, current, temperature, and other comprehensive information.

[0222] Merging can be performed using methods such as weighted averaging, maximum value selection, and linear interpolation. The combined target fusion information more comprehensively reflects the overall operating status of the supercapacitor, facilitating unified optimization of subsequent decisions. For example, if temperature and voltage control targets are combined, the system will combine 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 together, but more importantly, extracting useful feature information from it, which will directly affect subsequent optimization decisions.

[0224] The system then performs feature analysis on the combined target fusion information, extracting key operational characteristics of the capacitor, such as voltage fluctuation range, current variation trend, and temperature change rate. These characteristics serve as input parameters for subsequent decision-making, ensuring precise adjustment of the optimization strategy.

[0225] The system also performs state estimation based on the target fusion information to assess the current health status, charging efficiency, and energy storage status of the supercapacitor. This estimation result will provide an accurate system model for the optimization algorithm to facilitate subsequent performance scheduling and energy efficiency optimization. The target fusion information will ultimately serve as the control basis for the supercapacitor management system, helping the system to perform precise charge and discharge control, energy management, and performance scheduling. For example:

[0226] By analyzing the target fusion information, the system can optimize the current, power, and voltage during the charging and discharging process in real time to ensure that the capacitor reaches the target voltage in the shortest time while avoiding overcharging or over-discharging. The power and current data in the target fusion information will help the system adjust the energy allocation strategy to ensure that the energy storage and release process of the supercapacitor achieves optimal efficiency. Based on the fused target information, the system can dynamically schedule the operating strategy of the supercapacitor under different loads and working environments to maximize energy efficiency and extend its service life. For example, in the case of large load fluctuations, the system will use the target fusion information to adjust the charge and discharge rate of the supercapacitor to avoid unnecessary energy waste.

[0227] Target fusion information, as the core basis for system optimization, directly influences the execution of the adaptive optimization algorithm. By analyzing this target fusion information, the optimization algorithm can adjust the control strategy in real time to automatically respond to varying 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 optimal under all operating conditions. The adaptive optimization algorithm continuously monitors changes in the target fusion information and adjusts the control strategy. Whenever the system state changes, the target fusion information is promptly fed back to the optimization algorithm to guide the system's adjustment of its operating mode. For example, when voltage and temperature exceed preset thresholds, the optimization algorithm immediately adjusts the charging rate to avoid temperature increases caused by excessive charging. When load demand increases, the system adjusts the charge and discharge balance based on the target fusion information to avoid system overload.

[0228] Example:

[0229] Assume that at a given moment, the voltage, current, and temperature control targets each provide independent first-stage fusion information. The system then combines 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 determines that the current voltage is high and the temperature is close to the safety limit. Therefore, the optimization algorithm adjusts the charging rate based on the target fusion information, slowing the temperature rise and ensuring stable capacitor charging while avoiding overcharging and overheating.

[0230] By combining all first-order fusion information into target fusion information, the system provides a comprehensive and unified control basis, providing accurate data support for subsequent adaptive optimization algorithms. Target fusion information not only effectively integrates data from various targets but also provides a reliable decision-making basis for charging and discharging strategies, energy management, and performance scheduling, ensuring that supercapacitors maintain optimal operating conditions under variable operating conditions.

[0231] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these 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 any one or more embodiments or examples.

[0232] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present 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 by: The management method comprises the following steps: S1: Establish a hierarchical optimized topology of the supercapacitor management system based on the sensing range and position relationship of multiple sensors; S2: Acquire supercapacitor sensing data sensed by each sensor respectively; S3: Grouping multiple supercapacitor sensor data according to a hierarchical optimized topology structure, obtaining timestamps, and synchronously processing the supercapacitor sensor data to obtain synchronous sensing information; S4: sorting the supercapacitor management targets according to priority based on the synchronous sensing information; S5: Calculate the fusion confidence between each two supercapacitor management targets based on the sorting results; S6: Data fusion and retention based on fusion confidence; S7: taking all retained synchronous perception information as the first fusion information; S8: When all the first fusion information is finally merged into a total information, target fusion information is obtained, which is used as a 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 using an adaptive optimization algorithm according to claim 1, characterized in that: In step S4, the supercapacitor management targets are sorted according to priority based on the synchronous sensing information, including the following steps: Management objectives include charge and discharge control, voltage stability, temperature monitoring, health status monitoring, and power scheduling optimization; The importance of each management goal is determined by multiple factors, including security, real-time requirements, and operational efficiency; According to the priority score of each management objective, the management objectives are sorted from high to low priority.

3. The supercapacitor management method using an adaptive optimization algorithm according to claim 2, characterized in that: In step S5, the fusion confidence between each two supercapacitor management targets is calculated in sequence, including the following steps: By calculating the Pearson correlation coefficient between the perception data of management target A and management target B, the preliminary fusion confidence is obtained; According to the fusion weights of management target A and management target B, the fusion confidence is adjusted to obtain the weighted fusion confidence; If the weighted fusion confidence of management target A and management target B is greater than or equal to the confidence threshold, data fusion is performed. If the weighted fusion confidence of management target A and management target B is lower than the confidence threshold, it indicates that the perception data of management target A and management target B are quite different and need to be processed separately.

4. The supercapacitor management method using an adaptive optimization algorithm according to claim 3, wherein: The calculation expression of weighted fusion confidence is: , where To manage the fusion weight of target A, To manage the fusion weight of target B, is the Pearson correlation coefficient of the perception data, is the weighted fusion confidence.

5. The supercapacitor management method using an adaptive optimization algorithm according to claim 4, characterized in that: In step S6, data fusion and retention are performed according to the fusion confidence, including the following steps: If the weighted fusion confidence between the two management targets is greater than or equal to the confidence threshold, the synchronous perception information of the management targets is fused and updated to the fused information; If the weighted fusion confidence between two management targets is less than the confidence threshold, the synchronous perception information of the two management targets is retained; After the data fusion of the two management targets is completed, the data of the low-priority target is deleted and the data of the high-priority target is retained.

6. The supercapacitor management method using 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, target fusion information is obtained, which includes the following steps: Integrate the retained synchronous perception information into several groups of first fusion information, and merge the several groups of first fusion information into target fusion information according to the current optimization goal and data requirements; The first fusion information of the voltage control, temperature control, and charge and discharge management targets is merged to form total information including comprehensive information of voltage, current, and temperature, that is, target fusion information is obtained.

7. The supercapacitor management method using an adaptive optimization algorithm according to claim 6, characterized in that: In step S3, the plurality of supercapacitor sensor data are grouped according to a hierarchical optimized topology structure, and timestamps are obtained for synchronization, including the following steps: In the topology, the measurement data of each sensor is divided into several groups, including: Group 1: Voltage group, including 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, including internal resistance and leakage current, which are used to reflect the health status of capacitors; Every 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 using an adaptive optimization algorithm according to claim 7, characterized in that: In step S2, supercapacitor sensing data sensed by each sensor is obtained respectively, including the following steps: The voltage sensor monitors the terminal voltage of the supercapacitor in real time, the current sensor measures the current during the charging and discharging process, and the temperature sensor monitors the capacitor temperature.

9. The supercapacitor management method using an adaptive optimization algorithm according to claim 8, characterized in that: In step S1, based on the sensing range and position relationship of multiple sensors, a hierarchical optimized topology structure of the supercapacitor management system is established, including the following steps: Sensors are divided into different levels according to their functions and monitored targets; The data collected by sensors at each level will be preliminarily processed within the level where it is located; In highly dynamic environments, the topology is dynamically adjusted based on real-time monitoring data.

10. A supercapacitor management system with an adaptive optimization algorithm, used to implement the management method according to any one of claims 1 to 9, characterized in that: It includes topology building module, data grouping module, data sorting and retention module and adaptive optimization module; Topology building module: Based on the sensing range and position relationship of multiple sensors, a hierarchical optimized topology structure of the supercapacitor management system is established; Data grouping module: acquires the supercapacitor sensor data sensed by each sensor, groups multiple supercapacitor sensor data according to a hierarchical optimized topology structure, obtains the timestamp, and performs synchronous processing on the supercapacitor sensor data; Data sorting and retention module: Based on the synchronous perception information, the supercapacitor management targets are sorted according to priority. Based on the sorting results, the fusion confidence between each two supercapacitor management targets is calculated in sequence. Data is fused and retained based on the fusion confidence, and all retained synchronous perception 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 the supercapacitor optimization management, and the supercapacitor charging and discharging strategy is optimized according to the adaptive optimization algorithm.

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