Adaptability analysis method and system for overcharge load and multi-element energy storage equipment

Abstract

The invention relates to the technical field of data analysis, and discloses an overcharge load and multivariate energy storage equipment suitability analysis method and system, and the method comprises the steps: carrying out the multi-feature sudden change analysis of the real-time power data of an overcharge load, and obtaining a sudden change feature set of each load sudden change event, performing energy storage response matching analysis on the multi-element energy storage equipment according to the abrupt feature set and the operation state data of the multi-element energy storage equipment to obtain a response matching degree data set, generating a collaborative response strategy according to the abrupt feature set and the operation state data, and performing multi-energy storage layer collaborative analysis on the multi-element energy storage equipment by using the collaborative response strategy to obtain a response matching degree data set; and according to the response matching degree data set and the collaborative response capability, calculating a comprehensive adaptability score of the overcharge load and the multi-element energy storage equipment, and generating an adaptability target analysis result. According to the method, second-level and millisecond-level aperiodic overcharge load mutation can be effectively and accurately identified and predicted, and the response and cooperative adaptability of the multi-element energy storage equipment are improved.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and in particular to a method and system for adaptability analysis of overcharge load and multi-element energy storage devices. Background Technology

[0002] In recent years, with the rapid development of photovoltaic-storage supercharging stations and high-power charging stations, their internal loads have exhibited high randomness and transient characteristics. Supercharging loads are affected by user charging behavior, resulting in large power fluctuations and rapid abrupt changes, often occurring on the order of seconds or milliseconds. This rapid load fluctuation places high demands on the scheduling and response capabilities of energy storage devices.

[0003] Traditional load forecasting methods based on historical data typically rely on statistical patterns over minutes or longer periods, making it difficult to accurately predict load surges at the second or millisecond level. This can lead to energy storage devices failing to respond in a timely manner, thus affecting load balancing and energy management. Furthermore, existing photovoltaic-storage-supercharging stations are usually equipped with multiple energy storage devices, including high-power and high-energy energy storage. However, current technologies lack methods for analyzing the coordinated response of multiple energy storage layers under transient load conditions, making it difficult to quantify the adaptability of energy storage devices.

[0004] In the field of power system disturbance identification, although methods such as fast frequency fluctuation detection, wavelet analysis, or Fourier transform can be used for grid disturbance identification, overcharging load mutations often do not have obvious periodicity or frequency characteristics. Transient load mutations exhibit high randomness in the time domain, making it difficult for high-frequency wavelet decomposition or FFT analysis to effectively capture them, thus failing to provide reliable information for energy storage device scheduling.

[0005] In summary, existing technologies still have significant shortcomings in terms of second-level and millisecond-level load forecasting accuracy, rapid response of energy storage devices, and collaborative analysis of multiple energy storage layers, making it difficult to meet the needs of photovoltaic-storage-supercharging stations for optimized control and energy storage adaptability analysis under high random load conditions.

[0006] Therefore, existing technologies have the problem of difficulty in accurately identifying and predicting second-level and millisecond-level non-periodic overcharging load changes, resulting in insufficient response and collaborative adaptability of multi-energy storage devices. Summary of the Invention

[0007] This invention provides a method for adaptability analysis of overcharging load and multi-element energy storage devices. Its main purpose is to solve the problem that it is difficult to accurately identify and predict second-level and millisecond-level non-periodic overcharging load changes, which leads to insufficient response and collaborative adaptability of multi-element energy storage devices.

[0008] Firstly, to achieve the above objectives, the present invention provides a method for adaptability analysis of overcharge load and multi-element energy storage devices, comprising: Real-time power data of the overcharging load and operating status data of the multi-element energy storage devices are acquired. Multi-feature mutation analysis is performed on the real-time power data to obtain the mutation feature set of each load mutation event. Based on the mutation feature set and the operating status data, the energy storage response matching analysis is performed on the multi-element energy storage device to obtain the response matching degree dataset for each load mutation event; The available power range and response priority of the multi-element energy storage device are generated based on the mutation feature set and the operating status data. A collaborative response strategy is generated based on the available power range and the response priority. The collaborative response strategy is used to perform multi-layer collaborative analysis on the multi-electrode energy storage device to obtain the collaborative response capability. The comprehensive adaptability score of the overcharging load and the multi-element energy storage device is calculated based on the response matching degree dataset and the collaborative response capability, and the adaptability target analysis result is generated based on the comprehensive adaptability score.

[0009] Secondly, the present invention also provides a compatibility analysis system for overcharge load and multi-element energy storage devices, the system comprising: The feature mutation analysis module is used to acquire real-time power data of overcharge load and operating status data of multi-element energy storage devices, and to perform multi-feature mutation analysis on the real-time power data to obtain the mutation feature set of each load mutation event; The response matching analysis module is used to perform energy storage response matching analysis on the multi-element energy storage device based on the mutation feature set and the operating status data, and obtain the response matching degree dataset for each load mutation event. The response priority generation module is used to generate the available power range and response priority of the multi-element energy storage device based on the mutation feature set and the operating status data. A response strategy generation module is used to generate a collaborative response strategy based on the available power range and the response priority. The energy storage layer collaborative analysis module is used to perform multi-energy storage layer collaborative analysis on the multi-electro-energy storage device using the collaborative response strategy to obtain the collaborative response capability. The adaptability score calculation module is used to calculate the comprehensive adaptability score of the overcharging load and the multi-element energy storage device based on the response matching degree dataset and the collaborative response capability, and to generate adaptability target analysis results based on the comprehensive adaptability score.

[0010] This invention performs multi-feature mutation analysis on real-time power data of overcharging loads, accurately identifying aperiodic load mutations on timescales of seconds or even milliseconds. Combined with the operating status information of multi-energy storage devices, it determines the available power and energy state of each storage unit in real time, thereby quickly matching appropriate response targets. By dividing the available power range of energy storage units and setting response priorities, it achieves coordinated control between the fast and slow layers, enabling the high-power layer to respond promptly to instantaneous impacts, while the energy layer is responsible for subsequent balancing compensation. Simultaneously, it dynamically adjusts the coordination strategy based on the energy storage response results, ensuring continuous adaptation under different mutation scenarios. This effectively improves the identification accuracy of overcharging load mutations and the response coordination of energy storage devices, fundamentally improving the real-time response and coordinated adaptation capabilities of multi-energy storage systems under complex load conditions. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart illustrating a method for adaptability analysis of overcharge load and multi-element energy storage devices according to an embodiment of the present invention. Figure 2 This is a functional block diagram of an overcharge load and multi-element energy storage device adaptability analysis system provided in an embodiment of the present invention; The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0013] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0014] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0015] This application provides a method for adaptability analysis of overcharging load and multi-element energy storage devices. This method can be executed by software or hardware installed on terminal or server devices. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and demand forecasting platforms.

[0016] Reference Figure 1 The diagram shown is a flowchart illustrating a method for adaptability analysis of overcharging load and multi-element energy storage devices according to an embodiment of the present invention. In this embodiment, the method for adaptability analysis of overcharging load and multi-element energy storage devices includes: S1. Obtain real-time power data of the overcharge load and operating status data of the multi-element energy storage device, perform multi-feature mutation analysis on the real-time power data, and obtain the mutation feature set of each load mutation event.

[0017] In this embodiment of the invention, the overcharging load refers to the instantaneous high-power load change caused by high-power charging behavior in the supercharging station. Real-time power data is continuously collected power change data during operation, used to reflect the instantaneous power state of the load. The multi-element energy storage device is a composite energy storage system composed of various types of energy storage units, including electrochemical energy storage, supercapacitors, and flywheel energy storage. Operating status data includes the operating voltage, current, power, and charge / discharge status of each energy storage unit during the operation of the multi-element energy storage device.

[0018] Multi-feature abrupt change analysis identifies key characteristics of load abrupt changes by analyzing multi-dimensional features of the power curve (such as amplitude changes, rate of change, duration, and fluctuation frequency). A load abrupt change event is a moment or period in which power changes abnormally and drastically within a short period of time. The abrupt change feature set is a set of feature parameters extracted for each load abrupt change event, used to describe attributes such as the amplitude, rate, and duration of the abrupt change.

[0019] In detail, the multi-feature mutation analysis of the real-time power data to obtain the mutation feature set of each load mutation event includes: The real-time power data from different sampling channels are timestamped to obtain synchronous power data; Calculate the first-order rate of change of the synchronous power data, and calculate the second-order rate of change based on the first-order rate of change; When the second-order rate of change is greater than a preset change threshold, the load mutation event of the overcharge load is extracted; Calculate the statistical characteristics of the load mutation event within a preset time window; Based on the statistical characteristics, the magnitude and duration of the load mutation event are extracted; The mutation magnitude and the mutation duration are summarized into a mutation feature set.

[0020] In this embodiment of the invention, the original timestamp and sampling interval are recorded for each sampling source. A reference clock is selected, and the fixed offset and time drift rate of each channel are estimated by comparing the time difference of each channel on a known synchronization event (such as a time synchronization pulse, an external GPS pulse, or obvious signal characteristics). For channels with drift, a linear or higher-order drift model is established and the timestamp is corrected. Then, the corrected data is resampled to a unified time grid. During resampling, missing samples are interpolated (linear, spline, or nearest-neighbor based interpolation, depending on the signal characteristics), and the uncertainty of the interpolation is recorded. The aligned data is subjected to quality checks (such as cross-correlation verification and synchronization error statistics) to obtain synchronization power data.

[0021] In this embodiment of the invention, necessary preprocessing is performed on the synchronous power data to suppress noise (such as moving average, bandpass filtering, or Savitzky-Golay filtering to preserve transient characteristics). Then, the first-order rate of change is calculated using a numerical difference method based on the sampling interval (commonly using central difference or Savitzky-Golay derivative). Note that time-difference weighted difference formula is used when handling unequal sampling intervals. After obtaining the first-order rate of change, it is also appropriately smoothed to reduce the amplification effect of high-frequency noise on the second-order derivative. Then, the second-order rate of change is calculated using the same numerical difference method (differentiating the first-order rate).

[0022] In this embodiment of the invention, a threshold strategy is first determined (which may be an absolute threshold, multiple standard deviations based on background noise, or an adaptive threshold such as a sliding window percentile). Time points exceeding the threshold are found on the second-order rate of change sequence. To avoid noise triggering, a hysteresis / double threshold mechanism is introduced—a high threshold triggers the start of the event, and a low threshold determines the end of the event. Continuous over-threshold intervals are merged into a single event, and a minimum duration or minimum energy rule is applied to filter out short-pulse false positives, resulting in a load mutation event.

[0023] In this embodiment of the invention, a window is defined for each load mutation event (commonly a preset duration before the event begins and a delay after the event). Within this window, descriptive statistics are calculated, including mean, median, variance, standard deviation, maximum value, minimum value, quartiles, peak-to-peak value, root mean square energy, sum of signal energy, integral power (energy), rising slope and falling slope, duty cycle, and higher-order statistics such as skewness and kurtosis.

[0024] In this embodiment of the invention, the baseline power before the load mutation event is first estimated (the median or weighted average of several periods before the event can be used to resist noise), and then the mutation amplitude is determined with the baseline as a reference: the peak value minus the baseline can be taken, the peak-to-trough difference can be used, or a certain percentile (such as the 95th percentile) can be used to avoid overestimation caused by single-point anomalies; the mutation duration is: one is the cumulative time when the load mutation event exceeds a given amplitude threshold; the other is defined by energy contribution or area proportion (for example, the time period that accounts for 90% of the total energy of the event).

[0025] This invention acquires real-time power data of overcharge load and operating status data of multi-element energy storage devices, and performs multi-feature mutation analysis on the power data. It can accurately identify abnormal load change patterns in a short period of time, extract the amplitude, duration and other key features of each mutation event, monitor load mutation in real time, detect potential high-power impacts in advance, optimize the response strategy of multi-element energy storage devices, and improve the collaborative adaptability and safety of the energy storage system.

[0026] S2. Perform energy storage response matching analysis on the multi-element energy storage device based on the mutation feature set and the operating status data to obtain the response matching degree dataset for each load mutation event.

[0027] In this embodiment of the invention, energy storage response matching analysis is a process of comparing and analyzing the real-time operating status of multi-element energy storage devices with the characteristics of load change events to evaluate the energy storage system's responsiveness and adaptability to load changes. The response matching dataset is a collection of statistical data recording the response effects and matching degrees of multi-element energy storage devices in each load change event, used to evaluate the adaptability and control effectiveness of the energy storage system.

[0028] In detail, the step of performing energy storage response matching analysis on the multi-element energy storage device based on the mutation feature set and the operating status data to obtain a response matching degree dataset for each load mutation event includes: Based on the operating status data, extract the available power, current state of charge, response delay time, and energy capacity of each of the multi-element energy storage devices; Using the available power and the mutation amplitude of the mutation feature set, the matching degree of the multi-element energy storage device to the mutation power is calculated to obtain the power matching degree; Using the current state of charge, the energy capacity, and the mutation feature set, the energy matching degree of the multi-element energy storage device during the duration of the mutation is calculated to obtain the energy matching degree. The degree of matching between the response delay time and the mutation duration of the mutation feature set is analyzed to generate response timeliness; The power matching degree, the energy matching degree, and the response timeliness are summarized into a response matching degree dataset.

[0029] In this embodiment of the invention, real-time output power, voltage, current, and status flags of each energy storage unit are read from the monitoring data of the multi-energy storage device. Available power is generated by the device's rated parameters and current power, which is the maximum power that the energy storage unit can provide or absorb under the current conditions. The current state of charge (SOC) is obtained from the battery charge management system, which represents the percentage of the current charge relative to the rated capacity. The response delay time is extracted, which is the time from receiving the control command to the energy storage device actually starting to respond. At the same time, the energy capacity of each energy storage unit, including the rated capacity and available capacity, is recorded for subsequent calculation of energy matching degree.

[0030] In this embodiment of the invention, the amplitude of each load change event is compared with the available power of the energy storage device to assess whether the energy storage device can provide sufficient power to offset or compensate for load changes when the change event occurs. The calculation can be performed by taking the ratio, residual or relative difference between the change amplitude and the available power, and mapping the ratio, residual or relative difference to the matching degree value. The matching degree of multiple energy storage units is summarized, and a single event power matching degree can be generated by weighted average, maximum value selection or minimum value constraint.

[0031] In this embodiment of the invention, based on the current state of charge and energy capacity of the energy storage unit, and combined with the duration and amplitude of the load mutation event, it is calculated whether the energy storage unit can provide sufficient energy for compensation during the duration of the mutation: the required energy is obtained by integrating the mutation power, and then compared with the energy that the energy storage device can provide to obtain the energy matching ratio; the energy matching ratios of each energy storage unit are summarized to generate an event-level energy matching index.

[0032] In this embodiment of the invention, the response delay time of the energy storage device is compared with the duration of the load change event to determine whether the energy storage device can respond in a timely manner when the change event occurs. The calculation method may be the ratio of the delay time to the duration of the change event, the power shortage before and after the delay, or the trigger lag rate. Statistical or weighted processing is performed on multiple energy storage units to obtain the response timeliness of each event, so as to reflect the rapid response capability of the energy storage system.

[0033] This invention performs energy storage response matching analysis by matching the mutation feature set with the operating status data of multi-element energy storage devices. This can quantify the degree of matching of the power, energy and response timeliness of the energy storage system to each load mutation event, accurately assess the adaptability of energy storage devices under different mutation conditions, help identify potential insufficient response or delay problems, and provide data basis for optimizing energy storage scheduling strategies.

[0034] S3. Generate the available power range and response priority of the multi-element energy storage device based on the mutation feature set and the operating status data.

[0035] In this embodiment of the invention, the available power range is the power range that the multi-energy storage device or each energy storage unit can provide under the current operating state, including the minimum output power and the maximum output power. The response priority is the priority level of the multi-energy storage device or each energy storage unit in terms of response order and intensity when a load change event occurs, used to guide the coordinated adjustment and power distribution of the energy storage system.

[0036] Specifically, generating the available power range and response priority of the multi-element energy storage device based on the mutation feature set and the operating status data includes: Extract the maximum discharge power, maximum charging power, and device health status from the operating status data; Generate an upper limit for discharge and a lower limit for recharge based on the current state of charge and the device health status; The smaller value between the maximum discharge power and the discharge limit is selected as the target discharge limit. The smaller value between the maximum charging power and the lower rechargeable limit is selected as the target lower charging limit. A usable power range is generated based on the target upper discharge limit and the target lower charge limit; The mutation type of the multi-element energy storage device is generated based on the mutation feature set; Obtain the priority decision logic of the multi-element energy storage device, match the mutation type with the priority decision logic, and obtain the response priority.

[0037] In this embodiment of the invention, real-time power data and device status information are read from the multi-element energy storage device. The maximum discharge power and maximum charging power are obtained using the rated parameters of the energy storage unit and the current operating conditions. At the same time, device health status indicators, such as capacity decay rate, battery internal resistance, temperature anomaly and cycle life ratio, are obtained to evaluate the availability and safety of the energy storage unit in the current state.

[0038] In this embodiment of the invention, the maximum energy that the energy storage unit can continue to discharge and the remaining capacity that can continue to be charged are obtained by combining the current state of charge of the energy storage unit; at the same time, the upper and lower limits are adjusted according to the health status of the device. For example, the upper limit of discharge and the lower limit of charging of the energy storage unit with severe capacity decay will be reduced to protect the device, so as to obtain the upper limit of discharge and the lower limit of charging, which are used to constrain the energy storage operation.

[0039] In this embodiment of the invention, the maximum discharge power and the upper limit of discharge are compared, and the smaller of the two is taken as the safe and achievable target discharge upper limit. This ensures that the energy storage unit will not exceed its own limits to discharge while meeting energy storage capacity and safety constraints. Furthermore, the maximum charging power and the lower limit of rechargeability are compared, and the smaller of the two is taken as the target lower limit of charging. This ensures that the energy storage unit can be safely charged under load fluctuations or sudden events without exceeding the system or equipment limits, while also providing a buffer space for sudden load adjustments.

[0040] In this embodiment of the invention, the power output range of the energy storage unit is defined as the usable power range, with the target upper discharge limit and the target lower charge limit as boundaries, to ensure the safety and stability of the energy storage system under sudden load conditions.

[0041] In this embodiment of the invention, information such as amplitude, duration, and rate of change in the mutation feature set is analyzed, and load mutation events are classified according to predefined rules, such as short-term high-amplitude mutations, long-term medium-amplitude mutations, or low-amplitude fluctuation-type mutations; each energy storage unit is labeled with the corresponding mutation type according to its capacity and historical response performance.

[0042] Furthermore, rules are read from the preset priority decision logic of the energy storage system, such as priority for fast response units, priority for high-energy energy storage units, or priority for health status; the mutation type of the energy storage device is matched with the decision logic, and the response order and weight of each energy storage unit in the mutation event are generated according to the rules to form the final response priority, which is used to guide the coordinated scheduling and rapid power compensation of multiple energy storage devices.

[0043] This invention generates available power ranges and response priorities by combining mutation feature sets and operating status data of multiple energy storage devices. It can accurately define the operable range and response sequence of each energy storage unit in load mutation events. Under the premise of ensuring equipment safety and lifespan, it realizes efficient collaborative scheduling of the energy storage system, improves the rapid response capability to sudden power changes, optimizes energy distribution and load compensation effects, and thus improves the stability and control accuracy of the entire supercharging system.

[0044] S4. Generate a collaborative response strategy based on the available power range and the response priority.

[0045] In this embodiment of the invention, the coordinated response strategy is a multi-energy storage unit joint regulation scheme formulated based on the available power range and response priority of the energy storage device, which is used to achieve a fast, coordinated and efficient response of the energy storage system as a whole when a load change event occurs.

[0046] Specifically, the step of generating a collaborative response strategy based on the available power range and the response priority includes: Construct a feasible scheduling set based on the available power range; The multi-element energy storage devices are sorted in descending order according to the response priority to obtain a sorted energy storage device sequence. Within the feasible scheduling set, the target power demand is allocated sequentially according to the ordered sequence of energy storage devices to obtain the power allocation result; After the target power demand allocation is completed, obtain the remaining energy capacity and depth of discharge of each of the multi-electrode energy storage devices; The power allocation result is dynamically corrected using the remaining energy capacity and the depth of discharge to obtain a corrected allocation result. A collaborative response strategy is generated based on the corrected allocation result and the response priority.

[0047] In this embodiment of the invention, based on the available power range of each energy storage unit, the power value that the available power range can provide is discretized or divided into multiple operable power points to form a combination space; by considering system constraints, such as total power demand, minimum output power of the unit, energy capacity limit and safe operating range, all power combinations that meet the conditions are screened out to obtain a feasible scheduling set.

[0048] In this embodiment of the invention, energy storage units are sorted from high to low priority according to response priority; energy storage units with higher priority undertake the main regulation task in sudden load events and respond to power demand first.

[0049] In this embodiment of the invention, the sorted sequence of energy storage units is traversed, starting with high-priority devices, and the target power demand is allocated to each energy storage unit according to the available power range. During the allocation process, the allocation amount is adjusted according to the remaining capacity and power limit of each unit. When the target power demand has been partially met, the remaining power is allocated to the next priority energy storage unit until the total demand is met or the allocable power is exhausted.

[0050] In this embodiment of the invention, after power allocation is completed, the remaining energy capacity of each energy storage unit is obtained, which is the current state of charge minus the energy corresponding to the allocated power; at the same time, the depth of discharge is obtained, which is the proportion of the discharged energy of the energy storage unit to its total rated capacity, to evaluate the sustainable output capability and safe operation status of the energy storage unit, and to provide a basis for dynamic adjustment.

[0051] In this embodiment of the invention, the allocated power is checked based on the remaining energy capacity and depth of discharge to see if it exceeds the safe available range of the energy storage unit; for units that exceed the limits or have potential risks, the allocated power is readjusted proportionally or by priority; during the correction process, the coordination capability between energy storage units and the overall power balance are considered to ensure that the corrected power allocation meets the load demand and does not exceed the equipment safety limits.

[0052] In this embodiment of the invention, the final corrected power allocation value of each energy storage unit is combined with the response priority to form a unified scheduling execution scheme. The strategy clearly defines the output power, response order, duration and triggering conditions of each energy storage unit. The coordinated response strategy can be directly issued to the energy storage control system to realize the rapid, coordinated and safe response of multiple energy storage units in load change events.

[0053] This invention generates a collaborative response strategy by combining available power range and response priority. Under the premise of ensuring the safety and capacity constraints of energy storage devices, it can achieve efficient collaborative scheduling of multiple energy storage units, rationally allocate power demand, and enable high-priority and high-response energy storage units to participate in load regulation first. At the same time, it dynamically considers the remaining capacity and depth of discharge, improves the system's rapid response capability and stability to sudden load changes, and optimizes the overall energy utilization efficiency and control accuracy of the energy storage system.

[0054] S5. Using the aforementioned collaborative response strategy, perform multi-energy storage layer collaborative analysis on the multi-element energy storage device to obtain the collaborative response capability.

[0055] In this embodiment of the invention, multi-layer collaborative analysis analyzes the joint regulation effect of energy storage units of different types or power levels in an energy storage system under the guidance of a collaborative response strategy, and evaluates the degree of cooperation and regulation efficiency of each energy storage layer in the event of load abrupt changes. Collaborative response capability is the overall capability of a multi-layer energy storage system to respond jointly in the event of load abrupt changes, including power compensation efficiency, energy coordination, and response timeliness, used to quantify the adaptability and regulation effect of the energy storage system.

[0056] In detail, the step of using the collaborative response strategy to perform multi-layer collaborative analysis on the multi-element energy storage device to obtain collaborative response capability includes: According to the collaborative response strategy, each energy storage layer of the multi-energy storage device is assigned a corresponding target power command and adjustment priority; Based on the target power command and the adjustment priority, the multi-layer energy storage device is dynamically coordinated between layers to obtain response coordination data and energy transfer balance data. The response coordination data is used to analyze the response process of multiple energy storage layers to obtain response consistency. The energy transfer balance data is used to analyze the energy distribution process of multiple energy storage layers to obtain the energy transfer balance. The synergistic regulation effect of multiple energy storage layers is analyzed based on the response consistency and energy transfer balance to obtain the synergistic response capability.

[0057] In this embodiment of the invention, the energy storage system is divided into different layers according to the coordinated response strategy, such as a high-power fast response layer and a high-energy-capacity slow response layer; the target output power of each energy storage layer under the current load change event is obtained, taking into account the available power range and remaining energy of the energy storage unit; at the same time, combined with the response priority rule, the adjustment order and weight within the energy storage layer and between each layer are clarified, forming a hierarchical target power command and priority allocation table, providing a basis for dynamic coordination.

[0058] In this embodiment of the invention, the actual output power of each energy storage layer is generated according to the target power command and adjustment priority of each layer, either by time step or event triggering. During the allocation process, the available power range, remaining energy and equipment health status of the energy storage unit are considered, and the output of each layer is dynamically adjusted to avoid exceeding the limit. The power transfer between layers, the response time difference and the energy flow direction are recorded to generate response coordination data and energy transfer balance data, which are used to evaluate the cooperation effect and energy distribution between energy storage layers.

[0059] In this embodiment of the invention, response coordination data is used to statistically analyze the power response curves, trigger delays, and output fluctuations of each energy storage layer during load surge events; the synchronization degree of the responses of each energy storage layer is analyzed, such as the power peak time difference, response amplitude deviation, and response rate consistency; these indicators are used to quantify the level of coordinated action of multiple energy storage layers during events, and response consistency is obtained, which is used to evaluate the overall rapid response capability of the system.

[0060] In this embodiment of the invention, the step of analyzing the energy distribution process of multiple energy storage layers using the energy transfer balance data to obtain energy transfer balance includes: The interlayer energy differences of multiple energy storage layers are analyzed based on the energy transfer balance data to obtain the energy deviation matrix; Statistical feature analysis was performed on the energy deviation matrix to obtain energy difference characteristics; The energy flow relationship of multiple energy storage layers is analyzed based on the energy difference characteristics to obtain the energy transfer balance.

[0061] In detail, based on energy transfer balance data, the energy input and output between multiple energy storage layers are compared to obtain the energy distribution differences of each layer under the same time period or the same load event, forming an energy deviation matrix to reflect the degree of imbalance in energy transfer between layers. Statistical feature analysis is performed on this deviation matrix to extract energy difference characteristics, including average deviation, variance, and maximum deviation value, to quantify the fluctuation and concentration trend of energy distribution. Based on these energy difference characteristics, a comprehensive analysis of the energy flow relationship between each energy storage layer is conducted to assess the coordination and balance of energy transfer, thereby obtaining an overall energy transfer balance index to characterize the energy distribution stability of the energy storage system in multi-layer collaborative operation.

[0062] In this embodiment of the invention, the overall regulation effect of multiple energy storage layers in load abrupt events is evaluated by combining response consistency and energy transfer balance indicators; the coordinated response capability can be generated by weighted or comprehensive scoring methods to quantify the comprehensive performance of the energy storage system in terms of power compensation speed, response synchronization and energy coordination.

[0063] This invention utilizes a collaborative response strategy to perform multi-layer collaborative analysis of multi-element energy storage devices. This enables unified control and real-time monitoring of the power output, response sequence, and energy distribution of different energy storage layers during load surge events. During the analysis, by calculating the response consistency and energy transfer balance of each energy storage layer, the overall coordination and regulation efficiency of the energy storage system can be quantified. This helps identify potential response delays or uneven energy distribution problems, optimize inter-layer collaboration, and improve the system's rapid response capability, energy utilization efficiency, and overall stability to sudden load changes.

[0064] S6. Calculate the comprehensive adaptability score of the overcharge load and the multi-element energy storage device based on the response matching degree dataset and the collaborative response capability, and generate adaptability target analysis results based on the comprehensive adaptability score.

[0065] In this embodiment of the invention, the comprehensive adaptability score is an index calculated by combining the response matching degree dataset with the collaborative response capability, using a weighted or comprehensive evaluation method to measure the overall adaptability performance of the overcharge load and the multi-element energy storage equipment. It is used to evaluate the energy storage system's response capability and collaborative effect to load changes. The adaptability target analysis results are analytical conclusions or indicators generated based on the comprehensive adaptability score, used to guide the optimization of energy storage system control strategies, performance evaluation, and load regulation decisions.

[0066] Specifically, the step of calculating the comprehensive adaptability score of the overcharging load and the multi-element energy storage device based on the response matching dataset and the collaborative response capability includes: Obtain the weights of the power matching degree, energy matching degree, and response timeliness indicators in the response matching degree dataset; The response matching dataset is weighted and aggregated using the aforementioned indicator weights to obtain the aggregated response matching value. A response capability value is generated based on the collaborative response capability, and the aggregated response matching value and the response capability value are weighted and fused to obtain a fused response value; Calculate the average response matching value of the response matching dataset, and non-linearly amplify the average response matching value to obtain the amplified average value; Multiplying the fusion response value and the amplified average value yields a comprehensive compatibility score between the overcharge load and the multi-element energy storage device.

[0067] In this embodiment of the invention, the formula for calculating the overall suitability score is as follows:

[0068]

[0069]

[0070] in, Indicates the aggregated response matching value. This represents the i-th metric data in the response matching dataset. This represents the weight of the i-th indicator. This represents the total number of metrics in the response matching dataset. This indicates an amplified average value. This represents a nonlinear amplification operation through an exponential function. This indicates the preset nonlinear amplification factor. Indicates the average response matching value. This indicates the overall suitability score. Indicates the fusion weight. This indicates the response capability value.

[0071] In this embodiment of the invention, weights are assigned based on the importance of power matching degree, energy matching degree, and response timeliness in the response matching degree dataset. The weighted product aggregation of each index is performed to obtain the aggregated response matching value for each load change event, which reflects the overall adaptability of the energy storage device to load changes.

[0072] Furthermore, the aggregated response matching value is weighted and fused with the collaborative response capability of multiple energy storage layers to generate a fused response value. This comprehensively considers the performance of single-layer matching and multi-layer collaboration. The average response matching value of all events is calculated, and the response features with higher importance are enhanced through nonlinear amplification processing to obtain the amplified average value.

[0073] Furthermore, the fusion response value is multiplied by the amplified average value to form a comprehensive compatibility score between the overcharge load and the multi-element energy storage device, which is used to quantify the overall response capability and cooperation level of the energy storage system to load fluctuations.

[0074] In detail, the step of generating the fitness target analysis result based on the comprehensive fitness score includes: When the overall adaptability score is greater than or equal to the preset adaptability threshold, high adaptability is taken as the target analysis result of the adaptability of the overcharging load and the multi-element energy storage device; When the overall adaptability score is less than a preset adaptability threshold, the deviation between the response matching score dataset and the collaborative response capability is collected. The causes of the low compatibility between the overcharge load and the multi-element energy storage device are analyzed based on the deviation value, and the causes of low compatibility obtained from the analysis are used as the results of the compatibility target analysis.

[0075] In this embodiment of the invention, the calculated comprehensive adaptability score is compared with a preset adaptability threshold; if the score meets the conditions, it is directly determined that the adaptability of the energy storage device is good under the current load conditions.

[0076] In this embodiment of the invention, load mutation events with scores below a threshold are identified; differences between indicators such as power matching degree, energy matching degree, and response timeliness and ideal values ​​are extracted from the response matching degree dataset; simultaneously, deviations in response consistency and energy transfer balance among multiple energy storage layers are obtained from the collaborative response capability data. Further, using the collected deviation values, key factors leading to low adaptability are identified through rule analysis or statistical methods, such as excessive response delay, insufficient energy capacity, inadequate power matching, or insufficient collaborative response of a certain energy storage layer; each cause is associated with a specific energy storage unit or layer, generating a detailed analysis report including the problem type, severity, and scope of impact; finally, these analysis results are used as the adaptability target analysis results for the causes of low adaptability, providing a basis for optimizing energy storage regulation and improving strategies.

[0077] This invention calculates a comprehensive adaptability score by combining the response matching dataset with the collaborative response capability, and generates adaptability target analysis results accordingly. This can comprehensively quantify the overall adaptability performance of energy storage systems in response to sudden changes in supercharging load. It can not only accurately assess the matching degree of power, energy and response time, but also reflect the collaborative effect of multiple energy storage layers. This helps to identify potential adaptability deficiencies in a timely manner, and provides a scientific basis for optimizing energy storage scheduling strategies, improving the system's rapid response capability and stability, thereby ensuring the safe and efficient operation of supercharging stations under high power fluctuation conditions.

[0078] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0079] like Figure 2 The diagram shown is a functional block diagram of an overcharge load and multi-element energy storage device adaptability analysis system provided in an embodiment of the present invention.

[0080] In this embodiment of the disclosure, a compatibility analysis system for overcharging load and multi-element energy storage devices is provided. This compatibility analysis system corresponds one-to-one with the compatibility analysis method for overcharging load and multi-element energy storage devices described in the above embodiment. Figure 2 As shown, the compatibility analysis system 100 for overcharge load and multi-element energy storage devices includes a feature mutation analysis module 101, a response matching analysis module 102, a response priority generation module 103, a response strategy generation module 104, an energy storage layer collaborative analysis module 105, and a compatibility score calculation module 106. Detailed descriptions of each functional module are as follows: The feature mutation analysis module 101 is used to acquire real-time power data of overcharge load and operating status data of multi-element energy storage devices, and to perform multi-feature mutation analysis on the real-time power data to obtain the mutation feature set of each load mutation event. The response matching analysis module 102 is used to perform energy storage response matching analysis on the multi-element energy storage device based on the mutation feature set and the operating status data, and obtain the response matching degree dataset for each load mutation event. The response priority generation module 103 is used to generate the available power range and response priority of the multi-element energy storage device based on the mutation feature set and the operating status data. The response strategy generation module 104 is used to generate a collaborative response strategy based on the available power range and the response priority. The energy storage layer collaborative analysis module 105 is used to perform multi-energy storage layer collaborative analysis on the multi-electro-energy storage device using the collaborative response strategy to obtain the collaborative response capability. The adaptability score calculation module 106 is used to calculate the comprehensive adaptability score of the overcharging load and the multi-element energy storage device based on the response matching degree dataset and the collaborative response capability, and to generate adaptability target analysis results based on the comprehensive adaptability score.

[0081] In one embodiment, the feature mutation analysis module 101 performs multi-feature mutation analysis on the real-time power data to obtain a mutation feature set for each load mutation event, for the purpose of: The real-time power data from different sampling channels are timestamped to obtain synchronous power data; Calculate the first-order rate of change of the synchronous power data, and calculate the second-order rate of change based on the first-order rate of change; When the second-order rate of change is greater than a preset change threshold, the load mutation event of the overcharge load is extracted; Calculate the statistical characteristics of the load mutation event within a preset time window; Based on the statistical characteristics, the magnitude and duration of the load mutation event are extracted; The mutation magnitude and the mutation duration are summarized into a mutation feature set.

[0082] In one embodiment, the response matching analysis module 102 performs energy storage response matching analysis on the multi-element energy storage device based on the mutation feature set and the operating status data to obtain a response matching degree dataset for each load mutation event, which is used for: Based on the operating status data, extract the available power, current state of charge, response delay time, and energy capacity of each of the multi-element energy storage devices; Using the available power and the mutation amplitude of the mutation feature set, the matching degree of the multi-element energy storage device to the mutation power is calculated to obtain the power matching degree; Using the current state of charge, the energy capacity, and the mutation feature set, the energy matching degree of the multi-element energy storage device during the duration of the mutation is calculated to obtain the energy matching degree. The degree of matching between the response delay time and the mutation duration of the mutation feature set is analyzed to generate response timeliness; The power matching degree, the energy matching degree, and the response timeliness are summarized into a response matching degree dataset.

[0083] In one embodiment, the response priority generation module 103, when performing the generation of the available power range and response priority of the multi-element energy storage device based on the mutation feature set and the operating status data, is used for: Extract the maximum discharge power, maximum charging power, and device health status from the operating status data; Generate an upper limit for discharge and a lower limit for recharge based on the current state of charge and the device health status; The smaller value between the maximum discharge power and the discharge limit is selected as the target discharge limit. The smaller value between the maximum charging power and the lower rechargeable limit is selected as the target lower charging limit. A usable power range is generated based on the target upper discharge limit and the target lower charge limit; The mutation type of the multi-element energy storage device is generated based on the mutation feature set; Obtain the priority decision logic of the multi-element energy storage device, match the mutation type with the priority decision logic, and obtain the response priority.

[0084] In one embodiment, the response strategy generation module 104 generates a coordinated response strategy based on the available power range and the response priority, for the following purposes: Construct a feasible scheduling set based on the available power range; The multi-element energy storage devices are sorted in descending order according to the response priority to obtain a sorted energy storage device sequence. Within the feasible scheduling set, the target power demand is allocated sequentially according to the ordered sequence of energy storage devices to obtain the power allocation result; After the target power demand allocation is completed, obtain the remaining energy capacity and depth of discharge of each of the multi-electrode energy storage devices; The power allocation result is dynamically corrected using the remaining energy capacity and the depth of discharge to obtain a corrected allocation result. A collaborative response strategy is generated based on the corrected allocation result and the response priority.

[0085] In one embodiment, the energy storage layer collaborative analysis module 105 performs multi-energy storage layer collaborative analysis on the multi-element energy storage device using the collaborative response strategy to obtain collaborative response capabilities, and is used for: According to the collaborative response strategy, each energy storage layer of the multi-energy storage device is assigned a corresponding target power command and adjustment priority; Based on the target power command and the adjustment priority, the multi-layer energy storage device is dynamically coordinated between layers to obtain response coordination data and energy transfer balance data. The response coordination data is used to analyze the response process of multiple energy storage layers to obtain response consistency. The energy transfer balance data is used to analyze the energy distribution process of multiple energy storage layers to obtain the energy transfer balance. The synergistic regulation effect of multiple energy storage layers is analyzed based on the response consistency and energy transfer balance to obtain the synergistic response capability.

[0086] In one embodiment, the energy storage layer collaborative analysis module 105 analyzes the energy distribution process of multiple energy storage layers using the energy transfer balance data to obtain energy transfer balance, and is used for: The interlayer energy differences of multiple energy storage layers are analyzed based on the energy transfer balance data to obtain the energy deviation matrix; Statistical feature analysis was performed on the energy deviation matrix to obtain energy difference characteristics; The energy flow relationship of multiple energy storage layers is analyzed based on the energy difference characteristics to obtain the energy transfer balance.

[0087] In one embodiment, the adaptability score calculation module 106 performs the calculation of a comprehensive adaptability score for the overcharging load and the multi-element energy storage device based on the response matching dataset and the collaborative response capability, for the following purposes: Obtain the weights of the power matching degree, energy matching degree, and response timeliness indicators in the response matching degree dataset; The response matching dataset is weighted and aggregated using the aforementioned indicator weights to obtain the aggregated response matching value. A response capability value is generated based on the collaborative response capability, and the aggregated response matching value and the response capability value are weighted and fused to obtain a fused response value; Calculate the average response matching value of the response matching dataset, and non-linearly amplify the average response matching value to obtain the amplified average value; Multiplying the fusion response value and the amplified average value yields a comprehensive compatibility score between the overcharge load and the multi-element energy storage device.

[0088] In one embodiment, the adaptability score calculation module 106, when performing the generation of adaptability target analysis results based on the comprehensive adaptability score, is used for: When the overall adaptability score is greater than or equal to the preset adaptability threshold, high adaptability is taken as the target analysis result of the adaptability of the overcharging load and the multi-element energy storage device; When the overall adaptability score is less than a preset adaptability threshold, the deviation between the response matching score dataset and the collaborative response capability is collected. The causes of the low compatibility between the overcharge load and the multi-element energy storage device are analyzed based on the deviation value, and the causes of low compatibility obtained from the analysis are used as the results of the compatibility target analysis.

[0089] In this invention, the specific limitations of the compatibility analysis system for overcharging load and multi-element energy storage devices can be found in the above-described limitations of the compatibility analysis method for overcharging load and multi-element energy storage devices, and will not be repeated here. Each module in the aforementioned compatibility analysis system for overcharging load and multi-element energy storage devices can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0090] In the embodiments provided by this invention, it should be understood that the disclosed system can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0091] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0092] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0093] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0094] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0095] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0096] In the embodiments provided in this disclosure, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0097] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0098] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for analyzing the adaptability of an overloading and multi-element energy storage device, characterized by, The method comprises: acquiring real-time power data of overloading and operation state data of multi-element energy storage equipment; performing multi-feature mutation analysis on the real-time power data to obtain a mutation feature set of load mutation events; performing energy storage response matching analysis according to the mutation feature set and the operation state data to obtain a response matching degree data set; generating an available power interval and a response priority of the multi-element energy storage equipment according to the mutation feature set and the operation state data; generating a collaborative response strategy based on the available power interval and the response priority; performing multi-energy storage layer collaborative analysis by using the collaborative response strategy to obtain a collaborative response capability; calculating a comprehensive adaptability score according to the response matching degree data set and the collaborative response capability, and generating an adaptability target analysis result.

2. The method of claim 1, wherein the supercharged and multi-element energy storage device is a supercapacitor. The method comprises: acquiring multi-channel real-time power data and performing timestamp correction to obtain synchronous power data; calculating first and second order change rates of the synchronous power data; identifying load mutation events based on the second order change rate; extracting statistical features of the load mutation events, including mutation amplitude and mutation duration; summarizing the features and forming a mutation feature set.

3. The method of claim 2, wherein the supercharged and multi-element energy storage device is a supercapacitor. The method comprises: extracting available power, current state of charge, response delay time and energy capacity of the multi-element energy storage equipment according to the operation state data; calculating power matching degree based on the available power and the mutation amplitude; calculating energy matching degree based on the current state of charge, energy capacity and the mutation feature set; analyzing the matching degree of the response delay time and the mutation duration to generate response timeliness; summarizing the power matching degree, the energy matching degree and the response timeliness to form a response matching degree data set.

4. The method of claim 3, wherein the supercharged and multi-element energy storage device is a supercapacitor. The method comprises: extracting maximum charge and discharge power and equipment health status according to the operation state data; generating discharging upper limit and charging lower limit in combination with the current state of charge; selecting the smaller value between the discharging upper limit and the maximum discharge power as a target discharging upper limit, and selecting the smaller value between the charging lower limit and the maximum charge power as a target charging lower limit; determining an available power interval based on the target discharging upper limit and the target charging lower limit; identifying mutation types according to the mutation feature set, and matching preset priority decision logic to generate a response priority.

5. The method of claim 1, wherein the supercharged and multi-element energy storage device is a supercapacitor. The method comprises: constructing a feasible scheduling set according to the available power interval; sorting the multi-element energy storage equipment according to the response priority; allocating target power demand to the sorted energy storage equipment in the feasible scheduling set in sequence to obtain a power allocation result; dynamically correcting the power allocation result based on the residual energy capacity and the discharge depth of each energy storage equipment; generating a collaborative response strategy according to the corrected allocation result and the response priority.

6. The method of claim 1, wherein the supercharged and multi-element energy storage device is a supercapacitor. The method comprises: According to the cooperative response strategy, target power and priority are allocated to each energy storage layer; Based on the target power and priority, inter-layer dynamic coordination is carried out to obtain response coordination data and energy balance data; According to the response coordination data, the consistency of the responses of multiple energy storage layers is analyzed; According to the energy balance data, the balance of energy transmission among multiple energy storage layers is analyzed; The response consistency and energy transmission balance are comprehensively evaluated to obtain the cooperative response capability.

7. The method of claim 6, wherein the supercharged and multi-element energy storage device is a supercapacitor. The cooperative response strategy is used to analyze multiple energy storage layers to obtain the cooperative response capability, which includes: According to the energy transmission balance data, an energy deviation matrix among each energy storage layer is calculated; Statistical feature analysis is performed on the energy deviation matrix to obtain energy difference features; Based on the energy difference features, the energy transmission balance is evaluated.

8. The method of claim 1, wherein the supercharged and multi-element energy storage device is a supercapacitor. The comprehensive adaptability score is calculated based on the response matching degree data set and the cooperative response capability, which includes: The weights of each index in the response matching degree data set are obtained and weighted product aggregation is performed to obtain an aggregated response matching value; The aggregated response matching value and the response capability value generated based on the cooperative response capability are weighted and fused to obtain a fused response value; The average value of the response matching degree data set is nonlinearly amplified to obtain an amplified average value; The fused response value is multiplied by the amplified average value to generate a comprehensive adaptability score.

9. The method of claim 8, wherein the supercharged and multi-element energy storage device is a supercapacitor. The generation step of the adaptability target analysis result includes: When the comprehensive adaptability score is greater than or equal to a preset threshold, it is determined as a high adaptability result; When the comprehensive adaptability score is less than the preset threshold, the deviation value of the response matching degree and the cooperative response capability is collected; Based on the deviation value, low adaptability reason analysis is performed to generate an adaptability target analysis result.

10. A system for analyzing the adaptability of an overcharged and multi-element energy storage device, comprising: The system includes: A feature mutation analysis module is configured to obtain real-time power data of overloading and operating state data of multiple energy storage devices; multi-feature mutation analysis is performed on the real-time power data to obtain a mutation feature set of load mutation events; A response matching analysis module is configured to perform energy storage response matching analysis based on the mutation feature set and the operating state data to obtain a response matching degree data set; A response priority generation module is configured to generate available power intervals and response priorities of multiple energy storage devices based on the mutation feature set and the operating state data; A response strategy generation module is configured to generate a cooperative response strategy based on the available power intervals and the response priorities; An energy storage layer cooperative analysis module is configured to use the cooperative response strategy to analyze multiple energy storage layers to obtain a cooperative response capability; An adaptability score calculation module is configured to calculate a comprehensive adaptability score based on the response matching degree data set and the cooperative response capability, and generate an adaptability target analysis result.

Images