Parameter processing method and processing device of energy storage device and energy storage device

Abstract

The application provides a parameter processing method and device of an energy storage device and the energy storage device, relates to the technical field of energy storage, and the parameter processing method of the energy storage device comprises the following steps: in response to a selection operation on a component of the energy storage device, displaying a plurality of connection relationship models corresponding to the selected component on an editable interface; the connection relationship model comprises a plurality of connection node reference graphs, and each connection node reference graph is a connection relationship preview graph obtained based on the physical connection characteristics of the selected component; collecting real-time operation data of the energy storage device; selecting a connection node reference graph matched with the real-time operation data; in response to an operation instruction on the connection node reference graph, obtaining a connection node parameter group corresponding to the operation instruction; and controlling the operation parameters of the energy storage device based on the connection node parameter group. The application can optimize the operation efficiency of the energy storage device and realize global optimal scheduling.

Description

Technical Field

[0001] This application relates to the field of energy storage technology, and in particular to a parameter processing method, processing device and energy storage device for energy storage equipment. Background Technology

[0002] Traditional energy storage systems typically employ a distributed, independent configuration model, leading to problems such as high energy storage capacity requirements, large investment costs, and long payback periods. To address this issue, existing technologies have proposed optimization algorithms for centralized control of energy storage devices. However, when solving the energy storage scheduling problem, these algorithms are prone to getting trapped in local optima, resulting in overall low efficiency. Summary of the Invention

[0003] The main objective of this invention is to provide a parameter processing method for energy storage devices, which aims to optimize the operating efficiency of energy storage devices and achieve globally optimal scheduling.

[0004] To achieve the above objectives, the present invention provides a parameter processing method for an energy storage device, the parameter processing method for the energy storage device comprising:

[0005] In response to a selection operation for a component of an energy storage device, multiple connection relationship models corresponding to the selected component are displayed on an editable interface; the connection relationship model includes multiple connection node reference diagrams, each of which is a preview diagram of the connection relationship obtained based on the physical connection characteristics of the selected component;

[0006] Collect real-time operating data of energy storage devices, including state of charge, charging and discharging power, ambient temperature, and battery life loss coefficient;

[0007] Select a reference diagram of connection nodes that match the real-time running data;

[0008] In response to an operation command for the connection node reference graph, obtain the connection node parameter set corresponding to the operation command;

[0009] The operating parameters of the energy storage device are controlled based on the connection node parameter group.

[0010] Optionally, the step of selecting a reference graph of connection nodes that matches the real-time running data includes:

[0011] Acquire historical operating data of the energy storage device, including ambient temperature and battery life loss coefficient;

[0012] Based on the state of charge and charging / discharging power in the real-time operating data, the state of charge is divided into multiple first intervals, and the granularity of the interval division is adjusted based on the real-time fluctuation characteristics of the charging / discharging power to generate multiple target intervals, each interval corresponding to a set of preset connection node reference diagrams.

[0013] Based on the historical operation data, the validity of the connection node reference graph for each target interval is verified, and a corresponding matching degree score matrix is ​​generated.

[0014] A reference diagram of connection nodes that match the current operating mode of the energy storage device is selected based on the matching degree scoring matrix.

[0015] Based on the ambient temperature and the battery life loss coefficient, the parameter threshold range of the filtered connection node reference diagram is corrected.

[0016] Prioritize the corrected connection node reference graph and output the optimal matching connection node reference graph.

[0017] Optionally, the step of validating the connection node reference graph for each target interval based on the historical operational data and generating a corresponding matching score matrix includes:

[0018] Determine the correlation between the state of charge and the charging / discharging power in the historical operating data;

[0019] Construct an operation mode clustering model corresponding to the correlation between the state of charge and the charging and discharging power;

[0020] The similarity score between the current real-time running data and the reference graph of each connected node is calculated using a clustering model.

[0021] The similarity score is weighted and fused with the battery life loss coefficient to generate the matching score matrix.

[0022] Optionally, the step of calculating the similarity score between the current real-time running data and the reference graph of each connected node using a clustering model includes:

[0023] Obtain the state-of-charge sequence and charge / discharge power sequence within a continuous time window;

[0024] Feature vectors are extracted from the state-of-charge sequence and the charge-discharge power sequence using time series analysis methods;

[0025] The similarity score is determined by comparing the feature vectors with the feature distribution of each connected node reference graph using a clustering algorithm and quantifying the similarity score based on the similarity of the feature distributions.

[0026] The construction of the operation mode clustering model corresponding to the correlation between the state of charge and the charging / discharging power includes:

[0027] Extract the first feature vector of state of charge and the second feature vector of charge / discharge power from the historical operating data;

[0028] A multidimensional space model is constructed based on the first feature vector and the second feature vector;

[0029] Clustering algorithms are used to classify data points in a multidimensional space, forming clusters under different operating modes;

[0030] Based on the battery life loss coefficient, each cluster is optimized and adjusted to generate the operating mode clustering model.

[0031] Optionally, controlling the operating parameters of the energy storage device based on the connection node parameter group includes:

[0032] Based on the dynamic adjustment parameters and static configuration parameters in the connection node parameter group, the first control command for the energy storage device is generated.

[0033] Construct an objective function that maximizes the operating efficiency of the energy storage device;

[0034] The population parameters for the running efficiency in the objective function are initialized by chaotic mapping, and multiple iterative calculations are performed to generate an objective parameter adjustment strategy.

[0035] The first control command is adjusted according to the target parameter adjustment strategy to generate the target control command;

[0036] The operating parameters of the energy storage device are controlled according to the target control command.

[0037] Optionally, the step of initializing the population parameters for operational efficiency in the objective function through chaotic mapping and performing multiple iterative calculations to generate an objective parameter adjustment strategy includes:

[0038] By utilizing the randomness and ergodicity of chaotic mapping, initial population parameters corresponding to the running efficiency in the objective function are generated, and the population size is dynamically configured according to the real-time state of charge interval.

[0039] Multi-dimensional constraints are imposed on the population size for the operational efficiency of the objective function, and iterative optimization is performed through dynamically adjusted crossover and mutation probabilities. The multi-dimensional constraints include a charge / discharge power threshold based on ambient temperature correction, a charge / discharge depth boundary based on battery life loss coefficient, and a voltage balance tolerance range in the connection node parameter group. The mutation probability is correlated with the battery life loss coefficient in real time.

[0040] After iterative optimization, the corresponding iterative results are obtained. When the difference between the optimal solutions of the three consecutive iterations is less than the convergence threshold or the proportion of feasible solutions that satisfy all constraints of the multi-dimensional constraints exceeds the preset proportion, the target parameter adjustment strategy is output.

[0041] The step of adjusting the first control command according to the target parameter adjustment strategy to generate the target control command includes:

[0042] According to the target parameter adjustment strategy, determine the first deviation value of the charging and discharging power threshold and the second deviation value of the state of charge range in the first control command;

[0043] The target control command is generated by correcting the corresponding charge / discharge power threshold and state of charge range in the first control command based on the first deviation value and the second deviation value.

[0044] Optionally, the step of obtaining the connection node parameter group corresponding to the operation instruction in response to the operation instruction for the connection node reference graph includes:

[0045] In response to an operation command for a connection node reference graph, the parameter identifier in the operation command is parsed, and a composite parameter identifier set corresponding to the parameter identifier is generated based on the topological correlation of the connection node reference graph.

[0046] The system acquires multiple connection node parameters corresponding to the composite parameter identifier set and verifies their validity through a multi-dimensional verification mechanism. This acquisition and verification includes dynamically adjusting the allowable threshold range of the connection node parameters based on the ambient temperature and battery life loss coefficient in the real-time operating data of the energy storage device; and verifying the logical compatibility of associated parameters in the reference diagram of the same connection node. If a conflict exists, the system triggers a parameter dynamic adjustment module.

[0047] The parameter dynamic correction module adaptively corrects the parameters of the connection nodes that fail the verification. The adaptive correction of the parameters of the connection nodes that fail the verification by the parameter dynamic correction module includes replacing the parameters that exceed the threshold based on the historical optimal values ​​of the parameters in the same topology scenario in historical operation data; and recalculating the compatible solution of the conflict parameters according to the state of charge fluctuation characteristics in the real-time operation data.

[0048] The checked and / or corrected connection node parameters are aggregated according to their priority order in the connection node reference diagram to generate a connection node parameter group with a security verification label.

[0049] Optionally, the parameter processing method for the energy storage device further includes:

[0050] Acquire historical and real-time operating data of multiple associated energy storage devices, and construct a multi-user shared capacity optimization model based on the historical operating data. The multi-user shared capacity optimization model includes a shared capacity allocation strategy library and a capacity dynamic game adjustment mechanism.

[0051] Through the capacity dynamic game adjustment mechanism, the state of charge fluctuation characteristics and charging and discharging power requirements in the real-time operating data of each energy storage device are analyzed to generate the shared capacity competition coefficient of each energy storage device.

[0052] Based on the shared capacity competition coefficient, and combined with the historical contribution ratio and real-time demand ratio in the shared capacity allocation strategy library, the shared capacity allocation strategy of the multi-user shared capacity optimization model is dynamically adjusted.

[0053] According to the adjusted shared capacity allocation strategy, capacity allocation instructions corresponding to each energy storage device are generated, and the charging and discharging power thresholds and state of charge boundaries in the operating parameters of the energy storage devices are synchronously corrected based on the capacity allocation instructions.

[0054] In addition, to achieve the above objectives, the present invention also provides a processing apparatus, the processing apparatus comprising: a memory, a processor, and a parameter processing program for an energy storage device stored in the memory and executable on the processor, the parameter processing program for the energy storage device being configured to implement the parameter processing method for the energy storage device as described above.

[0055] In addition, to achieve the above objectives, the present invention also provides an energy storage device, including the processing apparatus described above.

[0056] This invention, in response to a component selection operation for an energy storage device, displays multiple connection relationship models corresponding to the selected component on an editable interface. These connection relationship models include multiple connection node reference diagrams. Each reference diagram is a preview of the connection relationship based on the physical connection characteristics of the selected component. Real-time operating data of the energy storage device is then collected, including state of charge, charge / discharge power, ambient temperature, and battery life loss coefficient. A connection node reference diagram matching the real-time operating data is selected, and in response to an operation command for the reference diagram, a connection node parameter set corresponding to the operation command is obtained. Finally, the operating parameters of the energy storage device are controlled based on the connection node parameter set. This enables the energy storage device to adaptively adjust under complex operating conditions, achieving globally optimal scheduling, improving overall system efficiency, reducing operating costs, extending equipment lifespan, and ensuring maximum energy utilization. Attached Figure Description

[0057] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0059] Figure 1 This is a schematic flowchart of a parameter processing method for an energy storage device according to an embodiment of the present invention;

[0060] Figure 2 for Figure 1 A flowchart illustrating step S300 in the process;

[0061] Figure 3 for Figure 2 A flowchart illustrating step S330 in the process;

[0062] Figure 4 for Figure 3 A flowchart illustrating step S332 in the process;

[0063] Figure 5 for Figure 3 A flowchart illustrating step S333 in the process;

[0064] Figure 6 for Figure 1 A flowchart illustrating step S500 in the process;

[0065] Figure 7 for Figure 6 A flowchart illustrating step S530 in the process;

[0066] Figure 8 for Figure 6 A flowchart illustrating step S540 in the process;

[0067] Figure 9 for Figure 1 A flowchart illustrating step S400 in the process;

[0068] Figure 10 This is a schematic flowchart of a parameter processing method for an energy storage device according to another embodiment of the present invention.

[0069] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0070] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Well-known modules, units, and their connections, links, communications, or operations are not shown or described in detail. Furthermore, the described features, architectures, or functions can be combined in any way in one or more embodiments. Those skilled in the art should understand that the various embodiments described below are only for illustrative purposes and not for limiting the scope of protection of the present invention. It is also readily understood that the modules, units, or processing methods in the various embodiments described herein and shown in the accompanying drawings can be combined and designed in various different configurations. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0071] The definitions of various terms or methods used in the following embodiments are, except where logically impossible, generally defined as broad concepts that can be implemented under the premise of the content disclosed in the embodiments. Under this understanding, all specific subordinate limitations of the terms or methods should be considered as part of the invention and should not be narrowly interpreted or biased simply because the specification does not disclose such a specific limitation. Similarly, provided that it is logically feasible, the order of the steps in the method is flexible and varied, and all specific subordinate limitations in the broad concepts of various terms or methods fall within the scope of protection of this invention.

[0072] Traditional energy storage systems typically employ a distributed, independent configuration model, leading to problems such as high energy storage capacity requirements, large investment costs, and long payback periods. To address this issue, existing technologies have proposed optimization algorithms for centralized control of energy storage devices. However, when solving the energy storage scheduling problem, these algorithms are prone to getting trapped in local optima, resulting in overall low efficiency.

[0073] The main solution of this application embodiment is as follows: In response to the selection operation of the components of the energy storage device, multiple connection relationship models corresponding to the selected components are displayed on the editable interface. The connection relationship model includes multiple connection node reference diagrams. Each connection node reference diagram is a preview diagram of the connection relationship obtained based on the physical connection characteristics of the selected components. Then, real-time operating data of the energy storage device is collected. The real-time operating data includes state of charge, charging and discharging power, ambient temperature and battery life loss coefficient. Then, the connection node reference diagram that matches the real-time operating data is selected. In response to the operation command of the connection node reference diagram, the connection node parameter group corresponding to the operation command is obtained. Finally, the operating parameters of the energy storage device are controlled based on the connection node parameter group.

[0074] In this embodiment, for ease of description, the following description uses the processing device as the execution subject.

[0075] This application provides a solution that enables energy storage devices to adaptively adjust under complex operating conditions, achieve globally optimal scheduling, improve overall system efficiency, reduce operating costs, extend equipment lifespan, and ensure maximum energy utilization.

[0076] Therefore, the present invention proposes a parameter processing method for energy storage devices; it is understood that the energy storage device is equipped with a processing device for storing and executing the following method. The processing device can be implemented using a main controller, such as an MCU (Micro Controller Unit), a DSP (Digital Signal Processor), an FPGA (Field Programmable Gate Array), or a SOC (System On Chip).

[0077] In existing technologies, traditional energy storage systems mostly adopt a distributed, independent configuration mode, which can easily lead to excessive capacity demand and increased investment costs. Although existing optimization algorithms attempt to solve this problem through centralized control, they are prone to getting trapped in local optima during energy storage scheduling, making it difficult to achieve global efficiency optimization. For example, in integrated wind-solar-storage projects, when photovoltaic output fluctuates significantly, traditional parameter adjustment methods cannot dynamically match the optimal operating strategy according to real-time operating conditions, resulting in accelerated battery life loss and decreased energy utilization.

[0078] To address the aforementioned issues, it is necessary to overcome the limitations of static parameter configuration and develop an optimization mechanism that can dynamically adapt to operating conditions. This embodiment approaches the problem from the perspective of physical connectivity characteristics and real-time data fusion: first, a component-level connectivity model is established to create a visual interactive interface; second, multi-dimensional operating data is introduced as a basis for dynamic adjustment; and finally, closed-loop optimization of the operating state is achieved through parameter group matching. The core of this solution lies in combining physical topology features with real-time operating condition analysis to avoid the algorithm getting trapped in local optima.

[0079] Based on the above, referring to Figure 1 In one embodiment of the present invention, the parameter processing method of the energy storage device includes steps S100-S500, wherein:

[0080] S100. In response to a selection operation for a component of an energy storage device, multiple connection relationship models corresponding to the component selected by the selection operation are displayed on an editable interface; the connection relationship model includes multiple connection node reference diagrams, each of which is a connection relationship preview diagram obtained based on the physical connection characteristics of the selected component.

[0081] S200: Collect real-time operating data of the energy storage device, including state of charge, charging and discharging power, ambient temperature and battery life loss coefficient;

[0082] S300. Select a connection node reference diagram that matches the real-time running data;

[0083] S400: In response to an operation command for a connection node reference diagram, obtain the connection node parameter group corresponding to the operation command;

[0084] S500: Control the operating parameters of the energy storage device based on the connection node parameter group.

[0085] The connection relationship model refers to the component connection logic framework established through topology analysis. Graph theory algorithms can be used to parse electrical connection paths, generating a node network model including buses, switches, and energy storage units. Its function is to intuitively display physical connection characteristics and provide spatial reference for parameter adjustment. Real-time operating data includes dynamic process parameters and static state parameters, such as charging and discharging power collected by current sensors and ambient temperature obtained by temperature probes. Its function is to construct a multi-dimensional parameter space for pattern matching. The connection node reference diagram refers to a preset typical connection topology scheme, such as energy storage unit layout defined by series-parallel combination, electrical connection paths designed with star or ring structures, and node schemes configured with different protection strategies. This facilitates rapid identification and matching of actual operating conditions. This connection node reference diagram can be generated by machine learning after pattern recognition of historical operating data, used to quickly match the optimal connection mode under the current operating conditions. The connection node parameter group includes dynamically adjusted parameters and static configuration parameters. For example, combining charging and discharging power thresholds and temperature compensation coefficients to form a parameter package, its function is to achieve multi-parameter coordinated control.

[0086] When an operator selects a specific component, the processing unit calls upon a pre-stored topology database to generate an associated connection model. By continuously acquiring the operating status parameters of the energy storage unit, it can use pattern recognition algorithms to match the topology scheme corresponding to the current data features. It then parses optimized parameter combinations from the selected reference map. Based on factors such as charge / discharge thresholds and temperature compensation coefficients in the parameter set, it dynamically adjusts the operating strategy of the energy storage device. For example, in low-temperature environments, the processing unit automatically increases the temperature compensation coefficient and adjusts the charge / discharge power limits according to the current state of charge, ensuring the device maintains efficient operation within a safe range.

[0087] Compared to existing technologies, current solutions rely on fixed parameter configuration tables and cannot adapt to changes in the dynamic operating environment. This embodiment establishes a dynamic parameter adjustment mechanism driven by both physical topology and real-time data. Traditional methods tend to overlook the coupling relationship between parameters when optimizing a single parameter, while this embodiment achieves global optimization of multi-dimensional parameters through the coordinated control of parameter groups. Existing technologies lack a visual interactive interface; the connection node reference diagram provided in this embodiment makes the parameter adjustment process interpretable.

[0088] The aforementioned technologies effectively solve the problem of lag in parameter adjustment in traditional energy storage systems, achieving dynamic optimization and matching of operating parameters. In application scenarios with significant wind and solar power fluctuations, the system can quickly respond to power changes and adjust charging and discharging strategies, improving the operating efficiency of the energy storage system. Through the coordinated control of multi-dimensional parameters, the risk of battery overcharging and over-discharging is reduced, extending equipment lifespan. The introduction of a visual interactive interface improves the accuracy and ease of operation of parameter adjustments, reducing equipment failures caused by human error.

[0089] This embodiment, in response to a component selection operation for an energy storage device, displays multiple connection relationship models corresponding to the selected component on an editable interface. These connection relationship models include multiple connection node reference diagrams. Each reference diagram is a preview of the connection relationship based on the physical connection characteristics of the selected component. Real-time operating data of the energy storage device is then collected, including state of charge, charge / discharge power, ambient temperature, and battery life loss coefficient. A connection node reference diagram matching the real-time operating data is selected, and in response to an operation command for the reference diagram, a connection node parameter set corresponding to the operation command is obtained. Finally, the operating parameters of the energy storage device are controlled based on the connection node parameter set to ensure the device operates in its optimal state. This enables the energy storage device to adaptively adjust under complex operating conditions, achieving globally optimal scheduling, improving overall system efficiency, reducing operating costs, extending equipment lifespan, and ensuring maximum energy utilization.

[0090] Optionally, refer to Figure 2 Another embodiment of the present invention provides a parameter processing method for an energy storage device, based on the above. Figure 1 The embodiment shown selects a connection node reference diagram that matches the real-time running data, including steps S310-S360, wherein:

[0091] S310. Obtain historical operating data of the energy storage device;

[0092] S320. Based on the state of charge and charging / discharging power in the real-time operating data, the state of charge is divided into multiple first intervals, and the granularity of the interval division is adjusted based on the real-time fluctuation characteristics of the charging / discharging power to generate multiple target intervals, each interval corresponding to a set of preset connection node reference diagrams.

[0093] S330. Based on the historical operation data, the validity of the connection node reference map of each target interval is verified, and a corresponding matching degree score matrix is ​​generated.

[0094] S340. Based on the matching degree scoring matrix, a reference diagram of connection nodes that match the current operating mode of the energy storage device is selected.

[0095] S350. Based on the ambient temperature and the battery life loss coefficient, correct the parameter threshold range of the selected connection node reference diagram.

[0096] S360. Prioritize the corrected connection node reference diagram and output the optimal matching connection node reference diagram.

[0097] Historical operating data includes ambient temperature and battery life loss coefficients. This data refers to the set of operating status parameters recorded by the energy storage device over a historical period. It can be stored in a database and retrieved from the most recent three months of data to verify the applicability of the connection node reference diagram. The first interval of the state of charge (SOC) refers to dividing the SOC into multiple consecutive sub-intervals based on a percentage range. A sliding window algorithm can be used to dynamically adjust the interval boundary values ​​based on real-time data; for example, dividing the SOC from 0% to 100% into five intervals. The real-time fluctuation characteristics of charging and discharging power refer to the dynamic trend of power value changes over time. The fluctuation amplitude can be calculated using standard deviation or frequency domain analysis methods to adjust the granularity of the interval division. The target interval is the jointly defined region of SOC and charging / discharging power after dynamic adjustment. A gridded method can be used to cross-combine the SOC interval and power fluctuation amplitude to generate this region. Validity verification involves verifying the applicability of the connection node reference diagram in different target intervals using historical data. A backpropagation neural network model can be used to calculate the operating error of the reference diagram in historical data. The matching score matrix stores the comprehensive score results of each connection node reference graph under different target intervals. A matrix data structure can be used to associate the score values ​​with the interval index. Operating mode matching involves selecting the connection node reference graph that best matches the current operating state of the energy storage device. A cosine similarity algorithm can be used to calculate the correlation between real-time data and historical operating modes. Ambient temperature and battery life loss coefficients refer to external factors and internal aging indicators affecting the performance of the energy storage device. Real-time data can be collected using temperature sensors, and life loss can be estimated using the number of battery cycles. Parameter threshold ranges refer to the adjustable charging and discharging parameter boundaries in the connection node reference graph. Dynamic programming algorithms can be used to correct the upper and lower limits based on temperature and life data. Prioritizing the corrected connection node reference graph can be achieved using a multi-objective optimization algorithm. Multi-objective optimization algorithms are decision-making methods that simultaneously optimize multiple operating indicators. The NSGA-II algorithm can be used to prioritize the connection node reference graph.

[0098] In implementing this solution, the historical operating data of the energy storage device is first retrieved from the database, and ambient temperature and battery life loss coefficient are extracted as verification criteria. Based on the real-time collected state of charge (SOC) values, the SOC is divided into multiple first intervals; for example, the SOC is divided into ten intervals with a step size of 10%. Simultaneously, the granularity of the interval division is adjusted based on the real-time fluctuation amplitude of the charging and discharging power; for example, when the power fluctuation amplitude exceeds a preset threshold, the SOC interval step size is reduced to 5%. Multiple target intervals are generated through dynamic adjustment, each interval corresponding to a set of preset connection node reference diagrams. Next, the effectiveness of the connection node reference diagrams for each target interval is verified using historical operating data; for example, a matching degree scoring matrix is ​​generated by calculating the average operating efficiency of each reference diagram in historical data. Based on the scoring matrix, the connection node reference diagram with the highest matching degree to the current operating mode is selected, and the parameter thresholds of the reference diagrams are corrected by combining real-time ambient temperature and battery life loss coefficient. Finally, a multi-objective optimization algorithm is used to sort the corrected reference diagrams; for example, with operating efficiency and battery life as optimization objectives, the optimal matching result is output.

[0099] Traditional methods directly match the connection node reference graph based solely on real-time operational data, without considering the impact of historical operating modes on the validity of the reference graph, and without dynamically adjusting the interval division granularity according to real-time fluctuations in charging and discharging power. This embodiment, by introducing historical data verification and dynamic granularity adjustment, avoids mismatch problems caused by real-time data noise. Simultaneously, it improves the screening accuracy of the connection node reference graph through a multi-objective optimization algorithm, ensuring that the parameter threshold range is adapted to the actual operating environment and equipment aging status.

[0100] Through the above-mentioned technical means, this embodiment can effectively improve the matching accuracy of the connection node reference diagram, reduce the problem of decreased operating efficiency of energy storage devices caused by parameter threshold deviation, and extend battery life by dynamically adjusting the interval division granularity and parameter correction mechanism, thereby optimizing the overall operating performance of energy storage devices.

[0101] Optionally, refer to Figure 3 Another embodiment of the present invention provides a parameter processing method for an energy storage device, based on the above. Figure 2 The illustrated embodiment verifies the validity of the connection node reference graph for each target interval based on the historical operation data, and generates a corresponding matching score matrix, including steps S331-S334, wherein:

[0102] S331. Determine the correlation between the state of charge and the charging / discharging power in the historical operating data;

[0103] S332. Construct an operation mode clustering model corresponding to the correlation between the state of charge and the charging and discharging power;

[0104] S333. Calculate the similarity score between the current real-time running data and the reference graph of each connected node using a clustering model;

[0105] S334. The similarity score and the battery life loss coefficient are weighted and fused to generate the matching score matrix.

[0106] The correlation between state of charge (SBC) and charge / discharge power refers to the statistical correlation between the two in historical operating data. This can be achieved using Pearson correlation coefficient or covariance matrix analysis to quantify the impact of SBC changes on charge / discharge power under different operating conditions. The operating pattern clustering model is a mathematical model that classifies multi-dimensional feature data using unsupervised learning algorithms. It can be implemented using K-means clustering or hierarchical clustering algorithms to group operating data with similar SBC and charge / discharge power correlation characteristics into the same category. The similarity score is a distance measure between real-time operating data and historical cluster centers in the feature space. This can be achieved using Euclidean distance or cosine similarity algorithms to characterize the degree of matching between the current operating state and historical patterns. Weighted fusion is the calculation process of superimposing different dimensional scoring indicators according to weights. This can be achieved using linear weighting or entropy weighting methods, where the battery life loss coefficient is used as a weighting factor to dynamically adjust the influence of the similarity score on the final score.

[0107] In implementing this scheme, the time-series features of state of charge and charge / discharge power are first extracted from historical operating data. Covariance analysis is then used to determine the correlation strength between these two parameters across multiple operating cycles. Based on this, a multi-dimensional feature space is constructed, and a clustering algorithm is employed to classify historical operating patterns, forming clusters of operating patterns with different correlation characteristics. When real-time operating data is input, the distance between the data and the center of each cluster is calculated to generate an initial similarity score. Furthermore, the battery life loss coefficient is used as a dynamic weighting factor to weight and correct the similarity score, ultimately generating a scoring matrix that includes the matching degree of the reference graph for each connection node. For example, when the battery life loss coefficient exceeds a preset threshold, the weighting of the similarity score related to charge / discharge power is reduced to avoid overcharging and discharging that accelerates battery aging.

[0108] Traditional methods typically employ fixed rules or single-parameter thresholds for pattern matching, such as directly mapping connection node reference diagrams based solely on state-of-charge intervals. This embodiment, however, constructs an operational pattern clustering model, incorporating multi-dimensional operational data features into the analysis, enabling more accurate identification of differences in correlation characteristics under various operating conditions. Simultaneously, it introduces a battery life loss coefficient to dynamically adjust the scoring weights, allowing the matching results to adapt to changes in battery health status and avoiding verification result biases caused by fixed parameters.

[0109] Therefore, this embodiment can effectively solve the problem of insufficient accuracy in operating mode matching in the prior art. By using dynamically weighted multidimensional feature analysis, it improves the accuracy of connection node reference diagram verification, thereby providing a more reliable basis for energy storage device parameter control. For example, when battery aging intensifies, by adjusting the weight allocation strategy, it prioritizes connection node parameter combinations that have a smaller impact on battery life, achieving a balance between operating efficiency and device lifespan optimization.

[0110] Optionally, refer to Figure 4 Another embodiment of the present invention provides a parameter processing method for an energy storage device, based on the above. Figure 3 The embodiment shown constructs an operating mode clustering model corresponding to the correlation between the state of charge and the charging / discharging power, including steps S3321-S3324, wherein:

[0111] S3321. Extract the first feature vector of the state of charge and the second feature vector of the charging and discharging power from the historical operating data;

[0112] S3322. Construct a multi-dimensional space model based on the first feature vector and the second feature vector;

[0113] S3323. Use clustering algorithms to classify data points in multidimensional space and form clusters under different operating modes;

[0114] S3324. Based on the battery life loss coefficient, optimize and adjust each cluster to generate the operating mode clustering model.

[0115] The first feature vector refers to the set of key indicators extracted from the time series of the state of charge using feature extraction methods. This can be achieved using sliding window statistical mean, variance, or frequency domain transform coefficients, and is used to quantify the dynamic changes in the state of charge. The second feature vector refers to the set of indicators reflecting power fluctuation characteristics extracted from the charge and discharge power data. These can be peak values, mean values, rates of change, or spectral features, and can be achieved using piecewise aggregation approximation methods, used to describe the pattern differences in charge and discharge behavior. The multidimensional space model refers to a high-dimensional data space constructed using the first and second feature vectors as coordinate axes. This can be achieved using principal component analysis or manifold learning dimensionality reduction methods, used to map multidimensional data to a computable spatial structure. The clustering algorithm is an unsupervised learning method that divides data points in the multidimensional space into different clusters based on similarity. This can be implemented using K-means, DBSCAN, or hierarchical clustering algorithms, used to identify the distribution patterns of different operating modes. Optimization and adjustment based on battery life loss coefficient refers to modifying the boundaries or weights of clusters according to the degree of battery life decay under different operating modes. This can be achieved by using weighted Euclidean distance or cluster center offset strategies to improve the matching degree between clustering results and actual battery loss.

[0116] In the implementation process, the state of charge (SOC) data from historical operating data is first processed by feature extraction. For example, the SOC sequence is divided into multiple time windows, and the mean, standard deviation, and slope of change within each window are calculated to form a first feature vector. Simultaneously, features are extracted from the charge / discharge power data, such as the maximum power, power fluctuation frequency, and duration percentage within each time window, forming a second feature vector. The first and second feature vectors are then combined by time alignment to form multidimensional data points, which are projected into three-dimensional space using principal component analysis to reduce computational complexity. Next, the K-means algorithm is used to cluster the projected data points, with the initial number of clusters determined according to the elbow rule. After clustering, the average lifespan loss of each cluster is calculated based on the battery lifespan loss coefficient corresponding to the data points within each cluster. If the lifespan loss of a particular cluster is significantly higher than that of other clusters, the boundary range of that cluster is adjusted or the data points are redistributed to make the clustering results more aligned with the battery lifespan optimization goals.

[0117] Compared to existing technologies, traditional methods typically consider only a single parameter or a simple linear combination when constructing operation mode classification models. For example, they may divide operation modes solely based on the state of charge interval, resulting in an inability to accurately reflect the correlation between the dynamic characteristics of charge and discharge power and battery life. This embodiment, however, constructs a multi-dimensional spatial model and introduces a clustering algorithm, which comprehensively considers the nonlinear relationship between state of charge and charge / discharge power. Furthermore, it optimizes the clustering results by incorporating the battery life loss coefficient, making the operation mode classification more closely aligned with the multi-factor coupling effects in real-world application scenarios.

[0118] Therefore, this embodiment can more accurately identify the operating modes of energy storage devices under different operating conditions, providing a reliable data foundation for subsequent parameter control strategies, thereby effectively reducing the problem of accelerated battery life loss caused by mode division deviation, and improving the overall operating efficiency of energy storage processing devices.

[0119] Optionally, refer to Figure 5 The present invention also provides a parameter processing method for an energy storage device in one embodiment, based on the above. Figure 3 The embodiment shown includes steps S3331-S3333, where: The step of calculating the similarity score between the current real-time running data and the reference graph of each connected node using a clustering model includes steps S3331-S3333, wherein:

[0120] S3331. Obtain the state-of-charge sequence and charge / discharge power sequence within a continuous time window;

[0121] S3332. Extract feature vectors from the state of charge sequence and the charge / discharge power sequence using time series analysis methods;

[0122] S3333: By comparing the feature vector with the feature distribution of each connected node reference graph through a clustering algorithm, and quantifying the similarity score according to the similarity of the feature distribution, the similarity score is determined.

[0123] The continuous time window sequence of state of charge (SOC) and charge / discharge power (CDP) refers to the continuously recorded SOC and CDP data during the operation of the energy storage device within a preset time period. This can be achieved using a sliding window or fixed-length window method to capture the temporal correlation characteristics during dynamic operation. Time series analysis methods refer to mathematical modeling methods for feature extraction from sequence data. These can be implemented using Fourier transform, wavelet decomposition, or autoregressive integral moving average models to transform raw data into a quantifiable and comparable mathematical representation. Feature distribution similarity quantification scoring involves calculating the statistical distance between feature vectors and features in a reference graph. This can be achieved using Euclidean distance, cosine similarity, or dynamic time warping algorithms to objectively evaluate the degree of data distribution matching. Multidimensional space model construction maps the SOC and CDP feature vectors to a high-dimensional mathematical space. This can be achieved using principal component analysis or manifold learning methods to comprehensively reflect the nonlinear correlation between parameters. Cluster optimization adjustment dynamically corrects the position or radius of cluster centers based on the battery life loss coefficient. This can be achieved using weighted clustering algorithms or adaptive radius adjustment mechanisms to adapt to parameter drift caused by battery aging.

[0124] The process involves collecting time-series data on state of charge (SOC) and charge / discharge power within a continuous time window, and then extracting feature vectors characterizing the dynamic operating properties of the equipment using time series analysis methods. For example, wavelet decomposition is used to extract high-frequency fluctuation features and low-frequency trend features from the charge / discharge power sequence, while an autoregressive model is used to extract short-term predictive features from the SOC sequence. The extracted feature vectors are then input into a clustering model, and similarity calculations are performed with the feature distribution of a pre-stored connection node reference graph. A dynamic time warping algorithm is used to handle the matching of time series of different lengths. During the model building phase, the SOC and charge / discharge power feature vectors from historical operating data are mapped to a three-dimensional space, and a density clustering algorithm is used to identify high-density regions to form clusters. The boundaries of each cluster are dynamically adjusted based on the battery life loss coefficient; for example, when battery life loss exceeds a preset threshold, the safe operating range of the SOC is expanded proportionally.

[0125] Traditional methods typically employ single-point data analysis or static threshold matching, failing to effectively capture the temporal correlation characteristics of energy storage device operating parameters. Existing clustering algorithms, mostly based on fixed parameter settings, cannot adapt to feature drift issues caused by battery aging. This embodiment, by introducing time window data acquisition and a dynamic clustering adjustment mechanism, accurately reflects the changing trends of device operating status while automatically correcting model parameters to adapt to device performance degradation. This embodiment effectively improves the matching accuracy between real-time operating data and the connection node reference graph, avoiding mismatches caused by local data deviations. By dynamically adjusting clustering model parameters, it ensures that the device maintains optimal operating status throughout its entire lifecycle, extending the lifespan of battery components. The construction of a multi-dimensional feature space enhances the ability to analyze parameter correlations under complex operating conditions, providing a reliable basis for the optimized control of energy storage systems.

[0126] Optionally, refer to Figure 6 Another embodiment of the present invention provides a parameter processing method for an energy storage device, based on the above. Figure 1 The illustrated embodiment controls the operating parameters of the energy storage device based on the connection node parameter group, including steps S510-S550, wherein:

[0127] S510. Generate the first control command for the energy storage device based on the dynamic adjustment parameters and static configuration parameters in the connection node parameter group;

[0128] S520. Construct an objective function that maximizes the operating efficiency of the energy storage device;

[0129] S530. Initialize the population parameters of the running efficiency in the objective function through chaotic mapping, and perform multiple iterative calculations to generate an objective parameter adjustment strategy;

[0130] S540. Adjust the first control command according to the target parameter adjustment strategy to generate the target control command;

[0131] S550. Control the operating parameters of the energy storage device according to the target control command.

[0132] Dynamic adjustment parameters refer to parameters that need to be adjusted in real time during equipment operation. These can be implemented using charge / discharge power thresholds, state of charge (SOC) ranges, and temperature compensation coefficients to optimize equipment operation based on real-time conditions. Static configuration parameters are fixed parameters related to the equipment topology and redundancy design. They can be implemented using the topology of connection nodes and the redundancy design ratio to maintain the stability of the processing device's infrastructure. Static configuration parameters include the topology of connection nodes and the redundancy design ratio. The objective function is a mathematical model that maximizes operating efficiency. It can be constructed using linear or nonlinear equations and is used to quantitatively evaluate the impact of different parameter combinations on efficiency. Chaotic mapping initialization refers to the optimization algorithm step of generating initial population parameters using chaotic sequences. This can be implemented using Logistic mapping or Tent mapping to expand the parameter search range and avoid local optima.

[0133] In this process, after generating the first control command, a multi-dimensional objective function is constructed based on the synergistic effect of dynamically adjusted parameters and statically configured parameters. A highly diverse initial parameter population is generated through chaotic mapping, and better parameter combinations are continuously selected during the iteration process, ultimately forming a target control command that balances real-time operating conditions and the stability of the processing device. For example, when the charging and discharging power threshold needs to be dynamically adjusted, chaotic mapping can effectively avoid the problem of traditional gradient descent methods getting trapped in local extrema, thereby finding the globally optimal power threshold range.

[0134] Existing methods for solving energy storage scheduling problems typically employ fixed parameters or a single optimization algorithm, which can easily converge to a suboptimal solution due to improper initial parameter selection. This embodiment, however, introduces chaotic mapping and multi-objective iterative optimization, enabling efficient exploration of the global optimum in a complex, multi-dimensional parameter space while balancing the synergistic relationship between dynamic adjustment and static configuration.

[0135] Through the above-mentioned technical means, this embodiment can dynamically optimize operating parameters to adapt to real-time operating condition changes while ensuring the stability of the energy storage equipment infrastructure. This effectively solves the problem that traditional methods are prone to getting stuck in local optima, resulting in low overall efficiency, and improves the operating efficiency and lifespan of the equipment.

[0136] Optionally, refer to Figure 7 Another embodiment of the present invention provides a parameter processing method for an energy storage device, based on the above. Figure 6 The illustrated embodiment initializes the population parameters for operational efficiency in the objective function through chaotic mapping and performs multiple iterative calculations to generate an objective parameter adjustment strategy, including steps S531-S533, wherein:

[0137] S531. Utilize the randomness and ergodicity of chaotic mapping to generate initial population parameters corresponding to the running efficiency in the objective function, and dynamically configure the population size according to the real-time state of charge interval.

[0138] S532. Apply multi-dimensional constraints to the population size on the operational efficiency of the objective function, and perform iterative optimization by dynamically adjusting the crossover probability and mutation probability.

[0139] S533. After iterative optimization, the corresponding iterative results are obtained. When the difference between the optimal solutions of the three consecutive iterations is less than the convergence threshold or the proportion of feasible solutions that satisfy all constraints of the multi-dimensional constraints exceeds the preset proportion, the target parameter adjustment strategy is output.

[0140] The multi-dimensional constraints include a charge / discharge power threshold based on ambient temperature correction, a charge / discharge depth boundary based on the battery life loss coefficient, and a voltage balance tolerance range in the connection node parameter group. The variability probability is correlated with the battery life loss coefficient in real time.

[0141] The randomness and ergodicity of the chaotic mapping refer to the generation of an initial parameter distribution with ergodic space characteristics through nonlinear dynamic equations. This can be achieved using Logistic or Tent mappings to avoid population aggregation problems caused by traditional random initialization. Dynamically configuring the population size involves adjusting the initial population size based on the width of the real-time state of charge interval. This can be calculated by multiplying the interval length by a preset density coefficient to adapt to parameter search requirements under different state of charge. Multi-dimensional constraints include charge / discharge power thresholds corrected for ambient temperature. These can be dynamically adjusted using a temperature-power reduction factor table to prevent battery overload risks under high-temperature conditions. The charge / discharge depth boundary based on the battery life loss coefficient establishes an inverse correlation model between the life loss rate and the charge / discharge depth. This can be generated using piecewise linear interpolation to balance operating efficiency and equipment lifespan. The voltage balance tolerance range refers to setting an allowable deviation range based on historical voltage fluctuation data in the connection node parameter group. This can be calculated using a sliding window statistical method to calculate the standard deviation threshold to maintain voltage balance between battery packs.

[0142] In the initialization phase, a chaotic mapping is used to generate an initial population covering different regions of the solution space, avoiding local clustering that may occur with traditional stochastic methods. The population size is dynamically expanded or contracted based on the length of the current state of charge (SOC) interval. For example, a baseline size is used when the SOC is in the stable range of 20%-80%, and the population size is increased to improve search accuracy when approaching the charge / discharge limit. During iterative optimization, the charge / discharge power threshold is dynamically adjusted based on the real-time ambient temperature. For example, when the temperature exceeds 35°C, the power limit is adjusted by decreasing by 2% for every 1°C increase. The charge / discharge depth boundary is updated in real-time based on the battery lifetime loss coefficient. For example, when the lifetime loss coefficient reaches 0.8, the charge / discharge depth limit is adjusted from 90% to 80%. The crossover probability and mutation probability are dynamically adjusted based on the number of generations of population evolution and the battery lifetime loss coefficient. For example, when the lifetime loss coefficient exceeds 0.7, the mutation probability is increased from 0.1 to 0.15 to enhance search diversity. The iteration termination condition is set to the difference between the optimal solutions for three consecutive generations being less than 1% or the proportion of feasible solutions exceeding 85%, ensuring that the algorithm converges stably after sufficient search. In the control command correction stage, the original control parameters are linearly compensated by calculating the deviation values ​​of the charging and discharging power threshold and the deviation value of the state of charge interval. For example, the power threshold is increased by 3% while the lower limit of the state of charge interval is decreased by 5%.

[0143] Traditional optimization algorithms employ fixed population sizes and static constraints, failing to adapt to the dynamic changes in the operating conditions of energy storage devices. Furthermore, the mutation probability setting is independent of the device state, easily leading to ineffective searches. This embodiment, however, generates an initial population with spatial ergodicity through chaotic mapping, combined with a state-of-charge-driven dynamic population size adjustment mechanism, significantly improving the efficiency of spatial exploration. Multi-dimensional constraints incorporate real-time parameters such as ambient temperature and battery life into the optimization process, ensuring that the generated parameter adjustment strategy always conforms to the actual operating limitations of the device. The real-time correlation mechanism between mutation probability and battery life depletion coefficient achieves a dynamic balance between algorithm robustness and device health status.

[0144] This embodiment effectively solves the problem of traditional optimization algorithms easily getting trapped in local optima. By combining chaotic initialization with dynamic constraint mechanisms, it achieves a simultaneous improvement in global optimization capability and adaptability to operating conditions. The synergistic effect of dynamic population size configuration and multi-dimensional constraints ensures the feasibility of parameter adjustment strategies in complex operating environments. The real-time correlation mechanism between mutation probability and device health status improves search efficiency while extending battery life. The dual criteria for iteration termination take into account both the algorithm's convergence speed and the quality of the solution. The final control command deviation correction mechanism achieves accurate mapping from optimization results to actual control parameters.

[0145] Optionally, refer to Figure 8Another embodiment of the present invention provides a parameter processing method for an energy storage device, based on the above. Figure 6 The illustrated embodiment adjusts the first control command according to the target parameter adjustment strategy to generate a target control command, including steps S541-S542, wherein:

[0146] S541. According to the target parameter adjustment strategy, determine the first deviation value of the charging and discharging power threshold and the second deviation value of the state of charge range in the first control command.

[0147] S542. Based on the first deviation value and the second deviation value, correct the corresponding charge / discharge power threshold and state of charge range in the first control command to generate the target control command.

[0148] The target parameter adjustment strategy refers to the set of parameters generated by an optimization algorithm for adjusting control commands. This can be achieved through iterative calculation of population parameters initialized by chaotic mapping, and its function is to dynamically adapt to the real-time operating conditions of the energy storage device. The first deviation value refers to the difference between the actual value and the preset value of the charging / discharging power threshold. This can be achieved through comparative analysis of real-time operating data and historical data, and is used to dynamically adjust the allowable range of charging / discharging power. The second deviation value refers to the difference between the actual boundary and the preset boundary of the state of charge interval. This can be achieved through dynamic adjustment of the interval division granularity, and is used to optimize the safe operating range of the state of charge. The deviation correction process involves adding the calculated deviation to the original parameters. This can be achieved using linear weighting or nonlinear mapping methods to ensure that the adjustment of the control commands meets the actual operating requirements.

[0149] In the process of generating target control commands, the deviation values ​​of the charge / discharge power threshold and the state of charge (SOC) range are first determined through a multi-objective optimization algorithm. For example, when real-time operating data shows that increased ambient temperature leads to decreased battery efficiency, the first deviation value of the charge / discharge power threshold may be adjusted to a certain percentage lower than the preset value to avoid overload risk. Furthermore, the second deviation value of the SOC range can be dynamically expanded based on the battery life loss coefficient, for example, adjusting the original range from 30%~80% to 25%~75% to slow down battery aging. Subsequently, by superimposing the first and second deviation values ​​onto the corresponding parameters of the original control command, a new target control command is generated. This process is implemented through a closed-loop feedback mechanism, enabling the operating parameters of the energy storage device to respond in real time to environmental changes and performance degradation.

[0150] Existing methods typically adjust control commands using fixed deviation values ​​or a single optimization objective, which can easily lead to parameter mismatches due to sudden environmental changes or equipment aging. For example, existing technologies may only adjust charging and discharging power based on the current state of charge, ignoring the dynamic impact of temperature on the power threshold. This embodiment introduces dynamic calculation and superposition correction of multi-dimensional deviation values, enabling control commands to simultaneously consider power limitations, state of charge ranges, and equipment aging factors, avoiding local optima problems caused by adjusting a single parameter.

[0151] Through the aforementioned technical means, this embodiment can dynamically adjust the threshold values ​​of key parameters in the control commands based on real-time operating data and historical performance degradation trends, thereby more accurately maintaining the energy storage device within its optimal operating range. For example, it can automatically reduce the upper limit of charging and discharging power to prevent overheating in high-temperature environments, or shrink the state of charge range to extend service life when battery life deteriorates. This dynamic adjustment mechanism effectively solves the inefficiency problem caused by fixed parameters in traditional methods, improving the adaptability and operating efficiency of the energy storage device under different operating conditions.

[0152] Optionally, refer to Figure 9 The present invention also provides a parameter processing method for an energy storage device in one embodiment, based on the above. Figure 1 The illustrated embodiment, in response to an operation command for a connection node reference diagram, obtains a connection node parameter group corresponding to the operation command, including steps S410-S430, wherein:

[0153] S410. In response to an operation instruction for a connection node reference graph, parse the parameter identifier in the operation instruction, and generate a composite parameter identifier set corresponding to the parameter identifier based on the topological correlation of the connection node reference graph.

[0154] S420. Obtain multiple connection node parameters corresponding to the composite parameter identifier set, and verify their validity through a multi-dimensional verification mechanism.

[0155] S430. The parameter dynamic correction module adaptively corrects the parameters of the connection nodes that fail the verification.

[0156] S440. Collect the checksum and / or corrected connection node parameters according to their priority order in the connection node reference diagram to generate a connection node parameter group with a security verification label.

[0157] The composite parameter identifier set refers to the parameter set generated by parsing the basic parameter identifiers in the operation instructions and extending them by combining the topological structure correlation. A topology graph traversal algorithm can be used to identify the parameters of related nodes and extract their identifiers, thus ensuring the comprehensiveness of parameter acquisition. The multi-dimensional verification mechanism refers to the verification rules that dynamically adjust parameter thresholds based on real-time running data and verify the logical compatibility between parameters. This can be implemented using a dynamic threshold mapping table and a logical conflict detection algorithm, thereby resolving parameter over-limit or logical conflict issues. The parameter dynamic correction module is a module that automatically adjusts parameters based on historical optimal values ​​and real-time fluctuation characteristics. This can be implemented using historical data matching and real-time interpolation algorithms, thus avoiding the inefficiency of manual intervention. The security verification label is an encrypted identifier attached to the parameter group, used to record the parameter source and correction process. Hash chain technology can be used to generate an immutable verification code, thereby achieving parameter traceability and security protection.

[0158] When an operation command for a connection node reference graph is received, the parameter identifiers in the command are first parsed, and a composite parameter identifier set is generated through topology analysis to ensure coverage of all associated parameters. Then, the corresponding connection node parameters are extracted from the database, and the parameter threshold range is dynamically adjusted based on ambient temperature and battery life loss coefficient, while simultaneously checking the logical compatibility between parameters. For parameters that fail verification, adaptive correction is performed by matching historical best values ​​or recalculating compatible solutions. Finally, the parameters that pass verification are sorted by topology priority and a safety verification tag is attached to form an executable parameter group. For example, when a node voltage parameter is detected to exceed the corrected threshold, it is automatically replaced with the historical best value, and the correction path is recorded in the safety tag.

[0159] Compared to existing technologies, traditional methods only use fixed thresholds for parameter validation, which cannot be dynamically adjusted according to the real-time environment and lack a logical conflict detection mechanism, resulting in a high risk of parameter group failure. This embodiment, however, achieves adaptive adjustment of parameter thresholds based on the running state through a combination of multi-dimensional validation and dynamic correction. It also ensures the rationality of parameter execution order through topology priority sorting and enhances data traceability using a security tagging mechanism.

[0160] This embodiment effectively solves the problem of control command failure caused by parameter logic conflicts or exceeding thresholds. It ensures the effectiveness of parameter groups in complex operating environments through dynamic verification and correction mechanisms, and uses security verification tags to achieve parameter traceability, avoiding efficiency losses caused by manual intervention, and ultimately forming a reliable parameter set that meets the actual needs of the equipment.

[0161] The step of obtaining multiple connection node parameters corresponding to the composite parameter identifier set and verifying their validity through a multi-dimensional verification mechanism includes:

[0162] Based on the ambient temperature and battery life loss coefficient in the real-time operating data of the energy storage device, the allowable threshold range of the connection node parameters is dynamically adjusted; and the logical compatibility of the associated parameters in the reference diagram of the same connection node is verified. If there is a conflict, the parameter dynamic adjustment module is triggered.

[0163] The dynamic adjustment of the allowable threshold range for connection node parameters refers to adjusting the upper and lower limits of parameter thresholds based on real-time ambient temperature and battery life loss coefficient. This can be achieved by using a temperature sensor to collect ambient temperature data in real time, combined with the loss coefficient output by the battery life monitoring module, and calculating the current allowable threshold range using a linear interpolation algorithm. This feature allows parameter thresholds to adaptively adjust with device aging and environmental changes, avoiding parameter failure issues caused by fixed thresholds. The logic compatibility verification refers to verifying the collaborative relationships between different parameters in the same connection node reference diagram. This can be achieved using parameter correlation analysis algorithms, such as verifying the matching degree of charging / discharging power and voltage parameters, by establishing a parameter constraint relationship matrix to detect any logical conflicts. This feature can identify contradictory settings when working collaboratively across parameters, ensuring the overall coordination of the parameter set.

[0164] Specifically, as the ambient temperature rises, the threshold correction coefficient corresponding to the battery life loss factor is calculated in real time. For example, for every 5 degrees Celsius increase in temperature, the upper limit of the charge / discharge power threshold is dynamically reduced. Simultaneously, logical compatibility checks between parameters are implemented by traversing the parameter association paths in the connection node reference graph. For instance, if the charge / discharge depth setting of a node is detected to exceed the voltage tolerance range of adjacent nodes, the dynamic parameter correction module is triggered to recalculate the compatible parameter values. This forms a closed-loop verification mechanism that retains the flexibility of manual settings while ensuring parameter validity through automated correction.

[0165] Traditional methods use fixed parameter thresholds and only perform single-parameter range verification, which cannot adapt to equipment aging and environmental changes, and ignores the synergistic relationships between parameters. This embodiment solves the problem of parameter settings being out of sync with actual equipment operating conditions by using dynamic threshold correction and cross-parameter logic compatibility verification, while eliminating operational risks caused by parameter coordination conflicts. This embodiment effectively improves the accuracy and reliability of energy storage device parameter verification, ensuring that parameter settings always match the actual operating state and environmental conditions of the equipment, avoiding system efficiency degradation caused by parameter threshold deviations or logic conflicts, and guaranteeing the stable operation of energy storage devices under different operating conditions.

[0166] The adaptive correction of connection node parameters that fail verification via the dynamic parameter correction module includes:

[0167] Based on the historical optimal values ​​of parameters under the same topological scenario in historical operation data, replace the parameters that exceed the threshold; and, based on the state of charge fluctuation characteristics in real-time operation data, recalculate the compatible solution of the conflict parameters.

[0168] The parameter dynamic correction module is an algorithm module for automatically adjusting parameter conflicts. It employs a mechanism combining historical data retrieval and real-time calculation, aiming to resolve parameter compatibility issues through a dual approach of historical experience and real-time analysis. The historical optimal parameter value refers to the optimal combination of parameters recorded during the operation of energy storage devices under the same topology. This value can be obtained by filtering operating efficiency indicators from a historical database, providing verified and effective alternative values ​​for parameters exceeding thresholds. The state-of-charge (SOC) fluctuation characteristic refers to the changing pattern of the SOC during real-time charging and discharging of the battery. Trend features can be extracted through time series analysis, providing a dynamic adjustment basis for correcting conflicting parameters.

[0169] When the verification mechanism detects that the parameters of a connected node exceed the allowable threshold range, the parameter dynamic correction module first retrieves the historical optimal values ​​of the parameters for the same topology scenario from historical operating data. For example, in a parallel battery pack topology, if the charging / discharging power threshold of a node is detected to exceed the safe range at the current temperature, it is automatically replaced with the historical optimal value. Subsequently, based on the real-time state of charge fluctuation characteristics, such as the periodic changes in charging / discharging rates, it recalculates logically conflicting parameter combinations. For example, when the state of charge drops rapidly, it adjusts the correlation parameters between voltage balancing tolerance and depth of charge / discharge to generate a corrected solution compatible with the current operating state. Through the synergy of historical optimal replacement and real-time dynamic calculation, system risks caused by parameter exceeding limits are avoided, while ensuring the dynamic adaptation of the corrected parameters to the current operating scenario.

[0170] In some specific implementations, when the energy storage device is operating in a high-temperature environment, if the temperature-related parameter verification of a certain connection node fails, the parameter dynamic correction module can call the optimal charge and discharge power threshold at the same ambient temperature in the historical database for replacement, and at the same time recalculate the voltage balancing parameters of adjacent nodes based on the real-time change slope of the state of charge, thereby maintaining the stable operation of the system under high-temperature conditions.

[0171] Compared to existing technologies, traditional parameter correction methods typically rely solely on static threshold replacement or single real-time adjustments. For example, they may directly use fixed safety values ​​to cover abnormal parameters or only adjust parameters locally based on current data. This embodiment, however, employs a dual correction mechanism that integrates historical best values ​​and real-time fluctuation characteristics. This not only inherits verified historical operating experience but also fully responds to dynamically changing operating states, thereby solving the problem of the disconnect between static correction and real-time scenarios.

[0172] This embodiment can quickly provide safe and effective alternative parameters when parameter verification fails, while eliminating logical conflicts between parameters. It ensures that the corrected parameter set not only meets the historical optimal operating conditions, but also adapts to real-time state of charge fluctuations, ultimately improving the parameter compatibility and system stability of energy storage devices under different operating scenarios.

[0173] Optionally, refer to Figure 10 Another embodiment of the present invention provides a parameter processing method for an energy storage device, based on the above. Figures 1 to 9 In any of the embodiments shown, the parameter processing method for the energy storage device further includes steps S600-S900, wherein:

[0174] S600: Obtain historical and real-time operating data of multiple associated energy storage devices, and construct a multi-user shared capacity optimization model based on the historical operating data;

[0175] S700. Through the capacity dynamic game adjustment mechanism, analyze the state of charge fluctuation characteristics and charging and discharging power requirements in the real-time operating data of each energy storage device, and generate the shared capacity competition coefficient of each energy storage device.

[0176] S800. Based on the shared capacity competition coefficient, and combined with the historical contribution ratio and real-time demand ratio in the shared capacity allocation strategy library, dynamically adjust the shared capacity allocation strategy of the multi-user shared capacity optimization model.

[0177] S900. According to the adjusted shared capacity allocation strategy, generate capacity allocation instructions corresponding to each energy storage device, and synchronously correct the charging and discharging power thresholds and state of charge boundaries in the operating parameters of the energy storage device based on the capacity allocation instructions.

[0178] The multi-user shared capacity optimization model includes a shared capacity allocation strategy library and a dynamic capacity game adjustment mechanism. The multi-user shared capacity optimization model is a mathematical model that integrates operational data from multiple related energy storage devices and establishes dynamic allocation rules. It can be implemented using a game theory-based multi-objective optimization algorithm to coordinate capacity demand conflicts among different devices. The shared capacity allocation strategy library is a dataset storing historical optimal allocation schemes. It can be implemented using a database combined with a machine learning model to update strategies, providing a reference allocation benchmark. The dynamic capacity game adjustment mechanism is the processing logic that dynamically calculates the resource competition relationship between devices based on real-time data. It can be implemented using a Nash equilibrium-based real-time game algorithm to balance real-time demand and historical contributions. The shared capacity competition coefficient is a quantitative indicator reflecting the current operating state of the devices and their shared capacity demand. It can be implemented using a weighted calculation method based on the state of charge fluctuation amplitude and charging / discharging power demand, used to dynamically adjust capacity allocation priorities. The historical contribution ratio refers to the proportion of resources provided by the devices for shared capacity during historical operation. It can be implemented using a time-weighted integral method to calculate historical charging / discharging contributions, ensuring fairness in long-term operation. The real-time demand ratio refers to the urgency of the current operating status of the equipment for the shared capacity. It can be calculated based on the degree of deviation of the state of charge from the target value and is used to prioritize the needs of critical equipment.

[0179] The historical operating data of multiple related energy storage devices are integrated and analyzed to form initial allocation rules by constructing a shared capacity allocation strategy library. During real-time operation, a dynamic capacity game adjustment mechanism continuously analyzes the fluctuation range of the state of charge (SOC) and changes in charging and discharging power requirements of each device. For example, when the SOC of a device drops rapidly, its shared capacity competition coefficient will dynamically increase. This competition coefficient, along with the historical contribution ratio, is input into a multi-objective optimization model, which generates a new shared capacity allocation strategy through a dynamic adjustment algorithm. For example, for devices with high historical contribution but low current demand, their allocation weight can be appropriately reduced; while for devices with low historical contribution but urgent real-time demand, their allocation priority is temporarily increased. The final generated capacity allocation instruction will synchronously adjust the charging and discharging power thresholds of each device, such as increasing the upper limit of the charging and discharging power of high-priority devices, while adjusting the safe boundary range of their SOC.

[0180] Traditional methods using independent configuration modes prevent devices from sharing redundant capacity, while centralized control algorithms are prone to getting stuck in local optima due to parameter fixation. This embodiment establishes a dynamic game theory mechanism, allowing the real-time operating status of each device to dynamically influence the allocation of shared capacity. For example, when a device suddenly experiences high power demand, the game theory mechanism can quickly adjust the operating parameters of other devices to release shared capacity. Simultaneously, the dual consideration of historical contribution and real-time demand avoids resource allocation imbalances that may result from relying solely on the current state; for instance, devices with long-term low utilization rates can still receive basic capacity guarantees during sudden demand surges.

[0181] This embodiment effectively reduces the total capacity requirement of multi-device systems and avoids redundant capacity configuration through a dynamic sharing mechanism. A multi-dimensional parameter adjustment strategy prevents the centralized control algorithm from getting trapped in local optima, and a real-time game theory mechanism ensures the dynamic rationality of capacity allocation. The fusion processing of historical and real-time data enhances the system's adaptability to sudden operating conditions, and synchronously corrected operating parameters guarantee the collaborative optimization effect between devices.

[0182] The present invention also proposes a processing apparatus, the processing apparatus comprising: a memory, a processor, and a parameter processing program for an energy storage device stored in the memory and executable on the processor, the parameter processing program for the energy storage device being configured to implement the parameter processing method for the energy storage device as described above.

[0183] It is worth noting that since the processing device of the present invention is based on the parameter processing method of the above-mentioned energy storage device, the embodiments of the processing device of the present invention include all the technical solutions of all embodiments of the parameter processing method of the above-mentioned energy storage device, and the technical effects achieved are exactly the same, so they will not be repeated here.

[0184] The present invention also proposes an energy storage device, which includes the processing apparatus as described in the above embodiments.

[0185] It is worth noting that since the energy storage device of the present invention is based on the above-mentioned processing device, the embodiments of the energy storage device of the present invention include all the technical solutions of all the embodiments of the above-mentioned processing device, and the technical effects achieved are exactly the same, so they will not be repeated here.

[0186] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system 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 system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0187] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0188] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0189] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A parameter processing method of an energy storage device, characterized by, The parameter processing method of the energy storage device comprises: in response to a selection operation on a component of the energy storage device, displaying a plurality of connection relationship models corresponding to the selected component on an editable interface; the connection relationship model comprises a plurality of connection node reference graphs, each connection node reference graph being a connection relationship preview graph based on the physical connection characteristics of the selected component; collecting real-time operation data of the energy storage device, the real-time operation data comprising state of charge, charging and discharging power, ambient temperature and battery life loss coefficient; selecting a connection node reference graph matching the real-time operation data; in response to an operation instruction on the connection node reference graph, obtaining a connection node parameter group corresponding to the operation instruction; controlling the operation parameters of the energy storage device based on the connection node parameter group; the selection of the connection node reference graph matching the real-time operation data comprises: obtaining historical operation data of the energy storage device, the historical operation data comprising ambient temperature and battery life loss coefficient; according to the state of charge and the charging and discharging power in the real-time operation data, dividing the state of charge into a plurality of first intervals, and adjusting the granularity of interval division based on the real-time fluctuation characteristics of the charging and discharging power to generate a plurality of target intervals, each interval corresponding to a group of preset connection node reference graphs; based on the historical operation data, verifying the effectiveness of the connection node reference graph of each target interval to generate a corresponding matching degree score matrix; based on the matching degree score matrix, filtering the connection node reference graph matching the operation mode of the current energy storage device; based on the ambient temperature and the battery life loss coefficient, correcting the parameter threshold range of the filtered connection node reference graph; prioritizing the corrected connection node reference graph and outputting the optimal matching connection node reference graph.

2. The parameter processing method of an energy storage device according to claim 1, wherein the verification of the effectiveness of the connection node reference graph of each target interval based on the historical operation data to generate a corresponding matching degree score matrix comprises: determining the relevance of the state of charge and the charging and discharging power in the historical operation data; constructing an operation mode clustering model corresponding to the relevance of the state of charge and the charging and discharging power; calculating the similarity score of the current real-time operation data and each connection node reference graph through the clustering model; weighting and fusing the similarity score and the battery life loss coefficient to generate the matching degree score matrix.

3. The parameter processing method of an energy storage device according to claim 2, wherein the calculation of the similarity score of the current real-time operation data and each connection node reference graph through the clustering model comprises: obtaining a state of charge sequence and a charging and discharging power sequence in a continuous time window; extracting feature vectors from the state of charge sequence and the charging and discharging power sequence using time series analysis method; comparing the feature vectors with the feature distribution of each connection node reference graph through clustering algorithm, and quantitatively scoring according to the similarity of the feature distribution to determine the similarity score; the construction of the operation mode clustering model corresponding to the relevance of the state of charge and the charging and discharging power comprises: extracting a first feature vector of the state of charge and a second feature vector of the charging and discharging power in the historical operation data; construct a multi-dimensional space model based on the first feature vector and the second feature vector; classify data points in the multi-dimensional space using a clustering algorithm to form clustering clusters under different operating modes; optimize and adjust each clustering cluster based on the battery life loss coefficient to generate the operating mode clustering model.

4. The parameter processing method of an energy storage device according to claim 1, wherein The control of the operating parameters of the energy storage device based on the connection node parameter group comprises: generating a first control instruction of the energy storage device according to the dynamic adjustment parameters and the static configuration parameters in the connection node parameter group; constructing an objective function for maximizing the operating efficiency of the energy storage device; initializing the population parameters of the operating efficiency in the objective function through chaotic mapping and performing multiple iterations to generate a target parameter adjustment strategy; adjusting the first control instruction according to the target parameter adjustment strategy to generate a target control instruction; controlling the operating parameters of the energy storage device according to the target control instruction.

5. The parameter processing method of an energy storage device according to claim 4, wherein The initialization of the population parameters of the operating efficiency in the objective function through chaotic mapping and the multiple iterations to generate a target parameter adjustment strategy comprise: using the randomness and ergodicity of chaotic mapping to generate initial population parameters corresponding to the operating efficiency in the objective function, and dynamically configuring the population size according to the real-time state of charge interval; applying multi-dimensional constraint conditions to the population of the operating efficiency in the objective function, and performing iterative optimization through dynamically adjusted crossover probability and mutation probability, the multi-dimensional constraint conditions including the charge and discharge power threshold corrected based on the environmental temperature, the charge and discharge depth boundary based on the battery life loss coefficient, and the voltage balance tolerance range in the connection node parameter group, and the mutation probability is real-time associated with the battery life loss coefficient; after iterative optimization, the corresponding iteration result is obtained, when the difference of the optimal solution of the iteration result for three generations is less than the convergence threshold or the proportion of feasible solutions satisfying all constraints of the multi-dimensional constraint conditions exceeds the preset proportion, the target parameter adjustment strategy is output; The adjustment of the first control instruction according to the target parameter adjustment strategy to generate a target control instruction comprises: determining a first deviation value of the charge and discharge power threshold and a second deviation value of the state of charge interval in the first control instruction according to the target parameter adjustment strategy; correcting the corresponding charge and discharge power threshold and state of charge interval in the first control instruction according to the first deviation value and the second deviation value to generate the target control instruction.

6. The parameter processing method of an energy storage device according to claim 1, wherein The acquisition of the connection node parameter group corresponding to the operation instruction in response to the operation instruction for the connection node reference graph comprises: In response to the operation instruction for the connection node reference graph, parse the parameter identifier in the operation instruction, and generate a composite parameter identifier set corresponding to the parameter identifier based on the topological structure association of the connection node reference graph; acquire a plurality of connection node parameters corresponding to the composite parameter identifier set, and verify the validity thereof through a multi-dimensional verification mechanism; the acquiring a plurality of connection node parameters corresponding to the composite parameter identifier set, and verifying the validity thereof through a multi-dimensional verification mechanism comprises dynamically correcting the allowable threshold range of the connection node parameters based on the ambient temperature and the battery life loss coefficient in the real-time operation data of the energy storage device; and verifying the logical compatibility of the associated parameters in the same connection node reference diagram, and triggering the parameter dynamic correction module if there is a conflict; adaptively correcting the connection node parameters that fail the verification through the parameter dynamic correction module; the adaptively correcting the connection node parameters that fail the verification through the parameter dynamic correction module comprises replacing the over-threshold parameters based on the historical optimal values of the parameters in the historical operation data under the same topology scenario; and recalculating the compatible solution of the conflicting parameters according to the state of charge fluctuation characteristics in the real-time operation data; collecting the connection node parameters after verification and / or correction according to their priority order in the connection node reference diagram, and generating a connection node parameter group with a safety verification label.

7. The parameter processing method of an energy storage device according to any one of claims 1 to 6, wherein The parameter processing method of the energy storage device further comprises: acquiring historical operation data and real-time operation data of a plurality of associated energy storage devices, constructing a multi-user shared capacity optimization model based on the historical operation data, the multi-user shared capacity optimization model comprising a shared capacity allocation strategy library and a capacity dynamic game adjustment mechanism; analyzing the state of charge fluctuation characteristics and the charging and discharging power demand in the real-time operation data of each energy storage device through the capacity dynamic game adjustment mechanism, and generating a shared capacity competition coefficient of each energy storage device; dynamically adjusting the shared capacity allocation strategy of the multi-user shared capacity optimization model based on the shared capacity competition coefficient, in combination with the historical contribution proportion and real-time demand proportion in the shared capacity allocation strategy library; generating a capacity allocation instruction corresponding to each energy storage device according to the adjusted shared capacity allocation strategy, and synchronously correcting the charging and discharging power threshold and the state of charge boundary in the operation parameters of the energy storage device based on the capacity allocation instruction.

8. A processing device, characterized by The processing device comprises a memory, a processor, and a parameter processing program of an energy storage device stored on the memory and executable on the processor, and the parameter processing program of the energy storage device is configured to implement the parameter processing method of the energy storage device according to any one of claims 1 to 7.

9. An energy storage device, characterized by, The processing device according to claim 8. The processing device according to claim 8.

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