A new energy battery multi-working condition linkage control system and method

Abstract

The application relates to the field of battery multi-working-condition linkage control, and discloses a new energy battery multi-working-condition linkage control system and method, which comprises the following steps: acquiring the running state information of each cell node of a battery, uniformly representing and processing the state information, and constructing a structure state network; correlatively analyzing the state changes of the cell nodes in the battery under different time scales to form a structure state parameter set; analyzing the response differences of the cell nodes in the battery and the evolution characteristics in the structure state network to form a dynamic power distribution scheme; on the basis of the dynamic power distribution scheme, fusing the node power, energy recovery and heat regulation information with the structure state network, generating cooperative power constraint information, and generating a dynamic cooperative power distribution scheme; and constraining the energy distribution relationship of the battery system under different running working conditions to form a continuous linkage adjustment mechanism. The application has the advantages of improving the battery utilization rate and safety.

Description

Technical Field

[0001] This invention relates to the field of multi-condition linkage control of batteries, specifically to a multi-condition linkage control system and method for new energy batteries. Background Technology

[0002] In existing new energy battery systems, due to differences in cell aging caused by long-term use, or the replacement of some cells during maintenance, the performance of new and old cells is inconsistent, resulting in significant differences in the response and thermal characteristics of each cell within the battery pack. Existing battery management systems are usually based on a unified power distribution strategy and static state monitoring of the entire battery pack, making it difficult to perceive in real time the differences in structural state and their coupling evolution characteristics caused by aging differences or partial replacement of individual cells. This can lead to problems such as uneven power load, abnormal local temperature rise, or decreased energy recovery efficiency in some cells during actual operation, making it difficult to ensure the overall safety, balance, and lifespan optimization of the battery system. This reduces the overall energy utilization efficiency of the battery and increases safety risks, especially under high load, rapid charge and discharge, or extreme temperature conditions. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a multi-condition linkage control system and method for new energy batteries, which has the advantages of improving battery utilization and safety, and solves the problems mentioned in the background technology.

[0004] To achieve the aforementioned goals of improving battery utilization and safety, this invention provides the following technical solution: a multi-condition linkage control method for new energy batteries, comprising the following steps: The system acquires the operating status information of each cell node in the battery, performs unified characterization processing on the status information, and constructs a structural state network of the cell nodes and their interrelationships within the battery. Based on the structural state network, the state changes of the internal cell nodes of the battery are analyzed in relation to different time scales, forming a set of structural state parameters that characterize the consistency of battery structure, differences in node response, and coupling evolution characteristics. Based on the set of structural state parameters, the differences in the response of the internal cell nodes of the battery and their evolution characteristics in the structural state network are analyzed. Based on the analysis results, the constraints of the battery structural state on power allocation are determined, and a dynamic power allocation scheme is formed. Based on the dynamic power allocation scheme, node power, energy recovery and thermal regulation information are fused with the structural state network to generate cooperative power constraint information. Based on the structural state network, the cooperative operation of energy output, energy recovery and thermal regulation modules is adjusted to generate a dynamic cooperative power allocation scheme. Based on the dynamic collaborative power allocation scheme, the energy allocation relationship of the battery system under different operating conditions is constrained, and the adjusted operating state is fed back to the set of structural state parameters, forming a continuous linkage adjustment mechanism between the set of structural state parameters and the energy allocation relationship.

[0005] Preferably, the process of constructing the structural state network of the internal cell nodes and their interrelationships is as follows: The battery cell voltage, current, temperature, internal resistance and heat distribution data streams are collected synchronously through multi-source sensing components. The collected data is spatiotemporally aligned and normalized to form unified state feature information; Based on historical cell performance database and unified state feature information, calculate the state similarity and coupling coefficient between nodes; A structural state network of the battery's internal cell nodes and their connections is constructed based on the coupling coefficient.

[0006] Preferably, the process of forming a set of structural state parameters characterizing the consistency of battery structure, differences in node response, and coupling evolution features is as follows: Based on the structural state network, the unified state feature information of each node in the network is mapped to the corresponding node; The state feature vector of each node is decomposed into three time scales: short-term dynamics, medium-term fluctuations, and long-term trends. The coupling evolution between nodes is then analyzed by combining the weights of the network topology. A cross-scale recursive correlation analysis method is adopted, combined with the coupling strength in the structural state network, to compare and correlate the state vectors between nodes, and calculate the differences in node responses and the coupling evolution trend. Based on the completion of multi-scale decomposition and the calculation of coupling strength and response differences between nodes in the structural state network, structural consistency index, response delay index and coupling strength parameter of the nodes are extracted to generate a set of structural state parameters.

[0007] Preferably, the process of analyzing the differences in the response of internal cell nodes and their evolution characteristics in the structural state network is as follows: Using the set of structural state parameters as input, the structural consistency index, response delay index, and coupling strength parameter of each node are standardized to form a unified index matrix that can be used for comparative analysis. Cluster analysis of node feature vectors in a unified index matrix is ​​performed using node coupling strength to identify nodes with high response differences and key coupling paths; Anomaly detection is performed on nodes with high response differences and key coupling paths. Combined with spatiotemporal correlation analysis, node response difference anomalies and coupling evolution anomalies are identified, generating a node response difference matrix and a set of evolution trajectories.

[0008] Preferably, the process of forming a dynamic power allocation scheme is as follows: Based on the node response difference matrix and evolution trajectory set, the response characteristics of each node under different operating conditions are evaluated, and the power carrying capacity, energy recovery capacity and thermal regulation constraint threshold are calculated. By combining the calculation results with unified state characteristic information, a structural state constraint function is constructed, incorporating node power allocation, energy recovery, and thermal management requirements into the constraint conditions. The dynamic power allocation scheme is used as the basis for constraint function calculation. The constraint function is used to constrain the overall power allocation of the battery system under different operating conditions, thus forming a dynamic power allocation scheme.

[0009] Preferably, the process of generating cooperative power constraint information is as follows: Based on the dynamic power allocation scheme, the power output, energy recovery and thermal regulation information of each cell node are fused with the response differences, coupling strength and key coupling path information of the nodes in the structural state network. By using a weighted mapping method, data from different sources are mapped to the same feature space to form a fused feature vector; Based on the fused feature vectors, the power constraints, energy recovery constraints, and thermal management constraints of each node under the current operating conditions are calculated. Based on the coupling relationship between nodes, calculate the collaborative constraints and assign priority weights to each node; Based on the cooperative constraints and node priorities, a cooperative optimization algorithm is used to generate the final cooperative power constraint information.

[0010] Preferably, the process of generating a dynamic cooperative power allocation scheme is as follows: By fusing cooperative power constraint information with node response differences, coupling strength, and key coupling path information in the structural state network, a multi-node linkage control graph is constructed. In the multi-node linkage control diagram, an associated edge is established for the power output, energy recovery capability and thermal regulation requirements of each node, and the edge weight and node priority are labeled. Based on the multi-node linkage control diagram, an adaptive adjustment algorithm is used to adjust the output power, recovery power and thermal regulation parameters in real time. During the adjustment process, the overall power allocation is dynamically optimized by combining the node state change trend and coupling evolution characteristics; Prioritize adjustments to critical nodes and highly coupled paths to form a dynamic collaborative power allocation scheme covering all nodes and modules.

[0011] Preferably, the process of constraining the energy distribution relationship of the battery system under different operating conditions is as follows: The system acquires the node power output, energy recovery, and thermal regulation status of the battery system under the current operating conditions, and predicts the future state change trend of each node by combining the dynamic collaborative power allocation scheme. Under different load, temperature and SOC conditions, the power output and energy recovery capability of each cell node are constrained in real time. By comparing the constraint results with historical operating data, the energy allocation strategy is adjusted in real time, and nodes that are overloaded or unbalanced are promptly regulated. Based on node constraints and overall system requirements, battery energy allocation constraint information under multiple operating conditions is dynamically generated and synchronously updated to the dynamic collaborative power allocation scheme, outputting the battery system energy allocation constraint scheme.

[0012] Preferably, the process of forming a continuous linkage and adjustment mechanism between the set of structural state parameters and the energy distribution relationship is as follows: The effectiveness of the energy distribution constraint scheme of the battery system is comprehensively monitored. The power output, energy recovery, temperature and SOC status of each cell node are collected and the collected data are uniformly mapped to a unified state feature information. Based on unified state feature information, the latest operating state is compared with the set of structural state parameters to update the node's response difference index, coupling strength, and key coupling path information. The updated structural state parameters are dynamically corrected to assess the coupling evolution trend and potential instability risk between nodes and generate feedback adjustment signals. The feedback adjustment signal is input into the dynamic collaborative power allocation scheme to form a continuous linkage adjustment mechanism between the set of structural state parameters and the energy allocation relationship.

[0013] A multi-condition linkage control system for new energy batteries, comprising: State-based network construction module: acquires and uniformly represents the operating status of cell nodes, and constructs a structural state network of internal battery nodes and their interrelationships; Multi-scale analysis module: Based on the structural state network, it analyzes the state changes of nodes at different time scales of short, medium and long time, and generates a set of structural state parameters; Power constraint module: Based on the set of structural state parameters, analyze the differences and evolution characteristics of node responses, determine the power allocation constraint requirements, and form a dynamic power allocation scheme; Collaborative optimization module: Integrates node power, energy recovery, and thermal regulation information with the structural state network to generate collaborative power constraint information and form a dynamic collaborative power allocation scheme; Linkage adjustment module: Based on the dynamic collaborative power allocation scheme, it constrains the energy allocation of the system and feeds back the operating status to the set of structural state parameters, forming a continuous linkage adjustment mechanism.

[0014] Compared with the prior art, the present invention provides a multi-condition linkage control system and method for new energy batteries, which has the following beneficial effects: This invention acquires the real-time operating status of each cell node and constructs a structural state network. This allows for a comprehensive understanding of the structural state differences and inter-node coupling evolution characteristics caused by cell aging variations or partial replacements. Based on multi-timescale correlation analysis and a set of structural state parameters, the method accurately assesses the response characteristics of each cell and the power, energy recovery, and thermal regulation constraints. This generates dynamic power allocation schemes and collaborative power constraint information, enabling real-time optimization of energy output, energy recovery, and thermal management of the battery system under different operating conditions. Through a continuous linkage adjustment mechanism, it dynamically adjusts power allocation, energy recovery, and thermal regulation parameters, prioritizing the stability of nodes with high response differences and critical coupling paths. This effectively improves the overall safety, balance, and lifespan optimization of the battery. Simultaneously, under complex conditions such as high load, rapid charging and discharging, and extreme temperatures, it significantly improves power load balance, local temperature rise consistency, and energy utilization efficiency, reducing operational risks and achieving comprehensive intelligent, highly reliable, and highly efficient management of the battery system. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the method of the present invention; Figure 2 This is a schematic diagram of the structure of the present invention. Detailed Implementation

[0016] 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, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1: Please refer to Figure 1 As shown in the figure, a multi-condition linkage control method for new energy batteries according to an embodiment of the present invention includes the following steps: S1: Obtain the operating status information of each cell node in the battery, perform unified characterization processing on the status information, and construct a structural state network of the cell nodes and their interrelationships within the battery.

[0018] The process of constructing the structural state network of the battery's internal cell nodes and their interrelationships in S1 is as follows: The battery cell voltage, current, temperature, internal resistance and heat distribution data streams are collected synchronously through multi-source sensing components. Voltage, current, temperature, and heat distribution sensors are arranged on the surface of each cell or on the outside of the cell module in the battery pack. At the same time, the internal resistance change of the cell is measured through the BMS interface to achieve comprehensive perception of the cell's operating status. The sensor data is collected by a high-speed data acquisition module and aligned with the time synchronization control unit to ensure that the data of each cell is collected synchronously under a unified time reference. The collected raw data includes instantaneous voltage, current fluctuations, surface temperature, and heat distribution information. The internal state is initially estimated through edge computing or model extrapolation to ensure that complete cell state information that can be used for analysis is obtained.

[0019] The collected data is spatiotemporally aligned and normalized to form unified state feature information; The collected multi-source cell data is aligned with a unified timestamp, and data from different sampling frequencies are resampled to a unified time scale to eliminate the influence of sampling period differences on the analysis results. At the same time, data of different dimensions such as voltage, current, temperature, internal resistance and heat distribution are normalized or standardized to make their values ​​fall into the same feature range, which facilitates cross-index comparison and fusion. The unified state feature information generated after processing not only includes the real-time state of each cell, but also reflects the relative differences between cells, providing directly usable input data for constructing a structural state network.

[0020] Based on historical cell performance database and unified state feature information, calculate the state similarity and coupling coefficient between nodes; By accessing the historical performance database of the battery pack, the performance and aging curves of each cell under different charge and discharge conditions are obtained. The historical data is compared with the current unified state characteristic information, and the state similarity between cells is calculated using algorithms based on Euclidean distance or cosine similarity. At the same time, the node coupling coefficient is calculated by combining factors such as temperature gradient, internal resistance change and response delay, which quantifies the strength of the electrical and thermodynamic correlation between cells. The coupling coefficient not only reflects the degree of mutual influence between cells, but also provides a key reference for the construction of network topology.

[0021] Construct a structural state network of the battery's internal cell nodes and their connections based on the coupling coefficient; Each cell is treated as a network node. Based on the calculated coupling coefficient, edges are established between the nodes, and the weight of the edge is the magnitude of the coupling coefficient. During the construction process, a threshold can be set to filter weakly coupled node connections to simplify the network structure and highlight key coupling relationships. The resulting structural state network comprehensively reflects the correlation and interaction between the electrical, thermal, and response characteristics of the cells inside the battery pack, providing an intuitive network model for analyzing cell response differences, coupling evolution, and power scheduling constraints.

[0022] S2: Based on the structural state network, the state changes of the internal cell nodes of the battery are analyzed at different time scales to form a set of structural state parameters that characterize the consistency of battery structure, the differences in node response and the coupling evolution characteristics.

[0023] The process of forming a set of structural state parameters in S2 that characterizes the consistency of battery structure, differences in node response, and coupling evolution features is as follows: Based on the structural state network, the unified state feature information of each node in the network is mapped to the corresponding node; The system maps each cell node in the structural state network to the voltage, current, temperature, internal resistance, and thermal distribution data collected by its corresponding sensors. This unified state characteristic information is then bound to the node's topological position in the network, forming a complete state vector for each node. This mapping process utilizes a data management module to structurally store the multi-source information of each cell, ensuring that each node's state vector contains the cell's own dynamic information and its coupling relationship with other nodes, providing basic data support for multi-scale analysis and coupled calculations.

[0024] The state feature vector of each node is decomposed into three time scales: short-term dynamics, medium-term fluctuations, and long-term trends. The coupling evolution between nodes is then analyzed by combining the weights of the network topology. The state vector of each cell node is decomposed on three time scales: short-time dynamics reflect instantaneous load or temperature fluctuations, medium-time fluctuations reflect the daily cycle charging and discharging patterns, and long-time trends reflect cell aging or capacity decay. During the decomposition process, the coupling relationship between each node and its neighboring nodes is assigned different weights based on the connection relationships between nodes in the structural state network. Weighted average or weighted recursive algorithms are used to analyze the impact of state changes between nodes on the overall network, so as to quantify the interaction and evolution characteristics between cells and achieve a fine characterization of response differences.

[0025] A cross-scale recursive correlation analysis method is adopted, combined with the coupling strength in the structural state network, to compare and correlate the state vectors between nodes, and calculate the differences in node responses and the coupling evolution trend. Based on the state vectors after multi-scale decomposition, a recursive correlation analysis algorithm is used to calculate the correlation and deviation of the state vectors of each node at different time scales. At the same time, the coupling strength between nodes in the network is referenced to identify the synchronicity or lag of the responses between nodes. Through cross-scale iterative comparison, the short-term fluctuations and long-term trends of nodes are combined to quantify the differences in node responses, and then the coupling evolution trend is derived, providing a data foundation for discovering nodes with potential performance imbalances or abnormal responses.

[0026] Based on the completion of multi-scale decomposition and the calculation of coupling strength and response differences between nodes in the structural state network, structural consistency index, response delay index and coupling strength parameter of nodes are extracted to generate a set of structural state parameters. Based on the analysis results, the response stability, structural consistency, response delay, and coupling strength with other nodes of each node are extracted as specific parameters to form node-level structural state indices. All node indices are aggregated to generate a set of structural state parameters covering all cells in the battery pack, which is stored in a database as the core input for dynamic power allocation and collaborative control. This enables a comprehensive quantitative description of cell state differences and network coupling evolution, providing sufficient basis for power constraints and optimization strategies.

[0027] S3: Based on the set of structural state parameters, analyze the differences in the response of the internal cell nodes of the battery and their evolution characteristics in the structural state network. Based on the analysis results, determine the constraints of the battery structural state on power allocation and form a dynamic power allocation scheme.

[0028] The process of analyzing the differences in the response of internal cell nodes of the battery and their evolution characteristics in the structural state network in S3 is as follows: Using the set of structural state parameters as input, the structural consistency index, response delay index, and coupling strength parameter of each node are standardized to form a unified index matrix that can be used for comparative analysis. Using the previous set of structural state parameters as input, the structural consistency index, response delay index, and coupling strength parameter of each node are normalized or standardized to ensure they are within a uniform numerical range, eliminating dimensional differences. The standardized node parameters are combined into node feature vectors, and then all node feature vectors are arranged in rows to form a unified index matrix that can be directly used for comparative analysis and subsequent clustering calculations. This process is implemented through a data processing module, ensuring the integrity of the matrix data structure and its dynamic updating capability, making it suitable for different battery pack sizes and different operating conditions.

[0029] Cluster analysis of node feature vectors in a unified index matrix is ​​performed using node coupling strength to identify nodes with high response differences and key coupling paths; Based on a unified index matrix, and combined with information on the coupling strength between nodes, weighted clustering analysis is performed on the node feature vectors. Algorithms such as hierarchical clustering, K-means, or density clustering are used. The clustering distance metric is adjusted according to the coupling weight between nodes. The clustering results identify node clusters with significant response differences, and the high coupling paths between nodes within the clusters are analyzed. This yields potential key coupling paths and nodes with high response differences in the battery pack, providing target objects for power allocation optimization and anomaly monitoring.

[0030] Anomaly detection is performed on nodes with high response differences and key coupling paths. Combined with spatiotemporal correlation analysis, node response difference anomalies and coupling evolution anomalies are identified, and node response difference matrices and evolution trajectory sets are generated. For the identified nodes with high response differences and key coupling paths, anomaly detection algorithms, such as those based on statistical thresholds, moving averages, or adaptive thresholds, are applied to analyze the power response, temperature changes, and coupling strength of the nodes in real time to determine whether there are any abnormal deviations. At the same time, spatiotemporal correlation analysis is performed by combining the topological relationship of the nodes in the network and the time series characteristics to identify abnormal node response differences and abnormal coupling evolution. Finally, all node response difference information is organized into a node response difference matrix, and the key path and abnormal evolution information are organized into an evolution trajectory set for use in the dynamic power allocation and collaborative optimization control module.

[0031] The process of forming a dynamic power allocation scheme in S3 is as follows: Based on the node response difference matrix and evolution trajectory set, the response characteristics of each node under different operating conditions are evaluated, and the power carrying capacity, energy recovery capacity and thermal regulation constraint threshold are calculated. Using the node response difference matrix and evolution trajectory set as input, the response characteristics of each cell node under different load, temperature and SOC conditions are quantitatively evaluated, including power output capability, energy recovery efficiency and thermal regulation capability. Using historical operating data, node topology information and real-time monitoring data, the maximum safe power carrying capacity, energy recovery constraint threshold and allowable temperature rise range of each node are calculated by the model to form a set of node-level constraint parameters, which provides basic data support for dynamic power allocation. This evaluation process can be implemented through software calculation module to ensure real-time performance and scalability.

[0032] By combining the calculation results with unified state characteristic information, a structural state constraint function is constructed, incorporating node power allocation, energy recovery, and thermal management requirements into the constraint conditions. By integrating node constraint parameters with unified state characteristic information, a mathematical constraint model is constructed, which transforms node power allocation, energy recovery capability, and thermal regulation requirements into computable constraint functions, such as power upper limit constraints, energy recovery efficiency constraints, and temperature control constraints. The coupling strength between nodes and the weight of critical coupling paths are introduced into the constraint functions to ensure that the constraint functions can reflect the mutual influence between nodes, thereby fully mapping the actual characteristics of the structural state network and node constraints to the power allocation optimization problem.

[0033] The dynamic power allocation scheme is used as the basis for constraint function calculation. The constraint function is used to constrain the overall power allocation of the battery system under different operating conditions, thus forming a dynamic power allocation scheme. With constraint functions as the core, the power distribution of the entire battery system under different operating conditions is optimized. By solving the constraint optimization problem, linear programming, quadratic programming or distributed optimization algorithms can be used to dynamically determine the power output, energy recovery and thermal regulation strategy of each node. The calculation results form a dynamic power distribution scheme that is updated in real time. It can be automatically adjusted according to the changes in node response and operating conditions during charging and discharging, so as to ensure balanced power load of the battery pack, maximize energy recovery efficiency and temperature safety, thereby realizing multi-condition linkage optimization control of the battery system.

[0034] S4: Based on the dynamic power allocation scheme, the node power, energy recovery and thermal regulation information are integrated with the structural state network to generate cooperative power constraint information. Based on the structural state network, the cooperative operation of the energy output, energy recovery and thermal regulation modules is adjusted to generate a dynamic cooperative power allocation scheme.

[0035] The process of generating cooperative power constraint information in S4 is as follows: Based on the dynamic power allocation scheme, the power output, energy recovery and thermal regulation information of each cell node are fused with the response differences, coupling strength and key coupling path information of the nodes in the structural state network. The system acquires the power output, energy recovery capability, and thermal regulation parameters of each cell node in the dynamic power allocation scheme. It also integrates the node response differences, inter-node coupling strength, and key coupling path information in the structural state network. This multi-source information is then processed and linked with the network topology and coupling characteristics through the data fusion module. This ensures that the multi-dimensional characteristics of each node can fully reflect its role and impact in the overall battery system, providing basic data for collaborative constraint calculation.

[0036] By using a weighted mapping method, data from different sources are mapped to the same feature space to form a fused feature vector; For the integrated multi-source information, a weighted mapping method is adopted to map data such as cell power, energy recovery and thermal regulation indicators, node response differences, and coupling strength to a unified feature space, so that various types of data can be quantitatively analyzed under the same dimension. The mapping can be achieved through weighted normalization, principal component analysis or multidimensional scaling transformation methods to form a fused feature vector for each node, providing standardized input for constraint calculation.

[0037] Based on the fused feature vectors, the power constraints, energy recovery constraints, and thermal management constraints of each node under the current operating conditions are calculated. By utilizing fused feature vectors and combining the current load state, temperature, and SOC condition of the battery system, constraint calculations are performed on the power output, energy recovery capability, and thermal management capability of each node. The upper limit of power, upper limit of energy recovery, and allowable temperature rise range of each node can be calculated through mathematical models or algorithm modules, ensuring that the constraint values ​​can reflect the characteristics of the node itself and its coupling relationship in the system, thereby realizing the dynamic and real-time nature of node-level constraints.

[0038] Based on the coupling relationship between nodes, calculate the collaborative constraints and assign priority weights to each node; Based on the node coupling strength and key coupling path information in the structural state network, the cooperative influence of each node in the overall battery system is analyzed. The interaction between nodes is incorporated into the constraint calculation. Priority weights can be assigned to each node through weighted superposition method, network topology analysis or cooperative optimization model to ensure that key nodes and highly coupled paths have higher adjustment priority in cooperative power regulation, thereby improving the overall safety and balance of the system.

[0039] Based on the cooperative constraints and node priorities, a cooperative optimization algorithm is used to generate the final cooperative power constraint information. Using the calculated cooperative constraints and node priority information, cooperative optimization algorithms, such as distributed optimization, reinforcement learning, or multi-objective optimization methods, are invoked to generate the final cooperative power constraint information. In actual implementation, the algorithm dynamically adjusts the power allocation, energy recovery, and thermal regulation constraints according to real-time operating conditions, node status, and network coupling characteristics to ensure the energy utilization efficiency, temperature safety, and balance of the battery system under multiple operating conditions, while providing accurate constraints for dynamic cooperative power allocation.

[0040] The process of generating a dynamic cooperative power allocation scheme in S4 is as follows: By fusing cooperative power constraint information with node response differences, coupling strength, and key coupling path information in the structural state network, a multi-node linkage control graph is constructed. By acquiring cooperative power constraint information and simultaneously collecting response differences, node coupling strength, and key coupling path data of each node in the structural state network, this information is integrated into a unified control model through a data fusion algorithm, forming a multi-node linkage control graph. This graph clearly represents the power output, energy recovery, and thermal regulation characteristics of each node and reflects the mutual influence relationships between nodes. Weighted mapping and topological mapping methods can be used to transform node characteristics and coupling relationships into attributes of graph nodes and edges, thereby achieving data visualization and computability.

[0041] In the multi-node linkage control diagram, an associated edge is established for the power output, energy recovery capability and thermal regulation requirements of each node, and the edge weight and node priority are labeled. Based on the coupling relationships and critical paths between nodes in the graph, the associated edges between nodes are established. The edge weights depend on the coupling strength between nodes and the importance of the cooperative constraints. At the same time, based on the power response characteristics, energy recovery capabilities, and thermal regulation effects of the nodes, priority weights are assigned to each node and marked in the graph. The degree of influence between nodes can be calculated through topology analysis algorithms, and the priority can be determined by combining the historical operating data of the nodes, thereby quantifying the node adjustment order and intensity.

[0042] Based on the multi-node linkage control diagram, an adaptive adjustment algorithm is used to adjust the output power, recovery power and thermal regulation parameters in real time. By utilizing the constructed linkage control graph and invoking the adaptive adjustment algorithm, the power output, energy recovery, and thermal regulation parameters of each node are dynamically adjusted, enabling the system to optimize power allocation based on real-time state changes. Model predictive control, reinforcement learning, or iterative optimization algorithms can be used, combined with the historical response characteristics of nodes and real-time measurement data, to generate the optimal output strategy for each node under the current operating conditions, thereby achieving a dynamic balance between power and thermal management.

[0043] During the adjustment process, the overall power allocation is dynamically optimized by combining the node state change trend and coupling evolution characteristics; During the adaptive adjustment process, the state change trends of each node are continuously tracked, including changes in voltage, current, temperature and SOC. At the same time, combined with the coupling evolution characteristics, the power distribution ratio between nodes is adjusted to optimize the energy utilization efficiency and thermal balance of the overall battery system. The node power distribution can be continuously optimized through rolling time window analysis and dynamic weight adjustment, so that the system can maintain stable, safe and efficient operation under multiple operating conditions.

[0044] Prioritize adjustments to critical nodes and highly coupled paths to form a dynamic collaborative power allocation scheme covering all nodes and modules; Based on the labeled node priorities and critical coupling path information, priority adjustments are made to critical nodes and nodes with high coupling paths to ensure that these nodes play a leading role in system regulation. At the same time, combined with the adjustment of non-critical nodes, a dynamic collaborative power allocation scheme covering the entire battery system is formed. Through multi-objective optimization algorithms, node power, energy recovery and thermal regulation constraints are considered simultaneously to generate a system-level dynamic collaborative allocation strategy, thereby achieving global optimal power scheduling and balanced management.

[0045] S5: Based on the dynamic cooperative power allocation scheme, the energy allocation relationship of the battery system under different operating conditions is constrained, and the adjusted operating state is fed back to the set of structural state parameters, forming a continuous linkage adjustment mechanism between the set of structural state parameters and the energy allocation relationship.

[0046] The process of constraining the energy distribution relationship of the battery system under different operating conditions in S5 is as follows: The system acquires the node power output, energy recovery, and thermal regulation status of the battery system under the current operating conditions, and predicts the future state change trend of each node by combining the dynamic collaborative power allocation scheme. The system collects real-time power output, energy recovery efficiency, and thermal regulation status of each cell node under current operating conditions through sensing components and monitoring modules. The collected data is then mapped to a unified state characteristic information. Combined with the aforementioned dynamic collaborative power allocation scheme, prediction algorithms are used to analyze the future state change trends of each node, including power changes, heat distribution, and SOC evolution. Time series prediction methods based on historical operating data or model-based predictive control methods can be used to predict node state trends, providing basic data for energy allocation constraints.

[0047] Under different load, temperature and SOC conditions, the power output and energy recovery capability of each cell node are constrained in real time. Based on the predicted future state of the nodes, the power output, energy recovery capability, and thermal regulation capability of each node under different load conditions, temperature environments, and SOC ranges are constrained in real time to prevent overload or overheating of the nodes. By setting power upper limits, recovery thresholds, and temperature control strategies, the constraint parameters of each node can be input into the control algorithm to achieve real-time monitoring and constraint adjustment, ensuring the safe and balanced operation of the battery system under various operating conditions.

[0048] By comparing the constraint results with historical operating data, the energy allocation strategy is adjusted in real time, and nodes that are overloaded or unbalanced are promptly regulated. By comparing real-time constraint results with historical operating data, it is possible to identify whether there are uneven power loads, decreased energy recovery efficiency, or abnormal local temperature rises at the current nodes. For nodes with abnormalities, their energy distribution strategies are adjusted in real time, including reducing power output, optimizing recovery power, or adjusting thermal management parameters. Through closed-loop control and rolling time window analysis methods, rapid response of abnormal nodes can be achieved, ensuring the overall energy distribution balance and safety of the system.

[0049] Based on node constraints and overall system requirements, battery energy allocation constraint information under multiple operating conditions is dynamically generated and synchronously updated to the dynamic collaborative power allocation scheme, outputting the battery system energy allocation constraint scheme. After real-time adjustment is completed, the constraint results of each node are integrated with the overall energy demand, power allocation strategy and thermal management requirements of the system to dynamically generate battery energy allocation constraint information covering multiple operating conditions. At the same time, the updated constraint information is synchronized to the dynamic collaborative power allocation scheme, so that the entire system can perform power and energy adjustment according to the latest constraints in subsequent operation. Finally, a complete battery system energy allocation constraint scheme is output. Through multi-objective optimization algorithms and data synchronization mechanisms, real-time linkage between the constraints of each node and global collaboration can be achieved, ensuring the safety, balance and energy utilization efficiency of the system under complex operating conditions.

[0050] The process of establishing a continuous linkage and adjustment mechanism between the set of structural state parameters and the energy distribution relationship in S5 is as follows: The effectiveness of the energy distribution constraint scheme of the battery system is comprehensively monitored. The power output, energy recovery, temperature and SOC status of each cell node are collected and the collected data are uniformly mapped to a unified state feature information. Through sensors, monitoring modules, and management systems, the power output, energy recovery efficiency, temperature, and SOC status of each cell node in the battery system are collected in real time under the current operating conditions. Data from different sources are time-aligned, normalized, and uniformly mapped to form unified state characteristic information that can be directly used for structural state analysis. Multi-source data acquisition and spatiotemporal synchronization algorithms can be used to map the operating information of each node to the corresponding node in the structural state network, providing complete basic data for coordinated regulation.

[0051] Based on unified state feature information, the latest operating state is compared with the set of structural state parameters to update the node's response difference index, coupling strength, and key coupling path information. By comparing and analyzing the unified state characteristic information with the existing set of structural state parameters, and by calculating the deviation between the current state and historical characteristics of each node, the response difference index, coupling strength and key coupling path information of the nodes are dynamically updated. Recursive correlation analysis and network weighted calculation methods across time scales can be used to accurately identify the changes in node response differences and coupling evolution trends, and realize the real-time updating of the set of structural state parameters.

[0052] The updated structural state parameters are dynamically corrected to assess the coupling evolution trend and potential instability risk between nodes and generate feedback adjustment signals. Based on the updated structural state parameters, combined with the coupling strength and response differences between nodes, the coupling evolution trend of node states and the entire battery system is evaluated, and the risks of potential local overload, thermal imbalance or power unevenness are analyzed. Feedback regulation signals that can be used to adjust power distribution, energy recovery and thermal management are formed. Model-based predictive control methods or real-time optimization algorithms can be used to quantify and weight the feedback regulation signals so that the signals can accurately reflect the actual operating state of nodes and the system.

[0053] The feedback adjustment signal is input into the dynamic cooperative power allocation scheme to form a continuous linkage adjustment mechanism between the set of structural state parameters and the energy allocation relationship. The feedback adjustment signal is input into the dynamic collaborative power allocation scheme to adjust the power output, energy recovery, and thermal regulation strategies of each node, thereby realizing closed-loop linkage adjustment between the set of structural state parameters and the energy allocation relationship. Through continuous cyclic execution of monitoring, analysis, evaluation, and feedback adjustment, the battery system maintains dynamic balance and optimized performance under different operating conditions. Through cyclic control structure and adaptive optimization algorithm, continuous linkage adjustment of multiple operating conditions and multiple nodes can be achieved to ensure the overall safety, balance, and energy utilization efficiency of the battery system are maximized.

[0054] Example 2: Please refer to Figure 2 As shown, a multi-condition linkage control system for new energy batteries includes: State-based network construction module: acquires and uniformly represents the operating status of cell nodes, and constructs a structural state network of internal battery nodes and their interrelationships; Multi-scale analysis module: Based on the structural state network, it analyzes the state changes of nodes at different time scales of short, medium and long time, and generates a set of structural state parameters; Power constraint module: Based on the set of structural state parameters, analyze the differences and evolution characteristics of node responses, determine the power allocation constraint requirements, and form a dynamic power allocation scheme; Collaborative optimization module: Integrates node power, energy recovery, and thermal regulation information with the structural state network to generate collaborative power constraint information and form a dynamic collaborative power allocation scheme; Linkage adjustment module: Based on the dynamic collaborative power allocation scheme, it constrains the energy allocation of the system and feeds back the operating status to the set of structural state parameters, forming a continuous linkage adjustment mechanism.

[0055] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0056] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-condition linkage control method for new energy batteries, characterized in that, Includes the following steps: The system acquires the operating status information of each cell node in the battery, performs unified characterization processing on the status information, and constructs a structural state network of the cell nodes and their interrelationships within the battery. Based on the structural state network, the state changes of the internal cell nodes of the battery are analyzed in relation to different time scales, forming a set of structural state parameters that characterize the consistency of battery structure, differences in node response, and coupling evolution characteristics. Based on the set of structural state parameters, the differences in the response of the internal cell nodes of the battery and their evolution characteristics in the structural state network are analyzed. Based on the analysis results, the constraints of the battery structural state on power allocation are determined, and a dynamic power allocation scheme is formed. Based on the dynamic power allocation scheme, node power, energy recovery and thermal regulation information are fused with the structural state network to generate cooperative power constraint information. Based on the structural state network, the cooperative operation of energy output, energy recovery and thermal regulation modules is adjusted to generate a dynamic cooperative power allocation scheme. Based on the dynamic collaborative power allocation scheme, the energy allocation relationship of the battery system under different operating conditions is constrained, and the adjusted operating state is fed back to the set of structural state parameters, forming a continuous linkage adjustment mechanism between the set of structural state parameters and the energy allocation relationship.

2. The multi-condition linkage control method for new energy batteries according to claim 1, characterized in that, The process of constructing the structural state network of the internal cell nodes and their interrelationships is as follows: The battery cell voltage, current, temperature, internal resistance and heat distribution data streams are collected synchronously through multi-source sensing components. The collected data is spatiotemporally aligned and normalized to form unified state feature information; Based on historical cell performance database and unified state feature information, calculate the state similarity and coupling coefficient between nodes; A structural state network of the battery's internal cell nodes and their connections is constructed based on the coupling coefficient.

3. The multi-condition linkage control method for new energy batteries according to claim 2, characterized in that, The process of forming a set of structural state parameters characterizing the consistency of battery structure, differences in node response, and coupling evolution features is as follows: Based on the structural state network, the unified state feature information of each node in the network is mapped to the corresponding node; The state feature vector of each node is decomposed into three time scales: short-term dynamics, medium-term fluctuations, and long-term trends. The coupling evolution between nodes is then analyzed by combining the weights of the network topology. A cross-scale recursive correlation analysis method is adopted, combined with the coupling strength in the structural state network, to compare and correlate the state vectors between nodes, and calculate the differences in node responses and the coupling evolution trend. Based on the completion of multi-scale decomposition and the calculation of coupling strength and response differences between nodes in the structural state network, structural consistency index, response delay index and coupling strength parameter of the nodes are extracted to generate a set of structural state parameters.

4. The multi-condition linkage control method for new energy batteries according to claim 3, characterized in that, The process of analyzing the differences in the responses of battery cell nodes and their evolution characteristics in the structural state network is as follows: Using the set of structural state parameters as input, the structural consistency index, response delay index, and coupling strength parameter of each node are standardized to form a unified index matrix that can be used for comparative analysis. Cluster analysis of node feature vectors in a unified index matrix is ​​performed using node coupling strength to identify nodes with high response differences and key coupling paths; Anomaly detection is performed on nodes with high response differences and key coupling paths. Combined with spatiotemporal correlation analysis, node response difference anomalies and coupling evolution anomalies are identified, generating a node response difference matrix and a set of evolution trajectories.

5. The multi-condition linkage control method for new energy batteries according to claim 4, characterized in that, The process of forming a dynamic power allocation scheme is as follows: Based on the node response difference matrix and evolution trajectory set, the response characteristics of each node under different operating conditions are evaluated, and the power carrying capacity, energy recovery capacity and thermal regulation constraint threshold are calculated. By combining the calculation results with unified state characteristic information, a structural state constraint function is constructed, incorporating node power allocation, energy recovery, and thermal management requirements into the constraint conditions. The dynamic power allocation scheme is used as the basis for constraint function calculation. The constraint function is used to constrain the overall power allocation of the battery system under different operating conditions, thus forming a dynamic power allocation scheme.

6. The multi-condition linkage control method for new energy batteries according to claim 5, characterized in that, The process of generating cooperative power constraint information is as follows: Based on the dynamic power allocation scheme, the power output, energy recovery and thermal regulation information of each cell node are fused with the response differences, coupling strength and key coupling path information of the nodes in the structural state network. By using a weighted mapping method, data from different sources are mapped to the same feature space to form a fused feature vector; Based on the fused feature vectors, the power constraints, energy recovery constraints, and thermal management constraints of each node under the current operating conditions are calculated. Based on the coupling relationship between nodes, calculate the collaborative constraints and assign priority weights to each node; Based on the cooperative constraints and node priorities, a cooperative optimization algorithm is used to generate the final cooperative power constraint information.

7. The multi-condition linkage control method for new energy batteries according to claim 6, characterized in that, The process of generating a dynamic cooperative power allocation scheme is as follows: By fusing cooperative power constraint information with node response differences, coupling strength, and key coupling path information in the structural state network, a multi-node linkage control graph is constructed. In the multi-node linkage control diagram, an associated edge is established for the power output, energy recovery capability and thermal regulation requirements of each node, and the edge weight and node priority are labeled. Based on the multi-node linkage control diagram, an adaptive adjustment algorithm is used to adjust the output power, recovery power and thermal regulation parameters in real time. During the adjustment process, the overall power allocation is dynamically optimized by combining the node state change trend and coupling evolution characteristics; Prioritize adjustments to critical nodes and highly coupled paths to form a dynamic collaborative power allocation scheme covering all nodes and modules.

8. The multi-condition linkage control method for new energy batteries according to claim 7, characterized in that, The process of constraining the energy distribution relationship of the battery system under different operating conditions is as follows: The system acquires the node power output, energy recovery, and thermal regulation status of the battery system under the current operating conditions, and predicts the future state change trend of each node by combining the dynamic collaborative power allocation scheme. Under different load, temperature and SOC conditions, the power output and energy recovery capability of each cell node are constrained in real time. By comparing the constraint results with historical operating data, the energy allocation strategy is adjusted in real time, and nodes that are overloaded or unbalanced are promptly regulated. Based on node constraints and overall system requirements, battery energy allocation constraint information under multiple operating conditions is dynamically generated and synchronously updated to the dynamic collaborative power allocation scheme, outputting the battery system energy allocation constraint scheme.

9. The multi-condition linkage control method for new energy batteries according to claim 8, characterized in that, The process of forming a continuous linkage and adjustment mechanism between the set of structural state parameters and the energy distribution relationship is as follows: The effectiveness of the energy distribution constraint scheme of the battery system is comprehensively monitored. The power output, energy recovery, temperature and SOC status of each cell node are collected and the collected data are uniformly mapped to a unified state feature information. Based on unified state feature information, the latest operating state is compared with the set of structural state parameters to update the node's response difference index, coupling strength, and key coupling path information. The updated structural state parameters are dynamically corrected to assess the coupling evolution trend and potential instability risk between nodes and generate feedback adjustment signals. The feedback adjustment signal is input into the dynamic collaborative power allocation scheme to form a continuous linkage adjustment mechanism between the set of structural state parameters and the energy allocation relationship.

10. A multi-condition linkage control system for new energy batteries, applied to the method described in any one of claims 1-9, characterized in that, include: State-based network construction module: acquires and uniformly represents the operating status of cell nodes, and constructs a structural state network of internal battery nodes and their interrelationships; Multi-scale analysis module: Based on the structural state network, it analyzes the state changes of nodes at different time scales of short, medium and long time, and generates a set of structural state parameters; Power constraint module: Based on the set of structural state parameters, analyze the differences and evolution characteristics of node responses, determine the power allocation constraint requirements, and form a dynamic power allocation scheme; Collaborative optimization module: Integrates node power, energy recovery, and thermal regulation information with the structural state network to generate collaborative power constraint information and form a dynamic collaborative power allocation scheme; Linkage adjustment module: Based on the dynamic collaborative power allocation scheme, it constrains the energy allocation of the system and feeds back the operating status to the set of structural state parameters, forming a continuous linkage adjustment mechanism.

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