Methods, equipment and storage media for dynamic evaluation of the operating status of energy storage systems

Abstract

A method, device, and storage medium for dynamic assessment of the operating status of an energy storage system are disclosed. The method involves analyzing multimodal operating data of the energy storage system based on a dynamic state-space model to obtain an estimated value of the system's current operating status. This estimated value is then fused with historical operating trends to construct a multidimensional state assessment index set. Finally, a weighted fusion model is used to perform weighted analysis on this multidimensional state assessment index set to predict the system's state in the next operating cycle. This aims to improve the accuracy of assessing the operating status of energy storage systems under complex operating environments.

Description

Technical Field

[0001] This application belongs to the field of energy storage system and battery management technology, and in particular relates to a method, device and storage medium for dynamic evaluation of the operating status of an energy storage system. Background Technology

[0002] Currently, energy storage systems are widely used in critical scenarios such as new energy power generation, power peak shaving, microgrids, and data centers. Stable system operation is crucial for ensuring energy supply security. Traditional energy storage systems mostly use wired BMS for operational status monitoring and evaluation. While these systems offer advantages in data transmission stability, they suffer from cumbersome wiring, poor scalability, and high maintenance costs. Against this backdrop, wireless BMS, due to its flexible deployment and ease of maintenance, is gradually becoming the development trend.

[0003] However, in wireless BMS systems, due to factors such as wireless channel interference, data packet loss, and transmission delay, the status data acquired by the system is incomplete, discontinuous, and subject to noise pollution. This results in unstable and inaccurate operational status assessments based on the raw data, making it difficult to meet the needs of system status security assessments in real-world complex operating environments. Summary of the Invention

[0004] In view of this, embodiments of this application provide a method, device and storage medium for dynamic evaluation of the operating status of an energy storage system, aiming to improve the accuracy of evaluating the operating status of an energy storage system under complex operating environments.

[0005] This application provides a method for dynamically evaluating the operating status of an energy storage system, including:

[0006] Collect multimodal operation data of energy storage systems;

[0007] The multimodal operating data is analyzed based on a dynamic state-space model to obtain an estimated value of the operating state of the energy storage system at the current moment.

[0008] The estimated operating status is integrated with historical operating trends to construct a multi-dimensional set of status assessment indicators;

[0009] The multidimensional state assessment index set is weighted and analyzed based on a weighted fusion model to predict the state of the energy storage system in the next operating cycle.

[0010] In one embodiment, the dynamic state-space model includes an extended Kalman filter model. The step of analyzing the multimodal operating data based on the dynamic state-space model to obtain an estimate of the current operating state of the energy storage system includes:

[0011] The multimodal operating data is input into the extended Kalman filter model, and the multimodal operating data is iteratively processed based on a nonlinear state transition function in the extended Kalman filter model to obtain the predicted state vector at the current time.

[0012] The predicted state vector is mapped to the observation space using the observation function to obtain the predicted observation vector;

[0013] The residual between the predicted observation vector and the actual acquired multimodal observation data is calculated, and the predicted state vector is updated and corrected based on the Kalman gain matrix to obtain the estimated operating state value at the current time.

[0014] In one embodiment, the step of iteratively processing the multimodal operating data based on a nonlinear state transition function to obtain the predicted state vector at the current moment includes:

[0015] Based on the nonlinear state transition function, the predicted state vector at the current moment is estimated according to the system state at the previous moment and the multimodal operation data at the current moment.

[0016] In one embodiment, the nonlinear state transition function includes: a battery state of charge update model, a battery health state update model, a temperature prediction model, and a voltage prediction model.

[0017] The step of estimating the predicted state vector at the current moment based on the nonlinear state transition function, according to the system state at the previous moment and the multimodal operation data at the current moment, includes:

[0018] Based on the battery state of charge update model, the battery state of charge at the previous moment, the current at the current moment, and the temperature are used to dynamically correct the state of charge, so as to obtain the predicted state of charge at the current moment.

[0019] Based on the battery health status update model, the battery health status at the previous moment and the current, temperature and communication quality at the current moment are analyzed to obtain the predicted health status at the current moment.

[0020] Based on the temperature prediction model, the temperature at the previous moment and the current at the current moment are analyzed to obtain the predicted temperature at the current moment.

[0021] Based on the voltage prediction model, the open-circuit voltage of the state of charge at the previous moment and the battery internal resistance at the current moment are analyzed to obtain the predicted voltage at the current moment.

[0022] The predicted state vector is constructed based on the predicted state of charge, the predicted health state, the predicted temperature, and the predicted voltage.

[0023] In one embodiment, the historical operating trend includes state, state evolution rate, and direction; before fusing the estimated operating state with the historical operating trend to construct a multidimensional state assessment index set, the method further includes:

[0024] For each state dimension, construct its evolution trend function; based on the evolution trend function, determine the state, state evolution rate, and direction of the different dimensions.

[0025] In one embodiment, the weight fusion model includes a hierarchical weighted model;

[0026] The weighted analysis of the multidimensional state assessment index set based on the weighted fusion model to predict the predicted state of the energy storage system in the next operating cycle includes:

[0027] The multidimensional state assessment index set is input into the hierarchical weighted model for hierarchical weight analysis to obtain the predicted state of the energy storage system in the next operating cycle.

[0028] In one embodiment, the step of inputting the multidimensional state assessment index set into the hierarchical weighted model for hierarchical weight analysis to obtain the predicted state of the energy storage system in the next operating cycle includes:

[0029] The multidimensional state assessment index set is input into the hierarchical weighted model. In the hierarchical weighted model, the multidimensional state assessment index is functionally classified, and each functional category index is further subdivided into several specific state indices. Weights are assigned to each functional category index and each specific state index. The functional category indexes and specific state indices of each dimension are weighted and fused based on their respective weights to predict the predicted state of the energy storage system in the next operating cycle.

[0030] A second aspect of this application provides a device for dynamically evaluating the operating status of an energy storage system, comprising:

[0031] The acquisition module is used to collect multimodal operating data of the energy storage system;

[0032] The first analysis module is used to analyze the multimodal operating data based on the dynamic state space model to obtain the estimated operating state of the energy storage system at the current moment.

[0033] The fusion module is used to fuse the estimated operating status with historical operating trends to construct a multi-dimensional set of status assessment indicators.

[0034] The second analysis module is used to perform weighted analysis on the multidimensional state assessment index set based on the weighted fusion model, and predict the predicted state of the energy storage system in the next operating cycle.

[0035] In one embodiment, the dynamic state-space model includes an extended Kalman filter model, and the first analysis module includes:

[0036] The processing unit is used to input the multimodal operation data into the extended Kalman filter model, and perform iterative processing on the multimodal operation data based on a nonlinear state transition function in the extended Kalman filter model to obtain the predicted state vector at the current time.

[0037] The mapping unit is used to map the predicted state vector to the observation space using the observation function to obtain the predicted observation vector.

[0038] The acquisition unit is used to calculate the residual between the predicted observation vector and the actual acquired multimodal observation data, and to update and correct the predicted state vector based on the Kalman gain matrix to obtain the estimated operating state value at the current time.

[0039] In one embodiment, the processing unit is specifically used for:

[0040] Based on the nonlinear state transition function, the predicted state vector at the current moment is estimated according to the system state at the previous moment and the multimodal operation data at the current moment.

[0041] In one embodiment, the nonlinear state transition function includes: a battery state of charge update model, a battery health state update model, a temperature prediction model, and a voltage prediction model.

[0042] The processing unit is specifically used for:

[0043] Based on the battery state of charge update model, the battery state of charge at the previous moment, the current at the current moment, and the temperature are used to dynamically correct the state of charge, so as to obtain the predicted state of charge at the current moment.

[0044] Based on the battery health status update model, the battery health status at the previous moment and the current, temperature and communication quality at the current moment are analyzed to obtain the predicted health status at the current moment.

[0045] Based on the temperature prediction model, the temperature at the previous moment and the current at the current moment are analyzed to obtain the predicted temperature at the current moment.

[0046] Based on the voltage prediction model, the open-circuit voltage of the state of charge at the previous moment and the battery internal resistance at the current moment are analyzed to obtain the predicted voltage at the current moment.

[0047] The predicted state vector is constructed based on the predicted state of charge, the predicted health state, the predicted temperature, and the predicted voltage.

[0048] In one embodiment, the historical operating trend includes state, state evolution rate, and direction; the device further includes a determining module, the determining module being used to:

[0049] For each state dimension, construct its evolution trend function; based on the evolution trend function, determine the state, state evolution rate, and direction of the different dimensions.

[0050] In one embodiment, the weight fusion model includes a hierarchical weighted model;

[0051] The second analysis module is specifically used for:

[0052] The multidimensional state assessment index set is input into the hierarchical weighted model for hierarchical weight analysis to obtain the predicted state of the energy storage system in the next operating cycle.

[0053] In one embodiment, the second analysis module is specifically used for:

[0054] The multidimensional state assessment index set is input into the hierarchical weighted model. In the hierarchical weighted model, the multidimensional state assessment index is functionally classified, and each functional category index is further subdivided into several specific state indices. Weights are assigned to each functional category index and each specific state index. The functional category indexes and specific state indices of each dimension are weighted and fused based on their respective weights to predict the predicted state of the energy storage system in the next operating cycle.

[0055] A third aspect of this application provides a dynamic evaluation device for the operating status of an energy storage system, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor; the processor executes the computer program to implement the steps of the method described in the first aspect above.

[0056] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described in the first aspect above.

[0057] The beneficial effects of this application's embodiments are as follows: After analyzing the collected multimodal operating data of the energy storage system based on a dynamic state-space model to obtain an estimated value of the energy storage system's current operating state, this estimated value is fused with historical operating trends to construct a multidimensional state assessment index set. A weighted analysis of this multidimensional state assessment index set is then performed based on a weighted fusion model to predict the predicted state of the energy storage system in the next operating cycle. This aims to improve the accuracy of energy storage system operating state assessment under complex operating environments. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of this application, 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 A schematic diagram illustrating the implementation process of a dynamic evaluation method for the operating status of an energy storage system provided in an embodiment of this application;

[0060] Figure 2 for Figure 1 A schematic diagram illustrating the specific implementation process of S120 in China;

[0061] Figure 3 A schematic diagram of a dynamic evaluation device for the operating status of an energy storage system provided in an embodiment of this application;

[0062] Figure 4 A schematic diagram of a dynamic evaluation device for the operating status of an energy storage system provided in an embodiment of this application. Detailed Implementation

[0063] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0065] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0066] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0067] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0068] In the description of the embodiments of this application, the term "multiple frames" refers to two or more (including two).

[0069] In the description of the embodiments of this application, the technical terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of this application and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0070] This application provides a method for dynamically assessing the operational status of an energy storage system. It involves analyzing multimodal operational data collected from the energy storage system based on a dynamic state-space model to obtain an estimated operational status of the system at the current moment. This estimated operational status is then fused with historical operational trends to construct a multidimensional state assessment index set. Finally, a weighted analysis is performed on this multidimensional state assessment index set using a weighted fusion model to predict the predicted state of the energy storage system in the next operational cycle. This method aims to improve the accuracy of operational status assessment for energy storage systems under complex operating environments.

[0071] Please see Figure 1 As shown, Figure 1 This is a schematic diagram illustrating the implementation flow of a dynamic evaluation method for the operating status of an energy storage system provided in an embodiment of this application. Figure 1 As can be seen, the dynamic evaluation method for the operating status of an energy storage system provided in this application includes steps S110 to S140. Details are as follows:

[0072] S110: Collects multimodal operation data of the energy storage system.

[0073] Multimodal operational data is generated by collecting heterogeneous data from multiple sources from the energy storage system. In this embodiment, multimodal operational data includes, but is not limited to: battery operating parameters such as voltage, current, temperature, state of charge, and health status; communication parameters such as wireless signal strength, packet loss rate, and retransmission count; environmental parameters such as external ambient temperature, humidity, and vibration intensity; and system operation information such as charging and discharging strategies. Multimodal operational data can provide comprehensive information reflecting the state of the energy storage system, providing an input basis for subsequent state estimation.

[0074] S120: Based on the dynamic state-space model, analyze the multimodal operating data to obtain the estimated operating state of the energy storage system at the current moment.

[0075] In this embodiment, the dynamic state-space model includes an extended Kalman filter model. The extended Kalman filter model is used to analyze the modal operating data in order to perform nonlinear state estimation of the energy storage system.

[0076] For example, please refer to Figure 2 , Figure 2 for Figure 1 A schematic diagram illustrating the specific implementation process of the S120. (By...) Figure 2 As can be seen, in this embodiment, S120 includes S121 to S123, which are detailed below:

[0077] S121: Input the multimodal operation data into the extended Kalman filter model. In the extended Kalman filter model, the multimodal operation data is processed iteratively based on the nonlinear state transition function to obtain the predicted state vector at the current time.

[0078] Specifically, the multimodal operating data is processed iteratively based on a nonlinear state transition function to obtain the predicted state vector at the current moment, including: estimating the predicted state vector at the current moment based on the system state at the previous moment and the multimodal operating data at the current moment using the nonlinear state transition function.

[0079] In one embodiment, the nonlinear state transition function includes: a battery state of charge update model, a battery health state update model, a temperature prediction model, and a voltage prediction model.

[0080] Based on the nonlinear state transition function, the predicted state vector for the current moment is estimated according to the system state at the previous moment and the multimodal operation data at the current moment. This includes: dynamically correcting the state of charge (SOC) at the previous moment based on the battery SOC, the current at the current moment, and the temperature based on the battery SOC update model to obtain the predicted SOC at the current moment; analyzing the battery health status at the previous moment and the current, temperature, and communication quality at the current moment based on the battery health status update model to obtain the predicted health status at the current moment; analyzing the temperature at the previous moment and the current at the current moment based on the temperature prediction model to obtain the predicted temperature at the current moment; and analyzing the open-circuit voltage of the SOC at the previous moment and the battery internal resistance at the current moment based on the voltage prediction model to obtain the predicted voltage at the current moment. The predicted state vector is constructed based on the predicted SOC, predicted health status, predicted temperature, and predicted voltage.

[0081] The battery state of charge update model is expressed as follows: ; This indicates the battery's state of charge at the moment before. This represents the current at the current moment (positive for discharging, negative for charging), and C represents the battery's rated capacity. Indicates the sampling time interval. Indicates the temperature correction factor. Indicates the current temperature. Indicates reference temperature; This indicates the predicted state of charge.

[0082] The battery health status update model is represented as follows: ; This indicates the battery health status at the previous moment. Indicates communication quality. , , These represent the degradation factor and its sensitivity to different levels of influence.

[0083] The temperature prediction model is expressed as: ; Indicates the current temperature. This indicates the temperature at the previous moment. Battery internal resistance, The temperature rise coefficient represents the heating effect of electric current.

[0084] The voltage prediction model is expressed as: ; Indicates the current predicted voltage; It represents the open-circuit voltage and the nonlinear function of the state of charge.

[0085] S122: The predicted state vector is mapped to the observation space using the observation function to obtain the predicted observation vector.

[0086] For example, a nonlinear observation function is used to transform the predicted state vector into an observable output, resulting in a predicted observation vector. Specifically, this can be expressed as: ; Represents the predicted observation vector. H( represents the predicted state vector) ) represents the nonlinear observation mapping function. This represents the observation noise, which follows a Gaussian distribution.

[0087] S123: Perform residual calculation between the predicted observation vector and the actual acquired multimodal observation data, and update and correct the predicted state vector based on the Kalman gain matrix to obtain the estimated operating state value at the current time.

[0088] Specifically, the residual calculation formula is expressed as: ;in, This represents the calculated residual value. This represents the actual observed values ​​collected. Indicates the predicted observation value. Represents modal data, This indicates the differential weight.

[0089] By fusing multi-source heterogeneous data and employing a differentiated weighting strategy for residual calculation, a multi-modal residual fusion strategy was realized. Based on the Kalman gain adaptive mechanism for updating, the filtering stability under abnormal fluctuations can be improved to obtain accurate operating state estimates.

[0090] S130: Integrate the estimated operating status with historical operating trends to construct a multi-dimensional set of status assessment indicators.

[0091] Historical operational trends include state, rate of state evolution, and direction. Before fusing the estimated operational state with historical operational trends to construct a multi-dimensional state assessment index set, the process includes: constructing an evolutionary trend function for each state dimension; and determining the state, rate of state evolution, and direction for different dimensions based on the evolutionary trend function. Specifically, the evolutionary trend function for the state dimensions includes, but is not limited to, sliding window functions, exponential smoothing functions, or adaptive filtering functions. Different types of evolutionary trend functions can be custom-constructed for different state dimensions to improve the flexibility and accuracy of historical operational trend prediction.

[0092] S140: Based on the weighted fusion model, a weighted analysis of the multi-dimensional state assessment index set is performed to predict the predicted state of the energy storage system in the next operating cycle.

[0093] The weighted fusion model includes a hierarchical weighted model; based on the weighted fusion model, the multidimensional state assessment index set is weighted and analyzed to predict the predicted state of the energy storage system in the next operating cycle, including: inputting the multidimensional state assessment index set into the hierarchical weighted model for hierarchical weight analysis to obtain the predicted state of the energy storage system in the next operating cycle.

[0094] The multidimensional state assessment index set is input into a hierarchical weighted model for hierarchical weight analysis to obtain the predicted state of the energy storage system in the next operating cycle. This includes: inputting the multidimensional state assessment index set into the hierarchical weighted model; classifying the multidimensional state assessment indexes by function in the hierarchical weighted model; subdividing each functional category index into several specific state indicators; assigning weights to each functional category index and each specific state indicator; and performing weighted fusion prediction of the functional category indexes and specific state indicators of each dimension based on their respective weights to obtain the predicted state of the energy storage system in the next operating cycle.

[0095] Specifically, functional category indicators are primary indicators, including but not limited to electrical performance, thermal management, health status, or communication quality; specific status indicators are secondary indicators, including but not limited to state of charge (SOC) and depth of discharge (DSD). By performing a two-level weighted fusion prediction of the functional category indicators and specific status indicators across all dimensions based on their respective weights, prediction accuracy can be improved. Furthermore, complex, multi-dimensional, and fuzzy input results can be transformed into standardized, actionable, and logically structured decision-making criteria.

[0096] As the above analysis shows, the embodiments of this application construct a wireless communication network for each battery cell's corresponding communication nodes; periodically send global time synchronization signals to achieve data synchronization among the communication nodes; further, by real-time monitoring of the battery voltage state, communication signal quality, and remaining power of each communication node, and analyzing the battery voltage state, communication signal quality, and remaining power based on a weighted scoring method, dynamically scheduling each communication node to improve scheduling efficiency. This aims to solve the problems of difficult data synchronization and low scheduling efficiency of communication nodes in the prior art.

[0097] Please see Figure 3 , Figure 3 This is a schematic diagram of a dynamic evaluation device for the operating status of an energy storage system provided in an embodiment of this application. The dynamic evaluation device for the operating status of the energy storage system includes modules or units used for performing... Figures 1 to 2 The steps in the corresponding embodiments. Please refer to the details. Figures 1 to 2 The relevant descriptions in the corresponding embodiments are shown below. For ease of explanation, only the parts relevant to this embodiment are shown. See also... Figure 3 A dynamic evaluation device 300 for the operating status of an energy storage system includes:

[0098] The acquisition module 310 is used to acquire multimodal operation data of the energy storage system;

[0099] The first analysis module 320 is used to analyze the multimodal operating data based on the dynamic state space model to obtain the estimated operating state of the energy storage system at the current moment.

[0100] The fusion module 330 is used to fuse the estimated operating status with the historical operating trend to construct a multi-dimensional state assessment index set;

[0101] The second analysis module 340 is used to perform weighted analysis on the multidimensional state assessment index set based on the weighted fusion model, and predict the predicted state of the energy storage system in the next operating cycle.

[0102] In one embodiment, the dynamic state-space model includes an extended Kalman filter model, and the first analysis module 320 includes:

[0103] The processing unit is used to input the multimodal operation data into the extended Kalman filter model, and perform iterative processing on the multimodal operation data based on a nonlinear state transition function in the extended Kalman filter model to obtain the predicted state vector at the current time.

[0104] The mapping unit is used to map the predicted state vector to the observation space using the observation function to obtain the predicted observation vector.

[0105] The acquisition unit is used to calculate the residual between the predicted observation vector and the actual acquired multimodal observation data, and to update and correct the predicted state vector based on the Kalman gain matrix to obtain the estimated operating state value at the current time.

[0106] In one embodiment, the processing unit is specifically used for:

[0107] Based on the nonlinear state transition function, the predicted state vector at the current moment is estimated according to the system state at the previous moment and the multimodal operation data at the current moment.

[0108] In one embodiment, the nonlinear state transition function includes: a battery state of charge update model, a battery health state update model, a temperature prediction model, and a voltage prediction model.

[0109] The processing unit is specifically used for:

[0110] Based on the battery state of charge update model, the battery state of charge at the previous moment, the current at the current moment, and the temperature are used to dynamically correct the state of charge, so as to obtain the predicted state of charge at the current moment.

[0111] Based on the battery health status update model, the battery health status at the previous moment and the current, temperature and communication quality at the current moment are analyzed to obtain the predicted health status at the current moment.

[0112] Based on the temperature prediction model, the temperature at the previous moment and the current at the current moment are analyzed to obtain the predicted temperature at the current moment.

[0113] Based on the voltage prediction model, the open-circuit voltage of the state of charge at the previous moment and the battery internal resistance at the current moment are analyzed to obtain the predicted voltage at the current moment.

[0114] The predicted state vector is constructed based on the predicted state of charge, the predicted health state, the predicted temperature, and the predicted voltage.

[0115] In one embodiment, the historical operating trend includes state, state evolution rate, and direction; the device further includes a determining module, the determining module being used to:

[0116] For each state dimension, construct its evolution trend function; based on the evolution trend function, determine the state, state evolution rate, and direction of the different dimensions.

[0117] In one embodiment, the weight fusion model includes a hierarchical weighted model;

[0118] The second analysis module 340 is specifically used for:

[0119] The multidimensional state assessment index set is input into the hierarchical weighted model for hierarchical weight analysis to obtain the predicted state of the energy storage system in the next operating cycle.

[0120] In one embodiment, the second analysis module 340 is specifically used for:

[0121] The multidimensional state assessment index set is input into the hierarchical weighted model. In the hierarchical weighted model, the multidimensional state assessment index is functionally classified, and each functional category index is further subdivided into several specific state indices. Weights are assigned to each functional category index and each specific state index. The functional category indexes and specific state indices of each dimension are weighted and fused based on their respective weights to predict the predicted state of the energy storage system in the next operating cycle.

[0122] Please see Figure 4 , Figure 4 This is a schematic diagram of a dynamic evaluation device for the operating status of an energy storage system provided in an embodiment of this application. Figure 4It is understood that the dynamic evaluation device 400 for the operating status of the energy storage system includes: a processor 410, a memory 420, and a computer program 430 stored in the memory 420 and executable on the processor 410; when the processor 410 executes the computer program 430, it implements the steps in the above-mentioned embodiments of the dynamic evaluation and management method for the operating status of the energy storage system, for example... Figure 1 The steps S110 to S140 are shown. Alternatively, when the processor 410 executes the computer program 430, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 3 The functions of modules 310 to 340 are shown.

[0123] For example, the computer program 430 can be divided into one or more modules / units, one or more of which are stored in the memory 420 and executed by the processor 410 to complete this application. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 430 in the dynamic evaluation device for the operating status of the energy storage system. For example, the computer program 430 can be divided into a data acquisition module, a first analysis module, a fusion module, and a second analysis module.

[0124] The dynamic evaluation device for the operating status of the energy storage system provided in this embodiment may include, but is not limited to, processors and memory. Those skilled in the art will understand that... Figure 4 This is merely an example of a dynamic evaluation device for the operating status of an energy storage system and does not constitute a limitation on such devices. It may include more or fewer components than shown in the illustration, or combine certain components, or different components. For example, a dynamic evaluation device for the operating status of an energy storage system may also include input / output devices, network access devices, buses, etc.

[0125] The processor 410 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0126] The memory 420 can be an internal storage unit of the dynamic evaluation device for the operating status of the energy storage system, such as a hard drive or memory. The memory 420 can also be an external storage device, such as a plug-in hard drive, smart media card (SMC), secure digital (SD) card, or flash card. Furthermore, the dynamic evaluation device for the operating status of the energy storage system can include both internal and external storage units. The memory 420 is used to store computer programs and other programs and data required by the dynamic evaluation device for the operating status of the energy storage system. The memory 420 can also be used to temporarily store data that has been output or will be output.

[0127] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0128] This application also provides a network device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above method embodiments.

[0129] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0130] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the above-described method embodiments.

[0131] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0132] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0133] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0134] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0135] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0136] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for dynamically evaluating the operating status of an energy storage system, characterized in that, include: Collect multimodal operation data of energy storage systems; The multimodal operating data is input into an extended Kalman filter model, and the multimodal operating data is iteratively processed based on a nonlinear state transition function in the extended Kalman filter model to obtain the predicted state vector at the current time. The predicted state vector is mapped to the observation space using the observation function to obtain the predicted observation vector; The residual between the predicted observation vector and the actual acquired multimodal observation data is calculated, and the predicted state vector is updated and corrected based on the Kalman gain matrix to obtain the estimated operating state value at the current time. The estimated operating status is integrated with historical operating trends to construct a multi-dimensional set of status assessment indicators; The multidimensional state assessment index set is weighted and analyzed based on a weighted fusion model to predict the state of the energy storage system in the next operating cycle.

2. The method for dynamic evaluation of the operating status of an energy storage system as described in claim 1, characterized in that, The step of iteratively processing the multimodal operating data based on a nonlinear state transition function to obtain the predicted state vector at the current moment includes: Based on the nonlinear state transition function, the predicted state vector at the current moment is estimated according to the system state at the previous moment and the multimodal operation data at the current moment.

3. The method for dynamic evaluation of the operating status of an energy storage system as described in claim 2, characterized in that, The nonlinear state transition function includes: a battery state of charge update model, a battery health state update model, a temperature prediction model, and a voltage prediction model. The step of estimating the predicted state vector at the current moment based on the nonlinear state transition function, according to the system state at the previous moment and the multimodal operation data at the current moment, includes: Based on the battery state of charge update model, the battery state of charge at the previous moment, the current at the current moment, and the temperature are used to dynamically correct the state of charge, so as to obtain the predicted state of charge at the current moment. Based on the battery health status update model, the battery health status at the previous moment and the current, temperature and communication quality at the current moment are analyzed to obtain the predicted health status at the current moment. Based on the temperature prediction model, the temperature at the previous moment and the current at the current moment are analyzed to obtain the predicted temperature at the current moment. Based on the voltage prediction model, the open-circuit voltage of the state of charge at the previous moment and the battery internal resistance at the current moment are analyzed to obtain the predicted voltage at the current moment. The predicted state vector is constructed based on the predicted state of charge, the predicted health state, the predicted temperature, and the predicted voltage.

4. The method for dynamic evaluation of the operating status of an energy storage system as described in claim 3, characterized in that, The historical operating trend includes the state, the rate of state evolution, and the direction; Before fusing the estimated operating status with historical operating trends to construct a multidimensional status assessment index set, the method further includes: For each state dimension, construct its evolution trend function; The state, state evolution rate, and direction in different dimensions are determined based on the evolutionary trend function.

5. The method for dynamic evaluation of the operating status of an energy storage system as described in claim 4, characterized in that, The weighted fusion model includes a hierarchical weighted model; The weighted analysis of the multidimensional state assessment index set based on the weighted fusion model to predict the predicted state of the energy storage system in the next operating cycle includes: The multidimensional state assessment index set is input into the hierarchical weighted model for hierarchical weight analysis to obtain the predicted state of the energy storage system in the next operating cycle.

6. The method for dynamic evaluation of the operating status of an energy storage system as described in claim 5, characterized in that, The step of inputting the multidimensional state assessment index set into the hierarchical weighted model for hierarchical weight analysis to obtain the predicted state of the energy storage system in the next operating cycle includes: The multidimensional state assessment index set is input into the hierarchical weighted model. In the hierarchical weighted model, the multidimensional state assessment index is functionally classified, and each functional category index is further subdivided into several specific state indices. Weights are assigned to each functional category index and each specific state index. The functional category indexes and specific state indices of each dimension are weighted and fused based on their respective weights to predict the predicted state of the energy storage system in the next operating cycle.

7. A dynamic evaluation device for the operating status of an energy storage system, characterized in that, include: The acquisition module is used to collect multimodal operating data of the energy storage system; The first analysis module is used to input the multimodal operating data into an extended Kalman filter model, and to perform iterative processing on the multimodal operating data based on a nonlinear state transition function in the extended Kalman filter model to obtain the predicted state vector at the current time. The predicted state vector is then mapped to the observation space using an observation function to obtain the predicted observation vector. The residual between the predicted observation vector and the actual acquired multimodal observation data is calculated, and the predicted state vector is updated and corrected based on the Kalman gain matrix to obtain the estimated operating state value at the current time. The fusion module is used to fuse the estimated operating status with historical operating trends to construct a multi-dimensional set of status assessment indicators. The second analysis module is used to perform weighted analysis on the multidimensional state assessment index set based on the weighted fusion model, and predict the predicted state of the energy storage system in the next operating cycle.

8. A dynamic evaluation device for the operating status of an energy storage system, characterized in that, include: Processor, memory, and computer programs stored in said memory and executable on said processor; When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.

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