Method and device for dynamically evaluating running state of energy storage system and storage medium

Abstract

The invention relates to an energy storage system operation state dynamic evaluation method and device and a storage medium, and the method comprises the steps: carrying out the analysis of collected multi-mode operation data of an energy storage system based on a dynamic state space model, obtaining an operation state estimation value of the energy storage system at the current moment, fusing the operation state estimation value with a historical operation trend, and obtaining an operation state estimation value of the energy storage system; constructing a multi-dimensional state evaluation index set; and performing weighted analysis on the multi-dimensional state evaluation index set based on the weight fusion model, and predicting the prediction state of the energy storage system in the next operation cycle. The objective of the invention is to improve the accuracy of energy storage system operation state evaluation in a complex operation environment.

Description

Technical Field

[0001] The present 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 dynamically evaluating the operating status of an energy storage system. Background Art

[0002] Energy storage systems are currently widely used in key scenarios such as renewable energy generation, power peak regulation, microgrids, and data centers. Stable system operation is crucial for ensuring energy supply security. Traditional energy storage systems often use wired BMSs for operating status monitoring and assessment. While these systems offer advantages in data transmission stability, they suffer from cumbersome wiring, poor scalability, and high maintenance costs. Against this backdrop, wireless BMSs are becoming a growing trend due to their flexible deployment and ease of maintenance.

[0003] However, in wireless BMS systems, due to the influence of factors such as wireless channel interference, data packet loss, and transmission delay, the status data obtained by the system is incomplete, discontinuous, and noise-contaminated, resulting in unstable and inaccurate operating status assessment results based on the original data, which makes it difficult to meet the needs of system status safety assessment in actual complex operating environments. Summary of the Invention

[0004] In view of this, embodiments of the present application provide a method, device, and storage medium for dynamically evaluating the operating status of an energy storage system, aiming to improve the accuracy of evaluating the operating status of an energy storage system in a complex operating environment.

[0005] The present invention provides a method for dynamically evaluating the operating status of an energy storage system, including: Collect multi-modal operating data of the energy storage system; Analyzing the multimodal operating data based on a dynamic state space model to obtain an estimated operating state of the energy storage system at a current moment; Fusion of the estimated operating status values with historical operating trends to construct a multi-dimensional status assessment indicator set; A weighted analysis is performed on the multi-dimensional state evaluation index set based on a weight fusion model to predict the predicted state of the energy storage system in the next operation cycle.

[0006] In one embodiment, the dynamic state space model includes an extended Kalman filter model, and the analysis of the multimodal operating data based on the dynamic state space model to obtain an estimated value of the operating state of the energy storage system at a current moment includes: Inputting the multimodal operation data into the extended Kalman filter model, and performing cyclic iterative processing on the multimodal operation data based on a nonlinear state transfer function in the extended Kalman filter model to obtain a predicted state vector at the current moment; Use the observation function to map the predicted state vector to the observation space to obtain the predicted observation vector; The residuals of the predicted observation vector and the actually collected multimodal observation data are calculated, and the predicted state vector is updated and corrected based on the Kalman gain matrix to obtain the estimated value of the operating state at the current moment.

[0007] In one embodiment, the performing cyclic iterative processing on the multimodal operation data based on the nonlinear state transfer function to obtain the predicted state vector at the current moment includes: Based on the nonlinear state transfer 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.

[0008] In one embodiment, the nonlinear state transfer 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 estimating the predicted state vector at the current moment based on the nonlinear state transfer function according to the system state at the previous moment and the multimodal operation data at the current moment includes: Dynamically correcting the battery state of charge at a previous moment, the current and the temperature based on the battery state of charge update model to obtain a predicted state of charge at the current moment; Analyzing the battery health status at a previous moment and the current, temperature, and communication quality at a current moment based on the battery health status update model to obtain a predicted health status at a current moment; Analyzing the temperature at a previous moment and the current at a current moment based on the temperature prediction model to obtain a predicted temperature at the current moment; Analyzing the state-of-charge open-circuit voltage at a previous moment and the battery internal resistance at a current moment based on the voltage prediction model to obtain a predicted voltage at the current moment; The predicted state vector is constructed based on the predicted state of charge, the predicted state of health, the predicted temperature, and the predicted voltage.

[0009] In one embodiment, the historical operating trend includes a state, a state evolution rate, and a direction; and before fusing the estimated operating state value with the historical operating trend to construct a multi-dimensional state assessment indicator set, the process further includes: For each state dimension, an evolution trend function is constructed; and based on the evolution trend function, the states, state evolution rates and directions of the different dimensions are determined.

[0010] In one embodiment, the weight fusion model includes a hierarchical weighted model; The weighted analysis of the multi-dimensional state evaluation indicator set based on the weight fusion model to predict the predicted state of the energy storage system in the next operation cycle includes: The multi-dimensional state evaluation index set is input into the hierarchical weighted model to perform hierarchical weight analysis to obtain the predicted state of the energy storage system in the next operation cycle.

[0011] In one embodiment, inputting the multi-dimensional state assessment indicator set into the hierarchical weighted model for hierarchical weight analysis to obtain a predicted state of the energy storage system in the next operating cycle includes: The multidimensional state evaluation indicator set is input into the hierarchical weighted model, the multidimensional state evaluation indicators are functionally classified in the hierarchical weighted model, and each functional category indicator is subdivided into a number of specific state indicators. Weights are assigned to each functional category indicator and each specific state indicator respectively, and the functional category indicators and specific state indicators of each dimension are weightedly fused and predicted based on their respective corresponding weights to obtain the predicted state of the energy storage system in the next operating cycle.

[0012] A second aspect of an embodiment of the present application provides a device for dynamically evaluating the operating status of an energy storage system, comprising: Acquisition module, used to collect multi-modal operation data of the energy storage system; a first analysis module, configured to analyze the multimodal operation data based on a dynamic state space model to obtain an estimated value of the operation state of the energy storage system at a current moment; A fusion module is used to fuse the estimated operating status value with the historical operating trend to construct a multi-dimensional status evaluation indicator set; The second analysis module is used to perform weighted analysis on the multi-dimensional state evaluation index set based on a weight fusion model to predict the predicted state of the energy storage system in the next operation cycle.

[0013] In one embodiment, the dynamic state space model includes an extended Kalman filter model, and the first analysis module includes: a processing unit, configured to input the multimodal operation data into the extended Kalman filter model, and perform cyclic iterative processing on the multimodal operation data based on a nonlinear state transfer function in the extended Kalman filter model to obtain a predicted state vector at a current moment; A mapping unit, configured to map the predicted state vector to the observation space using the observation function to obtain a predicted observation vector; The acquisition unit is used to calculate the residual between the predicted observation vector and the actually collected multimodal observation data, and to update and correct the predicted state vector based on the Kalman gain matrix to obtain the estimated value of the operating state at the current moment.

[0014] In one embodiment, the processing unit is specifically configured to: Based on the nonlinear state transfer 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.

[0015] In one embodiment, the nonlinear state transfer 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 processing unit is specifically configured to: Dynamically correcting the battery state of charge at a previous moment, the current and the temperature based on the battery state of charge update model to obtain a predicted state of charge at the current moment; Analyzing the battery health status at a previous moment and the current, temperature, and communication quality at a current moment based on the battery health status update model to obtain a predicted health status at a current moment; Analyzing the temperature at a previous moment and the current at a current moment based on the temperature prediction model to obtain a predicted temperature at the current moment; Analyzing the state-of-charge open-circuit voltage at a previous moment and the battery internal resistance at a current moment based on the voltage prediction model to obtain a predicted voltage at the current moment; The predicted state vector is constructed based on the predicted state of charge, the predicted state of health, the predicted temperature, and the predicted voltage.

[0016] In one embodiment, the historical operating trend includes a state, a state evolution rate, and a direction; the apparatus further includes a determination module, the determination module being configured to: For each state dimension, an evolution trend function is constructed; and based on the evolution trend function, the states, state evolution rates and directions of the different dimensions are determined.

[0017] In one embodiment, the weight fusion model includes a hierarchical weighted model; The second analysis module is specifically used to: The multi-dimensional state evaluation index set is input into the hierarchical weighted model to perform hierarchical weight analysis to obtain the predicted state of the energy storage system in the next operation cycle.

[0018] In one embodiment, the second analysis module is specifically configured to: The multidimensional state evaluation indicator set is input into the hierarchical weighted model, the multidimensional state evaluation indicators are functionally classified in the hierarchical weighted model, and each functional category indicator is subdivided into a number of specific state indicators. Weights are assigned to each functional category indicator and each specific state indicator respectively, and the functional category indicators and specific state indicators of each dimension are weightedly fused and predicted based on their respective corresponding weights to obtain the predicted state of the energy storage system in the next operating cycle.

[0019] A third aspect of an embodiment of the present application provides a device for dynamically evaluating 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; when the processor executes the computer program, the steps of the method described in the first aspect above are implemented.

[0020] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method described in the first aspect above are implemented.

[0021] The beneficial effects of the embodiments of this application include analyzing the collected multimodal operating data of the energy storage system based on a dynamic state space model to obtain an estimated operating state of the energy storage system at the current moment. This estimated operating state is then integrated with historical operating trends to construct a multidimensional state assessment indicator set. A weighted analysis of this multidimensional state assessment indicator set is then performed based on a weighted fusion model to predict the energy storage system's state for the next operating cycle. This approach aims to improve the accuracy of energy storage system operating state assessments in complex operating environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0023] Figure 1 A schematic diagram of the implementation flow of a method for dynamically evaluating the operating status of an energy storage system provided in one embodiment of the present application; Figure 2 for Figure 1 Schematic diagram of the specific implementation process of S120; Figure 3 A schematic diagram of a device for dynamically evaluating the operating status of an energy storage system provided in one embodiment of the present application; Figure 4 A schematic diagram of a device for dynamically evaluating the operating status of an energy storage system provided in one embodiment of the present application. DETAILED DESCRIPTION

[0024] The following embodiments of the technical solution of the present application will be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application and are therefore only examples and are not intended to limit the scope of protection of the present application.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0026] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0027] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0028] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0029] In the description of the embodiments of the present application, the term "multi-frame" refers to two or more (including two).

[0030] In the description of the embodiments of the present application, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the embodiments of the present application.

[0031] This embodiment of the application provides a method for dynamically assessing the operating status of an energy storage system. This method analyzes collected multimodal operating data of the energy storage system based on a dynamic state space model to obtain an estimated operating status of the energy storage system at the current moment. This estimated operating status is then fused with historical operating trends to construct a multidimensional state assessment indicator set. This multidimensional state assessment indicator set is then weighted and analyzed using a weighted fusion model to predict the energy storage system's state for the next operating cycle. This method aims to improve the accuracy of energy storage system operating status assessments in complex operating environments.

[0032] See also Figure 1 As shown, Figure 1 This is a schematic diagram of the implementation flow of a method for dynamically evaluating the operating status of an energy storage system provided in one embodiment of the present application. Figure 1 It can be seen that the method for dynamically evaluating the operating status of an energy storage system provided in the embodiment of the present application includes steps S110 to S140. The details are as follows: S110: Collect multi-modal operation data of the energy storage system.

[0033] Multimodal operating data is collected from multiple sources of heterogeneous data within the energy storage system. In this embodiment, this multimodal operating 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 number of retransmissions; environmental parameters such as ambient temperature, humidity, and vibration intensity; and system operating information such as charge and discharge strategies. This multimodal operating data can provide comprehensive information reflecting the state of the energy storage system, serving as an input foundation for subsequent state estimation.

[0034] S120: Analyze the multimodal operation data based on the dynamic state space model to obtain an estimated value of the operation state of the energy storage system at the current moment.

[0035] In this embodiment, the dynamic state space model includes an extended Kalman filter model, and the modal operation data is analyzed by the extended Kalman filter model to perform nonlinear state estimation on the energy storage system.

[0036] For example, see Figure 2, Figure 2 for Figure 1 Schematic diagram of the specific implementation process of S120. Figure 2 It can be seen that in this embodiment, S120 includes S121 to S123, which are described in detail as follows: S121: Input the multimodal operation data into the extended Kalman filter model, perform cyclic iterative processing on the multimodal operation data based on the nonlinear state transfer function in the extended Kalman filter model, and obtain the predicted state vector at the current moment.

[0037] Specifically, the multimodal operation data is iteratively processed based on the nonlinear state transfer function to obtain the predicted state vector at the current moment, including: estimating the predicted state vector at the current moment based on the nonlinear state transfer function according to the system state at the previous moment and the multimodal operation data at the current moment.

[0038] In one embodiment, the nonlinear state transfer function includes: a battery state of charge update model, a battery health state update model, a temperature prediction model, and a voltage prediction model; Based on the nonlinear state transfer 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, including: dynamically correcting the battery state of charge at the previous moment, the current and the temperature at the current moment based on the battery state of charge update model to obtain the predicted state of charge at the current moment; analyzing the battery state of health at the previous moment and the current, temperature and communication quality at the current moment based on the battery health state update model to obtain the predicted health state 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; analyzing the state of charge open circuit voltage 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; and constructing a predicted state vector based on the predicted state of charge, predicted health state, predicted temperature and predicted voltage.

[0039] Among them, the battery state of charge update model is expressed as: ; Indicates the battery charge status at the previous moment. Indicates the current at the current moment (positive for discharge, negative for charge), C indicates the rated capacity of the battery, represents the sampling time interval, represents the temperature correction factor, Indicates the current temperature. Indicates the reference temperature; Indicates the predicted state of charge.

[0040] The battery health status update model is expressed as: ; Indicates the battery health status at the previous moment. Indicates the communication quality, , , They represent the degradation factor and the sensitivity to different impact quantities.

[0041] The temperature prediction model is expressed as: ; Indicates the current temperature. Indicates the temperature at the last moment. Battery internal resistance, Temperature rise coefficient, which represents the heating effect of electric current.

[0042] The voltage prediction model is expressed as: ; Indicates the current predicted voltage; Represents the open circuit voltage, a nonlinear function of the state of charge.

[0043] S122: Map the predicted state vector to the observation space using the observation function to obtain a predicted observation vector.

[0044] For example, a nonlinear observation function is used to convert the predicted state vector into an observable output to obtain a predicted observation vector. Specifically, it can be expressed as: ; represents the predicted observation vector, represents the predicted state vector, h( ) represents the nonlinear observation mapping function, represents the observation noise, which obeys Gaussian distribution.

[0045] S123: Calculate the residual between the predicted observation vector and the actually collected multimodal observation data, and update and correct the predicted state vector based on the Kalman gain matrix to obtain the estimated value of the operating state at the current moment.

[0046] Specifically, the residual calculation formula is expressed as: ;in, represents the residual calculation value, represents the actual collected observation value, represents the predicted observation value, represents modal data, represents the differentiation weight.

[0047] By fusing multi-source heterogeneous data and adopting a differentiated weight strategy for residual calculation, a multimodal residual fusion strategy is implemented. Updates are made based on the Kalman gain adaptive mechanism, which can improve the filtering stability during abnormal fluctuations to obtain accurate operating status estimates.

[0048] S130: Fusion of the estimated operating status value and the historical operating trend to construct a multi-dimensional status evaluation indicator set.

[0049] Historical operating trends include states, state evolution rates, and directions. Before integrating the estimated operating state with historical operating trends to construct a multidimensional state assessment indicator set, the following steps are performed: constructing an evolution trend function for each state dimension; and determining the states, state evolution rates, and directions for different dimensions based on the evolution trend functions. Specifically, evolution trend functions for state dimensions include, but are not limited to, sliding window functions, exponential smoothing functions, or adaptive filtering functions. Different types of evolution trend functions can be customized for different state dimensions to improve the flexibility and accuracy of historical operating trend forecasting.

[0050] S140: Perform weighted analysis on the multi-dimensional state evaluation indicator set based on the weight fusion model to predict the predicted state of the energy storage system in the next operation cycle.

[0051] The weight fusion model includes a hierarchical weighted model; based on the weight fusion model, a weighted analysis is performed on the multidimensional state evaluation indicator set to predict the predicted state of the energy storage system in the next operating cycle, including: inputting the multidimensional state evaluation indicator 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.

[0052] The multidimensional state evaluation indicator 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 operation cycle, including: inputting the multidimensional state evaluation indicator set into the hierarchical weighted model, functionally classifying the multidimensional state evaluation indicators in the hierarchical weighted model, and subdividing each functional category indicator into several specific state indicators, assigning weights to each functional category indicator and each specific state indicator respectively, and performing weighted fusion prediction on the functional category indicators and specific state indicators of each dimension based on their respective corresponding weights to obtain the predicted state of the energy storage system in the next operation cycle.

[0053] 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 and depth of discharge. A two-layer weighted fusion prediction of functional category indicators and specific status indicators in each dimension based on their respective weights not only improves prediction accuracy but also transforms complex, multi-dimensional, and fuzzy input results into standardized, executable, and logically layered decision-making basis.

[0054] From the above analysis, it can be seen that the embodiments of the present application construct a wireless communication network with each battery cell corresponding to a communication node; periodically send a global time synchronization signal to achieve data synchronization between each communication node; further, after real-time monitoring of the battery voltage status, communication signal quality, and remaining power corresponding to each communication node, analyze the battery voltage status, communication signal quality, and remaining power based on a weighted scoring method, and dynamically schedule each communication node to improve the scheduling efficiency of each communication node. This aims to solve the problems of difficult communication node data synchronization and low scheduling efficiency in the prior art.

[0055] See Figure 3 , Figure 3 Schematic diagram of a device for dynamically evaluating the operating status of an energy storage system provided in one embodiment of the present application. The device for dynamically evaluating the operating status of an energy storage system includes modules or units for executing Figures 1 to 2 Each step in the corresponding embodiment. Please refer to Figures 1 to 2 For the convenience of explanation, only the parts related to this embodiment are shown. Figure 3 The energy storage system operation status dynamic evaluation device 300 includes: The acquisition module 310 is used to collect multi-modal operation data of the energy storage system; A first analysis module 320 is configured to analyze the multimodal operation data based on a dynamic state space model to obtain an estimated value of the operation state of the energy storage system at a current moment; A fusion module 330 is configured to fuse the estimated operating status value with the historical operating trend to construct a multi-dimensional status assessment indicator set; The second analysis module 340 is configured to perform a weighted analysis on the multi-dimensional state evaluation indicator set based on a weight fusion model to predict the predicted state of the energy storage system in the next operation cycle.

[0056] In one embodiment, the dynamic state space model includes an extended Kalman filter model, and the first analysis module 320 includes: a processing unit, configured to input the multimodal operation data into the extended Kalman filter model, and perform cyclic iterative processing on the multimodal operation data based on a nonlinear state transfer function in the extended Kalman filter model to obtain a predicted state vector at a current moment; A mapping unit, configured to map the predicted state vector to the observation space using the observation function to obtain a predicted observation vector; The acquisition unit is used to calculate the residual between the predicted observation vector and the actually collected multimodal observation data, and to update and correct the predicted state vector based on the Kalman gain matrix to obtain the estimated value of the operating state at the current moment.

[0057] In one embodiment, the processing unit is specifically configured to: Based on the nonlinear state transfer 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.

[0058] In one embodiment, the nonlinear state transfer 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 processing unit is specifically configured to: Dynamically correcting the battery state of charge at a previous moment, the current and the temperature based on the battery state of charge update model to obtain a predicted state of charge at the current moment; Analyzing the battery health status at a previous moment and the current, temperature, and communication quality at a current moment based on the battery health status update model to obtain a predicted health status at a current moment; Analyzing the temperature at a previous moment and the current at a current moment based on the temperature prediction model to obtain a predicted temperature at the current moment; Analyzing the state-of-charge open-circuit voltage at a previous moment and the battery internal resistance at a current moment based on the voltage prediction model to obtain a predicted voltage at the current moment; The predicted state vector is constructed based on the predicted state of charge, the predicted state of health, the predicted temperature, and the predicted voltage.

[0059] In one embodiment, the historical operating trend includes a state, a state evolution rate, and a direction; the apparatus further includes a determination module, the determination module being configured to: For each state dimension, an evolution trend function is constructed; and based on the evolution trend function, the states, state evolution rates and directions of the different dimensions are determined.

[0060] In one embodiment, the weight fusion model includes a hierarchical weighted model; The second analysis module 340 is specifically configured to: The multi-dimensional state evaluation index set is input into the hierarchical weighted model to perform hierarchical weight analysis to obtain the predicted state of the energy storage system in the next operation cycle.

[0061] In one embodiment, the second analysis module 340 is specifically configured to: The multidimensional state evaluation indicator set is input into the hierarchical weighted model, the multidimensional state evaluation indicators are functionally classified in the hierarchical weighted model, and each functional category indicator is subdivided into a number of specific state indicators. Weights are assigned to each functional category indicator and each specific state indicator respectively, and the functional category indicators and specific state indicators of each dimension are weightedly fused and predicted based on their respective corresponding weights to obtain the predicted state of the energy storage system in the next operating cycle.

[0062] See Figure 4 , Figure 4 A schematic diagram of a device for dynamically evaluating the operating status of an energy storage system provided in one embodiment of the present application. Figure 4 It can be seen that the energy storage system operating status dynamic evaluation device 400 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, the steps in the above-mentioned embodiments of the energy storage system operating status dynamic evaluation management method are implemented, such as Figure 1 Alternatively, when the processor 410 executes the computer program 430, the functions of the modules / units in the above-mentioned device embodiments are realized, for example Figure 3 Functions of modules 310 to 340 are shown.

[0063] Exemplarily, computer program 430 may be divided into one or more modules / units, one or more of which are stored in memory 420 and executed by processor 410 to implement the present application. One or more modules / units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of computer program 430 in the energy storage system's operating status dynamic assessment device. For example, computer program 430 may be divided into an acquisition module, a first analysis module, a fusion module, and a second analysis module.

[0064] The energy storage system operating status dynamic evaluation device provided in this embodiment may include, but is not limited to, a processor and a memory. It will be understood by those skilled in the art that Figure 4 The device for dynamically evaluating the operating status of an energy storage system is merely an example and does not constitute a limitation on the device for dynamically evaluating the operating status of an energy storage system. The device may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the device for dynamically evaluating the operating status of an energy storage system may also include input and output devices, network access devices, buses, etc.

[0065] The processor 410 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), 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.

[0066] Memory 420 can be an internal storage unit of the energy storage system's dynamic operating status assessment device, such as a hard drive or memory. Memory 420 can also be an external storage device of the energy storage system's dynamic operating status assessment device, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the energy storage system's dynamic operating status assessment device can include both an internal storage unit and an external storage device. Memory 420 is used to store computer programs and other programs and data required by the energy storage system's dynamic operating status assessment device. Memory 420 can also be used to temporarily store data that has been output or is about to be output.

[0067] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0068] An embodiment of the present 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 implements the steps of any of the above-mentioned method embodiments when executing the computer program.

[0069] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned various method embodiments can be implemented.

[0070] An embodiment of the present application provides a computer program product. When the computer program product is run on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned various method embodiments when executing the computer program product.

[0071] If the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a camera / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, removable hard drives, magnetic disks, or optical disks. In some jurisdictions, based on legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunication signals.

[0072] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0073] Those skilled in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel 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.

[0074] In the embodiments provided in this application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0075] The units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0076] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for dynamically evaluating the operating status of an energy storage system, characterized in that: include: Collect multi-modal operating data of the energy storage system; Analyzing the multimodal operating data based on a dynamic state space model to obtain an estimated operating state of the energy storage system at a current moment; Fusion of the estimated operating status values with historical operating trends to construct a multi-dimensional status assessment indicator set; A weighted analysis is performed on the multi-dimensional state evaluation index set based on a weight fusion model to predict the predicted state of the energy storage system in the next operation cycle.

2. The method for dynamically evaluating the operating status of an energy storage system according to claim 1, wherein: The dynamic state space model includes an extended Kalman filter model. The multimodal operating data is analyzed based on the dynamic state space model to obtain an estimated operating state value of the energy storage system at a current moment, including: Inputting the multimodal operation data into the extended Kalman filter model, and performing cyclic iterative processing on the multimodal operation data based on a nonlinear state transfer function in the extended Kalman filter model to obtain a predicted state vector at the current moment; Use the observation function to map the predicted state vector to the observation space to obtain the predicted observation vector; The residuals of the predicted observation vector and the actually collected multimodal observation data are calculated, and the predicted state vector is updated and corrected based on the Kalman gain matrix to obtain the estimated value of the operating state at the current moment.

3. The method for dynamically evaluating the operating status of an energy storage system according to claim 2, wherein: The multimodal operation data is subjected to cyclic iterative processing based on the nonlinear state transfer function to obtain a predicted state vector at the current moment, including: Based on the nonlinear state transfer 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.

4. The method for dynamically evaluating the operating status of an energy storage system according to claim 3, wherein: The nonlinear state transfer 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 estimating the predicted state vector at the current moment based on the nonlinear state transfer function according to the system state at the previous moment and the multimodal operation data at the current moment includes: Dynamically correcting the battery state of charge at a previous moment, the current and the temperature based on the battery state of charge update model to obtain a predicted state of charge at the current moment; Analyzing the battery health status at a previous moment and the current, temperature, and communication quality at a current moment based on the battery health status update model to obtain a predicted health status at a current moment; Analyzing the temperature at a previous moment and the current at a current moment based on the temperature prediction model to obtain a predicted temperature at the current moment; Analyzing the state-of-charge open-circuit voltage at a previous moment and the battery internal resistance at a current moment based on the voltage prediction model to obtain a predicted voltage at the current moment; The predicted state vector is constructed based on the predicted state of charge, the predicted state of health, the predicted temperature, and the predicted voltage.

5. The method for dynamically evaluating the operating status of an energy storage system according to claim 3, wherein: The historical operating trend includes state, state evolution rate and direction; Before fusing the estimated operating status value with the historical operating trend to construct a multi-dimensional status evaluation indicator set, the method further includes: For each state dimension, construct its evolution trend function; The states, state evolution rates and directions of the different dimensions are determined based on the evolution trend function.

6. The method for dynamically evaluating the operating status of an energy storage system according to claim 5, wherein: The weight fusion model includes a hierarchical weighted model; The weighted analysis of the multi-dimensional state evaluation indicator set based on the weight fusion model to predict the predicted state of the energy storage system in the next operation cycle includes: The multi-dimensional state evaluation index set is input into the hierarchical weighted model to perform hierarchical weight analysis to obtain the predicted state of the energy storage system in the next operation cycle.

7. The method for dynamically evaluating the operating status of an energy storage system according to claim 6, characterized in that: Inputting the multi-dimensional state evaluation indicator set into the hierarchical weighted model to perform hierarchical weight analysis to obtain a predicted state of the energy storage system in the next operation cycle includes: The multidimensional state evaluation indicator set is input into the hierarchical weighted model, the multidimensional state evaluation indicators are functionally classified in the hierarchical weighted model, and each functional category indicator is subdivided into a number of specific state indicators. Weights are assigned to each functional category indicator and each specific state indicator respectively, and the functional category indicators and specific state indicators of each dimension are weightedly fused and predicted based on their respective corresponding weights to obtain the predicted state of the energy storage system in the next operating cycle.

8. A device for dynamically evaluating the operating status of an energy storage system, characterized in that: include: Acquisition module, used to collect multi-modal operation data of the energy storage system; a first analysis module, configured to analyze the multimodal operation data based on a dynamic state space model to obtain an estimated value of the operation state of the energy storage system at a current moment; A fusion module is used to fuse the estimated operating status value with the historical operating trend to construct a multi-dimensional status evaluation indicator set; The second analysis module is used to perform weighted analysis on the multi-dimensional state evaluation index set based on a weight fusion model to predict the predicted state of the energy storage system in the next operation cycle.

9. A device for dynamically evaluating the operating status of an energy storage system, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor; When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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