Communication and optimization method and system of energy storage system

Abstract

The invention discloses a communication and optimization method and system of an energy storage system, and relates to the technical field of communication. Real-time state parameters of the battery pack are obtained through wireless communication, the state parameters are predicted through time sequence analysis and a data compensation mechanism, the problems of numerous battery packs, communication time delay and packet loss are solved, the real-time performance and accuracy of data are ensured, and the real-time performance of the battery pack is improved by combining a digital twinborn model. The system can optimize a control strategy based on battery aging evaluation and simulation analysis, realizes dynamic adjustment and refined energy management, adopts a differential privacy technology to encrypt and transmit data, ensures privacy protection of the control strategy, and ensures the safety of the control strategy through real-time monitoring and self-adaptive adjustment mechanism and minimization of the difference between a digital twin model and an actual system. The stability, efficiency and safety of the energy storage system are further improved, and long-term reliable operation of the system is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a communication and optimization method and system for an energy storage system. Background Art

[0002] During the communication process, the energy storage system needs to monitor the status parameters of the battery pack in real time, such as voltage, current, temperature, etc., and adjust the system's operating strategy based on these parameters to ensure the safe and stable operation of the energy storage system. However, due to the large number of battery packs in the energy storage system and their wide distribution, there are delays and packet loss problems in the communication between the battery packs, making it difficult to ensure the real-time and accuracy of the monitoring data; at the same time, the aging and failure of the battery pack will also affect the communication quality, and thus affect the control decision of the energy storage system. Summary of the invention

[0003] The purpose of the present invention is to provide a communication and optimization method and system for an energy storage system, which realizes intelligent scheduling, data compensation and privacy protection of the energy storage system in a complex environment, and improves the stability, efficiency and safety of the system.

[0004] The purpose of the present invention can be achieved through the following technical solutions: The present application provides a communication and optimization method for an energy storage system, comprising the following steps: The state parameters of the battery pack in the energy storage system, including voltage, current and temperature data, are obtained through wireless communication, and then the trend characteristics of parameter changes are extracted using time series analysis methods; Based on the extracted features, determine whether there is communication delay or data packet loss. If there is communication anomaly, start the data compensation mechanism and use historical data to predict the current state parameters. Based on the acquired state parameters and prediction data, the digital twin model of the energy storage system is constructed by combining the geometric, physical, behavioral and rule models of the digital twin. The support vector machine algorithm is used to input the historical operation data of the battery pack and output the aging assessment results. According to the aging assessment results, the parameter settings of the digital twin model are adjusted, and the optimized control strategy parameters are obtained through model simulation. The optimized control strategy parameters are transmitted to the actual energy storage system, and the transmitted data is encrypted using differential privacy technology.

[0005] Furthermore, the state parameters of the battery pack in the energy storage system, including voltage, current and temperature data, are obtained through wireless communication, and then the time series analysis method is used to extract the parameter change trend characteristics, including: A communication link is established with the energy storage system through a wireless communication module, and voltage, current and temperature state parameters of the battery pack are periodically acquired. The acquired state parameter data are stored in chronological order to form a time series data set; Preprocess the time series data to improve data quality, and use the time series analysis algorithm to model the preprocessed data to extract the change trend characteristics of voltage, current, and temperature parameters; The health status and remaining life of the battery pack are judged based on the extracted change trend characteristics. When an abnormal trend is found, the early warning mechanism is triggered. Combined with the status parameters and change trends of the battery pack, the charging and discharging strategy of the energy storage system is dynamically adjusted.

[0006] Furthermore, based on the extracted features, it is determined whether there is communication delay or data packet loss. When there is communication anomaly, the data compensation mechanism is activated and the current state parameters are predicted using historical data, including: By monitoring the network communication status in real time, key indicators are obtained to determine whether there is a communication anomaly. When a communication anomaly is detected, the data compensation mechanism is triggered to estimate the current state parameters based on the pre-established prediction model using historical data; When predicting state parameters, we use a variety of machine learning algorithms to improve prediction accuracy through ensemble learning. For different types of state parameters, we select appropriate historical data features as input and dynamically adjust the hyperparameters of the prediction model to adapt to different data distributions and change trends. The predicted state parameters are integrated with the actual received data, and the state estimation value is obtained through weighted average or Bayesian estimation method; During the data fusion process, the weights of the predicted and measured values ​​are dynamically adjusted according to the severity and duration of the communication anomaly to smooth data fluctuations and reduce the impact of the anomaly. The compensated state parameters are then tested for anomalies, and when the abnormal deviation is found to exceed the preset threshold, the alarm mechanism is triggered.

[0007] Furthermore, based on the acquired state parameters and prediction data, combined with the geometric, physical, behavioral and rule models of the digital twin, a digital twin model of the energy storage system is constructed, including: Obtain the real-time status parameters and prediction data of the energy storage system, establish a three-dimensional geometric model based on the physical structure and spatial layout of the energy storage system, and characterize the appearance and internal structure of the system; Use equivalent circuit models or thermal models to describe the physical characteristics of the energy storage system, and then model the behavior mode and state transition law of the energy storage system under different working conditions; By using the business rule engine, the grid dispatching rules and energy management strategies are embedded into the digital twin model for global optimization. Multi-source heterogeneous data are integrated with various models to build a mapping of the energy storage system from the physical world to the digital space, forming a real-time updated digital twin model.

[0008] Furthermore, through model simulation, the optimized control strategy parameters are obtained, including: Obtain the historical operating data of the battery pack, pre-process the data, remove outliers and noise data, obtain a standardized data set, use the support vector machine algorithm to train the standardized historical operating data of the battery pack, establish a battery aging assessment model, and determine the optimal model parameters through cross-validation and grid search; Use the trained battery aging assessment model to predict new battery pack operating data, obtain the battery pack aging assessment results, determine the health status and remaining life of the battery pack, and dynamically adjust the parameter settings of the digital twin model based on the battery pack aging assessment results; Use the adjusted digital twin model to conduct multi-scenario and multi-operating condition simulation analysis on the battery pack to obtain the performance data and degradation trend of the battery pack under different conditions; Optimize the charge and discharge control strategy of the battery pack through simulation results and machine learning algorithms; The optimized control strategy parameters are applied to the actual battery pack management system to perform adaptive control and energy management on the battery pack, and to perform online monitoring and real-time optimization of the battery pack performance.

[0009] Furthermore, the optimized control strategy parameters are transmitted to the actual energy storage system, and the transmission data is encrypted using differential privacy technology, including: According to the optimized control strategy parameters, differential privacy technology is used to encrypt data, and random noise is added to protect parameter privacy. The encrypted control strategy parameters are transmitted to the control module of the actual energy storage system through a secure communication protocol. After receiving the encrypted control strategy parameters, the control module of the energy storage system uses the corresponding decryption algorithm to decrypt them and obtain the optimized control strategy parameters; The control module monitors and analyzes the operating status of the energy storage system in real time based on the decrypted control strategy parameters to determine whether the control strategy needs to be adjusted; When the control strategy needs to be adjusted, the control module generates corresponding control instructions based on the optimized control strategy parameters and sends them to the actuators of the energy storage system. The actuators then dynamically adjust the parameters of the energy storage equipment based on the received control instructions to optimize the control of the energy storage system.

[0010] Furthermore, after encrypting the transmitted data, it also includes: real-time monitoring of the system operation status of the control strategy deployed in the actual system, judging the degree of difference between the digital twin model and the actual system, and when the difference exceeds a preset threshold, triggering the model parameter adaptive adjustment mechanism to update the digital twin model parameters.

[0011] Furthermore, the system operation status of the control strategy deployed in the actual system is monitored in real time to determine the degree of difference between the digital twin model and the actual system. When the difference exceeds the preset threshold, the model parameter adaptive adjustment mechanism is triggered to update the digital twin model parameters, including: Obtain the actual system operation data, including the execution of the control strategy, system status parameters, etc., as the input for real-time monitoring, input the real-time monitoring data into the digital twin model, run the digital twin model, and obtain the model output results; Calculate the difference between the output of the digital twin model and the actual system operation data, use indicators such as mean square error and relative error to measure the degree of difference, and determine whether the degree of difference exceeds the preset threshold. If it exceeds the threshold, the model parameter adaptive adjustment mechanism is triggered. If it does not exceed the threshold, real-time monitoring continues; After the model parameter adaptive adjustment mechanism is triggered, optimization algorithms such as gradient descent algorithm and evolutionary algorithm are used to adjust the parameters of the digital twin model to minimize the difference between the model output and the actual system operation data; The adjusted model parameters are updated to the digital twin model to obtain an updated digital twin model so that it is synchronized with the actual system.

[0012] Furthermore, after updating the parameters of the digital twin model, it also includes: continuously collecting battery pack status parameters and communication quality indicators, using a random forest algorithm to establish a fault prediction model, and dynamically adjusting the simulation accuracy and computing resource allocation of the digital twin model according to the fault prediction results; through cyclic iterative optimization, the digital twin model is continuously synchronized with the actual system.

[0013] The present invention provides a communication and optimization system for an energy storage system, which is used to implement a communication and optimization method for an energy storage system, including: The data acquisition and preprocessing module establishes a communication link with the energy storage system through the wireless communication module, periodically obtains the state parameters of the battery pack, and stores them in chronological order to form a time series data set; The communication anomaly detection module uses a time series analysis algorithm to model the preprocessed data, extract the trend characteristics of voltage, current and temperature parameters, and then judge the health status and remaining life of the battery pack based on the characteristics. At the same time, it monitors key indicators to determine whether there is a communication anomaly. When an anomaly is detected, the data compensation mechanism is triggered to use historical data and machine learning algorithms to predict and compensate for the current state parameters. The model building and optimization module combines the geometric, physical, behavioral and rule models of digital twins to build a digital twin model of the energy storage system based on the acquired state parameters and predicted data. By clarifying the hierarchical relationship and assembly order of the model and adding spatial constraints, the model is assembled from parts to components to equipment. Multi-source heterogeneous data is integrated with various models, and simulation states and measurement data are modeled using random finite sets. The real-time measurement data is integrated during the simulation operation through the Bayesian reasoning method to dynamically correct the model. The evaluation and control strategy optimization module uses the support vector machine algorithm to input the historical operation data of the battery pack and output the aging evaluation results. According to the aging evaluation results, the parameter settings of the digital twin model are dynamically adjusted. The adjusted digital twin model is then used to perform simulation analysis of multiple scenarios and multiple working conditions to obtain the performance data and degradation trends of the battery pack under different conditions. Through the simulation results and machine learning algorithms, the charging and discharging control strategy of the battery pack is optimized to extend the battery pack life. The optimized control strategy parameters are applied to the actual battery pack management system to perform online monitoring and real-time optimization of the battery pack performance. The data encryption and transmission module uses differential privacy technology to encrypt data according to the optimized control strategy parameters, protects parameter privacy by adding random noise, and transmits the encrypted control strategy parameters to the control module of the actual energy storage system through a secure communication protocol. The control module decrypts the encrypted control strategy parameters after receiving them, and monitors and analyzes the operating status of the energy storage system in real time according to the decrypted parameters to determine whether the control strategy needs to be adjusted. If the control strategy needs to be adjusted, the energy storage system is optimized. The adaptive adjustment and fault prediction module monitors the system operation status of the control strategy deployed in the actual system in real time, determines the degree of difference between the digital twin model and the actual system, and triggers the model parameter adaptive adjustment mechanism when the difference exceeds the preset threshold. The optimization algorithm is used to adjust the parameters of the digital twin model to minimize the difference between the model output and the actual system operation data, and update the model parameters. At the same time, the battery pack status parameters and communication quality indicators are continuously collected, and the random forest algorithm is used to establish a fault prediction model to obtain fault prediction results. The simulation accuracy and computing resource allocation of the digital twin model are dynamically adjusted according to the fault prediction results. Through cyclic iterative optimization, the digital twin model and the actual system are continuously synchronized.

[0014] The beneficial effects of the present invention are: By introducing wireless communication, time series analysis, data compensation mechanism and digital twin technology, the problems of large number of battery packs, communication delay and packet loss in the energy storage system are solved, and the real-time and accuracy of monitoring data are improved. By real-time monitoring of battery pack status parameters such as voltage, current and temperature, time series analysis is used to extract change trend characteristics, identify communication anomalies and start data compensation mechanism, and use historical data for status prediction, thus ensuring the stable operation of the system and data accuracy in an unstable communication environment; Combining digital twin models and machine learning algorithms, by establishing a digital twin system including geometric, physical, behavioral and rule models, it is possible to adjust the control strategy according to the health status and remaining life of the battery pack, achieve more sophisticated energy management and dynamic regulation, evaluate the battery aging degree through the support vector machine algorithm, and optimize the charging and discharging strategy based on the simulation results, thereby improving the overall efficiency of the energy storage system and extending the battery life; The application of differential privacy technology solves the privacy protection problem of data transmission during the optimization of control strategies. The optimized control strategy parameters are encrypted and transmitted to the actual energy storage system through a secure communication protocol, which effectively prevents the leakage of sensitive data. On this basis, real-time monitoring and model adaptive adjustment mechanisms can be used to ensure the synchronization of the digital twin model with the actual system, further optimize the operating status of the energy storage system, and ensure its stability and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] For better understanding and implementation, the technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0016] Figure 1 A flow chart of a communication and optimization method for an energy storage system provided in Example 1 of the present application; Figure 2 A schematic diagram of a flow chart of extracting parameter change trend characteristics of a communication and optimization method for an energy storage system provided in Example 1 of the present application; Figure 3 A structural diagram of a communication and optimization system for an energy storage system provided in Example 2 of the present application. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects taken by the present invention to achieve the predetermined invention purpose, exemplary embodiments will be described in detail here, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are only examples of methods and systems consistent with some aspects of the present application as detailed in the attached claims.

[0018] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0019] The specific implementation methods, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0020] Example 1 See also Figure 1-Figure 2 , this embodiment provides a communication and optimization method for an energy storage system, comprising the following steps: S1. Obtain the state parameters of the battery pack in the energy storage system through wireless communication, including voltage, current and temperature data, and then use time series analysis method to extract parameter change trend characteristics; Furthermore, the state parameters of the battery pack in the energy storage system, including voltage, current and temperature data, are obtained through wireless communication, and then the time series analysis method is used to extract the parameter change trend characteristics, including: S11, establishing a communication link with the energy storage system through a wireless communication module, periodically acquiring state parameters such as voltage, current and temperature of the battery pack, and storing the acquired state parameter data in chronological order to form a time series data set; S12. Preprocess the time series data, including removing outliers and smoothing noise, to improve data quality, and use time series analysis algorithms, such as the ARIMA model, to model the preprocessed data and extract the trend characteristics of parameters such as voltage, current, and temperature; S13. Determine the health status and remaining life of the battery pack based on the extracted change trend characteristics. When an abnormal trend is found, trigger the early warning mechanism. Combined with the state parameters and change trend of the battery pack, dynamically adjust the charging and discharging strategy of the energy storage system to optimize system efficiency and extend battery life.

[0021] Specifically, in energy storage systems, it is crucial to choose the right wireless communication technology, because different technologies have their own characteristics in terms of transmission distance, power consumption, bandwidth, etc. For example, Zigbee is a short-range, low-power wireless communication technology suitable for battery-powered devices, with a transmission distance of between 10 and 100 meters and a rate of 20 kbps to 250 kbps. LoRa is a long-distance communication technology with operating frequency bands including 433 MHz, 868 MHz, 915 MHz, etc. The transmission distance can reach several kilometers to more than ten kilometers, which is suitable for long-distance data transmission and low-power devices. For energy storage systems, if data transmission is required over a larger range, LoRa may be a better choice; if low power consumption and short-range communication are of concern, Zigbee is more suitable.

[0022] More specifically, the state parameters of the battery pack are periodically acquired through wireless communication technology, and the trend characteristics of parameter changes are extracted using time series analysis methods, realizing real-time monitoring and early warning of the health status and remaining life of the battery pack. At the same time, the charging and discharging strategy of the energy storage system is dynamically adjusted according to the extracted characteristics to optimize system efficiency and extend battery life. This process not only improves the real-time and accuracy of the data, but also enhances the intelligent management level of the energy storage system, ensuring the safe and stable operation of the system.

[0023] S2. Based on the extracted features, determine whether there is communication delay or data packet loss. If there is communication anomaly, start the data compensation mechanism and use historical data to predict the current state parameters; Furthermore, based on the extracted features, it is determined whether there is communication delay or data packet loss. When there is communication anomaly, the data compensation mechanism is activated and the current state parameters are predicted using historical data, including: By monitoring the network communication status in real time, key indicators such as communication delay and data packet loss rate are obtained to determine whether there is communication anomaly. When communication anomaly is detected, the data compensation mechanism is triggered to estimate the current state parameters based on the pre-established prediction model using historical data; When predicting state parameters, we use a variety of machine learning algorithms, such as decision trees, support vector machines, and neural networks, to improve prediction accuracy through ensemble learning. For different types of state parameters, we select appropriate historical data features as input and dynamically adjust the hyperparameters of the prediction model to adapt to different data distributions and changing trends. The predicted state parameters are integrated with the actual received data to obtain a more accurate and stable state estimation value through weighted averaging or Bayesian estimation. During the data fusion process, the weights of the predicted and measured values ​​are dynamically adjusted according to the severity and duration of the communication anomaly to smooth data fluctuations and reduce the impact of the anomaly. The compensated state parameters are then subjected to anomaly detection. When the abnormal deviation is found to exceed the preset threshold, the alarm mechanism is triggered to notify relevant personnel for further processing and analysis.

[0024] Specifically, the predicted state parameters are fused with the actual received data. This process emphasizes the importance of data fusion in improving data accuracy and reliability. Data fusion can eliminate noise and erroneous data by integrating information from multiple data sources, thereby improving data quality. Specifically in terms of methods, weighted averaging is a simple and effective way. It assigns different weights according to the credibility and accuracy of each data source, and then calculates the average value. This method is simple and easy to implement, but it requires accurate evaluation of the weights of each data source. Bayesian estimation is a method based on probability theory. It updates the understanding and prediction of data by calculating the posterior probability, which is suitable for scenarios that process uncertain information. Both methods can effectively improve the accuracy and reliability of data and provide more solid data support for the monitoring and optimization of energy storage systems.

[0025] More specifically, by real-time monitoring of network communication conditions, detecting communication anomalies and initiating data compensation mechanisms, using historical data and a variety of machine learning algorithms to predict current state parameters, and then integrating them with actual data, the accuracy and reliability of the data are improved, data fluctuations are smoothed, the impact of anomalies is reduced, and the stable operation of the energy storage system is ensured. The safety and reliability of the system are further enhanced through anomaly detection and alarm mechanisms.

[0026] S3. Construct a digital twin model of the energy storage system based on the acquired state parameters and predicted data, combined with the geometric, physical, behavioral and rule models of the digital twin. Specifically, constructing a digital twin model of the energy storage system realizes the mapping between the physical entity and the virtual model, so that the model can reflect the operating status of the energy storage system in real time, and provide support for the monitoring, diagnosis and optimization of the system. This not only improves the accuracy and reliability of data processing, but also enhances the intelligent management level of the energy storage system.

[0027] Furthermore, based on the acquired state parameters and prediction data, combined with the geometric, physical, behavioral and rule models of the digital twin, a digital twin model of the energy storage system is constructed, including: Obtain the real-time status parameters and prediction data of the energy storage system, establish a three-dimensional geometric model based on the physical structure and spatial layout of the energy storage system, and accurately depict the appearance and internal structure of the system; Use equivalent circuit models and thermal models to describe the physical characteristics of the energy storage system, including processes such as energy conversion and heat transfer; use finite state machines, petri nets and other methods to model the behavior patterns and state transition laws of the energy storage system under different working conditions; By using the business rule engine, grid dispatching rules, energy management strategies, etc. are embedded into the digital twin model to achieve global optimization, integrate multi-source heterogeneous data with various models, build a mapping of the energy storage system from the physical world to the digital space, and form a real-time updated digital twin model, providing a basis for monitoring, prediction, optimization, control and other applications.

[0028] Specifically, by combining the acquired state parameters and prediction data, the digital twin model of the energy storage system is constructed using the geometric, physical, behavioral and rule models of the digital twin, realizing real-time mapping between the physical entity and the virtual model. This not only enables the model to accurately reflect the operating status of the energy storage system, providing strong support for the monitoring, diagnosis and optimization of the system, but also significantly improves the accuracy and reliability of data processing, thereby enhancing the intelligent management level of the energy storage system and providing a solid guarantee for the efficient and stable operation of the energy storage system.

[0029] It should be explained that to build a digital twin model of an energy storage system, it is first necessary to clarify the hierarchical relationship and assembly order of the model, add appropriate spatial constraints such as angles, contacts, offsets, etc., to realize model assembly from parts to components and then to equipment; then, the geometric model, physical model, behavioral model and rule model are organically integrated. The geometric model is verified by measuring key geometric features; the physical model is verified based on anchor point change comparison analysis; the behavioral model constructs timing diagrams and state diagrams, and adopts formal verification methods; the rule model is verified by comparing the drive response and the actual response. Finally, based on a data-driven approach, the simulation state and measurement data are modeled using random finite sets (RFS), and the real-time measurement data is integrated during the simulation operation through the Bayesian reasoning method to realize dynamic correction of the model, thereby improving the accuracy and credibility of the model.

[0030] S4. Use the support vector machine algorithm, input the historical operation data of the battery pack, output the aging evaluation results, adjust the parameter settings of the digital twin model according to the aging evaluation results, reduce the model calculation complexity, and obtain the optimized control strategy parameters through model simulation; Furthermore, through model simulation, the optimized control strategy parameters are obtained, including: Obtain the historical operating data of the battery pack, including parameters such as voltage, current, and temperature, pre-process the data, remove outliers and noise data, obtain a standardized data set, use the support vector machine algorithm to train the standardized historical operating data of the battery pack, establish a battery aging assessment model, and determine the optimal model parameters through cross-validation and grid search; Use the trained battery aging assessment model to predict new battery pack operating data, obtain the battery pack aging assessment results, judge the health status and remaining life of the battery pack, and dynamically adjust the parameter settings of the digital twin model, including material properties, physical characteristics, etc., according to the battery pack aging assessment results, to reduce the model calculation complexity and improve simulation efficiency and accuracy; Use the adjusted digital twin model to conduct multi-scenario and multi-operating condition simulation analysis on the battery pack to obtain the performance data and degradation trend of the battery pack under different conditions, providing a basis for optimizing the control strategy; Through simulation results and machine learning algorithms, such as reinforcement learning and genetic algorithms, the charge and discharge control strategy of the battery pack is optimized, including parameters such as charging current, cut-off voltage, and temperature control, to extend the life of the battery pack; The optimized control strategy parameters are applied to the actual battery pack management system to perform adaptive control and energy management on the battery pack, realize online monitoring and real-time optimization of battery pack performance, and ensure safe and reliable operation of the battery pack.

[0031] Specifically, by using the support vector machine algorithm to train the historical operating data of the battery pack and establish an aging assessment model, an accurate assessment of the health status and remaining life of the battery pack is achieved. The digital twin model parameters are dynamically adjusted based on the evaluation results, which reduces the model calculation complexity and improves the simulation efficiency and accuracy. Furthermore, the adjusted digital twin model is used for multi-scenario and multi-operating condition simulation analysis, and the charging and discharging control strategy is optimized in combination with the machine learning algorithm. This not only extends the life of the battery pack, but also realizes online monitoring and real-time optimization of the battery pack performance by applying the optimized control strategy parameters to the actual battery pack management system, ensuring the safe and reliable operation of the battery pack.

[0032] S5. Transmit the optimized control strategy parameters to the actual energy storage system, and use differential privacy technology to encrypt the transmitted data.

[0033] Furthermore, the optimized control strategy parameters are transmitted to the actual energy storage system, and the transmission data is encrypted using differential privacy technology, including: According to the optimized control strategy parameters, differential privacy technology is used to encrypt data, and random noise is added to protect parameter privacy. The encrypted control strategy parameters are transmitted to the control module of the actual energy storage system through a secure communication protocol. After receiving the encrypted control strategy parameters, the control module of the energy storage system uses the corresponding decryption algorithm to decrypt them and obtain the optimized control strategy parameters; The control module monitors and analyzes the operating status of the energy storage system in real time based on the decrypted control strategy parameters to determine whether the control strategy needs to be adjusted; When the control strategy needs to be adjusted, the control module generates corresponding control instructions based on the optimized control strategy parameters and sends them to the actuators of the energy storage system. The received control instructions are then used to dynamically adjust the charging and discharging power, voltage and other parameters of the energy storage equipment to achieve optimized control of the energy storage system.

[0034] Specifically, by using differential privacy technology to encrypt the optimized control strategy parameters and using a secure communication protocol to transmit them to the control module of the actual energy storage system, the parameter privacy is effectively protected to prevent the data from being stolen or tampered with during transmission. After decryption, the control module monitors and analyzes the operating status of the energy storage system in real time according to the optimized control strategy parameters, dynamically adjusts the control strategy, generates and issues control instructions to the actuator, and achieves precise adjustment of the charging and discharging power, voltage and other parameters of the energy storage equipment, thereby ensuring the safe, reliable and efficient operation of the energy storage system and improving the overall performance and stability of the system.

[0035] Furthermore, after encrypting the transmission data, the method further includes: real-time monitoring of the system operation status of the control strategy deployed in the actual system, judging the degree of difference between the digital twin model and the actual system, and when the difference exceeds a preset threshold, triggering the model parameter adaptive adjustment mechanism to update the digital twin model parameters; Furthermore, the system operation status of the control strategy deployed in the actual system is monitored in real time to determine the degree of difference between the digital twin model and the actual system. When the difference exceeds the preset threshold, the model parameter adaptive adjustment mechanism is triggered to update the digital twin model parameters, including: Obtain the actual system operation data, including the execution of the control strategy, system status parameters, etc., as the input for real-time monitoring, input the real-time monitoring data into the digital twin model, run the digital twin model, and obtain the model output results; Calculate the difference between the output of the digital twin model and the actual system operation data, use indicators such as mean square error and relative error to measure the degree of difference, and determine whether the degree of difference exceeds the preset threshold. If it exceeds the threshold, the model parameter adaptive adjustment mechanism is triggered. If it does not exceed the threshold, real-time monitoring continues; After the model parameter adaptive adjustment mechanism is triggered, optimization algorithms such as gradient descent algorithm and evolutionary algorithm are used to adjust the parameters of the digital twin model to minimize the difference between the model output and the actual system operation data; Update the adjusted model parameters to the digital twin model to obtain an updated digital twin model, so that it is synchronized with the actual system, ensuring that the digital twin model can accurately reflect the operating status of the actual system and provide support for optimizing the control strategy.

[0036] Specifically, it achieves dynamic synchronization between the digital twin model and the actual system, improves the accuracy and reliability of the model, provides strong support for optimizing the control strategy, and ensures the efficient and stable operation of the energy storage system.

[0037] Furthermore, after updating the parameters of the digital twin model, it also includes: continuously collecting battery pack status parameters and communication quality indicators, using the random forest algorithm to establish a fault prediction model, and dynamically adjusting the simulation accuracy and computing resource allocation of the digital twin model according to the fault prediction results; through iterative optimization, the digital twin model is continuously synchronized with the actual system to ensure the effectiveness of the control strategy and the safety of the system operation.

[0038] Furthermore, the battery pack status parameters and communication quality indicators are continuously collected, and the random forest algorithm is used to establish a fault prediction model. According to the fault prediction results, the simulation accuracy and computing resource allocation of the digital twin model are dynamically adjusted, including: Continuously collect the status parameters of the battery pack, including voltage, current, temperature, etc., and obtain the quality indicators of the communication link such as signal strength and bit error rate, and transmit the collected data to the cloud server; then use the random forest algorithm to train the fault prediction model, optimize the model parameters through cross-validation and other methods to improve the prediction accuracy, deploy the trained fault prediction model to the cloud, receive the battery pack status parameters and communication quality indicators in real time, and predict the failure probability of the battery pack; When the predicted failure probability exceeds the preset threshold, the simulation accuracy of the digital twin model is dynamically adjusted to improve the time resolution and spatial resolution of the simulation calculation to obtain more detailed battery pack status information.

[0039] According to the simulation results of the digital twin model, optimize the allocation strategy of cloud computing resources, dynamically adjust the number and configuration of virtual machines, and ensure the real-time and reliability of simulation calculations; The optimized digital twin model simulation results are fed back to the fault prediction model, and the prediction algorithm is continuously iterated and optimized to improve the accuracy and explainability of fault prediction, providing a reliable basis for the operation and maintenance decisions of the battery pack.

[0040] Example 2 See also Figure 3 This embodiment provides a communication and optimization system for an energy storage system, which is used to implement a communication and optimization method for an energy storage system, including: Data acquisition and preprocessing module: establishes a communication link with the energy storage system through the wireless communication module, periodically obtains the voltage, current, temperature and other state parameters of the battery pack, and stores them in chronological order to form a time series data set; Communication anomaly detection module: Use time series analysis algorithms, such as the ARIMA model, to model the preprocessed data, extract the trend characteristics of parameters such as voltage, current, and temperature, and then judge the health status and remaining life of the battery pack based on the characteristics. At the same time, monitor key indicators such as communication delay and data packet loss rate to determine whether there is a communication anomaly. When an anomaly is detected, the data compensation mechanism is triggered to use historical data and machine learning algorithms to predict and compensate for the current state parameters. The model building and optimization module combines the geometric, physical, behavioral and rule models of digital twins, builds a digital twin model of the energy storage system based on the acquired state parameters and predicted data, assembles the model from parts to components to equipment by clarifying the hierarchical relationship and assembly sequence of the model, and adds spatial constraints. It also integrates multi-source heterogeneous data with various models, models the simulation state and measurement data using random finite sets (RFS), and integrates real-time measurement data during the simulation process using the Bayesian reasoning method to achieve dynamic correction of the model and improve the accuracy and credibility of the model. The evaluation and control strategy optimization module uses a support vector machine algorithm to input the historical operating data of the battery pack and output the aging assessment results. According to the aging assessment results, the parameter settings of the digital twin model are dynamically adjusted to reduce the calculation complexity of the model. The adjusted digital twin model is then used to perform simulation analysis of multiple scenarios and multiple working conditions to obtain the performance data and degradation trends of the battery pack under different conditions. Through the simulation results and machine learning algorithms, the charging and discharging control strategy of the battery pack is optimized to extend the battery pack life. The optimized control strategy parameters are applied to the actual battery pack management system to achieve online monitoring and real-time optimization of the battery pack performance. The data encryption and transmission module uses differential privacy technology to encrypt data according to the optimized control strategy parameters, protects parameter privacy by adding random noise, and transmits the encrypted control strategy parameters to the control module of the actual energy storage system through a secure communication protocol. The control module decrypts the encrypted control strategy parameters after receiving them, and monitors and analyzes the operating status of the energy storage system in real time according to the decrypted parameters, determines whether the control strategy needs to be adjusted, and realizes the optimized control of the energy storage system; The adaptive adjustment and fault prediction module monitors the system operation status of the control strategy deployed in the actual system in real time, determines the degree of difference between the digital twin model and the actual system, and triggers the model parameter adaptive adjustment mechanism when the difference exceeds the preset threshold. The optimization algorithm is used to adjust the parameters of the digital twin model to minimize the difference between the model output and the actual system operation data, and update the model parameters to ensure that the model can accurately reflect the operation status of the actual system. At the same time, the battery pack status parameters and communication quality indicators are continuously collected, and the random forest algorithm is used to establish a fault prediction model to obtain fault prediction results. The simulation accuracy and computing resource allocation of the digital twin model are dynamically adjusted according to the fault prediction results. Through iterative optimization, the continuous synchronization of the digital twin model and the actual system is achieved to ensure the effectiveness of the control strategy and the safety of system operation.

[0041] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A communication and optimization method for an energy storage system, characterized in that: The steps include: The state parameters of the battery pack in the energy storage system, including voltage, current and temperature data, are obtained through wireless communication, and then the trend characteristics of parameter changes are extracted using time series analysis methods; Based on the extracted features, determine whether there is communication delay or data packet loss. If there is communication anomaly, start the data compensation mechanism and use historical data to predict the current state parameters. Based on the acquired state parameters and prediction data, the digital twin model of the energy storage system is constructed by combining the geometric, physical, behavioral and rule models of the digital twin. The support vector machine algorithm is used to input the historical operation data of the battery pack and output the aging assessment results. According to the aging assessment results, the parameter settings of the digital twin model are adjusted, and the optimized control strategy parameters are obtained through model simulation. The optimized control strategy parameters are transmitted to the actual energy storage system, and the transmitted data is encrypted using differential privacy technology.

2. The communication and optimization method of an energy storage system according to claim 1, characterized in that: The state parameters of the battery pack in the energy storage system are obtained through wireless communication, including voltage, current and temperature data. Then, the time series analysis method is used to extract the trend characteristics of parameter changes, including: A communication link is established with the energy storage system through a wireless communication module, and voltage, current and temperature state parameters of the battery pack are periodically acquired. The acquired state parameter data are stored in chronological order to form a time series data set; Preprocess the time series data to improve data quality, and use the time series analysis algorithm to model the preprocessed data and extract the change trend characteristics of voltage, current and temperature parameters; The health status and remaining life of the battery pack are judged based on the extracted change trend characteristics. When an abnormal trend is found, the early warning mechanism is triggered. Combined with the status parameters and change trends of the battery pack, the charging and discharging strategy of the energy storage system is dynamically adjusted.

3. The communication and optimization method of an energy storage system according to claim 1, characterized in that: Based on the extracted features, determine whether there is communication delay or data packet loss. If there is communication anomaly, start the data compensation mechanism and use historical data to predict the current state parameters, including: By monitoring the network communication status in real time, key indicators are obtained to determine whether there is a communication anomaly. When a communication anomaly is detected, the data compensation mechanism is triggered to estimate the current state parameters based on the pre-established prediction model using historical data; When predicting state parameters, we use a variety of machine learning algorithms to improve prediction accuracy through ensemble learning. For different types of state parameters, we select appropriate historical data features as input and dynamically adjust the hyperparameters of the prediction model to adapt to different data distributions and change trends. The predicted state parameters are integrated with the actual received data, and the state estimation value is obtained through weighted average or Bayesian estimation method; During the data fusion process, the weights of the predicted and measured values ​​are dynamically adjusted according to the severity and duration of the communication anomaly to smooth data fluctuations and reduce the impact of the anomaly. The compensated state parameters are then tested for anomalies, and when the abnormal deviation is found to exceed the preset threshold, the alarm mechanism is triggered.

4. The communication and optimization method of an energy storage system according to claim 1, characterized in that: Based on the acquired state parameters and prediction data, combined with the geometric, physical, behavioral and rule models of the digital twin, a digital twin model of the energy storage system is constructed, including: Obtain the real-time status parameters and prediction data of the energy storage system, establish a three-dimensional geometric model based on the physical structure and spatial layout of the energy storage system, and characterize the appearance and internal structure of the system; Use equivalent circuit models or thermal models to describe the physical characteristics of the energy storage system, and then model the behavior mode and state transition law of the energy storage system under different working conditions; Utilizing the business rule engine, grid dispatching rules and energy management strategies are embedded into the digital twin model for global optimization. Multi-source heterogeneous data are integrated with various models to build a mapping of the energy storage system from the physical world to the digital space, forming a real-time updated digital twin model.

5. The communication and optimization method of an energy storage system according to claim 1, characterized in that: Through model simulation, the optimized control strategy parameters are obtained, including: Obtain historical operating data of the battery pack, pre-process the data, remove outliers and noise data, obtain a standardized data set, use the support vector machine algorithm to train the standardized historical operating data of the battery pack, establish a battery aging assessment model, and determine the optimal model parameters through cross-validation and grid search; Use the trained battery aging assessment model to predict new battery pack operating data, obtain the battery pack aging assessment results, determine the health status and remaining life of the battery pack, and dynamically adjust the parameter settings of the digital twin model based on the battery pack aging assessment results; Use the adjusted digital twin model to conduct multi-scenario and multi-operating condition simulation analysis on the battery pack to obtain the performance data and degradation trend of the battery pack under different conditions; Optimize the charge and discharge control strategy of the battery pack through simulation results and machine learning algorithms; The optimized control strategy parameters are applied to the actual battery pack management system to perform adaptive control and energy management on the battery pack, and to perform online monitoring and real-time optimization of the battery pack performance.

6. The communication and optimization method of an energy storage system according to claim 1, characterized in that: The optimized control strategy parameters are transmitted to the actual energy storage system, and the transmission data is encrypted using differential privacy technology, including: According to the optimized control strategy parameters, differential privacy technology is used to encrypt data, and random noise is added to protect parameter privacy. The encrypted control strategy parameters are transmitted to the control module of the actual energy storage system through a secure communication protocol. After receiving the encrypted control strategy parameters, the control module of the energy storage system uses the corresponding decryption algorithm to decrypt them and obtain the optimized control strategy parameters; The control module monitors and analyzes the operating status of the energy storage system in real time based on the decrypted control strategy parameters to determine whether the control strategy needs to be adjusted; When the control strategy needs to be adjusted, the control module generates corresponding control instructions based on the optimized control strategy parameters and sends them to the actuators of the energy storage system. The actuators then dynamically adjust the parameters of the energy storage equipment based on the received control instructions to optimize the control of the energy storage system.

7. The communication and optimization method of an energy storage system according to claim 6, characterized in that: After encrypting the transmitted data, it also includes: real-time monitoring of the system operation status of the control strategy deployed in the actual system, judging the degree of difference between the digital twin model and the actual system, and when the difference exceeds the preset threshold, triggering the model parameter adaptive adjustment mechanism to update the digital twin model parameters.

8. The communication and optimization method of an energy storage system according to claim 7, characterized in that: Real-time monitoring of the system operation status of the control strategy deployed in the actual system, judging the degree of difference between the digital twin model and the actual system. When the difference exceeds the preset threshold, the model parameter adaptive adjustment mechanism is triggered to update the digital twin model parameters, including: Obtain the actual system operation data, including the execution of the control strategy and system status parameters, as the input for real-time monitoring, input the real-time monitoring data into the digital twin model, run the digital twin model, and obtain the model output results; Calculate the difference between the digital twin model output and the actual system operation data, use mean square error and relative error indicators to measure the difference, and determine whether the difference exceeds the preset threshold. If it exceeds the threshold, the model parameter adaptive adjustment mechanism is triggered. If it does not exceed the threshold, real-time monitoring continues; After the model parameter adaptive adjustment mechanism is triggered, an optimization algorithm is used to adjust the parameters of the digital twin model to minimize the difference between the model output and the actual system operation data; The adjusted model parameters are updated to the digital twin model to obtain an updated digital twin model so that it is synchronized with the actual system.

9. The communication and optimization method of an energy storage system according to claim 8, characterized in that: After updating the digital twin model parameters, it also includes: continuously collecting battery pack status parameters and communication quality indicators, using the random forest algorithm to establish a fault prediction model, and dynamically adjusting the simulation accuracy and computing resource allocation of the digital twin model based on the fault prediction results; through cyclic iterative optimization, the digital twin model is continuously synchronized with the actual system.

10. A communication and optimization system for an energy storage system, used to implement a communication and optimization method for an energy storage system as claimed in any one of claims 1 to 9, characterized in that: include: The data acquisition and preprocessing module establishes a communication link with the energy storage system through the wireless communication module, periodically obtains the state parameters of the battery pack, and stores them in chronological order to form a time series data set; The communication anomaly detection module uses a time series analysis algorithm to model the preprocessed data, extract the change trend characteristics, and then judge the health status and remaining life of the battery pack based on the characteristics. When an anomaly is detected, the data compensation mechanism is triggered to use historical data and machine learning algorithms to predict and compensate for the current state parameters; The model building and optimization module builds a digital twin model of the energy storage system. By clarifying the hierarchical relationship and assembly order of the model and adding spatial constraints, the model is assembled from parts to components and then to equipment. Multi-source heterogeneous data is integrated with various models, and simulation states and measurement data are modeled using random finite sets. The real-time measurement data is integrated through the Bayesian reasoning method to dynamically correct the model. The evaluation and control strategy optimization module uses the support vector machine algorithm to dynamically adjust the parameter settings of the digital twin model according to the aging assessment results. The adjusted digital twin model is then used to perform simulation analysis of multiple scenarios and multiple working conditions to obtain the performance data and degradation trends of the battery pack under different conditions. The charging and discharging control strategy of the battery pack is optimized through simulation results and machine learning algorithms. The data encryption and transmission module uses differential privacy technology to encrypt data based on the optimized control strategy parameters, transmits the encrypted control strategy parameters to the control module of the actual energy storage system through a secure communication protocol, and decrypts them. Based on the decrypted parameters, the operating status of the energy storage system is monitored and analyzed in real time to determine whether the control strategy needs to be adjusted; The adaptive adjustment and fault prediction module monitors the system operation status of the control strategy deployed in the actual system in real time, determines the degree of difference between the digital twin model and the actual system, and then uses the optimization algorithm to adjust the parameters of the digital twin model. At the same time, the random forest algorithm is used to establish a fault prediction model to obtain fault prediction results, and the simulation accuracy and computing resource allocation of the digital twin model are dynamically adjusted according to the fault prediction results.

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