Energy storage operation safety control method, system, device and storage medium

Abstract

The application relates to the technical field of energy storage system thermal management, and particularly relates to an energy storage operation safety control method, system, device and storage medium, which comprises collecting characteristic data of each battery cluster and respectively calculating corresponding thermal risk indexes; when the thermal risk index corresponding to any battery cluster reaches a preset intervention threshold, a system average thermal risk index is calculated based on the thermal risk indexes of the battery clusters, and an original value of a charge-discharge current limit of the battery cluster is derated according to a relative thermal deviation between the thermal risk index corresponding to the battery cluster and the system average thermal risk index, so as to obtain a derated charge-discharge current limit value corresponding to the battery cluster; a communication response message containing the derated charge-discharge current limit value is returned, so that an energy management system can perform charge-discharge power scheduling on a power conversion system in the energy storage system according to the derated charge-discharge current limit value. The application can improve the timely response capability of the energy storage system to early thermal risks below the BMS alarm condition.

Description

Technical Field

[0001] This application relates to the field of thermal management technology for energy storage systems, and in particular to a method, system, device and storage medium for safe operation control of energy storage. Background Technology

[0002] Battery energy storage systems typically consist of multiple battery clusters, a battery management system (BMS), an energy management system (EMS), a thermal management system, and a power conversion system (PCS). The BMS monitors operational data such as temperature, current, and state of charge (SOC) of each battery cluster and reports corresponding charge / discharge limits to the EMS. The EMS generates scheduling instructions based on the data reported by the BMS, which are then executed by the PCS for corresponding charge / discharge control. The thermal management system regulates the operating temperature of the battery clusters. During the operation of multiple battery clusters, the temperature state of different clusters may change with operating load and heat dissipation conditions. Therefore, timely identification and response to the thermal risks of each battery cluster are crucial for ensuring the safe operation of the energy storage system.

[0003] Existing energy storage systems typically involve a Battery Management System (BMS) that collects and reports operational data and charge / discharge limit parameters for each battery cluster at preset intervals. Within these limits, the Energy Management System (EMS) performs power scheduling for the Process Control System (PCS) based on the system's operational needs. The Thermal Management System (EMS) then adjusts the temperature based on detected temperatures and its preset temperature control conditions. When the temperature, temperature difference, or other operational parameters of a battery cluster reach alarm conditions set by the BMS, the BMS generates an alarm and adjusts the corresponding charge / discharge limit parameters. The EMS then limits the charge / discharge power of the PCS according to the adjusted parameters, thereby providing safety protection for the malfunctioning battery cluster.

[0004] However, when the operating parameters of the battery cluster have not yet reached the BMS alarm conditions, the BMS typically continues to report charge and discharge limit parameters as if under normal conditions, the EMS continues to perform power scheduling according to predetermined operating requirements, and the thermal management system operates according to its own temperature control conditions. At this time, even if some battery clusters have shown a trend of continuous temperature increase, accelerated temperature rise, or increased temperature difference within the cluster, the existing system is unable to promptly translate these thermal state changes into corresponding operational controls. This results in a certain response gap between the emergence of thermal risk trends and the triggering of BMS alarms, and battery clusters with higher thermal risks may continue to bear the original charge and discharge loads. Therefore, how to improve the timely response capability of energy storage systems to early thermal risks that have not yet reached the BMS alarm conditions has become an urgent problem to be solved. Summary of the Invention

[0005] This application provides a method, system, device, and storage medium for safe operation control of energy storage, which can improve the timely response capability of energy storage systems to early thermal risks below BMS alarm conditions, and provide preventive and differentiated intervention for early thermal risks. This application provides the following technical solutions: In a first aspect, this application provides a method for safe control of energy storage operation, the method comprising: Collect characteristic data of each battery cluster in the energy storage system, and calculate the thermal risk index corresponding to each battery cluster based on the characteristic data; When the thermal risk index corresponding to any of the battery clusters reaches the preset intervention threshold, the system average thermal risk index is calculated based on the thermal risk index corresponding to each battery cluster. Based on the relative thermal deviation between the thermal risk index corresponding to the battery cluster and the system average thermal risk index, the original value of the charging and discharging current limit of the battery cluster is derated to obtain the derated charging and discharging current limit value corresponding to the battery cluster. The energy management system of the energy storage system returns a communication response message containing the derated charge / discharge current limit value, so that the energy management system can perform charge / discharge power scheduling on the power conversion system in the energy storage system according to the derated charge / discharge current limit value.

[0006] In one specific implementation, the step of calculating the thermal risk index corresponding to each battery cluster based on the feature data includes: The feature data includes the current highest single cell temperature of each battery cluster, the difference between the highest and lowest single cell temperatures within the cluster, the state of charge, and the charging and discharging current. Regarding the first For each battery cluster, an electrical state coupling coefficient is determined based on the state of charge and charge / discharge current. Based on the current highest single-cell temperature, the real-time rate of change of the highest single-cell temperature, and the difference between the highest and lowest single-cell temperatures within the cluster, risk characterization values ​​corresponding to temperature levels, temperature rise rates, and intra-cluster temperature differences are determined. These risk characterization values ​​are then weighted and fused with the electrical state coupling coefficient to obtain the first... Thermal risk index corresponding to each battery cluster; The formula for calculating the heat risk index is as follows: ; in, For the first The thermal risk index corresponding to each battery cluster For the first The electrical state coupling coefficient corresponding to each battery cluster For the first The highest single-cell temperature of the current battery cluster. This is the optimal operating temperature reference value. This is the alarm threshold temperature for the battery management system. It is a non-linear penalty exponent. For the first Real-time rate of change of the highest single cell temperature in the battery cluster. The maximum allowable rate of temperature rise threshold, For the first The difference between the highest and lowest individual cell temperatures within a battery cluster. For the maximum allowable temperature difference, , and Let be the weight coefficient, and satisfy... ; The electrical coupling coefficient is calculated according to the following formula: ; in, For the first The state of charge of each battery cluster, For the first The charging and discharging current of each battery cluster For the first The maximum allowable current of each battery cluster and This is the adjustment coefficient.

[0007] In one specific implementation, before calculating the thermal risk index corresponding to each battery cluster based on the feature data, the method further includes: Acquire historical operating data and thermal runaway test data of the energy storage battery cabinet, and use the historical operating data and thermal runaway test data as training samples; A thermal risk index algorithm model is established using the thermal risk index calculation formula, and the thermal risk index calculation formula is set as a custom physical constraint layer of the model. Based on the training samples, the thermal risk index algorithm model is trained using backpropagation and gradient descent. During training, the parameter learning and updating in the thermal risk index algorithm model are constrained by the custom physical constraint layer, ensuring that the parameter learning and optimization are performed within the calculation relationships defined by the thermal risk index calculation formula, thereby obtaining the optimal parameter combination under different battery aging states and environmental conditions. The optimal parameter combination includes weight coefficients. , and Nonlinear penalty index and adjustment coefficient and ; The optimal parameter combination is encapsulated as an update parameter set to generate a parameter configuration package, and the parameter configuration package is periodically sent to the edge controller.

[0008] In one specific implementation, after calculating the thermal risk index corresponding to each battery cluster based on the feature data, the method further includes: When the thermal risk index corresponding to each battery cluster is lower than the preset intervention threshold, a basic polling mode is adopted to poll the battery management system corresponding to each battery cluster in equal time slices. When the thermal risk index of any of the battery clusters reaches the preset intervention threshold, switch to the asymmetric high-frequency polling mode, determine the battery clusters whose thermal risk index reaches the preset intervention threshold as high-risk battery clusters, and determine the battery clusters whose thermal risk index is lower than the preset intervention threshold as low-risk battery clusters. In the asymmetric high-frequency polling mode, the polling frequency of the battery management system corresponding to the low-risk battery cluster is reduced, or data polling of the battery management system corresponding to the low-risk battery cluster is suspended and a basic heartbeat is maintained; the full data reading of the battery management system corresponding to the high-risk battery cluster is adjusted to reading only the highest single cell temperature, temperature rise trend indicator and intra-cluster temperature difference, and the released communication resources are concentratedly allocated to the battery management system corresponding to the high-risk battery cluster, thereby increasing the polling frequency of the battery management system corresponding to the high-risk battery cluster.

[0009] In one specific implementation, the method further includes: When the thermal risk index corresponding to the high-risk battery cluster is lower than the preset recovery threshold for a consecutive preset number of high-frequency polling cycles, and the liquid cooling unit in the energy storage system controls the temperature of the high-risk battery cluster within the target temperature range, the smooth recovery mechanism is entered. Under the smooth recovery mechanism, the derating charge and discharge current limit value corresponding to the high-risk battery cluster is gradually increased according to a preset step size until it is restored to the original charge and discharge current limit value, and the polling frequency and polling time slot of each battery management system are gradually restored. When the derating current limit values ​​for each of the high-risk battery clusters are restored to their original values ​​and the battery management systems resume normal polling, the system switches to the basic polling mode.

[0010] In one specific implementation scheme, the step of derating the original value of the charge / discharge current limit of the battery cluster based on the relative thermal deviation between the thermal risk index corresponding to the battery cluster and the system average thermal risk index includes: Based on the thermal risk index corresponding to each battery cluster, the system average thermal risk index is calculated, and the difference between the thermal risk index corresponding to each battery cluster and the system average thermal risk index is calculated to obtain the relative thermal deviation corresponding to each battery cluster. The initial safety degradation coefficient corresponding to the battery cluster is calculated based on the relative thermal deviation. A global safety attenuation coefficient is set based on whether the system's average thermal risk index reaches a preset global threshold. Specifically, when the system's average thermal risk index is lower than the preset global threshold, the global safety attenuation coefficient is set to 1. When the system's average thermal risk index reaches the preset global threshold, the global safety attenuation coefficient is set to a preset global attenuation value that is not less than 0 and less than 1. Based on the initial safety attenuation coefficient and the global safety attenuation coefficient, the original values ​​of the charging current limit and the discharge current limit corresponding to the battery cluster are derated to obtain the corresponding derated charging current limit and derated discharge current limit. Regarding the first For each battery cluster, based on the initial safety degradation coefficient and the global safety degradation coefficient, the corresponding derating charge / discharge current limit value is calculated according to the following formula: ; ; in, For the first The derating charging current limit value for each battery cluster. For the first The original value of the charging current limit for each battery cluster. For the first The derating discharge current limit value for each battery cluster For the first The original value of the discharge current limit corresponding to each battery cluster. For the first The initial safe degradation coefficient corresponding to each battery cluster is the global safety attenuation coefficient.

[0011] In one specific implementation, calculating the initial safety degradation coefficient corresponding to the battery cluster based on the relative thermal deviation includes: Based on the relationship between the relative thermal deviation and the allowable threshold of the relative thermal deviation, and in combination with the nonlinear order and the amplification or attenuation coefficient, the influence of the relative thermal deviation on the initial safety attenuation coefficient is determined, and the value of the initial safety attenuation coefficient is restricted by the lower limit and the upper limit of the initial safety attenuation coefficient to obtain the corresponding initial safety attenuation coefficient. Regarding the first For each battery cluster, the corresponding initial safety degradation coefficient is calculated according to the following formula: ; in, For the first The initial safe degradation coefficient corresponding to each battery cluster This is the lower limit of the initial safety attenuation factor. This is the upper limit of the initial safety attenuation coefficient. For the first The relative thermal deviation corresponding to each battery cluster The relative thermal deviation allowable threshold, It is a nonlinear order. This is the amplification or attenuation factor.

[0012] In one specific implementation, the method further includes: When the thermal risk index corresponding to any of the battery clusters reaches the preset intervention threshold and the corresponding battery management system does not trigger an alarm, a forced power command is issued to the liquid cooler unit in the energy storage system based on the highest cluster temperature and temperature rise rate of the battery cluster obtained at high frequency, so as to dynamically adjust the frequency of the liquid cooler compressor and the speed of the water pump before the temperature control conditions of the liquid cooler unit itself are triggered. When increasing the liquid cooling power of the liquid chiller unit, the dehumidifier in the energy storage system is controlled to perform preventative dehumidification.

[0013] Secondly, this application provides an energy storage operation safety control system for executing an energy storage operation safety control method as described in the first aspect, the system comprising: Cloud platform layer, edge control layer, device physical layer, and energy management system; The edge control layer includes an edge controller, which is disposed in the communication link between multiple battery management systems and the energy management system in the device physical layer, and is communicatively connected to each of the battery management systems and the energy management system respectively. The edge controller includes a thermal risk index calculation engine, a charging and discharging current limit derating module, a communication processing module, and a communication scheduling and reconfiguration module. The cloud platform layer is communicatively connected to the edge controller, and the device physical layer includes multiple battery management systems, liquid cooling units, and dehumidifiers.

[0014] Thirdly, this application provides an electronic device, the device including a processor and a memory; the memory stores a program, the program being loaded and executed by the processor to implement an energy storage operation safety control method as described in the first aspect.

[0015] Fourthly, this application provides a computer-readable storage medium storing a program that, when executed by a processor, is used to implement an energy storage operation safety control method as described in the first aspect.

[0016] By collecting characteristic data of each battery cluster in the energy storage system and calculating the corresponding thermal risk index, when the thermal risk index of any battery cluster reaches a preset intervention threshold, the system average thermal risk index is calculated based on the thermal risk index of each battery cluster. Based on the relative thermal deviation between the thermal risk index of the battery cluster and the system average thermal risk index, the original value of the charge and discharge current limit of the battery cluster is derated to obtain the corresponding derated charge and discharge current limit value. Furthermore, a communication response message containing the derated charge and discharge current limit value is returned to the energy management system, enabling the energy management system to perform charge and discharge power scheduling of the power conversion system based on the derated charge and discharge capacity boundary. Therefore, the energy storage system does not need to rely on battery management system alarms as the trigger for charging and discharging power limits. Instead, it can promptly convert the thermal risk status of a battery cluster into an adjustment of the charging and discharging current limit value when the thermal risk index of the battery cluster reaches the preset intervention conditions. At the same time, the corresponding derating treatment is determined by the deviation of the battery cluster from the system's average thermal risk level, so that battery clusters with relatively high thermal risks can be subject to targeted charging and discharging capacity limits. This is then applied to the power conversion system through the existing power scheduling process of the energy management system, thereby shortening the response process between the thermal risk reaching the intervention conditions and the implementation of charging and discharging power limits. This improves the timeliness of the energy storage system's response to early thermal risks of battery clusters and enhances its operational safety.

[0017] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, the preferred embodiments of this application are described in detail below with reference to the accompanying drawings. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the application architecture of the energy storage operation safety control system in the embodiments of this application.

[0019] Figure 2 This is a flowchart illustrating the energy storage operation safety control method in the embodiments of this application.

[0020] Figure 3 This is a flowchart illustrating step S100 in an embodiment of this application.

[0021] Figure 4 This is a schematic diagram of the communication session reconstruction based on the thermal risk index in the embodiments of this application.

[0022] Figure 5 This is a flowchart illustrating step S200 in an embodiment of this application.

[0023] Figure 6 This is a schematic diagram of the thermoelectric synergistic control process in the embodiments of this application.

[0024] Figure 7 This is a schematic diagram of the smooth recovery process in the embodiments of this application.

[0025] Figure 8 This is a schematic diagram of the overall process of the energy storage operation safety control method in the embodiments of this application.

[0026] Figure 9 This is a block diagram of an electronic device for energy storage operation safety control in an embodiment of this application. Detailed Implementation

[0027] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.

[0028] Optionally, this application uses the energy storage operation safety control method provided in various embodiments in an electronic device as an example for illustration. The electronic device is a terminal or a server. The terminal can be a computer, tablet computer, etc. This embodiment does not limit the type of electronic device.

[0029] To facilitate understanding of the embodiments of this application, the main abbreviations and key terms involved in this application are explained as follows: BMS (Battery Management System) is used to collect and manage operational data such as temperature, state of charge, charging and discharging current, and charging and discharging current limits of the corresponding battery clusters.

[0030] EMS (Energy Management System) is used to acquire the operating data and charging / discharging capacity boundaries of energy storage systems, and to schedule charging and discharging power accordingly.

[0031] PCS (Power Conversion System) is used to convert DC power from the battery side to AC power from the grid side and to execute charging and discharging power scheduling commands sent by the energy management system.

[0032] RS485 is a serial communication interface standard used for data transmission between edge controllers, battery management systems, and energy management systems.

[0033] Modbus is a master-slave communication protocol that uses request and response messages to read and write register data.

[0034] CCL (Charge Current Limit) is the maximum allowable charging current provided by the battery management system.

[0035] DCL (Discharge Current Limit) is the maximum allowable discharge current provided by the battery management system.

[0036] SOC (State of Charge) is used to characterize the relationship between the current remaining charge of a battery and its rated capacity.

[0037] SOH (State of Health) is used to characterize the degree to which a battery retains its performance relative to its initial state.

[0038] DO (Digital Output) is a digital output interface used to output switch control signals.

[0039] CRC (Cyclic Redundancy Check) is used to verify the data integrity of communication messages during transmission.

[0040] TRI (Thermal Risk Index) is used to characterize the current thermal risk level of a battery cluster.

[0041] The edge controller is used to perform data acquisition, thermal risk index calculation, charging and discharging current limit derating, communication scheduling reconstruction, and thermal management linkage control.

[0042] The bandwidth concession flag is used to record the communication scheduling status, device suspension status, liquid cooling control status, and charge / discharge current limit derating status within the edge controller.

[0043] Reference Figure 1 This is a schematic diagram of the application architecture of an energy storage operation safety control system provided in one embodiment of this application. This application first provides an energy storage operation safety control method. To execute this method, this application also provides an energy storage operation safety control system. Figure 1The application architecture of the system is illustrated. The system includes a cloud platform layer, an edge control layer, a device physical layer, and an energy management system. The edge control layer includes an edge controller, which is located in the communication link between multiple battery management systems and the energy management system in the device physical layer, and communicates with each battery management system and the energy management system respectively. The edge controller includes a thermal risk index calculation engine, a charge / discharge current limit derating module, a communication processing module, and a communication scheduling and reconfiguration module. The cloud platform layer communicates with the edge controller, and the device physical layer includes multiple battery management systems, liquid cooling units, and dehumidifiers.

[0044] exist Figure 1 In the system shown, the cloud platform layer and the edge control layer transmit operational data and distribute parameter configuration packages. The edge control layer interacts with the energy management system and the device physical layer, respectively, and performs thermal risk calculation, communication message processing, charging and discharging current limit derating and communication scheduling reconstruction between the battery management system and the energy management system. It can also perform thermal management linkage control of the liquid chiller and the dehumidifier.

[0045] In the aforementioned system architecture, the edge controller intervenes in the existing communication links between each battery management system (BMS) and the energy management system. Each BMS continues to provide operational data and original charge / discharge current limits according to its original data response method. The energy management system continues to obtain charge / discharge current limits according to its original data reading method and schedules the power conversion system according to its original power scheduling logic. By derating the charge / discharge current limits read by the energy management system, the edge controller enables thermal risk intervention to be implemented without altering the original control logic of the BMS and energy management systems.

[0046] In this embodiment, the energy storage battery cabinet includes multiple battery clusters, each battery cluster comprising multiple individual cells, and each battery cluster is configured with a corresponding battery management system. The battery management system is used to collect temperature data of each individual cell within the corresponding battery cluster, and to acquire operational data such as the state of charge, charging / discharging current, and original charging / discharging current limits of the corresponding battery cluster. Specifically, the highest individual cell temperature in a battery cluster is the maximum value among the temperatures of the multiple individual cells within that cluster, the lowest individual cell temperature is the minimum value among the temperatures of the multiple individual cells within that cluster, and the temperature difference within the cluster is the difference between the highest and lowest individual cell temperatures. Each battery management system generates operational data for its corresponding battery cluster, and the operational data corresponding to multiple battery clusters collectively constitute the operational data of the energy storage battery cabinet. The operational data generated by each battery cluster at different operating times is stored in chronological order to constitute the historical operational data of the energy storage battery cabinet.

[0047] The cloud platform layer includes a large-scale model training engine. This engine acquires historical operational data and thermal runaway test data from each battery cluster in the energy storage battery cabinet. Based on this data, it trains a thermal risk index algorithm model to obtain the optimal parameter combination under different battery aging states and environmental conditions. The cloud platform layer encapsulates this optimal parameter combination as an update parameter set to generate a parameter configuration package, which is periodically distributed to the edge controller. Simultaneously, it receives operational data uploaded by the edge controller, enabling the thermal risk index algorithm model's calculation parameters to be updated according to battery operating status and environmental conditions.

[0048] The edge control layer is centered around an edge controller. In this embodiment, the edge controller also includes bandwidth concession flag management functionality and liquid cooling and dehumidification linkage control functionality.

[0049] The thermal risk index calculation engine acquires feature data for each battery cluster and calculates the corresponding thermal risk index based on this data. The calculated thermal risk indices are then transmitted to the charge / discharge current limit derating module and the communication scheduling reconfiguration module. When the thermal risk index for any battery cluster reaches a preset intervention threshold, the charge / discharge current limit derating module calculates the system average thermal risk index based on the thermal risk indices for each battery cluster. Based on the relative thermal deviation between the thermal risk index for that battery cluster and the system average thermal risk index, the module drates the original charge / discharge current limit for that battery cluster, resulting in a drated charge / discharge current limit. The communication scheduling reconfiguration module adjusts the polling frequency, data reading range, and communication resource allocation method for each battery management system according to the thermal risk index for each battery cluster, enabling battery clusters in different thermal risk states to adopt corresponding communication scheduling methods.

[0050] The communication processing module is used to realize the transmission, reception, and processing of communication data between the edge controller and each battery management system and energy management system. In one specific embodiment, the edge controller communicates with each battery management system and energy management system using Modbus. When communicating with each battery management system, the edge controller sends data read requests to each battery management system according to the Modbus master communication mode, and receives the characteristic data and original charge / discharge current limit values ​​returned by each battery management system. When communicating with the energy management system, the edge controller receives charge / discharge current limit value read requests sent by the energy management system according to the Modbus slave communication mode, and returns a communication response message containing the derating charge / discharge current limit values ​​to the energy management system.

[0051] In one specific embodiment, the edge controller is internally configured with two cooperating buffer areas. One buffer area stores data collected from various battery management systems, as well as data generated after thermal risk calculation and derating. The other buffer area stores response data to the energy management system. The two buffer areas are synchronized according to a preset update method, enabling the edge controller to respond promptly to read requests sent by the energy management system while performing thermal risk calculation and derating.

[0052] The communication processing module parses the communication response messages returned by each battery management system, extracts the feature data and original charge / discharge current limit values ​​from the communication response messages, and maps the extracted data to the corresponding register area inside the edge controller. After derating, the drated charge / discharge current limit values ​​are written to the corresponding response register area. When a request to read the charge / discharge current limit value is received from the energy management system, the edge controller generates the corresponding communication response message based on the data in the response register area and returns it to the energy management system. Through the above communication processing, the edge controller can complete the reception, parsing, mapping, processing, and response of communication data between the battery management system and the energy management system.

[0053] The bandwidth concession flag is used to record the communication status information generated during the communication scheduling reconfiguration process. The communication status information includes the current communication mode, the device information of the paused polling, the derating status, and parameters related to the derating degree. It is used for communication status management and recovery control within the edge controller.

[0054] The liquid cooling and dehumidification linkage control function generates corresponding linkage control commands based on the thermal risk index of each battery cluster and sends these commands to the liquid cooling unit or dehumidifier. The liquid cooling unit adjusts the temperature of the energy storage battery cabinet according to the received linkage control commands, and the dehumidifier adjusts the humidity of the energy storage battery cabinet according to the received linkage control commands, so that the charging and discharging power scheduling and thermal management control work together.

[0055] The physical layer of the equipment includes multiple battery management systems, liquid cooling units, and dehumidifiers. Figure 1 BMS cluster 1, BMS cluster 2, and BMS cluster N represent the battery management systems corresponding to multiple battery clusters. Each battery management system collects data such as temperature, state of charge, charge / discharge current, and raw values ​​of charge / discharge current limits for its corresponding battery cluster, and sends the collected data to the edge controller. The liquid chiller and dehumidifier receive the linkage control commands sent by the edge controller and provide feedback on the corresponding equipment operating status to the edge controller.

[0056] In one specific embodiment, the edge controller communicates with each battery management system (BMS) and with the energy management system using Modbus communication based on an RS485 bus. When communicating with each BMS, the edge controller polls each BMS using Modbus master communication. When communicating with the energy management system, the edge controller responds to read requests from the energy management system using Modbus slave communication, and the communication processing module handles message parsing, register mapping, and data connection between different communication directions.

[0057] In other embodiments, RS485 communication can be replaced by CAN bus or Ethernet communication, including Modbus TCP communication. Using CAN bus or Ethernet communication typically requires hardware upgrades to the communication interface, communication chip, and associated wiring, increasing system modification costs. This application's embodiment, while retaining the original RS485 communication hardware, improves the timeliness of data acquisition and transmission related to high-risk battery clusters through software processing such as communication scheduling reconstruction, data reading range adjustment, dual-buffered response, and communication resource reallocation. This enables the RS485 bus-based communication link to achieve near-high-speed bus real-time response capabilities, thereby improving thermal risk response efficiency while reducing hardware upgrade costs.

[0058] Reference Figure 2 This is a flowchart illustrating an embodiment of an energy storage operation safety control method provided in this application. The method includes at least the following steps: Step S100: Collect the characteristic data of each battery cluster in the energy storage system, and calculate the thermal risk index corresponding to each battery cluster based on the characteristic data.

[0059] In step S100, the edge controller establishes a basic polling queue according to the communication load of the battery management system corresponding to each battery cluster. In each basic polling cycle, it collects temperature characteristic data and electrical status data of each battery cluster in a time-sharing manner according to different polling time periods. For the same battery cluster, it saves the highest single-cell temperature collected in the corresponding polling time period of adjacent basic polling cycles in the order of collection time, and calculates the corresponding real-time change rate of the highest single-cell temperature based on the highest single-cell temperature collected in adjacent basic polling cycles. The edge controller calls the thermal risk index algorithm model to calculate each characteristic data cluster by cluster to obtain the thermal risk index corresponding to each battery cluster.

[0060] Specifically, combined Figure 3First, during device initialization and team building, the edge controller allocates corresponding polling time slots to each battery management system based on the communication load of the corresponding battery management system for each battery cluster, and establishes a basic polling queue according to the execution order of each polling time slot. The communication load is determined based on the amount of data in a single communication by the battery management system, the data update cycle, and the communication response time, enabling each battery management system to interact with the edge controller according to its corresponding polling time slot.

[0061] In one specific embodiment, when any battery management system (BMS) malfunctions or goes offline, the edge controller does not directly remove the BMS from the basic polling queue. Instead, it puts the BMS into a suspended state. The edge controller stores the device address, last valid data snapshot, suspension timestamp, and suspension reason code corresponding to the suspended BMS in a suspension register table, and records the suspension identifier in the corresponding polling period. When the suspended BMS returns to online, the edge controller reads the corresponding device address and original polling configuration from the suspension register table and restores the corresponding polling period for that BMS, thereby maintaining the stability of the polling position of each device in the basic polling queue.

[0062] Subsequently, the edge controller sends data read requests to each battery management system in sequence according to the polling order in the basic polling queue, and receives the response data returned by each battery management system to collect characteristic data of each battery cluster. The characteristic data includes the current highest single-cell temperature of each battery cluster, historical data of the highest single-cell temperature, the difference between the highest and lowest single-cell temperatures within the cluster, the state of charge, and the charging and discharging current. Specifically, the edge controller records the corresponding acquisition time each time it acquires the highest single-cell temperature, and stores the highest single-cell temperatures and acquisition times acquired by the same battery cluster in different polling cycles in chronological order into the historical temperature data set corresponding to that battery cluster. The historical temperature data set constitutes the historical data of the highest single-cell temperature of that battery cluster.

[0063] Then, the edge controller calculates the real-time rate of change of the highest single-cell temperature based on historical data of the highest single-cell temperature for each battery cluster. For the first... For each battery cluster, using the current polling time as the current acquisition time, the highest single-cell temperature acquired at the current polling time is compared with the highest single-cell temperature acquired at the previous valid polling time. Based on the difference in the highest single-cell temperature between the two polling times and the acquisition time difference, the rate of change of the highest single-cell temperature corresponding to the current polling time is determined. Therefore, the real-time rate of change of the highest single-cell temperature simultaneously reflects the highest single-cell temperature at the current polling time and its change relative to the previous valid polling time. When the historical temperature data set contains multiple sets of valid temperature data, the edge controller performs a weighted calculation on the multiple sets of highest single-cell temperature change rates, including the rate of change of the highest single-cell temperature corresponding to the current polling time, to obtain the [number of data points]. The real-time rate of change of the highest single-cell temperature in each battery cluster. By weighting multiple sets of historical temperature data, the impact of fluctuations in a single temperature acquisition on the calculation results of the real-time rate of change of the highest single-cell temperature can be reduced.

[0064] In practice, when a newly connected battery cluster lacks historical data on the highest single-cell temperature, the edge controller acquires the ambient temperature of the energy storage battery cabinet and uses it as a reference temperature for the missing historical temperature to supplement the initial calculation conditions for the real-time change rate of the highest single-cell temperature. After the newly connected battery cluster generates continuous and valid highest single-cell temperature data, the edge controller calculates the real-time change rate of the highest single-cell temperature based on the actual collected highest single-cell temperature and the collection time.

[0065] Finally, the edge controller calculates the thermal risk index for each battery cluster based on the current highest single-cell temperature, historical data of the highest single-cell temperature, the difference between the highest and lowest single-cell temperatures within the cluster, the state of charge, and the charge / discharge current. The historical data of the highest single-cell temperature is used to determine the real-time rate of change of the highest single-cell temperature for the corresponding battery cluster.

[0066] Regarding the first For each battery cluster, the corresponding electrical state coupling coefficient is determined based on the state of charge and charge / discharge current. Based on the current highest single-cell temperature, the real-time rate of change of the highest single-cell temperature, and the difference between the highest and lowest single-cell temperatures within the cluster, risk characterization values ​​corresponding to temperature level, temperature rise rate, and intra-cluster temperature difference are determined. These risk characterization values ​​are then weighted and fused with the electrical state coupling coefficient to obtain the [missing value]. Thermal risk index corresponding to each battery cluster; The formula for calculating the thermal risk index is shown in formula (1): (1) in, For the first Thermal risk index corresponding to each battery cluster; ; For the first The highest single-cell temperature of the current battery cluster; The optimal operating temperature reference value is, for example, 25°C; This is the alarm threshold temperature for the battery management system, for example, 45°C. It is a non-linear penalty exponent; For the first The real-time rate of change of the highest single-cell temperature in each battery cluster is expressed as: That is, the edge controller calculates the real-time rate of change of the highest temperature based on the time difference between two consecutive polls and the highest single-unit temperature difference; The maximum allowable rate of temperature rise threshold, for example, 1°C / min; ; The maximum permissible temperature difference, for example, 5°C; , and Let be the weighting coefficients, and satisfy: ; In the formula, the temperature term corresponding to the highest single-cell temperature characterizes the relationship between the current temperature of the battery cluster and the optimal operating temperature benchmark and the alarm critical temperature of the battery management system. The nonlinear penalty exponent is used to nonlinearly amplify the temperature term, making its influence on the thermal risk index more significant as the highest single-cell temperature approaches the alarm critical temperature of the battery management system. In a specific embodiment, the nonlinear penalty exponent... Choose 2 or 3. The temperature rise rate term corresponding to the real-time change rate of the highest single-cell temperature is used to characterize how quickly the highest single-cell temperature in the battery cluster changes over time. The temperature difference term corresponding to the difference between the highest and lowest single-cell temperatures within the cluster is used to characterize the temperature consistency within the same battery cluster. Weighting coefficients , and These are used to adjust the maximum single-cell temperature, the real-time rate of change of the maximum single-cell temperature, and the degree of influence of intra-cluster temperature difference on the thermal risk index, respectively.

[0067] The formula for calculating the electrical coupling coefficient is shown in formula (2): (2) in, For the first The state of charge of each battery cluster; For the first The charging and discharging current of each battery cluster; Maximum allowable current; and This is the adjustment coefficient. The electrical state coupling coefficient is used to couple the state of charge and charge / discharge current of the battery cluster with temperature characteristics. Under the same conditions of maximum single-cell temperature, real-time rate of change of maximum single-cell temperature, and intra-cluster temperature difference, the higher the state of charge or the larger the charge / discharge current, the larger the electrical state coupling coefficient, and the higher the corresponding thermal risk index. This allows the thermal risk index to simultaneously reflect the thermal state and electrical operating state of the battery cluster.

[0068] Before calculating the thermal risk index for each battery cluster based on feature data, the process includes: acquiring historical operating data and thermal runaway test data of the energy storage battery cabinet, and using these data as training samples; establishing a thermal risk index algorithm model using the thermal risk index calculation formula, and setting the thermal risk index calculation formula as a custom physical constraint layer of the model; training the thermal risk index algorithm model based on the training samples through backpropagation and gradient descent, and constraining the parameter learning and updating in the thermal risk index algorithm model through the custom physical constraint layer during training, so that the parameter learning and optimization are carried out under the calculation relationship defined by the thermal risk index calculation formula, to obtain the optimal parameter combination under different battery aging states and environmental conditions; wherein, the optimal parameter combination includes weight coefficients. , and Nonlinear penalty index and adjustment coefficient and The optimal parameter combination is encapsulated as an update parameter set to generate a parameter configuration package, and the parameter configuration package is periodically sent to the edge controller.

[0069] Specifically, the energy storage battery cabinet comprises multiple battery clusters, and historical operating data is recorded with each battery cluster as the data organization unit. For any given battery cluster, the highest single-cell temperature, historical data of the highest single-cell temperature, the difference between the highest and lowest single-cell temperatures within the cluster, the state of charge, and the charging and discharging current of the battery cluster under different battery aging states and environmental conditions are associated and stored, forming the historical operating data for that battery cluster. The historical operating data corresponding to each battery cluster collectively constitute the historical operating data of the energy storage battery cabinet. Thus, all thermal characteristics and electrical state data in the training samples correspond to specific battery clusters and are consistent with the data objects for calculating the thermal risk index for each battery cluster separately.

[0070] For any given battery cluster, historical data on the highest individual cell temperature are recorded sequentially according to the acquisition time, showing the highest individual cell temperature at multiple times. The rate of change of the highest individual cell temperature is determined based on the highest individual cell temperature and acquisition time at adjacent acquisition times. When multiple sets of valid historical temperature data are included, the rate of change of the highest individual cell temperature is weighted and calculated to obtain the real-time rate of change of the highest individual cell temperature corresponding to that battery cluster. Therefore, the temperature rise rate characteristic used for calculating the thermal risk index in the training samples corresponds to the historical data of the highest individual cell temperature of the corresponding battery cluster.

[0071] Thermal runaway test data includes temperature change data and electrical state data generated during laboratory thermal runaway destructive tests. Historical operating data is used to characterize the thermal characteristics and electrical state of each battery cluster under normal operation and different aging states and environmental conditions. Thermal runaway test data is used to provide samples of temperature changes and electrical state during the continuous development of thermal risk. Both types of data serve as training samples for the thermal risk index algorithm model.

[0072] Specifically, the custom physical constraint layer is used to embed the calculation relationships corresponding to the aforementioned thermal risk index calculation formula and electrical state coupling coefficient calculation formula into the training process of the thermal risk index algorithm model. For any training sample input into the thermal risk index algorithm model, the highest single-cell temperature, the real-time rate of change of the highest single-cell temperature, the difference between the highest and lowest single-cell temperatures within the cluster, the state of charge, and the charge / discharge current of the corresponding battery cluster are input into the custom physical constraint layer. The custom physical constraint layer forms a temperature term corresponding to the highest single-cell temperature, a temperature rise rate term corresponding to the real-time rate of change of the highest single-cell temperature, and a temperature difference term corresponding to the temperature difference within the cluster, respectively, according to the thermal risk index calculation formula. And according to the electrical state coupling coefficient calculation formula, the corresponding electrical state coupling coefficient is determined based on the state of charge and the charge / discharge current.

[0073] In the above calculation process, the weighting coefficient , and Nonlinear penalty index and adjustment coefficient and As parameters to be optimized in the thermal risk index algorithm model, during the model training process, the thermal characteristics and electrical states are not directly obtained through unrestricted data mapping relationships to obtain the thermal risk index. Instead, they participate in the thermal risk index calculation according to the coupling relationship between the temperature term, the rate of temperature rise term, the temperature difference term, and the electrical state, which are defined by the thermal risk index calculation formula. The model training adjusts the above-mentioned parameters to be optimized, so that the parameter training process is constrained by the physical calculation relationship of the thermal risk index.

[0074] In a specific training process, the training samples are first computed using the current parameter combination. Specifically, the thermal characteristics and electrical states corresponding to the training samples are input into the thermal risk index algorithm model. The custom physical constraint layer calculates the thermal risk index according to the thermal risk index calculation formula and the electrical state coupling coefficient calculation formula, thus obtaining the thermal risk index calculation result corresponding to the current parameter combination and forming the training error corresponding to the current training process.

[0075] Backpropagation is used to perform reverse calculations from the output of the heat risk index algorithm model along the computational relationships in the model towards the parameters to be optimized, based on the training error, to determine the training error relative to the weight coefficients. , and Nonlinear penalty index and adjustment coefficient and The gradient of each parameter is used to characterize the direction and degree of influence of changes in the corresponding parameter on the current training error, thereby determining the adjustment direction of each parameter to be optimized.

[0076] Gradient descent is used to update the corresponding parameter values ​​based on the gradients of each parameter obtained from backpropagation. Specifically, based on the gradients of each parameter, the weight coefficients are adjusted in the direction that reduces the training error. , and Nonlinear penalty index and adjustment coefficient and After each parameter update, the updated parameters are re-introduced into the thermal risk index algorithm model, and forward calculation is performed again on the training samples. Backpropagation and gradient descent are then performed again based on the new training error. By repeatedly executing forward calculation, backpropagation, and parameter updates, the parameters to be optimized are gradually adjusted to obtain the optimal parameter combination suitable for different battery aging states and environmental conditions.

[0077] Therefore, backpropagation is used to determine the relationship between the training error and each parameter to be optimized, and gradient descent iteratively updates each parameter based on this relationship. A custom physical constraint layer maintains the computational relationships between the thermal characteristics, electrical states, and parameters to be optimized as specified in the thermal risk index calculation formula throughout the parameter training process, ensuring that parameter learning and updating always occur within the established computational framework of the thermal risk index. The cloud platform encapsulates the optimal parameter combination obtained from training as an update parameter set to generate a parameter configuration package, and sends the parameter configuration package to the edge controller according to a preset period. After receiving the parameter configuration package, the edge controller parses the update parameter set and uses it to update the weight coefficients in its local thermal risk index algorithm model. , and Nonlinear penalty index and adjustment coefficient and .

[0078] After updating the parameters, the edge controller inputs the currently collected highest single-cell temperature, historical data of the highest single-cell temperature, the difference between the highest and lowest single-cell temperatures within the cluster, the state of charge, and the charge / discharge current into the thermal risk index algorithm model. Specifically, the real-time rate of change of the current highest single-cell temperature is determined based on the historical data of the highest single-cell temperature, and then calculated cluster by cluster according to the updated thermal risk index calculation formula to obtain the thermal risk index for each battery cluster. The thermal risk index, after normalization, is between 0 and 1; a higher thermal risk index indicates a higher degree of thermal risk for the corresponding battery cluster.

[0079] It should be noted that, in other embodiments, the model training method in the cloud platform is not limited to a specific network structure or training algorithm. In cases where the thermal risk characteristics of battery clusters can be learned based on training samples and parameter combinations for calculating the thermal risk index can be output, methods such as recurrent neural networks, generative adversarial networks, support vector machines, or random forests can also be used to train or optimize the parameters to be optimized in the thermal risk index calculation formula. When different training methods are used, the output results are still used to determine the weight coefficients, nonlinear penalty exponents, and adjustment coefficients required for calculating the thermal risk index, and the edge controller uses the corresponding parameter combinations to calculate the thermal risk index corresponding to each battery cluster.

[0080] In one optional implementation, after calculating the thermal risk index corresponding to each battery cluster based on the feature data in step S100, the method further includes reconstructing the communication session between the edge controller and each battery management system according to the thermal risk index corresponding to each battery cluster.

[0081] In this embodiment, the edge controller dynamically adjusts the polling mode, polling frequency, data reading range, and communication resource allocation method of each battery management system based on the thermal risk index corresponding to each battery cluster. When the thermal risk index corresponding to each battery cluster is lower than the preset intervention threshold, each battery management system participates in data communication using the basic polling mode; when the thermal risk index corresponding to any battery cluster reaches the preset intervention threshold, the edge controller switches to the asymmetric high-frequency polling mode and reallocates communication resources according to the thermal risk index corresponding to each battery cluster.

[0082] Specifically, refer to Figure 4 , Figure 4 This is a schematic diagram illustrating a communication session reconstruction process based on a thermal risk index, as provided in one embodiment of this application. In each polling cycle, the edge controller compares the thermal risk index corresponding to each battery cluster with a preset intervention threshold, and selects either a basic polling mode or an asymmetric high-frequency polling mode based on the comparison result.

[0083] When the thermal risk index for each battery cluster is below the preset intervention threshold, the edge controller adopts a basic polling mode, polling the battery management system (BMS) corresponding to each battery cluster in equal time slices. Each BMS obtains a communication time slot sequentially according to the polling order in the basic polling queue. Within the corresponding communication time slot, the edge controller sends a data read request, collecting characteristic data such as the highest single-cell temperature, intra-cluster temperature difference, state of charge, and charge / discharge current of each battery cluster, and updates the thermal risk index corresponding to each battery cluster based on the newly collected characteristic data. In the basic polling mode, the polling time slots occupied by each BMS are relatively evenly distributed, allowing the edge controller to continuously acquire the operating status of each battery cluster. After completing one basic polling cycle, the edge controller again compares the updated thermal risk indices with the preset intervention threshold, thereby adjusting the communication session according to changes in the thermal risk status of the battery cluster.

[0084] When the thermal risk index of any battery cluster reaches the preset intervention threshold, the edge controller switches to the asymmetric high-frequency polling mode, identifies the battery cluster with the thermal risk index reaching the preset intervention threshold as a high-risk battery cluster, and identifies the battery cluster with the thermal risk index below the preset intervention threshold as a low-risk battery cluster.

[0085] In the asymmetric high-frequency polling mode, the polling frequency of the battery management system corresponding to the low-risk battery cluster is reduced, or data polling of the battery management system corresponding to the low-risk battery cluster is suspended and basic heartbeat is maintained; the full data reading of the battery management system corresponding to the high-risk battery cluster is adjusted to reading only the highest single cell temperature, temperature rise trend indicator and intra-cluster temperature difference, and the released communication resources are concentrated and allocated to the battery management system corresponding to the high-risk battery cluster, increasing the polling frequency of the battery management system corresponding to the high-risk battery cluster.

[0086] Upon entering the asymmetric high-frequency polling mode, the edge controller first reduces the communication resources occupied by the battery management systems corresponding to low-risk battery clusters. Specifically, the edge controller reduces the polling frequency of the battery management systems corresponding to low-risk battery clusters and extends the time interval between two adjacent data polls. When it is necessary to further release communication resources, the edge controller suspends data polling of the battery management systems corresponding to some low-risk battery clusters and maintains basic heartbeat communication to confirm that the corresponding battery management systems are still online.

[0087] For battery management systems that have suspended data polling, the edge controller stores the device address, last valid data snapshot, suspension timestamp, suspension reason code, and original polling configuration in the suspension register, and marks the suspension status in the corresponding polling period. The suspension register is used to save the communication status and polling configuration of the suspended polling device, so that low-risk devices can still maintain an identifiable and recoverable communication status during the reallocation of communication resources.

[0088] Meanwhile, the edge controller streamlines the data reads from the battery management system (BMS) corresponding to high-risk battery clusters. In basic polling mode, the edge controller acquires multiple operational data points from the BMS according to a preset full register read range. Upon entering asymmetric high-frequency polling mode, the edge controller adjusts its full register read of the BMS corresponding to high-risk battery clusters to only read registers storing the highest single-cell temperature, temperature rise trend flag, and intra-cluster temperature difference. The highest single-cell temperature reflects the current highest temperature level of the high-risk battery cluster, the temperature rise trend flag reflects the changing trend of the highest single-cell temperature, and the intra-cluster temperature difference reflects the temperature consistency within the same battery cluster. These data are directly related to the thermal risk changes of the high-risk battery clusters; reading only the corresponding registers reduces the amount of data carried in a single request and response message, shortening the communication time for each request and response process.

[0089] In one specific embodiment, the edge controller uses Modbus function code 0x03 to read register data from the battery management system. In basic polling mode, the edge controller reads a preset full range of register addresses; in asymmetric high-frequency polling mode, the reading range is reduced to 3 to 5 registers storing the highest single-cell temperature, temperature rise trend flag, and intra-cluster temperature difference. By reducing the number of registers read in a single operation, the Modbus request and response cycle is shortened from approximately 5ms to 10ms to approximately 1ms to 2ms.

[0090] The communication time released after the polling frequency of low-risk battery clusters is reduced or data polling is paused, together with the communication time saved after the single register read range of high-risk battery clusters is reduced, forms the redistributed communication resources. The edge controller centrally allocates the released communication resources to the battery management system corresponding to the high-risk battery clusters to increase the polling frequency of the battery management system corresponding to the high-risk battery clusters.

[0091] In one specific embodiment, the refresh cycle of key temperature data for high-risk battery clusters is shortened from approximately 1 second in the basic polling mode to approximately 100ms to 200ms. This allows the edge controller to obtain the highest single-cell temperature, temperature rise trend, and intra-cluster temperature difference of high-risk battery clusters more frequently, and to update the thermal risk index corresponding to the high-risk battery clusters in a timely manner.

[0092] Through the above processing, the basic polling mode uses equal time slices to maintain the regular data collection of each battery cluster, while the asymmetric high-frequency polling mode increases the refresh frequency of key thermal state data of high-risk battery clusters by compressing the communication occupation of low-risk nodes, simplifying the reading content of high-risk nodes, and centrally allocating communication resources, thereby realizing the dynamic reconstruction of communication sessions driven by the thermal risk index.

[0093] Step S200: When the thermal risk index corresponding to any battery cluster reaches the preset intervention threshold, calculate the system average thermal risk index based on the thermal risk index corresponding to each battery cluster, and reduce the original value of the charging and discharging current limit of the battery cluster according to the relative thermal deviation between the thermal risk index corresponding to the battery cluster and the system average thermal risk index, so as to obtain the reduced charging and discharging current limit value corresponding to the battery cluster.

[0094] In step S200, the edge controller first calculates the system average thermal risk index and the relative thermal deviation of each battery cluster relative to the system average thermal risk index based on the thermal risk index corresponding to each battery cluster. Then, it calculates the initial safety attenuation coefficient corresponding to each battery cluster based on the relative thermal deviation. Finally, it sets the global safety attenuation coefficient based on whether the system average thermal risk index reaches the preset global threshold. Finally, it applies the initial safety attenuation coefficient and the global safety attenuation coefficient together to the original value of the charge and discharge current limit to obtain the derating charge and discharge current limit value corresponding to each battery cluster.

[0095] Figure 5 This is a flowchart illustrating step S200 in one embodiment of this application. (Refer to...) Figure 5 Step S200 includes at least the following sub-steps: Step S2001: Based on the thermal risk index corresponding to each battery cluster, calculate the system average thermal risk index, and calculate the difference between the thermal risk index corresponding to the battery cluster and the system average thermal risk index to obtain the relative thermal deviation corresponding to the battery cluster.

[0096] In step S2001, the edge controller obtains the thermal risk index corresponding to each battery cluster within the current calculation cycle, and averages each thermal risk index to obtain the system average thermal risk index. The system average thermal risk index is used to characterize the overall thermal risk level of multiple battery clusters in the current energy storage system.

[0097] For the The edge controller calculates the first battery cluster. The difference between the thermal risk index corresponding to each battery cluster and the system average thermal risk index is used as the calculated difference. The relative thermal deviation corresponding to each battery cluster is expressed as shown in formula (3): (3) in, For the first The relative thermal deviation corresponding to each battery cluster For the first The thermal risk index corresponding to each battery cluster This is the system's average thermal risk index.

[0098] When the relative thermal deviation is greater than zero, it indicates that the first... The thermal risk level of this individual battery cluster is higher than the overall system average; when the relative thermal deviation is less than zero, it indicates that the first... The thermal risk level of the individual battery cluster is lower than the overall system average; when the relative thermal deviation is zero, it indicates that the first... The thermal risk level of each battery cluster is the same as the average level of the entire system. Therefore, the relative thermal deviation can reflect the differences in thermal risk between battery clusters.

[0099] Step S2002: Calculate the initial safety degradation coefficient corresponding to the battery cluster based on the relative thermal deviation.

[0100] In step S2002, the edge controller calculates the initial safety attenuation coefficient for each battery cluster based on the relative thermal deviation of each battery cluster, so that the initial safety attenuation coefficient changes with the degree of deviation of the battery cluster from the average thermal risk level of the system.

[0101] Specifically, based on the relationship between relative thermal deviation and the allowable threshold of relative thermal deviation, and combined with the nonlinear order and amplification or attenuation coefficient, the influence of relative thermal deviation on the initial safety attenuation coefficient is determined. The value of the initial safety attenuation coefficient is restricted by the lower limit and the upper limit of the initial safety attenuation coefficient, thus obtaining the corresponding initial safety attenuation coefficient.

[0102] Regarding the first For each battery cluster, the corresponding initial safety degradation coefficient is calculated according to the following formula (4): (4) in, For the first The initial safe degradation coefficient corresponding to each battery cluster This is the lower limit of the initial safety attenuation factor. This is the upper limit of the initial safety attenuation coefficient. For the first The relative thermal deviation corresponding to each battery cluster The relative thermal deviation allowable threshold, It is a nonlinear order. This refers to the amplification or attenuation coefficient. The lower and upper limits of the initial safe attenuation coefficient are used to constrain the range of values ​​for the initial safe attenuation coefficient, preventing it from exceeding the preset control boundary. The deviation allowance threshold is used to normalize the relative thermal deviation, ensuring that relative thermal deviations under different operating conditions can participate in the calculation of the initial safe attenuation coefficient on a uniform scale. Nonlinear order. Used to adjust the nonlinearity of the initial safety attenuation coefficient as a function of relative thermal deviation, amplifying or attenuating coefficient. This is used to adjust the impact of relative thermal deviation on the initial safety degradation coefficient. Through the above nonlinear calculation relationship, battery clusters with thermal risk levels higher than the system average level correspond to more significant degradation adjustment, gradually bringing the thermal risk levels of each battery cluster closer together.

[0103] Step S2003: Set the global safety attenuation coefficient based on whether the system's average thermal risk index has reached the preset global threshold.

[0104] In step S2003, the edge controller compares the system's average thermal risk index with a preset global threshold and sets a global safety attenuation coefficient based on the comparison result. The preset global threshold is used to determine whether the overall thermal risk level of the current energy storage system requires global derating.

[0105] Specifically, when the system's average thermal risk index is below a preset global threshold, the global safety degradation coefficient is set to 1. In this case, the global safety degradation coefficient does not have an additional degradation effect on the original charging and discharging current limits of each battery cluster; the derating degree of each battery cluster is mainly determined by its corresponding initial safety degradation coefficient. When the system's average thermal risk index reaches the preset global threshold, the global safety degradation coefficient is set to a preset global degradation value that is not less than 0 and less than 1. In this case, the global safety degradation coefficient and the initial safety degradation coefficient corresponding to each battery cluster work together to further reduce the overall voltage of the charging and discharging current limits of each battery cluster on top of the differentiated derating.

[0106] Therefore, the initial safety degradation coefficient is used to reflect the difference in thermal risk level of a single battery cluster relative to the system average, while the global safety degradation coefficient is used to reflect the overall thermal risk level of the energy storage system. These two factors respectively form a primary differentiated degradation targeting the thermal risk differences of a single cluster and a secondary global degradation targeting the overall thermal risk of the system.

[0107] Step S2004: Based on the initial safety attenuation coefficient and the global safety attenuation coefficient, calculate the derating charge and discharge current limit value corresponding to the battery cluster.

[0108] In step S2004, the edge controller obtains the original values ​​of the charging current limit and the discharge current limit for each battery cluster. Based on the initial safety attenuation coefficient and the global safety attenuation coefficient, it performs derating on the original values ​​of the charging current limit and the discharge current limit for each battery cluster to obtain the corresponding drated charging current limit and drated discharge current limit.

[0109] Regarding the first For each battery cluster, based on the initial safety attenuation coefficient and the global safety attenuation coefficient, the corresponding derating charge and discharge current limit values ​​are calculated according to the following formulas (5) and (6): (5) (6) in, For the first The derating charging current limit value for each battery cluster. For the first The original value of the charging current limit for each battery cluster. For the first The derating discharge current limit value for each battery cluster For the first The original value of the discharge current limit corresponding to each battery cluster. For the first The initial safe degradation coefficient corresponding to each battery cluster This is the global safety attenuation coefficient.

[0110] When the system's average thermal risk index is lower than the preset global threshold, the global safety degradation coefficient is 1. The charge and discharge current limit value after derating is calculated jointly from the original charge and discharge current limit value and the initial safety degradation coefficient. When the system's average thermal risk index reaches the preset global threshold, the preset global degradation value further applies to the original charge and discharge current limit value corresponding to each battery cluster, so that all battery clusters implement overall derating synchronously on the basis of differentiated derating.

[0111] Through steps S2001 to S2004, the edge controller implements differentiated derating based on the thermal risk differences between each battery cluster, and implements global derating based on the overall thermal risk level of the system, so that the charging and discharging current limit value after derating reflects both the relative thermal risk of a single battery cluster and the overall thermal risk of the energy storage system.

[0112] Step S300: Return a communication response message containing the derated charge and discharge current limit value to the energy management system of the energy storage system, so that the energy management system can perform charge and discharge power scheduling on the power conversion system in the energy storage system according to the derated charge and discharge current limit value.

[0113] In step S300, the edge controller receives a charge / discharge current limit value read request from the energy management system, obtains the derating charge / discharge current limit value corresponding to the current battery cluster, and writes the derating charge / discharge current limit value into a communication response message. The edge controller sends the communication response message to the energy management system, causing the energy management system to use the derating charge / discharge current limit value as the current charge / discharge capacity boundary of the corresponding battery cluster, and to perform charge / discharge power scheduling on the power conversion system according to the original power scheduling logic. In this process, the energy management system does not need to add judgment logic for the thermal risk index, nor does it need to change the original charge / discharge current limit value read method and power scheduling logic. Instead, it directly uses the received derating charge / discharge current limit value as the current charge / discharge capacity boundary for power scheduling.

[0114] Specifically, the edge controller maintains two sets of battery management system data caches, which operate asynchronously. One set of data caches receives and stores high-frequency characteristic data and raw charge / discharge current limit values ​​collected from each battery management system, enabling the edge controller to calculate the thermal risk index for each battery cluster and perform charge / discharge current limit derating. The other set of data caches stores response data to the energy management system, including drated charge current limit values ​​and drated discharge current limit values.

[0115] After completing the derating process in step S200, the edge controller writes the calculated drated charging current limit and drated discharging current limit into the response cache for the energy management system. Data acquisition, thermal risk index calculation, and derating on the battery management system side, along with read request responses on the energy management system side, are executed based on their respective caches. This allows the edge controller to continuously acquire and update battery cluster data while responding to read requests sent by the energy management system.

[0116] In one specific embodiment, the energy management system sends a Modbus read request to the edge controller according to a preset polling cycle. The Modbus read request uses function code 0x03 to read the charging current limit register and discharging current limit register of the corresponding battery cluster. The edge controller receives and parses the Modbus read request from the energy management system using Modbus slave communication, and reads the corresponding dated charging current limit and dated discharging current limit from the response cache according to the register address in the request.

[0117] Subsequently, the edge controller writes the drated charging current limit and the drated discharging current limit into the Modbus response message and returns the Modbus response message to the energy management system according to the Modbus slave communication method. Thus, the energy management system still obtains the charging and discharging current limit values ​​according to the original register address and reading method, but the data actually read has been drated by the edge controller according to the thermal risk index.

[0118] After receiving the Modbus response message, the energy management system parses it to obtain the dated charging current limit and the dated discharging current limit. It then uses the dated charging current limit as the maximum allowed charging current for the corresponding battery cluster and the dated discharging current limit as the maximum allowed discharging current for the corresponding battery cluster. Following its inherent power scheduling logic, the energy management system allocates charging and discharging power within the range defined by the dated charging and discharging current limits and sends corresponding power scheduling commands to the power conversion system, which then performs charging or discharging power adjustment.

[0119] In the above process, the edge controller does not directly send control commands to the power conversion system to reduce charging and discharging power. Instead, it modifies the charging and discharging current limit values ​​read by the energy management system, thereby changing the charging and discharging capacity boundaries used by the energy management system when performing power scheduling. The energy management system is still responsible for generating power scheduling commands, and the power conversion system still operates according to the power scheduling commands sent by the energy management system.

[0120] Furthermore, the bandwidth concession flag is used to record the current communication scheduling state, device suspension state, liquid cooling control state, and charge / discharge current limit derating state of the edge controller, and is used for communication state management and recovery control within the edge controller. In one specific embodiment, the bandwidth concession flag is set in the state register area inside the edge controller, occupying a 16-bit register, with the register address located in a vendor-defined address range. Bit 0 of the bandwidth concession flag is used to indicate the communication mode. When bit 0 is 0, the edge controller is in basic polling mode; when bit 0 is 1, the edge controller is in asymmetric high-frequency polling mode. Bits 1 to 3 are used to indicate the group number of the suspended device. Code 000 indicates that there is currently no suspended device group, and codes 001 to 111 indicate that the first to seventh groups of devices are in a suspended state, respectively. Bit 4 is used to indicate the liquid cooling power overwrite state. When bit 4 is 0, the liquid cooling unit operates according to its own temperature control logic; when bit 4 is 1, the liquid cooling unit is in the edge controller's forced overwrite control state. Bit 5 is used to indicate the derating state of the charge / discharge current limit. When bit 5 is 0, it indicates that the current charge / discharge current limit has not been derating; when bit 5 is 1, it indicates that the current charge / discharge current limit has been derating. Bits 6 and 7 are reserved. Bits 8 to 15 are used to store the safety attenuation coefficient γ, which is the product of the initial safety attenuation coefficient and the global safety attenuation coefficient. The safety attenuation coefficient γ is stored as an integer multiplied by 100. For example, when the safety attenuation coefficient γ is 0.5, the value stored in bits 8 to 15 is 50.

[0121] The bandwidth concession flag is only used for communication status management and recovery control within the edge controller and is not used as an input for the energy management system to perform charge and discharge power scheduling. The energy management system still obtains the charge and discharge current limit value according to the original register address and reading method, and performs charge and discharge power scheduling on the power conversion system according to the original power scheduling logic.

[0122] In one specific embodiment, the edge controller calculates the thermal risk index of the corresponding battery cluster once for each high-frequency polling cycle, with the calculation cycle for the thermal risk index not exceeding 100ms. The energy management system's polling cycle for reading the charge / discharge current limit value is 100ms to 1s. Therefore, the time required from the thermal risk index corresponding to the battery cluster reaching the preset intervention threshold to the energy management system receiving the communication response message containing the derating charge / discharge current limit value is approximately 200ms to 1.1s.

[0123] In step S300, the edge controller embeds the derating charge / discharge current limit value obtained in step S200 into the Modbus communication response process between the battery management system and the energy management system. Before the battery management system triggers an alarm, the energy management system can adjust the charge / discharge power of the power conversion system according to the derating charge / discharge capacity boundary, so that the battery cluster is preventively power-limited before it continues to heat up and reaches the battery management system alarm condition.

[0124] Furthermore, in an optional embodiment, in addition to limiting the charging and discharging power of the battery cluster through steps S200 and S300, the edge controller also coordinates the control of the liquid cooling unit and dehumidifier in the energy storage system according to the thermal risk status of the battery cluster, so that the power scheduling on the electrical side and the temperature regulation on the thermal management side jointly respond to the early thermal risk of the battery cluster.

[0125] Specifically, refer to Figure 6 , Figure 6 This is a schematic diagram of a thermoelectric co-control process provided in one embodiment of this application. The edge controller continuously acquires the maximum cluster temperature and temperature rise rate of each battery cluster, and determines whether thermal management intervention is required based on the thermal risk index corresponding to each battery cluster. The maximum cluster temperature is the maximum value among the individual cell temperatures within the battery cluster, and the temperature rise rate characterizes how quickly the maximum cluster temperature increases over time.

[0126] When the thermal risk index corresponding to any battery cluster reaches a preset intervention threshold and the corresponding battery management system does not trigger an alarm, the edge controller, based on the acquired highest cluster temperature and temperature rise rate of the battery cluster, issues a forced power command to the liquid cooling unit in the energy storage system. Before the liquid cooling unit's own temperature control conditions are triggered, the controller dynamically adjusts the frequency of the liquid cooling compressor and the speed of the water pump. At this point, the battery cluster is already showing a thermal risk trend requiring intervention, but the liquid cooling unit has not yet started or increased its liquid cooling power based on its own temperature control conditions.

[0127] Upon receiving a forced power command, the liquid cooling unit dynamically adjusts the frequency of the liquid cooling compressor and the speed of the water pump to enhance the cooling and circulation capacity of the cooling medium. This allows the battery cluster to begin enhanced cooling even before the liquid cooling unit's own temperature control conditions are triggered. Therefore, without waiting for the battery cluster temperature to reach the liquid cooling unit's preset temperature control conditions, the edge controller can proactively implement liquid cooling intervention based on the thermal risk trends reflected by the cluster's highest temperature and temperature rise rate.

[0128] Furthermore, when increasing the liquid cooling power of the liquid-cooled unit, the edge controller controls the dehumidifier in the energy storage system to perform preventative dehumidification. The dehumidifier reduces the humidity inside the energy storage battery cabinet, suppressing the risk of condensation caused by local temperature drops after the liquid cooling power is increased, thus coordinating the pre-cooling process of the liquid-cooled unit with the humidity regulation inside the energy storage battery cabinet.

[0129] In one specific embodiment, the edge controller establishes communication connections with both the liquid-cooled chiller and the dehumidifier. When the thermal risk index reaches a preset intervention threshold, the edge controller sends a forced power command to the liquid-cooled chiller and a start command to the dehumidifier. The liquid-cooled chiller adjusts the frequency of its liquid-cooled compressor and the speed of its water pump according to the forced power command, while the dehumidifier performs preventative dehumidification according to the start command.

[0130] Through the above processing, before the battery management system triggers an alarm and the liquid cooling unit's own temperature control conditions are triggered, the edge controller has already intervened in thermal management based on the highest cluster temperature, temperature rise rate, and thermal risk index of the battery cluster. Simultaneously, steps S200 and S300 reduce the charge and discharge load on the corresponding battery cluster by lowering the derating charge and discharge current limit value, while the liquid cooling unit and dehumidifier respectively enhance cooling and reduce ambient humidity, thus forming a thermoelectric synergistic control process where electrical control and thermal management control work together.

[0131] In one optional implementation, after the communication response message is sent in step S300, the edge controller continues to monitor the thermal risk index and temperature status of each high-risk battery cluster. When the thermal risk status continues to decrease and the temperature is stabilized, the charging and discharging current limit value and communication polling status are smoothly restored to avoid sudden changes in charging and discharging capacity and communication resources in a short period of time.

[0132] Specifically, refer to Figure 7 , Figure 7 This is a schematic diagram of a smooth recovery process provided in one embodiment of this application. The edge controller continuously determines whether each high-risk battery cluster meets the preset recovery conditions. When a high-risk battery cluster does not meet the preset recovery conditions, the edge controller maintains the current derating state and asymmetric high-frequency polling mode, and continues to monitor the corresponding thermal risk index and temperature changes.

[0133] When the thermal risk index corresponding to a high-risk battery cluster remains below a preset recovery threshold for a predetermined number of consecutive high-frequency polling cycles, and the liquid cooling unit in the energy storage system maintains the temperature of the high-risk battery cluster within the target temperature range, a smooth recovery mechanism is initiated. The fact that the thermal risk index remains below the preset recovery threshold for a predetermined number of consecutive high-frequency polling cycles indicates that the thermal risk index of the high-risk battery cluster has been continuously decreasing, rather than only temporarily decreasing due to data fluctuations within a single polling cycle. The liquid cooling unit maintaining the temperature of the high-risk battery cluster within the target temperature range indicates that the liquid cooling intervention has restored the temperature of the corresponding battery cluster to a stable state. By simultaneously judging the thermal risk index and temperature status, premature termination of the derating process can be avoided before the thermal risk has stabilized and subsided.

[0134] In one specific embodiment, the preset quantity is denoted as... , The setting is associated with the high-frequency polling cycle to enable continuous... Each high-frequency polling cycle covers a predetermined stable observation period. A preset recovery threshold is used to determine whether high-risk battery clusters have escaped the thermal risk state requiring continued intervention, and a target temperature range is used to determine whether the liquid cooling unit's temperature control of the battery clusters meets stability requirements. After entering the smooth recovery mechanism, the edge controller gradually increases the derating charge / discharge current limit value corresponding to the high-risk battery clusters according to a preset step size until it returns to the original charge / discharge current limit value, and gradually restores the polling frequency and polling time slots of each battery management system.

[0135] Specifically, the safety degradation coefficient is the product of the initial safety degradation coefficient corresponding to the high-risk battery cluster and the global safety degradation coefficient. The edge controller gradually increases the safety degradation coefficient in preset steps, gradually restoring it to 1.0. As the safety degradation coefficient increases, the corresponding derating charging current limit and derating discharging current limit also gradually increase until they are restored to their original values. By gradually restoring the charging and discharging current limits, the abrupt change in the charging and discharging capability boundaries adopted by the energy management system when thermal risk intervention is lifted can be avoided, allowing the charging and discharging power of the power conversion system to recover smoothly according to the gradually relaxed charging and discharging current limits.

[0136] While restoring the charging and discharging current limits, the edge controller gradually restores the communication resources compressed in the asymmetric high-frequency polling mode. For battery management systems with reduced polling frequencies, the edge controller gradually shortens the time interval between adjacent polls, allowing the corresponding polling frequency to gradually recover. For battery management systems that have suspended data polling, the edge controller restores the corresponding polling slots one by one based on the device address, original polling configuration, and suspended status recorded in the pending register. After restoring the polling slot for each battery management system, the edge controller re-adds the corresponding battery management system to the polling queue and verifies whether the data request and response are normal. After the corresponding battery management system resumes normal communication, the edge controller clears the pending records related to the corresponding device from the pending register.

[0137] Furthermore, the edge controller clears the corresponding bits in the bandwidth concession flag according to the recovery status of each control state. When the communication mode returns to normal, the bit corresponding to the communication mode is cleared; when the suspended device resumes normal polling, the bit corresponding to the device's suspended state is cleared; when the liquid-cooled unit exits the forced power control state, the bit corresponding to the liquid-cooling control state is cleared; when the charging and discharging current limit value returns to its original value, the bit corresponding to the derating state is cleared.

[0138] Once the derating charge / discharge current limits for each high-risk battery cluster have been restored to their original values, and all battery management systems have resumed normal polling, the edge controller switches to basic polling mode. In basic polling mode, the edge controller re-polles the battery management systems corresponding to each battery cluster using equal time slices and resumes the regular feature data acquisition and thermal risk index calculation process.

[0139] Therefore, the smooth recovery mechanism does not immediately release all control when the thermal risk index first falls below the recovery threshold. Instead, it restores the charging and discharging capacity boundary, equipment polling status, and communication mode sequentially after confirming that the thermal risk state continues to decrease and the temperature stabilizes, thereby avoiding sudden changes in the allocation of charging and discharging power and communication resources.

[0140] In summary, combining Figure 8This application collects characteristic data such as the highest single-cell temperature, historical data of the highest single-cell temperature, the difference between the highest and lowest single-cell temperatures within the cluster, state of charge, and charge / discharge current of each battery cluster in the energy storage system to calculate the thermal risk index corresponding to each battery cluster. When the thermal risk index corresponding to any battery cluster reaches a preset intervention threshold, the system average thermal risk index is calculated based on the thermal risk indices corresponding to each battery cluster. Based on the relative thermal deviation between the thermal risk index corresponding to that battery cluster and the system average thermal risk index, the original charge / discharge current limit value of that battery cluster is derated to obtain the corresponding derated charge / discharge current limit value. A communication response message containing the derated charge / discharge current limit value is returned to the energy management system of the energy storage system, enabling the energy management system to perform charge / discharge power scheduling on the power conversion system in the energy storage system according to the derated charge / discharge current limit value. In addition, the edge controller can reconstruct the communication session with each battery management system according to the thermal risk index corresponding to each battery cluster, and centrally allocate the released communication resources to the battery management system corresponding to the high-risk battery cluster. When the thermal risk index reaches the preset intervention threshold and the corresponding battery management system does not trigger an alarm, it can also link the liquid cooling unit and dehumidifier to implement thermal management intervention. After the thermal risk state continues to decrease and the temperature recovers to the target temperature range, it gradually restores the charging and discharging current limit value and the communication polling state.

[0141] By quantifying the thermal state and electrical operating state of each battery cluster into a corresponding thermal risk index, and when the thermal risk index of any battery cluster reaches a preset intervention threshold, the relative thermal deviation of the thermal risk index relative to the system average thermal risk index is converted into a corresponding adjustment of the charge and discharge current limit value. This allows the energy storage system to adjust the charge and discharge capacity boundary of the corresponding battery cluster when the thermal risk reaches the preset intervention condition without relying solely on the battery management system alarm as the trigger for charge and discharge power limitation. Furthermore, the power scheduling of the energy management system acts on the power conversion system, thereby shortening the response process between the thermal risk reaching the intervention condition and the implementation of charge and discharge power limitation. Meanwhile, by reducing or suspending data polling of the battery management system corresponding to low-risk battery clusters, simplifying the data reading content of the battery management system corresponding to high-risk battery clusters, and reallocating communication resources, the refresh frequency of key thermal state data of high-risk battery clusters can be improved. By increasing liquid cooling power in advance and performing preventive dehumidification under appropriate conditions, temperature and humidity regulation under thermal risk conditions can be strengthened. By smoothly restoring the charge and discharge current limit values ​​and communication polling status, abrupt changes in charge and discharge capacity boundaries and communication resource allocation during the thermal risk dissipation process can be reduced, thereby improving the timeliness of energy storage system response to battery cluster thermal risks, the continuity of control, and operational safety.

[0142] Figure 9This is a block diagram of an electronic device provided in one embodiment of this application. The device includes at least a processor 901 and a memory 902.

[0143] Processor 901 includes one or more processing cores, such as a quad-core processor or an octa-core processor. Processor 901 is implemented in at least one hardware form selected from CPU (Central Processing Unit), DSP (Digital Signal Processor), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). In some embodiments, processor 901 includes a main processor and a coprocessor. The main processor performs operations such as battery cluster characteristic data processing, thermal risk index calculation, charge / discharge current limit derating, communication message parsing, and communication response message generation. The coprocessor performs communication status monitoring, data cache updates, and data processing in low-power operation. In another embodiment, processor 901 further includes an AI (Artificial Intelligence) processor, which performs parameter calculations related to the thermal risk index algorithm model.

[0144] The memory 902 includes one or more non-transitory computer-readable storage media, and also includes high-speed random access memory and non-volatile memory, including disk storage devices or flash memory devices. The memory 902 is used to store battery cluster characteristic data, thermal risk index algorithm parameters, preset intervention thresholds, preset recovery thresholds, charge / discharge current limits, communication polling configurations, and communication status data. The non-transitory computer-readable storage media in the memory 902 stores at least one instruction, which, when executed by the processor 901, implements the aforementioned energy storage operation safety control method.

[0145] Optionally, this application also provides a computer-readable storage medium storing a program that is loaded and executed by a processor to implement the energy storage operation safety control method of the above method embodiments.

[0146] Optionally, this application also provides a computer product including a computer-readable storage medium storing a program, which is loaded and executed by a processor to implement the energy storage operation safety control method of the above method embodiments.

[0147] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0148] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for safe control of energy storage operation, characterized in that, The method includes: The system collects characteristic data of each battery cluster in the energy storage system, and calculates the thermal risk index corresponding to each battery cluster based on the characteristic data; for the first... For each battery cluster, an electrical state coupling coefficient is determined based on the state of charge and charge / discharge current. Based on the current highest single-cell temperature, the real-time rate of change of the highest single-cell temperature, and the difference between the highest and lowest single-cell temperatures within the cluster, risk characterization values ​​corresponding to temperature levels, temperature rise rates, and intra-cluster temperature differences are determined. These risk characterization values ​​are then weighted and fused with the electrical state coupling coefficient to obtain the first... The thermal risk index corresponding to each battery cluster; When the thermal risk index corresponding to any of the battery clusters reaches the preset intervention threshold, the system average thermal risk index is calculated based on the thermal risk index corresponding to each battery cluster. Based on the relative thermal deviation between the thermal risk index corresponding to the battery cluster and the system average thermal risk index, the original value of the charging and discharging current limit of the battery cluster is derated to obtain the derated charging and discharging current limit value corresponding to the battery cluster. The energy management system of the energy storage system returns a communication response message containing the derated charge / discharge current limit value, so that the energy management system can perform charge / discharge power scheduling on the power conversion system in the energy storage system according to the derated charge / discharge current limit value.

2. The energy storage operation safety control method according to claim 1, characterized in that, The calculation of the thermal risk index corresponding to each battery cluster based on the feature data includes: The feature data includes the current highest single cell temperature of each battery cluster, the difference between the highest and lowest single cell temperatures within the cluster, the state of charge, and the charging and discharging current. The formula for calculating the heat risk index is as follows: ； in, For the first The thermal risk index corresponding to each battery cluster For the first The electrical state coupling coefficient corresponding to each battery cluster For the first The highest single-cell temperature of the current battery cluster. This is the optimal operating temperature reference value. This is the alarm threshold temperature for the battery management system. It is a non-linear penalty exponent. For the first Real-time rate of change of the highest single cell temperature in the battery cluster. The maximum allowable rate of temperature rise threshold, For the first The difference between the highest and lowest individual cell temperatures within a battery cluster. For the maximum allowable temperature difference, , and Let be the weight coefficient, and satisfy... ; The electrical coupling coefficient is calculated according to the following formula: ； in, For the first The state of charge of each battery cluster, For the first The charging and discharging current of each battery cluster For the first The maximum allowable current of each battery cluster and This is the adjustment coefficient.

3. The energy storage operation safety control method according to claim 2, characterized in that, Before calculating the thermal risk index corresponding to each battery cluster based on the feature data, the method further includes: Acquire historical operating data and thermal runaway test data of the energy storage battery cabinet, and use the historical operating data and thermal runaway test data as training samples; A thermal risk index algorithm model is established using the thermal risk index calculation formula, and the thermal risk index calculation formula is set as a custom physical constraint layer of the model. Based on the training samples, the thermal risk index algorithm model is trained using backpropagation and gradient descent. During training, the parameter learning and updating in the thermal risk index algorithm model are constrained by the custom physical constraint layer, ensuring that the parameter learning and optimization are performed within the calculation relationships defined by the thermal risk index calculation formula, thereby obtaining the optimal parameter combination under different battery aging states and environmental conditions. The optimal parameter combination includes weight coefficients. , and Nonlinear penalty index and adjustment coefficient and ; The optimal parameter combination is encapsulated as an update parameter set to generate a parameter configuration package, and the parameter configuration package is periodically sent to the edge controller.

4. The energy storage operation safety control method according to claim 1, characterized in that, After calculating the thermal risk index corresponding to each battery cluster based on the feature data, the method further includes: When the thermal risk index corresponding to each battery cluster is lower than the preset intervention threshold, a basic polling mode is adopted to poll the battery management system corresponding to each battery cluster in equal time slices. When the thermal risk index of any of the battery clusters reaches the preset intervention threshold, switch to the asymmetric high-frequency polling mode, determine the battery clusters whose thermal risk index reaches the preset intervention threshold as high-risk battery clusters, and determine the battery clusters whose thermal risk index is lower than the preset intervention threshold as low-risk battery clusters. In the asymmetric high-frequency polling mode, the polling frequency of the battery management system corresponding to the low-risk battery cluster is reduced, or data polling of the battery management system corresponding to the low-risk battery cluster is suspended and a basic heartbeat is maintained; the full data reading of the battery management system corresponding to the high-risk battery cluster is adjusted to reading only the highest single cell temperature, temperature rise trend indicator and intra-cluster temperature difference, and the released communication resources are concentratedly allocated to the battery management system corresponding to the high-risk battery cluster, thereby increasing the polling frequency of the battery management system corresponding to the high-risk battery cluster.

5. The energy storage operation safety control method according to claim 4, characterized in that, The method further includes: When the thermal risk index corresponding to the high-risk battery cluster is lower than the preset recovery threshold for a consecutive preset number of high-frequency polling cycles, and the liquid cooling unit in the energy storage system controls the temperature of the high-risk battery cluster within the target temperature range, the smooth recovery mechanism is entered. Under the smooth recovery mechanism, the derating charge and discharge current limit value corresponding to the high-risk battery cluster is gradually increased according to a preset step size until it is restored to the original charge and discharge current limit value, and the polling frequency and polling time slot of each battery management system are gradually restored. When the derating current limit values ​​for each of the high-risk battery clusters are restored to their original values ​​and the battery management systems resume normal polling, the system switches to the basic polling mode.

6. The energy storage operation safety control method according to claim 1, characterized in that, The step of derating the original value of the charge / discharge current limit of the battery cluster based on the relative thermal deviation between the thermal risk index corresponding to the battery cluster and the average thermal risk index of the system includes: The initial safety degradation coefficient corresponding to the battery cluster is calculated based on the relative thermal deviation. A global safety attenuation coefficient is set based on whether the system's average thermal risk index reaches a preset global threshold. Specifically, when the system's average thermal risk index is lower than the preset global threshold, the global safety attenuation coefficient is set to 1. When the system's average thermal risk index reaches the preset global threshold, the global safety attenuation coefficient is set to a preset global attenuation value that is not less than 0 and less than 1. Based on the initial safety attenuation coefficient and the global safety attenuation coefficient, the original values ​​of the charging current limit and the discharge current limit corresponding to the battery cluster are derated to obtain the corresponding derated charging current limit and derated discharge current limit. Regarding the first For each battery cluster, based on the initial safety degradation coefficient and the global safety degradation coefficient, the corresponding derating charge / discharge current limit value is calculated according to the following formula: ； ； in, For the first The derating charging current limit value for each battery cluster. For the first The original value of the charging current limit for each battery cluster. For the first The derating discharge current limit value for each battery cluster For the first The original value of the discharge current limit corresponding to each battery cluster. For the first The initial safe degradation coefficient corresponding to each battery cluster is the global safety attenuation coefficient.

7. The energy storage operation safety control method according to claim 6, characterized in that, The calculation of the initial safe degradation coefficient corresponding to the battery cluster based on the relative thermal deviation includes: Based on the relationship between the relative thermal deviation and the allowable threshold of the relative thermal deviation, and in combination with the nonlinear order and the amplification or attenuation coefficient, the influence of the relative thermal deviation on the initial safety attenuation coefficient is determined, and the value of the initial safety attenuation coefficient is restricted by the lower limit and the upper limit of the initial safety attenuation coefficient to obtain the corresponding initial safety attenuation coefficient. Regarding the first For each battery cluster, the corresponding initial safety degradation coefficient is calculated according to the following formula: ； in, For the first The initial safe degradation coefficient corresponding to each battery cluster This is the lower limit of the initial safety attenuation factor. This is the upper limit of the initial safety attenuation coefficient. For the first The relative thermal deviation corresponding to each battery cluster The relative thermal deviation allowable threshold, It is a nonlinear order. This is the amplification or attenuation factor.

8. The energy storage operation safety control method according to any one of claims 1-7, characterized in that, The method further includes: When the thermal risk index corresponding to any of the battery clusters reaches the preset intervention threshold and the corresponding battery management system does not trigger an alarm, a forced power command is issued to the liquid cooler unit in the energy storage system based on the highest cluster temperature and temperature rise rate of the battery cluster. Before the temperature control conditions of the liquid cooler unit itself are triggered, the frequency of the liquid cooler compressor and the speed of the water pump are dynamically adjusted. When increasing the liquid cooling power of the liquid chiller unit, the dehumidifier in the energy storage system is controlled to perform preventative dehumidification.

9. An energy storage operation safety control system, used to execute an energy storage operation safety control method as described in any one of claims 1 to 8, characterized in that, The system includes: Cloud platform layer, edge control layer, device physical layer, and energy management system; The edge control layer includes an edge controller, which is disposed in the communication link between multiple battery management systems and the energy management system in the device physical layer, and is communicatively connected to each of the battery management systems and the energy management system respectively. The edge controller includes a thermal risk index calculation engine, a charging and discharging current limit derating module, a communication processing module, and a communication scheduling and reconfiguration module. The cloud platform layer is communicatively connected to the edge controller, and the device physical layer includes multiple battery management systems, liquid cooling units, and dehumidifiers.

10. An electronic device, characterized in that, The device includes a processor and a memory; the memory stores a program, which is loaded and executed by the processor to implement an energy storage operation safety control method as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The storage medium stores a program, which, when executed by a processor, is used to implement an energy storage operation safety control method as described in any one of claims 1 to 8.

Images