Thermal management control method, device and equipment for energy storage system

Abstract

The embodiment of the invention provides a thermal management control method, device and equipment for an energy storage system, and relates to the technical field of energy storage. The method comprises the following steps: acquiring historical operation data of the energy storage system in different scenes; constructing a target prediction model according to the historical operation data; acquiring actual operation data of the to-be-managed energy storage system in real time; inputting the actual operation data into the target prediction model to obtain a prediction result of the to-be-managed energy storage system; and performing thermal management control operation on the to-be-managed energy storage system according to the prediction result of the to-be-managed energy storage system so as to adjust the operation parameters of the to-be-managed energy storage system in real time and complete thermal management control on the to-be-managed energy storage system. The method is used for achieving the effect of improving the safety and reliability of thermal management of the energy storage system.

Description

Technical Field

[0001] The present application relates to the field of energy storage technology, specifically to the field of thermal management technology of energy storage systems, and in particular to a thermal management control method, device and equipment for energy storage systems. Background Art

[0002] In recent years, with the development of renewable energy technology, battery energy storage technology has been increasingly used in power systems. Battery energy storage systems have the advantages of fast response and flexible operation. They can provide power support during peak load periods of the grid and store excess power during low load periods of the grid. However, battery energy storage systems face problems such as thermal management and thermal runaway during operation, which directly affect the reliability and safety of battery energy storage systems. Therefore, it is necessary to monitor the energy storage system and use the monitoring data for thermal management.

[0003] In the prior art, the thermal management of the energy storage system is achieved by monitoring the temperature of the energy storage system. When the temperature exceeds a preset value, the refrigeration device is controlled to control the temperature of the energy storage system to achieve the purpose of thermal management.

[0004] The inventors found that in the prior art, the chemical reactions inside the battery and the thermal effects generated by the current will cause the battery temperature to rise. Excessively high temperatures will accelerate battery aging and even cause thermal runaway, thereby affecting the overall performance of the system. However, since the energy storage system is easily affected by multiple factors, the safety and reliability of the thermal management system are relatively low. Summary of the invention

[0005] The embodiments of the present application provide a thermal management control method, device and equipment for an energy storage system, so as to achieve the effect of improving the safety and reliability of thermal management of the energy storage system.

[0006] In a first aspect, an embodiment of the present application provides a thermal management control method for an energy storage system, comprising:

[0007] Obtain historical operation data of energy storage systems in different scenarios;

[0008] Building a target prediction model based on the historical operation data;

[0009] Obtain the actual operating data of the energy storage system to be managed in real time;

[0010] Inputting the actual operation data into the target prediction model to obtain a prediction result of the energy storage system to be managed;

[0011] A thermal management control operation is performed on the energy storage system to be managed according to the prediction result of the energy storage system to be managed, so as to adjust the operating parameters of the energy storage system to be managed in real time and complete the thermal management control of the energy storage system to be managed.

[0012] In a possible implementation, the prediction result of the energy storage system to be managed includes a fault prediction probability and a fault prediction time; accordingly, performing a thermal management control operation on the energy storage system according to the prediction result of the energy storage system to be managed includes: determining a target thermal management control time and a target control strategy according to the fault prediction probability and the fault prediction time; and performing a thermal management control operation on the energy storage system within the target thermal management control time according to the target control strategy.

[0013] In a possible implementation, the energy storage system includes a cooling system, a dehumidification system and a charging and discharging device; accordingly, the thermal management control operation is performed on the energy storage system within the target thermal management control time according to the target control strategy, including: controlling the operating frequency of the cooling system to increase or decrease within the target thermal management control time according to the target control strategy, so that the operating temperature of the energy storage system is maintained within a preset temperature range; controlling the operating power of the dehumidification system to increase or decrease within the target thermal management control time according to the target control strategy, so that the operating humidity of the energy storage system is maintained within a preset humidity range; when it is detected that the operating state of the energy storage station obtained according to the actual operating data is the preset operating state, stopping changing the thermal management control strategy of the energy storage system, and completing the thermal management control of the energy storage system.

[0014] In a possible implementation, constructing a target prediction model based on the historical operation data includes: performing data extraction processing based on the historical operation data to obtain training data that has a significant impact on thermal management failures of the energy storage system; inputting the training data into an initial prediction model for machine learning training processing to obtain an intermediate prediction model; determining environmental factor data and equipment supplier data in different regions based on the historical operation data; using the environmental factor data as random effect data and the equipment supplier data as fixed effect data to construct a mixed effect model; and constructing a target prediction model based on the mixed effect model and the intermediate prediction model.

[0015] In a possible implementation, after constructing the target prediction model according to the historical operating data, it also includes: determining critical point data of external environmental parameters according to the historical operating data; performing a temperature adjustment operation on the target prediction model according to the critical point data of the external environmental parameters and a preset regression prediction fault analysis method to obtain a first adjusted target prediction model for determining the operating environment parameter range of the energy storage system.

[0016] In a possible implementation, after obtaining the first adjustment target prediction model for determining the operating temperature range of the energy storage system, it also includes: determining business knowledge data based on the historical operating data; performing a data screening operation on the first adjustment target prediction model based on the business knowledge data and the association inference method to obtain a second adjustment target prediction model for eliminating influencing factors.

[0017] In a possible implementation, the method further includes: performing data comparison processing on the actual operation data and the historical operation data to obtain a comparison result; determining an adjusted control strategy and an adjusted thermal management control time according to the comparison result, the actual operation data and the target control strategy; and performing a thermal management control operation on the energy storage system within the adjusted thermal management control time according to the adjusted control strategy.

[0018] In a possible implementation, it also includes: displaying and processing the historical operation data, actual operation data, and energy storage system adjustment data; determining fault prediction information based on the actual operation data; and generating and displaying alarm information when it is detected that the fault prediction information reaches a preset critical point.

[0019] In a second aspect, an embodiment of the present application provides a thermal management control device for an energy storage system, comprising:

[0020] Data collection module, used to obtain historical operation data of energy storage systems under different scenarios;

[0021] A model building module, used to build a target prediction model based on the historical operation data;

[0022] The data collection module is also used to obtain the actual operation data of the energy storage system to be managed in real time;

[0023] A prediction module, used for inputting the actual operation data into the target prediction model to obtain a prediction result of the energy storage system to be managed;

[0024] The control module is used to perform thermal management control operations on the energy storage system to be managed according to the prediction results of the energy storage system to be managed, so as to adjust the operating parameters of the energy storage system to be managed in real time and complete the thermal management control of the energy storage system to be managed.

[0025] In a third aspect, an embodiment of the present application provides a thermal management control device for an energy storage system, including: a plurality of monitoring sensors, a memory, and a processor;

[0026] The plurality of monitoring sensors are used to collect operating data of the energy storage system;

[0027] The memory stores computer-executable instructions;

[0028] The processor executes the computer-executable instructions stored in the memory, so that the processor executes various possible implementations of the first aspect as described above.

[0029] An embodiment of the present application provides a thermal management control method, device and equipment for an energy storage system, wherein the method first constructs a target prediction model by acquiring historical operating data of the energy storage system under different scenarios, and then predicts the energy storage system to be managed using the actual operating data of the system to be managed through the target prediction model, and outputs the prediction result. Then, according to the prediction result, a thermal management control operation is performed on the energy storage system to be managed, so as to adjust the operating parameters of the energy storage system to be managed in real time, and complete the thermal management control of the energy storage system to be managed. This enables the present application to adjust the thermal management strategy according to the energy storage system under different scenarios, intervene in the thermal management control in advance according to the prediction results, and thereby achieve the purpose of improving the safety and reliability of thermal management of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0031] Figure 1 A schematic diagram of a scenario of a thermal management control method for an energy storage system provided in this application;

[0032] Figure 2 A schematic diagram of a thermal management control method for an energy storage system provided in this application;

[0033] Figure 3 Schematic diagram of the structure of the thermal management control device of the energy storage system provided in this application Figure 1 ;

[0034] Figure 4 Schematic diagram of the structure of the thermal management control device of the energy storage system provided in this application Figure 2 ;

[0035] Figure 5 A schematic diagram of the structure of the thermal management control device of the energy storage system provided in this application.

[0036] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0037] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0038] The inventors found in the practice that in the prior art, the liquid cooling system is used to monitor the temperature of a single energy storage system, which lacks the ability to uniformly monitor and collect data for systems in different environments within a specific range, and only focuses on temperature management, but fails to integrate multiple factors such as external temperature, humidity, geographical location, and equipment suppliers, and cannot simulate the comprehensive impact of different factors on the reliability of the thermal management system. As a result, the thermal management method obtained in the end has poor adaptability under different geographical and supplier conditions, and a more comprehensive and reliable thermal management solution is needed.

[0039] In response to the above technical problems, the inventors came up with the following idea: by collecting fault data of thermal management systems of liquid-cooled energy storage power stations distributed across the country, constructing a fault model under the influence of multiple factors, and combining long-term fault prediction with real-time monitoring, the inventors optimized the better operation strategy under multi-dimensional conditions of different regions, external environments (temperature, humidity) and supplier equipment, thereby achieving the purpose of improving the safety and reliability of thermal management of energy storage systems.

[0040] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0041] Figure 1 A schematic diagram of a thermal management control method for an energy storage system provided in this application, such as Figure 1 As shown, the specific application scenarios of the present application include: an operation terminal 101 of an operation and maintenance personnel, a cloud platform 102 and an energy storage system 103 in multiple different scenarios.

[0042] The operation and maintenance personnel operation terminal 101 may be a computer-related device and is connected to the cloud platform 102 for communication. The operation and maintenance personnel operation terminal 101 is used to display the prediction results sent by the cloud platform 102 and the operation data of each energy storage system 103 .

[0043] The cloud platform 102 is used to collect and store the operating data of the energy storage system 103 from multiple different scenarios, forming a large-scale energy storage system data pool covering different regions. The cloud platform 102 also supports multi-dimensional data analysis, such as regional differences, climate differences, location differences, supplier equipment performance and historical fault records, providing a basis for subsequent data analysis and optimization. It can also regularly clean and pre-process the operating data in the energy storage system data pool, eliminate noise and abnormal data, and ensure the accuracy of the results of subsequent model training and analysis and prediction.

[0044] The energy storage system 103 may be an energy storage power station. A variety of sensors are deployed in the energy storage system 103 for real-time collection of parameters related to thermal management of the energy storage system. The energy storage system 103 may be connected to the cloud platform 102 via an Internet of Things (IoT) module. After the connection, wireless or wired communication protocols (such as Modbus, CAN bus, or based on the IEC61850 standard) may be used for data transmission to ensure the real-time and integrity of the data.

[0045] Figure 2 This is a flow chart of the thermal management control method for the energy storage system provided in this application. The execution subject of this embodiment can be Figure 1 The cloud platform 102 in the illustrated embodiment may also be other computer-related devices having the same functions as the cloud platform 102, and there is no particular limitation on this embodiment.

[0046] like Figure 2 As shown, the thermal management control method of the energy storage system includes the following steps:

[0047] S201: Obtain historical operation data of the energy storage system under different scenarios.

[0048] In this embodiment, different scenarios may be energy storage system working scenarios with multi-dimensional differences, such as: different regions (such as geographical location, climate, temperature and humidity), different supplier equipment performance, supplier equipment maintenance years, etc. The historical operation data may be the operation data of all energy storage systems collected by the thermal management control method of the energy storage system. These historical operation data may include: supplier equipment performance data, operating environment data, and historical fault data of the energy storage system.

[0049] S202: Build a target prediction model based on historical operation data.

[0050] In this embodiment, building the target prediction model may be a process of using historical operation data to perform model training through a machine learning method to obtain the target prediction model.

[0051] Specifically, in an optional embodiment of the present application, step S202 includes:

[0052] S202a: Perform data extraction and processing based on historical operation data to obtain training data that has a significant impact on thermal management failures of the energy storage system.

[0053] In this embodiment, data extraction processing can be a process of extracting key factors that have a significant impact on the thermal management failure of the energy storage system from multi-dimensional historical operation data in combination with a feature selector or based on feature replacement as training data. Among them, the key factors may include ambient temperature, humidity, charge and discharge rate, liquid cooling unit model, cumulative start and stop times and operating time of liquid cooling unit components, thermal management strategy of the liquid cooling unit, different equipment suppliers, equipment service life and maintenance records, etc.

[0054] S202b: Input the training data into the initial prediction model for machine learning training to obtain an intermediate prediction model.

[0055] In this embodiment, the machine learning training process can be a process of using the Bayesian Cox proportional risk model and the random survival forest model as the initial prediction model to analyze the input training data and predict whether the equipment will fail and the failure time under multiple covariant factors. The predicted results are then compared with the historical fault records in the training data, where the predicted results can be presented in the form of a risk proportional function or survival probability. For example, for each power station and equipment, the model can output the probability of failure risk within a specific time range (for example, the failure risk in the next 30 days increases by 2%), and optimize and adjust the initial prediction model to obtain an intermediate prediction model. Among them, the prediction results output by the intermediate prediction model have a higher accuracy than those output by the initial prediction model.

[0056] S202c: Determine environmental factor data and equipment supplier data for different regions based on historical operation data.

[0057] In this embodiment, the environmental factor data of different regions may include ambient temperature and humidity. The equipment supplier data of different regions may include charge and discharge ratio, liquid cooling unit model, cumulative start and stop times and operation time of liquid cooling unit components, thermal management strategy of liquid cooling unit, different equipment suppliers, equipment service life and maintenance records, etc.

[0058] S202d: Use environmental factor data as random effect data and equipment supplier data as fixed effect data to construct a mixed effect model.

[0059] In this embodiment, the mixed effect model can use regional environmental factors (such as temperature and humidity) as random effects, and the performance parameters and usage of the equipment supplier as fixed effects. Through this distinction, the mixed effect model can identify the fixed factors and random factors that affect the failure under different regional and supplier conditions, thereby providing customized thermal management control solutions for different power stations.

[0060] S202e: Construct a target prediction model based on the mixed effect model and the intermediate prediction model.

[0061] In this embodiment, building a target prediction model can be a process of adding a mixed effect model on the basis of an intermediate prediction model to obtain a prediction model that can identify fixed factors and random factors that affect failures under different geographical and supplier conditions, and adjust the prediction results based on these fixed factors and random factors.

[0062] S203: Acquire actual operation data of the energy storage system to be managed in real time.

[0063] In this embodiment, the energy storage system to be managed can be selected by the operation and maintenance personnel or all energy storage systems in operation under the management of the cloud platform. The actual operation data refers to the current real-time operation data of the energy storage system.

[0064] S204: Input the actual operation data into the target prediction model to obtain the prediction result of the energy storage system to be managed.

[0065] In this embodiment, after the actual operation data is input into the target prediction model, after analysis and prediction by the target prediction model, a prediction result including data such as the failure probability data and the failure occurrence time point of the energy storage system to be managed can be obtained.

[0066] S205: Performing thermal management control operations on the energy storage system to be managed according to the prediction results of the energy storage system to be managed, so as to adjust the operating parameters of the energy storage system to be managed in real time and complete the thermal management control of the energy storage system to be managed.

[0067] In this embodiment, a dynamic automation controller is integrated on the cloud platform, which sends thermal management control instructions to the energy storage system to be managed in advance based on the prediction results output by the target prediction model, so as to adjust the operating parameters of the energy storage system to be managed in real time and complete the thermal management control of the energy storage system to be managed.

[0068] Specifically, in an optional embodiment of the present application, the prediction result of the energy storage system to be managed includes a fault prediction probability and a fault prediction time; accordingly, in step S205, a thermal management control operation is performed on the energy storage system according to the prediction result of the energy storage system to be managed, including:

[0069] S205a: Determine a target thermal management control time and a target control strategy according to the fault prediction probability and the fault prediction time.

[0070] In this embodiment, the fault prediction probability and the fault prediction time can determine that the energy storage system to be managed may fail within a specific time period in the future, and generate a target control strategy for adjusting the operating parameters of the relevant equipment in the energy storage system to be managed before the specific time period (target thermal management control time) based on the judgment result.

[0071] S205b: Performing thermal management control operations on the energy storage system within the target thermal management control time according to the target control strategy.

[0072] In this embodiment, executing the thermal management control operation may be a process of generating corresponding control instructions according to the target control strategy and the target thermal management control time, and then sending the control instructions to the energy storage system to be managed to adjust the operating parameters of each device in the energy storage system to be managed.

[0073] Specifically, in an optional embodiment of the present application, the energy storage system includes a cooling system, a dehumidification system and a charging and discharging device; accordingly, step S205b includes:

[0074] Step b1: Control the operating frequency of the cooling system to increase or decrease within the target thermal management control time according to the target control strategy, so as to maintain the operating temperature of the energy storage system within a preset temperature range.

[0075] Step b2: According to the target control strategy, the operating power of the dehumidification system is controlled to increase or decrease within the target thermal management control time, so that the operating humidity of the energy storage system is maintained within a preset humidity range.

[0076] Step b3: When it is detected that the operating state of the energy storage station obtained according to the actual operating data is the preset operating state, the thermal management control strategy of the energy storage system is stopped to complete the thermal management control of the energy storage system.

[0077] In this embodiment, based on the target control strategy and the target thermal management control time, the energy storage system to be managed can adjust the operating parameters of the cooling system, dehumidification system and charging and discharging equipment in its own system in real time. Changing the thermal management control strategy of the energy storage system means maintaining the current control state of the energy storage system unchanged.

[0078] For example, when the target prediction model predicts that the outside temperature of area A will rise to the critical temperature value within the next three hours, the target control strategy is to control the operating frequency of the cooling system to increase to the target frequency in advance within the next two hours to reduce the operating temperature of the battery pack. For areas with high humidity, the energy storage system to be managed can be controlled to dynamically adjust the operating strategy of the dehumidification system to avoid condensation in the energy storage system to be managed.

[0079] In summary, the thermal management control method for the energy storage system provided in the embodiment of the present application first constructs a target prediction model by acquiring the historical operating data of the energy storage system under different scenarios, and then predicts the energy storage system to be managed using the actual operating data of the system to be managed through the target prediction model, and outputs the prediction result. Then, according to the prediction result, a thermal management control operation is performed on the energy storage system to be managed, so as to adjust the operating parameters of the energy storage system to be managed in real time, and complete the thermal management control of the energy storage system to be managed. This enables the present application to adjust the thermal management strategy according to the energy storage system under different scenarios, intervene in the thermal management control in advance according to the prediction results, and thereby achieve the purpose of improving the safety and reliability of thermal management of the energy storage system.

[0080] Based on the above embodiment, a thermal management control method for an energy storage system provided in an optional embodiment of the present application further includes:

[0081] Step A: Determine the critical point data of external environmental parameters based on historical operating data.

[0082] In this embodiment, the external environmental parameter critical point data may include environmental parameter critical points such as the highest temperature critical point or the highest humidity critical point for the normal operation of the energy storage device. The critical point is a pre-set specific threshold. When the ambient temperature exceeds the temperature critical point, the equipment failure rate may increase.

[0083] Step B: Performing a temperature adjustment operation on the target prediction model according to the critical point data of the external environmental parameters and the preset regression prediction fault analysis method to obtain a first adjusted target prediction model for determining the operating environmental parameter range of the energy storage system.

[0084] In this embodiment, the preset regression prediction fault analysis method can identify and analyze whether the ambient temperature in the use scenario of the energy storage system to be managed exceeds a specific temperature threshold, so as to determine a more optimal operating temperature range for the energy storage system to be managed. The prediction results output by the first adjustment target prediction model obtained in this way can be used to dynamically adjust the target management strategy to ensure that the energy storage system to be managed always maintains a better operating state under different environmental conditions. This makes the control accuracy and reliability of the thermal management of the energy storage system higher, and the thermal management control effect better.

[0085] On the basis of the above embodiment, as an optional embodiment of the present application, the thermal management control method of the energy storage system provided, after obtaining the first adjustment target prediction model for determining the operating temperature range of the energy storage system in step B, further includes:

[0086] Step C: Determine business knowledge data based on historical operation data.

[0087] In this embodiment, the business knowledge data may be industry knowledge of the energy storage system.

[0088] Step D: Performing a data screening operation on the first adjustment target prediction model according to the business knowledge data and the correlation inference method to obtain a second adjustment target prediction model for eliminating influencing factors.

[0089] In this embodiment, the correlation inference method can be a first adjustment target model combined with business knowledge data and correlation inference to avoid the first adjustment target prediction model mistakenly taking factors that only have correlation but no direct causal relationship as the decisive factor of the fault. Here, whether the factors with correlation and whether there is a direct causal relationship can be comprehensively judged based on the business knowledge data. Here, the prediction results output by the second adjustment target prediction model are more accurate and effective, making the subsequent thermal management control of the energy storage system to be managed safer and more reliable.

[0090] Based on the above embodiment, a thermal management control method for an energy storage system provided in an optional embodiment of the present application further includes:

[0091] Step E: Compare the actual operation data with the historical operation data to obtain a comparison result.

[0092] Step F: Determine the adjusted control strategy and the adjusted thermal management control time according to the comparison results, actual operation data and target control strategy.

[0093] Step G: Performing thermal management control operations on the energy storage system within the adjusted thermal management control time according to the adjusted control strategy.

[0094] In this embodiment, by comparing with the historical operating data, comparison data such as the failure probability and failure time under a working environment close to or similar to the historical operating data are obtained, and the target control strategy and thermal management control time are further adjusted according to the existing data. Compared with the predicted results, the predicted results can be output more accurately to ensure that the energy storage system to be managed always maintains a better operating state in a constantly changing environment.

[0095] The thermal management control method of the energy storage system provided in an optional embodiment of the present application further includes:

[0096] Step H: Display and process the historical operation data, actual operation data, and energy storage system adjustment data.

[0097] In this embodiment, the display processing can send historical operation data, actual operation data, and energy storage system adjustment data to the operation and maintenance personnel's operation end through the cloud platform for data visualization, so that the operation and maintenance personnel can intuitively see the temperature, humidity, cooling system status, fault prediction time and other information of each energy storage system.

[0098] Step I: Determine fault prediction information based on actual operation data.

[0099] Step J: When it is detected that the fault prediction information reaches a preset critical point, an alarm message is generated and displayed.

[0100] In this embodiment, the fault prediction information may be a predicted fault time point. When it is detected that the predicted fault time point reaches a preset critical point of a pre-set fault, an alarm message is generated when the fault is about to occur and displayed to the operation and maintenance personnel. The alarm message here may be a data visualization content such as a sound message, a text message, or a light signal that can remind the operation and maintenance personnel. Displaying the alarm message can ensure that the operation and maintenance personnel can detect the fault in time and take corresponding intervention measures, thereby reducing the downtime risk of related equipment in the energy storage system.

[0101] Figure 3 Schematic diagram of the structure of the thermal management control device of the energy storage system provided in this application Figure 1 ,like Figure 3 As shown, the thermal management control device of the energy storage system provided in this embodiment includes: a data collection module 31 , a model building module 32 , a prediction module 33 and a control module 34 .

[0102] The data collection module 31 is used to obtain the historical operation data of the energy storage system under different scenarios.

[0103] The model building module 32 is used to build a target prediction model based on historical operation data.

[0104] The data collection module 31 is also used to obtain the actual operation data of the energy storage system to be managed in real time.

[0105] The prediction module 33 is used to input the actual operation data into the target prediction model to obtain the prediction result of the energy storage system to be managed.

[0106] The control module 34 is used to perform thermal management control operations on the energy storage system to be managed according to the prediction results of the energy storage system to be managed, so as to adjust the operating parameters of the energy storage system to be managed in real time and complete the thermal management control of the energy storage system to be managed.

[0107] In an optional embodiment of the present application, the prediction result of the energy storage system to be managed includes a fault prediction probability and a fault prediction time; accordingly, the control module 34 is specifically used to: determine a target thermal management control time and a target control strategy according to the fault prediction probability and the fault prediction time; and perform thermal management control operations on the energy storage system within the target thermal management control time according to the target control strategy.

[0108] In an optional embodiment of the present application, the energy storage system includes a cooling system, a dehumidification system and a charging and discharging device; accordingly, the control module 34 is specifically used to: control the operating frequency of the cooling system to increase or decrease within the target thermal management control time according to the target control strategy, so that the operating temperature of the energy storage system is maintained within a preset temperature range; control the operating power of the dehumidification system to increase or decrease within the target thermal management control time according to the target control strategy, so that the operating humidity of the energy storage system is maintained within a preset humidity range; when it is detected that the operating state of the energy storage station obtained according to the actual operating data is the preset operating state, stop changing the thermal management control strategy of the energy storage system, and complete the thermal management control of the energy storage system.

[0109] In an optional embodiment of the present application, the model building module 32 is specifically used to: perform data extraction processing based on historical operating data to obtain training data that has a significant impact on thermal management failures of the energy storage system; input the training data into the initial prediction model for machine learning training processing to obtain an intermediate prediction model; determine environmental factor data and equipment supplier data in different regions based on historical operating data; use the environmental factor data as random effect data and the equipment supplier data as fixed effect data to construct a mixed effect model; and construct a target prediction model based on the mixed effect model and the intermediate prediction model.

[0110] In an optional embodiment of the present application, the model building module 32 is also used to: determine the critical point data of the external environmental parameters based on the historical operating data; perform a temperature adjustment operation on the target prediction model based on the critical point data of the external environmental parameters and a preset regression prediction fault analysis method to obtain a first adjusted target prediction model for determining the operating environment parameter range of the energy storage system.

[0111] In an optional embodiment of the present application, the model building module 32 is also used to: determine the business knowledge data based on the historical operation data; perform data screening operations on the first adjustment target prediction model based on the business knowledge data and the correlation inference method to obtain a second adjustment target prediction model for eliminating influencing factors.

[0112] In an optional embodiment of the present application, the control module 34 is also used to: compare the actual operation data with the historical operation data to obtain a comparison result; determine the adjusted control strategy and the adjusted thermal management control time according to the comparison result, the actual operation data and the target control strategy; and perform thermal management control operations on the energy storage system within the adjusted thermal management control time according to the adjusted control strategy.

[0113] Figure 4 Schematic diagram of the structure of the thermal management control device of the energy storage system provided in this application Figure 2 ,like Figure 4As shown, the thermal management control device of the energy storage system provided in an optional embodiment of the present application further includes: a display warning device 35.

[0114] Among them, the display warning device 35 is specifically used to: display and process historical operation data, actual operation data, and energy storage system adjustment data; determine fault prediction information based on actual operation data; and generate and display alarm information when it is detected that the fault prediction information reaches a preset critical point.

[0115] The thermal management control device for the energy storage system provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effect are similar, which will not be described in detail in this embodiment.

[0116] Figure 5 This is a schematic diagram of the structure of the thermal management control device of the energy storage system provided in this application. Figure 5 As shown, the thermal management control device of the energy storage system provided in this embodiment includes: a plurality of monitoring sensors 501 , a memory 502 and a processor 503 .

[0117] Among them, multiple monitoring sensors 501 are used to collect operating data of the energy storage system.

[0118] Memory 502 stores computer executable instructions.

[0119] In a specific implementation process, the processor 503 executes the computer-executable instructions stored in the memory 502, so that at least one processor 503 executes the above method.

[0120] Optionally, the thermal management control device of the energy storage system further includes a communication component 504 . The processor 503 , the memory 502 and the communication component 504 are connected via a bus 505 .

[0121] The specific implementation process of the processor 503 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0122] The embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above-mentioned thermal management control method of the energy storage system is implemented.

[0123] An embodiment of the present application also provides a computer program product, including a computer program, which implements the above thermal management control method for the energy storage system when executed by a processor.

[0124] In the above embodiments, it should be understood that the processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the invention can be directly implemented as a hardware processor, or can be implemented by a combination of hardware and software modules in the processor.

[0125] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (NVM), such as at least one disk storage.

[0126] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

[0127] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0128] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0129] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0130] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0131] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0132] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0133] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0134] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0135] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0136] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A thermal management control method for an energy storage system, characterized in that: include: Obtain historical operation data of energy storage systems in different scenarios; Building a target prediction model based on the historical operation data; Obtain the actual operating data of the energy storage system to be managed in real time; Inputting the actual operation data into the target prediction model to obtain a prediction result of the energy storage system to be managed; A thermal management control operation is performed on the energy storage system to be managed according to the prediction result of the energy storage system to be managed, so as to adjust the operating parameters of the energy storage system to be managed in real time and complete the thermal management control of the energy storage system to be managed.

2. The method according to claim 1, characterized in that The prediction result of the energy storage system to be managed includes a fault prediction probability and a fault prediction time; Accordingly, performing a thermal management control operation on the energy storage system according to the prediction result of the energy storage system to be managed includes: Determine a target thermal management control time and a target control strategy according to the fault prediction probability and the fault prediction time; A thermal management control operation is performed on the energy storage system within the target thermal management control time according to the target control strategy.

3. The method according to claim 2, characterized in that The energy storage system includes a cooling system, a dehumidification system and a charging and discharging device; Accordingly, performing a thermal management control operation on the energy storage system within the target thermal management control time according to the target control strategy includes: Controlling the operating frequency of the cooling system to increase or decrease within the target thermal management control time according to the target control strategy, so that the operating temperature of the energy storage system is maintained within a preset temperature range; Controlling the operating power of the dehumidification system to increase or decrease within the target thermal management control time according to the target control strategy, so that the operating humidity of the energy storage system is maintained within a preset humidity range; When it is detected that the operating state of the energy storage station obtained according to the actual operating data is a preset operating state, the thermal management control strategy of the energy storage system is stopped from being changed, and the thermal management control of the energy storage system is completed.

4. The method according to claim 1, characterized in that: The step of constructing a target prediction model according to the historical operation data includes: Performing data extraction processing based on the historical operation data to obtain training data that has a significant impact on thermal management failures of the energy storage system; Inputting the training data into an initial prediction model for machine learning training processing to obtain an intermediate prediction model; Determine environmental factor data and equipment supplier data for different regions based on the historical operation data; The environmental factor data are used as random effect data, and the equipment supplier data are used as fixed effect data to construct a mixed effect model; A target prediction model is constructed based on the mixed effect model and the intermediate prediction model.

5. The method according to claim 1, characterized in that After the target prediction model is constructed according to the historical operation data, the method further includes: Determining critical point data of external environmental parameters based on the historical operating data; A temperature adjustment operation is performed on the target prediction model according to the critical point data of the external environmental parameters and a preset regression prediction fault analysis method to obtain a first adjustment target prediction model for determining the operating environmental parameter range of the energy storage system.

6. The method according to claim 5, characterized in that After obtaining the first adjustment target prediction model for determining the operating temperature range of the energy storage system, the method further includes: Determining business knowledge data based on the historical operation data; A data screening operation is performed on the first adjustment target prediction model according to the business knowledge data and the correlation inference method to obtain a second adjustment target prediction model for eliminating influencing factors.

7. The method according to claim 2, characterized in that Also includes: Performing data comparison processing on the actual operation data and the historical operation data to obtain a comparison result; Determining an adjusted control strategy and an adjusted thermal management control time according to the comparison result, the actual operation data and the target control strategy; A thermal management control operation is performed on the energy storage system within the adjusted thermal management control time according to the adjusted control strategy.

8. The method according to any one of claims 1 to 7, characterized in that Also includes: Displaying and processing the historical operation data, actual operation data, and energy storage system adjustment data; Determining fault prediction information according to the actual operation data; When it is detected that the fault prediction information reaches a preset critical point, an alarm message is generated and displayed.

9. A thermal management control device for an energy storage system, characterized in that: include: Data collection module, used to obtain historical operation data of energy storage systems under different scenarios; A model building module, used to build a target prediction model based on the historical operation data; The data collection module is also used to obtain the actual operation data of the energy storage system to be managed in real time; A prediction module, used for inputting the actual operation data into the target prediction model to obtain a prediction result of the energy storage system to be managed; The control module is used to perform thermal management control operations on the energy storage system to be managed according to the prediction results of the energy storage system to be managed, so as to adjust the operating parameters of the energy storage system to be managed in real time and complete the thermal management control of the energy storage system to be managed.

10. A thermal management control device for an energy storage system, characterized in that: include: multiple monitoring sensors, memory and processors; The plurality of monitoring sensors are used to collect operating data of the energy storage system; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 8.

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