A stress monitoring method and system for an energy storage battery compartment

Abstract

This invention relates to a stress monitoring method and system for an energy storage battery compartment. On one hand, it utilizes a high-precision, integrated displacement sensor to quickly and accurately collect displacement data of the energy storage battery compartment. Combined with a finite element simulation model, the measured displacement data is projected onto the finite element simulation model and cleverly transformed into stress distribution data of the energy storage battery compartment, achieving stress monitoring with simple structural design, convenient data acquisition, and accurate stress analysis. On the other hand, it directly constructs a simulation result library containing various working conditions. Based on the data matching between the actual displacement data and the data in the simulation result library, the current working condition is quickly identified, and the stress distribution results are obtained, saving stress monitoring time, improving stress analysis efficiency, and expanding the scope of application.

Description

Technical Field

[0001] This invention relates to a stress monitoring method and system for an energy storage battery compartment, belonging to the field of energy storage batteries. Background Technology

[0002] As a crucial support for the new energy field, the safety, stability, and efficiency of energy storage battery technology have become a focus of industry attention. The structural design of the energy storage battery compartment, a key component of the battery system, directly affects the operational efficiency and safety of the entire energy storage system. However, in actual operation, the energy storage battery compartment is often affected by various external factors (such as mechanical vibration and temperature changes) and internal factors (such as battery expansion and thermal runaway), leading to uneven structural stress distribution and potentially causing safety accidents.

[0003] During the transportation of energy storage battery compartments, due to complex road conditions and variable vehicle driving conditions, vibrations of varying degrees are often generated. These vibrations may not only damage the internal structure of the battery, affecting its lifespan and performance, but may also cause safety hazards. In particular, during the lifting of energy storage battery compartments, factors such as the operating techniques of the lifting equipment or the design of the battery compartment itself may cause uneven or excessive stress on the battery compartment, leading to battery pack deformation, internal short circuits, and other safety hazards. These hazards not only affect the performance and lifespan of the battery, but may also pose a threat to personnel and equipment.

[0004] However, existing stress monitoring methods for energy storage battery compartments have the following technical problems: 1. Limited stress monitoring means: Currently, most energy storage battery compartment stress monitoring relies mainly on external sensors. These sensors have limitations in measurement accuracy, real-time performance, and reliability, and cannot comprehensively and accurately reflect the stress distribution inside the battery compartment; 2. Insufficient structural optimization design: Due to the lack of effective stress monitoring data, the structural optimization design of energy storage battery compartments often relies on empirical design and simulation calculations, making it difficult to achieve precise optimization, resulting in structural redundancy or insufficient strength; 3. Imperfect early warning mechanism: Most existing early warning systems are based on monitoring parameters such as battery voltage, current, and temperature, and are slow to react to changes in structural stress, failing to detect and warn of potential safety hazards in a timely manner. Summary of the Invention

[0005] The purpose of this invention is to provide a stress monitoring method and system for energy storage battery compartments, in order to solve the problem of inaccurate stress monitoring in energy storage battery compartments. By combining displacement sensors and finite element simulation models, the method can accurately monitor the stress distribution of energy storage battery compartments under current operating conditions.

[0006] To achieve the above objectives, on the one hand, this invention proposes a stress monitoring method for an energy storage battery compartment, which involves installing a displacement sensor capable of monitoring structural changes in the battery compartment to be monitored. The method includes: Establish a finite element simulation model of the energy storage battery compartment; Under the current operating conditions, acquire the displacement data collected by the displacement sensor; The displacement data is input into the finite element simulation model to generate stress distribution results.

[0007] Furthermore, in the stress monitoring method for the aforementioned energy storage battery compartment, a displacement sensor is installed in the middle of the top crossbeam of the energy storage battery compartment to be monitored.

[0008] Furthermore, the displacement data acquired by the stress monitoring method for the aforementioned energy storage battery compartment is preprocessed in the following manner: The displacement data is filtered, denoised, and calibrated in sequence.

[0009] Furthermore, the stress monitoring method for the aforementioned energy storage battery compartment also includes: Preset the stress warning threshold for the energy storage battery compartment under the current operating conditions; Based on the stress distribution results, when the stress value in a stress area exceeds the stress warning threshold, the warning system is triggered to generate an alarm signal.

[0010] Furthermore, the stress monitoring method for the aforementioned energy storage battery compartment also includes: Based on the stress distribution results, high-stress areas and structural weaknesses of the energy storage battery compartment are identified, and the structure of the energy storage battery compartment under the current operating conditions is optimized based on the high-stress areas and structural weaknesses.

[0011] Furthermore, the stress monitoring method for the aforementioned energy storage battery compartment is applicable under either the hoisting condition or the vibration condition.

[0012] On the other hand, the present invention proposes a stress monitoring method for an energy storage battery compartment, comprising: The stress distribution of the energy storage battery compartment under different working conditions is simulated and calculated in advance to obtain the stress simulation results under each working condition, and the simulation results under all working conditions are stored in the simulation result library. Acquire actual displacement data collected by displacement sensors installed on the energy storage battery compartment; Select the target working condition that matches the actual displacement data from all working conditions stored in the simulation result library, and determine the simulation result of the target working condition as the stress distribution result.

[0013] Furthermore, the stress monitoring method for the aforementioned energy storage battery compartment also includes: Obtain the stress distribution characteristics under the target working condition; Based on the stress distribution characteristics and actual displacement data, the structure of the energy storage battery compartment is optimized, and the stress simulation results corresponding to the target working condition in the simulation result library are updated.

[0014] Furthermore, the stress monitoring method for the aforementioned energy storage battery compartment also includes: Based on the stress distribution results, determine whether the current stress distribution of the energy storage battery compartment exceeds the safety threshold of each stress area under the target operating condition. If so, trigger the early warning system to generate an alarm signal.

[0015] On the other hand, the present invention also proposes a stress monitoring system for an energy storage battery compartment, including a processor, the processor being used to execute a computer program to implement the steps of the stress monitoring method for the energy storage battery compartment described above.

[0016] The beneficial effects of this invention are as follows: On the one hand, by installing displacement sensors to monitor structural changes in the energy storage battery compartment, a finite element simulation model of the battery compartment is established; under the current operating conditions, displacement data collected by the displacement sensors is acquired; the displacement data is input into the finite element simulation model to generate stress distribution results; by rationally installing displacement sensors on the energy storage battery compartment and combining them with the finite element simulation model, the displacement changes of the energy storage battery compartment can be analyzed efficiently and accurately, thereby obtaining the stress distribution results of the energy storage battery compartment, timely observing the state of the energy storage battery compartment, improving the environmental safety factor of the energy storage battery compartment, and also helping to extend the life of the energy storage battery compartment; on the other hand, by pre-testing the energy storage battery compartment under different operating conditions... The battery compartment undergoes stress distribution simulation calculations to obtain stress simulation results under various operating conditions, and these results are stored in a simulation result library. Actual displacement data collected by displacement sensors installed on the battery compartment is acquired. Target operating conditions matching the actual displacement data are selected from all operating conditions stored in the simulation result library, and the simulation results of these target operating conditions are determined as the stress distribution results. Using the constructed simulation result library that integrates simulation results from various operating conditions, the actual displacement data is directly matched with the target operating conditions, thereby determining the current stress distribution result. This saves stress monitoring time, efficiently completes stress analysis of the battery compartment under the current operating conditions, and improves stress monitoring efficiency while ensuring the accuracy of stress result analysis. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a stress monitoring method for an energy storage battery compartment according to one aspect of this application. Figure 2 This is a schematic diagram of the structure of an energy storage battery compartment equipped with a displacement sensor in a practical application scenario, which is one aspect of the stress monitoring method for an energy storage battery compartment proposed in this application. Figure 3 This is a schematic diagram of the displacement distribution of an energy storage battery compartment under hoisting conditions in a practical application scenario, based on a stress monitoring method for an energy storage battery compartment proposed in one aspect of this application. Figure 4This is a schematic diagram of the displacement distribution of an energy storage battery compartment under vibration conditions in a practical application scenario, based on a stress monitoring method for an energy storage battery compartment proposed in one aspect of this application. Figure 5 This is a flowchart of a stress monitoring method for an energy storage battery compartment proposed in one aspect of this application in a practical application scenario; Figure label: A-Energy storage battery compartment; a1-Displacement sensor; a2-Side beam; a3-Battery placement rack. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0019] The invention is conceived to monitor and understand the stress distribution of the energy storage battery compartment in real time. On the one hand, it utilizes high-precision, integrated displacement sensors to quickly and accurately collect displacement data of the battery compartment. Combined with a finite element simulation model, the measured displacement data is projected onto the finite element simulation model and cleverly transformed into stress distribution data of the battery compartment, achieving stress monitoring with simple structural design, convenient data acquisition, and accurate stress analysis. On the other hand, it directly constructs a simulation result library containing various working conditions. Based on the data matching between the actual displacement data and the data in the simulation result library, the current working condition is quickly identified, and the stress distribution results are obtained, saving stress monitoring time, improving stress analysis efficiency, and expanding the scope of application.

[0020] Method Example 1: like Figure 1 The diagram shown is a flowchart illustrating a stress monitoring method for an energy storage battery compartment according to one aspect of this application. The method includes steps S11-S14, specifically: Step S11 involves installing displacement sensors to monitor structural changes in the energy storage battery compartment, ensuring comprehensive coverage of key monitoring points. A reasonable displacement data acquisition frequency is set according to different operating conditions or the specific requirements of the energy storage battery compartment, integrating the displacement sensor and the battery compartment. Preferably, the displacement sensor is installed in the middle of the top crossbeam of the battery compartment. Specifically, for example... Figure 2 This is a structural diagram of an energy storage battery compartment equipped with a displacement sensor in a practical application scenario, according to a stress monitoring method for an energy storage battery compartment proposed in one aspect of this application. The energy storage battery compartment A consists of a top crossbeam, side beams a2, and a battery placement rack a3. A displacement sensor a1 is installed in the middle of the crossbeam at the top of the compartment.

[0021] Furthermore, this application preferably uses a magnetostrictive displacement sensor, which is a high-precision, long-stroke absolute position measurement sensor manufactured based on the magnetostrictive principle. It employs a non-contact measurement method; since the moving magnetic ring used for measurement and the sensor itself do not directly contact each other, they are not subject to friction or wear. Therefore, it has a long service life, strong environmental adaptability, high reliability, good safety, and is easy to automate in the system. It can even operate normally in harsh industrial environments. In addition, it can withstand high temperatures, high pressures, and strong vibrations, fully meeting the displacement data acquisition requirements of various operating conditions in energy storage battery compartments.

[0022] Step S12: Establish the finite element simulation model of the energy storage battery compartment. It should be noted that the finite element simulation model is a model established using the finite element analysis method. It has high adaptability, wide application range, and can handle various loads and boundary conditions, satisfying the construction of the energy storage battery compartment model under various working conditions. At the same time, a detailed finite element simulation model is also established based on the actual geometric structure, material properties, and working environment of the energy storage battery compartment.

[0023] Step S13: Under the current working conditions, acquire the displacement data collected by the displacement sensor. Here, in the actual application scenario, after acquiring the displacement data collected by the displacement sensor, the acquired displacement data is preprocessed. Specifically, the displacement data is filtered, denoised, and calibrated in sequence to eliminate noise and environmental interference when the displacement sensor acquires the displacement data, and to ensure the accuracy and consistency of the measurement results.

[0024] The preprocessed displacement data is integrated into the input format of the finite element simulation model, and step S14 is executed to input the displacement data into the finite element simulation model to generate stress distribution results. Here, after receiving the displacement data, the finite element simulation model performs stress distribution simulation calculations, which also considers factors such as the nonlinear characteristics of the energy storage battery compartment material, contact effects, and boundary conditions to ensure the accuracy of the simulation results. The stress distribution results include, but are not limited to, stress distribution diagrams and stress distribution values. In the preferred embodiment of this application, the preferred stress distribution results include stress distribution diagrams and stress distribution values.

[0025] Through the above steps S11-S14, an integrated displacement sensor and energy storage battery compartment are realized. The overall structure is not only compact, but also easy to install and maintain. It can adapt to the complex environment of the energy storage battery compartment and reduce construction costs and time. By combining the finite element simulation model and simulation calculation technology, the measured displacement data is converted into stress distribution data, thereby observing the stress distribution results of the energy storage battery compartment and gaining a deeper understanding of the real-time stress changes of the energy storage battery compartment.

[0026] This application provides a preferred embodiment of a stress monitoring method for an energy storage battery compartment. A magnetostrictive displacement sensor is designed and installed in the middle of the crossbeam at the top of the energy storage battery compartment M to be tested. A finite element simulation model M' is established based on the geometry, material properties, and working environment of the energy storage battery compartment M. Under the current operating conditions, the magnetostrictive displacement sensor is used to collect displacement data of the energy storage battery compartment M in real time, and the collected displacement data is sequentially filtered, denoised, and calibrated to complete data preprocessing. The preprocessed displacement data is integrated into the input format of the finite element simulation model M' and input into the finite element simulation model M'. The finite element simulation model M' performs stress distribution simulation calculations on the input data to generate a stress distribution map and stress distribution values ​​for the energy storage battery compartment M under the current operating conditions.

[0027] Method Example 2: Following the above embodiments, the stress monitoring method for an energy storage battery compartment provided in this application further includes: A preset stress warning threshold for the energy storage battery compartment under the current operating conditions is provided. Here, the stress warning threshold is used to determine whether the stress at various locations in the energy storage battery compartment has reached a high stress threshold. It can be a certain stress value or a certain stress range. Specifically, it can be set according to different needs.

[0028] Based on the stress distribution results, when the stress value in a stress area exceeds the stress warning threshold, the warning system is triggered to generate an alarm signal, alerting maintenance personnel that the energy storage battery compartment is in stress crisis. This enables real-time monitoring and immediate feedback of the stress state of the energy storage battery compartment, improving the timeliness and accuracy of monitoring.

[0029] This application provides a preferred embodiment of a stress monitoring method for an energy storage battery compartment. The preset stress warning threshold for the energy storage battery compartment M is [0, 50]. A finite element simulation model M' is used to perform stress distribution simulation calculations, generating a stress distribution map and stress distribution values ​​for the energy storage battery compartment M. It is then determined whether the stress distribution values ​​of the energy storage battery compartment fall within the stress warning threshold [0, 50]. If they do, the warning system is triggered, generating an alarm signal to alert maintenance personnel that the energy storage battery compartment M is experiencing a stress crisis. If they do not fall within the threshold, the displacement data of the energy storage battery compartment M is continuously collected and monitored.

[0030] Method Example 3: Following the above embodiments, the stress monitoring method for an energy storage battery compartment provided in this application further includes: Based on the stress distribution results, high-stress areas and structural weaknesses of the energy storage battery compartment are identified, and the structure of the energy storage battery compartment under the current operating conditions is optimized based on these high-stress areas and structural weaknesses. Here, the optimization of the energy storage battery compartment includes, but is not limited to, the arrangement of stiffeners, material replacement, and redesign of the structure. This provides a scientific basis for structural optimization by utilizing high-precision calculations of the stress distribution of the energy storage battery compartment, thereby improving the quality and lifespan of the energy storage battery compartment.

[0031] It should also be noted that, in this application, the current working condition includes either the hoisting working condition or the vibration working condition.

[0032] like Figure 3 The diagram shows the displacement distribution of an energy storage battery compartment under lifting conditions in a practical application scenario, based on a stress monitoring method for an energy storage battery compartment proposed in one aspect of this application. Under lifting conditions, the stress distribution is simulated and calculated within a finite element simulation model, generating a stress distribution map of the energy storage battery compartment A. Based on the stress distribution map, the high-stress area and structural weakness of the energy storage battery compartment A are identified as the crossbeam at the top of the compartment. Specifically, the red area in the middle of the crossbeam represents the high-stress area, which is the weakest structurally and has the greatest impact on the structure under lifting conditions. The gradual change in color to blue on both sides of the red high-stress area indicates that the high stress gradually decreases, and the impact on the structure under lifting conditions gradually weakens. Therefore, under lifting conditions, the material of the crossbeam at the top of the energy storage battery compartment will be strengthened to prevent the energy storage battery compartment from being in a high-risk state during lifting.

[0033] like Figure 4 The diagram illustrates the displacement distribution of an energy storage battery compartment under vibration conditions in a practical application scenario, based on a stress monitoring method for an energy storage battery compartment proposed in one aspect of this application. Under vibration conditions, the stress distribution is simulated and calculated within a finite element simulation model, generating a stress distribution map of the energy storage battery compartment. Based on the stress distribution map, the high-stress areas and structural weaknesses of the energy storage battery compartment are identified as the crossbeam and side beam a2 at the top of the compartment. Specifically, the red area in the middle of the crossbeam represents the high-stress area, which is the weakest structurally and experiences the greatest structural impact during hoisting. The color gradually changes to blue on both sides of the red high-stress area until it reaches side beam a2, indicating that the high stress gradually decreases and the impact on the structure under vibration conditions gradually weakens. Therefore, under vibration conditions, reinforcing ribs are arranged on the crossbeam and side beam a2 at the top of the compartment to prevent the energy storage battery compartment from being in a high-risk state under vibration conditions.

[0034] Method Example 4: For example, the process involves five steps: Step 1: Designing and installing displacement sensors on the energy storage battery compartment to ensure coverage of key monitoring points and setting a reasonable data acquisition frequency; Step 2: Establishing a finite element simulation model of the energy storage battery compartment based on geometric modeling, material property definitions, and boundary condition settings; Step 3: Real-time acquisition of displacement data using displacement sensors, followed by preprocessing to extract effective and high-precision displacement data; Step 4: Inputting the preprocessed displacement data into the simulation model to perform stress distribution simulation calculations and generate stress distribution results for the energy storage battery compartment; Step 5: Evaluating the structural state of the energy storage battery compartment based on the stress distribution results, identifying potential high-stress areas, and developing corresponding structural optimization schemes. Furthermore, based on a preset stress warning threshold, when the monitored stress level exceeds the threshold, the warning system automatically triggers the warning mechanism, sending warning information to maintenance personnel.

[0035] This application enables real-time monitoring and immediate feedback of the stress state of the energy storage battery compartment, improving the timeliness and accuracy of monitoring. By utilizing simulation technology and combining real-time displacement data, it achieves high-precision calculation of the stress distribution of the energy storage battery compartment, providing a scientific basis for subsequent structural optimization. Furthermore, it establishes an intelligent early warning system based on stress levels, which can automatically identify potential safety hazards and issue early warnings, effectively reducing the risk of safety accidents.

[0036] Method Example 5: like Figure 5 This is a structural diagram of a stress monitoring method for an energy storage battery compartment proposed in one aspect of this application, applied in a practical scenario. High-precision displacement sensors are installed at key structural locations within the energy storage battery compartment. The sensors transmit real-time collected displacement data to a data processing unit via wired or wireless means. Upon receiving the raw displacement data, the data processing unit preprocesses it and organizes the preprocessed displacement data into a format suitable for input to the simulation model.

[0037] Within the simulation calculation module, a finite element simulation model is established under the current working conditions based on the geometric and material parameters of the energy storage battery compartment. The preprocessed displacement data is then input into the finite element simulation model as boundary or loading conditions. The finite element simulation model starts the simulation calculation program and performs stress distribution simulation calculations based on the input displacement data and the finite element simulation model, generating stress distribution results.

[0038] Within the structural optimization and early warning system, the stress distribution results obtained from simulation calculations are analyzed to identify high-stress areas and potential structural weaknesses. Based on the analysis results, structural optimization schemes are proposed. Furthermore, a stress early warning threshold is set; when simulation results show that the stress in a certain area exceeds the threshold, the early warning system is triggered. The early warning system sends warning signals to maintenance personnel through audible and visual alarms, SMS notifications, or remote monitoring platforms.

[0039] In another aspect of this application, a stress monitoring method for an energy storage battery compartment is provided, comprising: The stress distribution of the energy storage battery compartment under different working conditions is simulated and calculated in advance to obtain the stress simulation results under each working condition, and the simulation results under all working conditions are stored in the simulation result library; here, the working conditions include, but are not limited to, normal working conditions, extreme temperature working conditions, vibration working conditions, wind load conditions, and hoisting working conditions, etc., which are the working conditions of the energy storage battery compartment.

[0040] Acquire actual displacement data collected by displacement sensors installed on the energy storage battery compartment; here, the displacement sensors need to be installed in a position that can monitor changes in the compartment structure, for example, a displacement sensor is installed in the middle of the crossbeam at the top of the energy storage battery compartment.

[0041] The system selects target working conditions that match the actual displacement data from all working conditions stored in the simulation results library, and determines the simulation results of the target working conditions as stress distribution results, thereby achieving more efficient and wider-ranging stress monitoring of energy storage battery compartments and facilitating the management of energy storage battery compartments.

[0042] Meanwhile, in order to improve the accuracy of the simulation results library, the stress distribution characteristics under the target working conditions are obtained. Here, the stress distribution characteristics refer to the stress distribution of each part of the energy storage battery compartment under the target working conditions. For example, under the hoisting condition, the top beam of the compartment is a high-stress area and other locations are low-stress areas; under the vibration condition, the top beam and side beam of the compartment are high-stress areas and other locations are low-stress areas.

[0043] Based on the stress distribution characteristics and actual displacement data, the structure of the energy storage battery compartment is optimized, and the stress simulation results corresponding to the target working condition in the simulation result library are updated.

[0044] Furthermore, in order for maintenance personnel to respond quickly to high-stress areas, the system determines whether the stress distribution of the current energy storage battery compartment exceeds the safety threshold of each stress area under the target operating condition based on the stress distribution results. If so, the system triggers the early warning system to generate an alarm signal.

[0045] Method Example 6: For example, high-precision displacement sensors are installed at key structural locations in the energy storage battery compartment to ensure comprehensive coverage and accurate monitoring of displacement changes in the energy storage battery compartment under various operating conditions. After collecting the raw displacement data, noise reduction processing is first performed to eliminate sensor noise and environmental interference. Then, data calibration is performed to ensure the accuracy and consistency of the measurement results. Finally, the preprocessed displacement data is organized into a format suitable for input to the simulation model.

[0046] The stress distribution of the energy storage battery compartment under different operating conditions is simulated and calculated in advance, and the results are stored in the simulation result library. The displacement data measured in real time by displacement sensors is compared and analyzed with the data in the simulation result library. Through algorithm matching, the operating condition that is closest to the real-time displacement data is found and determined as the target operating condition. The simulation results under the target operating condition are directly used as the current stress distribution result.

[0047] Meanwhile, the target operating condition is used as the evaluation basis for the current energy storage battery compartment. Specifically, based on the stress distribution results, it is determined whether the stress distribution of the current energy storage battery compartment exceeds the safety threshold of each stress region under the target operating condition. If it does, the early warning system is triggered. In addition, actual displacement data is combined with the stress distribution characteristics under the target operating condition to continuously update the simulation result library and early warning thresholds, thereby improving the accuracy and adaptability of the system.

[0048] In summary, the significant benefits of this application regarding the structural optimization and safety monitoring of energy storage battery compartments include: Precise stress distribution monitoring: By installing high-precision displacement sensors on the energy storage battery compartment, the displacement changes of the compartment under different operating conditions can be measured in real time and accurately. Combined with simulation calculation models, these displacement data are transformed into detailed stress distribution maps, providing structural designers with unprecedentedly accurate data support. This data-driven stress analysis method greatly improves the accuracy and comprehensiveness of structural stress assessment compared to traditional experience-based design or finite-point stress testing.

[0049] Structural optimization and weight reduction: Based on stress distribution data obtained from real-time monitoring and simulation analysis, engineers can accurately identify high-stress concentration areas and low-stress redundancy areas in the structure. Through targeted structural optimization design, such as strengthening high-stress areas, optimizing material layout, or adopting more efficient structural forms, not only is the load-bearing capacity and safety performance of the energy storage battery compartment significantly improved, but the overall weight is also effectively reduced, energy utilization efficiency is improved, and production and transportation costs are lowered.

[0050] Early warning and fault prevention: Real-time monitoring of the stress state of the energy storage battery compartment triggers an early warning mechanism immediately upon detection of abnormal stress concentration or displacement trends. This early warning capability enables maintenance personnel to respond quickly and take necessary maintenance measures, such as adjusting the workload, reinforcing the structure, or performing preventative maintenance. This effectively avoids structural failure, battery damage, and even safety accidents, ensuring the safe and stable operation of the equipment and extending its service life.

[0051] Intelligent Management and Decision Support: The energy storage battery compartment, integrating displacement sensors and a data analysis system, enables intelligent monitoring of its structural health. Through big data analytics, the system can learn and predict trends in structural performance, providing scientific decision support for operations managers. This intelligent management approach not only improves management efficiency but also reduces the risk of human error, ensuring the long-term reliable operation of the energy storage system.

[0052] Promoting the Development of the New Energy Industry: The application of this invention not only enhances the technological level and market competitiveness of energy storage battery compartments, but also drives the new energy industry towards a safer, more efficient, and intelligent direction. With the widespread application of energy storage technology in new energy vehicles, smart grids, and renewable energy, the promotion of this application will foster sustainable development in these fields and contribute to building a clean, low-carbon, safe, and efficient energy system.

[0053] In another aspect of this application, a stress monitoring system for an energy storage battery compartment is also provided, including a processor for executing a computer program to implement the steps of the stress monitoring method for the energy storage battery compartment described above. The description of the above method is sufficiently clear in the embodiments and will not be repeated here.

Claims

1. A method for stress monitoring of an energy storage battery compartment, characterized in that, The method involves installing displacement sensors capable of monitoring structural changes in an energy storage battery compartment to be monitored. Establish a finite element simulation model of the energy storage battery compartment; Under the current operating conditions, acquire the displacement data collected by the displacement sensor; The displacement data is input into the finite element simulation model to generate stress distribution results.

2. The stress monitoring method for the energy storage battery compartment according to claim 1, characterized in that, A displacement sensor is installed in the middle of the top crossbeam of the energy storage battery compartment to be monitored.

3. The stress monitoring method for the energy storage battery compartment according to claim 1, characterized in that, The acquired displacement data is preprocessed in the following manner: The displacement data is filtered, denoised, and calibrated in sequence.

4. The stress monitoring method for the energy storage battery compartment according to claim 1, characterized in that, Also includes: Preset the stress warning threshold for the energy storage battery compartment under the current operating conditions; Based on the stress distribution results, when the stress value in a stress area exceeds the stress warning threshold, the warning system is triggered to generate an alarm signal.

5. The stress monitoring method for the energy storage battery compartment according to claim 1, characterized in that, Also includes; Based on the stress distribution results, high-stress areas and structural weaknesses of the energy storage battery compartment are identified, and the structure of the energy storage battery compartment under the current operating conditions is optimized based on the high-stress areas and structural weaknesses.

6. The stress monitoring method for the energy storage battery compartment according to any one of claims 1-5, characterized in that, The current working condition includes either the hoisting condition or the vibration condition.

7. A method for stress monitoring of an energy storage battery compartment, characterized in that, include: The stress distribution of the energy storage battery compartment under different working conditions is simulated and calculated in advance to obtain the stress simulation results under each working condition, and the simulation results under all working conditions are stored in the simulation result library. Acquire actual displacement data collected by displacement sensors installed on the energy storage battery compartment; Select the target working condition that matches the actual displacement data from all working conditions stored in the simulation result library, and determine the simulation result of the target working condition as the stress distribution result.

8. The stress monitoring method for the energy storage battery compartment according to claim 7, characterized in that, Also includes: Obtain the stress distribution characteristics under the target working condition; Based on the stress distribution characteristics and actual displacement data, the structure of the energy storage battery compartment is optimized, and the stress simulation results corresponding to the target working condition in the simulation result library are updated.

9. The stress monitoring method for the energy storage battery compartment according to any one of claims 7-8, characterized in that, Also includes: Based on the stress distribution results, determine whether the current stress distribution of the energy storage battery compartment exceeds the safety threshold of each stress area under the target operating condition. If so, trigger the early warning system to generate an alarm signal.

10. A stress monitoring system for an energy storage battery compartment, comprising a processor, characterized in that, The processor is used to execute a computer program to implement the steps of the stress monitoring method for the energy storage battery compartment according to any one of claims 1-9.

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