Thermal management optimization method for energy storage device

Abstract

The invention relates to the technical field of energy storage management, in particular to a thermal management optimization method for an energy storage device, and the method comprises the steps: obtaining the basic feature information of the energy storage device, building a finite element simulation model of the energy storage device according to the basic feature information, and carrying out the preprocessing operation; carrying out transient heat conduction analysis on the finite element simulation model subjected to pretreatment, and extracting a transient heat conduction analysis result; the steady-state feature information of the energy storage device under safe operation is obtained, the finite element simulation model is corrected and solved according to the steady-state feature information until the analysis result output by transient heat conduction analysis reaches the optimization target, and the risk of thermal runaway of the energy storage device is reduced.

Description

Background Art

[0002] Thermal management is crucial in energy storage devices. Excessively high temperatures will accelerate the aging process of the battery, increase internal resistance, reduce capacity, and even cause thermal runaway. On the contrary, too low a temperature will reduce the reaction rate of the battery and affect its performance. Therefore, optimizing the thermal management system and ensuring that the energy storage device operates within the ideal temperature range have become the key to ensuring its reliability and life. Energy storage devices include many types, such as lithium-ion batteries, sodium-sulfur batteries, flywheel energy storage, supercapacitors, compressed air energy storage, etc. Among them, lithium-ion batteries have become the most important energy storage technology due to their high energy density and wide application. However, different types of energy storage devices will generate heat during operation. If not managed in time, it may lead to performance degradation, shortened life, and even cause safety problems such as thermal runaway. Summary of the invention

[0003] The main purpose of the present invention is to provide a thermal management optimization method for an energy storage device, which effectively solves the above-mentioned problems mentioned in the background technology.

[0004] The technical solution of the present invention is as follows:

[0005] A thermal management optimization method for an energy storage device is proposed, the method comprising the following steps:

[0006] S1. Obtain basic characteristic information of the energy storage device, establish a finite element simulation model of the energy storage device according to the basic characteristic information, and perform pre-processing operations;

[0007] S2. Perform transient heat conduction analysis on the finite element simulation model after pre-processing, and extract transient heat conduction analysis results;

[0008] S3. Obtain steady-state characteristic information of the energy storage device under safe operation, correct and solve the finite element simulation model according to the steady-state characteristic information, until the analysis result output by the transient heat conduction analysis reaches the optimization target, and output the transient heat conduction analysis result at this time.

[0009] A further improvement of the present invention is that the basic feature information in S1 includes geometric feature information, material feature information, and boundary feature information. The geometric feature information includes geometric size information of the energy storage device; the material feature information includes density information, thermal conductivity information, and specific heat capacity information of the energy storage device; and the boundary feature information includes ambient temperature information, convective heat transfer coefficient, heat generation rate information, and thermal conductivity information.

[0010] A further improvement of the present invention is that S1 comprises the following specific steps:

[0011] S11, obtaining geometric characteristic information, material characteristic information, and boundary characteristic information of the energy storage device;

[0012] S12. Establish energy storage device components in ABAQUS finite element software according to the geometric feature information of the energy storage device, select a heat conduction constitutive model, add materials in the property module according to the material feature information and assign material properties, and assign the material properties to the energy storage device components;

[0013] S13, enter the assembly module to create an instance;

[0014] S14. Enter the meshing module to define the global mesh size, select the DC3C8 unit type to mesh the created instance, and output the pre-processing model.

[0015] A further improvement of the present invention is that S2 comprises the following specific steps:

[0016] S21. Set the solver for the pre-processing model in ABAQUS finite element software and set the analysis type to transient heat conduction analysis;

[0017] S22, define the total analysis time and analysis step in the solver and specify the output variables;

[0018] S23. Enter the contact module to create a contact pair and select the contact type as surface-to-surface contact. Enter the thermal conductivity information in the contact properties. After solving the calculation, output the transient heat conduction analysis results.

[0019] A further improvement of the present invention is that the transient heat conduction analysis result in S23 is a trend of temperature changes at different locations inside the energy storage system over time.

[0020] A further improvement of the present invention is that the specific steps of judging whether the analysis result output by the transient heat conduction analysis reaches the optimization target in S3 are:

[0021] S31, extracting the temperature variation trend of different positions inside the energy storage system over time, and obtaining a function curve of the temperature variation trend of a single position over time;

[0022] S32, extracting the maximum value in the function curve of the temperature variation trend over time of all single locations;

[0023] S33. When all the maximum values ​​extracted in S32 are less than or equal to the maximum value under safe operation of the energy storage device, the optimization target is achieved, and the transient heat conduction analysis result of the finite element simulation model at this time is output.

[0024] The technical effects of the present invention are as follows:

[0025] A thermal management optimization method for an energy storage device is constructed. The present invention obtains basic characteristic information of the energy storage device, establishes a finite element simulation model of the energy storage device according to the basic characteristic information, and performs pre-processing operations; performs transient heat conduction analysis on the finite element simulation model after the pre-processing is completed, and extracts the transient heat conduction analysis results; obtains steady-state characteristic information of the energy storage device under safe operation, corrects the finite element simulation model according to the steady-state characteristic information, and solves it until the analysis result output by the transient heat conduction analysis reaches the optimization target, thereby reducing the risk of thermal runaway of the energy storage device. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0027] Figure 1 A schematic flow chart of a thermal management optimization method for an energy storage device according to the present invention. DETAILED DESCRIPTION

[0028] The present invention aims to propose a thermal management optimization method for an energy storage device, by acquiring basic characteristic information of the energy storage device, establishing a finite element simulation model of the energy storage device according to the basic characteristic information and performing pre-processing operations; performing transient heat conduction analysis on the finite element simulation model after the pre-processing, and extracting transient heat conduction analysis results; obtaining steady-state characteristic information of the energy storage device under safe operation, correcting the finite element simulation model according to the steady-state characteristic information and solving it, until the analysis results output by the transient heat conduction analysis reach the optimization target, thereby reducing the risk of thermal runaway of the energy storage device.

[0029] Embodiment 1:

[0030] This embodiment proposes a thermal management optimization method for an energy storage device, such as Figure 1 As shown, the following specific steps are included:

[0031] S1. Obtain basic characteristic information of the energy storage device, establish a finite element simulation model of the energy storage device according to the basic characteristic information, and perform pre-processing operations;

[0032] S2. Perform transient heat conduction analysis on the finite element simulation model after pre-processing, and extract transient heat conduction analysis results;

[0033] S3. Obtain steady-state characteristic information of the energy storage device under safe operation, correct and solve the finite element simulation model according to the steady-state characteristic information, until the analysis result output by the transient heat conduction analysis reaches the optimization target, and output the transient heat conduction analysis result at this time.

[0034] A further improvement of the present invention is that the basic feature information in S1 includes geometric feature information, material feature information, and boundary feature information, and the geometric feature information includes geometric dimension information of the energy storage device, such as the length, width, height, etc. of the battery unit. These geometric parameters are the basis for establishing a simulation model; the material feature information includes density information, thermal conductivity information, and specific heat capacity information of the energy storage device, which determine the behavior of the material in the heat conduction process, such as the speed of heat transfer and the ability to absorb heat; the boundary feature information includes ambient temperature information, convection heat transfer coefficient, heat generation rate information, and thermal conductivity information, which define the heat exchange between the energy storage device and the external environment and affect the accuracy of the simulation results.

[0035] A further improvement of the present invention is that S1 comprises the following specific steps:

[0036] S11, obtaining geometric characteristic information, material characteristic information, and boundary characteristic information of the energy storage device;

[0037] S12. According to the geometric feature information of the energy storage device, establish the energy storage device components in the ABAQUS finite element software, select the heat conduction constitutive model, add materials and assign material properties in the property module according to the material feature information, and assign the material properties to the energy storage device components; in ABAQUS, build a three-dimensional model of the energy storage device based on the acquired geometric feature information. The model should reflect the shape and structure of the actual device as accurately as possible to ensure the reliability of the simulation results. The constitutive model is used to describe the thermal conduction behavior of the material, usually based on Fourier's law. Material properties such as thermal conductivity, density, and specific heat capacity are assigned to each component in the model at this time to ensure that the heat conduction process can be correctly simulated. In the property module of ABAQUS, create and assign material properties. This step ensures that the thermophysical properties of each component are correctly described.

[0038] S13, enter the assembly module to create an instance;

[0039] S14. Enter the meshing module to define the global mesh size, select the DC3C8 unit type to mesh the created instance, and output the pre-processing model.

[0040] In the mesh module, set the global mesh size to ensure that the model is reasonably divided in space. The fineness of the mesh affects the balance between calculation accuracy and calculation amount. Select the DC3C8 unit type (8-node three-dimensional heat conduction unit), which is suitable for heat conduction analysis. This unit type can simulate the heat conduction process well. After the mesh division is completed, generate the pre-processing model to prepare for the subsequent simulation analysis.

[0041] A further improvement of the present invention is that S2 comprises the following specific steps:

[0042] S21. Set the solver for the pre-processing model in ABAQUS finite element software and set the analysis type to transient heat conduction analysis; select the transient heat conduction analysis type to simulate the temperature distribution of the energy storage device as it changes over time. Transient analysis takes the time factor into account, so that the dynamic change of temperature over time can be observed.

[0043] S22, define the total analysis time and analysis step in the solver and specify the output variables;

[0044] S23. Enter the contact module to create a contact pair and select the contact type as surface-to-surface contact. Enter the thermal conductivity information in the contact properties, and output the transient heat conduction analysis results after solving the calculation. Enter the contact properties such as thermal conductivity to simulate the heat transfer between components. This information directly affects the accuracy of the simulation results.

[0045] A further improvement of the present invention is that the transient heat conduction analysis result in S23 is the temperature variation trend of different positions inside the energy storage system over time, which is used as a basis for judging whether the system has achieved the optimization goal.

[0046] A further improvement of the present invention is that the specific steps of judging whether the analysis result output by the transient heat conduction analysis reaches the optimization target in S3 are:

[0047] S31, extracting the temperature variation trend of different positions inside the energy storage system over time, and obtaining a function curve of the temperature variation trend of a single position over time;

[0048] S32. Extract the maximum value in the function curve of the temperature change trend over time at all single locations, analyze the temperature curve of each location, and extract its maximum value. The maximum value is usually the most concerned indicator in the design, which directly reflects the thermal management effect of the system;

[0049] S33. When all the maximum values ​​extracted in S32 are less than or equal to the maximum value under safe operation of the energy storage device, the optimization target is achieved, and the transient heat conduction analysis result of the finite element simulation model at this time is output. The simulation result at this time can be output as the final result.

[0050] The core of this invention is to systematically analyze and optimize the thermal management of energy storage devices through finite element simulation methods. By acquiring and correcting basic characteristic information and combining transient and steady-state analysis methods, the thermal management performance of energy storage devices can be effectively improved to ensure that they operate within a safe and stable temperature range. This method not only improves the accuracy and reliability of simulation, but also provides a powerful tool for the design and optimization of actual energy storage systems.

[0051] Embodiment 2:

[0052] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-mentioned thermal management optimization method for an energy storage device by calling the computer program stored in the memory.

[0053] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement a thermal management optimization method for an energy storage device provided in the above method embodiment. The electronic device may also include other components for implementing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0054] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), and it can also be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0055] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0056] The present invention is described with reference to flowcharts and block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process or block in the flowchart and block diagram, as well as the combination of processes and blocks in the flowchart or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts. Figure 1 Process or multiple processes and boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0057] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 Process or multiple processes and boxes Figure 1 The steps for the functions specified in one or more boxes.

[0058] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A thermal management optimization method for an energy storage device, characterized in that: The specific steps include: S1. Obtain basic characteristic information of the energy storage device, establish a finite element simulation model of the energy storage device according to the basic characteristic information, and perform pre-processing operations; S2. Perform transient heat conduction analysis on the finite element simulation model after pre-processing, and extract transient heat conduction analysis results; S3. Obtain steady-state characteristic information of the energy storage device under safe operation, correct and solve the finite element simulation model according to the steady-state characteristic information, until the analysis result output by the transient heat conduction analysis reaches the optimization target, and output the transient heat conduction analysis result at this time.

2. A thermal management optimization method for an energy storage device according to claim 1, characterized in that: The basic feature information in S1 includes geometric feature information, material feature information, and boundary feature information. The geometric feature information includes geometric size information of the energy storage device; the material feature information includes density information, thermal conductivity information, and specific heat capacity information of the energy storage device; the boundary feature information includes ambient temperature information, convection heat transfer coefficient, heat generation rate information, and thermal conductivity information.

3. A thermal management optimization method for an energy storage device according to claim 2, characterized in that: The S1 comprises the following specific steps: S11, obtaining geometric characteristic information, material characteristic information, and boundary characteristic information of the energy storage device; S12. Establish energy storage device components in ABAQUS finite element software according to the geometric feature information of the energy storage device, select a heat conduction constitutive model, add materials in the property module according to the material feature information and assign material properties, and assign the material properties to the energy storage device components; S13, enter the assembly module to create an instance; S14. Enter the meshing module to define the global mesh size, select the DC3C8 unit type to mesh the created instance, and output the pre-processing model.

4. A thermal management optimization method for an energy storage device according to claim 3, characterized in that: The S2 comprises the following specific steps: S21. Set the solver for the pre-processing model in ABAQUS finite element software and set the analysis type to transient heat conduction analysis; S22, define the total analysis time and analysis step in the solver and specify the output variables; S23. Enter the contact module to create a contact pair and select the contact type as surface-to-surface contact. Enter the thermal conductivity information in the contact properties. After solving the calculation, output the transient heat conduction analysis results.

5. A thermal management optimization method for an energy storage device according to claim 4, characterized in that: The transient heat conduction analysis result in S23 is the temperature variation trend of different positions inside the energy storage system over time.

6. A thermal management optimization method for an energy storage device according to claim 5, characterized in that: The specific steps of judging whether the analysis result output by the transient heat conduction analysis reaches the optimization target in S3 are: S31, extracting the temperature variation trend of different positions inside the energy storage system over time, and obtaining a function curve of the temperature variation trend of a single position over time; S32, extracting the maximum value in the function curve of the temperature variation trend over time of all single locations; S33. When all the maximum values ​​extracted in S32 are less than or equal to the maximum value under safe operation of the energy storage device, the optimization target is achieved, and the transient heat conduction analysis result of the finite element simulation model at this time is output.

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