Battery cell exhaust analysis method and device, storage medium and program product

Abstract

The invention provides a battery cell exhaust analysis method and device, a storage medium and a program product, and relates to the technical field of simulation. The exhaust analysis method comprises the following steps: acquiring gas production state data of a naked battery cell in a thermal runaway process of the battery cell; inputting the gas production state data into a three-dimensional model of the bare cell, predicting time-varying data of exhaust states at two ends of the bare cell, and constructing the three-dimensional model based on the size of the bare cell; and inputting the time-varying data of the exhaust states at the two ends of the naked battery cell into a one-dimensional model of a flow channel between the naked battery cell and a shell of the battery cell, predicting the state of the gas in the flow channel, and constructing the one-dimensional model based on an exhaust path between the naked battery cell and the shell.

Description

Technical Field

[0001] The present application relates to the field of simulation technology, and in particular to a method, device, storage medium and program product for analyzing exhaust gas of a battery cell. Background Art

[0002] During the cell structure design process, cell safety testing is required. Current cell safety testing focuses on cell failure phenomena and cannot predict the exhaust process during thermal runaway.

[0003] The above statements are only used to provide background information related to the present application and do not necessarily constitute prior art. Summary of the Invention

[0004] A technical problem to be solved by the present disclosure is to provide a method, device, storage medium and program product for analyzing exhaust of a battery cell, which can quickly predict the exhaust process of the battery cell.

[0005] According to one aspect of the present disclosure, a method for analyzing exhaust gas of a battery cell is proposed, comprising: obtaining gas production status data of a bare battery cell during thermal runaway of the battery cell; inputting the gas production status data into a three-dimensional model of the bare battery cell, and predicting the exhaust status data of both ends of the bare battery cell as a function of time, wherein the three-dimensional model is constructed based on the size of the bare battery cell; inputting the exhaust status data of both ends of the bare battery cell as a function of time into a one-dimensional model of a flow channel between the bare battery cell and the battery cell shell, and predicting the state of the gas in the flow channel, wherein the one-dimensional model is constructed based on the exhaust path between the bare battery cell and the shell.

[0006] In the technical solution of the embodiment of the present application, a three-dimensional model of the bare cell and a one-dimensional model of the flow channel between the bare cell and the cell shell are constructed, and then the gas production state data of the bare cell during the thermal runaway process is input into the three-dimensional model, which can predict the exhaust state data at both ends of the bare cell over time, and input the exhaust state data at both ends of the bare cell over time into the one-dimensional model to predict the state of the gas in the internal space of the cell. By combining the one-dimensional model and the three-dimensional model, the cell exhaust process is numerically simulated to predict the state of the gas in the internal space of the cell. Compared with predicting the state of the gas in the internal space of the cell only through the three-dimensional model, it can reduce the computational complexity and computational cycle, reduce redundant processing, and give play to the complementarity of scenarios, thereby improving the prediction efficiency of the state of the gas in the internal space of the cell.

[0007] In some embodiments, the gas production state data includes gas production rate data, and the gas production state data is input into the three-dimensional model of the bare cell. Predicting the exhaust state data at both ends of the bare cell over time includes: inputting the gas production rate data into the three-dimensional model to predict the exhaust flow data at both ends of the bare cell over time; inputting the exhaust state data at both ends of the bare cell over time into the one-dimensional model of the flow channel between the bare cell and the cell shell, and predicting the state of the gas in the flow channel includes: inputting the exhaust flow data at both ends of the bare cell over time into the one-dimensional model of the flow channel between the bare cell and the cell shell, and predicting the flow state of the gas in the flow channel. By building a one-dimensional and three-dimensional combined model at the single cell level and performing numerical model simulation on the exhaust process, it is possible to quickly predict the flow state of the gas in the flow channel before and after thermal runaway of the cell, thereby improving the efficiency of the simulation.

[0008] In some embodiments, the flow state includes flow velocity, and the exhaust analysis method further includes: determining the pressure distribution of the gas in the flow channel based on the flow velocity of the gas in the flow channel; determining the flow resistance value of each region of the flow channel based on the pressure distribution of the gas in the flow channel; and identifying risk points of the battery cell based on the flow resistance value of each region. In this embodiment, the flow resistance value of each region within the battery cell is evaluated using the pressure distribution of the gas in the flow channel, providing data support for identifying weak areas in the battery cell design.

[0009] In some embodiments, identifying risk points in the battery cell based on the flow resistance value of each region of the flow channel includes identifying regions with flow resistance values ​​greater than a threshold as risk points. In this embodiment, the flow resistance value of each region can be used to identify the location of pressure buildup, thereby quickly locating weak areas in the battery cell's mechanical design. Furthermore, identifying weak areas in the battery cell's mechanical design can provide optimization directions for structural optimization design.

[0010] In some embodiments, the exhaust path includes the bare cell boundary area, the gap between the bare cell and the bottom of the housing, the corner area of ​​the housing, the gap area between the internal mechanical components of the housing, and the explosion-proof valve area. By determining the exhaust path within the cell when thermal runaway occurs, a one-dimensional model of the flow path between the bare cell and the housing can be constructed.

[0011] In some embodiments, the gas production state data includes gas production temperature data, and the gas production state data is input into a three-dimensional model of the bare cell. Predicting the exhaust state data at both ends of the bare cell over time includes: inputting the gas production temperature data into the three-dimensional model to predict the exhaust temperature data at both ends of the bare cell over time; inputting the exhaust state data at both ends of the bare cell over time into a one-dimensional model of the flow channel between the bare cell and the cell shell, and predicting the state of the gas in the flow channel includes: inputting the exhaust temperature data over time into the one-dimensional model to predict the temperature distribution of the gas in the flow channel. In this embodiment, the combination of the one-dimensional model and the three-dimensional model can quickly predict the temperature distribution in the cell flow channel.

[0012] In some embodiments, the dimensions of any one or more regions in the one-dimensional model are modified to predict the state of gas in the flow channel; based on the gas state in the flow channel, the performance of the battery cell is determined; and based on the battery cell performance, the optimal battery cell structure is determined. In this embodiment, by modifying the cross-sectional area of ​​the one-dimensional model, the impact of different battery cell structures on the exhaust path of the battery cell in the event of thermal runaway can be quickly identified, thereby selecting the optimal battery cell structure to improve battery cell safety.

[0013] According to the second aspect of the present disclosure, a battery cell exhaust analysis device is also proposed, including: a data acquisition module, configured to obtain gas production status data of a bare battery cell during thermal runaway; a first prediction module, configured to input the gas production status data into a three-dimensional model of the bare battery cell, and predict the exhaust status data at both ends of the bare battery cell that changes with time, and the three-dimensional model is constructed based on the size of the bare battery cell; a second prediction module, configured to input the exhaust status data at both ends of the bare battery cell that changes with time into a one-dimensional model of a flow channel between the bare battery cell and the battery cell shell, and predict the state of the gas in the flow channel, and the one-dimensional model is constructed based on the exhaust path between the bare battery cell and the shell.

[0014] In the technical solution of the embodiment of the present application, a three-dimensional model of the bare cell and a one-dimensional model of the flow channel between the bare cell and the cell shell are constructed, and then the gas production state data of the bare cell during the thermal runaway process is input into the three-dimensional model, which can predict the exhaust state data at both ends of the bare cell over time. The exhaust state data at both ends of the bare cell over time is input into the one-dimensional model to predict the state of the gas in the internal space of the cell. By combining the one-dimensional model and the three-dimensional model, the exhaust process of the cell is numerically simulated to predict the state of the gas in the internal space of the cell. Compared with predicting the state of the gas in the internal space of the cell only through the three-dimensional model, it can reduce the computational complexity and computational cycle, reduce redundant processing, and give play to the complementarity of scenarios, thereby improving the prediction efficiency of the flow state of the gas in the internal space of the cell.

[0015] In some embodiments, the gas production state data includes gas production rate data. The first prediction module is configured to input the gas production rate data into a three-dimensional model to predict the exhaust flow rate at both ends of the bare cell over time. The second prediction module is configured to input the exhaust flow rate data at both ends of the bare cell over time into a one-dimensional model of the flow channel between the bare cell and the cell casing to predict the flow state of the gas in the flow channel. By building a combined one-dimensional and three-dimensional model at the cell level and performing numerical model simulation of the exhaust process, it is possible to quickly predict the flow state of the gas in the flow channel before and after thermal runaway occurs in the cell, thereby improving the efficiency of the simulation.

[0016] In some embodiments, the flow state includes flow velocity, and the exhaust gas analysis device further includes: a processing module configured to determine the pressure distribution of the gas in the flow channel based on the flow velocity of the gas in the flow channel, determine the flow resistance value of each region of the flow channel based on the pressure distribution of the gas in the flow channel, and identify risk points of the battery cell based on the flow resistance value of each region. In this embodiment, the flow resistance value of each region within the battery cell is evaluated using the pressure distribution of the gas in the flow channel, providing data support for identifying weak areas in the battery cell design.

[0017] In some embodiments, the processing module is configured to identify areas with flow resistance values ​​greater than a threshold as risk points. In this embodiment, the flow resistance values ​​of each area can be used to identify the location of pressure buildup, thereby quickly locating weak areas in the cell's mechanical design. Furthermore, identifying weak areas in the cell's mechanical design can provide optimization directions for structural optimization.

[0018] In some embodiments, the exhaust path includes the bare cell boundary area, the gap between the bare cell and the bottom of the housing, the corner area of ​​the housing, the gap area between the internal mechanical components of the housing, and the explosion-proof valve area. By determining the exhaust path within the cell when thermal runaway occurs, a one-dimensional model of the flow path between the bare cell and the housing can be constructed.

[0019] In some embodiments, the gas production status data includes gas production temperature data. The first prediction module is configured to input the gas production temperature data into a three-dimensional model to predict the exhaust gas temperature variation over time at both ends of the bare cell. The second prediction module is configured to input the exhaust gas temperature variation over time data into a one-dimensional model to predict the temperature distribution of the gas in the flow channel. In this embodiment, the combination of the one-dimensional and three-dimensional models can quickly predict the temperature distribution within the cell flow channel.

[0020] In some embodiments, the parameter modification module is configured to modify the dimensions of any one or more regions in the one-dimensional model so that the second prediction module can predict the state of the gas in the flow channel. The processing module is configured to determine the performance of the battery cell based on the state of the gas in the flow channel, and then determine the optimal battery cell structure based on the battery cell performance. In this embodiment, by modifying the cross-sectional area of ​​the one-dimensional model, the impact of different battery cell structures on the exhaust path of the battery cell in the event of thermal runaway can be quickly identified, thereby selecting the optimal battery cell structure to improve battery cell safety.

[0021] According to a third aspect of the present disclosure, a battery cell exhaust analysis device is also proposed, comprising: a processor; and a memory coupled to the processor, for storing instructions, which, when executed by the processor, causes the processor to execute the exhaust analysis method as described above.

[0022] According to a fourth aspect of the present disclosure, a computer-readable storage medium is further provided, on which computer instructions are stored. When the computer instructions are executed by a processor, the above-mentioned exhaust gas analysis method is implemented.

[0023] According to a fifth aspect of the present disclosure, a computer program product is further proposed, comprising: computer instructions, which implement the above-mentioned exhaust gas analysis method when executed by a processor.

[0024] Other features and advantages of the present disclosure will become apparent from the following detailed description of exemplary embodiments of the present disclosure with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the drawings without creative work.

[0026] Figure 1 is a schematic diagram of a method for analyzing exhaust gas of a battery cell according to one or more embodiments; Figure 2 is a schematic diagram of a method for analyzing exhaust gas of a battery cell according to one or more embodiments; Figure 3 is a schematic diagram of a three-dimensional model according to one or more embodiments; Figure 4 A one-dimensional model architecture diagram according to one or more embodiments; Figure 5 is a schematic diagram of a method for analyzing exhaust gas of a battery cell according to one or more embodiments; Figure 6 is a schematic diagram of a method for analyzing exhaust gas of a battery cell according to one or more embodiments; Figure 7 is a schematic diagram of a method for analyzing exhaust gas of a battery cell according to one or more embodiments; Figure 8 is a schematic diagram of an exhaust gas analysis device for a battery cell according to one or more embodiments; Figure 9 is a schematic diagram of an exhaust gas analysis device for a battery cell according to one or more embodiments; Figure 10 Schematic diagram of an exhaust gas analysis device for a battery cell according to one or more embodiments. DETAILED DESCRIPTION

[0027] The following detailed description of the embodiments of the present application is provided in conjunction with the accompanying drawings and examples. The following detailed description of the embodiments and the accompanying drawings are used to illustrate the principles of the present application, but are not intended to limit the scope of the present application, i.e., the present application is not limited to the described embodiments.

[0028] In the description of this application, it should be noted that, unless otherwise specified, "multiple" means more than two; the terms "upper", "lower", "left", "right", "inside", "outside", etc., indicating directions or positional relationships, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as a limitation on this application. In addition, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. "Vertical" is not strictly perpendicular, but is within the allowable error range. "Parallel" is not strictly parallel, but is within the allowable error range.

[0029] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0030] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0031] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0032] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0033] It should be noted that like reference numerals and letters refer to like items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0034] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0035] During thermal runaway, capturing the exhaust status of battery cells is crucial for risk assessment, safety design optimization, and effective protection. Current battery cell safety thermal runaway assessment standards primarily focus on testing. This is limited by the inability to capture the gas state in the gap between the JR (JellyRoll) and the battery casing during thermal runaway. Using only a three-dimensional model to predict the gas state in the flow path between the JR and the casing is inefficient.

[0036] The present disclosure provides a battery cell exhaust analysis solution that can improve the efficiency of predicting the state of gas in the battery cell's internal space, thereby providing data support for other analyses. The solution of the present disclosure will be introduced below in conjunction with specific embodiments.

[0037] Figure 1 Schematic diagram of a method for analyzing exhaust gas of a battery cell according to one or more embodiments, which includes steps S11 to S13.

[0038] In step S11 , gas generation status data of the bare battery cell during thermal runaway is obtained.

[0039] The battery cell is, for example, a lithium-ion square shell wound battery cell, a lithium-ion cylindrical battery cell, or a lithium-ion laminated battery cell, etc. The present disclosure does not limit the type of battery cell.

[0040] In step S12, the gas production state data is input into a three-dimensional model of the bare cell to predict the exhaust state data at both ends of the bare cell that changes with time. The three-dimensional model is constructed based on the size of the bare cell.

[0041] In some embodiments, a bare cell flow channel simulation model may be obtained based on the size information of the bare cell, for example, a bare cell three-dimensional CAD (Computer Aided Design) model.

[0042] The input condition of this three-dimensional model is the gas production status data of the bare battery cell during the thermal runaway test of the battery cell. The three-dimensional model can be used to simulate and analyze the fluid between the layers of the bare battery cell, and obtain the exhaust status change data at both ends of the bare battery cell over time.

[0043] In step S13, the exhaust state data of both ends of the bare cell changing with time is input into a one-dimensional model of the flow channel between the bare cell and the cell shell to predict the state of the gas in the flow channel. The one-dimensional model is constructed based on the exhaust path between the bare cell and the shell.

[0044] In some embodiments, a flow channel model between the bare cell and the housing is constructed. This flow channel model is a one-dimensional model. This one-dimensional model represents the gap between the bare cell and the housing, which constitutes the flow channel. During the thermal runaway exhaust process of the cell, gas passes through the flow channel. The input condition of the one-dimensional model is the exhaust state data of the two ends of the bare cell over time. Through simulation, the state of the gas in the flow channel is obtained.

[0045] In this embodiment, by building a combined one- and three-dimensional model at the cell level and performing numerical simulation of the exhaust process, it is possible to predict the state of gas in the flow path before and after thermal runaway occurs. Compared to predicting the gas state within the cell using only a three-dimensional model, this reduces computational complexity and cycle time, reduces redundant processing, and leverages scenario complementarity, thereby improving the efficiency of predicting the gas state within the cell.

[0046] In some embodiments, the gas production status data includes gas production rate data, and the exhaust analysis method is as follows: Figure 2 As shown, Figure 2 Schematic diagram of a method for analyzing exhaust gas of a battery cell according to one or more embodiments, including steps S111 to S131 .

[0047] In step S111 , gas generation rate data of a bare cell during thermal runaway of the cell is obtained.

[0048] For example, the gas production rate of the bare cell from the beginning to the end of thermal aging is obtained, which is used as the input condition for subsequent numerical model simulation.

[0049] The gas production rate of a bare cell can be determined through pressure vessel testing. For example, by placing the cell in a pressure vessel and measuring the volume and pressure of the gas in the vessel, the gas production rate can be determined using the gas state equation.

[0050] In step S121 , the gas production rate data is input into the three-dimensional model of the bare cell to predict the exhaust flow rate at both ends of the bare cell over time.

[0051] The input condition of this three-dimensional model is the gas production rate data of the bare battery cell during the thermal runaway test of the battery cell. The three-dimensional model can be used to simulate and analyze the fluid between the layers of the bare battery cell, and obtain the exhaust flow rate change data at both ends of the bare battery cell over time.

[0052] like Figure 3As shown, the two ends 31 and 32 of the bare cell are exhaust flow statistics positions. By counting the exhaust flow at these positions, the exhaust flow change data of the two ends of the bare cell over time can be obtained.

[0053] In step S131 , the exhaust flow rate variation data at both ends of the bare cell over time is input into a one-dimensional model of the flow channel between the bare cell and the cell shell to predict the flow state of the gas in the flow channel.

[0054] The input condition of this one-dimensional model is the exhaust flow rate variation data at both ends of the bare battery cell over time. Through simulation, the flow state of the gas in the flow channel is obtained.

[0055] In this embodiment, by building a one-dimensional and three-dimensional combined model at the single cell level and performing a numerical model simulation on the exhaust process, the flow state of the gas in the flow channel before and after the thermal runaway of the cell can be quickly predicted, thereby improving the efficiency of the simulation.

[0056] In some embodiments, the exhaust path includes the boundary area of ​​the bare cell, the gap area between the bare cell and the bottom of the shell, the corner area of ​​the shell, the gap area of ​​the internal mechanical parts of the shell, and the explosion-proof valve area.

[0057] Based on the typical positions of the flow channels inside the battery cell, a one-dimensional model can be created for the flow channel gap between the bare battery cell and the shell. Figure 4 As shown, Figure 4 A one-dimensional model architecture diagram according to one or more embodiments.

[0058] Reference numeral 41 denotes the bare cell boundary flow input parameter area, where the parameters output by the 3D model are input. JR represents the bare cell, 411 represents the flow monitor, and A represents a port that does not input information. Reference numeral 412 denotes the first display, which displays the pressure information of the bare cell boundary area over time at 4121 and the current pressure information at 4122.

[0059] Reference numeral 42 denotes the gap between the bare cell and the bottom of the housing. Gas in the gap can exchange heat with the ambient fluid through convection. Reference numeral 421 denotes a second display, which displays pressure information of the gap between the bare cell and the bottom of the housing over time via 4211 and current pressure information via 4212.

[0060] Reference numeral 43 denotes a corner region of the housing. Gas in this region can also undergo convective heat exchange with the ambient fluid. Those skilled in the art will appreciate that, similar to the configuration of first display 412 and second display 421, a display can also be provided in this region to display pressure information over time and current pressure information for the region. To simplify the drawing, a third display is not shown in this region.

[0061] Reference numeral 44 denotes the gap area between the internal mechanical components of the housing. The gas in this area can also undergo convective heat exchange with the ambient fluid. Examples of internal mechanical components include tabs and poles. Those skilled in the art will appreciate that, similar to the first and second displays 412 and 421, this area can also be equipped with a display to display the pressure information of the area over time, as well as the current pressure information. To simplify the drawing, a fourth display is not shown in this area.

[0062] Reference numeral 45 denotes the explosion-proof valve area. Those skilled in the art will appreciate that, similar to the first and second displays 412 and 421, this area can also be provided with a display to display the pressure information of the area over time and the current pressure information. To simplify the drawing, a fifth display is not shown in this area.

[0063] Depending on the project requirements, the display can be set up in different locations.

[0064] Reference numeral 46 denotes a solver, which is used to input information such as simulation time and the size of each gap.

[0065] In the above embodiment, based on the typical position of the flow channel inside the battery cell, a one-dimensional model describing the flow channel gap between the bare battery cell and the shell is constructed, so that the one-dimensional model can be used to predict the flow state of the gas in the flow channel.

[0066] In some embodiments, the flow state of the gas in the flow channel includes the flow velocity of the gas in the flow channel. Figure 5 As shown, Figure 5 Schematic diagram of a method for analyzing exhaust gas of a battery cell according to one or more embodiments, including steps S51 to S53.

[0067] In step S51 , the pressure distribution of the gas in the flow channel is determined based on the flow velocity of the gas in the flow channel.

[0068] The gas pressure distribution in the flow channel, for example, includes the pressure at the cell boundary, the gap between the cell and the bottom of the housing, the corners of the housing, the gaps between the housing's internal mechanical components, and the explosion-proof valve. Pressure monitoring allows you to visualize the static pressure curve over time within the cell's flow channel.

[0069] In step S52 , the flow resistance value of each region of the flow channel is determined based on the pressure distribution of the gas in the flow channel.

[0070] During thermal runaway, cell casing damage occurs primarily due to high fluid resistance in that area, leading to pressure buildup. The static pressure difference between any two points can be used to determine the flow resistance between different areas.

[0071] In step S53 , based on the flow resistance value of each region, the risk points of the battery cell are identified.

[0072] In this embodiment, the flow resistance value of each area of ​​the flow channel is determined by the pressure distribution of the gas in the flow channel, and the flow resistance value of each area inside the battery cell is then evaluated, providing data support for identifying weak areas in the battery cell design.

[0073] In some embodiments, identifying risk points of the battery cell based on the flow resistance value of each region of the flow channel includes: identifying a region having a flow resistance value greater than a threshold as a risk point.

[0074] In this embodiment, the flow resistance value of each area can be used to identify the location of the pressure buildup, thereby quickly locating the weak areas of the battery cell's mechanical design. In addition, identifying the weak areas of the battery cell's mechanical design can also provide optimization directions for structural optimization design.

[0075] In some embodiments, as Figure 6 As shown, Figure 6 Schematic diagram of a method for analyzing exhaust gas of a battery cell according to one or more embodiments, including steps S61 and S62.

[0076] In step S61 , the size of any one or more regions in the one-dimensional model is changed to predict the state of the gas in the flow channel.

[0077] In step S62 , the performance of the battery cell is determined based on the state of the gas in the flow channel.

[0078] In step S63 , the optimal structure of the battery cell is determined based on the performance of the battery cell.

[0079] For example, by changing the dimensions of any one or more regions in a one-dimensional model, the battery cell structure is altered. The gas flow velocity in the flow channel is predicted, and based on this velocity, the gas pressure distribution in the flow channel is determined. Based on this pressure distribution, the flow resistance of each region of the flow channel is determined. The presence of regions with flow resistance values ​​greater than a threshold is determined. If so, this indicates a risk point in the battery cell; if not, this indicates no risk point. The optimal battery cell structure is then selected, eliminating any risk points.

[0080] In this embodiment, by adjusting the size of any one or more areas in the one-dimensional model, the flow area of ​​the fluid can be adjusted, and the impact of different battery cell structures on the exhaust path of thermal runaway of the battery cell can be quickly identified, thereby selecting the optimal battery cell structure to improve the safety of the battery cell.

[0081] In some embodiments, the gas production state data includes gas production temperature data, such as gas production heating power. Figure 7 As shown, Figure 7Schematic diagram of a method for analyzing exhaust gas of a battery cell according to one or more embodiments, including steps S112 - S132 .

[0082] In step S112 , gas generation temperature data of the bare battery cell during thermal runaway is obtained.

[0083] In step S122, the gas production temperature data is input into the three-dimensional model to predict the exhaust gas temperature variation data at both ends of the bare cell over time.

[0084] In step S132, the exhaust temperature variation data is input into a one-dimensional model to predict the temperature distribution of the gas in the flow channel. As the gas diffuses in the flow channel, different temperatures may be present in different areas through heat conduction and heat exchange.

[0085] In this embodiment, by combining the one-dimensional model and the three-dimensional model, the temperature distribution of the entire process of gas being generated from the inside of the bare battery cell and flowing to the explosion-proof valve position can be quickly simulated, thereby providing data support for subsequent safety analysis.

[0086] like Figure 4 As shown, the gas production temperature data of the bare cell is input into the bare cell boundary area in the label 41, and then the temperature distribution of the gas in the gap area between the bare cell and the bottom of the shell, the corner area of ​​the shell, the gap area of ​​the internal mechanical parts of the shell, and the explosion-proof valve area is determined in sequence. Among them, the first display 412 displays the temperature data through the label 4124, and the second display 421 displays the temperature data through the label 4214. Of course, the temperature data of the corresponding areas can also be displayed on the third display, the fourth display, and the fifth display.

[0087] In some embodiments, the size of any one or more regions in the one-dimensional model is changed to predict the temperature distribution of the gas in the flow channel; based on the temperature distribution of the gas in the flow channel, the performance of the battery cell is determined; and based on the performance of the battery cell, the optimal structure of the battery cell is determined.

[0088] For example, by changing the size of any one or more areas in the one-dimensional model, that is, changing the structure of the battery cell, the temperature distribution of the gas in the flow channel is predicted, and then it is determined whether the temperature of a certain area is greater than the temperature threshold. If so, it means that the structural design of the battery cell is unreasonable and it is easy to cause safety failures; if not, it means that the structural design of the battery cell is reasonable, so that the optimal battery cell structure can be quickly identified to improve the safety of the battery cell.

[0089] Alternatively, after predicting the temperature distribution of the gas in the flow channel, the temperature of each region is compared with the corresponding temperature threshold. If any region exceeds the corresponding temperature threshold, the battery cell structure is identified as defective. If the temperature of each region meets the corresponding temperature threshold requirement, the battery cell structure is properly designed.

[0090] When determining the optimal structure, the pressure distribution and temperature distribution of the gas in the flow channel can be comprehensively judged to determine the optimal battery cell structure. For example, if there is no area with a flow resistance greater than the flow resistance threshold, and no area has a temperature greater than the temperature threshold, it means that the battery cell structure meets the design requirements.

[0091] Those skilled in the art will understand that in the above method of the specific embodiment, the writing order of each step does not mean a strict execution order and does not constitute any limitation on the real-time process. The specific execution order of each step should be determined by its function and possible internal logic.

[0092] The above are schematic diagrams of some embodiments of the battery cell exhaust analysis method. Below, the battery cell exhaust analysis device will be further introduced with reference to the accompanying drawings.

[0093] Figure 8 Schematic diagram of an exhaust gas analysis device for a battery cell according to one or more embodiments, the exhaust gas analysis device includes a data acquisition module 81 , a first prediction module 82 and a second prediction module 83 .

[0094] The data acquisition module 81 is configured to obtain the gas production status data of the bare cell during the thermal runaway process; the first prediction module 82 is configured to input the gas production status data into the three-dimensional model of the bare cell, and predict the exhaust status data at both ends of the bare cell that changes with time, and the three-dimensional model is constructed based on the size of the bare cell; the second prediction module 83 is configured to input the exhaust status data at both ends of the bare cell that changes with time into the one-dimensional model of the flow channel between the bare cell and the cell shell, and predict the state of the gas in the flow channel, and the one-dimensional model is constructed based on the exhaust path between the bare cell and the shell.

[0095] The exhaust path includes the boundary area of ​​the bare cell, the gap area between the bare cell and the bottom of the shell, the corner area of ​​the shell, the gap area of ​​the internal mechanical parts of the shell, and the explosion-proof valve area.

[0096] In this embodiment, by building a combined one- and three-dimensional model at the cell level and performing numerical simulation of the exhaust process, it is possible to predict the state of gas in the flow path before and after thermal runaway occurs. Compared to predicting the gas state within the cell using only a three-dimensional model, this reduces computational complexity and cycle time, reduces redundant processing, and leverages scenario complementarity, thereby improving the efficiency of predicting the gas state within the cell.

[0097] In some embodiments, the gas production status data includes gas production rate data, and the first prediction module is configured to input the gas production rate data into a three-dimensional model to predict the exhaust flow rate at both ends of the bare battery cell as a function of time; the second prediction module is configured to input the exhaust flow rate at both ends of the bare battery cell as a function of time into a one-dimensional model of the flow channel between the bare battery cell and the battery cell shell to predict the flow state of the gas in the flow channel.

[0098] In this embodiment, by building a one-dimensional and three-dimensional combined model at the single cell level and performing a numerical model simulation on the exhaust process, the flow state of the gas in the flow channel before and after the thermal runaway of the cell can be quickly predicted, thereby improving the efficiency of the simulation.

[0099] In some embodiments, the flow state includes flow velocity, such as Figure 9 As shown, the exhaust analysis device also includes: a processing module 91, which is configured to determine the pressure distribution of the gas in the flow channel based on the flow velocity of the gas in the flow channel, determine the flow resistance value of each area of ​​the flow channel based on the pressure distribution of the gas in the flow channel, and identify the risk points of the battery cell based on the flow resistance value of each area.

[0100] In this embodiment, the flow resistance value of each area of ​​the flow channel is determined by the pressure distribution of the gas in the flow channel, and the flow resistance value of each area inside the battery cell is then evaluated, providing data support for identifying weak areas in the battery cell design.

[0101] In some embodiments, the processing module is configured to identify an area where the flow resistance value is greater than a threshold as a risk point.

[0102] In this embodiment, the flow resistance value of each area can be used to identify the location of the pressure buildup, thereby quickly locating the weak areas of the battery cell's mechanical design. In addition, identifying the weak areas of the battery cell's mechanical design can also provide optimization directions for structural optimization design.

[0103] In some embodiments, the gas production status data includes gas production temperature data, and the first prediction module is configured to input the gas production temperature data into a three-dimensional model to predict the exhaust temperature change data at both ends of the bare battery cell over time; the second prediction module is configured to input the exhaust temperature change data over time into a one-dimensional model to predict the temperature distribution of the gas in the flow channel.

[0104] In this embodiment, by combining the one-dimensional model and the three-dimensional model, the temperature distribution of the entire process of gas being generated from the inside of the bare battery cell and flowing to the explosion-proof valve position can be quickly simulated, thereby providing data support for subsequent safety analysis.

[0105] In some embodiments, as Figure 9As shown, the exhaust analysis device also includes a parameter modification module 92, which is configured to change the size of any one or more areas in the one-dimensional model so that the second prediction module predicts the state of the gas in the flow channel; the processing module 91 is also configured to determine the performance of the battery cell based on the state of the gas in the flow channel, and determine the optimal structure of the battery cell based on the performance of the battery cell.

[0106] For example, by changing the dimensions of any one or more regions in a one-dimensional model, the battery cell structure is altered. The gas flow velocity in the flow channel is predicted, and based on this velocity, the gas pressure distribution in the flow channel is determined. Based on this pressure distribution, the flow resistance of each region of the flow channel is determined. The presence of regions with flow resistance values ​​greater than a threshold is determined. If so, this indicates a risk point in the battery cell; if not, this indicates no risk point. The optimal battery cell structure is then selected, eliminating any risk points.

[0107] Alternatively, the size of any one or more areas in the one-dimensional model can be changed to predict the temperature distribution of the gas in the flow channel, and then determine whether there is an area with a temperature greater than a threshold. If so, it means that the structural design of the battery cell is unreasonable and may easily cause a safety failure; if not, it means that the structural design of the battery cell is reasonable, so that the optimal battery cell structure can be quickly identified to improve the safety of the battery cell.

[0108] Figure 10 Schematic diagram of an exhaust gas analysis device for a battery cell according to one or more embodiments. The exhaust gas analysis device can be implemented as an electronic device. The exhaust gas analysis device 10 includes a memory 1010 and a processor 1020. The memory 1010 can be a disk, flash memory, or any other non-volatile storage medium. The memory is used to store instructions in the above embodiments. The processor 1020 is coupled to the memory 1010 and can be implemented as one or more integrated circuits, such as a microprocessor or microcontroller. The processor 1020 is used to execute instructions stored in the memory.

[0109] In some embodiments, processor 1020 is coupled to memory 1010 via BUS 1030. The exhaust gas analysis device can also be connected to an external storage device 1050 via a storage interface 1040 to access external data, and can also be connected to a network or another computer system (not shown) via a network interface 1060. This will not be described in detail here.

[0110] In this embodiment, the electronic device stores data instructions in memory and processes these instructions through a processor. This improves the efficiency of predicting the state of gas within the battery cell. Furthermore, it allows for rapid identification of weak areas in the battery cell design and rapid assessment of the optimal battery cell structure.

[0111] In other embodiments, the present application provides a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement the steps of the method in the above-described embodiment. Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, apparatus, or computer program products. Therefore, the present application may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable non-transitory storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0112] In some embodiments of the application, a computer program product is further provided, comprising computer program instructions, which implement the method of any one of the above embodiments when executed by a processor.

[0113] The above description of the various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced with each other and will not be repeated herein for the sake of brevity.

[0114] So far, the present application has been described in detail. In order to avoid obscuring the concept of the present application, some details well known in the art have not been described. Based on the above description, those skilled in the art can fully understand how to implement the technical solutions disclosed herein.

[0115] The methods and systems of the present application may be implemented in many ways. For example, the methods and systems of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present application may also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the methods according to the present application. Therefore, the present application also covers recording media that store programs for executing the methods according to the present application.

[0116] Although some specific embodiments of the present application have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present application. It should be understood by those skilled in the art that the above examples may be modified without departing from the scope and spirit of the present application. The scope of the present application is defined by the appended claims.

Claims

1. A method for analyzing exhaust gas of a battery cell, characterized in that: include: Obtaining gas production status data of the bare battery cell during thermal runaway of the battery cell; Inputting the gas production state data into a three-dimensional model of the bare cell to predict the time-varying data of the exhaust state at both ends of the bare cell, wherein the three-dimensional model is constructed based on the size of the bare cell; The exhaust state data of both ends of the bare cell changing with time is input into a one-dimensional model of the flow channel between the bare cell and the cell shell to predict the state of the gas in the flow channel. The one-dimensional model is constructed based on the exhaust path between the bare cell and the shell.

2. The exhaust gas analysis method according to claim 1, characterized in that: The gas production state data includes gas production rate data, Inputting the gas production state data into the three-dimensional model of the bare cell, and predicting the time-varying data of the gas exhaust state at both ends of the bare cell includes: Inputting the gas production rate data into the three-dimensional model to predict the exhaust flow rate at both ends of the bare cell over time; Inputting the data of the exhaust state change over time at both ends of the bare cell into a one-dimensional model of the flow channel between the bare cell and the cell shell, and predicting the state of the gas in the flow channel includes: The exhaust flow rate variation data at both ends of the bare cell over time is input into a one-dimensional model of the flow channel between the bare cell and the cell shell to predict the flow state of the gas in the flow channel.

3. The exhaust gas analysis method according to claim 2, characterized in that: The flow state includes a flow velocity, and the exhaust gas analysis method further includes: determining a pressure distribution of the gas in the flow channel based on a flow velocity of the gas in the flow channel; determining a flow resistance value of each region of the flow channel based on a pressure distribution of the gas in the flow channel; Based on the flow resistance value of each area, the risk points of the battery cell are identified.

4. The exhaust gas analysis method according to claim 3, characterized in that: Based on the flow resistance value of each area of ​​the flow channel, identifying the risk points of the battery cell includes: The area where the flow resistance value is greater than the threshold is identified as the risk point.

5. The exhaust gas analysis method according to claim 1, characterized in that: The exhaust path includes the boundary area of ​​the bare cell, the gap area between the bare cell and the bottom of the shell, the corner area of ​​the shell, the gap area of ​​the internal mechanical parts of the shell, and the explosion-proof valve area.

6. The exhaust gas analysis method according to claim 1, characterized in that: The gas production state data includes gas production temperature data, Inputting the gas production state data into the three-dimensional model of the bare cell, and predicting the time-varying data of the gas exhaust state at both ends of the bare cell includes: Inputting the gas production temperature data into the three-dimensional model to predict the exhaust temperature variation data at both ends of the bare cell over time; Inputting the data of the exhaust state change over time at both ends of the bare cell into a one-dimensional model of the flow channel between the bare cell and the cell shell, and predicting the state of the gas in the flow channel includes: The exhaust gas temperature variation data over time is input into the one-dimensional model to predict the temperature distribution of the gas in the flow channel.

7. The exhaust gas analysis method according to any one of claims 1 to 6, characterized in that: Also includes: Changing the size of any one or more regions in the one-dimensional model to predict the state of the gas in the flow channel; determining the performance of the battery cell based on the state of the gas in the flow channel; Based on the performance of the battery cell, the optimal structure of the battery cell is determined.

8. An exhaust gas analysis device for a battery cell, characterized in that: include: a data acquisition module configured to acquire gas production status data of the bare battery cell during thermal runaway of the battery cell; A first prediction module is configured to input the gas production state data into a three-dimensional model of the bare cell to predict the time-varying data of the gas exhaust state at both ends of the bare cell, wherein the three-dimensional model is constructed based on the size of the bare cell; The second prediction module is configured to input the exhaust status change data of the two ends of the bare cell over time into a one-dimensional model of the flow channel between the bare cell and the shell of the cell, and predict the state of the gas in the flow channel, wherein the one-dimensional model is constructed based on the exhaust path between the bare cell and the shell.

9. The exhaust gas analysis device according to claim 8, characterized in that The gas production state data includes gas production rate data, The first prediction module is configured to input the gas production rate data into the three-dimensional model to predict the exhaust flow rate at both ends of the bare cell over time; The second prediction module is configured to input the exhaust flow rate variation data at both ends of the bare cell over time into a one-dimensional model of the flow channel between the bare cell and the cell shell, and predict the flow state of the gas in the flow channel.

10. The exhaust gas analysis device according to claim 9, characterized in that The flow state includes a flow velocity, and the exhaust gas analysis device further includes: The processing module is configured to determine the pressure distribution of the gas in the flow channel based on the flow velocity of the gas in the flow channel, determine the flow resistance value of each area of ​​the flow channel based on the pressure distribution of the gas in the flow channel, and identify the risk points of the battery cell based on the flow resistance value of each area.

11. The exhaust gas analysis device according to claim 10, characterized in that: The processing module is configured to identify the area where the flow resistance value is greater than a threshold as the risk point.

12. The exhaust gas analysis device according to claim 8, characterized in that The exhaust path includes the boundary area of ​​the bare cell, the gap area between the bare cell and the bottom of the shell, the corner area of ​​the shell, the gap area of ​​the internal mechanical parts of the shell, and the explosion-proof valve area.

13. The exhaust gas analysis device according to claim 8, characterized in that The gas production state data includes gas production temperature data, The first prediction module is configured to input the gas production temperature data into the three-dimensional model to predict the exhaust gas temperature change data at both ends of the bare cell over time; The second prediction module is configured to input the exhaust temperature variation data over time into the one-dimensional model to predict the temperature distribution of the gas in the flow channel.

14. The exhaust gas analysis device according to any one of claims 8 to 13, characterized in that: Also includes: a parameter modification module configured to change the size of any one or more regions in the one-dimensional model so that the second prediction module can predict the state of the gas in the flow channel; The processing module is configured to determine the performance of the battery cell based on the state of the gas in the flow channel, and determine the optimal structure of the battery cell based on the performance of the battery cell.

15. An exhaust gas analysis device for a battery cell, characterized in that: include: processor; as well as A memory coupled to the processor is used to store instructions, and when the instructions are executed by the processor, the processor is caused to perform the exhaust gas analysis method according to any one of claims 1 to 7.

16. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by a processor, the exhaust gas analysis method according to any one of claims 1 to 7 is implemented.

17. A computer program product, characterized in that include: The method comprises computer instructions, which, when executed by a processor, implement the exhaust gas analysis method according to any one of claims 1 to 7.

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