Method, device, equipment, medium and program product for establishing battery thermal model

Abstract

This application provides a method, apparatus, device, medium, and program product for establishing a battery thermal model, belonging to the field of battery technology. The method for establishing a battery thermal model includes: establishing an initial reduced-order thermal model based on at least one thermodynamic parameter of a battery device, wherein the battery device includes at least one cell, and the initial reduced-order thermal model includes at least one thermal resistance to be identified corresponding to each cell; identifying parameters of the initial reduced-order thermal model based on a target temperature to obtain simulated thermal resistance values ​​corresponding to each of the at least one thermal resistance to be identified; and determining the battery thermal model based on the simulated thermal resistance values ​​corresponding to each of the at least one thermal resistance to be identified and the initial reduced-order thermal model. The battery thermal model is used for thermal simulation of the battery device. This method can reduce computational load and improve simulation efficiency.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to a method, apparatus, device, medium, and program product for establishing a battery thermal model. Background Technology

[0002] Energy conservation and emission reduction are key to the sustainable development of the automotive industry, and electric vehicles, due to their energy-saving and environmentally friendly advantages, have become an important component of this sustainable development. For electric vehicles, battery technology is a crucial factor in their development.

[0003] Temperature has a significant impact on battery performance, lifespan, and safety. Thermal simulation of batteries can model temperature distribution under different operating conditions, allowing for optimized thermal management design, cost savings, and a reduction in the risk of thermal runaway. Currently, most mainstream thermal simulation methods are based on the finite volume method or finite element method, using a three-dimensional model of the battery for calculations. Due to the high complexity of the model, the thermal simulation process is time-consuming, inefficient, and requires high-performance simulation equipment. Summary of the Invention

[0004] This application aims to at least address the technical problem of low efficiency in battery thermal simulation in the prior art. Therefore, one objective of this application is to provide a method for establishing a battery thermal model, thereby simplifying the battery thermal model and improving thermal simulation efficiency.

[0005] An embodiment of the first aspect of this application provides a method for establishing a battery thermal model, comprising: establishing an initial reduced-order thermal model based on at least one thermodynamic parameter of a battery device, the battery device including at least one cell, the initial reduced-order thermal model including at least one thermal resistance to be identified corresponding to each cell; identifying parameters of the initial reduced-order thermal model based on a target temperature to obtain simulated thermal resistance values ​​corresponding to the at least one thermal resistance to be identified; and determining a battery thermal model based on the simulated thermal resistance values ​​corresponding to the at least one thermal resistance to be identified and the initial reduced-order thermal model, the battery thermal model being used for thermal simulation of the battery device.

[0006] In the technical solution of this application embodiment, the three-dimensional model of the battery device is designed with reduced order, and the parameters can be determined by the properties of the battery device. This simplifies the battery model, reduces the model parameters that need to be identified, significantly reduces the amount of calculation in the parameter identification process, improves the parameter identification efficiency, shortens the time required for the battery thermal simulation process, and improves the simulation efficiency while improving the accuracy of the simulation results.

[0007] In some embodiments, establishing an initial reduced-order thermal model based on at least one thermodynamic parameter of the battery device includes: for each of the at least one battery cell: establishing a cell model corresponding to the cell based on the cell's thermal mass parameter. Establishing a cell model based on the thermal mass of each cell can effectively simplify the individual cell models in the battery device and simplify the calculation process.

[0008] In some embodiments, the battery device further includes at least one cooling component, and each of the at least one battery cell is respectively configured as a corresponding cooling component adjacent to the at least one cooling component. Establishing an initial reduced-order thermal model based on at least one thermodynamic parameter of the battery device includes: for each of the at least one cooling component: establishing a cooling component model corresponding to the cooling component based on at least one of the fluid parameters, physical parameters, and thermodynamic parameters of the cooling component. Decomposing the simulation model of the battery device into a cell model and a cooling component model, and establishing corresponding reduced-order models based on their respective performance parameters, can simplify the model and reduce the computational load while improving the accuracy of the simulation results.

[0009] In some embodiments, establishing a cooling component model corresponding to the cooling component based on at least one of the fluid parameters, physical parameters, and thermodynamic parameters of the cooling component includes: establishing a cooling component mass model corresponding to the cooling component based on the mass flow rate at the first end of the cooling component, the mass flow rate at the second end of the cooling component, the fluid density of the coolant in the cooling component, the fluid volume of the coolant in the cooling component, the isothermal bulk modulus of the coolant, the isobaric coefficient of thermal expansion of the coolant, the fluid pressure of the coolant in the cooling component, and the fluid temperature of the coolant in the cooling component. For the cooling component, the principle of mass conservation must be satisfied. Based on the various relevant parameters of the cooling component, an accurate mass model can be established to ensure that it conforms to mass conservation, thereby enabling the final battery thermal model to accurately simulate the battery device.

[0010] In some embodiments, establishing a cooling component model corresponding to the cooling component based on at least one of the fluid parameters, physical parameters, and thermodynamic parameters of the cooling component includes: determining the viscous frictional pressure loss generated by the coolant flowing through the cooling component based on the shell structure of the cooling component and the hydrodynamic viscosity of the coolant in the cooling component. Accurate viscous frictional pressure loss can be obtained through the structural parameters of the cooling component and the viscosity parameters of the coolant. Using this viscous frictional pressure loss, the resulting cooling component model can simulate the cooling component under actual conditions, improving the accuracy of the simulation results.

[0011] In some embodiments, establishing a cooling component model based on at least one of the fluid parameters, physical parameters, and thermodynamic parameters of the cooling component includes: establishing a cooling component momentum model based on the fluid pressure of the coolant in the cooling component and the viscous frictional pressure loss generated by the coolant flowing through the cooling component. For the cooling component, the principle of momentum conservation must be satisfied. Based on the various relevant parameters of the cooling component, an accurate momentum model can be established to ensure that it conforms to momentum conservation, thereby enabling the final battery thermal model to accurately simulate the battery device.

[0012] In some embodiments, establishing a cooling component model corresponding to the cooling component based on at least one of the fluid parameters, physical parameters, and thermodynamic parameters of the cooling component includes: establishing a cooling component energy model corresponding to the cooling component based on the energy flow rate flowing through a first end of the cooling component, the energy flow rate flowing through a second end of the cooling component, and the heat flow rate from the casing of the cooling component. For the cooling component, the principle of energy conservation must be satisfied. Based on the various relevant parameters of the cooling component, an accurate energy model can be established to conform to energy conservation, thereby enabling the final battery thermal model to accurately simulate the battery device.

[0013] In some embodiments, establishing a cooling component model corresponding to the cooling component based on at least one of the fluid parameters, physical parameters, and thermodynamic parameters of the cooling component includes: establishing a cooling component heat flow model corresponding to the cooling component based on the convective heat of the coolant in the cooling component, the fluid thermal conductivity of the coolant, the shell area of ​​the cooling component, and the shell temperature of the cooling component, wherein the convective heat of the coolant indicates the heat generated by convection when the coolant flow rate is not zero. Based on the various relevant parameters of the cooling component and the coolant, an accurate heat flow model can be obtained, enabling accurate simulation of the actual heat flow state of the cooling component during thermal simulation of the battery device, thus improving the accuracy of the simulation results.

[0014] In some embodiments, when the battery device includes multiple battery cells, establishing an initial reduced-order thermal model based on at least one thermodynamic parameter of the battery device includes: establishing an initial reduced-order thermal model based on a cell model corresponding to each of the multiple battery cells, a cooling component model corresponding to each of the at least one cooling component, the arrangement of the multiple battery cells, and the connection relationship between the multiple battery cells and the at least one cooling component. Obtaining the reduced-order thermal model of the battery device based on the layout of the battery cells and cooling components in the battery device can accurately simulate the real situation of the battery device, obtaining thermal simulation results that are closer to reality.

[0015] In some embodiments, the battery device further includes at least one first heat conductor, each of the at least one first heat conductor being disposed between any one of the at least one battery cell and a corresponding cooling component adjacent to the battery cell. Establishing an initial reduced-order thermal model based on at least one thermodynamic parameter of the battery device includes: for each of the at least one first heat conductor: establishing a first heat conductor model corresponding to the first heat conductor based on the thermal mass parameter of the first heat conductor. When establishing the reduced-order thermal model of the battery device, considering the first heat conductor between the battery cell and the cooling component, and establishing the first heat conductor model based on the thermal mass of the heat conductor, can accurately simulate the battery device while effectively simplifying the first heat conductor model in the battery device and simplifying the calculation process.

[0016] In some embodiments, when the battery device includes multiple battery cells, the battery device also includes at least one second heat conductor. Each of the at least one second heat conductor is disposed between any two adjacent battery cells. Based on at least one thermodynamic parameter of the battery device, establishing an initial reduced-order thermal model includes: for each of the at least one second heat conductor: establishing a second heat conductor model corresponding to the second heat conductor based on the contact area between the second heat conductor and the battery cell and the thickness of the second heat conductor. When establishing the reduced-order thermal model of the battery device, considering the second heat conductor between adjacent battery cells and establishing the second heat conductor model based on the thermal mass of the heat conductor can accurately simulate the battery device while effectively simplifying the second heat conductor model in the battery device and simplifying the calculation process.

[0017] In some embodiments, based on the target temperature, parameter identification is performed on the initial reduced-order thermal model to obtain simulated thermal resistance values ​​corresponding to at least one thermal resistance to be identified. This includes: for any one of the at least one thermal resistance to be identified: setting the thermal resistance to be identified as an initial thermal resistance value; determining the objective function for the thermal resistance to be identified based on the target temperature; and performing parameter identification on the initial reduced-order thermal model based on the initial thermal resistance value and the objective function to obtain simulated thermal resistance values ​​corresponding to the thermal resistance to be identified. After obtaining the initial reduced-order thermal model, by assigning initial values ​​to the thermal resistances to be identified and setting the objective function, accurate simulated thermal resistance values ​​can be obtained while reducing the computational load of the parameter identification process and improving computational efficiency.

[0018] In some embodiments, parameter identification of the initial reduced-order thermal model based on the initial thermal resistance value and the objective function to obtain the simulated thermal resistance value corresponding to the thermal resistance to be identified includes: iterating the parameters of the thermal resistance value corresponding to the thermal resistance to be identified based on the initial thermal resistance value and the objective function; and stopping the parameter iteration of the thermal resistance value corresponding to the thermal resistance to be identified in response to meeting a pre-set cutoff condition, thus obtaining the simulated thermal resistance value corresponding to the thermal resistance to be identified. By setting reasonable objective functions and cutoff conditions, the number of parameter iterations can be effectively reduced, and the efficiency of parameter identification can be improved.

[0019] In some embodiments, when the battery device includes multiple battery cells, the method further includes: dividing the multiple battery cells into multiple initial battery cell groups according to the arrangement of the multiple battery cells, each initial battery cell group including at least one battery cell; updating the battery cell groups of the multiple battery cells based on the multiple initial battery cell groups and an objective function to obtain at least one simulated battery cell group, each simulated battery cell group including at least one battery cell. Reasonable grouping of the battery cells in the battery device allows the same simulated thermal resistance value to be used for the cells in the same simulated battery cell group, effectively reducing the number of thermal resistances to be identified during parameter identification, thus improving computational efficiency while obtaining accurate simulation results.

[0020] In some embodiments, when the battery device includes multiple cells, the cutoff condition includes at least one of the following: the number of iterations for parameter iteration of the thermal resistance value corresponding to the thermal resistance to be identified reaches a threshold number; the first cell error of each simulated cell group in at least one simulated cell group satisfies a first error condition, the first cell error indicating the average error between the simulated temperature and the target temperature of all cells in the simulated cell group, the simulated temperature indicating the temperature of the cell calculated based on the objective function; and the second cell error of multiple cells satisfies a second error condition, the second cell error indicating the average error between the simulated temperature and the target temperature of the multiple cells. By setting reasonable cutoff conditions, accurate simulated thermal resistance values ​​can be obtained while improving parameter identification efficiency.

[0021] In some embodiments, updating the cell groups of multiple cells based on multiple initial cell groups and an objective function to obtain at least one simulated cell group includes: dividing at least one cell in a cell group into a new cell group in response to a third cell error not satisfying a third error condition, wherein the third cell error indicates the error between the simulated temperature and the target temperature of any one of the at least one cells; and / or merging the multiple cell groups in response to each cell in the multiple cell groups having a thermal resistance value corresponding to the thermal resistance to be identified satisfying a first thermal resistance condition. By splitting and / or merging the cell groups, the cell grouping results can be optimized, and the accuracy of the simulation results can be improved.

[0022] An embodiment of the second aspect of this application provides an apparatus for establishing a battery thermal model, comprising: a first module for establishing an initial reduced-order thermal model based on at least one thermodynamic parameter of a battery device, the battery device including at least one cell, the initial reduced-order thermal model including at least one thermal resistance to be identified corresponding to each cell; a second module for identifying parameters of the initial reduced-order thermal model based on a target temperature to obtain simulated thermal resistance values ​​corresponding to the at least one thermal resistance to be identified; and a third module for determining a battery thermal model based on the simulated thermal resistance values ​​corresponding to the at least one thermal resistance to be identified and the initial reduced-order thermal model, the battery thermal model being used for thermal simulation of the battery device.

[0023] An embodiment of the third aspect of this application provides a computing device, including: at least one processor; and at least one memory communicatively connected to the at least one processor, the at least one memory storing instructions that, when executed individually or jointly by the at least one processor, cause the computing device to perform the methods described above.

[0024] An embodiment of the fourth aspect of this application provides a computer-readable storage medium storing instructions that, when executed individually or jointly by one or more processors of a computing device, cause the computing device to perform the methods described above.

[0025] An embodiment of the fifth aspect of this application provides a computer program product including instructions that, when executed individually or jointly by one or more processors of a computing device, cause the computing device to perform the methods described above.

[0026] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0027] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments disclosed in this application and should not be construed as limiting the scope of this application.

[0028] Figure 1 This is a flowchart illustrating a method for establishing a battery thermal model according to some embodiments of this application;

[0029] Figure 2 This is a schematic diagram illustrating the process of parameter identification for an initial reduced-order thermal model in some embodiments of this application;

[0030] Figure 3This is a schematic diagram illustrating the process of obtaining simulated thermal resistance values ​​in some embodiments of this application;

[0031] Figure 4 This is a schematic diagram illustrating the process of grouping multiple battery cells according to some embodiments of this application;

[0032] Figure 5 This is a schematic diagram illustrating the process of updating cell groups in some embodiments of this application;

[0033] Figure 6 A schematic block diagram of an apparatus for establishing a battery thermal model according to some embodiments of this application;

[0034] Figure 7 This is a schematic block diagram of a computing device according to some embodiments of this application;

[0035] Figure 8 This is a schematic diagram of the initial reduced-order thermal model for a single cell in some embodiments of this application;

[0036] Figure 9 This is a schematic diagram of the initial reduced-order thermal model for multiple cell cases in some embodiments of this application;

[0037] Figure 10 This is a schematic diagram of the initial cell grouping under natural cooling conditions according to some embodiments of this application;

[0038] Figure 11 This is a schematic diagram of the initial cell grouping under water cooling conditions according to some embodiments of this application;

[0039] Figure 12 This is a schematic diagram of the gradient method iterative process in some embodiments of this application;

[0040] Figure 13 This is a flowchart illustrating a method for establishing a battery thermal model according to some embodiments of this application. Detailed Implementation

[0041] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0043] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0044] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0045] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0046] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0047] In the description of the embodiments of this application, the technical terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of this application and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0048] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0049] Currently, judging from market trends, the application of power batteries is becoming increasingly widespread. Power batteries are not only used in energy storage systems such as hydropower, thermal power, wind power, and solar power plants, but also extensively used in electric vehicles such as electric bicycles, electric motorcycles, and electric cars, as well as in military equipment and aerospace. With the continuous expansion of power battery applications, market demand is also constantly increasing.

[0050] Temperature significantly impacts battery performance, lifespan, and safety during use. To understand a battery's state under different operating conditions, thermal simulation is typically required. This simulation allows for the modeling of temperature distribution under varying conditions, enabling optimization of battery thermal management design, reducing design costs, and mitigating the risk of thermal runaway.

[0051] Currently, most mainstream thermal simulation methods use three-dimensional models of batteries for simulation, such as those based on the finite volume method or the finite element method. However, due to the high complexity of the three-dimensional model, the thermal simulation process is time-consuming, inefficient, and places high demands on the performance of the simulation equipment.

[0052] To improve simulation efficiency, a reduced-order thermal model of the battery can be used for thermal simulation, which simplifies the three-dimensional model of the battery by reducing its order. In this model, each component of the battery device can be simplified into a corresponding reduced-order model. Several parameters exist that reflect the performance of the battery device itself and the relationships between its components and the external environment. Some parameters can be obtained based on the battery device's inherent performance, while others, such as certain thermal resistance values, require identification. By designing a suitable initial reduced-order thermal model, the parameter identification process can be simplified. Based on this model, parameter identification is performed to obtain the simulated thermal resistance values ​​corresponding to the thermal resistance values ​​to be identified in the initial model, effectively reducing the computational load and improving efficiency. After obtaining the simulated thermal resistance values, the battery thermal model can be derived from the initial reduced-order thermal model and the simulated thermal resistance values.

[0053] The battery thermal model obtained using this method is a reduced-order model, and accurate simulated thermal resistance values ​​are obtained through parameter identification. This battery thermal model can accurately simulate the real state of the battery, effectively improving the efficiency of thermal simulation while improving the accuracy of simulation results.

[0054] The method for establishing a battery thermal model disclosed in this application can be used, but is not limited to, in the thermal simulation of battery devices used in electrical devices such as vehicles, ships, or aircraft. Using the method for establishing a battery thermal model disclosed in this application helps reduce the computational load during the battery thermal model establishment process, improves computational efficiency, shortens the time required for battery thermal simulation, and improves simulation efficiency.

[0055] This application provides a method for establishing a battery thermal model. (See also...) Figure 1 Method 100 includes steps 110 to 130.

[0056] Step 110: Establish an initial reduced-order thermal model based on at least one thermodynamic parameter of the battery device. The battery device includes at least one battery cell. The initial reduced-order thermal model includes at least one thermal resistance to be identified for each battery cell.

[0057] Step 120: Based on the target temperature, perform parameter identification on the initial reduced-order thermal model to obtain the simulated thermal resistance value corresponding to at least one thermal resistance to be identified.

[0058] Step 130: Determine the battery thermal model based on the simulated thermal resistance values ​​corresponding to at least one thermal resistance to be identified and the initial reduced-order thermal model. The battery thermal model is used for thermal simulation of the battery device.

[0059] In the embodiments of this application, a "battery device" may include a single battery cell, or a series, parallel, or hybrid structure of multiple battery cells (e.g., a battery pack or battery module). A reduced-order model refers to a lower-order model obtained by reducing the order of a higher-order model relative to that of a higher-order model. In the embodiments of this application, a "reduced-order thermal model" refers to a lower-order model obtained by reducing the order of a three-dimensional thermal simulation model of the battery device, based on parameters related to the battery device (e.g., relevant thermodynamic parameters describing the thermodynamic state of the battery device). The method for establishing a low-order thermal model will be described in detail below.

[0060] Reduced-order thermal models of battery devices typically include parameters such as heat capacity and thermal resistance. Heat capacity indicates a material's ability to absorb or release heat during temperature changes, while thermal resistance indicates the ratio of the temperature difference between the two ends of an object to the power of the heat source during heat transfer. Reduced-order thermal models can be built and simulated on various simulation platforms (e.g., Simulink, Amesim).

[0061] The battery device includes at least one battery cell. In step 110, when establishing the initial reduced-order thermal model of the battery device, the battery cell can be represented as an equivalent heat capacity, including the heat generation power. For each battery cell, its interaction with the surrounding environment can be represented as an equivalent thermal resistance. The effect of thermal resistance on temperature can be expressed as follows:

[0062] RQ0=ΔT0

[0063] In the formula, R is the thermal resistance value, Q0 indicates the power at the thermal resistance, and ΔT0 is the temperature difference across the thermal resistance.

[0064] In the initial reduced-order thermal model of the battery device, equivalent thermal capacities and thermal resistances can be interconnected to form a thermal circuit model similar to a circuit. In this initial reduced-order thermal model, the thermal resistance between each cell and its surrounding environment (e.g., cell-air thermal resistance, cell-cooling component thermal resistance, etc.) cannot be accurately determined when establishing the reduced-order thermal model. Step 120 requires parameter identification of these thermal resistances to obtain accurate simulated thermal resistance values ​​suitable for thermal simulation. The method for parameter identification will be detailed below.

[0065] After obtaining the simulated thermal resistance value corresponding to each thermal resistance to be identified, these simulated thermal resistance values ​​can be substituted into the initial reduced-order thermal model to obtain the battery thermal model. This battery thermal model is also a reduced-order thermal model, and the simulation platform mentioned above can be used to perform thermal simulation on it to obtain the thermal simulation results of the battery device.

[0066] The three-dimensional model of the battery device is reduced in size, and the parameters can be determined by the properties of the battery device. This simplifies the battery model, reduces the number of model parameters that need to be identified, significantly reduces the amount of computation in the parameter identification process, improves the efficiency of parameter identification, and shortens the time required for battery thermal simulation. This improves both the accuracy and efficiency of the simulation results.

[0067] According to some embodiments of this application, step 110 includes:

[0068] For each cell in at least one battery cell:

[0069] Based on the thermal mass parameters of the battery cell, a battery cell model corresponding to the battery cell is established.

[0070] For each cell in a battery device, a cell model can be built based on its thermal mass parameters. Thermal mass parameters are parameters that indicate the heat of an object. In this example, thermal mass can be expressed as the product of the cell's mass and its specific heat capacity. In the cell model, the temperature change of the cell can be represented by the following formula:

[0071]

[0072] In the formula, T represents the cell temperature, t represents time, Q represents the total power of the cell, including the heat generated by the cell itself and the heat transfer power from other cells and / or the environment to the cell. The total power of the cell can be obtained using methods such as the equivalent circuit model or electrochemical model of the cell. M represents the thermal mass of the cell, m represents the mass of the cell, and c represents the specific heat capacity of the cell. The parameters such as the total power, mass, and specific heat capacity of the cell can be set when establishing the initial reduced-order thermal model, for example, by inputting these parameters into the simulation platform.

[0073] By establishing cell models based on the thermal mass of each cell, the individual cell models in the battery device can be effectively simplified, thus simplifying the calculation process.

[0074] According to some embodiments of this application, the battery device further includes at least one cooling assembly. Each of the at least one battery cell is respectively configured to be adjacent to a corresponding cooling assembly in the at least one cooling assembly. Step 110 includes:

[0075] For each of at least one cooling component:

[0076] A cooling component model corresponding to the cooling component is established based on at least one of the fluid parameters, physical parameters, and thermodynamic parameters of the cooling component.

[0077] In battery devices, cooling components (such as water-cooled plates) are typically included to dissipate heat. These cooling components contain a coolant (such as coolant liquid or cooling gas). The cooling components are positioned close to the battery cells to conduct heat generated by the cells, reducing the risk of overheating.

[0078] When establishing the initial reduced-order thermal model, the cooling components in the battery device need to be considered. For each cooling component, a corresponding cooling component model needs to be established. Since there may be flowing coolant in the cooling components, relevant fluid parameters, physical parameters, and / or thermodynamic parameters can be considered when establishing the cooling component model to obtain a more accurate cooling component model. For example, the fluid characteristics of the coolant in the cooling component, the convective heat transfer between the coolant and the cooling component shell, and the fluid volume and temperature at the inlet and / or outlet of the cooling component can be considered to obtain a more realistic coolant temperature change.

[0079] In the example using Simulink as the simulation platform, the Thermal LiquidPipe model can be used to simulate the cooling components, and the relevant parameters can be set.

[0080] Decomposing the simulation model of the battery device into a cell model and a cooling component model, and establishing corresponding reduced-order models based on their respective performance parameters, can simplify the model and reduce the amount of computation while improving the accuracy of the simulation results.

[0081] According to some embodiments of this application, establishing a cooling component model corresponding to the cooling component based on at least one of the fluid parameters, physical parameters, and thermodynamic parameters of the cooling component includes:

[0082] Based on the mass flow rate at the first end of the cooling component, the mass flow rate at the second end of the cooling component, the fluid density of the coolant in the cooling component, the fluid volume of the coolant in the cooling component, the isothermal bulk modulus of the coolant, the isobaric thermal expansion coefficient of the coolant, the fluid pressure of the coolant in the cooling component, and the fluid temperature of the coolant in the cooling component, a mass model of the cooling component corresponding to the cooling component is established.

[0083] In the embodiments of this application, "mass flow rate" refers to the mass of fluid passing through the effective cross-section of a closed pipe or open channel per unit time, "fluid density" refers to the mass of fluid per unit volume, "fluid volume" is used to indicate the total volume of coolant in the cooling assembly, "isothermal bulk modulus" refers to the measure of the volume change of a fluid under pressure while the temperature remains constant, "coefficient of thermal expansion at constant pressure" refers to the change in fluid volume caused by a unit temperature change under constant pressure, "fluid pressure" is used to indicate the pressure of the coolant in the cooling assembly, and "fluid temperature" is used to indicate the temperature of the coolant in the cooling assembly.

[0084] The cooling component mass model is a part of the cooling component model and is used to represent the mass of the coolant in the cooling component. For a cooling component, the principle of mass conservation must be satisfied. In the cooling component mass model, the mass conservation of the coolant in the cooling component can be expressed by the following equation:

[0085]

[0086] In the formula, This refers to the mass flow rate at the first end of the cooling assembly (e.g., an opening in the cooling assembly). Here, ρ is the mass flow rate at the second end of the cooling assembly (e.g., another opening in the cooling assembly), ρ is the fluid density of the coolant, V is the fluid volume of the coolant in the cooling assembly, β is the isothermal bulk modulus of the coolant, α is the isobaric coefficient of thermal expansion of the coolant, p is the fluid pressure of the coolant in the cooling assembly, T is the fluid temperature of the coolant in the cooling assembly, and t indicates time. These parameters can be set in the simulation platform, and the set values ​​can be determined according to the different designs of the battery device.

[0087] Based on the various relevant parameters of the cooling components, an accurate mass model can be established to conform to the law of mass conservation, thereby enabling the final battery thermal model to accurately simulate the battery device.

[0088] According to some embodiments of this application, establishing a cooling component model corresponding to the cooling component based on at least one of the fluid parameters, physical parameters, and thermodynamic parameters of the cooling component includes:

[0089] Based on the housing structure of the cooling assembly and the hydrodynamic viscosity of the coolant in the cooling assembly, the viscous frictional pressure loss generated by the coolant flowing through the cooling assembly is determined.

[0090] In the embodiments of this application, "viscous frictional pressure loss" refers to the pressure loss caused by friction due to fluid viscosity during fluid flow. "Fluid dynamic viscosity," also known as fluid dynamic viscosity, fluid absolute viscosity, or fluid simple viscosity, is defined as the ratio of stress to strain rate and can be used to characterize the internal friction coefficient of fluid viscosity. The shell structure of the cooling component can be represented by relevant parameters, such as the shape factor of the cooling component (i.e., a parameter measuring the structural shape of the cooling component), the length of the cooling component, the total equivalent length of the cooling component's resistance (i.e., the equivalent length corresponding to the pipe resistance of the cooling component), and the hydraulic diameter of the cooling component (i.e., four times the ratio of the cross-sectional area of ​​the cooling component's pipe to its perimeter).

[0091] In one example, the coolant is assumed to flow laminarly within the cooling assembly, neglecting turbulence. The viscous frictional pressure loss generated by the coolant flowing through the cooling assembly can then be expressed by the following formula:

[0092]

[0093] In the formula, Δp V,A Δp represents the viscous frictional pressure loss between the center of the cooling component and its first end. V,B This refers to the viscous frictional pressure loss between the center of the cooling component and the second end of the cooling component. This refers to the mass flow rate at the first end of the cooling assembly. Let λ be the mass flow rate at the second end of the cooling assembly, λ be the shape factor of the cooling assembly, v be the hydrodynamic viscosity of the coolant, and L be the length of the cooling assembly. eq Let D be the total equivalent length of the cooling component, D be the hydraulic diameter of the cooling component, and S be the cross-sectional area of ​​the cooling component. These parameters can be set in the simulation platform, and the set values ​​can be determined according to the different designs of the battery device.

[0094] By using the structural parameters of the cooling component and the viscosity parameters of the coolant, an accurate viscous frictional pressure loss can be obtained. Using this viscous frictional pressure loss, the resulting cooling component model can simulate the cooling component under actual conditions, improving the accuracy of the simulation results.

[0095] According to some embodiments of this application, establishing a cooling component model corresponding to the cooling component based on at least one of the fluid parameters, physical parameters, and thermodynamic parameters of the cooling component includes:

[0096] Based on the fluid pressure of the coolant in the cooling assembly and the viscous frictional pressure loss generated by the coolant flowing through the cooling assembly, a momentum model of the cooling assembly corresponding to the cooling assembly is established.

[0097] The momentum model of a cooling component is part of the overall cooling component model and is used to represent the momentum of the coolant within the component. For a cooling component, the principle of momentum conservation must be satisfied. In the mass model of a cooling component, the momentum conservation of the coolant can be expressed using the following equation:

[0098] p A -p=Δp V,A

[0099] p B -p=Δp V,B

[0100] In the formula, p A p is the fluid pressure of the coolant at the first end of the cooling assembly. B Let Δp be the fluid pressure of the coolant at the second end of the cooling assembly, and p be the fluid pressure of the coolant in the cooling assembly. V,A Δp represents the viscous frictional pressure loss between the center of the cooling component and its first end. V,B This refers to the viscous frictional pressure loss between the center of the cooling component and its second end. The aforementioned parameters can be set in the simulation platform, and the set values ​​can be determined based on the different designs of the battery device.

[0101] Based on the various relevant parameters of the cooling components, an accurate momentum model can be established to conform to the law of conservation of momentum, thereby enabling the final battery thermal model to accurately simulate the battery device.

[0102] According to some embodiments of this application, establishing a cooling component model corresponding to the cooling component based on at least one of the fluid parameters, physical parameters, and thermodynamic parameters of the cooling component includes:

[0103] Based on the energy flow rate through the first end of the cooling component, the energy flow rate through the second end of the cooling component, and the heat flow rate from the shell of the cooling component, an energy model of the cooling component corresponding to the cooling component is established.

[0104] In the embodiments of this application, "energy flow rate" refers to the energy flow rate through a certain cross section per unit time, and "heat flow rate" refers to the heat transferred through a given surface per unit time through conduction, convection, radiation, etc., also known as heat flow.

[0105] The cooling component energy model is part of the cooling component model and is used to represent the energy situation within the cooling component. For a cooling component, the principle of energy conservation must be satisfied. In the cooling component mass model, the energy conservation within the cooling component can be expressed using the following equation:

[0106]

[0107] In the formula, ρ is the fluid density of the coolant, V is the fluid volume of the coolant in the cooling assembly, u indicates the energy per unit mass of fluid, and Φ A Φ is the energy flow rate flowing through the first end of the cooling component. B Q is the energy flow rate flowing through the second end of the cooling component. H The heat flow rate from the housing of the cooling components is given by t, which indicates time. These parameters can be set in the simulation platform, and the set values ​​can be determined according to the different designs of the battery device.

[0108] Based on the various relevant parameters of the cooling components, an accurate energy model can be established to conform to the law of energy conservation, thereby enabling the final battery thermal model to accurately simulate the battery device.

[0109] According to some embodiments of this application, establishing a cooling component model corresponding to the cooling component based on at least one of the fluid parameters, physical parameters, and thermodynamic parameters of the cooling component includes:

[0110] Based on the convective heat of the coolant in the cooling component, the fluid thermal conductivity of the coolant, the shell area of ​​the cooling component, and the shell temperature of the cooling component, a heat flow model of the cooling component corresponding to the cooling component is established.

[0111] In embodiments of this application, the convective heat of the coolant indicates the heat generated by convection when the coolant flow rate is not zero, and the "fluid thermal conductivity" indicates the fluid's ability to conduct heat.

[0112] The heat flux model of a cooling component is part of the overall cooling component model and is used to represent the heat flux of the cooling component. In the heat flux model, the heat flux within the cooling component can be expressed using the following equation:

[0113]

[0114] In the formula, Q GThis represents the heat flow of the cooling component (in this example, it can be considered as the heat flow rate Q from the housing of the cooling component mentioned above). H ), Q conv S is the convective heat of the coolant, k is the fluid thermal conductivity of the coolant, and S is the heat transfer coefficient of the coolant. H Let T be the housing area of ​​the cooling component (in this example, it can be expressed as the area in the cooling component that is in contact with the coolant), D be the hydraulic diameter of the cooling component, and T be the area of ​​the cooling component's housing. H T represents the casing temperature of the cooling assembly, and T represents the fluid temperature of the coolant within the cooling assembly. These parameters can be set within the simulation platform, and the set values ​​can be determined based on the specific design of the battery device.

[0115] Based on the various relevant parameters of the cooling components and coolant, an accurate heat flow model can be obtained, enabling accurate simulation of the actual heat flow state of the cooling components when performing thermal simulation of the battery device, thereby improving the accuracy of the simulation results.

[0116] According to some embodiments of this application, when the battery device includes multiple cells, step 110 includes:

[0117] An initial reduced-order thermal model is established based on the cell model corresponding to each of the multiple cells, the cooling component model corresponding to each of the at least one cooling component, the arrangement of the multiple cells, and the connection relationship between the multiple cells and the at least one cooling component.

[0118] Figure 8 The image illustrates an example of an initial reduced-order thermal model for a single battery cell. For example... Figure 8 As shown, the battery cell and cooling assembly can be represented by battery cell models and cooling assembly models, respectively. This model illustrates two thermal resistances to be identified: the battery cell-air thermal resistance and the battery cell-cooling assembly thermal resistance, which will be obtained through parameter identification in step 120.

[0119] exist Figure 8 Based on a single battery cell, Figure 9 The diagram illustrates the initial reduced-order thermal model for a battery device comprising 64 cells. For example... Figure 9 As shown, in this model, the cell models are connected according to the arrangement of the cells in the battery device, and connected to each cooling component according to the actual connection relationship in the battery device. In this model, a total of 64 cell-air thermal resistances and 64 cell-water cooling plate thermal resistances need to be identified and obtained in step 120.

[0120] In some embodiments, all sensors detecting temperature and / or flow rate can be ignored in the initial reduced-order thermal model. In real-world applications, parameters such as cell and coolant temperature and flow rate need to be monitored according to usage requirements.

[0121] In some embodiments, the initial reduced-order thermal model can be further simplified, thereby further simplifying the calculation process. For example, multiple cell models located close to each other can be connected to the same cooling component model. Figure 9 In the example shown, the battery device includes 64 battery cells and 16 cooling components. Four adjacent battery cell models are connected to the same cooling component model, meaning the coolant properties in the cooling components corresponding to these four cells are considered to be the same. It should be understood that... Figure 9 This is just an example. The connection between the cell model and the cooling component model can be designed according to the usage requirements. For example, the number and position of cells sharing the same cooling component can be adjusted, or each cell can be connected to a separate cooling component. This application does not limit this.

[0122] In some embodiments, the cooling component model may also be represented by a fixed coolant temperature, further simplifying the calculation process.

[0123] In some embodiments, the air temperature in the initial reduced-order thermal model is set to the same temperature, without considering the air temperature difference at different locations, because the air temperature difference at different locations will be determined by the cell-air thermal resistance response obtained from subsequent parameters.

[0124] By obtaining a reduced-order thermal model of the battery device based on the layout of the battery cells and cooling components, the actual situation of the battery device can be accurately simulated, resulting in thermal simulation results that are closer to reality.

[0125] According to some embodiments of this application, the battery device further includes at least one first thermal conductor. Each of the at least one first thermal conductor is respectively disposed between any one of the at least one battery cell and a corresponding cooling component adjacent to the battery cell. Step 110 includes:

[0126] For each of at least one first heat conductor:

[0127] Based on the thermal mass parameters of the first thermal conductor, a model of the first thermal conductor corresponding to the first thermal conductor is established.

[0128] like Figure 8 As shown, to improve heat transfer efficiency, a first heat conductor is sometimes placed between the battery cell and the cooling assembly. The first heat conductor can be made of thermally conductive adhesive, etc. In the initial reduced-order thermal model, the first heat conductor can be represented as a first heat conductor model. Similar to the battery cell model, the first heat conductor model can also be established based on the thermal mass parameters of the first heat conductor. An example of the first heat conductor model can be found in the example of the battery cell model, and will not be repeated here.

[0129] When establishing a reduced-order thermal model of a battery device, considering the first thermal conductor between the cell and the cooling components, and establishing the first thermal conductor model based on the thermal mass of this thermal conductor, can accurately simulate the battery device, while effectively simplifying the first thermal conductor model in the battery device and simplifying the calculation process.

[0130] According to some embodiments of this application, in the case where the battery device includes multiple battery cells, the battery device also includes at least one second heat conductor. Each of the at least one second heat conductor is respectively disposed between any two adjacent battery cells in the plurality of battery cells. Step 110 includes:

[0131] For each of at least one second heat conductor:

[0132] Based on the contact area between the second heat conductor and the battery cell, and the thickness of the second heat conductor, a model of the second heat conductor corresponding to the second heat conductor is established.

[0133] like Figure 8 and Figure 9 As shown, a second heat conductor is sometimes provided between two adjacent battery cells. The second heat conductor can be a heat insulation pad or the like. In some embodiments, the second heat conductor can be provided between the contact surfaces of adjacent battery cells. In other embodiments, the second heat conductor can also be provided only between the larger contact surfaces (i.e., the large surface of the battery cell) between two adjacent battery cells, and the influence between other adjacent surfaces is converted into the cell-to-air thermal resistance, which is obtained by parameter identification in step 120. In this case, the battery cells located at the edge of the battery device are less affected by the surrounding battery cells and have easier heat dissipation, so their corresponding cell-to-air thermal resistance is also smaller. Conversely, the battery cells in the center of the battery device are more affected by the surrounding battery cells and have more difficulty in heat dissipation, so their corresponding cell-to-air thermal resistance is also larger.

[0134] The second heat conductor model can be expressed by the following formula:

[0135]

[0136] In the formula, Q R k is the power at the second heat conductor. R Let be the thermal conductivity of the second heat conductor, A be the contact area between the second heat conductor and the battery cell, d be the thickness of the second heat conductor, and ΔT be the temperature difference between the two ends of the second heat conductor (i.e., the ends where the second heat conductor contacts the two adjacent battery cells respectively). In the simulation platform, the above-mentioned parameters can be set, and the set values ​​can be determined according to different battery device designs.

[0137] When establishing a reduced-order thermal model of a battery device, a second thermal conductor between adjacent cells is considered, and a model of the second thermal conductor is established based on the thermal mass of this thermal conductor. This can accurately simulate the battery device and effectively simplify the model of the second thermal conductor in the battery device, thus simplifying the calculation process.

[0138] According to some embodiments of this application, reference is made to Figure 2 For any one of the at least thermal resistors to be identified, step 120 includes steps 210 to 230.

[0139] Step 210: Set the thermal resistance to be identified to the initial thermal resistance value.

[0140] Step 220: Determine the objective function for the thermal resistance to be identified based on the target temperature.

[0141] Step 230: Based on the initial thermal resistance value and the objective function, perform parameter identification on the initial reduced-order thermal model to obtain the simulated thermal resistance value corresponding to the thermal resistance to be identified.

[0142] For each thermal resistance to be identified, an initial thermal resistance value can first be assigned to it. In step 210, for Figure 8 For the cell-to-air thermal resistance, the initial thermal resistance value can be set in the range of 5 to 20 Kelvin per watt (K / W). For the cell-to-water cooling plate thermal resistance, the initial thermal resistance value can be set in the range of 0.3 to 1.0 K / W. In some embodiments, the battery device includes multiple cells, and the particle swarm optimization (PSO) algorithm can be used to assign an initial value to the thermal resistance to be identified for each cell. The PSO algorithm initializes a random combination of the initial thermal resistance values ​​corresponding to the thermal resistance to be identified for all groups (e.g., 50 to 200 groups), i.e., "particles," and initializes the velocity of all particles, which is generally randomly selected within the range of the initial thermal resistance value corresponding to the thermal resistance to be identified.

[0143] In step 220, the target temperature can be the actual temperature of the battery device during operation, obtained by actual testing, or it can be the simulated temperature obtained by performing a three-dimensional thermal simulation on the three-dimensional model of the battery device. This application does not limit this.

[0144] In some embodiments, the target temperature can be determined separately for different operating conditions, such as the target temperature under natural cooling and the target temperature under water cooling conditions.

[0145] Taking the cell-air thermal resistance to be identified as an example, the target temperature is selected as the actual temperature under natural cooling conditions. The objective function can be determined as the error between the simulated temperature of the cell obtained from the initial reduced-order thermal model simulation and the target temperature, as shown in the following formula:

[0146]

[0147] In the formula, e i Let n be the error between the simulated temperature and the target temperature of cell i. time T represents the total number of time points. sim,i,j and T real,i,j Let represent the simulated temperature of cell i at the j-th time point and the target temperature at that time point, respectively. The formula does not include e. i Taking the absolute value preserves the "direction" of the error, that is, compared with the target temperature, e i A negative value indicates that the simulated temperature is low, and the corresponding parameter needs to be adjusted to increase it during the parameter identification process. i A positive value indicates that the simulated temperature is high, and the corresponding parameters need to be adjusted to make it smaller during the parameter identification process.

[0148] When a battery device comprises multiple cells, the objective function can be determined as the average error between the simulated temperature and the target temperature of each cell. For example, it can be the average error of all cells in the battery device, as shown in the following formula:

[0149]

[0150] In the formula, fitness is the average error of all cells, e i To represent the error between the simulated temperature and the target temperature of cell i mentioned above, n cell This represents the number of battery cells in the battery pack.

[0151] After obtaining the initial reduced-order thermal model, by assigning initial values ​​to the thermal resistance to be identified and setting the objective function, accurate simulated thermal resistance values ​​can be obtained while reducing the computational load of the parameter identification process and improving computational efficiency.

[0152] According to some embodiments of this application, reference is made to Figure 3 Step 230 includes steps 310 to 320.

[0153] Step 310: Based on the initial thermal resistance value and the objective function, perform parameter iteration on the thermal resistance value corresponding to the thermal resistance to be identified.

[0154] Step 320: In response to the satisfaction of the preset cutoff condition, stop the parameter iteration of the thermal resistance value corresponding to the thermal resistance to be identified, and obtain the simulated thermal resistance value corresponding to the thermal resistance to be identified.

[0155] As mentioned above, after determining the initial thermal resistance value and the objective function, parameter identification is performed based on the initial reduced-order thermal model, and the thermal resistance value to be identified is iterated. The parameter obtained in each iteration, i.e., the thermal resistance value, is used in the initial reduced-order thermal model for simulation to obtain the simulated temperature of the battery cell. Parameter iteration can use gradient methods or heuristic algorithms, etc.

[0156] If there is a clear monotonic relationship between the thermal resistance to be identified and the objective function (e.g., the simulation temperature increases as the thermal resistance increases), the gradient method can be used. The gradient method can quickly obtain the simulated thermal resistance value, typically yielding good results within 10 iterations. If the gradient method is not applicable, heuristic algorithms can be used, such as particle swarm optimization, simulated annealing, and genetic algorithms. The specific calculation processes of the gradient method and heuristic algorithms will be detailed below.

[0157] For different objective functions, cutoff conditions can be set separately. When the cutoff condition is met during the parameter iteration process, the simulated thermal resistance value is considered to be good and can be determined as the simulated thermal resistance value, at which point the parameter iteration process stops. Specific examples of cutoff conditions will be detailed below.

[0158] By setting reasonable objective functions and cutoff conditions, the number of parameter iterations can be effectively reduced, and the efficiency of parameter identification can be improved.

[0159] According to some embodiments of this application, when the battery device includes multiple cells, method 100 further includes process 400. (See reference...) Figure 4 Process 400 includes steps 410 to 420.

[0160] Step 410: Divide the multiple battery cells into multiple initial battery cell groups according to their arrangement. Each initial battery cell group includes at least one battery cell.

[0161] Step 420: Based on multiple initial cell groups and the objective function, update the cell groups of multiple cells to obtain at least one simulated cell group. Each simulated cell group includes at least one cell.

[0162] To improve computational efficiency, it can be assumed that multiple cells within a certain region have the same thermal resistance value, thus significantly reducing the number of thermal resistances to be identified. Therefore, multiple cells can be divided into multiple cell groups, with each cell in each group using the same simulated thermal resistance value.

[0163] Figure 10 The middle indicates Figure 9 An example of the initial cell grouping of 64 cells under natural cooling conditions. Figure 10 The grouping method described above can be applied to identify the cell-air thermal resistance of 64 battery cells. According to... Figure 10 The two axes of symmetry shown can divide the battery cell into sections as follows: Figure 10 The four regions shown are the top left, top right, bottom left, and bottom right regions. For Figure 10 The 16 cells in the upper left area can be arranged according to Figure 10 The division shown divides the cells into four initial cell groups. Within each initial cell group, the cells have the same cell-to-air thermal resistance (CTR). Specifically: cells 1 and 17 have the same CTR; cells 2, 3, 4, and 5 have the same CTR; cells 18, 19, 20, and 21 have the same CTR; and cells 6, 7, 8, 22, 23, and 24 have the same CTR. Then... Figure 10 The two axes of symmetry in the diagram divide the upper right, lower left, and lower right regions into four initial cell groups each, using the same grouping method. At this point, it can be assumed that the cell groups at corresponding positions in the upper left, upper right, lower left, and lower right regions also have the same cell-to-air thermal resistance. For example, cells 1 and 17 in the upper left region, cells 33 and 49 in the upper right region, cells 9 and 25 in the lower left region, and cells 41 and 57 in the lower right region have the same cell-to-air thermal resistance. Therefore, the number of thermal resistance values ​​to be identified is reduced from 64 to 4.

[0164] Figure 11 The middle indicates Figure 9 An example of the initial cell grouping of 64 cells under water cooling conditions. Figure 11 The grouping method described above can be applied to identify the thermal resistance of the cell-cooling assembly for 64 battery cells. Based on the coolant flow direction in the cooling assembly (shown in the diagram as water cooling flow), the following can be obtained: Figure 11 One axis of symmetry in [the structure]. According to [the...] Figure 11 The axis of symmetry shown divides the battery cell into two regions, as illustrated: the left region and the right region. For Figure 11 The 32 cells in the left-middle area can be arranged according to Figure 11The shown division method divides the cells into 7 initial cell groups. Within each initial cell group, the cell-cooling assembly thermal resistance is the same for all cells. Specifically: cells 1 and 17 have the same thermal resistance; cells 2, 3, 18, and 19 have the same thermal resistance; and cells 4, 5, 6, 20, 21, and 22 have the same thermal resistance. Cells 7, 8, 9, 10, 23, 24, 25, and 26 have the same thermal resistance in their cell-cooling assemblies. Cells 11, 12, 13, 27, 28, and 29 have the same thermal resistance in their cell-cooling assemblies. Cells 14, 15, 30, and 31 have the same thermal resistance in their cell-cooling assemblies. Cells 16 and 32 have the same thermal resistance in their cell-cooling assemblies. Then press... Figure 11 The axis of symmetry divides the right-side region into 7 initial cell groups using the same grouping method. At this point, it can be assumed that the corresponding cell groups in the left and right regions also have the same cell-cooling assembly thermal resistance. For example, cells 1 and 17 in the left-side region, and cells 33 and 49 in the right-side region have the same cell-cooling assembly thermal resistance. Therefore, the number of thermal resistance values ​​to be identified is reduced from 64 to 7.

[0165] It should be understood that Figure 10 and Figure 11 These are just two examples of initial cell grouping; the initial cell grouping can be designed according to usage requirements. In other embodiments, cell grouping may not be performed, but rather the parameters of the resistors to be identified for each cell may be identified separately to obtain the simulated thermal resistance value for each cell.

[0166] After obtaining the initial cell grouping, the cell grouping can also be updated during the parameter identification process. For example, it can be updated based on the error e between the simulated temperature and the target temperature of each cell in each cell group. i The process involves splitting and / or merging the cell groups to obtain the final simulated cell groups. These simulated cell groups, as part of the final battery thermal model, can be used for thermal simulation of the battery device. Specific examples of updating cell groups will be detailed below.

[0167] By rationally grouping the cells in the battery device, the same simulated thermal resistance value can be used for cells in the same simulated cell group, which effectively reduces the number of thermal resistances to be identified during the parameter identification process, thereby improving computational efficiency while obtaining accurate simulation results.

[0168] According to some embodiments of this application, when the battery device includes multiple cells, the cutoff condition includes at least one of the following:

[0169] The number of iterations of the parameter iteration for the thermal resistance value corresponding to the thermal resistance to be identified reaches the threshold number;

[0170] The first cell error of each simulated cell group in at least one simulated cell group satisfies the first error condition;

[0171] The second cell error of multiple cells satisfies the second error condition.

[0172] The first cell error indicates the average error between the simulated temperature and the target temperature of all cells in the simulated cell group. The simulated temperature indicates the temperature of the cell calculated based on the objective function. The second cell error indicates the average error between the simulated temperature and the target temperature of multiple cells.

[0173] As mentioned above, the parameter iteration process will stop when a pre-set cutoff condition is met. The cutoff condition can be determined based on factors such as the number of iterations and / or the error between the simulated temperature and the target temperature of the battery cell.

[0174] In one example, a maximum number of iterations can be preset. When the maximum number of parameter iterations is reached, the parameter iteration process stops, and the simulated thermal resistance value is obtained.

[0175] In one example, given cell grouping, for each updated simulated cell group, if the average error between the simulated temperature and the target temperature of all cells within that group satisfies the first error condition, then parameter iteration can be stopped, and the simulated thermal resistance value can be obtained. The following formula illustrates an example of the first error condition:

[0176] For all simulated cell group a:

[0177]

[0178] In the formula, e i Let n be the error between the simulated temperature and the target temperature of cell i. cell,a TOL represents the number of cells in simulated cell group a. group This is a pre-set error threshold. In one example, TOL group It can be set within the range of 0.02 to 0.2.

[0179] In one example, parameter iteration can also be stopped and the simulated thermal resistance value obtained when the average error between the simulated temperature and the target temperature of all cells satisfies the second error condition. The following formula illustrates an example of the second error condition:

[0180] fitness <TOL pack

[0181] In the formula, fitness is the average of the errors of all the cells mentioned above, and TOL pack This is a pre-set error threshold. In one example, TOL pack It can be set within a value range of 0.2 to 0.5.

[0182] For the different cutoff conditions mentioned above, you can choose any one of them, that is, stop parameter iteration when the condition is met, or you can choose two or three of them at the same time, that is, stop parameter iteration only when two or three of the selected conditions are met at the same time.

[0183] In some embodiments, parameter iteration for the thermal resistance to be identified and cell grouping update can be performed simultaneously. When the cutoff condition is met, the parameter iteration process and cell grouping update process are stopped, and the simulated thermal resistance value and simulated cell grouping of the thermal resistance to be identified are obtained.

[0184] By setting reasonable cutoff conditions, accurate simulated thermal resistance values ​​can be obtained while improving parameter identification efficiency.

[0185] According to some embodiments of this application, reference is made to Figure 5 Step 420 includes steps 510 and / or 520.

[0186] Step 510: In response to the fact that at least one cell in the cell group has a third cell error that does not meet the third error condition, at least one cell is divided into a new cell group. The third cell error indicates the error between the simulated temperature and the target temperature of any one of the at least one cells.

[0187] Step 520: In response to the fact that the thermal resistance value corresponding to the thermal resistance to be identified for each cell in the multiple cell groups all satisfies the first thermal resistance condition, the multiple cell groups are merged.

[0188] In step 510, the error e between the simulated temperature and the target temperature of cell i in a certain cell group h can be determined. i Does the third error condition satisfy? The following formula illustrates an example of the third error condition:

[0189] |e i |>TOL cell

[0190] In the formula, e i To account for the error between the simulated temperature and the target temperature of cell i, TOL cell This is a pre-set error threshold. In one example, TOL cell It can be set within a value range of 0.2 to 0.5.

[0191] If one or more cells i in cell group h do not meet the third error condition, then the error of cell i is considered to be an outlier, and cell i should not be classified in cell group h. Instead, cell i is classified into a separate group.

[0192] In step 520, if the thermal resistance values ​​of the cells to be identified in each of the multiple cell groups all satisfy the first thermal resistance condition, then the multiple cell groups can be merged into one cell group. In step 520, the previously separated cells can be merged back into other cell groups, or multiple cells can be reassembled into a single cell group. The following formula illustrates an example of the first thermal resistance condition:

[0193]

[0194] In the formula, The thermal resistance value of the cell to be identified in cell group k1. TOL represents the thermal resistance value of the cell to be identified in cell group k2. R This is a pre-set error threshold. In one example, for cell-air thermal resistance, this error threshold (which can be expressed as...) This error threshold (which can be set within a range of 0.05 to 0.2) can be set for the cell-cooling assembly thermal resistance. The value can be set within the range of 0.01 to 0.03. When this condition is met, cell group k1 and cell group k2 can be merged into one cell group.

[0195] As mentioned above, algorithms such as gradient methods or heuristic algorithms can be used to obtain the simulated thermal resistance value. The following will use cell grouping h as an example to explain the two algorithms respectively.

[0196] First, we will introduce the gradient method.

[0197] In the m-th parameter iteration, the average error between the simulated temperature and the target temperature of all cells i in cell group h in this iteration can be calculated, as shown in the following formula:

[0198]

[0199] In the formula, E h,m e is the average error between the simulated temperature and the target temperature of all cells i in cell group h during the m-th parameter iteration. i Let n be the error between the simulated temperature and the target temperature of cell i. cell,h The number of cells in cell group h.

[0200] Based on the average error of the current iteration (m-th iteration) and the average error of the previous iteration (m-1-th iteration), a linear extrapolation is performed to estimate the thermal resistance value when the error is 0. This value is then set as the thermal resistance value of the thermal resistance to be identified and substituted into the initial reduced-order thermal model for simulation, proceeding to the next iteration. (Reference) Figure 12 The thermal resistance value R at the next iteration, i.e., the (m+1)th iteration. h,m+1 for:

[0201]

[0202] In the formula, E h,m R is the average error between the simulated temperature and the target temperature of all cells i in cell group h during the m-th parameter iteration. h,m E is the thermal resistance value obtained in the m-th parameter iteration. h,m-1 R is the average error between the simulated temperature and the target temperature of all cells i in cell group h during the (m-1)th parameter iteration. h,m-1 This is the thermal resistance value obtained in the (m-1)th parameter iteration.

[0203] It should be noted that if m = 1, the thermal resistance value for the second iteration can be directly set, and it is usually set as follows:

[0204]

[0205] When the cutoff condition is met, the iteration process stops, and the simulated thermal resistance value is obtained.

[0206] The principles of heuristic algorithms will be introduced next. The particle swarm optimization algorithm will be used as an example.

[0207] The inertia weight w in the particle swarm optimization algorithm is calculated as follows:

[0208]

[0209] In the formula, w max The maximum value for inertia weight is typically set within the range of 0.9 to 1.0. min The minimum inertia weight is typically set within the range of 0.3 to 0.5, m. now Let m be the current iteration number. max This is a limit on the maximum number of iterations.

[0210] Calculate the velocity v of particle y in the m-th iteration. y,m As shown in the following formula:

[0211] v y,m =wv y,m-1 +c1r1(Py,best -POP y,m-1 )+c2r2(G best -POP y,m-1 )

[0212] In the formula, w is the inertia weight, and POP y,m-1 Let P be the position of particle y in the (m-1)th iteration, c1 and c2 be acceleration coefficients, usually set in the range of 1 to 3, r1 and r2 be random numbers, usually taking values ​​from 0 to 1, and P be the position of particle y in the (m-1)th iteration. y,best G represents the best value for the particle to date. best This represents the best possible value for all particles so far. It is also necessary to ensure that v... min ≤v y,m ≤v max , where v max The maximum value of the particle position, POP. max 0.1 to 0.3 times, v min =-v max .

[0213] Update the positions of all particles, for example, for particle y:

[0214] POP y,m =POP y,m-1 +v y,m

[0215] In the formula, POP y,m Let be the position of particle y in the m-th iteration, and POP must be guaranteed. min ≤POP y,m ≤POP max POP min POP represents the minimum particle position. max This represents the maximum value of the particle's position.

[0216] When the cutoff condition is met, the iteration process stops, that is, the particle position is stopped. The final particle position obtained is the simulated thermal resistance value.

[0217] In some embodiments, for the cell-air thermal resistance and the cell-cooling component thermal resistance, the corresponding simulated thermal resistance values ​​can be determined separately. Alternatively, after determining the cell-air thermal resistance, the cell-air thermal resistance can be substituted into the initial reduced-order thermal model to further determine the cell-cooling component thermal resistance.

[0218] By splitting and / or merging the cell groups, the cell grouping results can be optimized, thereby improving the accuracy of the simulation results.

[0219] Based on the same technical concept, embodiments of this application provide an apparatus for establishing a battery thermal model. Embodiments of the apparatus for establishing a battery thermal model can be referenced from embodiments of the method for establishing a battery thermal model; details that are repeated will not be repeated. Reference Figure 6 The apparatus 600 for establishing a battery thermal model includes a first module 610, a second module 620, and a third module 630.

[0220] The first module 610 is used to establish an initial reduced-order thermal model based on at least one thermodynamic parameter of the battery device. The battery device includes at least one battery cell. The initial reduced-order thermal model includes at least one thermal resistance to be identified for each battery cell.

[0221] The second module 620 is used to identify parameters of the initial reduced-order thermal model based on the target temperature, and obtain simulated thermal resistance values ​​corresponding to at least one thermal resistance to be identified.

[0222] The third module 630 is used to determine the battery thermal model based on the simulated thermal resistance values ​​corresponding to at least one thermal resistance to be identified and the initial reduced-order thermal model. The battery thermal model is used to perform thermal simulation of the battery device.

[0223] The first module 610, the second module 620, and the third module 630 in the apparatus 600 for establishing a battery thermal model can correspond to steps 110 to 130 in the method 100 for establishing a battery thermal model, and will not be described in detail here for the sake of brevity. It should be understood that, corresponding to the embodiment of the method 100 for establishing a battery thermal model, the embodiment of the apparatus 600 for establishing a battery thermal model may also include more modules.

[0224] It should be noted that the functions of the modules discussed herein can be divided into multiple modules, and / or at least some functions of multiple modules can be combined into a single module. The specific actions performed by a particular module discussed herein include the specific module itself performing the action, or alternatively, the specific module calling or otherwise accessing another component or module that performs the action (or performs the action in conjunction with the specific module). Therefore, a specific module performing an action can include the specific module performing the action itself and / or another module that performs the action, called or otherwise accessed by the specific module.

[0225] It should also be understood that this article can describe various technologies in the general context of software and hardware components or program modules. The above regarding... Figure 6The described modules can be implemented in hardware or in hardware in combination with software and / or firmware. For example, these modules can be implemented as computer program code / instructions configured to execute in one or more processors and stored in a computer-readable storage medium. Alternatively, these modules can be implemented as hardware logic / circuit. Hardware logic / circuit may include integrated circuit chips (which include processors (e.g., Central Processing Unit (CPU), microcontrollers, microprocessors, digital signal processors (DSPs), etc.), memory, one or more communication interfaces, and / or one or more components of other circuitry), and may optionally execute received program code and / or include embedded firmware to perform functions.

[0226] This application provides a computing device 700, such as... Figure 7 As shown. Figure 7 An example configuration of a computing device 700 that can be used to implement the method 100 for establishing a battery thermal model described herein is shown. For example, the apparatus 600 for establishing a battery thermal model described above may be implemented wholly or at least partially by the computing device 700 or a similar device or system.

[0227] The computing device 700 may include at least one processor 705 capable of communicating with each other, such as via a bus 704 or other suitable connection, a memory 707, multiple communication interfaces 702, a display device 701, other input / output (I / O) devices 703, and one or more mass storage devices 706. Instructions are stored on the memory 707 that, when executed by the processor 705, cause the processor 705 to perform the battery replacement control method as described in the above embodiments.

[0228] The computing device 700 can be a variety of different types of devices. Examples of the computing device 700 include, but are not limited to: desktop computers, server computers, laptop or netbook computers, mobile devices (e.g., tablet computers, cellular or other wireless phones (e.g., smartphones), notebook computers, mobile stations), wearable devices (e.g., glasses, watches), entertainment devices (e.g., entertainment appliances, set-top boxes communicatively coupled to a display device, game consoles), televisions or other display devices, automotive computers, and so on.

[0229] Processor 705 may be a single processing unit or multiple processing units, and all processing units may include single or multiple computing units or multiple cores. Processor 705 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuits, and / or any device that manipulates signals based on operating instructions. Among other capabilities, processor 705 may be configured to fetch and execute computer-readable instructions stored in memory 707, mass storage device 706, or other computer-readable media, such as program code of operating system 708, program code of application program 709, program code of other program 710, etc.

[0230] Memory 707 and mass storage device 706 are examples of computer-readable storage media for storing instructions executed by processor 705 to perform the various functions described above. For example, memory 707 can generally include both volatile and non-volatile memory (e.g., RAM, ROM, etc.). Furthermore, mass storage device 706 can generally include hard disk drives, solid-state drives, removable media, including external and removable drives, memory cards, flash memory, floppy disks, optical disks (e.g., CDs, DVDs), storage arrays, network-attached storage, storage area networks, etc. Both memory 707 and mass storage device 706 can be collectively referred to herein as memory or computer-readable storage media, and can be non-transitory media capable of storing computer-readable, processor-executable program instructions as computer program code, which can be executed by processor 705 as a specific machine configured to perform the operations and functions described in the examples herein.

[0231] Multiple programs may be stored on mass storage device 706. These programs include operating system 708, one or more application programs 709, other programs 710, and program data 711, and they may be loaded into memory 707 for execution. Examples of such application programs or program modules may include, for example, computer program logic (e.g., computer program code or instructions) for implementing the following components / functions: means 600 for building a battery thermal model (including first module 610, second module 620, and third module 630), method 100 for building a battery thermal model (including any suitable steps of method 100 for building a battery thermal model), and / or other embodiments described herein.

[0232] Although Figure 7 The data is illustrated as being stored in memory 707 of computing device 700, but operating system 708, application program 709, other programs 710 and program data 711 or portions thereof may be implemented using any form of computer-readable medium accessible by computing device 700.

[0233] One or more communication interfaces 702 are used for exchanging data with other devices, such as via a network, direct connection, etc. Such communication interfaces can be one or more of the following: any type of network interface (e.g., a network interface card (NIC)), wired or wireless (such as IEEE 802.11 Wireless LAN (WLAN)) wireless interface, Wi-MAX interface, Ethernet interface, Universal Serial Bus (USB) interface, cellular network interface, Bluetooth™ interface, Near Field Communication (NFC) interface, etc. Communication interface 702 can facilitate communication across various network and protocol types, including wired networks (e.g., LAN, cable, etc.) and wireless networks (e.g., WLAN, cellular, satellite, etc.), the Internet, etc. Communication interface 702 can also provide communication with external storage devices (not shown), such as storage arrays, network-attached storage, storage area networks, etc.

[0234] In some examples, a display device 701, such as a monitor, may be included for displaying information and images to the user. Other I / O devices 703 may be devices that receive various inputs from the user and provide various outputs to the user, and may include touch input devices, gesture input devices, cameras, keyboards, remote controls, mice, printers, audio input / output devices, and so on.

[0235] The technologies described herein can be supported by these various configurations of computing device 700, and are not limited to specific examples of the technologies described herein. For example, the functionality can also be implemented wholly or partially on a “cloud” using a distributed system. A cloud includes and / or represents a platform for resources. The platform abstracts the underlying functionality of the cloud’s hardware (e.g., servers) and software resources. Resources may include applications and / or data that can be used when performing computational processing on servers remote from computing device 700. Resources may also include services provided via the Internet and / or via subscriber networks such as cellular or Wi-Fi networks. The platform can abstract resources and functionality to connect computing device 700 to other computing devices. Therefore, the implementation of the functionality described herein can be distributed throughout the cloud. For example, the functionality can be implemented partly on computing device 700 and partly through a platform that abstracts the functionality of the cloud.

[0236] This application also provides a computer-readable storage medium storing instructions that, when executed individually or jointly by one or more processors of a computing device, cause the computing device to perform the control method as described in any of the above embodiments.

[0237] Computer-readable storage media include volatile and non-volatile, removable and non-removable media implemented by any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, DVD, or other optical storage devices, magnetic cassettes, magnetic tapes, disk storage devices or other magnetic storage devices, or any other non-transmission medium that can be used to store information for access by computer equipment.

[0238] This application also provides a computer program product, including instructions that, when executed individually or jointly by one or more processors of a computing device, cause the computing device to perform the control method as described in any of the above embodiments.

[0239] A specific embodiment of this application is described below. It should be understood that this specific embodiment is described for illustrative purposes only and should not be construed as limiting the scope of this application.

[0240] like Figure 13 As shown, when establishing the battery thermal model, an initial reduced-order thermal model of the battery device is first established based on the thermodynamic parameters of the battery device, such as... Figure 8 and Figure 9 As shown. The battery device includes at least one battery cell and at least one cooling assembly. The initial reduced-order thermal model includes at least one thermal resistance to be identified for each battery cell, such as the cell-air thermal resistance and the cell-cooling assembly thermal resistance.

[0241] For each cell in a battery device, a cell model can be established based on the cell's thermal mass parameters.

[0242] For each cooling component in the battery device, a mass model of the corresponding cooling component can be established based on the mass flow rate at the first end of the cooling component, the mass flow rate at the second end of the cooling component, the fluid density of the coolant in the cooling component, the fluid volume of the coolant in the cooling component, the isothermal bulk modulus of the coolant, the isobaric coefficient of thermal expansion of the coolant, the fluid pressure of the coolant in the cooling component, and the fluid temperature of the coolant in the cooling component. A momentum model of the corresponding cooling component can be established based on the fluid pressure of the coolant in the cooling component and the viscous frictional pressure loss generated by the coolant flowing through the cooling component. An energy model of the corresponding cooling component can be established based on the energy flowing through the first end of the cooling component, the energy flowing through the second end of the cooling component, and the energy from the casing of the cooling component. A heat flux model of the corresponding cooling component can be established based on the convective heat of the coolant in the cooling component, the fluid thermal conductivity of the coolant, the casing area of ​​the cooling component, and the casing temperature of the cooling component. In the simulation platform, the above-mentioned parameters can be set, and the set values ​​can be determined according to the different designs of the battery device.

[0243] A first heat conductor is disposed between the battery cell and the adjacent cooling component. The model of the first heat conductor can be established based on the thermal mass parameters of the first heat conductor. A second heat conductor is disposed between the large surfaces of two adjacent battery cells. The model of the second heat conductor can be established based on the contact area between the second heat conductor and the battery cell and the thickness of the second heat conductor.

[0244] When a battery device includes multiple cells, the cells can be divided into multiple cell groups. The cells in each cell group use the same simulated thermal resistance value. Therefore, the same thermal resistance to be identified can be used to identify the parameters of the cells in the same cell group.

[0245] For each thermal resistance to be identified, an initial thermal resistance value is set, and an objective function is determined based on the target temperature. When the battery device comprises multiple cells, these cells are divided into multiple initial cell groups. Using a gradient method or heuristic algorithm, the parameters corresponding to the thermal resistance values ​​of the cells to be identified are iterated. The thermal resistance value obtained in each iteration can be substituted into the initial reduced-order thermal model for simulation, thereby obtaining the error between the simulated temperature and the target temperature of each cell, and determining whether the cutoff condition is met.

[0246] During the iteration process, the cell groups can be updated simultaneously. If one or more cells in a cell group do not meet the third error condition, then the cell should not be classified into the cell group, and the cells that do not meet the third error condition are classified into a separate group. If the thermal resistance values ​​of the cells to be identified in multiple cell groups all meet the first thermal resistance condition, then the multiple cell groups can be merged into one cell group.

[0247] When the cutoff condition is met, stop parameter iteration to obtain the final simulated cell grouping and the simulated thermal resistance value corresponding to each thermal resistance to be identified.

[0248] Substituting the obtained simulated thermal resistance value into the initial reduced-order thermal model, and considering the grouping of simulated battery cells, the final battery thermal model can be obtained, which can be used for thermal simulation of the battery device.

[0249] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and they should all be covered within the scope of the claims and specification of this application. In particular, as long as there is no structural conflict, the various technical features mentioned in the embodiments can be combined in any way. This application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for establishing a battery thermal model, characterized in that, include: An initial reduced-order thermal model is established based on at least one thermodynamic parameter of the battery device, wherein the battery device includes at least one cell and the initial reduced-order thermal model includes at least one thermal resistance to be identified for each cell. Based on the target temperature, the parameters of the initial reduced-order thermal model are identified to obtain the simulated thermal resistance values ​​corresponding to the at least one thermal resistance to be identified. The battery thermal model is determined based on the simulated thermal resistance value corresponding to the at least one thermal resistance to be identified and the initial reduced-order thermal model. The battery thermal model is used to perform thermal simulation on the battery device.

2. The method according to claim 1, characterized in that, The establishment of an initial reduced-order thermal model based on at least one thermodynamic parameter of the battery device includes: For each of the at least one battery cell: Based on the thermal mass parameters of the battery cell, a battery cell model corresponding to the battery cell is established.

3. The method according to claim 1, characterized in that, The battery device further includes at least one cooling component, and each of the at least one battery cells is respectively configured to be a corresponding cooling component adjacent to the at least one cooling component. Establishing an initial reduced-order thermal model based on at least one thermodynamic parameter of the battery device includes: For each of the at least one cooling component: A cooling component model corresponding to the cooling component is established based on at least one of the fluid parameters, physical parameters, and thermodynamic parameters of the cooling component.

4. The method according to claim 3, characterized in that, The step of establishing a cooling component model corresponding to the cooling component based on at least one of the fluid parameters, physical parameters, and thermodynamic parameters of the cooling component includes: A mass model of the cooling component corresponding to the cooling component is established based on the mass flow rate at the first end of the cooling component, the mass flow rate at the second end of the cooling component, the fluid density of the coolant in the cooling component, the fluid volume of the coolant in the cooling component, the isothermal bulk modulus of the coolant, the isobaric coefficient of thermal expansion of the coolant, the fluid pressure of the coolant in the cooling component, and the fluid temperature of the coolant in the cooling component.

5. The method according to claim 3, characterized in that, The step of establishing a cooling component model corresponding to the cooling component based on at least one of the fluid parameters, physical parameters, and thermodynamic parameters of the cooling component includes: Based on the housing structure of the cooling assembly and the hydrodynamic viscosity of the coolant in the cooling assembly, the viscous frictional pressure loss generated by the coolant flowing through the cooling assembly is determined.

6. The method according to claim 5, characterized in that, The step of establishing a cooling component model corresponding to the cooling component based on at least one of the fluid parameters, physical parameters, and thermodynamic parameters of the cooling component includes: Based on the fluid pressure of the coolant in the cooling assembly and the viscous frictional pressure loss generated by the coolant flowing through the cooling assembly, a momentum model of the cooling assembly corresponding to the cooling assembly is established.

7. The method according to claim 3, characterized in that, The step of establishing a cooling component model corresponding to the cooling component based on at least one of the fluid parameters, physical parameters, and thermodynamic parameters of the cooling component includes: Based on the energy flow rate through the first end of the cooling component, the energy flow rate through the second end of the cooling component, and the heat flow rate from the shell of the cooling component, an energy model of the cooling component corresponding to the cooling component is established.

8. The method according to claim 3, characterized in that, The step of establishing a cooling component model corresponding to the cooling component based on at least one of the fluid parameters, physical parameters, and thermodynamic parameters of the cooling component includes: Based on the convective heat of the coolant in the cooling assembly, the fluid thermal conductivity of the coolant, the shell area of ​​the cooling assembly, and the shell temperature of the cooling assembly, a heat flow model of the cooling assembly corresponding to the cooling assembly is established, wherein the convective heat of the coolant indicates the heat generated by convection when the flow rate of the coolant is not zero.

9. The method according to claim 3, characterized in that, In the case where the battery device includes multiple cells, establishing an initial reduced-order thermal model based on at least one thermodynamic parameter of the battery device includes: The initial reduced-order thermal model is established based on the cell model corresponding to each of the plurality of cells, the cooling component model corresponding to each of the at least one cooling component, the arrangement of the plurality of cells, and the connection relationship between the plurality of cells and the at least one cooling component.

10. The method according to claim 3, characterized in that, The battery device further includes at least one first heat conductor, each of the at least one first heat conductor being disposed between any one of the at least one battery cell and a corresponding cooling component adjacent to the battery cell. The establishment of an initial reduced-order thermal model based on at least one thermodynamic parameter of the battery device includes: For each of the at least one first thermal conductor: Based on the thermal mass parameters of the first thermal conductor, a model of the first thermal conductor corresponding to the first thermal conductor is established.

11. The method according to claim 1, characterized in that, In the case where the battery device includes multiple battery cells, the battery device also includes at least one second heat conductor, each of the at least one second heat conductor being disposed between any two adjacent battery cells among the multiple battery cells. The establishment of an initial reduced-order thermal model based on at least one thermodynamic parameter of the battery device includes: For each of the at least one second heat conductor: Based on the contact area between the second thermal conductor and the battery cell and the thickness of the second thermal conductor, a second thermal conductor model corresponding to the second thermal conductor is established.

12. The method according to any one of claims 1-11, characterized in that, The step of identifying parameters in the initial reduced-order thermal model based on the target temperature to obtain simulated thermal resistance values ​​corresponding to the at least one thermal resistance to be identified includes: For any one of the at least one thermal resistors to be identified: Set the thermal resistance to be identified to the initial thermal resistance value; Based on the target temperature, determine the objective function for the thermal resistance to be identified; Based on the initial thermal resistance value and the objective function, the parameters of the initial reduced-order thermal model are identified to obtain the simulated thermal resistance value corresponding to the thermal resistance to be identified.

13. The method according to claim 12, characterized in that, The step of identifying parameters of the initial reduced-order thermal model based on the initial thermal resistance value and the objective function to obtain the simulated thermal resistance value corresponding to the thermal resistance to be identified includes: Based on the initial thermal resistance value and the objective function, the thermal resistance value corresponding to the thermal resistance to be identified is iterated by parameters. In response to the satisfaction of a pre-set cutoff condition, the parameter iteration of the thermal resistance value corresponding to the thermal resistance to be identified is stopped, and the simulated thermal resistance value corresponding to the thermal resistance to be identified is obtained.

14. The method according to claim 13, characterized in that, When the battery device includes multiple battery cells, the method further includes: Based on the arrangement of the multiple battery cells, the multiple battery cells are divided into multiple initial battery cell groups, and each initial battery cell group includes at least one battery cell; Based on the multiple initial cell groups and the objective function, the cell groups of the multiple cells are updated to obtain at least one simulated cell group, and each simulated cell group includes at least one cell.

15. The method according to claim 14, characterized in that, When the battery device comprises multiple cells, the cutoff condition includes at least one of the following: The number of iterations of the parameter iteration for the thermal resistance value corresponding to the thermal resistance to be identified reaches a threshold number; The first cell error of each of the at least one simulated cell group satisfies a first error condition, wherein the first cell error indicates the average error between the simulated temperature of all cells in the simulated cell group and the target temperature, and the simulated temperature indicates the temperature of the cell calculated based on the target function; The second cell error of the plurality of cells satisfies the second error condition, and the second cell error indicates the average value of the error between the simulated temperature and the target temperature of the plurality of cells.

16. The method according to claim 15, characterized in that, The step of updating the cell grouping of the multiple initial cell groups and the objective function to obtain at least one simulated cell grouping includes: In response to the presence of at least one cell in the cell group whose third cell error does not meet the third error condition, the at least one cell is divided into a new cell group, wherein the third cell error indicates the error between the simulated temperature and the target temperature of any one of the at least one cells; and / or In response to the fact that the thermal resistance value corresponding to the thermal resistance to be identified for each cell in the multiple cell groups all satisfies the first thermal resistance condition, the multiple cell groups are merged.

17. An apparatus for establishing a battery thermal model, characterized in that, include: The first module is used to establish an initial reduced-order thermal model based on at least one thermodynamic parameter of the battery device, wherein the battery device includes at least one cell, and the initial reduced-order thermal model includes at least one thermal resistance to be identified for each cell. The second module is used to identify the parameters of the initial reduced-order thermal model based on the target temperature, and obtain the simulated thermal resistance values ​​corresponding to the at least one thermal resistance to be identified. The third module is used to determine the battery thermal model based on the simulated thermal resistance value corresponding to the at least one thermal resistance to be identified and the initial reduced-order thermal model. The battery thermal model is used to perform thermal simulation on the battery device.

18. A computing device, comprising: At least one processor; as well as At least one memory communicatively connected to the at least one processor, the at least one memory storing instructions that, when executed individually or jointly by the at least one processor, cause the computing device to perform the method of any one of claims 1 to 16.

19. A computer-readable storage medium storing instructions that, when executed individually or jointly by one or more processors of a computing device, cause the computing device to perform the method of any one of claims 1 to 16.

20. A computer program product comprising instructions that, when executed individually or jointly by one or more processors of a computing device, cause the computing device to perform the method of any one of claims 1 to 16.

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