Simulation method and system for quickly predicting battery temperature, computing equipment and storage medium

Abstract

The invention discloses a simulation method and system for rapidly predicting the temperature of a battery, computing equipment and a storage medium, relates to the technical field of batteries, and solves the problem that an existing battery thermal management simulation model is relatively high in complexity. The method comprises the following steps: establishing a three-dimensional numerical model of a battery module; symmetrical boundary conditions are set for the three-dimensional numerical model, so that the order of the three-dimensional numerical model is reduced; establishing a 0D lumped model based on the reduced three-dimensional numerical model and the energy differential equation; and performing lithium battery temperature field prediction based on the 0D lumped model, and outputting a prediction result. According to the method, the three-dimensional numerical model is established based on the battery module, and then the three-dimensional numerical model is subjected to order reduction, so that the complexity of the model is greatly reduced while the high precision of the model is kept, and the model is easier to deploy and calculate in practical application; meanwhile, due to the fact that the complexity of the provided model is smaller, the model is easier to characterize, and higher practicability and transportability are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of batteries, and particularly to a simulation method, system, computing device, and storage medium for quickly predicting battery temperature. Background Art

[0002] With the rapid development of the new energy field, lithium batteries have become an important part of this field due to their high energy density, high power output, and good cycle life. These superior performance characteristics enable lithium batteries to be widely used in applications such as new energy energy storage systems and portable electronic devices. However, when the temperature of the lithium battery module is too low, the ion mobility of the battery will slow down, reducing the battery capacity and power. On the other hand, a relatively high temperature of the battery module will accelerate the deterioration of the electrode materials, reducing the service life. During the charging and discharging process, heat is inevitably generated. If this heat cannot be effectively controlled, it may lead to an increase in the internal temperature and pressure, an increase in the internal pressure of the battery, causing the overpressure device to open or the battery casing to burst. In addition, the escape of hot and combustible gases may trigger explosions and fires. The dense packaging of batteries makes it very likely that the thermal runaway of one battery will spread to adjacent batteries, ultimately resulting in battery fires. Therefore, it is crucial to ensure that the heat transfer in the battery module is within a controllable range. The ideal temperature range is 15°C to 60°C, which not only helps prevent thermal runaway or thermal instability of the battery but also significantly improves the overall performance of the battery, maintains the operating balance of the battery, and thus extends the health state and cycle life of the battery.

[0003] Therefore, in order to achieve such temperature control, the role of the battery management system (BMS), especially the battery thermal management system ( T MS), is particularly important. The battery thermal management system monitors and regulates the current and temperature of the battery to ensure that they are maintained within a safe and efficient operating range, usually requiring a temperature difference of less than 6°C. This precise control can effectively reduce the capacity attenuation and safety risks caused by temperature fluctuations. In order to perform more accurate battery thermal management, it is usually necessary to establish a corresponding simulation model for calculation.

[0004] Currently, during the creation of the simulation model, researchers tend to use an equivalent circuit model with relatively high accuracy, combined with conditions such as a superposition model and an empirical model to describe the thermal control process of the battery. However, such a method will result in a relatively high complexity of the simulation model, a large amount of calculation, and difficulty in deployment.

[0005] In view of this, there is a need for a simulation method, system, computing device, and storage medium for quickly predicting battery temperature. Summary of the Invention

[0006] In view of the problems in the prior art that the complexity of the battery thermal management simulation model is relatively high, the calculation amount is large, and it is difficult to deploy, the present invention provides a simulation method, system, computing device and storage medium for quickly predicting the battery temperature, which can reduce the complexity of the simulation model and make it easier to deploy and calculate in practical applications. The specific technical solutions are as follows:

[0007] In a first aspect, an embodiment of the present application provides a simulation method for quickly predicting the battery temperature, including:

[0008] Establish a three-dimensional numerical model of the battery module; set symmetric boundary conditions for the three-dimensional numerical model to reduce the order of the three-dimensional numerical model; establish a 0D lumped model based on the reduced-order three-dimensional numerical model and the energy differential equation; predict the lithium battery temperature based on the 0D lumped model and output the prediction result.

[0009] Preferably, the three-dimensional numerical model includes a first model and a second model. The first model is the three-dimensional numerical model corresponding to the battery module under forced ventilation of the fan, and the second model is the three-dimensional numerical model corresponding to the battery module under natural ventilation.

[0010] Preferably, the three-dimensional numerical model is the first model; establishing the three-dimensional numerical model of the battery module includes: determining the flow pattern inside the battery module by calculating the Reynolds number; determining the mass continuity equation and the momentum equation in the case where the flow pattern is turbulent; establishing a turbulent model of the battery module based on the mass continuity equation and the momentum equation; establishing a temperature field model inside the battery module based on the power balance equation; and obtaining the first model based on the turbulent model and the temperature field model.

[0011] Preferably, the three-dimensional numerical model is the second model; establishing the three-dimensional numerical model of the battery module includes: establishing a temperature field model inside the battery module based on the power balance equation; calculating the heat transfer coefficient of the second model through the convection-enhanced conductivity correlation equation, the Nusselt number and the Rayleigh number; and obtaining the second model based on the temperature field model and the heat transfer coefficient.

[0012] Preferably, the three-dimensional numerical model includes a first model and a second model; setting symmetric boundary conditions for the three-dimensional numerical model includes: for the first model, setting the fan action as the driving force for half-module air convection and setting symmetric flow field boundary conditions based on the middle horizontal plane and the middle vertical plane; for the first model and the second model, setting the bottom and the top of the temperature field as adiabatic and setting symmetric temperature field boundary conditions based on the middle vertical plane.

[0013] Preferably, the three-dimensional numerical model is the first model; based on the reduced three-dimensional numerical model and the energy differential equation, a 0D lumped model is established, including: determining the equivalent heat capacity and thermal resistance according to the material properties and geometric structure of the battery module; obtaining the heat generated by the battery module, and calculating the heat generation rate of the battery module through the internal resistance value measured by the constant current intermittent titration technique; based on the heat capacity, the thermal resistance, the heat, the heat generation rate, and the energy differential equation, solving the energy differential equation to obtain the variation trend of the average temperature of the battery module with time.

[0014] Preferably, the three-dimensional numerical model is the second model; based on the reduced three-dimensional numerical model and the energy differential equation, a 0D lumped model is established, including: calculating the equivalent heat capacity according to the material properties of the battery module, and estimating the thermal resistance of the battery module based on the structure and natural convection characteristics of the battery module; obtaining the heat generated by the battery module, and calculating the heat generation rate of the battery module through the internal resistance value measured by the constant current intermittent titration technique; using the empirical formula of the Nusselt number to estimate the heat transfer coefficient; based on the equivalent heat capacity, the thermal resistance, the heat, the heat generation rate, the heat transfer coefficient, and the energy differential equation, solving the energy differential equation to obtain the variation trend of the average temperature of the battery module with time.

[0015] In a second aspect, an embodiment of the present application provides a simulation system for quickly predicting the battery temperature, and the system includes:

[0016] A first modeling module for establishing a three-dimensional numerical model of the battery module; a model reduction module for setting symmetric boundary conditions for the three-dimensional numerical model to reduce the order of the three-dimensional numerical model; a second modeling module for establishing a 0D lumped model based on the reduced three-dimensional numerical model and the energy differential equation; a model prediction module for predicting the lithium battery temperature by the 0D lumped model and outputting a prediction result.

[0017] In a third aspect, an embodiment of the present application provides a computing device, including: a memory for storing a program; a processor for loading the program to execute the method as described in the first aspect.

[0018] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a program, and when the program is executed by a processor, the method as described in the first aspect is implemented.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention establishes a three-dimensional numerical model based on the battery module, and then reduces the order of the three-dimensional numerical model. While maintaining the high accuracy of the model, the complexity of the model is greatly reduced, making it easier to be deployed and calculated in practical applications; at the same time, due to the smaller complexity of the proposed model, the model is easier to characterize, and has stronger practicability and portability. Description of the Drawings

[0020] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts do not necessarily draw to scale.

[0021] Figure 1 Schematic flow chart of a simulation method for quickly predicting battery temperature provided by an embodiment of the present invention;

[0022] Figure 2 Schematic diagrams and physical diagrams of a battery cell and a battery module provided by an embodiment of the present invention;

[0023] Figure 3 Schematic structural diagram and thermocouple layout diagram of a battery module provided by an embodiment of the present invention;

[0024] Figure 4 Battery voltage diagram measured in a discharge constant current intermittent titration test at ambient temperature provided by an embodiment of the present invention;

[0025] Figure 5 Experimental temperature and simulated temperature diagrams collected by sA and sB probes in a battery module provided by an embodiment of the present invention;

[0026] Figure 6 Schematic structural diagram of a simulation system for quickly predicting battery temperature provided by an embodiment of the present invention;

[0027] Figure 7 Schematic structural diagram of a computing device provided by an embodiment of the present invention. Specific embodiments

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] It should be understood that when used in this specification and the appended claims, the terms "comprises" and "comprising" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0030] It should also be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0031] It should be further understood that the term "and / or" used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0032] Please refer to Figure 1 , Figure 1 A simulation method for quickly predicting the battery temperature provided by the present invention, which is applied to a computing device, specifically includes the following steps:

[0033] Step 101: The computing device establishes a three-dimensional numerical model of the battery module.

[0034] Preferably, the three-dimensional numerical model includes a first model and a second model, where the first model is the three-dimensional numerical model corresponding to the battery module under forced ventilation of the fan, and the second model is the three-dimensional numerical model corresponding to the battery module under natural ventilation.

[0035] In the embodiments of the present application, by establishing a three-dimensional numerical model of the battery module under two fan operating modes, the second model corresponding to the natural ventilation condition is reasonable under low power load conditions.

[0036] Specifically, in a possible implementation, the three-dimensional numerical model is the first model; the computing device can determine the flow pattern inside the battery module by calculating the Reynolds number; in the case where the flow pattern is turbulent, determine the mass continuity equation and the momentum equation; establish a turbulent model of the battery module based on the mass continuity equation and the momentum equation; establish a temperature field model inside the battery module based on the power balance equation; and obtain the first model based on the turbulent model and the temperature field model.

[0037] Among them, when the fan is turned on, it is assumed that the heat exchange is mainly driven by forced convection of the fan. Therefore, it is first necessary to solve the air velocity field inside the module under static conditions to provide input data for solving the time-varying temperature field.

[0038] Specifically, when the fan is working, the air flow is driven by convection, and the computing device can calculate the Reynolds number to determine the flow pattern inside the battery module:

[0039]

[0040] Among them, Re is the Reynolds number, ρ air and uin are the air density and the dynamic viscosity respectively, and the specific values are shown in Table 1 below. d in = 0.0768 m is the fan inlet diameter, μ air = 1 m s-1 is the inlet velocity (determined by measurement with an anemometer). This value of the Reynolds number indicates that the flow near the fan is turbulent. Therefore, in order to capture the flow behavior within the entire module volume, due to its robustness, the computational device can select the turbulent k-reizable model to describe the flow field of this first model.

[0041]

[0042]

[0043] Table 1 Physical properties of the medium

[0044] Among them, the air is assumed to be incompressible because the velocity of the cooling flow is low anyway. The mass continuity equation (2) and the momentum equation (3) can be expressed as:

[0045]

[0046] Among them, u represents the flow velocity of the flow field, t represents time, x represents position, and ρ represents density. Based on the above mass continuity equation (2) and momentum equation (3), the turbulent model can be expressed by the equation:

[0047]

[0048] Among them, k represents the turbulent kinetic energy, ε represents the kinetic energy dissipation rate, C1 ε represents the first lumped parameter (JK -1 ), C2 ε represents the second lumped parameter (WK -1 ), G k is the generation term of k, μ t is the turbulent viscosity.

[0049] In the embodiments of the present application, the generation term of k and the turbulent viscosity are respectively expressed as:

[0050]

[0051] Exemplarily, C μ = 0.09, C 1ε = 1.44, C 2ε = 1.92, σ ε = 1.3.

[0052] In the embodiments of the present application, the temperature field model is expressed by the power balance equation as:

[0053]

[0054] where ρ, c p , μ, λ are the medium density, heat capacity, viscosity, and thermal conductivity respectively, and the values are shown in Table 1; T is the temperature, t is the time, and q s is the specific heat generation term. q s The calculation formula of q cell is the volume of a single cell.

[0055]

[0056] It should be noted that q s is non-zero only inside the battery, and takes into account the irreversible heat generated due to ohmic losses and the reversible heat generated due to entropy change. R int is the internal resistance of the battery, which depends on the state of charge (SOC), battery temperature, and battery load conditions during charging or discharging; I(t) is the module current, is the temperature derivative of the open-circuit voltage dependent on the battery SOC, which illustrates the entropy change.

[0057] Based on the above turbulent flow equations (4)(5) and temperature field model equations (8)(9), the first model can be described.

[0058] In the fan-on state, the computing device can assume that the fan action is the driving force for half-module air convection. According to the decoupling method, first solve the stationary flow field, and then calculate the time-dependent temperature field based on the flow field results. This assumption allows another simplification: considering that the air volume is symmetric with respect to the middle horizontal plane, the flow field is solved only in half of the modeling volume (one-fourth of the module volume), and then mirrored to the other half, which significantly reduces the computational burden.

[0059] Specifically, in one possible implementation, the three-dimensional numerical model is the second model; the computing device can establish a temperature field model inside the battery module based on the power balance equation; calculate the heat transfer coefficient of the second model through the convection-enhanced conductivity correlation equation, Nusselt number, and Rayleigh number; and obtain the second model based on the temperature field model and the heat transfer coefficient.

[0060] Among them, in natural convection, the air flow strongly depends on the temperature change. In addition, when natural ventilation occurs, the internal air volume is still connected to the external air through the grille openings on the front and back of the battery module. To solve this problem, the Boussinesq approximation method is used to analyze a single battery cell in the open air, as a simple small battery module study.

[0061] In the embodiments of the present application, when the fan is not working, the module is cooled by exchanging heat with the external stable air. Assuming that the air inside the module is stable, the temperature field is modeled by the above equations (8) and (9). Under the condition of the fan being turned off, the flow field modeling can be ignored, and the heat exchange of the internal module air volume is calculated through empirical correlations. In order to consider the heat exchange between the unit narrow sides facing the external air volume through the grille opening, the convective air volume through the grille opening is as follows. Considering the following convective enhanced conductivity correlation is expressed as:

[0062]

[0063] where q represents conductivity, k represents turbulent kinetic energy, represents the temperature difference, and N u is the Nusselt number,

[0064] P r = μ air C p,air / k air is the Prandtl number, H is the height of the internal volume, and L is the width of the gap between the cell and the grille. Ra L is the Rayleigh number and can be expressed as:

[0065]

[0066] where g is the acceleration due to gravity, α p is the air thermal expansion coefficient, λ air is the thermal conductivity, and ΔT is the temperature difference between the battery surface and the grille wall.

[0067] The Nusselt number can also be expressed as: Nu = h L / λ air ; Combining the relevant equations (11) and (12) of the above Nusselt number and Rayleigh number, the heat transfer coefficient h of the second model can be calculated.

[0068] Based on the above temperature field model equations (8) and (9), and the heat transfer coefficient, the second model can be described.

[0069] Step 102, the computing device sets symmetric boundary conditions for the three-dimensional numerical model to reduce the order of the three-dimensional numerical model.

[0070] Among them, for the first model under the condition of forced ventilation by the fan, the computing device can set the fan action as the driving force for the convection of the air in the half-module, and set symmetric flow field boundary conditions based on the middle horizontal plane and the middle vertical plane; for the temperature fields of the first model and the second model, the bottom and the top of the temperature field can be set as adiabatic, and symmetric temperature field boundary conditions can be set based on the middle vertical plane.

[0071] Among them, on the premise that the function of the fan is assumed to be the driving force for half-module air convection, the computing device can be further simplified: considering that the air volume is symmetric with respect to the middle horizontal plane, the flow field is solved only in half of the modeled volume in the half-module (i.e., one-fourth of the battery module volume), and then mirrored to the other half, which can greatly reduce the computational burden.

[0072] In the embodiments of the present application, the boundary conditions are applied according to the actual experimental conditions during the experiment to ensure the consistency of the model verification conditions. Different sets of boundary conditions are adopted according to two fan operating modes:

[0073] For the fan in the working state in the first model, a uniform velocity of 1m / s is applied at the fan inlet, and a non-backflow condition is applied when modeling at the outlet grille. The uniform pressure and the local pressure drop are characterized as equivalent to 0.3. Anti-slip flow conditions are applied on all inner walls except the middle horizontal plane and the vertical plane, where symmetric conditions are applied. The bottom and upper surfaces of the battery module are considered adiabatic, while the heat transfer coefficient on the side is 3W / (m²·K). -1 On the middle vertical plane, symmetric thermal boundary conditions are applied. The inlet air flow is applied at a constant ambient temperature of 25°C, while a heat open boundary condition is applied at the outlet. -2 K -1 。In the case of natural ventilation, the bottom and upper surfaces of the battery module are also considered adiabatic, and a heat transfer coefficient of 3W / (m²·K) is applied on the side module walls.

[0074] In the case of the fan being turned off, no flow field modeling is performed, and the internal module air volume heat exchange is calculated through empirical correlations. -2 K -1 。

[0075] Step 103, the computing device establishes a 0D lumped model based on the reduced-order three-dimensional numerical model and the energy differential equation.

[0076] Preferably, the three-dimensional numerical model is the first model; the computing device can determine the equivalent heat capacity and thermal resistance according to the material properties and geometric structure of the battery module; obtain the heat generated by the battery module, and calculate the heat generation rate of the battery module through the internal resistance value obtained by the constant current intermittent titration technique; based on the heat capacity, the thermal resistance, the heat, the heat generation rate, and the energy differential equation, solve the energy differential equation to obtain the variation trend of the average temperature of the battery module with time.

[0077] Among them, the process of solving the energy differential equation is the process of jointly solving the above formulas (12) and (9).

[0078] In an embodiment of the present application, when the fan is running, the computing device can obtain a steady-state flow field solution using a separation method with pseudo-time stepping settings, and use the obtained steady-state flow pattern as the input for calculating the time-varying temperature field, together with the domain initial temperature and the module load profile.

[0079] Preferably, the three-dimensional numerical model is the second model; the computing device can calculate the equivalent heat capacity according to the material properties of the battery module, and estimate the thermal resistance of the battery module based on the structure and natural convection characteristics of the battery module; obtain the heat generated by the battery module, and calculate the heat generation rate of the battery module by using the internal resistance value obtained through the constant current intermittent titration technique; use the empirical formula of the Nusselt number to estimate the heat transfer coefficient; based on the equivalent heat capacity, the thermal resistance, the heat, the heat generation rate, the heat transfer coefficient and the energy differential equation, solve the energy differential equation to obtain the change trend of the average temperature of the battery module over time.

[0080] Step 104: The computing device predicts the temperature of the lithium battery based on the 0D lumped model and outputs the prediction result.

[0081] Wherein, the computing device can obtain the relevant data of the lithium battery to be predicted, and input the relevant data into the 0D lumped model for simulation to obtain the prediction result of the average temperature change of the lithium battery over time.

[0082] It should be noted that the purpose of this method is to predict and analyze the electrical and thermal behaviors of battery components more conveniently, quickly and effectively, which can reduce the resource consumption in the experimental process, improve the R & D efficiency, and make the design and implementation of the battery thermal management system more economical and efficient. This invention not only opens up a new perspective for the development of battery technology, but also provides a solid theoretical basis and technical guarantee for the R & D of new energy energy storage systems and the applications in other related high-tech fields.

[0083] The present invention establishes a three-dimensional numerical model based on the battery module, and then reduces the order of the three-dimensional numerical model. While maintaining the high accuracy of the model, it greatly reduces the complexity of the model, making it easier to deploy and calculate in practical applications; compared with traditional prediction methods, the present invention more deeply represents the thermal-fluid coupling mechanism inside the battery module, and can provide better support for battery thermal optimization management; at the same time, due to the smaller complexity of the proposed model, the model is easier to characterize and has stronger practicability and portability.

[0084] Please refer to Figures 2 - 7 , as well as Table 1 and Table 2, and the following will describe an embodiment of the present application and the verification process of its beneficial effects.

[0085] Step 1: Establishment of the numerical model

[0086] To verify the content of the present invention, an experimental environment under the same simulation conditions was built. The thermal study of a battery module composed of 20 prismatic lithium-ion batteries was carried out under two conditions: with the fan on and natural ventilation, and the experimental data was collected. At the same time, a three-dimensional numerical model was established to study the numerical and thermal analysis of the battery module under different simulation conditions, and the corresponding thermal resistance model was used to describe the heat transfer inside the battery module. The temperature field and flow field inside the battery module were analyzed on the simulation platform. Finally, a 0D lumped model was established using model reduction technology and the energy differential equation. By comparing the numerical values in the experimental environment and the simulation environment, it was determined whether this model could more quickly evaluate the temperature distribution inside the battery module, thus providing important data support for the thermal management of the battery.

[0087] In the embodiment of the present application, the prismatic lithium-ion battery used has a battery capacity of 20 Ah. Figure 2 Figures a and b are the pictures and dimensional drawings of the lithium-ion battery, and Table 2 shows the main parameters of the lithium-ion battery.

[0088] Parameter Value Unit Nominal Capacity 20 Ah Rated Voltage 2.3 V Operating Voltage 2.7 / 1.5 V Dimensions 116×103×22 mm Maximum Steady-State Current 100 A Maximum Peak Current 200 A Operating Temperature -30 / +55 ℃

[0089] Table 2 Main parameter table of the lithium-ion battery

[0090] In the embodiment of the present application, Figure 2 (c) and Figure 2 (d) respectively show the external and internal styles of the battery module. The batteries are in series and are connected to the terminals through welded aluminum tags. The batteries are arranged in two rows with a spacing of 7 mm, and the spacing between the narrow sides of each row of batteries and the front and back surfaces of the module is 5 mm. All sides of the module have an aluminum shell, with two holes for installing fans on the front and two circular grilles on the back. The bottom and the upper part are composed of plastic covers, and the latter houses the BMS electronic components and is covered by a polymer cover ( Figure 2 c). Seven stainless steel rods ensure the mechanical stiffness of the module. The size of the battery module is 260 mm * 270 mm * 115 mm. Each battery cell in the battery module is equipped with a K-type thermocouple (accuracy of ±1 K), which is set on the narrow side of the battery cell: the temperature data of each battery cell can be collected from the Figure 3 20 thermocouple arrays shown in (c) and (d) through a remote acquisition system. The picture of the thermocouple array is shown in Figure 2 d. Sensors sA and sB are respectively used to measure the highest battery temperatures representing when the fan is off and when the fan is on, and thus are regarded as verification temperatures. As Figure 3 shown is the battery module (excluding the shell, rods, fans, and grilles) viewed from the front (a) and from the bottom (b). The three-dimensional modeling adopts a vertical symmetry plane: the upper and lower polymer shells, battery terminals and connectors, the fan shell with an air inlet (1), the aluminum outer wall, and the batteries. The air outlet is located at the grille (2).

[0091] In an embodiment of the present application, R int is the internal resistance of the battery, which depends on the state of charge (SOC), the battery temperature, and the battery load conditions during charging or discharging; I(t) is the module current, is the temperature derivative of the open-circuit voltage dependent on the battery SOC, which illustrates the entropy change. To determine the average internal heat generation of the battery, a galvanostatic intermittent titration technique (GITT) test was conducted at ambient temperature, which is characterized by 10 current pulses (40 A, 3 minutes each), first discharging and then charging, starting from SOC = 100%, as Figure 4 shown in the battery voltage measured in the discharge galvanostatic intermittent titration method test at ambient temperature. Based on the experimental data and common battery parameters, it is assumed here that the average resistance is equal to 1.5 mΩ to simulate the internal heat generation of the battery, which can meet the need for quickly modeling the battery pack without complex battery characterization tests.

[0092] In an embodiment of the present application, g is the acceleration due to gravity, α p is the air thermal expansion coefficient, λ air is the thermal conductivity, and ΔT is the temperature difference between the battery surface and the grille wall. For the studied battery module, L≈0.01 m, H≈0.08 m, and taking the average temperature difference ΔT avg = 6 °C, Pr≈0.77, and Ra L ≈1000 are obtained. Since the Nusselt number can also be written as Nu = h L / λ air , the heat transfer coefficient can be derived as: h≈2.04 Wm -2 k -2 .

[0093] Step 2: Boundary conditions

[0094] In an embodiment of the present application, the boundary conditions are applied according to the experimental conditions used during the experiment to ensure the consistency of the model verification conditions. According to two fan operating modes, different sets of boundary conditions are adopted: for the fan in the working state, a uniform velocity of 1 m / s -1 is applied at the fan inlet, and a non-backflow condition is applied when modeling the outlet grille, with a uniform pressure and a local pressure drop characterized as equivalent to 0.3. Anti-slip flow conditions are applied on all inner walls except the middle horizontal plane and the vertical plane, where symmetric conditions are applied. The bottom and upper surfaces of the battery module are considered adiabatic, while the heat transfer coefficient on the sides is 3 Wm -2 K -1Symmetric thermal boundary conditions are applied on the middle vertical plane. The inlet air flow is applied at a constant ambient temperature of 25 °C, while a thermal open boundary condition is applied at the outlet.

[0095] In the case of the fan being off, no flow field modeling is performed, and the air volume heat exchange of the internal module is calculated through empirical correlations. Just as in the case of natural ventilation, in the case of natural ventilation, the bottom and upper surfaces of the module are also considered adiabatic, while a heat transfer coefficient of 3 Wm -2 K -1 is applied on the side module walls.

[0096] Step 3: Numerical method

[0097] In the embodiment of the present application, in the case of the fan running, a steady-state flow field solution is obtained using a segregated method with a pseudo-time stepping setting, where the relative tolerance is set to 1×10 -3 . A 9-step parameter ramp is performed on the inlet velocity to reach a value of 1 m / s -1 . The obtained steady-state flow pattern is used as the input for calculating the time-varying temperature field, together with the domain initial temperature and the module load profile. An intermediate time step method with a maximum time step of 10 s is used to calculate the temperature field when the fan is on and off.

[0098] Step 4: Calculation results

[0099] In the embodiment of the present application, the simulation results are compared with the experimental measurement results to verify the effectiveness of the model. In the fan-on and natural ventilation verification tests, the current nominal value is kept constant at 40 A, so the load curves have different shapes under fan charging and discharging. Figure 5 Verification tests in the fan-on (a) and fan-off (b) modes are shown. Temperature evolution during an 80-minute constant current charge-discharge cycle. The experimental and simulated temperatures of sensors sA and sB are plotted, and the calculated values are in good agreement with the experimental data: the mean absolute error Δε is less than 0.3 °C in the fan-on mode and less than 0.52 °C in the fan-off mode.

[0100] Since the accompanying drawings of the specification are in grayscale, a special explanation is given here for Figure 5 as follows: Figure 5 (a) The thin solid line is the load. The thick solid line relatively above is the simulated temperature at the sB sensor, and the square dots are the experimental temperatures collected by the sB sensor; the thick solid line relatively below is the simulated temperature at the sA sensor, and the round dots are the experimental temperatures collected by the sA sensor; Figure 5 (b) The thin solid line is the load. The thick solid line relatively below is the simulated temperature at the sB sensor, and the square dots are the experimental temperatures collected by the sB sensor; the thick solid line relatively above is the simulated temperature at the sA sensor, and the round dots are the experimental temperatures collected by the sA sensor.

[0101] In the embodiments of the present application, from the comparison between the test results and the model simulation results, the method proposed by the present invention can effectively predict the thermal behavior of the battery module.

[0102] The method part of the embodiments of the present application has been described above. Next, the simulation system for quickly predicting the battery temperature provided by the embodiments of the present application will be described.

[0103] Please refer to Figure 6 , Figure 6 which is a schematic structural diagram of a simulation system for quickly predicting the battery temperature provided by the embodiments of the present application; as Figure 6 shown, the system 600 includes:

[0104] A first modeling module 601 for establishing a three-dimensional numerical model of the battery module; a model reduction module 602 for setting symmetric boundary conditions for the three-dimensional numerical model to reduce the order of the three-dimensional numerical model; a second modeling module 603 for establishing a 0D lumped model based on the reduced three-dimensional numerical model and the energy differential equation; a model prediction module 604 for predicting the lithium battery temperature based on the 0D lumped model and outputting the prediction result.

[0105] Preferably, the three-dimensional numerical model includes a first model and a second model. The first model is the three-dimensional numerical model corresponding to the battery module under forced ventilation of the fan, and the second model is the three-dimensional numerical model corresponding to the battery module under natural ventilation.

[0106] Preferably, the three-dimensional numerical model is the first model; the first modeling module 601 is specifically configured to: determine the flow pattern inside the battery module by calculating the Reynolds number; determine the mass continuity equation and the momentum equation in the case where the flow pattern is turbulent; establish a turbulent model of the battery module based on the mass continuity equation and the momentum equation; establish a temperature field model inside the battery module based on the power balance equation; and obtain the first model based on the turbulent model and the temperature field model.

[0107] Preferably, the three-dimensional numerical model is the second model; the first modeling module 601 is specifically configured to: establish a temperature field model inside the battery module based on the power balance equation; calculate the heat transfer coefficient of the second model through the convection-enhanced conductivity correlation equation, the Nusselt number, and the Rayleigh number; and obtain the second model based on the temperature field model and the heat transfer coefficient.

[0108] Preferably, the model order reduction module 602 is specifically configured to: for the first model, set the fan action as the driving force for half-module air convection, and set symmetric flow field boundary conditions based on the middle horizontal plane and the middle vertical plane; for the first model and the second model, set the bottom and the top of the temperature field as adiabatic, and set symmetric temperature field boundary conditions based on the middle vertical plane.

[0109] Preferably, the three-dimensional numerical model is the first model; the second modeling module 603 is specifically configured to: determine the equivalent heat capacity and thermal resistance according to the material properties and geometric structure of the battery module; obtain the heat generated by the battery module, and calculate the heat generation rate of the battery module by using the internal resistance value obtained through the constant current intermittent titration technique; based on the heat capacity, the thermal resistance, the heat, the heat generation rate, and the energy differential equation, solve the energy differential equation to obtain the change trend of the average temperature of the battery module over time.

[0110] Preferably, the three-dimensional numerical model is the second model; the second modeling module 603 is specifically configured to: calculate the equivalent heat capacity according to the material properties of the battery module, and estimate the thermal resistance of the battery module based on the structure and natural convection characteristics of the battery module; obtain the heat generated by the battery module, and calculate the heat generation rate of the battery module by using the internal resistance value obtained through the constant current intermittent titration technique; estimate the heat transfer coefficient by using the empirical formula of the Nusselt number; based on the equivalent heat capacity, the thermal resistance, the heat, the heat generation rate, the heat transfer coefficient, and the energy differential equation, solve the energy differential equation to obtain the change trend of the average temperature of the battery module over time.

[0111] The system provided in the embodiments of the present application can be understood by referring to the corresponding content in the foregoing method embodiment part, and will not be repeated here.

[0112] As Figure 7 shown, Figure 7 FIG. is a possible logical structure diagram of a computing device provided in an embodiment of the present application. The computing device 700 includes: a processor 701, a communication interface 702, a memory 703, and a bus 704. The processor 701, the communication interface 702, and the memory 703 are interconnected through the bus 704. In the embodiments of the present application, the processor 701 is used to control and manage the actions of the computing device 700. For example, the processor 701 is used to execute Figure 1 the steps in the embodiment and / or other processes for the technologies described herein. The communication interface 702 is used to support the computing device 700 to communicate. The memory 703 is used to store the program code and data of the computing device 700.

[0113] Among them, the processor 701 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in connection with the disclosure of this application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, and so on. The bus 704 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 only a thick line is used to represent it in Figure 7 , but it does not mean that there is only one bus or one type of bus.

[0114] In another embodiment of the present application, a computer-readable storage medium is further provided. The computer-readable storage medium includes instructions that, when run on a computer, cause the computer to execute the above Figure 1 method described in the embodiments.

[0115] Those of ordinary skill in the art can realize that the units of each example described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components of each example have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0116] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0117] In several embodiments provided by the embodiments of the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0118] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0119] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0120] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The foregoing storage medium includes: various media such as USB flash drives, read-only memory (ROM), random access memory (RAM), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and the specification of the present invention.

Claims

1. A simulation method for quickly predicting battery temperature, characterized in that: include: Establish a three-dimensional numerical model of the battery module; Setting symmetric boundary conditions for the three-dimensional numerical model to reduce the order of the three-dimensional numerical model; Based on the reduced three-dimensional numerical model and energy differential equation, a 0D lumped model is established; The lithium battery temperature is predicted based on the 0D lumped model and the prediction result is output.

2. The method according to claim 1, characterized in that: The three-dimensional numerical model includes a first model and a second model, wherein the first model is a three-dimensional numerical model corresponding to the battery module under fan forced ventilation conditions, and the second model is a three-dimensional numerical model corresponding to the battery module under natural ventilation conditions.

3. The method according to claim 2, characterized in that The three-dimensional numerical model is the first model; the three-dimensional numerical model of the battery module is established, including: Determining the flow pattern inside the battery module by calculating the Reynolds number; In the case where the flow pattern is turbulent, determining the mass continuity equation and the momentum equation; Establishing a turbulence model of the battery module based on the mass continuity equation and the momentum equation; Based on the power balance equation, a temperature field model inside the battery module is established; The first model is obtained based on the turbulence model and the temperature field model.

4. The method according to claim 2, characterized in that The three-dimensional numerical model is the second model; the three-dimensional numerical model of the battery module is established, including: Based on the power balance equation, a temperature field model inside the battery module is established; Calculating the heat transfer coefficient of the second model by using the convection-enhanced conductivity correlation equation, Nusselt number and Rayleigh number; The second model is obtained based on the temperature field model and the heat transfer coefficient.

5. The method according to any one of claims 1 to 4, characterized in that The three-dimensional numerical model includes a first model and a second model; and setting a symmetric boundary condition for the three-dimensional numerical model includes: For the first model, the fan action is set as the driving force of the air convection of the half module, and the symmetric flow field boundary conditions are set based on the middle horizontal plane and the middle vertical plane; For the first model and the second model, the bottom and the top of the temperature field are set to be adiabatic, and based on the middle vertical surface, symmetric temperature field boundary conditions are set.

6. The method according to claim 2, characterized in that The three-dimensional numerical model is the first model; the 0D lumped model is established based on the reduced three-dimensional numerical model and the energy differential equation, including: Determining equivalent thermal capacity and thermal resistance based on material properties and geometric structure of the battery module; Obtaining the heat generated by the battery module, and calculating the heat generation rate of the battery module using the internal resistance value obtained by constant current intermittent titration technology testing; Based on the heat capacity, the thermal resistance, the heat, the heat generation rate and the energy differential equation, the energy differential equation is solved to obtain a time trend of an average temperature of the battery module.

7. The method according to claim 6, characterized in that: The three-dimensional numerical model is the second model; the 0D lumped model is established based on the reduced three-dimensional numerical model and the energy differential equation, including: calculating an equivalent heat capacity according to material properties of the battery module, and estimating a thermal resistance of the battery module based on a structure and natural convection characteristics of the battery module; Obtaining the heat generated by the battery module, and calculating the heat generation rate of the battery module using the internal resistance value obtained by constant current intermittent titration technology testing; Use the empirical formula of Nusselt number to estimate the heat transfer coefficient; Based on the equivalent heat capacity, the thermal resistance, the heat, the heat generation rate, the heat transfer coefficient and the energy differential equation, the energy differential equation is solved to obtain a time trend of an average temperature of the battery module.

8. A simulation system for rapidly predicting battery temperature, characterized in that: The method according to any one of claims 1 to 7 is applied, wherein the system comprises: A first modeling module is used to establish a three-dimensional numerical model of a battery module; A model order reduction module, used for setting symmetric boundary conditions for the three-dimensional numerical model to reduce the order of the three-dimensional numerical model; A second modeling module is used to establish a 0D lumped model based on the reduced three-dimensional numerical model and the energy differential equation; The model prediction module is used to predict the temperature of the lithium battery based on the 0D lumped model and output the prediction result.

9. A computing device, characterized in that include: Memory, used to store programs; A processor, configured to load the program to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a program, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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