A structural optimization method for air-cooled dual-system lithium-ion battery pack

Abstract

The present invention relates to the technical field of air-cooling and heat dissipation of power batteries, and discloses a method for optimizing the structure of an air-cooled dual-system lithium-ion battery pack. The method includes establishing a geometric model of the air-cooled dual-system lithium-ion battery pack and dividing the grid. The dual-system lithium-ion battery pack is composed of a lithium iron phosphate battery and a ternary lithium battery in series. The design variables are specified as: the width of the air inlet and outlet, the length of the air inlet and outlet ducts, and the optimization targets are specified as the maximum temperature, the average temperature, and the maximum temperature difference. A full-factor experiment is designed to obtain the optimization targets of the design variables at different levels. A variance analysis is performed on the experimental data to obtain a regression equation for the optimization target. The regression equation is substituted into a genetic algorithm for optimization to obtain the optimal structural parameters. The present invention utilizes full-factor experimental design and variance analysis to establish a model, and implements multi-objective optimization through a genetic algorithm. The optimized parameters can effectively improve the cooling performance of the battery pack and reduce the volume of the battery pack.

Description

Technical Field

[0001] The present invention relates to the technical field of air cooling and heat dissipation of power batteries, and in particular to a method for optimizing the structure of an air-cooled dual-system lithium-ion battery pack. Background Art

[0002] Currently, lithium-ion batteries are the primary power source for electric vehicles, with ternary lithium batteries and lithium iron phosphate batteries being the most widely used. Ternary lithium batteries are widely used in high-performance electric vehicles due to their high energy density and excellent low-temperature performance, while lithium iron phosphate batteries dominate the market due to their improved safety, long lifespan, and low cost. Combining ternary lithium batteries with lithium iron phosphate batteries creates a hybrid battery pack with complementary advantages, which not only improves overall performance but also achieves a balance between safety and energy density.

[0003] However, these two types of batteries have different thermal characteristics, and research on thermal management of dual-system battery packs is currently lacking. To ensure the safe operation of dual-system battery packs, research on thermal management systems for dual-system lithium-ion battery packs is necessary. Air cooling is widely used in thermal management of lithium-ion battery packs due to its advantages such as simple design, low operating costs, and easy maintenance. To improve the heat dissipation efficiency of dual-system lithium-ion battery packs while reducing the battery pack's space utilization, optimized design is required. Summary of the Invention

[0004] In order to improve the heat dissipation effect of a dual-system lithium-ion battery pack and reduce the space occupancy of the battery pack, the present invention provides a method for optimizing the structure of an air-cooled dual-system lithium-ion battery pack.

[0005] To implement the above technical solution, the specific steps are as follows:

[0006] S1. Establish a simulation model of an air-cooled dual-system lithium-ion battery pack and divide the grid;

[0007] The dual-system lithium-ion battery pack includes: a lithium iron phosphate battery and a ternary lithium battery; the lithium iron phosphate battery and the ternary lithium battery are arranged in series; wherein the positive electrode material of the lithium iron phosphate battery is lithium iron phosphate, the positive electrode material of the ternary lithium battery is lithium nickel cobalt manganese oxide or lithium nickel cobalt aluminum oxide, and the negative electrode material of both batteries is carbon-based material;

[0008] The simulation model is a three-dimensional geometric model of an air-cooled dual-system lithium-ion battery pack, including: lithium iron phosphate battery, ternary lithium battery, blower and air chamber;

[0009] Specifically, the Bernardi thermal model was coupled to the 3D geometric model of the air-cooled dual-system lithium-ion battery pack to simulate heat generation. Simultaneously, the single-particle lumped model was coupled to the 3D geometric model of the air-cooled dual-system lithium-ion battery pack to simulate charge balance.

[0010] Among them, the flow channel of the air chamber adopts a Z-type flow channel, and the cooling fluid is modeled according to the material properties of air;

[0011] The boundary conditions include: battery pack state of charge, battery pack load discharge rate, ambient temperature and battery pack initial temperature, fluid inlet boundary condition as velocity condition and outlet boundary condition as pressure outlet;

[0012] Since the density change of the cooling fluid (air) can be ignored, the air is considered as an incompressible fluid and can be expressed by the Reynolds number; the expression of the Reynolds number is as follows:

[0013]

[0014] Where, ρ air is the air density; v air is the inlet wind speed, which is 5 m / s in this embodiment; L is the characteristic length, which is determined according to the size of the air inlet; μ air is the air dynamic viscosity;

[0015] For incompressible fluids, the flow field within the battery pack can be simultaneously described by the following governing equations:

[0016] The mass conservation equation:

[0017]

[0018] Where u is the velocity vector of the fluid;

[0019] Momentum equation:

[0020]

[0021] Where p is pressure, F is external force, and t is time;

[0022] The energy conservation equation for cooling air is:

[0023]

[0024] Where C ρ,air is the specific heat of air, k air is the thermal conductivity of air, T is the temperature;

[0025] Finite element simulation software was used to construct the geometric structure and generate the mesh. To reduce computational cost and improve convergence, a swept mesh was used for the dual-system lithium-ion battery pack, and a free tetrahedral mesh was used for the air chamber.

[0026] Due to the large variations in air velocity and temperature in the battery pack area, a finer mesh is used in this area using smaller cells.

[0027] To ensure that the simulation results are independent of the mesh size, the maximum temperature and maximum temperature difference under six common different numbers of meshes are compared. The six common different numbers of meshes include: 0-10000 (mesh), 10000-15000 (mesh), 15000-20000 (mesh), 20000-25000 (mesh), 25000-30000 (mesh) and 30000-∞ (mesh);

[0028] When the inlet wind speed is 5m / s and the temperature is 25℃, the number of grids gradually increases from 87691 to 312512. After the number of grids exceeds 200000, the simulation results of the maximum temperature and the maximum temperature difference differ by less than 0.01℃.

[0029] S2. Preset design variables through the simulation model and set optimization targets based on the design variables;

[0030] The design variables include: air chamber inlet width, air chamber outlet width, air chamber inlet duct length, and air chamber outlet duct length;

[0031] Optimization objectives include: maximum temperature, average temperature, and maximum temperature difference;

[0032] Specifically, the air chamber adopts a Z-shaped flow channel, and the air inlet width and air outlet width are the widths of the air chamber flow channel inlet and outlet; the air inlet duct length and air outlet duct length are the distance from the air inlet to the air outlet of the Z-shaped flow channel; the battery spacing is the distance between two adjacent battery cells; to make the battery pack structure symmetrical, the air inlet width and air outlet width are the same, and the air inlet duct length and air outlet length are the same; in this embodiment, the air inlet width and air outlet width are 30mm, the air inlet duct length and air outlet length are 100mm, and the battery spacing is 2.5mm;

[0033] S3. Obtain the optimization targets of the design variables at different levels through full factorial experiments, fit the experimental data using variance analysis, establish a regression model to describe the relationship between the design variables and the optimization targets, and obtain the regression equation of the optimization targets;

[0034] A full factorial experiment is a systematic experimental method that analyzes the impact of each variable on the experimental results by studying all possible combinations of variables. This experiment can provide complete information to help researchers understand the interactions between different factors and the impact of each factor on the response variable.

[0035] The experimental data were fitted with a quadratic polynomial regression model using the variance analysis method, and the expression is as follows:

[0036]

[0037] Where x i is the i-th design variable, n = 3, x1 represents the width of the air inlet and outlet, x2 represents the length of the air inlet and outlet duct, and x3 represents the battery spacing; α0 is a constant term, which represents the response value when all design variables are zero; a is a linear regression coefficient, which describes the relationship between each design variable x i The linear effect on the response variable; b is the quadratic term coefficient, which describes the nonlinear effect of the square term of each design variable on the response; c is the interaction term coefficient, which describes the effect of the interaction effect between different design variables on the response;

[0038] The relationship between design variables and optimization objectives is as follows:

[0039]

[0040] Where Y represents the optimization target; Represents the estimated value of the regression model, which is close to the true value of the objective function; x is the design variable, including all design variables; δ is the error term, including random error and systematic error;

[0041] According to the design variables, a three-factor four-level experimental design scheme is adopted, which requires a total of 4 3 = 64 experiments, each experiment was repeated 3 times, and the different factors (design variables) and levels are shown in Table 1;

[0042] Table 1 Different factors and levels

[0043]

[0044] Analysis of Variance (ANOVA) can be used to analyze the interaction between various factors. ANOVA is performed on the experimental results of the full factorial experimental design to quantitatively analyze the significance of the three design variables on the battery pack temperature performance. The relevant calculation formula is as follows:

[0045]

[0046] SST=SSB+SSW

[0047] df within =Mk

[0048] df between =k-1

[0049]

[0050] In the formula, SSW is the within-group sum of squares, SSB is the between-group sum of squares, and Yie is the i-th observation value of the e-th group, is the mean value of the e-th group, n is the number of samples in the e-th group, is the grand mean of all observations, df withn is the within-group degrees of freedom, df between is the degrees of freedom between groups, M is the total number of observations (192), and k is the number of groups (64); MSW is the within-group mean square, and MSB is the between-group mean square; the z-score is used to determine whether the differences in the mean values ​​between groups are large enough;

[0051] The results of variance analysis are shown in Table 2:

[0052] Table 2 Variance analysis of battery pack temperature indicators

[0053]

[0054] Among them, the sum of squares represents the total variation of the data explained by each factor; the degrees of freedom refers to the number of values ​​that can change independently in the data; the mean square is the value obtained by dividing the sum of squares by the corresponding degrees of freedom, which represents the variance estimate of each factor; the F ratio is the ratio of the mean square of each factor to the mean square of the error, which shows the significant effect of the factor on the result. The higher the F ratio, the more significant the effect of the factor; usually a P value less than 0.05 means that the factor has a significant effect on the outcome variable.

[0055] Furthermore, the regression equation of the optimization target is specifically:

[0056]

[0057] Where x1 represents the width of the air inlet and outlet, x2 represents the length of the air inlet and outlet duct, x3 represents the battery spacing, y1 represents the maximum temperature, y2 represents the average temperature, and y3 represents the maximum temperature difference;

[0058] Analysis of variance shows that factor x1 has a significant impact on all three temperature indicators and is the primary factor affecting battery pack cooling. Although factors x1 and x3 also have significant effects on the temperature indicators, their effects are relatively small. In addition, the mean square values ​​of the error terms are all low, indicating that the model performs well in fitting the temperature indicators and has low unexplained variance.

[0059] By the coefficient of determination R 2 To measure the proportion of the variation explained by the model relative to the total variation, the coefficient of determination R 2 The expression is as follows:

[0060]

[0061] R 2 The closer it is to 1, the more variability the regression model explains; the R2 =0.989, R of regression model y2 2 =0.996, R of regression model y3 2 =0.959, R of the three regression models 2 The values ​​are all greater than 0.95, indicating that the model performs well in capturing the relationship between the design variables and the response variables;

[0062] S4. Utilize the regression equation and the weighted sum of the volume function to construct a multi-objective optimization objective function. Optimize the objective function using a genetic algorithm to obtain the optimal solution to optimize the air-cooled dual-system lithium-ion battery pack structure. The details are as follows:

[0063] After performing variance analysis on the design variables and establishing a regression model, the present invention selected a genetic algorithm (GA) for multi-objective optimization to minimize the maximum temperature, average temperature, and maximum temperature difference of the battery pack while reducing the battery pack volume (V) as much as possible.

[0064] The objective function of multi-objective optimization is constructed using the weighted sum of the regression equation and the volume function, and its expression is as follows:

[0065]

[0066] Where f(x) is the objective function, x is the n design variables, y i (x) is the regression model of the ith response variable, V(x) is the volume function of the battery pack, ω i is the weight of the ith response variable, ω V is the weight of the volume function;

[0067] Analyze the iterative curve and obtain the converged global optimal solution, which is the optimal structural parameters of the battery pack;

[0068] In this embodiment, the objective function expression of the multi-objective optimization is as follows:

[0069]

[0070] The volume V is calculated as follows:

[0071]

[0072] In the formula, the maximum temperature (y1) is directly related to the safety of the battery pack, and the optimization weight of the maximum temperature (y1) is w1 = 0.4; the maximum temperature difference (y3) affects the uniformity of the temperature in the battery pack, and its importance is second only to the maximum temperature. The weight of the maximum temperature difference (y3) is w3 = 0.3; the volume (V) affects the compactness of the system. In order to take into account the rationality of the structure, the weight of the volume (V) is w V=0.2; the average temperature (y2) generally reflects the heat dissipation effect, but has little impact on safety. The weight of the average temperature (y2) is w2 = 0.1;

[0073] In order to effectively optimize the objective function, the key parameters of the genetic algorithm are set as shown in Table 3;

[0074] Table 3 Key parameters of genetic algorithm

[0075]

[0076] Beneficial effects of the present invention

[0077] The present invention analyzes the influence of different structural parameters on the heat dissipation performance of the battery pack by designing a full-factor experiment; adopts variance analysis to analyze the experimental data, establishes a regression model of the optimization target, and obtains the influence of different design variables and their interactions on the optimization target; constructs the objective function of multi-objective optimization using the weighted sum of regression equations, optimizes the objective function through genetic algorithm, obtains the optimal structural parameters, and the optimized battery pack achieves the goal of balancing heat dissipation effect and system compactness. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 It is a schematic diagram of the process of the present invention;

[0079] Figure 2 Schematic diagram of the geometric model and mesh division of the air-cooled dual-system lithium-ion battery pack of the present invention, wherein part (a) is a schematic diagram of the three-dimensional model of the battery pack, and part (b) is a schematic diagram of the mesh division of the battery pack;

[0080] Figure 3 The two-dimensional diagram and grid independence verification diagram of the air-cooled dual-system lithium-ion battery pack of the present invention, wherein part (a) is the two-dimensional diagram of the battery pack, and part (b) is the grid independence verification diagram;

[0081] Figure 4 It is an iterative curve diagram of the fitness value of the genetic algorithm of the present invention;

[0082] Figure 5 A comparison chart showing the results and errors of different batteries at different temperatures. DETAILED DESCRIPTION

[0083] The present invention is further described in detail below with reference to specific embodiments.

[0084] like Figure 1 As shown, a method for optimizing the structure of an air-cooled dual-system lithium-ion battery pack specifically includes the following steps:

[0085] S1. Establish a simulation model of an air-cooled dual-system lithium-ion battery pack and divide the grid;

[0086] The dual-system lithium-ion battery pack includes: a lithium iron phosphate battery and a ternary lithium battery; the lithium iron phosphate battery and the ternary lithium battery are arranged in series; wherein the positive electrode material of the lithium iron phosphate battery is lithium iron phosphate, the positive electrode material of the ternary lithium battery is lithium nickel cobalt manganese oxide or lithium nickel cobalt aluminum oxide, and the negative electrode material of both batteries is carbon-based material;

[0087] like Figure 2 As shown in (a), the simulation model is a three-dimensional geometric model of an air-cooled dual-system lithium-ion battery pack, including: lithium iron phosphate battery, ternary lithium battery, blower and air chamber;

[0088] Specifically, the Bernardi thermal model was coupled to the 3D geometric model of the air-cooled dual-system lithium-ion battery pack to simulate heat generation. Simultaneously, the single-particle lumped model was coupled to the 3D geometric model of the air-cooled dual-system lithium-ion battery pack to simulate charge balance.

[0089] Among them, the flow channel of the air chamber adopts a Z-type flow channel, and the cooling fluid is modeled according to the material properties of air;

[0090] In this embodiment, the boundary conditions are set as follows: the battery pack state of charge is set to 100%, the battery pack load discharge rate is set to 3C, the ambient temperature and the battery pack initial temperature are set to 25°C, the fluid inlet boundary condition is a velocity condition, and the outlet boundary condition is a pressure outlet, with atmospheric pressure applied at the outlet;

[0091] Since the density change of the cooling fluid (air) can be ignored, the air is considered as an incompressible fluid and can be expressed by the Reynolds number; the expression of the Reynolds number is as follows:

[0092]

[0093] Where, ρ air is the air density; v air is the inlet wind speed, which is 5 m / s in this embodiment; L is the characteristic length, which is determined according to the size of the air inlet; μ air is the air dynamic viscosity;

[0094] For incompressible fluids, the flow field within the battery pack can be simultaneously described by the following governing equations:

[0095] The mass conservation equation:

[0096]

[0097] Where u is the velocity vector of the fluid;

[0098] Momentum equation:

[0099]

[0100] Where p is pressure, F is external force, and t is time;

[0101] The energy conservation equation for cooling air is:

[0102]

[0103] Where C ρ,air is the specific heat of air, k air is the thermal conductivity of air, T is the temperature;

[0104] like Figure 2 As shown in (b), the geometric structure is constructed and meshed using finite element simulation software. In order to reduce computational cost and improve convergence, a swept mesh is used to mesh the dual-system lithium-ion battery pack, and a free tetrahedral mesh is used to mesh the air chamber.

[0105] like Figure 3 As shown in (a), further, due to the large changes in air velocity and temperature in the battery pack area, the grid in this area is divided into finer grids using smaller-sized cells. In this embodiment, the maximum cell size in the battery pack area is 1.03×10 -3 m, and the minimum cell size is 1.58×10 -5 m; the maximum cell size of the air chamber grid is 5.3×10 -3 m, and the minimum cell size is 1.58×10 -3 m;

[0106] To ensure that the simulation results are independent of the mesh size, the maximum temperature and maximum temperature difference under six common different numbers of meshes are compared. The six common different numbers of meshes include: 0-10000 (mesh), 10000-15000 (mesh), 15000-20000 (mesh), 20000-25000 (mesh), 25000-30000 (mesh) and 30000-∞ (mesh);

[0107] When the inlet wind speed is 5m / s and the temperature is 25°C, the number of grids gradually increases from 87691 to 312512. After the number of grids exceeds 200,000, the simulation results of the maximum temperature and the maximum temperature difference differ by less than 0.01°C. To ensure the accuracy of the simulation model while effectively saving calculation time, the number of grids in this simulation model is 219799.

[0108] S2. Preset design variables through the simulation model and set optimization targets based on the design variables;

[0109] The design variables include: air chamber inlet width, air chamber outlet width, air chamber inlet duct length, and air chamber outlet duct length;

[0110] Optimization objectives include: maximum temperature, average temperature, and maximum temperature difference;

[0111] Specifically, the air chamber adopts a Z-shaped flow channel, and its air inlet width and air outlet width are the widths of the air chamber flow channel inlet and outlet; the air inlet duct length and air outlet duct length are the distance from the air inlet to the air outlet of the Z-shaped flow channel; the battery spacing is the distance between two adjacent battery cells; to make the battery pack structure symmetrical, the air inlet width and air outlet width are the same, and the air inlet duct length and air outlet length are the same; in this embodiment, the air inlet width and air outlet width are 30mm, the air inlet duct length and air outlet length are 100mm, and the battery spacing is 2.5mm;

[0112] S3. Obtain the optimization targets of the design variables at different levels through full factorial experiments, fit the experimental data using variance analysis, establish a regression model to describe the relationship between the design variables and the optimization targets, and obtain the regression equation of the optimization targets;

[0113] A full factorial experiment is a systematic experimental method that analyzes the impact of each variable on the experimental results by studying all possible combinations of variables. This experiment can provide complete information to help researchers understand the interactions between different factors and the impact of each factor on the response variable.

[0114] The experimental data were fitted with a quadratic polynomial regression model using the variance analysis method, and the expression is as follows:

[0115]

[0116] Where x i is the i-th design variable, n = 3, x1 represents the width of the air inlet and outlet, x2 represents the length of the air inlet and outlet duct, and x3 represents the battery spacing; α0 is a constant term, which represents the response value when all design variables are zero; a is a linear regression coefficient, which describes the relationship between each design variable x i The linear effect on the response variable; b is the quadratic term coefficient, which describes the nonlinear effect of the square term of each design variable on the response; c is the interaction term coefficient, which describes the effect of the interaction effect between different design variables on the response;

[0117] The relationship between design variables and optimization objectives is as follows:

[0118]

[0119] Where Y represents the optimization target; Represents the estimated value of the regression model, which is close to the true value of the objective function; x is the design variable, including all design variables; δ is the error term, including random error and systematic error;

[0120] According to the design variables, a three-factor four-level experimental design scheme is adopted, which requires a total of 4 3 = 64 experiments, each experiment was repeated 3 times, and the different factors (design variables) and levels are shown in Table 1;

[0121] Table 1 Different factors and levels

[0122]

[0123] Analysis of Variance (ANOVA) can be used to analyze the interaction between various factors. ANOVA is performed on the experimental results of the full factorial experimental design to quantitatively analyze the significance of the three design variables on the battery pack temperature performance. The relevant calculation formula is as follows:

[0124]

[0125] SST=SSB+SSW

[0126] df within =Mk

[0127] df between =k-1

[0128]

[0129] In the formula, SSW is the within-group sum of squares, SSB is the between-group sum of squares, and Y ie is the i-th observation value of the e-th group, is the mean value of group e, n is the number of samples in group e, is the grand mean of all observations, df within is the within-group degrees of freedom, df between is the degrees of freedom between groups, M is the total number of observations (192), and k is the number of groups (64); MSW is the within-group mean square, and MSB is the between-group mean square; the z-score is used to determine whether the differences in the mean values ​​between groups are large enough;

[0130] The results of variance analysis are shown in Table 2:

[0131] Table 2 Variance analysis of battery pack temperature indicators

[0132]

[0133] Among them, the sum of squares represents the total variation of the data explained by each factor; the degrees of freedom refers to the number of values ​​that can change independently in the data; the mean square is the value obtained by dividing the sum of squares by the corresponding degrees of freedom, which represents the variance estimate of each factor; the F ratio is the ratio of the mean square of each factor to the mean square of the error, which shows the significant effect of the factor on the result. The higher the F value, the more significant the effect of the factor; usually a P value less than 0.05 means that the factor has a significant effect on the outcome variable.

[0134] Furthermore, the regression equation of the optimization target is specifically:

[0135]

[0136] Where x1 represents the width of the air inlet and outlet, x2 represents the length of the air inlet and outlet duct, x3 represents the battery spacing, y1 represents the maximum temperature, y2 represents the average temperature, and y3 represents the maximum temperature difference;

[0137] Analysis of variance shows that factor x1 has a significant impact on all three temperature indicators and is the primary factor affecting battery pack cooling. Although factors x1 and x3 also have significant effects on the temperature indicators, their effects are relatively small. In addition, the mean square values ​​of the error terms are all low, indicating that the model performs well in fitting the temperature indicators and has low unexplained variance.

[0138] By the coefficient of determination R 2 To measure the proportion of the variation explained by the model relative to the total variation, the coefficient of determination R 2 The expression is as follows:

[0139]

[0140] R 2 The closer it is to 1, the more variability the regression model explains; the R 2 =0.989, R of regression model y2 2 =0.996, R of regression model y3 2 =0.959, R of the three regression models 2 The values ​​are all greater than 0.95, indicating that the model performs well in capturing the relationship between the design variables and the response variables;

[0141] S4. Utilize the regression equation and the weighted sum of the volume function to construct a multi-objective optimization objective function. Optimize the objective function using a genetic algorithm to obtain the optimal solution to optimize the air-cooled dual-system lithium-ion battery pack structure. The details are as follows:

[0142] After performing variance analysis on the design variables and establishing a regression model, the present invention selected a genetic algorithm (GA) for multi-objective optimization to minimize the maximum temperature, average temperature, and maximum temperature difference of the battery pack while reducing the battery pack volume (V) as much as possible.

[0143] The objective function of multi-objective optimization is constructed using the weighted sum of the regression equation and the volume function, and its expression is as follows:

[0144]

[0145] Where f(x) is the objective function, x is the n design variables, y i (x) is the regression model of the ith response variable, V(x) is the volume function of the battery pack, ω i is the weight of the ith response variable, ω V is the weight of the volume function;

[0146] Analyze the iterative curve and obtain the converged global optimal solution, which is the optimal structural parameters of the battery pack;

[0147] In this embodiment, the objective function expression of the multi-objective optimization is as follows:

[0148]

[0149] The volume V is calculated as follows:

[0150]

[0151] In the formula, the maximum temperature (y1) is directly related to the safety of the battery pack, and the optimization weight of the maximum temperature (y1) is w1 = 0.4; the maximum temperature difference (y3) affects the uniformity of the temperature in the battery pack, and its importance is second only to the maximum temperature. The weight of the maximum temperature difference (y3) is w3 = 0.3; the volume (V) affects the compactness of the system. In order to take into account the rationality of the structure, the weight of the volume (V) is w V =0.2; the average temperature (y2) generally reflects the heat dissipation effect, but has little impact on safety. The weight of the average temperature (y2) is w2 = 0.1;

[0152] In order to effectively optimize the objective function, the key parameters of the genetic algorithm are set as shown in Table 3;

[0153] Table 3 Key parameters of genetic algorithm

[0154]

[0155] Figure 4The iterative curve of the fitness value of the genetic algorithm during the optimization process is shown. It can be seen that as the number of generations increases, the objective function value gradually decreases and tends to be stable, indicating that the algorithm has converged to a better solution by the 40th generation. The design variable combination corresponding to the optimal fitness value is x1 = 36.41, x2 = 55, and x3 = 1.5.

[0156] In order to verify the reliability of genetic algorithm optimization, the optimized design variable combination was substituted into the simulation model for simulation, and the simulation results were compared with the optimization results and the original design (x1 = 30, x2 = 100, x3 = 2.5) results. The results are as follows: Figure 5 As shown in the figure, it can be seen that the difference between the optimization results and the simulation results in terms of temperature indicators is very small, with the maximum temperature difference being 0.192°C, the average temperature difference being 0.155°C, and the maximum temperature difference being 0.416°C, indicating the effectiveness of the optimization method and the reliability of the results.

[0157] Furthermore, a comparison of the optimized temperature indicators with those of the original structure shows significant improvements across all temperature indicators. The maximum temperature decreased by approximately 2.39%, the average temperature decreased by approximately 3.69%, and the maximum temperature difference decreased by approximately 3.78%. Furthermore, the optimized battery pack volume was reduced by approximately 40.35%, significantly improving the system's compactness.

[0158] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the structure of an air-cooled dual-system lithium-ion battery pack, characterized in that: The following steps are involved: S1. Establish a simulation model of an air-cooled dual-system lithium-ion battery pack and divide the grid; The dual-system lithium-ion battery pack includes: a lithium iron phosphate battery and a ternary lithium battery; and the lithium iron phosphate battery and the ternary lithium battery are arranged in series; The simulation model is a three-dimensional geometric model of an air-cooled dual-system lithium-ion battery pack, including: lithium iron phosphate battery, ternary lithium battery, blower and air chamber; Specifically, the Bernardi thermal model was coupled to the 3D geometric model of the air-cooled dual-system lithium-ion battery pack to simulate heat generation. Simultaneously, the single-particle lumped model was coupled to the 3D geometric model of the air-cooled dual-system lithium-ion battery pack to simulate charge balance. Among them, the flow channel of the air chamber adopts a Z-type flow channel, and the cooling fluid is modeled according to the material properties of air; The boundary conditions include: battery pack state of charge, battery pack load discharge rate, ambient temperature and battery pack initial temperature, fluid inlet boundary condition as velocity condition and outlet boundary condition as pressure outlet; S2. Preset design variables through the simulation model and set optimization targets based on the design variables; The design variables include: air chamber air inlet width, air chamber air outlet width, air chamber air inlet duct length, and air chamber air outlet duct length; Optimization objectives include: maximum temperature, average temperature and maximum temperature difference; S3. Obtain the optimization targets of the design variables at different levels through full factorial experiments, fit the experimental data using variance analysis, establish a regression model to describe the relationship between the design variables and the optimization targets, and obtain the regression equation of the optimization targets; The expression for fitting the experimental data using analysis of variance is as follows: ； Where, For the There are three design variables, n=3, x1 represents the width of the air inlet and outlet, x2 represents the length of the air inlet and outlet duct, and x3 represents the battery spacing; is a constant term, which represents the response value when all design variables are zero; a is a linear regression coefficient, which describes the response value of each design variable. The linear effect on the response variable; b is the quadratic term coefficient, which describes the nonlinear effect of the square term of each design variable on the response; c is the interaction term coefficient, which describes the effect of the interaction effect between different design variables on the response; The relationship between the design variables and the optimization objectives is specifically as follows: ； Where, represents the optimization objective; Represents the estimated value of the regression model, which is close to the true value of the objective function; is the design variable, including all design variables; is the error term, including random error and systematic error; S4. Constructing a multi-objective optimization objective function using a regression equation and a weighted sum of volume functions, optimizing the objective function using a genetic algorithm, and obtaining an optimal solution to optimize the air-cooled dual-system lithium-ion battery pack structure. The expression of the objective function of the multi-objective optimization constructed by using the weighted sum of the regression equation and the volume function is as follows: ； Where, is the objective function, are n said design variables, It is The regression model of the response variable, is a function of the volume of the battery pack, It is The weight of the response variable, is the weight of the volume function.

2. The air-cooled dual-system lithium-ion battery pack structure optimization method according to claim 1, characterized in that: In the meshing, the dual-system lithium-ion battery pack is meshed using a swept mesh, and the air chamber is meshed using a tetrahedron.

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