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

Abstract

The invention relates to the technical field of air-cooling heat dissipation of power batteries, and discloses an air-cooling dual-system lithium ion battery pack structure optimization method, a geometric model of an air-cooling dual-system lithium ion battery pack is established and grids are divided, and the dual-system lithium ion battery pack is formed by connecting lithium iron phosphate batteries and ternary lithium batteries in series; the specified design variables are the width of an air inlet and an air outlet and the length of an air inlet duct and an air outlet duct, and the specified optimization targets are the highest temperature, the average temperature and the maximum temperature difference; designing a full-factor experiment to obtain optimization objectives of the design variables under different levels; performing variance analysis on the experimental data to obtain a regression equation of the optimization target; and substituting the regression equation into a genetic algorithm for optimization to obtain an optimal structure parameter. According to the method, the model is established by using full-factor experimental design and variance analysis, multi-objective optimization is realized through the genetic algorithm, the optimized parameters can effectively improve the cooling performance of the battery pack, and the size of the battery pack is reduced.

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] At present, lithium-ion batteries are the main power source for electric vehicles, and among them, ternary lithium batteries and lithium iron phosphate batteries are 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 occupy the market with higher safety, longer life and lower cost. By combining ternary lithium batteries and lithium iron phosphate batteries to form a mixed battery pack with complementary advantages, it can not only improve the overall performance, but also achieve a balance between safety and energy density.

[0003] However, there are differences in the thermal characteristics of these two types of batteries, and there is still a gap in the research on the thermal management of dual-system battery packs. In order to ensure the safe operation of dual-system battery packs, it is necessary to study the thermal management system of dual-system lithium-ion battery packs. Air cooling is widely used in the thermal management of lithium-ion battery packs due to its advantages such as simple design, low operating cost and convenient maintenance. In order to improve the heat dissipation effect of dual-system lithium-ion battery packs and reduce the space occupancy of the battery pack, an 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; and 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 materials of the two batteries are carbon-based materials;

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

[0009] Specifically, the Bernardi thermal model is coupled to the three-dimensional geometric model of the air-cooled dual-system lithium-ion battery pack to perform heat generation simulation; at the same time, the single particle lumped model is coupled to the three-dimensional geometric model of the air-cooled dual-system lithium-ion battery pack to perform charge balance simulation;

[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 regarded as an incompressible fluid and can be expressed by the Reynolds number; the expression of the Reynolds number is as follows:

[0013]

[0014] In the formula, 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] In the formula, p is pressure, F is external force, and t is time;

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

[0023]

[0024] In the formula, 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 divide the mesh. In order to reduce the computational cost and improve the convergence, the dual-system lithium-ion battery pack was meshed using a swept mesh, and the air chamber was meshed using a free tetrahedral mesh.

[0026] Since the air velocity and temperature in the battery pack area vary greatly, a finer mesh is used in this area using smaller-sized cells;

[0027] To ensure that the simulation results are independent of the mesh size, the maximum temperature and maximum temperature difference under 6 common different numbers of meshes are compared. The 6 common different numbers of meshes include: 0-10000 (pieces), 10000-15000 (pieces), 15000-20000 (pieces), 20000-25000 (pieces), 25000-30000 (pieces) and 30000-∞ (pieces);

[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 according to the design variables;

[0030] Among them, the design variables include: air chamber air inlet width, air chamber air outlet width, air chamber air inlet duct length, air chamber air outlet duct length;

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

[0032] Specifically, the flow channel of the air chamber adopts a Z-shaped flow channel, and the width of the air inlet and the width of the air outlet are the widths of the air inlet and the air outlet of the flow channel of the air chamber; the length of the air inlet and the length of the air outlet are the distances 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; in order to make the battery pack structure symmetrical, it is stipulated that the width of the air inlet and the width of the air outlet are the same, and the length of the air inlet and the length of the air outlet are the same; in this embodiment, the width of the air inlet and the width of the air outlet are 30mm, the length of the air inlet and the length of the air outlet are 100mm, and the battery spacing is 2.5mm;

[0033] S3. Obtain the optimization targets of the design variables at different levels through full factor experiments, and use variance analysis to fit the experimental data, 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] Using the variance analysis method, the experimental data were fitted by a quadratic polynomial regression model, and the expression is as follows:

[0036]

[0037] In the formula, 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, 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 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 the design variables and the optimization objectives is as follows:

[0039]

[0040] In the formula, 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 was adopted, and 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, perform variance analysis on the experimental results of the full factorial experimental design, and quantitatively analyze the significance of the three design variables on the temperature performance of the battery pack. 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 ith observation of the eth 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, which is 192, and k is the number of groups, which is 64; MSW is the within-group mean square, and MSB is the between-group mean square; the z value is used to determine whether the difference in the mean values ​​between groups is 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 refer 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 error mean square, which shows the significant impact of the factor on the result. The higher the F ratio, the more significant the impact of the factor; usually a P value less than 0.05 means that the factor has a significant impact on the outcome variable.

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

[0056]

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

[0058] The variance analysis shows that factor x1 has a significant impact on the three temperature indicators and is the main factor affecting the cooling effect of the battery pack. Although factors x1 and x3 also have a significant impact 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 less unexplained variation.

[0059] 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. The objective function of multi-objective optimization is constructed by using the weighted sum of the regression equation and the volume function, and the objective function is optimized by a genetic algorithm to obtain the optimal solution to complete the optimization of 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, in order 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, the present invention selects a genetic algorithm (GA) for multi-objective optimization;

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

[0065]

[0066] Where f(x) is the objective function, x is the n design variables, and y is 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 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 maximum temperature (y1) optimization weight 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, and the weight of the average temperature (y2) w2 = 0.1;

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

[0074] Table 3 Key parameters of genetic algorithm

[0075]

[0076] Beneficial Effects of the Invention

[0077] The present invention designs a full-factor experiment to analyze the influence of different structural parameters on the heat dissipation performance of the battery pack; 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 the regression equation, optimizes the objective function through a genetic algorithm, obtains the optimal structural parameters, and the optimized battery pack achieves the goal of balancing the heat dissipation effect and the 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 The schematic diagram of the geometric model and the grid 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 grid division of the battery pack;

[0080] Figure 3 It is a two-dimensional diagram of the air-cooled dual-system lithium-ion battery pack of the present invention and a grid independence verification diagram, wherein part (a) is a two-dimensional diagram of the battery pack, and part (b) is a 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 It shows the comparison chart of the results and errors of different batteries at different temperatures. DETAILED DESCRIPTION

[0083] The present invention is further described in detail below in conjunction with 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; and 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 materials of the two batteries are carbon-based materials;

[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: a lithium iron phosphate battery, a ternary lithium battery, a blower and an air chamber;

[0088] Specifically, the Bernardi thermal model is coupled to the three-dimensional geometric model of the air-cooled dual-system lithium-ion battery pack to perform heat generation simulation; at the same time, the single particle lumped model is coupled to the three-dimensional geometric model of the air-cooled dual-system lithium-ion battery pack to perform charge balance simulation;

[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 discharge rate of the battery pack load is set to 3C, the ambient temperature and the initial temperature of the battery pack are set to 25°C, the fluid inlet boundary condition is the velocity condition, the outlet boundary condition is the pressure outlet, and an atmospheric pressure is applied at the outlet;

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

[0092]

[0093] In the formula, 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] In the formula, p is pressure, F is external force, and t is time;

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

[0102]

[0103] In the formula, 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 finite element simulation software is used to construct the geometric structure and divide the mesh. In order to reduce the computational cost and improve the convergence, the dual-system lithium-ion battery pack is meshed using a swept mesh, and the air chamber is meshed using a free tetrahedral mesh;

[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 units; in this embodiment, the maximum unit size of the battery pack area is 1.03×10 -3 m, 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, 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 6 common different numbers of meshes are compared. The 6 common different numbers of meshes include: 0-10000 (pieces), 10000-15000 (pieces), 15000-20000 (pieces), 20000-25000 (pieces), 25000-30000 (pieces) and 30000-∞ (pieces);

[0107] 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℃. In order to ensure the accuracy of the simulation model and effectively save 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 according to the design variables;

[0109] Among them, the design variables include: air chamber air inlet width, air chamber air outlet width, air chamber air inlet duct length, air chamber air outlet duct length;

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

[0111] Specifically, the flow channel of the air chamber adopts a Z-shaped flow channel, and its air inlet width and air outlet width are the widths of the air inlet and air outlet of the flow channel of the air chamber; the air inlet length and the air outlet length are the distances 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; in order to make the battery pack structure symmetrical, it is stipulated that the air inlet width and the air outlet width are the same, and the air inlet length and the air outlet length are the same; in this embodiment, the air inlet width and the air outlet width are 30mm, the air inlet length and the 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 factor experiments, and use variance analysis to fit the experimental data, 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] Using the variance analysis method, the experimental data were fitted by a quadratic polynomial regression model, and the expression is as follows:

[0115]

[0116] In the formula, 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, 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 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 the design variables and the optimization objectives is as follows:

[0118]

[0119] In the formula, 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 was adopted, and 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, perform variance analysis on the experimental results of the full factorial experimental design, and quantitatively analyze the significance of the three design variables on the temperature performance of the battery pack. 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 ith observation of the eth 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 within is the within-group degrees of freedom, df between is the degrees of freedom between groups, M is the total number of observations, which is 192, and k is the number of groups, which is 64; MSW is the within-group mean square, and MSB is the between-group mean square; the z value is used to determine whether the difference in the mean values ​​between groups is 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 refer 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 error mean square, which shows the significant impact of the factor on the result. The higher the F value, the more significant the impact of the factor; usually a P value less than 0.05 means that the factor has a significant impact on the outcome variable.

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

[0135]

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

[0137] The variance analysis shows that factor x1 has a significant impact on the three temperature indicators and is the main factor affecting the cooling effect of the battery pack. Although factors x1 and x3 also have a significant impact 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 less unexplained variation.

[0138] 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. The objective function of multi-objective optimization is constructed by using the weighted sum of the regression equation and the volume function, and the objective function is optimized by a genetic algorithm to obtain the optimal solution to complete the optimization of 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, in order 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, the present invention selects a genetic algorithm (GA) for multi-objective optimization;

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

[0144]

[0145] Where f(x) is the objective function, x is the n design variables, and y is 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 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 maximum temperature (y1) optimization weight 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, and the weight of the average temperature (y2) w2 = 0.1;

[0152] In order to effectively optimize the objective function, the key parameter settings of the genetic algorithm are 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 with the increase of generations, the objective function value gradually decreases and tends to be stable, indicating that the algorithm has converged to a better solution when it reaches the 40th generation. The design variable combination corresponding to the optimal fitness value is x1=36.41, x2=55, 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). Figure 5 As shown in the figure, it can be seen that the difference between the optimization result and the simulation result in terms of temperature index is very small, the maximum temperature difference is 0.192℃, the average temperature difference is 0.155℃, and the maximum temperature difference is 0.416℃, which shows the effectiveness of the optimization method and the reliability of the results.

[0157] Furthermore, the optimized temperature index is compared with the temperature index of the original structure. It can be seen that the optimized structure has significant improvements in various temperature indicators, with the maximum temperature reduced by about 2.39%, the average temperature reduced by about 3.69%, and the maximum temperature difference reduced by about 3.78%; in addition, the volume of the optimized battery pack is reduced by about 40.35%, which significantly improves the compactness of the system.

[0158] Although 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present 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: a lithium iron phosphate battery, a ternary lithium battery, a blower and an air chamber; S2. Preset design variables through the simulation model and set optimization targets according to 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; The 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; S4. The objective function of multi-objective optimization is constructed by using the weighted sum of the regression equation and the volume function. The objective function is optimized by a genetic algorithm to obtain the optimal solution to complete the optimization of the air-cooled dual-system lithium-ion battery pack structure.

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.

3. The air-cooled dual-system lithium-ion battery pack structure optimization method according to claim 1, characterized in that: The expression for fitting the experimental data using the variance analysis method is as follows: In the formula, 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 the linear regression coefficient, describing each design variable x i The linear influence on the response variable; b is the quadratic term coefficient, which describes the nonlinear influence of the square term of each design variable on the response; c is the interaction term coefficient, which describes the influence of the interaction effect between different design variables on the response.

4. The air-cooled dual-system lithium-ion battery pack structure optimization method according to claim 1, characterized in that: The relationship between the design variables and the optimization objectives is specifically as follows: In the formula, 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, which includes random error and systematic error.

5. The air-cooled dual-system lithium-ion battery pack structure optimization method according to claim 1, characterized in that: The expression of the objective function of multi-objective optimization constructed by using the weighted sum of the regression equation and the volume function is as follows: Where f(x) is the objective function, x is the n design variables, and y is 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.

Images