A design method for a liquid-cooled circulating aluminum battery tray

Abstract

The present invention discloses a method for designing a liquid-cooled circulating aluminum battery tray, comprising the following steps: designing the outer frame, partition and tray cover of the battery tray; performing distributed optimization design on the liquid cooling channel inside the tray through a multi-stage evolutionary game model; optimizing the mechanical strength and weight of the tray structure; setting up multiple support points inside the liquid cooling channel, and performing a trade-off optimization between the carrying capacity and the cooling efficiency through an evolutionary multi-stage evolutionary game model; optimizing the manufacturing process and cost of the tray using a multi-stage evolutionary game model; optimizing the performance of the tray in different application scenarios in the system integration stage; the results of each game stage are transmitted to the next stage through a feedback mechanism, and global optimization is performed based on the adjustment of the game strategy at each stage. The design method enables the tray to achieve the best cooling efficiency and carrying performance at different power densities, reduces production costs, and improves the manufacturing flexibility of the tray.

Description

Technical Field

[0001] The present invention relates to the technical field of liquid-cooled circulating aluminum battery trays, and in particular to a design method for a liquid-cooled circulating aluminum battery tray based on distributed liquid-cooling flow channel optimization and structural support. Background Art

[0002] In the existing liquid-cooled battery tray design, a single serpentine flow channel or a simple branch flow channel is usually used for cooling. The above-mentioned flow channel design allows the coolant to flow along a fixed path through a liquid cooling circulation system to take away the heat generated by the battery module, thereby achieving the purpose of lowering the temperature. The tray structure is usually made of aluminum alloy material and is divided into a load-bearing structure and a cooling plate. The two are designed separately. The load-bearing structure is responsible for bearing the weight of the battery and providing the necessary strength, while the cooling plate provides heat dissipation for the battery. Such a design is generally assembled by welding or riveting during the manufacturing process. The structure is complex, the production process is cumbersome and the cost is high. The main goal of the prior art is to achieve basic heat dissipation and load-bearing functions through relatively simple cooling channels and mechanical support structures.

[0003] The existing technology has exposed some key defects in application. First, the serpentine flow channel design of the traditional liquid cooling tray is fixed and cannot be dynamically adjusted according to the power density or heat load of different battery modules, resulting in uneven cooling and easy local overheating in high power density areas, which not only affects the service life of the battery but also leads to performance degradation and even safety hazards; secondly, the tray structure is too complicated, and the cooling plate and the load-bearing structure are separated, which increases the overall weight of the tray and reduces the lightweight level of the system. At the same time, it cannot effectively adapt to the needs of different application scenarios. When modular design is required to adapt to electric vehicles or energy storage systems with different power densities and sizes, the design flexibility of the existing tray is poor, and it is difficult to achieve efficient system integration.

[0004] In addition, the existing manufacturing process of liquid cooling trays is single, usually using welding and riveting processes, and lacks process combination optimization, resulting in high manufacturing costs and low production efficiency, which is not conducive to large-scale production applications. How to solve the above technical problems is the research direction that technical personnel in this field are committed to. Summary of the invention

[0005] The purpose of the present invention is to provide a liquid-cooled circulating aluminum battery tray design method, which aims to solve the problems of insufficient cooling efficiency, heavy tray structure, poor adaptability of modular design and high manufacturing cost by introducing innovative design of distributed liquid cooling flow channel optimization and structural support, and establishing a multi-stage evolutionary game model.

[0006] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0007] A method for designing a liquid-cooled circulating aluminum battery tray comprises the following steps:

[0008] S1, design the outer frame, partition and tray cover of the battery tray, and open a liquid cooling channel inside the battery tray;

[0009] S2, a distributed optimization design of the liquid cooling channel inside the tray is performed through a multi-stage evolutionary game model. The participants of the multi-stage evolutionary game model include liquid cooling channel designers and coolant performance selectors. The liquid cooling channel design adopts a multi-path layout to make the coolant flow evenly through various areas of the battery module. The coolant performance selector adjusts the coolant flow rate, specific heat capacity and flow path through the game to maximize the cooling efficiency;

[0010] S3, using a multi-stage evolutionary game model to optimize the mechanical strength and weight of the pallet structure. The participants of the multi-stage evolutionary game model include the pallet designer and the material selector;

[0011] S4, multiple support points are set up inside the liquid cooling channel, and a trade-off optimization between load-bearing capacity and cooling efficiency is performed through an evolutionary multi-stage evolutionary game model;

[0012] S5, using a multi-stage evolutionary game model to optimize the manufacturing process and cost of pallets. The game participants include manufacturing process engineers and production cost controllers. The process options include riveting, laser welding and modular design. The best process combination is determined through the game.

[0013] S6, in the system integration stage, a multi-stage evolutionary game model is used to optimize the performance of the pallet in different application scenarios. The game participants include system integration designers and user demand analysts. According to the needs of the battery system, the cooling system integration method is adjusted to achieve modularization of the pallet to adapt to electric vehicles and energy storage systems with different power densities;

[0014] S7, the result of each game stage is transmitted to the next stage through the feedback mechanism, and global optimization is performed based on the adjustment of the game strategy at each stage. When the game evolution at each stage reaches the Nash equilibrium state, the system design solution is the optimal solution.

[0015] As a specific implementation method, the design method also includes designing a liquid cooling circulation system before system integration to ensure the circulation of the coolant and optimize the flow rate and direction in real time through game theory.

[0016] As a specific implementation, step S2 specifically includes the following sub-steps:

[0017] S21, establish a multi-stage evolutionary game model, the initial conditions of the game are expressed as:

[0018] ;

[0019] Among them, U initial represents the total energy loss at the beginning of the game, is the temperature gradient in region i, V i is the volume of region i, ΔT i is the temperature change in region i, C pi is the specific heat capacity of the coolant in region i, Q i is the coolant flow rate in region i, λ i is the thermal conductivity of the coolant in region i;

[0020] S22, determining the thermal load distribution T(x, y, z) of the battery module, calculating the heat flux density q(x, y, z) of different regions of the battery module to obtain the thermal load distribution of each region, and inputting the thermal load distribution into the multi-stage evolutionary game model as a basis for the liquid cooling channel designer to adjust the channel layout and path;

[0021] S23, through strategy optimization in a multi-stage evolutionary game model, the liquid cooling channel designer designs a multi-cooling channel path layout based on the heat load distribution T(x, y, z) to make the coolant flow evenly through each area of ​​the battery module, ensuring that the temperature difference ΔT in the area remains within the set range;

[0022] S24, in each iteration of the game, the coolant performance selector adjusts the coolant flow rate Q and specific heat capacity C according to the heat flux density q(x, y, z) and cooling channel path length L(x, y, z) in different regions p , calculate the cooling efficiency η of each path:

[0023] ;

[0024] Where η represents the cooling efficiency, L(x, y, z) represents the length of the cooling channel, and ΔT represents the temperature difference in the area;

[0025] S25, in each round of game, the cooling channel path length L in area i i (x,y,z) and coolant flow rate Q i and coolant specific heat capacity C pi The adjustment is optimized through feedback mechanism;

[0026] S26, through multiple iterative games, the liquid cooling channel layout and coolant parameters of each area are determined. When the Nash equilibrium is reached, the optimized cooling channel path and coolant flow configuration are:

[0027] ;

[0028] ;

[0029] ;

[0030] Among them, L i * represents the optimized cooling channel path length in region i, Q i * represents the optimized coolant flow in region i, C pi * represents the optimized specific heat capacity of the coolant in region i, λ i represents the thermal conductivity of the coolant in region i, Q i represents the coolant flow rate in area i, is the temperature gradient in region i, ΔT i represents the temperature difference in region i, T i,in -T i,out represents the inlet and outlet temperature difference of the coolant in area i, L i is the cooling channel length in region i, C pi is the specific heat capacity of the coolant in region i, V i is the volume of region i, and L is the total length of all liquid cooling channels.

[0031] As a specific implementation, step S3 specifically includes the following sub-steps:

[0032] S31, the tray designer selects a high-strength thick-wall design or a lightweight thin-wall design based on the mechanical strength requirements of the battery tray, and the material selector is responsible for selecting the appropriate material;

[0033] S32, the pallet designer defines the stress σ(x, y, z) of the pallet under the action of external load by establishing a mechanical model of the pallet, where the stress σ(x, y, z) represents the stress borne by the pallet at different coordinate points x, y, z, and calculates the overall mechanical strength S of the pallet:

[0034] ;

[0035] Among them, S is the overall mechanical strength of the pallet, in N; V is the volume of the pallet, in m 3 ;

[0036] S33, the pallet designer selects the pallet wall thickness t through game optimization i and the elastic modulus E of the material i , the maximum stress σ of the tray max The following conditions are met:

[0037] ;

[0038] Among them, E i Indicates the elastic modulus of the material, in N / m2 , t i is the wall thickness of the pallet in mm, f s is the safety factor;

[0039] S34, the material selector selects different materials according to the strength requirements of the pallet and determines the density ρ of the material. i and intensity σ i Carry out the evaluation and calculate the mass M of the material i :

[0040] M i =ρ i •V;

[0041] Among them, M i represents the mass of the pallet material, ρ i is the density of the material, V is the volume of the tray;

[0042] S35, material selectors optimize between strength and weight through a multi-stage game model, achieving lightweight pallets while meeting strength requirements;

[0043] S36, in each round of the game, the pallet designer adjusts the pallet design according to the performance and wall thickness of different materials, and optimizes the mechanical properties and weight of the pallet through the feedback mechanism to achieve a balance between lightness and strength;

[0044] S37, finally through multiple rounds of iterative games, when the strategies of material selectors and pallet designers reach Nash equilibrium, the optimal structure and material configuration of the pallet are determined to achieve the best combination of pallet strength and weight;

[0045] Optimal wall thickness of the pallet i * The trade-off between strength and lightness is calculated to meet the strength requirements of the pallet under maximum stress:

[0046] ;

[0047] Among them, t i * is the optimized tray wall thickness, σ max E is the maximum stress that the pallet bears during use. i is the elastic modulus of the material, f s is the safety factor;

[0048] Optimal pallet quality i * Calculated by material density and pallet volume:

[0049] M i * =ρ i·V·t i * ;

[0050] Among them, M i * is the mass of the optimized pallet, V is the volume of the pallet;

[0051] Optimal stress distribution of the tray σ i * By calculating the stress of each part of the pallet, the material strength and wall thickness can meet the use requirements:

[0052] ;

[0053] Among them, σ i * is the optimized stress distribution.

[0054] As a specific implementation, step S4 specifically includes the following sub-steps:

[0055] S41, multiple support points are set up inside the liquid cooling channel, and the arrangement of the support points is optimized through a multi-stage evolutionary game model;

[0056] S42, establish the mechanical model of the liquid cooling channel, the bearing capacity of each support point is C s , its total bearing force F s Calculated as:

[0057] ;

[0058] Among them, C si is the bearing capacity of the ith support point, A si is the contact area of ​​the ith support point, F s is the total bearing capacity of all support points;

[0059] S43, optimize the distribution position P of the support points si (x, y, z) and the number n, the optimized support point distribution must meet the following conditions:

[0060] ;

[0061] Among them, P si * represents the optimized support point position, λ is the thermal conductivity of the coolant, Q s is the coolant flow rate in the flow channel, is the temperature gradient in the flow channel, F s is the force on the support point at a specific location, P si represents the position vector of the support point, and Respectively represent Fs and P si Find partial derivatives;

[0062] S44, the optimization of the support point position is dynamically adjusted through the feedback mechanism to ensure that the flow efficiency of the coolant is not affected, and the flow efficiency of the coolant η is calculated. s :

[0063] ;

[0064] Among them, η s represents the flow efficiency, Q s is the coolant flow rate in the flow channel, L s is the flow channel length, C p is the specific heat capacity of the coolant, ΔT s is the temperature difference in the flow channel;

[0065] S45, optimizes the number and position of support points through a multi-round and multi-stage evolutionary game model, so that the tray can achieve a balance between load-bearing capacity and cooling efficiency under different working conditions;

[0066] S46, finally through iterative game, when the support point position and coolant flow reach Nash equilibrium, the optimal distribution of the support points is determined.

[0067] As a specific implementation, step S5 specifically includes the following sub-steps:

[0068] S51, the manufacturing process engineer selects the manufacturing process of the pallet, and the production cost controller is responsible for optimizing the manufacturing cost, and determines the best process combination and cost control plan related to the pallet structure, material selection, support point arrangement and liquid cooling channel through game theory;

[0069] S52, manufacturing process engineers select appropriate process methods based on material selection, support point layout and optimization, and the complexity of the liquid cooling channel, including riveting, laser welding, and modular design, and calculate the cost of each process. total Perform the calculation:

[0070] ;

[0071] Among them, C total is the total manufacturing cost, C ri , C wi , C mi are the costs of press riveting, laser welding and modular design, C m_mat is the cost of material selection, C s Cost of arranging support points;

[0072] S53, manufacturing process engineers calculate the production efficiency E of the process based on the optimized tray material selection, support point layout and liquid cooling channel design.i , process complexity D i and material loss rate L i , and calculate the manufacturing efficiency η of the process combination c :

[0073] ;

[0074] Among them, η c represents the manufacturing efficiency of the process combination, E i is production efficiency, D i is the process complexity, L i is the material loss rate;

[0075] S54, the production cost controller combines the material cost, support point layout and flow channel optimization factors, dynamically adjusts the balance between process cost and production efficiency through the feedback mechanism, optimizes production efficiency and cost while ensuring the tray strength, heat dissipation performance and load-bearing capacity, and the cost optimization goal is to minimize the total manufacturing cost C total While maximizing manufacturing efficiency η c :

[0076] ;

[0077] Among them, α is the weight coefficient, which is used to balance cost and efficiency;

[0078] S55, through a multi-round and multi-stage evolutionary game model, determines the optimal process combination of press riveting, laser welding and modular design to maximize production efficiency and minimize material loss;

[0079] S56, ultimately through iterative game, when process selection, material cost, support point layout and flow channel design reach Nash equilibrium, the optimal process combination is determined and applied to the mass production process of pallets.

[0080] As a specific implementation, step S6 specifically includes the following sub-steps:

[0081] S61, the system integration designer designs the system integration method of the liquid cooling tray according to the power density requirements of the battery system, and the user demand analyst feedbacks the user demand function U(x, y, z) according to the performance requirements of the battery system in different application scenarios;

[0082] S62, system integration designers calculate the power density P of the battery module based on the power density P of the battery module. d (x, y, z) and heat load distribution q(x, y, z), adjust the cooling system integration method of the liquid cooling tray modular design;

[0083] S63, the user demand analyst inputs the user demand function U(x, y, z) of each scenario according to the demand for electric vehicles or energy storage systems in different application scenarios. The user demand function includes the power density demand P d (x,y,z), temperature limit T max and coolant flow requirement Q m , and use it as one of the input parameters of the system integrated multi-stage evolutionary game model;

[0084] S64, the system integration designer dynamically adjusts the cooling system according to the user demand function U(x, y, z) and cooling efficiency, so that the liquid cooling tray can adapt to electric vehicles and energy storage systems with different power densities. The power density and cooling system matching degree of each module are calculated through a multi-stage evolutionary game model. m :

[0085] ;

[0086] Among them, K m is the matching degree of modular design, ΔT m is the battery module temperature difference; P d (x,y,z) is the power density, Q m is the coolant flow rate; C p is the specific heat capacity of the coolant;

[0087] S65, adjusts the integration mode of the cooling system through the feedback mechanism. In each round of the game, the system integration designer and the user demand analyst optimize the system integration design according to the cooling efficiency, power density requirements and user demand function, so that the modular pallet can adapt to different application scenarios;

[0088] S66, finally, through multiple rounds of iterative game, when the cooling system integration mode and power density requirements reach Nash equilibrium, the optimal modular integration solution of the liquid cooling tray is determined to maximize the cooling performance, power density adaptability and system integration efficiency of the battery tray in different application scenarios.

[0089] Compared with the prior art, the present invention is beneficial in that:

[0090] 1) The invention not only optimizes the distributed design of the liquid cooling channel through a multi-stage evolutionary game model, so that the coolant can dynamically adjust the flow path according to the battery thermal load to prevent overheating, but also achieves a balance between lightweight and high strength through the integrated design of structural support and flow channel. In addition, this solution combines modular design with system integration, and can dynamically adjust the tray structure according to the different needs of electric vehicles and energy storage systems, so that the tray can achieve the best cooling efficiency and load-bearing performance at different power densities.

[0091] 2) The present invention optimizes the combination of riveting, laser welding and modular design through a game model, ensuring the minimization of manufacturing costs and maximization of production efficiency, and realizing automation and flexibility of production. Compared with the existing technology, this solution greatly reduces production costs, while improving the manufacturing flexibility of pallets, and can effectively meet the needs of different application scenarios.

[0092] 3) The present invention optimizes and updates in real time through a feedback mechanism to ensure that the tray design always maintains the best state in terms of heat dissipation, load-bearing, modularity and cost control. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required to be used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0094] Figure 1 This is an overall flow chart of a liquid-cooled circulating aluminum battery tray design method according to the present invention;

[0095] Figure 2 A schematic diagram of a flow channel of a liquid-cooled circulating aluminum battery tray design method according to the present invention;

[0096] Figure 3 It is a schematic diagram of the overall bearing structure of a liquid-cooled circulating aluminum battery tray design method described in the present invention;

[0097] Figure 4 The overall structure diagram of a tray of a liquid-cooled circulating aluminum battery tray design method described in the present invention;

[0098] Among them: 1. flow channel; 2. support point; 3. outer frame. DETAILED DESCRIPTION

[0099] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention:

[0100] See also Figures 1 to 4 As shown, this example provides a liquid-cooled circulating aluminum battery tray design method, including the following steps:

[0101] S1, designing the outer frame 3, partition and tray cover of the battery tray, and opening a liquid cooling channel 1 inside the battery tray;

[0102] S2, a distributed optimization design of the liquid cooling channel inside the tray is performed through a multi-stage evolutionary game model. The participants of the multi-stage evolutionary game model include liquid cooling channel designers and coolant performance selectors. The liquid cooling channel design adopts a multi-path layout to make the coolant flow evenly through various areas of the battery module. The coolant performance selector adjusts the coolant flow rate, specific heat capacity and flow path through the game to maximize the cooling efficiency;

[0103] S3, using a multi-stage evolutionary game model to optimize the mechanical strength and weight of the pallet structure. The participants of the multi-stage evolutionary game model include pallet designers and material selectors. The pallet designer chooses a high-strength thick-wall design or a lightweight thin-wall design, while the material selector chooses 6061-T6 aluminum alloy, high-strength alloy or composite material to achieve the optimal strength and lightweight effect in different application scenarios;

[0104] S4, multiple support points 2 are set up inside the liquid cooling channel, and a trade-off optimization is performed between the load-bearing capacity and the cooling efficiency through an evolutionary multi-stage evolutionary game model. The support points must not only enable the tray to bear the weight of the battery and external impact, but also ensure the uniformity of the liquid cooling channel without affecting the flow of the coolant and the heat dissipation effect;

[0105] S5, using a multi-stage evolutionary game model to optimize the manufacturing process and cost of pallets. The game participants include manufacturing process engineers and production cost controllers. The process options include riveting, laser welding and modular design. The best process combination is determined through the game.

[0106] S6, in the system integration stage, a multi-stage evolutionary game model is used to optimize the performance of the pallet in different application scenarios. The game participants include system integration designers and user demand analysts. According to the needs of the battery system, the cooling system integration method is adjusted to achieve modularization of the pallet to adapt to electric vehicles and energy storage systems with different power densities;

[0107] S7, the result of each game stage is transmitted to the next stage through the feedback mechanism, and global optimization is performed based on the adjustment of the game strategy at each stage. When the game evolution at each stage reaches the Nash equilibrium state, the system design solution is the optimal solution;

[0108] S8, through fluid mechanics and structural mechanics simulation, the optimization results of each game stage are verified to achieve a balance between heat dissipation, strength and weight. According to the simulation results, the liquid cooling channel depth, wall thickness, coolant flow rate and tray support structure are further optimized, and finally the overall performance of the liquid cooling tray meets the use requirements of the battery system.

[0109] Here, a liquid cooling circulation system is designed before system integration to ensure the circulation of the coolant. The flow rate and direction are optimized in real time through game theory, the coolant flow is increased in areas with higher battery temperatures, and the flow is reduced in areas with lower temperatures, thereby dynamically adjusting the distribution of the coolant.

[0110] Here, step S2 specifically includes the following sub-steps:

[0111] S21, a multi-stage evolutionary game model is established. The liquid cooling channel designer optimizes the path and layout of the liquid cooling channel, and the coolant performance selector adjusts the specific heat capacity C of the coolant. pi 、Flow Q i and thermal conductivity λ i , and finally the flow channel layout matches the coolant performance. The initial condition of the game is expressed as:

[0112] ;

[0113] Among them, U initial represents the total energy loss at the beginning of the game, is the temperature gradient in region i, V i is the volume of region i, ΔT i is the temperature change in region i, C pi is the specific heat capacity of the coolant in region i, Q i is the coolant flow rate in region i, λ i is the thermal conductivity of the coolant in region i;

[0114] S22, determining the thermal load distribution T(x, y, z) of the battery module, calculating the heat flux density q(x, y, z) of different regions of the battery module to obtain the thermal load distribution of each region, and inputting the thermal load distribution into the multi-stage evolutionary game model as a basis for the liquid cooling channel designer to adjust the channel layout and path;

[0115] S23, through strategy optimization in a multi-stage evolutionary game model, the liquid cooling channel designer designs a multi-cooling channel path layout according to the heat load distribution so that the coolant flows evenly through each area of ​​the battery module, ensuring that the temperature difference ΔT within the area remains within the set range;

[0116] S24, in each iteration of the game, the coolant performance selector adjusts the coolant flow rate Q and specific heat capacity C according to the heat flux density and cooling channel path L(x, y, z) in different regions. p , calculate the cooling efficiency η of each path:

[0117] ;

[0118] Where η represents the cooling efficiency in W / K, L(x,y,z) represents the path length of the liquid cooling channel, and ΔT represents the temperature difference in the area;

[0119] S25, in each round of game, the cooling channel path L i (x,y,z) and coolant flow rate Q i and C pi The adjustment of is optimized through the feedback mechanism, and the feedback function φ i (x, y, z) represents the comprehensive effect of path and coolant parameters, which is used to adjust the path and coolant parameters in real time;

[0120] Here the feedback function φ i (x, y, z) does not require a specific calculation formula. It only needs to be able to monitor and adjust the flow path and flow rate of the coolant in each area. By responding to the regional temperature gradient, coolant flow rate, specific heat capacity, and flow path length, the cooling efficiency can be optimized. The adjustment method can use PID or fuzzy control or MPC;

[0121] S26, through multiple iterative games, the layout of the liquid cooling channel and the coolant parameters of each area are determined. When the Nash equilibrium is reached, the optimized cooling channel path and coolant flow configuration are:

[0122] ;

[0123] ;

[0124] ;

[0125] Among them, L i * represents the optimized cooling channel path length in region i, Q i * represents the optimized coolant flow in region i, C pi * represents the optimized specific heat capacity of the coolant in region i, λ i represents the thermal conductivity of the coolant in region i, Q i represents the coolant flow rate in area i, ΔT i represents the temperature difference in region i, T i,in -T i,out represents the inlet and outlet temperature difference of the coolant in area i, L i is the cooling channel length in region i, C pi is the specific heat capacity of the coolant in region i, V i is the volume of region i, and L is the total length of all liquid cooling channels.

[0126] In this implementation, step S3 specifically includes the following sub-steps:

[0127] S31, the tray designer selects a high-strength thick-wall design or a lightweight thin-wall design based on the mechanical strength requirements of the battery tray, and the material selector is responsible for selecting appropriate materials, including 6061-T6 aluminum alloy, high-strength alloy, or composite material, to strike a balance between strength and weight;

[0128] S32, the pallet designer defines the stress σ(x, y, z) of the pallet under the action of external load by establishing a mechanical model of the pallet, where the stress σ(x, y, z) represents the stress borne by the pallet at different coordinate points x, y, z, and calculates the overall mechanical strength S of the pallet:

[0129] ;

[0130] Where S is the overall mechanical strength of the pallet, in N, and V is the volume of the pallet, in m 3 ;

[0131] S33, the pallet designer selects the pallet wall thickness t through game optimization i and the elastic modulus E of the material i , by calculating the stress distribution σ(x,y,z) and the maximum stress σ max , so that the pallet has sufficient strength under various working conditions, the maximum stress σ max The following conditions are met:

[0132] ;

[0133] Among them, E i Indicates the elastic modulus of the material, in N / m 2 , t i is the wall thickness of the pallet in mm, f s It is a safety factor to prevent the structure of the pallet from being damaged during use;

[0134] S34, the material selector selects different materials according to the strength requirements of the pallet and determines the density ρ of the material. i and intensity σ i Carry out the evaluation and calculate the mass M of the material i :

[0135] M i =ρ i •V;

[0136] Among them, M i represents the mass of the pallet material, ρ i is the density of the material, V is the volume of the tray;

[0137] S35, material selectors optimize between strength and weight through a multi-stage game model, and select 6061-T6 aluminum alloy, high-strength alloy or composite materials to achieve the best balance between strength and weight in different application scenarios, and achieve lightweight pallets while meeting strength requirements;

[0138] S36, in each round of the game, the pallet designer adjusts the pallet design according to the performance and wall thickness of different materials, and optimizes the mechanical properties and weight of the pallet through the feedback mechanism so that the pallet can maintain a low dead weight while bearing the weight of the battery and external loads, thus achieving a balance between lightness and strength;

[0139] S37, finally through multiple rounds of iterative games, when the strategies of material selectors and pallet designers reach Nash equilibrium, the optimal structure and material configuration of the pallet are determined to achieve the best combination of pallet strength and weight;

[0140] Optimal wall thickness of the pallet i * The trade-off between strength and lightness is calculated to meet the strength requirements of the pallet under maximum stress:

[0141] ;

[0142] Among them, t i * is the optimized tray wall thickness, σ max E is the maximum stress that the pallet bears during use. i is the elastic modulus of the material, f s is the safety factor;

[0143] Optimal pallet quality i * Calculated by material density and pallet volume:

[0144] M i * =ρ i ·V·t i * ;

[0145] Among them, M i * is the mass of the optimized pallet, V is the volume of the pallet;

[0146] Optimal stress distribution of the tray σ i * By calculating the stress of each part of the pallet, the material strength and wall thickness can meet the use requirements:

[0147] ;

[0148] Among them, σi * is the optimized stress distribution.

[0149] In this implementation, step S4 specifically includes the following sub-steps:

[0150] S41, multiple support points are set up inside the liquid cooling channel, and the arrangement of the support points is optimized through the evolutionary multi-stage evolutionary game model. The support points are used to bear the weight of the battery and resist external impact, while maintaining the uniformity of the liquid cooling channel;

[0151] S42, establish the mechanical model of the liquid cooling channel, the bearing capacity of each support point is C s , its total bearing force F s Calculated as:

[0152] ;

[0153] Among them, C si is the bearing capacity of the ith support point, A si is the contact area of ​​the ith support point, F s is the total bearing capacity of all support points;

[0154] S43, optimize the distribution position P of the support points according to the mechanical requirements of the pallet as a whole si (x, y, z) and the number n. Each support point ensures the load-bearing capacity of the tray without affecting the flow path and efficiency of the coolant. The optimized support point distribution must meet the following conditions:

[0155] ;

[0156] Among them, P si * represents the optimized support point position, λ is the thermal conductivity of the coolant, Q s is the coolant flow rate in the flow channel, is the temperature gradient in the flow channel, F s is the force on the support point at a specific location, P si represents the position vector of the support point, and Respectively represent F s and P si Find partial derivatives;

[0157] S44, the optimization of the support point position is dynamically adjusted through the feedback mechanism. In each round of the game, a trade-off is made between the bearing capacity of the support point and the flow of the coolant, so that the support point can withstand external impact and battery weight without affecting the flow efficiency of the coolant. The flow efficiency of the coolant η is calculated s :

[0158] ;

[0159] Among them, η s represents the flow efficiency, Q s is the coolant flow rate in the flow channel, L s is the flow channel length, C p is the specific heat capacity of the coolant, ΔT s is the temperature difference in the flow channel;

[0160] S45, through a multi-round and multi-stage evolutionary game model, optimizes the number and position of support points, so that the tray can achieve a balance between load-bearing capacity and cooling efficiency under different working conditions, and the arrangement of support points does not affect the overall heat dissipation effect and flow uniformity of the liquid cooling system;

[0161] S46, finally through iterative game, when the support point position and coolant flow reach Nash equilibrium, the optimal distribution of the support points is determined.

[0162] In this implementation, step S5 specifically includes the following sub-steps:

[0163] S51, the manufacturing process engineer selects the manufacturing process of the pallet, and the production cost controller is responsible for optimizing the manufacturing cost, and determines the best process combination and cost control plan related to the pallet structure, material selection, support point arrangement and liquid cooling channel through game theory;

[0164] S52, manufacturing process engineers select appropriate process methods based on material selection, support point layout and optimization, and the complexity of the liquid cooling channel, including riveting, laser welding, and modular design, and calculate the cost of each process. total Perform the calculation:

[0165] ;

[0166] Among them, C total is the total manufacturing cost, C ri , C wi , C mi are the costs of press riveting, laser welding and modular design, C m_mat is the cost of material selection, C s Cost of arranging support points;

[0167] S53, manufacturing process engineers calculate the production efficiency E of the process based on the optimized tray material selection, support point layout and liquid cooling channel design. i , process complexity D i and material loss rate L i , and calculate the manufacturing efficiency η of the process combination c :

[0168] ;

[0169] Among them, η c represents the manufacturing efficiency of the process combination, E i is production efficiency, D i is the process complexity, L i is the material loss rate;

[0170] S54, the production cost controller combines the material cost, support point layout and flow channel optimization factors, dynamically adjusts the balance between process cost and production efficiency through the feedback mechanism, optimizes production efficiency and cost while ensuring the tray strength, heat dissipation performance and load-bearing capacity, and the cost optimization goal is to minimize the total manufacturing cost C total While maximizing manufacturing efficiency η c :

[0171] ;

[0172] Among them, α is the weight coefficient, which is used to balance cost and efficiency;

[0173] S55, through a multi-round and multi-stage evolutionary game model, manufacturing process engineers and production cost controllers make optimal choices between production efficiency, manufacturing cost, support point layout, material selection and liquid cooling channel complexity, and gradually determine the best process combination of press riveting, laser welding and modular design to maximize production efficiency and minimize material loss;

[0174] S56, ultimately through iterative game, when process selection, material cost, support point layout and flow channel design reach Nash equilibrium, the optimal process combination is determined and applied to the mass production process of pallets.

[0175] In this implementation, step S6 specifically includes the following sub-steps:

[0176] S61, the system integration designer designs the system integration method of the liquid cooling tray according to the power density requirements of the battery system. The user demand analyst feeds back the user demand function U(x, y, z) according to the performance requirements of the battery system in different application scenarios. The multi-stage evolutionary game model is used to optimize the integration of the cooling system;

[0177] S62, system integration designers calculate the power density P of the battery module based on the power density P of the battery module. d (x, y, z) and heat load distribution q(x, y, z), adjust the cooling system integration method of the liquid cooling tray modular design, the modular design needs to meet the heat dissipation requirements under different power densities to calculate the cooling efficiency η m ;

[0178] S63, the user demand analyst inputs the user demand function U(x, y, z) of each scenario according to the demand for electric vehicles or energy storage systems in different application scenarios. The user demand function includes the power density demand P d (x,y,z), temperature limit T max and coolant flow requirement Q m , and use it as one of the input parameters of the system integrated multi-stage evolutionary game model;

[0179] S64, the system integration designer calculates the cooling efficiency η based on the user demand function U(x, y, z) and the cooling efficiency η m Dynamically adjust the cooling system to adapt the liquid cooling tray to electric vehicles and energy storage systems with different power densities. Calculate the power density and cooling system matching degree of each module through a multi-stage evolutionary game model. m :

[0180] ;

[0181] Among them, K m is the matching degree of modular design, ΔT m is the battery module temperature difference; P d (x,y,z) is the power density, Q m is the coolant flow rate; C p is the specific heat capacity of the coolant;

[0182] S65, adjusts the integration mode of the cooling system through the feedback mechanism. In each round of the game, the system integration designer and the user demand analyst optimize the system integration design according to the cooling efficiency, power density requirements and user demand function, so that the modular pallet can adapt to different application scenarios;

[0183] S66, finally, through multiple rounds of iterative game, when the cooling system integration mode and power density requirements reach Nash equilibrium, the optimal modular integration solution of the liquid cooling tray is determined to maximize the cooling performance, power density adaptability and system integration efficiency of the battery tray in different application scenarios. Example 1

[0184] In this example, the implementer considers a high-power density electric vehicle. The power density of the battery module used in the vehicle needs to reach 150W / kg. The design requires that its battery tray can operate stably under high power and maintain efficient heat dissipation performance under different ambient temperatures (hot urban roads in summer or long-term high-speed driving in mountainous areas). This embodiment demonstrates the advantages of applying a liquid-cooled battery tray to this scenario through design, optimization, simulation and performance evaluation.

[0185] Traditional design scheme: 1) The liquid cooling channel adopts a serpentine channel design with a fixed channel path. The coolant flows in from one end of the battery module and flows out from the other end; 2) The tray design adopts a design in which the aluminum alloy frame and the cooling plate are separated; 3) The production of the tray adopts a single welding process.

[0186] Adopt the design scheme of the present invention:

[0187] 1) A combination of multiple coolants (including water-based coolants and ethylene glycol-based coolants) was used, and different flow channel designs were carried out according to the changes in heat loads in different road conditions (urban, suburban, and mountainous areas). A distributed flow channel optimization design was adopted. Through a multi-stage evolutionary game model, the coolant flow channel was optimized according to the heat load distribution of the battery module, and the flow channel layout adopted a dynamic distributed multi-path layout. Through simulation, it was found that the coolant could evenly cover the entire battery module, and the flow path was automatically adjusted, so that the coolant flow in different areas could be adjusted in real time according to its heating conditions; 2) In the design of the tray structure, the support points and flow channels were integrated; 3) The production process of the tray was optimized through a multi-stage evolutionary game model, and a combination of riveting, laser welding and modular design was selected.

[0188] By comparison, the traditional design scheme has the following differences compared with the technical scheme of the present invention:

[0189] 1) The traditional design scheme adopts a serpentine flow channel design for the liquid cooling flow channel, which shows a large local overheating problem in the simulation, especially in the area far from the liquid inlet where the temperature difference is large. In the simulation operation, it is found that the temperature of the area closest to the liquid inlet in the battery module is maintained at about 35°C, while the temperature at the farthest end is as high as 48°C, which is obviously beyond the safe temperature range. In addition, the flow channel design has a fixed flow channel path and uneven distribution of the coolant, resulting in uneven heat dissipation. However, the distributed flow channel optimization design of the present invention is adopted. Through simulation, it is found that the coolant can evenly cover the entire battery module, and the flow path is automatically adjusted, so that the coolant flow in different areas can be adjusted in real time according to its heating situation. The optimized flow channel design keeps the highest temperature of the battery module at 38°C and the lowest temperature at 34°C. The temperature difference is greatly reduced to only 4°C, successfully avoiding the problem of local overheating.

[0190] 2) In the traditional solution, the tray design adopts a design scheme in which the aluminum alloy frame and the cooling plate are separated, which leads to the use of redundant materials and increases the weight burden. The weight of the entire tray is as high as 15kg, accounting for 20% of the total weight of the entire battery module. The design scheme of the present invention realizes the integrated design of the support point and the flow channel. The support point not only plays the role of supporting the battery module, but also serves as an auxiliary part of the coolant flow channel, reducing the material usage in the structure. The weight of the optimized tray is only 9.8kg, successfully realizing the lightweight design and reducing the weight by nearly 35%;

[0191] 3) In the traditional solution, a single welding process was used, which resulted in high production costs and low production efficiency. However, the technical solution of the present invention selected a combination of riveting, laser welding and modular design. During the process of process optimization, the implementers compared the production efficiency and material loss of different processes and finally determined the best process combination. After simulation calculation, the optimized production process not only reduced the manufacturing cost by 20%, but also increased the production efficiency by 30%. The production time of each pallet was shortened from the original 7.5 hours to 5.2 hours.

[0192] In order to further prove the feasibility of the design method of the present invention, the implementers conducted simulation tests in multiple scenarios. The test sites were selected on mountain roads in Beijing, Guangzhou and Yunnan, and the time was August 2024. In the simulation test, the extreme conditions of urban congested road conditions, high-speed driving and long-term climbing in mountainous areas were simulated.

[0193] The test shows that after the electric vehicle using the pallet made by the traditional design runs for 2 hours in a high temperature environment, the battery temperature reaches above 45°C, the heat dissipation effect is poor, and the battery performance decreases by about 15%. However, for the electric vehicle using the pallet design of the present invention, after testing for the same time and road conditions, the battery temperature is always maintained below 37°C, the temperature difference is controlled within 4°C, the battery performance only decreases by 5%, and the heat dissipation performance is excellent.

[0194] The following table shows the comparison data between the method of the present invention and the traditional method under different road conditions:

[0195]

[0196] The above test data show that the liquid-cooled circulating aluminum battery tray design of the present invention exhibits significant heat dissipation advantages in various test scenarios. Not only is the temperature difference reduced, but the energy efficiency decline is also greatly reduced, which effectively improves the stability and performance of electric vehicles under high-load operation. At the same time, in terms of manufacturing process, the present invention reduces production costs and improves production efficiency by optimizing the process combination, further verifying the feasibility and effectiveness of the design of the present invention.

[0197] In summary, through the simulation process and data comparison of this embodiment, it is clearly demonstrated that the present invention has significant advantages in heat dissipation performance, structural optimization, manufacturing process and energy efficiency improvement, solves many problems in the prior art, and has broad application prospects.

[0198] It should be emphasized that the above are only preferred embodiments of the present invention and do not limit the present invention in any form. Any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention are still within the scope of the case.

Claims

1. A liquid-cooled circulating aluminum battery tray design method, characterized in that: The following steps are involved: S1, design the outer frame, partition and tray cover of the battery tray, and open a liquid cooling channel inside the battery tray; S2, a distributed optimization design of the liquid cooling channel inside the tray is performed through a multi-stage evolutionary game model. The participants of the multi-stage evolutionary game model include liquid cooling channel designers and coolant performance selectors. The liquid cooling channel design adopts a multi-path layout to make the coolant flow evenly through various areas of the battery module. The coolant performance selector adjusts the coolant flow rate, specific heat capacity and flow path through the game to maximize the cooling efficiency; S3, using a multi-stage evolutionary game model to optimize the mechanical strength and weight of the pallet structure. The participants of the multi-stage evolutionary game model include the pallet designer and the material selector; S4, multiple support points are set up inside the liquid cooling channel, and a trade-off optimization between load-bearing capacity and cooling efficiency is performed through an evolutionary multi-stage evolutionary game model; S5, using a multi-stage evolutionary game model to optimize the manufacturing process and cost of pallets. The game participants include manufacturing process engineers and production cost controllers. The process options include riveting, laser welding and modular design. The best process combination is determined through the game. S6, in the system integration stage, a multi-stage evolutionary game model is used to optimize the performance of the pallet in different application scenarios. The game participants include system integration designers and user demand analysts. According to the needs of the battery system, the cooling system integration method is adjusted to achieve modularization of the pallet to adapt to electric vehicles and energy storage systems with different power densities; S7, the result of each game stage is transmitted to the next stage through the feedback mechanism, and global optimization is performed based on the adjustment of the game strategy at each stage. When the game evolution at each stage reaches the Nash equilibrium state, the system design solution is the optimal solution; Wherein, step S3 specifically includes the following sub-steps: S31, the tray designer selects a high-strength thick-wall design or a lightweight thin-wall design based on the mechanical strength requirements of the battery tray, and the material selector is responsible for selecting the appropriate material; S32, the pallet designer defines the stress σ(x, y, z) of the pallet under the action of external load by establishing a mechanical model of the pallet, where the stress σ(x, y, z) represents the stress borne by the pallet at different coordinate points x, y, z, and calculates the overall mechanical strength S of the pallet: ; Where S is the overall mechanical strength of the pallet, in N, and V is the volume of the pallet, in m 3 ; S33, the pallet designer selects the pallet wall thickness t through game optimization i and the elastic modulus E of the material i , the maximum stress σ of the tray max The following conditions are met: ； Among them, E i Indicates the elastic modulus of the material, in N / m 2 , t i is the wall thickness of the pallet in mm, f s is the safety factor; S34, the material selector selects different materials according to the strength requirements of the pallet and calculates the density ρ of the material. i and intensity σ i Carry out the evaluation and calculate the mass M of the material i : M i =ρ i •V; Among them, M i represents the mass of the pallet material, ρ i is the density of the material, V is the volume of the tray; S35, material selectors optimize between strength and weight through a multi-stage game model to achieve lightweight pallets while meeting strength requirements; S36, in each round of the game, the pallet designer adjusts the pallet design according to the performance and wall thickness of different materials, and optimizes the mechanical properties and weight of the pallet through the feedback mechanism to achieve a balance between lightness and strength; S37, finally through multiple rounds of iterative games, when the strategies of material selectors and pallet designers reach Nash equilibrium, the optimal structure and material configuration of the pallet are determined to achieve the best combination of pallet strength and weight; Optimal wall thickness of the pallet i * The trade-off between strength and lightness is calculated to meet the strength requirements of the pallet under maximum stress: ； Among them, t i * is the optimized tray wall thickness, σ max E is the maximum stress that the pallet bears during use. i is the elastic modulus of the material, f s is the safety factor; Optimal pallet quality i * Calculated by material density and pallet volume: M i * =ρ i ·V·t i * ; Among them, M i * is the mass of the optimized pallet, V is the volume of the pallet; Optimal stress distribution of the tray σ i * By calculating the stress of each part of the pallet, the material strength and wall thickness can meet the use requirements: ； Among them, σ i * is the optimized stress distribution.

2. A liquid-cooled circulating aluminum battery tray design method according to claim 1, characterized in that: The design method also includes designing a liquid cooling circulation system before system integration to ensure the circulation of the coolant and optimizing the flow rate and direction in real time through game theory.

3. The method for designing a liquid-cooled circulating aluminum battery tray according to claim 1, characterized in that: Step S2 specifically includes the following sub-steps: S21, establish a multi-stage evolutionary game model, the initial conditions of the game are expressed as: ； Among them, U initial represents the total energy loss at the beginning of the game, is the temperature gradient in region i, V i is the volume of region i, ΔT i is the temperature change in region i, C pi is the specific heat capacity of the coolant in region i, Q i is the coolant flow rate in region i, λ i is the thermal conductivity of the coolant in region i; S22, determining the thermal load distribution T(x, y, z) of the battery module, calculating the heat flux density q(x, y, z) of different regions of the battery module to obtain the thermal load distribution of each region, and inputting the thermal load distribution into the multi-stage evolutionary game model as a basis for the liquid cooling channel designer to adjust the channel layout and path; S23, through strategy optimization in a multi-stage evolutionary game model, the liquid cooling channel designer designs a multi-cooling channel path layout based on the heat load distribution T(x, y, z) to make the coolant flow evenly through each area of ​​the battery module, ensuring that the temperature difference ΔT in the area remains within the set range; S24, in each iteration of the game, the coolant performance selector adjusts the coolant flow rate Q and specific heat capacity C according to the heat flux density q(x, y, z) and cooling channel path length L(x, y, z) in different regions p , calculate the cooling efficiency η of each path: ； Where η represents the cooling efficiency, L(x, y, z) represents the length of the cooling channel, and ΔT represents the temperature difference in the area; S25, in each round of game, the cooling channel path length L in area i i (x,y,z) and coolant flow rate Q i and coolant specific heat C pi The adjustment is optimized through feedback mechanism; S26, through multiple iterative games, the liquid cooling channel layout and coolant parameters of each area are determined. When the Nash equilibrium is reached, the optimized cooling channel path and coolant flow configuration are: ； ； ； Among them, L i * represents the optimized cooling channel path length in region i, Q i * represents the optimized coolant flow in region i, C pi * represents the optimized specific heat capacity of the coolant in region i, λ i represents the thermal conductivity of the coolant in region i, Q i represents the coolant flow rate in area i, is the temperature gradient in region i, ΔT i represents the temperature difference in region i, T i,in -T i,out represents the inlet and outlet temperature difference of the coolant in area i, L i is the cooling channel length in region i, C pi is the specific heat capacity of the coolant in region i, V i is the volume of region i, and L is the total length of all liquid cooling channels.

4. The method for designing a liquid-cooled circulating aluminum battery tray according to claim 1, characterized in that: Step S4 specifically includes the following sub-steps: S41, multiple support points are set up inside the liquid cooling channel, and the arrangement of the support points is optimized through a multi-stage evolutionary game model; S42, establish the mechanical model of the liquid cooling channel, the bearing capacity of each support point is C s , its total bearing force F s Calculated as: ； Among them, C si is the bearing capacity of the ith support point, A si is the contact area of ​​the ith support point, F s is the total bearing capacity of all support points; S43, optimize the distribution position P of the support points si (x, y, z) and the number n, the optimized support point distribution must meet the following conditions: ； Among them, P si * represents the optimized support point position, λ is the thermal conductivity of the coolant, Q s is the coolant flow rate in the flow channel, is the temperature gradient in the flow channel, F s is the force on the support point at a specific location, P si represents the position vector of the support point, and Respectively represent F s and P si Find partial derivatives; S44, the optimization of the support point position is dynamically adjusted through the feedback mechanism to ensure that the flow efficiency of the coolant is not affected, and the flow efficiency of the coolant η is calculated. s : ； Among them, η s represents the flow efficiency, Q s is the coolant flow rate in the flow channel, L s is the flow channel length, C p is the specific heat capacity of the coolant, ΔT s is the temperature difference in the flow channel; S45, optimizes the number and position of support points through a multi-round and multi-stage evolutionary game model, so that the tray can achieve a balance between load-bearing capacity and cooling efficiency under different working conditions; S46, finally through iterative game, when the support point position and coolant flow reach Nash equilibrium, the optimal distribution of the support points is determined.

5. The method for designing a liquid-cooled circulating aluminum battery tray according to claim 1, characterized in that: Step S5 specifically includes the following sub-steps: S51, the manufacturing process engineer selects the manufacturing process of the pallet, and the production cost controller is responsible for optimizing the manufacturing cost, and determines the best process combination and cost control plan related to the pallet structure, material selection, support point arrangement and liquid cooling channel through game theory; S52, manufacturing process engineers select appropriate process methods based on material selection, support point layout and optimization, and the complexity of the liquid cooling channel, including riveting, laser welding, and modular design, and calculate the cost of each process. total Perform the calculation: ； Among them, C total is the total manufacturing cost, C ri , C wi , C mi are the costs of press riveting, laser welding and modular design, C m_mat is the cost of material selection, C s Cost of arranging support points; S53, manufacturing process engineers calculate the production efficiency E of the process based on the optimized tray material selection, support point layout and liquid cooling channel design. i , process complexity D i and material loss rate L i , and calculate the manufacturing efficiency η of the process combination c : ； Among them, η c represents the manufacturing efficiency of the process combination, E i is production efficiency, D i is the process complexity, L i is the material loss rate; S54, the production cost controller combines the material cost, support point layout and flow channel optimization factors, dynamically adjusts the balance between process cost and production efficiency through the feedback mechanism, optimizes production efficiency and cost while ensuring the tray strength, heat dissipation performance and load-bearing capacity, and the cost optimization goal is to minimize the total manufacturing cost C total While maximizing manufacturing efficiency η c : ； Among them, α is the weight coefficient, which is used to balance cost and efficiency; S55, through a multi-round and multi-stage evolutionary game model, determines the optimal process combination of press riveting, laser welding and modular design to maximize production efficiency and minimize material loss; S56, ultimately through iterative game, when process selection, material cost, support point layout and flow channel design reach Nash equilibrium, the optimal process combination is determined and applied to the mass production process of pallets.

6. The method for designing a liquid-cooled circulating aluminum battery tray according to claim 1, characterized in that: Step S6 specifically includes the following sub-steps: S61, the system integration designer designs the system integration method of the liquid cooling tray according to the power density requirements of the battery system, and the user demand analyst feeds back the user demand function U(x, y, z) according to the performance requirements of the battery system in different application scenarios; S62, the system integration designer shall calculate the power density P of the battery module according to the power density P of the battery module. d (x, y, z) and heat load distribution q(x, y, z), adjust the cooling system integration method of the liquid cooling tray modular design; S63, the user demand analyst inputs the user demand function U(x, y, z) of each scenario according to the demand for electric vehicles or energy storage systems in different application scenarios. The user demand function includes the power density demand P d (x,y,z), temperature limit T max and coolant flow requirement Q m , and use it as one of the input parameters of the system integrated multi-stage evolutionary game model; S64, the system integration designer dynamically adjusts the cooling system according to the user demand function U(x, y, z) and cooling efficiency, so that the liquid cooling tray can adapt to electric vehicles and energy storage systems with different power densities. The power density and cooling system matching degree of each module are calculated through a multi-stage evolutionary game model. m : ； Among them, K m is the matching degree of modular design, ΔT m is the battery module temperature difference; P d (x,y,z) is the power density, Q m is the coolant flow rate; C p is the specific heat capacity of the coolant; S65, adjusts the integration mode of the cooling system through the feedback mechanism. In each round of the game, the system integration designer and the user demand analyst optimize the system integration design according to the cooling efficiency, power density requirements and user demand function, so that the modular pallet can adapt to different application scenarios; S66, finally, through multiple rounds of iterative game, when the cooling system integration mode and power density requirements reach Nash equilibrium, the optimal modular integration solution of the liquid cooling tray is determined to maximize the cooling performance, power density adaptability and system integration efficiency of the battery tray in different application scenarios.

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