An optimization method and device for a heat dissipation system of a power battery

Abstract

The application provides an optimization method and device for a heat dissipation system of a power battery, the leader update strategy in the Ctenomycterus algorithm is used to improve the change mechanism of the position of the finder in the sparrow algorithm, and then the global ergodicity and early search capability of the sparrow algorithm are improved, the improved algorithm is used to optimize the structural parameters of the heat dissipation system of the power battery, the optimal structural parameter value of the heat dissipation system is obtained, and the temperature uniformity between the single batteries and the battery modules can be maintained while ensuring that each single battery in the battery pack works in a reasonable temperature range.

Description

Technical Field

[0001] This invention belongs to the field of thermal management technology for new energy powertrains, and relates to an optimization method and device for a heat dissipation system for power batteries. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Power batteries are a crucial component of new energy powertrains, significantly impacting key indicators such as driving range, lifespan, and safety. Battery temperature is a critical factor affecting battery life and performance; therefore, safe and efficient thermal management of battery packs is essential for improving battery pack operating characteristics, extending lifespan, and enhancing electric vehicle safety. Lithium-ion battery thermal management, based on the heating mechanism of lithium-ion batteries, designs an efficient battery pack cooling system to ensure that each individual cell within the pack operates within a reasonable temperature range while maintaining temperature uniformity among the cells and modules. Therefore, optimizing the battery pack cooling system structure is necessary to achieve optimal thermal management results.

[0004] Currently, existing technologies employ some intelligent algorithms to optimize heat dissipation systems, such as particle swarm optimization, ant colony optimization, and sparrow optimization. Compared to other algorithms, sparrow optimization has advantages such as fast convergence and high accuracy. However, its method for updating sparrow positions is lacking in balancing the relationship between global and local exploration, which may lead to premature convergence. In the later stages of iteration, population diversity may also decline rapidly, causing it to get trapped in local optima and making it difficult to find the optimal structural parameters of the heat dissipation system. Summary of the Invention

[0005] To address the aforementioned problems, this invention proposes an optimization method and apparatus for a power battery heat dissipation system. This invention utilizes the leader update strategy from the tunic group algorithm to improve the discoverer position change mechanism in the sparrow algorithm, thereby enhancing the global traversal and early-stage search capabilities of the sparrow algorithm. The improved algorithm optimizes the structural parameters of the power battery heat dissipation system, obtaining optimal values ​​for these parameters. This ensures that each individual cell within the battery pack operates within a reasonable temperature range while maintaining temperature uniformity between individual cells and battery modules.

[0006] According to some embodiments, the present invention adopts the following technical solution:

[0007] An optimization method for a heat dissipation system for a power battery includes the following steps:

[0008] Design the basic structure of the heat dissipation system for power batteries, and determine the structural parameters in the basic structure to be optimized;

[0009] The leader update strategy from the tunic group algorithm is incorporated into the discoverer position change mechanism from the sparrow algorithm.

[0010] Using the improved Sparrow Algorithm, the structural parameters of the determined power battery cooling system are optimized by taking the minimum maximum temperature difference of the power battery as the fitness function.

[0011] As an alternative implementation, the basic structure of the heat dissipation system for the power battery is a chain-lock type heat dissipation system structure, which includes battery sleeves arranged in an array to accommodate the batteries, a number of heat dissipation fins between adjacent battery sleeves, and phase change material filling the gaps between adjacent battery sleeves.

[0012] As an alternative implementation, the structural parameters include fin thickness, fin bending angle, and fin length.

[0013] As an alternative implementation, the specific process of incorporating the leader update strategy in the tunic swarm algorithm into the finder position change mechanism in the sparrow algorithm includes modifying the sparrow finder position update expression when the warning value is less than the safety value, so that it is based on the relevant parameters for balancing the global and local search capabilities of the tunic swarm, which are considered by the tunic swarm leader update strategy.

[0014] Furthermore, the formula for the discoverer's position in the improved Sparrow Algorithm is as follows:

[0015]

[0016] t represents the current iteration number, j represents the dimension of the variable being computed, and X represents the current iteration number. i,j This represents the position of the i-th sparrow in the j-th dimension. F represents the position of the first individual in dimension d. d Let u be the position of the food source in dimension d. b and l b The upper and lower bounds of the solution space, c2 and c3 are both random numbers between [0,1], c1((u b -l b c2+l b R2 is used to balance the global and local search capabilities of the tunicate swarm, where R2 is the warning value and ST is the safety value.

[0017] Furthermore, the expression for c1 is as follows:

[0018]

[0019] t represents the current iteration number, iter max It is the maximum number of iterations.

[0020] Furthermore, the fitness value in the Sparrow Algorithm is represented as follows:

[0021] F x =[f(x1),f(x2),…,f(x N )] T

[0022] f(x i )=[f(x i,1 ),f(x i,2 ),…,f(x i,d )]

[0023] Among them, F x Each value in the table represents the fitness value of a different individual.

[0024] A structural optimization device for a power battery heat dissipation system, comprising:

[0025] The structural parameter determination module is configured to design a basic structure for a heat dissipation system for a power battery and determine the structural parameters in the basic structure to be optimized.

[0026] The algorithm improvement module is configured to incorporate the leader update strategy from the tunic group algorithm into the finder position change mechanism from the sparrow algorithm.

[0027] The parameter optimization module is configured to use an improved sparrow algorithm, with the minimum maximum temperature difference of the power battery as the fitness function, to optimize the determined structural parameters of the power battery cooling system.

[0028] Furthermore, the fitness function is specifically as follows:

[0029] dtf(h1, ha1, hl1) = b1 * h1 3 -b2*ha1 3 -b3*hl1 3 -b4*h1 2 *ha1+b5*h1 2 *hl1+b6*ha1 2 *hl1+b7*h1*ha1*hl1

[0030] The range of values ​​for each variable is as follows:

[0031] The fin thickness h1 is 0.1-0.5 mm;

[0032] The fin bending angle ha1 is 10-50°;

[0033] The fin length hl1 is 5-10mm.

[0034] Among them, b i The correlation coefficient.

[0035] A heat dissipation system for power batteries is designed using the method described above.

[0036] A powertrain including the aforementioned cooling system for a power battery.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0038] This invention first constructs a chain-locked heat dissipation system structure for power batteries, selecting phase change materials for heat dissipation to form the power battery's heat dissipation system. Then, it uses the leader update strategy from the tunic group algorithm to improve the discoverer position change mechanism in the sparrow algorithm, thereby enhancing the global traversal and local search capabilities of the sparrow algorithm, resulting in a sparrow algorithm that integrates the tunic group algorithm. Using the minimum maximum temperature difference of the power battery as the fitness function, the fused algorithm optimizes the structural parameters of the power battery heat dissipation system, obtaining optimal heat dissipation system structural parameter values. This ensures the high efficiency of the heat dissipation system structure, guaranteeing that each individual cell within the battery pack operates within a reasonable temperature range while maintaining temperature uniformity between individual cells and battery modules.

[0039] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0040] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0041] Figure 1 This is a flowchart illustrating the process in this embodiment;

[0042] Figure 2 This is a structural diagram of the chain-lock type heat dissipation system in this embodiment. Detailed Implementation

[0043] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0044] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0045] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0046] Example 1

[0047] A method for structural optimization of a heat dissipation system for a power battery includes the following steps:

[0048] The constructed heat dissipation system for the power battery involves embedding the battery within a prefabricated battery sleeve, which is entirely made of aluminum, with phase change material filled into the gaps of the heat sink. For example... Figure 2 As shown, the heat sink in this embodiment is a chain-lock type heat sink structure. This structure has two rows of heat dissipation fins arranged laterally in each row of batteries, and the heat dissipation fins are symmetrically arranged along the battery axis. The structural design parameters include the fin thickness h1, fin bending angle ha1, and fin length hl1 in the internal structural network of the heat dissipation system.

[0049] In this embodiment, a heat dissipation material is selected for the power battery cooling system. The selected heat dissipation material is a composite of paraffin wax and nano-sized Fe2O3. The power battery cooling system is then obtained.

[0050] The Sparrow Algorithm is improved using the tunic group algorithm. The Sparrow Algorithm set matrix is ​​as follows:

[0051] X = [x1, x2, ..., x N ] T ,x i =[x i,1 ,x i,2 ,…,x i,d ]

[0052] Where N is the number of sparrows, i is the current iteration number, and d is the dimension of the variable.

[0053] The fitness value of the Sparrow Algorithm is represented as follows:

[0054] F x =[f(x1),f(x2),…,f(x N )] T

[0055] f(x i )=[f(x i,1 ),f(x i,2 ),…,f(x i,d )]

[0056] Among them, F x Each value in it represents the fitness value of a different individual.

[0057] The sparrows with better fitness values are regarded as discoverers, responsible for providing the foraging direction for the followers. The position update method of the discoverers is as follows:

[0058]

[0059] Where: t represents the current iteration number, j represents the variable dimension being currently calculated, X i,j represents the position of the i-th sparrow in the j-th dimension. iter max is the maximum number of iterations, α ∈ (0, 1] is a random number, R2 ∈ [0, 1], ST ∈ [0.5, 1] represent the early warning value and the safety value in turn. Q is a random number obeying the normal distribution of [0, 1]. L is a 1×d matrix with each element being 1. When R2 < ST, it means there are no natural enemies nearby, and the discoverers conduct extensive searches. If R2 ≥ ST, this means that some sparrows have detected natural enemies, and the entire population needs to move to other safe areas.

[0060] The update strategy of the leader of the salp swarm is as follows:

[0061]

[0062] In the formula: represents the position of the first individual in d dimensions, F d is the position of the food source in d dimensions, u b and l b are the upper and lower limits of the solution space, and both c2 and c3 are random numbers between [0, 1]. The c1((u b -l b )c2 + l b ) in the formula is used to balance the global and local search capabilities of the salp swarm and improve the search range in the early stage of the algorithm. The expression of c1 is as follows:

[0063]

[0064] Where: t represents the current iteration number, iter max is the maximum number of iterations,

[0065] Furthermore, the salp swarm algorithm is used to improve the sparrow algorithm. When the early warning value R2 is less than the safety value ST in the sparrow algorithm, it is easy to lose the optimal solution at non-zero points. To increase the global traversability and the search ability in the early stage, the update strategy of the leader in the salp swarm algorithm is used to improve the change mechanism of the discoverer's position.

[0066] The formula for the position of the improved sparrow discoverer is as follows:

[0067]

[0068] This resulted in an improvement of the Sparrow Algorithm by the Sea Coleoides Group Algorithm, yielding a Sparrow Algorithm that incorporates the Sea Coleoides Group Algorithm.

[0069] Using the sparrow algorithm derived from the fused tunicate group, the structural parameters of the constructed power battery cooling system are optimized with the minimum maximum temperature difference of the power battery as the fitness function.

[0070] dtf(h1,ha1,hl1)=-253*h1 3 -0.000073*ha1 3 -0.0011*hl1 3 -1.4*h1 2 *ha1+11.5*h1 2 *hl1+0.00042*ha1 2 *hl1+0.0081h1*ha1*hl1

[0071] The range of values ​​for the relevant parameters in the fitness function described above is as follows:

[0072] The fin thickness h1 is 0.1-0.5 mm.

[0073] The fin bending angle ha1 is 10-50°

[0074] The fin length hl1 is 5-10mm.

[0075] The values ​​of the above parameters are for illustrative purposes only. In other embodiments, they may be adjusted according to specific circumstances.

[0076] Other processes not detailed in detail can be carried out using existing technologies and will not be elaborated here.

[0077] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. An optimization method for a heat dissipation system of a power battery, characterized in that, Includes the following steps: Design the basic structure of the heat dissipation system for power batteries, and determine the structural parameters in the basic structure to be optimized; The leader update strategy from the tunic group algorithm is incorporated into the discoverer position change mechanism from the sparrow algorithm. Using the improved Sparrow Algorithm, the maximum temperature difference of the power battery is minimized as the fitness function to optimize the structural parameters of the determined power battery cooling system. The formula for the discoverer's position in the improved Sparrow Algorithm is as follows: Indicates the current iteration number. This indicates the dimension of the variable currently being calculated. Indicates the first The sparrow in the first The position of the dimension Representing the first individual in The position of the dimension For food source The position of the dimension and These are the upper and lower limits of the solution space. and All are random numbers between [0,1]. Used to balance the global and local search capabilities of tunicate swarms. This is a warning value. This is a safe value; The expression is as follows: Indicates the current iteration number. It is the maximum number of iterations; The fitness value in the Sparrow Algorithm is represented as follows: in, Each value in the table represents the fitness value of a different individual.

2. The optimization method for a heat dissipation system for a power battery as described in claim 1, characterized in that, The basic structure of the heat dissipation system for the power battery is a chain-lock type heat dissipation system structure, which includes battery sleeves arranged in an array to accommodate the batteries, several heat dissipation fins between adjacent battery sleeves, and phase change material filling the gaps between adjacent battery sleeves.

3. An optimization method for a heat dissipation system for a power battery as described in claim 1 or 2, characterized in that, The structural parameters include fin thickness, fin bending angle, and fin length.

4. The optimization method for a heat dissipation system for a power battery as described in claim 1, characterized in that, The specific process of incorporating the leader update strategy from the tunic swarm algorithm into the finder position change mechanism in the sparrow algorithm involves modifying the sparrow finder position update expression when the warning value is less than the safety value, so that it considers relevant parameters for balancing the global and local search capabilities of the tunic swarm based on the update strategy of the tunic swarm leader.

5. A structural optimization device for a power battery cooling system, using the method described in any one of claims 1-4, characterized in that, include: The structural parameter determination module is configured to design a basic structure for a heat dissipation system for a power battery and determine the structural parameters in the basic structure to be optimized. The algorithm improvement module is configured to incorporate the leader update strategy from the tunic group algorithm into the finder position change mechanism from the sparrow algorithm. The parameter optimization module is configured to use an improved sparrow algorithm, with the minimum maximum temperature difference of the power battery as the fitness function, to optimize the determined structural parameters of the power battery cooling system.

6. The structural optimization device for a power battery heat dissipation system as described in claim 5, characterized in that, The fitness function is specifically: The range of values ​​for each variable is as follows: Fin thickness It is 0.1-0.5mm; Fin bending angle 10-50°; fin length It is 5-10mm; b i Let be the correlation coefficient, and i be 1, 2, ..., 7.

7. A heat dissipation system for a power battery, characterized in that, It is designed by any one of the methods of claims 1-4 or the apparatus of claim 5 or 6.

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