A high-precision temperature prediction layered modeling method, system and medium for cylindrical batteries

Abstract

The present invention discloses a high-precision temperature prediction layered modeling method, system, and medium for cylindrical batteries, including: establishing a single-layer cylindrical battery model; based on the single-layer cylindrical battery model, establishing double-layer cylindrical battery models with different inner diameters, and performing numerical simulation on each double-layer cylindrical battery model; statistically analyzing the average error and maximum error between the simulated and experimental temperature rises of the batteries under different inner layer radii, constructing a genetic algorithm, and obtaining the optimal inner layer radius; continuing layering to obtain the subsequent optimal layering radius, and when the error value between the simulation and the experiment of the next layer is greater than the error value of the current layer, stopping layering to obtain a cylindrical battery layered model that reflects the actual temperature rise of the battery; and connecting the determined cylindrical battery layered models in series and parallel to obtain a cylindrical battery pack. The present invention is applicable to different battery heat generation models and provides a theoretical basis for cylindrical battery layering. While improving the accuracy of battery temperature prediction, it greatly reduces the time required for detailed battery modeling and improves modeling efficiency.

Description

Technical Field

[0001] The present invention belongs to the field of battery model design optimization, and in particular relates to a high-precision temperature prediction layered modeling method, system and medium for cylindrical batteries. Background Art

[0002] With the transformation of the global energy structure and growing awareness of environmental protection, the new energy vehicle industry has experienced rapid growth. This transformation has driven an urgent demand for high-performance battery technologies, particularly cylindrical lithium-ion batteries, which, due to their high energy density, long lifespan, and excellent charge and discharge performance, have become a core component of new energy vehicle powertrains. Cylindrical batteries are widely used beyond automotive applications and also demonstrate significant potential in the energy storage sector. They are considered a reliable energy storage device, providing an effective solution for smart grids, home energy storage systems, and renewable energy storage.

[0003] However, battery performance and lifespan are significantly affected by temperature. Excessively high or low temperatures can negatively impact the battery's electrochemical activity, thermal stability, and safety. In extreme cases, battery overheating can even trigger thermal runaway, leading to fire or explosion, posing a serious threat to personnel and property. Therefore, ensuring battery thermal safety is a key consideration in the design of new energy vehicles and energy storage systems.

[0004] Temperature prediction, as an important means of ensuring battery thermal safety, is crucial for optimizing battery management systems (BMS). Accurate temperature prediction enables real-time monitoring of battery operating conditions, effectively preventing overheating, extending battery life, and improving overall system reliability and safety. Furthermore, temperature prediction provides a scientific basis for battery thermal management design, helping develop more efficient cooling strategies to adapt to varying thermal loads in diverse environments and operating conditions.

[0005] This paper proposes a high-precision temperature prediction method for cylindrical battery layered models, enabling more accurate simulation of battery temperature changes during charging and discharging. Furthermore, for more complex cylindrical battery models, such as electrothermal and electrochemical-thermal coupling models, layered processing allows for better understanding of heat generation in various battery components. This provides a more reliable numerical model for cylindrical battery thermal management systems in new energy vehicles and energy storage systems. Summary of the Invention

[0006] The purpose of the present invention is to overcome the above problems existing in the prior art and provide a high-precision temperature prediction layered modeling method, system and medium for cylindrical batteries to accurately reflect the temperature changes of the battery during the charging and discharging process.

[0007] In order to achieve the above technical objectives and the above technical effects, the present invention is implemented through the following technical solutions:

[0008] A high-precision temperature prediction hierarchical modeling method for cylindrical batteries, comprising the following steps:

[0009] Step 1: Establish a single-layer cylindrical battery model with the boundary conditions consistent with the experimental conditions. Verify the reliability of the model and determine whether the battery parameters are reliable based on whether the error between the simulation results and the experimental results is less than or equal to the error threshold.

[0010] Step 2: Based on the single-layer cylindrical battery model, double-layer cylindrical battery models with different inner diameters are established, and numerical simulations are performed on each double-layer cylindrical battery model. When setting parameters, the inner and outer layers of the battery are treated as independent calculation domains for parameter setting;

[0011] Step 3: Calculate the average error and maximum error between the simulated and experimental temperature rises of the battery under different inner layer radii, and use this as a standard to write a genetic algorithm to obtain the optimal inner layer radius of the double-layer cylindrical battery;

[0012] Step 4: Fix the inner layer radius, analyze the case where the number of layers is three based on the double-layer cylindrical battery, repeat steps 2 and 3 to obtain the optimal layer radius of the three-layer cylindrical battery;

[0013] Step 5: Repeat the above steps to obtain a temperature prediction layered cylindrical battery model. When the error between the next layer of cylindrical batteries and the experiment is greater than the error between the current layer of cylindrical batteries and the experiment, stop layering to obtain a cylindrical battery layered model that reflects the actual temperature rise of the battery.

[0014] Step 6: Connect the determined cylindrical battery layered model in series and parallel through the busbar to obtain a cylindrical battery pack model. The specific parameters of the model are determined according to the actual series and parallel conditions.

[0015] Furthermore, establishing a single-layer cylindrical battery model in step one includes sequentially determining geometric parameters, basic parameters, electrochemical parameters, battery material parameters, and boundary conditions of the air domain of the battery model.

[0016] Furthermore, the error threshold in step 1, that is, if the error δ between the simulation result and the experimental result is ≤5%, the battery parameters are considered reliable.

[0017] Furthermore, step 2 specifically includes:

[0018] Each battery layer is considered as an independent calculation domain. In addition to the battery geometric parameters and material parameters, the total amount of parameters such as voltage and battery capacity should be kept unchanged to ensure the authenticity of the simulation;

[0019] For different inner radii involved, after the inner radius is initially set, the subsequent radius conditions are designed using an incremental method.

[0020] Furthermore, the establishment of the double-layer cylindrical battery model with different inner diameters in step 2 includes: assuming that the initial inner radius r0 = 0.1 mm, r n+1 =r n +0.2, where n is an integer.

[0021] Furthermore, the average error calculation formula in step 3 is as follows:

[0022] The maximum error calculation formula is as follows:

[0023] Furthermore, step three is implemented by Matlab software.

[0024] Furthermore, the specific steps implemented by Matlab software include: importing data, initializing variables, calculating errors, defining objective functions, setting parameters, iterative operations, and outputting results.

[0025] The present invention also provides a high-precision temperature prediction and layered modeling system for cylindrical batteries, comprising

[0026] The single-layer construction module is used to build a single-layer cylindrical battery model. The boundary conditions are consistent with the experimental conditions. The reliability of the model is verified by determining whether the error between the simulation results and the experimental results is less than or equal to the error threshold to determine whether the battery parameters are reliable.

[0027] The double-layer construction module is used to build double-layer cylindrical battery models with different inner diameters based on the single-layer cylindrical battery model, and perform numerical simulation on each double-layer cylindrical battery model. When setting parameters, the inner and outer layers of the battery are treated as independent calculation domains for parameter setting;

[0028] The error statistics module is used to calculate the average error and maximum error between the simulated and experimental temperature rises of batteries under different inner layer radii. Based on this, a genetic algorithm is written to obtain the optimal inner layer radius of double-layer cylindrical batteries.

[0029] The radius analysis module is used to fix the inner layer radius. Based on the double-layer cylindrical battery, the case where the number of delamination layers is three is analyzed. Steps 2 and 3 are repeated to obtain the optimal delamination radius of the three-layer cylindrical battery.

[0030] The model analysis module is used to repeat the above steps to obtain a temperature prediction layered cylindrical battery model. When the error value between the next layer of cylindrical batteries and the experiment is greater than the error value between the current layer of cylindrical batteries and the experiment, the layering is stopped to obtain a cylindrical battery layered model that reflects the actual temperature rise of the battery.

[0031] The series-parallel module is used to connect the determined cylindrical battery layered model in series and parallel through the busbar to obtain a cylindrical battery pack model. The specific parameters of the model are determined according to the actual series-parallel situation.

[0032] The present invention also provides a computer storage medium having a computer program stored thereon, wherein the computer program implements the above-mentioned modeling method when executed.

[0033] The beneficial effects of the present invention are:

[0034] (1) The present invention provides a high-precision temperature prediction and layered modeling method for cylindrical batteries, which provides a theoretical basis for the layering of cylindrical batteries; and while enabling the battery to have high-precision temperature prediction capabilities, it greatly reduces the time for detailed battery modeling and improves modeling efficiency.

[0035] (2) The method provided by the present invention can be used in a variety of cylindrical battery models, such as heat generation rate models, electrothermal coupling models, and electrochemical thermal coupling models. The layered model used in combination with these battery models can not only accurately reflect battery temperature changes, but also reflect the causes of battery temperature changes through the characteristics of each battery model. For example, the electrothermal coupling model can reflect the current density of each battery layer, and the electrochemical thermal coupling model can reflect the lithium ion concentration of each layer. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0037] Figure 1 is a flow chart of the method of the present invention;

[0038] Figure 2 It is a flow chart of the genetic algorithm in the present invention;

[0039] Figure 3 It is a top view of the battery layer in the present invention;

[0040] Figure 4 It is a layered cross-sectional view of the battery in the present invention;

[0041] Figure 5 is a schematic diagram of a battery pack in the present invention;

[0042] Figure 6 Schematic diagram comparing the temperature curves at different layer radii in the present invention with experimental results;

[0043] Figure 7 is a graph showing the average error and maximum error of the simulation data and the experimental data as a function of the inner radius;

[0044] Figure 8 This is a cloud diagram of the temperature rise curves of three double-layer cylindrical battery models in the present invention;

[0045] Figure 9It is the temperature cloud diagram of three double-layer cylindrical battery models in the present invention;

[0046] Figure 10 It is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0048] like Figure 1-9 A high-precision temperature prediction hierarchical modeling method for cylindrical batteries shown in the figure specifically includes the following steps:

[0049] Step 1: Build a single-layer cylindrical battery model, keeping the boundary conditions consistent with the experimental conditions. Verify the model's reliability by determining whether the battery parameters are reliable based on whether the error between the simulation and experimental results is less than or equal to the error threshold. If the error between the simulation and experimental results is δ ≤ 5%, the battery parameters are considered reliable.

[0050] As a specific embodiment of the present invention, establishing a single-layer cylindrical battery model includes: sequentially determining the geometric parameters, basic parameters, electrochemical parameters, battery material parameters, and boundary conditions of the air domain of the battery model. Geometric parameters include battery radius and battery height; basic parameters include voltage, battery capacity, and current density; electrochemical parameters include initial lithium ion concentration, maximum lithium ion concentration, and particle size; battery material parameters include density, specific heat capacity, thermal conductivity, and electrical conductivity; and boundary conditions of the air domain include temperature and convective heat transfer coefficient.

[0051] Step 2: Based on the single-layer cylindrical battery model, a double-layer cylindrical battery model with different inner diameters is established. Assume that the initial inner radius r0 = 0.1 mm, r n+1 =r n +0.2 (n=0, 1, 2, 3…), and perform numerical simulation on each double-layer cylindrical battery model. When setting parameters, the inner and outer layers of the battery are treated as independent calculation domains for parameter setting.

[0052] As a specific embodiment of the present invention, step 2 specifically includes:

[0053] Step 201: Each battery layer is considered as an independent calculation domain. In addition to the battery geometry parameters and material parameters, the total amount of parameters such as voltage and battery capacity should be kept unchanged to ensure the authenticity of the simulation.

[0054] Step 202: After the initial setting of the inner radius r0, the subsequent radius is designed using an incremental method, i.e., r n+1 =r n +0.2 (n=0, 1, 2, 3, ...) to ensure that every case is taken into account;

[0055] Step 3: Count the average error and maximum error between the simulated temperature rise and the experimental temperature rise of the battery under different inner layer radii, and use this as the standard to write a genetic algorithm to obtain the optimal inner layer radius of the double-layer cylindrical battery.

[0056] As a specific embodiment of the present invention, step three specifically includes:

[0057] Step 301: The average error (MAE) and maximum error (ME) of the simulated battery temperature rise and the experimental temperature rise under different inner layer radii are statistically analyzed, and a genetic algorithm is written based on this standard. The objective function of the genetic algorithm comprehensively considers the average error and the maximum error. In order to enable the layered cylindrical battery model to accurately reflect the temperature rise process of the actual battery, the average error is given priority. When the average error is not much different, the maximum error is comprehensively considered. The formulas for calculating the average error and the maximum error are as follows:

[0058] Mean Average Error (MAE):

[0059]

[0060] Where n is the total number of observations, y i is the ith simulation value, is the ith experimental value. represents the absolute value function.

[0061] Maximum error ME:

[0062]

[0063] The genetic algorithm involved in step 302 is implemented using Matlab software. Its main steps are: importing data, initializing variables, calculating errors, defining objective functions, setting parameters, iterating operations, and outputting results. The main code is:

[0064] Import data:

[0065] dataTable=readtable('duibi.xlsx','Sheet',1)

[0066] Initialize variables:

[0067] max_errors=zeros(size(dataTable,2)-1,1);

[0068] min_max_error=inf;

[0069] best_simulation_index=NaN;

[0070] Calculation error:

[0071] errors=abs(simulation_data-experimental_data);

[0072] Define the objective function:

[0073] poly_function=@(r,p)polyval(p,r);

[0074] Parameter settings:

[0075] [optimal_r,f_min]=ga(@(x)poly_function(x,p),1,[],[],[],[],lb,ub,[],options).

[0076] Step 4: Fix the inner layer radius, analyze the case where the number of stratification layers is three based on the double-layer cylindrical battery, repeat steps 2 and 3 to obtain the optimal stratification radius of the three-layer cylindrical battery.

[0077] Step 5: Repeat the above steps to obtain a temperature prediction layered cylindrical battery model. When the error value between the next layer of cylindrical batteries and the experiment is greater than the error value between the current layer of cylindrical batteries and the experiment, stop the layering and obtain a cylindrical battery layered model that reflects the actual temperature rise of the battery.

[0078] Step 6: Connect the determined cylindrical battery layered model in series and parallel through the busbar to obtain a cylindrical battery pack model. The specific parameters of the model are determined according to the actual series and parallel conditions.

[0079] As a specific embodiment of the present invention, step six specifically includes:

[0080] Step 601: If n batteries are connected in series, then

[0081] Voltage of series battery pack: V total =V1+V2+...+V n

[0082] Capacity of series battery pack: C total =C 单体

[0083] Series battery pack resistance: R total =R1+R2+...+R n

[0084] Step 602: If n batteries are connected in parallel, then

[0085] Parallel battery pack voltage: V total =V 单体

[0086] Parallel battery pack capacity: C total =C1+C2+...+C n

[0087] Parallel battery pack resistance: 1 / R total =1 / R1+1 / R2+...+1 / R n

[0088] like Figure 10 As shown, the second aspect of the present invention also provides a high-precision temperature prediction hierarchical modeling system for cylindrical batteries, comprising

[0089] The single-layer construction module is used to build a single-layer cylindrical battery model. The boundary conditions are consistent with the experimental conditions. The reliability of the model is verified by determining whether the error between the simulation results and the experimental results is less than or equal to the error threshold to determine whether the battery parameters are reliable.

[0090] The double-layer construction module is used to build double-layer cylindrical battery models with different inner diameters based on the single-layer cylindrical battery model, and perform numerical simulation on each double-layer cylindrical battery model. When setting parameters, the inner and outer layers of the battery are treated as independent calculation domains for parameter setting;

[0091] The error statistics module is used to calculate the average error and maximum error between the simulated and experimental temperature rises of batteries under different inner layer radii. Based on this, a genetic algorithm is written to obtain the optimal inner layer radius of double-layer cylindrical batteries.

[0092] The radius analysis module is used to fix the inner layer radius. Based on the double-layer cylindrical battery, the case where the number of delamination layers is three is analyzed. Steps 2 and 3 are repeated to obtain the optimal delamination radius of the three-layer cylindrical battery.

[0093] The model analysis module is used to repeat the above steps to obtain a temperature prediction layered cylindrical battery model. When the error value between the next layer of cylindrical batteries and the experiment is greater than the error value between the current layer of cylindrical batteries and the experiment, the layering is stopped to obtain a cylindrical battery layered model that reflects the actual temperature rise of the battery.

[0094] The series-parallel module is used to connect the determined cylindrical battery layered model in series and parallel through the busbar to obtain a cylindrical battery pack model. The specific parameters of the model are determined according to the actual series-parallel situation.

[0095] A third aspect of the present invention further provides a computer storage medium having a computer program stored thereon, which implements the above method when executed. The storage medium may include any medium capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0096] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0097] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. A high-precision temperature prediction hierarchical modeling method for cylindrical batteries, characterized in that: The specific steps include: Step 1: Establish a single-layer cylindrical battery model with the boundary conditions consistent with the experimental conditions. Verify the reliability of the model and determine whether the battery parameters are reliable based on whether the error between the simulation results and the experimental results is less than or equal to the error threshold. Step 2: Based on the single-layer cylindrical battery model, double-layer cylindrical battery models with different inner diameters are established, and numerical simulations are performed on each double-layer cylindrical battery model. When setting parameters, the inner and outer layers of the battery are treated as independent calculation domains for parameter setting; Step 3: Calculate the average error and maximum error between the simulated and experimental temperature rises of the battery under different inner layer radii, and use this as a standard to write a genetic algorithm to obtain the optimal inner layer radius of the double-layer cylindrical battery; Step 4: Fix the inner layer radius, analyze the case where the number of layers is three based on the double-layer cylindrical battery, repeat steps 2 and 3 to obtain the optimal layer radius of the three-layer cylindrical battery; Step 5: Repeat the above steps to obtain a temperature prediction layered cylindrical battery model. When the error between the next layer of cylindrical batteries and the experiment is greater than the error between the current layer of cylindrical batteries and the experiment, stop layering to obtain a cylindrical battery layered model that reflects the actual temperature rise of the battery. Step 6: Connect the determined cylindrical battery layered model in series and parallel through the busbar to obtain a cylindrical battery pack model. The specific parameters of the model are determined according to the actual series and parallel conditions.

2. A high-precision temperature prediction hierarchical modeling method for cylindrical batteries according to claim 1, characterized in that: Establishing a single-layer cylindrical battery model in step 1 includes: determining the geometric parameters, basic parameters, electrochemical parameters, battery material parameters and boundary conditions of the air domain of the battery model in sequence.

3. A high-precision temperature prediction hierarchical modeling method for cylindrical batteries according to claim 2, characterized in that: The error threshold in step 1, that is, if the error δ between the simulation result and the experimental result is ≤ 5%, the battery parameters are considered reliable.

4. A high-precision temperature prediction hierarchical modeling method for cylindrical batteries according to claim 1, characterized in that: Step 2 specifically includes: Each battery layer is considered as an independent calculation domain. In addition to the battery geometric parameters and material parameters, the total amount of voltage and battery capacity parameters should be kept unchanged to ensure the authenticity of the simulation; For different inner radii involved, after the inner radius is initially set, the subsequent radius conditions are designed using an incremental method.

5. A high-precision temperature prediction hierarchical modeling method for cylindrical batteries according to claim 4, characterized in that: The double-layer cylindrical battery model with different inner diameters is established in step 2. Assuming the initial inner radius r0 = 0.1 mm, r n+1 =r n +0.2, where n is an integer.

6. The high-precision temperature prediction hierarchical modeling method for cylindrical batteries according to claim 1, characterized in that: The formula for calculating the average error in step 3 is as follows: ; where n is the total number of observations is the ith simulation value, is the ith experimental value, represents the absolute value function; The maximum error calculation formula is as follows: .

7. The high-precision temperature prediction hierarchical modeling method for cylindrical batteries according to claim 6, characterized in that: Step three is implemented through Matlab software.

8. A high-precision temperature prediction hierarchical modeling method for cylindrical batteries according to claim 7, characterized in that: The specific steps implemented by Matlab software include: importing data, initializing variables, calculating errors, defining objective functions, setting parameters, iterative operations, and outputting results.

9. A high-precision temperature prediction and hierarchical modeling system for cylindrical batteries, characterized by: It includes a single-layer construction module, which is used to build a single-layer cylindrical battery model. The boundary conditions are consistent with the experimental conditions. The reliability of the model is verified by whether the error between the simulation results and the experimental results is less than or equal to the error threshold to determine whether the battery parameters are reliable; The double-layer construction module is used to build double-layer cylindrical battery models with different inner diameters based on the single-layer cylindrical battery model, and perform numerical simulation on each double-layer cylindrical battery model. When setting parameters, the inner and outer layers of the battery are treated as independent calculation domains for parameter setting; The error statistics module is used to calculate the average error and maximum error between the simulated and experimental temperature rises of batteries under different inner layer radii. Based on this, a genetic algorithm is written to obtain the optimal inner layer radius of double-layer cylindrical batteries. The radius analysis module is used to fix the inner layer radius. Based on the double-layer cylindrical battery, the case where the number of delamination layers is three is analyzed. The double-layer construction module and the error statistics module are repeatedly processed to obtain the optimal delamination radius of the three-layer cylindrical battery. The model analysis module is used to repeatedly process the single-layer construction module, the double-layer construction module, the error statistics module, and the radius analysis module to obtain a temperature prediction layered cylindrical battery model. When the error value between the next layer of cylindrical batteries and the experiment is greater than the error value between the current layer of cylindrical batteries and the experiment, the layering is stopped to obtain a cylindrical battery layered model that reflects the actual temperature rise of the battery. The series-parallel module is used to connect the determined cylindrical battery layered model in series and parallel through the busbar to obtain a cylindrical battery pack model. The specific parameters of the model are determined according to the actual series-parallel situation.

10. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the modeling method according to any one of claims 1 to 8 is implemented.

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