Cylindrical battery high-precision temperature prediction layered modeling method and system and medium

Abstract

The invention discloses a high-precision temperature prediction layered modeling method and system for a cylindrical battery and a medium. The method comprises the following steps: establishing a single-layer cylindrical battery model; on the basis of 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; the average error and the maximum error of the simulated temperature rise and the experimental temperature rise of the battery under different inner layer radiuses are counted, a genetic algorithm is constructed, and the optimal inner layer radius is obtained; layering continues to obtain a subsequent optimal layering radius, and when the error value of simulation and experiment of the next layer is larger than the error value of the current layer, layering is stopped, and a cylindrical battery layering model reflecting the actual temperature rise of the battery is obtained; and performing series-parallel connection on the determined cylindrical battery layering model to obtain a cylindrical battery pack. The method is suitable for different battery heat generation models, and a theoretical basis is provided for layering of the cylindrical battery; and the battery temperature prediction precision is improved, the detailed modeling time of the battery is greatly shortened, and the modeling efficiency is improved.

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 the enhancement of environmental protection awareness, the new energy vehicle industry has ushered in rapid development. This change has promoted the urgent demand for high-performance battery technology, especially cylindrical lithium-ion batteries, which have become the core components of new energy vehicle power systems due to their high energy density, long life and excellent charge and discharge performance. The wide application of cylindrical batteries is not limited to automobiles. They also show great potential in the field of energy storage. They are regarded as a reliable energy storage device and provide effective solutions for smart grids, home energy storage systems and renewable energy storage.

[0003] However, the performance and life of batteries are significantly affected by temperature. Too high or too low temperatures will have a negative impact on the electrochemical activity, thermal stability and safety of the battery. In extreme cases, overheating of the battery may even cause thermal runaway, leading to fire or explosion, posing a serious threat to personal safety and property. Therefore, ensuring the thermal safety of batteries is an important consideration in the design of new energy vehicles and energy storage systems.

[0004] Temperature prediction is an important means to ensure battery thermal safety and is crucial for the optimization of the battery management system (BMS). Through accurate temperature prediction, the working status of the battery can be monitored in real time, overheating can be effectively prevented, battery life can be extended, and the reliability and safety of the overall system can be improved. In addition, temperature prediction can also provide a scientific basis for the thermal management design of the battery and help develop more efficient cooling strategies to adapt to changes in thermal loads under different environments and operating conditions.

[0005] The present invention provides a modeling method for a cylindrical battery layered model with high-precision temperature prediction, which can more accurately simulate the temperature changes of the battery during the charging and discharging process. At the same time, for more complex cylindrical battery models, such as electrothermal coupling models and electrochemical thermal coupling models, layered processing can better explore the heat generation of each part of the battery. It provides a more reliable numerical model for the cylindrical battery thermal management system 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 to 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, specifically comprising the following steps:

[0009] Step 1: Establish a single-layer cylindrical battery model, keep 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, a double-layer cylindrical battery model with different inner diameters is established, and each double-layer cylindrical battery model is numerically simulated. When setting parameters, the inner and outer layers of the battery are treated as independent calculation domains for parameter setting;

[0011] 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 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 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;

[0013] 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 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: determining the geometric parameters, basic parameters, electrochemical parameters, battery material parameters and boundary conditions of the air domain of the battery model in sequence.

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

[0017] Furthermore, step 2 specifically includes:

[0018] Each layer of the battery is regarded 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;

[0019] For different inner radii involved, after the initial setting of the inner radius, an incremental method is used to design subsequent radius conditions.

[0020] Furthermore, in step 2, the double-layer cylindrical battery model with different inner diameters is established, including: 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 hierarchical 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. The reliability of the battery parameters is determined based on whether the error between the simulation results and the experimental results is less than or equal to the error threshold.

[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 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 a standard to write a genetic algorithm to obtain the optimal inner layer radius of the double-layer cylindrical battery;

[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 on which a computer program is stored, and the computer program implements the above modeling method when executed.

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

[0034] (1) The present invention provides a high-precision temperature prediction 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 model, electrothermal coupling model, and electrochemical thermal coupling model. The layered model used in combination with these battery models can not only accurately reflect the battery temperature change, but also reflect the cause of the battery temperature change through the characteristics of each battery model. For example, the electrothermal coupling model can reflect the current density of each layer of the battery, 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 is a top view of the battery layer in the present invention;

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

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

[0042] Figure 6 It is a schematic diagram comparing the temperature curves under different layering radii in the present invention with the 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 in the present invention;

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

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

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

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are 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: Establish a single-layer cylindrical battery model, keep 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. If the error between the simulation results and the 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: determining the geometric parameters, basic parameters, electrochemical parameters, battery material parameters and boundary conditions of the air domain of the battery model in sequence. The geometric parameters include battery radius and battery height, the basic parameters include voltage, battery capacity, current density, electrochemical parameters include initial lithium ion concentration, maximum lithium ion concentration, particle size, etc., battery material parameters include density, specific heat capacity, thermal conductivity, electrical conductivity, and boundary conditions of the air domain include temperature and convection 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, assuming 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 implementation of the present invention, step 2 specifically includes:

[0053] Step 201, each layer of the battery is regarded 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;

[0054] Step 202: After the initial setting of the inner radius r0, the subsequent radius conditions are designed by 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 implementation of the present invention, step three specifically includes:

[0057] Step 301, the average error MAE and the maximum error ME of the battery simulation temperature rise and the experimental temperature rise under different inner layer radii are counted, and the genetic algorithm is written based on this standard, wherein 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 calculation formulas for the average error and the maximum error are as follows:

[0058] 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 and step 3 is implemented by Matlab software, and 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 to 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 implementation 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] Resistance of series battery pack: 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 Fig.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. The reliability of the battery parameters is determined based on whether the error between the simulation results and the experimental results is less than or equal to the error threshold.

[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 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 a standard to write a genetic algorithm to obtain the optimal inner layer radius of the double-layer cylindrical battery;

[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] The third aspect of the present invention also provides a computer storage medium, on which a computer program is stored, and the computer program implements the above method when executed. The storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0096] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0097] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

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, keep 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, a double-layer cylindrical battery model with different inner diameters is established, and each double-layer cylindrical battery model is numerically simulated. When setting parameters, the inner and outer layers of the battery are treated as independent calculation domains for parameter setting; 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 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 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; 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 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 cylindrical battery high-precision temperature prediction hierarchical modeling method according to claim 1, characterized in that: Establishing a single-layer cylindrical battery model in step one 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 cylindrical battery high-precision temperature prediction hierarchical modeling method 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 cylindrical battery high-precision temperature prediction hierarchical modeling method according to claim 1, characterized in that: Step 2 specifically includes: Each layer of the battery is regarded 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; For different inner radii involved, after the initial setting of the inner radius, an incremental method is used to design subsequent radius conditions.

5. A cylindrical battery high-precision temperature prediction hierarchical modeling method according to claim 4, characterized in that: 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.

6. A cylindrical battery high-precision temperature prediction hierarchical modeling method according to claim 1, characterized in that: The formula for calculating the average error in step 3 is as follows: The maximum error calculation formula is as follows:

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

8. A cylindrical battery high-precision temperature prediction hierarchical modeling method 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 hierarchical modeling system for cylindrical batteries, characterized by: It includes a single-layer construction module, which is used to establish a single-layer cylindrical battery model. The boundary conditions are consistent with the experimental conditions. The reliability of the model is verified. The reliability of the battery parameters is determined based on whether the error between the simulation results and the experimental results is less than or equal to the error threshold. 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 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 a standard to write a genetic algorithm to obtain the optimal inner layer radius of the double-layer cylindrical battery; 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. 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. 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.

Images