Multi-disciplinary design optimization method for lithium battery thermal management system

Abstract

The invention provides a lithium battery thermal management system multidisciplinary design optimization method based on a structure self-adaptive digital twinning model, which comprises the following steps: carrying out multidisciplinary decomposition on BTMS by utilizing a multidisciplinary hierarchical decoupling method, and determining key design variables influencing BTMS performance; based on the determined BTMS key design variables, constructing a BTMS structure adaptive digital twinborn model set; based on the constructed BTMS structure adaptive digital twinning model set, multidisciplinary analysis is carried out to ensure that multidisciplinary coupling variables meet the multidisciplinary coupling relationship among the variables, and the interdisciplinary consistency of values is ensured; if the multi-disciplinary consistency requirement is not met, multi-disciplinary analysis iterative calculation of the next cycle is carried out; if the consistency requirement is met, an optimizer is used for solving the multidisciplinary design optimization problem; if the optimization solution iteration process is converged, a multidisciplinary design optimal solution is obtained; and if the optimization solution iteration process is not convergent, the design variables are updated until a multidisciplinary design optimal solution is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium battery management systems for electric vehicles, and in particular to a multidisciplinary design optimization method for a lithium battery thermal management system based on a structurally adaptive digital twin model. Background Art

[0002] Lithium batteries have become the preferred core power unit for electric vehicles, electric ships and other equipment due to their high energy density, long cycle life and relatively low cost. However, the optimal operating temperature range of lithium batteries is relatively narrow (20-35°C). Such a compact internal structure is very likely to cause thermal runaway risks such as overheating and inconsistent temperatures inside the battery module when the lithium battery continuously generates heat during charging and discharging. The lithium battery thermal management system (BTMS) is a complex integrated system that uses specific heat dissipation and temperature control strategies to ensure that the lithium battery pack is in the optimal thermal state. It involves thermodynamics (heat transfer between battery cells and cooling medium), fluid dynamics (interaction between cooling medium and cooling channel), structural mechanics, etc., which poses a challenge to improving the thermal management performance of BTMS.

[0003] For the complex coupling system of BTMS, the existing design methods generally adopt the "discipline island" approach, which lacks a high-precision system model and ignores the complex coupling between different disciplines, resulting in mismatch of multidisciplinary variables in the system and poor comprehensive operation performance of the complex system. Digital twin is a digital technology that can realize the interactive integration of physical systems and virtual worlds in real time and accurately. Any information that can be obtained from the real physical system can be obtained from its corresponding twin data in real time and accurately. Therefore, digital twin technology provides a potential solution for multidisciplinary design optimization (MDO) of complex systems.

[0004] However, digital twin technology is currently mainly used in real-time monitoring, operation and maintenance, fault warning and other aspects of complex systems, and has not been applied to multidisciplinary design optimization of complex coupled systems. The reason is that, on the one hand, the current digital twin technology lacks the ability to adapt to the potential structural changes of physical systems, and on the other hand, the current multidisciplinary collaborative design method lacks an effective solution framework for digital twin models. Summary of the invention

[0005] According to the technical problems raised above, a multidisciplinary design optimization (MDO) method for a lithium battery thermal management system (Battery Thermal Management System, BTMS) based on a structural adaptive digital twin model is provided. Aiming at the shortcoming that the current digital twin technology lacks the ability to adapt to the potential structural changes of the physical system, the present invention proposes a method for fixing the number of grids between partitions for variable structures; in view of the problem that the current MDO method lacks an effective solution framework for digital twin models, a multidisciplinary design optimization framework based on digital twin models is proposed. Through the multidisciplinary design optimization method for lithium battery BTMS based on a structural adaptive digital twin model proposed by the present invention, the optimal design scheme of the system can be found from a global perspective with high precision, which effectively improves the thermal management performance of the lithium battery BTMS and extends the service life of the lithium battery.

[0006] The technical means adopted by the present invention are as follows:

[0007] A multidisciplinary design optimization method for a lithium battery thermal management system, comprising:

[0008] S1. Use the multidisciplinary hierarchical decoupling method to perform multidisciplinary decomposition of BTMS and determine the key design variables that affect the performance of BTMS;

[0009] S2. Based on the determined BTMS key design variables, a set of BTMS structural adaptive digital twin models is constructed;

[0010] S3. Relying on the constructed BTMS structure adaptive digital twin model set, multidisciplinary analysis is carried out to ensure that the multidisciplinary coupling variables satisfy the multidisciplinary coupling relationship between the variables and ensure the interdisciplinary consistency of the values;

[0011] S4. If the multidisciplinary consistency requirement is not met, return to step S3 to perform the next cycle of multidisciplinary analysis and iterative calculation; if the consistency requirement is met, use the optimizer to solve the multidisciplinary design optimization problem;

[0012] S5. If the optimization solution iteration process converges, the optimal solution of the multidisciplinary design is obtained; if the optimization solution iteration process does not converge, return to step S1 to update the design variables until the optimal solution of the multidisciplinary design is obtained.

[0013] Furthermore, step S1 specifically includes:

[0014] S11. The BTMS is divided into three core subsystems, including the gas supply system, battery cells and battery modules, using a multidisciplinary hierarchical decomposition method. Each subsystem belongs to a different discipline. Battery life is a key performance indicator for determining battery performance. In the multidisciplinary design optimization of BTMS, battery life is treated as an important virtual subsystem (sub-discipline), which coordinates the multiple disciplines included in the above BTMS to achieve the best thermal management performance of lithium battery BTMS.

[0015] S12. Determine the cooling air mass flow rate based on the principles of thermodynamics and fluid dynamics Battery cell surface heat flux And the battery cell spacing b is used as the design variable, the design variables are:

[0016]

[0017] Further, step S2 specifically includes:

[0018] S21, using the method of fixing the number of grids between partitions for variable structures to divide the BTMS fluid domain grid;

[0019] S22, establish BTMS computational fluid dynamics model;

[0020] S23. Build a BTMS digital twin model.

[0021] Furthermore, step S21 specifically includes:

[0022] S211. For the structure that needs to be designed during the optimization process, or the structure whose size of the system itself changes, the structure corresponding to each changed structural parameter is processed into a separate partition; for each structural partition, a fixed number of grids are evenly divided, so as to determine the order of nodes in each structural partition;

[0023] S212. In the subsequent experimental design process, the variable structure parameters will change each time the simulation calculation is analyzed, but the number and order of the grids in each structural interval remain unchanged, and only the size of the grid itself changes; based on this, in the subsequent experimental design process, the order of the grid nodes of all variable structures remains unified, so the system performance index vector obtained by the simulation calculation is combined with the node order data to construct the subsequent variable structure digital twin model;

[0024] S213. For lithium battery BTMS, the battery cell spacing is regarded as a structure to be designed, and each battery cell spacing is processed into a separate structural partition. In each battery cell spacing structural partition, the number of nodes in the horizontal and vertical directions of the battery cell spacing is fixed, and does not change with the structural changes in the experimental design, but only the grid size changes.

[0025] Further, step S22 specifically includes:

[0026] S221. According to the actual working conditions, the cooling air temperature is set to room temperature;

[0027] S222. Select cooling air mass flow rate based on thermodynamics and fluid dynamics Heat flux on battery cell surface and the battery cell spacing b as the input of the twin model;

[0028] S223, determine the BTMS inlet and outlet pressure drop Δp, the BTMS internal maximum temperature difference ΔT and the BTMS overall average temperature T A As three BTMS performance indicators;

[0029] S224. In the computational fluid dynamics calculation process, the heat transfer process is taken into account, the k-ξ turbulence model and SIMPLEC method are used for solution, and the second-order upwind algorithm is used to process the pressure, density and momentum equations; the overall pressure and temperature distribution of BTMS is obtained by calculation, that is, the thermal state of each grid node in BTMS can be accurately captured.

[0030] Further, step S23 specifically includes:

[0031] S231. Use the K nearest neighbor algorithm to realize the feature mapping of spatial data, and then use the radial basis function neural network based on the node temperature data to establish the working temperature distribution model of the BTMS overall fluid domain, so as to realize the factual mapping relationship between the BTMS digital twin and the physical model;

[0032] S232, setting a data acquisition system in the real physical model to collect working status data of the real physical model in real time;

[0033] S233, performing noise reduction processing on the collected working status data, and feeding it back to the digital twin through the communication system, and then using Unity to realize real-time dynamic visualization of the working performance of the lithium battery BTMS, and completing the construction of the digital twin model of the lithium battery BTMS;

[0034] S234. The accuracy of the constructed BTMS digital twin model was verified using the mean absolute error and complex correlation coefficient indicators. The mean absolute error values ​​were concentrated below 0.2, and the complex correlation coefficient values ​​were mainly concentrated between 0.94 and 0.98, indicating that the accuracy of the established lithium battery BTMS thermal performance digital twin model met the requirements.

[0035] Furthermore, in step S4, if the consistency requirement is met, the multidisciplinary design optimization problem is solved using an optimizer, specifically including:

[0036] S41. Determine the optimization goal: By analyzing the factors that affect the performance and life of lithium batteries, determine the minimum cooling system inlet and outlet pressure Δp, the minimum maximum temperature difference ΔT and the minimum overall temperature T A The three BTMS thermal management performance indicators are used as optimization targets, and maximizing the lithium battery life is determined as one of the MDO optimization targets; minimizing the fan's real-time maximum power is used as one of the MDO optimization targets, and minimizing the BTMS installation volume is processed as one of the MDO model's optimization targets;

[0037] S42, the multi-objective optimization problem is transformed into a single-objective optimization problem by multiplying several single objectives, and different weights are assigned to each objective according to the expert parameters in the process; in addition, in the MDO process, objectives and constraints can be converted to each other, and the optimization objective in one subsystem can be used as a constraint in another subsystem, so the inlet and outlet pressure Δp and the average temperature T inside the lithium battery module are combined. A After being processed into MDO constraints, the MDO objective function for BTMS thermal management performance is constructed as follows:

[0038]

[0039] Among them, λ i is the single target weight;

[0040] S43. Based on the requirements of different disciplines for physical structure and performance, involving fluid dynamics, thermodynamics, and structure, the MDO constraints for BTMS are finally determined as follows:

[0041]

[0042] S44. The battery provides power to the cooling fan and the vehicle at the same time, so the heat generated by the battery unit is divided into two parts, as follows:

[0043]

[0044] In the formula, ξ represents the heat generation coefficient; η represents the proportional coefficient of fan heat generation; A represents the surface area of ​​the battery unit; ρ represents the air density;

[0045] S45. Combining the above steps S41 to S44, construct an MDO model of BTMS thermal management performance as follows:

[0046]

[0047] In the formula, f g (·) represents the BTMS digital twin model.

[0048] Compared with the prior art, the present invention has the following advantages:

[0049] 1. The present invention provides a multidisciplinary design optimization method for a lithium battery thermal management system based on a structurally adaptive digital twin model, and proposes a method for fixing the number of grid partitions for variable structures, which solves the problem that the current digital twin technology lacks the ability to adapt to potential structural changes in physical systems.

[0050] 2. The present invention provides a multidisciplinary design optimization method for a lithium battery thermal management system based on a structural adaptive digital twin model, and proposes a multidisciplinary design optimization framework based on a digital twin model, which solves the problem that the current MDO method lacks an effective solution framework for the digital twin model.

[0051] 3. The multidisciplinary design optimization method of the lithium battery thermal management system based on the structural adaptive digital twin model provided by the present invention can find the optimal design scheme of the system from a global perspective with high precision, effectively improve the thermal management performance of the lithium battery BTMS, and extend the service life of the lithium battery.

[0052] 4. The multidisciplinary design optimization method of a lithium battery thermal management system based on a structural adaptive digital twin model provided by the present invention can lay a technical foundation for the collaborative design of a class of complex coupled systems to improve the operating stability and safety of the system.

[0053] Based on the above reasons, the present invention can be widely promoted in the fields of electric vehicle lithium battery management systems and the like. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0055] Figure 1 This is a flow chart of the multidisciplinary design optimization method of the lithium battery thermal management system based on the structural adaptive digital twin model of the present invention.

[0056] Figure 2 A schematic diagram of the air-cooled BTMS structure provided in an embodiment of the present invention.

[0057] Figure 3 A multi-disciplinary hierarchical decomposition block diagram of BTMS provided in an embodiment of the present invention.

[0058] Figure 4 A schematic diagram of a method for fixing the number of grids between partitions for a variable structure provided by an embodiment of the present invention.

[0059] Figure 5A schematic diagram of the battery cell spacing structure partitioning provided in an embodiment of the present invention.

[0060] Figure 6 A theoretical framework diagram of the battery BTMS thermal performance digital twin model provided in an embodiment of the present invention.

[0061] Figure 7 Schematic diagram of the lithium battery BTMS digital twin model provided in an embodiment of the present invention.

[0062] Figure 8 Iteration diagram of the time history process of MDO optimization solution provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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 should fall within the scope of protection of the present invention.

[0064] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0065] like Figure 1 As shown, the present invention provides a flow chart of a multidisciplinary design optimization method for a lithium battery thermal management system based on a structural adaptive digital twin model, including:

[0066] S1. Use the multidisciplinary hierarchical decoupling method to perform multidisciplinary decomposition of BTMS and determine the key design variables that affect the performance of BTMS;

[0067] S2. Based on the determined BTMS key design variables, a set of BTMS structural adaptive digital twin models is constructed;

[0068] S3. Relying on the constructed BTMS structure adaptive digital twin model set, multidisciplinary analysis is carried out to ensure that the multidisciplinary coupling variables satisfy the multidisciplinary coupling relationship between the variables and ensure the interdisciplinary consistency of the values;

[0069] S4. If the multidisciplinary consistency requirement is not met, return to step S3 to perform the next cycle of multidisciplinary analysis and iterative calculation; if the consistency requirement is met, use the optimizer to solve the multidisciplinary design optimization problem;

[0070] S5. If the optimization solution iteration process converges, the optimal solution of the multidisciplinary design is obtained; if the optimization solution iteration process does not converge, return to step S1 to update the design variables until the optimal solution of the multidisciplinary design is obtained.

[0071] In the specific implementation, as a preferred embodiment of the present invention, the present embodiment takes the air-cooled BTMS as the research object to illustrate the multidisciplinary design optimization method of lithium battery BTMS based on the structure adaptive digital twin model provided by the present invention. Figure 2 As shown, it contains 10 lithium-ion battery cells and an air cooling system, where the size of each battery cell is 16mm×150mm×65mm. The battery cells are arranged vertically at equal intervals inside the BTMS to form air cooling channels of the same size. The BTMS is equipped with a cooling fan to provide cooling air. The cooling air enters from the inlet on one side and flows out from the outlet on the same side, thereby taking away the heat of the lithium battery to improve the thermal environment of the battery module. The flow channel size of the air inlet and outlet is 10mm.

[0072] In specific implementation, as a preferred embodiment of the present invention, step S1 specifically includes:

[0073] S11. The BTMS is divided into three core subsystems using a multidisciplinary hierarchical decomposition method, including the gas supply system, battery cells and battery modules. Each subsystem belongs to a different discipline, such as the battery module belongs to thermodynamics, fluid dynamics and structural science. Battery life is a key performance indicator of battery performance in BTMS design and actual engineering evaluation. Therefore, in the multidisciplinary design optimization of BTMS, battery life is treated as an important virtual subsystem (sub-discipline), such as Figure 3 As shown. The above BTMS includes multiple disciplines to achieve the best thermal management performance of lithium battery BTMS, so as to improve the working performance and life of lithium battery;

[0074] S12. Determine the cooling air mass flow rate based on the principles of thermodynamics and fluid dynamics Battery cell surface heat flux And the battery cell spacing b is used as the design variable, the design variables are:

[0075]

[0076] In this embodiment, the key design variables of BTMS are determined from different multidisciplinary principles for the above optimization objectives. For air-cooled BTMS, flowing air is used as a heat transfer medium. Therefore, when the temperature of the battery module reaches equilibrium, the heat generated by the battery module is equal to the heat carried away by the flowing air. Based on the principles of thermodynamics and fluid dynamics, in order to evaluate the thermal management performance of BTMS, three performance indicators are finally selected, namely the average temperature of the battery module, the maximum temperature difference of the battery module, and the pressure drop at the inlet and outlet of the air flow channel. Correspondingly, three key design variables are selected, including the mass flow rate of cooling air (kg / s), heat flux on the battery cell surface (W / m 2 ) and the cell spacing b (mm). Since the Latin hypercube experimental design method has a sample memory function and can avoid repeated sampling of sample points that have already appeared, the embodiment of the present invention adopts the Latin hypercube sampling method to calculate the mass flow rate of cooling air. (kg / s), heat flux on the battery cell surface (W / m 2 ) and the cell spacing b (mm) are used for experimental design, and the experimental design training data is determined to be 300. According to the set lithium battery BTMS system structure and performance requirements, the value ranges of each design variable in the corresponding experimental design are shown in Table 1.

[0077] Table 1 Range of parameters to be optimized

[0078]

[0079] In specific implementation, as a preferred embodiment of the present invention, step S2 specifically includes:

[0080] S21, using the method of fixing the number of grids between partitions for variable structures to divide the BTMS fluid domain grid;

[0081] S22, establish BTMS computational fluid dynamics model;

[0082] S23. Build a BTMS digital twin model.

[0083] In specific implementation, as a preferred embodiment of the present invention, high-fidelity simulation analysis and calculation is the key to ensure that the BTMS twin model maps the actual physical model with high precision. In view of the shortcomings of the current digital twin technology that lacks the ability to adapt to the potential structural changes of the physical system, this patent invents a method for fixing the number of grids between partitions for variable structures. The specific steps of the method for fixing the number of grids between partitions for variable structures are as follows: Figure 4As shown, step S21 specifically includes:

[0084] S211. For the structure that needs to be designed during the optimization process, or the structure whose size of the system itself changes, the structure corresponding to each changed structural parameter is processed into a separate partition; for each structural partition, a fixed number of grids are evenly divided, so as to determine the order of nodes in each structural partition;

[0085] S212. In the subsequent experimental design process, the variable structure parameters will change each time the simulation calculation is analyzed, but the number and order of the grids in each structural interval remain unchanged, and only the size of the grid itself changes; based on this, in the subsequent experimental design process, the order of the grid nodes of all variable structures remains unified, so the system performance index vector obtained by the simulation calculation is combined with the node order data to construct the subsequent variable structure digital twin model;

[0086] S213. For lithium battery BTMS, the battery cell spacing (i.e., the cooling air flow channel size) plays a key role in the thermal performance of the lithium battery pack. Therefore, the battery cell spacing is regarded as a structure to be designed, and each battery cell spacing is further processed into a separate structural partition, such as Figure 5 In each battery cell spacing structure partition, the number of nodes in the horizontal and vertical directions of the battery cell spacing is fixed, and does not change with the structural changes in the experimental design, but only the grid size changes.

[0087] In specific implementation, as a preferred embodiment of the present invention, step S22 specifically includes:

[0088] S221. According to the actual working conditions, the cooling air temperature is set to room temperature 20°C;

[0089] S222. Select cooling air mass flow rate based on thermodynamics and fluid dynamics Heat flux on battery cell surface and the battery cell spacing b as the input of the twin model;

[0090] S223, determine the BTMS inlet and outlet pressure drop Δp (Pa), the maximum temperature difference ΔT (°C) inside the BTMS and the overall average temperature T of the BTMS A (℃) as three BTMS performance indicators;

[0091] S224. In the computational fluid dynamics calculation process, the heat transfer process is taken into account, the k-ξ turbulence model and SIMPLEC method are used for solution, and the second-order upwind algorithm is used to process the pressure, density and momentum equations; the overall pressure and temperature distribution of BTMS is obtained by calculation, that is, the thermal state of each grid node in BTMS can be accurately captured, thereby laying a data foundation for the construction of the digital twin model of BTMS thermal management performance.

[0092] In specific implementation, as a preferred embodiment of the present invention, step S23 specifically includes:

[0093] S231. The present invention constructs a theoretical framework of a digital twin model of thermal performance of a lithium battery BTMS. Figure 6 As shown in the figure, the K-Nearest Neighbors (KNN) algorithm is used to realize the feature mapping of spatial data, and then the radial basis function neural network is used to establish the working temperature distribution model of the BTMS overall fluid domain based on the node temperature data, realizing the factual mapping relationship between the BTMS digital twin and the physical model;

[0094] S232, setting a data acquisition system in the real physical model, including a temperature sensor and an air pressure sensor, to collect working status data of the real physical model in real time;

[0095] S233, the collected working status data is subjected to noise reduction processing, and is fed back to the digital twin through the communication system, and then Unity is used to realize the real-time dynamic visualization of the working performance of the lithium battery BTMS, and the construction of the digital twin model of the lithium battery BTMS is completed; Figure 7 The digital twin model not only visualizes the real-time operating temperature and distribution of the lithium battery BTMS, but also can pop up an alarm and sound an alarm when a local high temperature occurs in the lithium battery BTMS.

[0096] S234, using the mean absolute error (MAE), multiple correlation coefficient (R 2 ) indicators were used to verify the accuracy of the constructed BTMS digital twin model. The average absolute error values ​​were concentrated below 0.2, and the complex correlation coefficient values ​​were mainly concentrated between 0.94 and 0.98, indicating that the accuracy of the established lithium battery BTMS thermal performance digital twin model meets the requirements.

[0097] In specific implementation, as a preferred embodiment of the present invention, in step S4, if the consistency requirement is met, the multidisciplinary design optimization problem is solved by using an optimizer, which specifically includes:

[0098] S41. Determine the optimization goal: By analyzing the factors that affect the performance and life of lithium batteries, determine the minimum cooling system inlet and outlet pressure Δp, the minimum maximum temperature difference ΔT and the minimum overall temperature T A The three BTMS thermal management performance indicators are used as optimization targets, and maximizing the lithium battery life is determined as one of the MDO optimization targets; minimizing the fan's real-time maximum power is used as one of the MDO optimization targets, and minimizing the BTMS installation volume is processed as one of the MDO model's optimization targets;

[0099] S42, the multi-objective optimization problem is transformed into a single-objective optimization problem by multiplying several single objectives, and different weights are assigned to each objective according to the expert parameters in the process; in addition, in the MDO process, objectives and constraints can be converted to each other, and the optimization objective in one subsystem can be used as a constraint in another subsystem, so the inlet and outlet pressure Δp and the average temperature T inside the lithium battery module are combined. A After being processed into MDO constraints, the MDO objective function for BTMS thermal management performance is constructed as follows:

[0100]

[0101] Among them, λ i is a single objective weight, and its value is determined based on expert experience. In this embodiment, the goal is to seek the best battery safety and life, so large weights are assigned to battery life and battery temperature difference, that is, λ1=0.6, λ2=0.2, λ3=0.1, λ4=0.1;

[0102] S43. Based on the requirements of different disciplines for physical structure and performance, involving fluid dynamics, thermodynamics, and structure, the MDO constraints for BTMS are finally determined as follows:

[0103]

[0104] S44. The battery provides power to the cooling fan and the vehicle at the same time, so the heat generated by the battery unit is divided into two parts, as follows:

[0105]

[0106] In the formula, ξ represents the heat generation coefficient; η represents the proportional coefficient of fan heat generation; A(m 2 ) represents the surface area of ​​the battery cell; ρ(kg / m 3 ) represents the density of air;

[0107] S45. Combining the above steps S41 to S44, construct an MDO model of BTMS thermal management performance as follows:

[0108]

[0109] In the formula, f g (·) represents the BTMS digital twin model.

[0110] Example

[0111] Initial pressure drop Δp and heat flux Set to 130.00Pa and 250.00W / m 2 , the air temperature is set to 20° C. In this embodiment, a genetic algorithm (GA) is used to solve the established MDO problem, and the corresponding population size, generation number, crossover rate and mutation rate are set to 50, 50, 0.95 and 0.95 respectively.

[0112] The time history of MDO optimization solution is shown in the iterative diagram. Figure 8 As shown in Table 2, it can be seen that after a slight fluctuation, the convergence curve obtains the optimal solution at the 63rd iteration, and the optimal optimization target value is 0.2764. The various indicators of BTMS thermal management performance have improved compared with the initial state, as shown in Table 2. The minimum temperature difference ΔT of the lithium battery is 21.30℃ (reduced by 3.75%), and the optimal volume is 0.0034m 3 (reduced by 2.29%), the optimal fan power is 3.08W (reduced by 7.99%), and the optimal lifespan is 11.10 years (extended by 10.89%).

[0113] Table 2 Comparison of relevant performance before and after ESBTMS optimization

[0114]

[0115] When lithium batteries continuously generate heat during charging and discharging, it is very easy to cause thermal runaway risks such as overheating and temperature inconsistency inside the battery module. To this end, the present invention proposes a multidisciplinary design optimization (MDO) method for a lithium battery thermal management system (Battery Thermal Management System, BTMS) based on a structural adaptive digital twin model, which makes up for the shortcomings of existing BTMS design methods that lack high-precision system models and ignore the complex coupling effects between different disciplines.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multidisciplinary design optimization method for a lithium battery thermal management system, characterized in that: include: S1. Use the multidisciplinary hierarchical decoupling method to perform multidisciplinary decomposition of BTMS and determine the key design variables that affect BTMS performance; S2. Based on the determined BTMS key design variables, a set of BTMS structural adaptive digital twin models is constructed; S3. Relying on the constructed BTMS structure adaptive digital twin model set, multidisciplinary analysis is carried out to ensure that the multidisciplinary coupling variables satisfy the multidisciplinary coupling relationship between the variables and ensure the interdisciplinary consistency of the values; S4, if the multidisciplinary consistency requirement is not met, return to step S3 to perform the next cycle of multidisciplinary analysis iterative calculation; If the consistency requirements are met, the optimizer is used to solve the multidisciplinary design optimization problem; S5. If the optimization solution iteration process converges, the optimal solution for the multidisciplinary design is obtained; If the optimization solution iteration process does not converge, return to step S1 to update the design variables until the optimal solution for the multidisciplinary design is obtained.

2. A multidisciplinary design optimization method for a lithium battery thermal management system according to claim 1, characterized in that: Step S1 specifically includes: S11. The BTMS is divided into three core subsystems using a multidisciplinary hierarchical decomposition method, including the gas supply system, battery cells and battery modules. Each subsystem belongs to a different discipline. Battery life is a key performance indicator for determining battery performance. In the multidisciplinary design optimization of BTMS, battery life is treated as an important virtual subsystem, which coordinates the multiple disciplines included in the above BTMS to achieve the best thermal management performance of lithium battery BTMS. S12. Determine the cooling air mass flow rate based on the principles of thermodynamics and fluid dynamics Battery cell surface heat flux And the battery cell spacing b is used as the design variable, the design variables are:

3. A multidisciplinary design optimization method for a lithium battery thermal management system according to claim 1, characterized in that: Step S2 specifically includes: S21, using the method of fixing the number of grids between partitions for variable structures to divide the BTMS fluid domain grid; S22, establish BTMS computational fluid dynamics model; S23. Build a BTMS digital twin model.

4. A multidisciplinary design optimization method for a lithium battery thermal management system according to claim 3, characterized in that: Step S21 specifically includes: S211. For the structure that needs to be designed during the optimization process, or the structure whose size of the system itself changes, the structure corresponding to each changed structural parameter is processed into a separate partition; for each structural partition, a fixed number of grids are evenly divided, so as to determine the order of nodes in each structural partition; S212. In the subsequent experimental design process, the variable structure parameters will change each time the simulation calculation is analyzed, but the number and order of the grids in each structural interval remain unchanged, and only the size of the grid itself changes; based on this, in the subsequent experimental design process, the order of the grid nodes of all variable structures remains unified, so the system performance index vector obtained by the simulation calculation is combined with the node order data to construct the subsequent variable structure digital twin model; S213. For lithium battery BTMS, the battery cell spacing is regarded as a structure to be designed, and each battery cell spacing is processed into a separate structural partition. In each battery cell spacing structural partition, the number of nodes in the horizontal and vertical directions of the battery cell spacing is fixed, and does not change with the structural changes in the experimental design, but only the grid size changes.

5. A multidisciplinary design optimization method for a lithium battery thermal management system according to claim 3, characterized in that: Step S22 specifically includes: S221. According to the actual working conditions, the cooling air temperature is set to room temperature; S222. Select cooling air mass flow rate based on thermodynamics and fluid dynamics Heat flux on battery cell surface and the battery cell spacing b as the input of the twin model; S223, determine the BTMS inlet and outlet pressure drop Δp, the BTMS internal maximum temperature difference ΔT and the BTMS overall average temperature T A As three BTMS performance indicators; S224. In the computational fluid dynamics calculation process, the heat transfer process is taken into account, the k-ξ turbulence model and SIMPLEC method are used for solution, and the second-order upwind algorithm is used to process the pressure, density and momentum equations; the overall pressure and temperature distribution of BTMS is obtained by calculation, that is, the thermal state of each grid node in BTMS can be accurately captured.

6. A multidisciplinary design optimization method for a lithium battery thermal management system according to claim 3, characterized in that: Step S23 specifically includes: S231. Use the K nearest neighbor algorithm to realize the feature mapping of spatial data, and then use the radial basis function neural network based on the node temperature data to establish the working temperature distribution model of the BTMS overall fluid domain, so as to realize the factual mapping relationship between the BTMS digital twin and the physical model; S232, setting a data acquisition system in the real physical model to collect working status data of the real physical model in real time; S233, performing noise reduction processing on the collected working status data, and feeding it back to the digital twin through the communication system, and then using Unity to realize real-time dynamic visualization of the working performance of the lithium battery BTMS, and completing the construction of the digital twin model of the lithium battery BTMS; S234. Use the mean absolute error and complex correlation coefficient indicators to verify the accuracy of the constructed BTMS digital twin model.

7. A multidisciplinary design optimization method for a lithium battery thermal management system according to claim 1, characterized in that: In step S4, if the consistency requirement is met, the optimizer is used to solve the multidisciplinary design optimization problem, which specifically includes: S41. Determine the optimization goal: By analyzing the factors that affect the performance and life of lithium batteries, determine the minimum cooling system inlet and outlet pressure Δp, the minimum maximum temperature difference ΔT and the minimum overall temperature T A The three BTMS thermal management performance indicators are used as optimization targets, and maximizing the lithium battery life is determined as one of the MDO optimization targets; minimizing the fan's real-time maximum power is used as one of the MDO optimization targets, and minimizing the BTMS installation volume is processed as one of the MDO model's optimization targets; S42, the multi-objective optimization problem is transformed into a single-objective optimization problem by multiplying several single objectives, and different weights are assigned to each objective according to the expert parameters in the process; in addition, in the MDO process, objectives and constraints can be converted to each other, and the optimization objective in one subsystem can be used as a constraint in another subsystem, so the inlet and outlet pressure Δp and the average temperature T inside the lithium battery module are combined. A After being processed into MDO constraints, the MDO objective function for BTMS thermal management performance is constructed as follows: Among them, λ i is the single target weight; S43. Based on the requirements of different disciplines for physical structure and performance, involving fluid dynamics, thermodynamics, and structure, the MDO constraints for BTMS are finally determined as follows: S44. The battery provides power to both the cooling fan and the vehicle, so the heat generated by the battery unit is divided into two parts, as follows: In the formula, ξ represents the heat generation coefficient; η represents the proportional coefficient of fan heat generation; A represents the surface area of ​​the battery unit; ρ represents the air density; S45. Combining the above steps S41 to S44, construct an MDO model of BTMS thermal management performance as follows: In the formula, f g (·) represents the BTMS digital twin model.

Images