Heavy truck liquid cooling over-charging topology dynamic optimization method and system

Abstract

The invention provides a heavy truck liquid cooling overcharging topology dynamic optimization method and system, and relates to the technical field of heavy truck liquid cooling overcharging, and the method comprises the steps: generating time-space thermal load distribution through a transient thermal load equation, and recognizing a thermal aggregation region coordinate set; generating a runner diameter grading mapping table; an objective function and constraint conditions of topological optimization are defined, a topological optimization algorithm is selected, parameters are set, and an optimal topological structure of the cold plate channel is obtained; an optimal topological structure is adopted, a fatigue life map is calculated through a modal superposition method, and meanwhile a thermal runaway risk matrix is quantified; and according to the fatigue life map, the thermal runaway risk matrix and the sensitivity field, establishing a dynamic adjustment health degree model of the cold plate channel, and outputting an optimized flow channel topology and a parameter set. The method has the beneficial effects that the service life of the battery pack is effectively prolonged, the maintenance cost is reduced, and the overall performance and the economic benefit of the liquid cooling overcharging system of the heavy truck are improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of heavy truck liquid cooling supercharging, in particular to a heavy truck liquid cooling supercharging topology dynamic optimization method and system. BACKGROUND

[0002] With the rapid development of new energy vehicles, heavy trucks as important transportation tools, their electric trend is becoming increasingly evident. In the process of heavy truck electrification, the charging efficiency and safety of the battery pack are key issues. Liquid cooling supercharging technology has become an important solution for heavy truck battery pack charging due to its high cooling performance.

[0003] However, the existing liquid cooling supercharging system mostly uses a fixed topology structure of the cold plate channel, which cannot adapt to the thermal load changes of the battery under different working conditions, resulting in low cooling efficiency, uneven battery temperature distribution, affecting the battery performance and life. The existing technology usually increases the cooling liquid flow or reduces the cooling liquid temperature to improve the cooling effect, but this will increase the system energy consumption and cost, and cannot fundamentally solve the thermal management problem. SUMMARY

[0004] The purpose of the present application is to provide a heavy truck liquid cooling supercharging topology dynamic optimization method and system to improve the above problems. In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0005] In a first aspect, the application provides a heavy truck liquid cooling supercharging topology dynamic optimization method, comprising:

[0006] Obtain the real-time heat generation power of the battery pack and the cooling liquid parameters, generate the space-time thermal load distribution through the transient thermal load equation, drive the phase field evolution equation to output the phase field state, and identify the thermal aggregation region coordinate set;

[0007] Couple the thermal aggregation region coordinate set with the flow channel pressure drop and the material thermal expansion characteristics, construct the sensitivity field through the coupling sensitivity function, extract the high sensitivity flow channel candidate area, and generate the flow channel diameter hierarchical mapping table;

[0008] According to the flow channel diameter hierarchical mapping table, define the objective function and constraint conditions of topology optimization, select the topology optimization algorithm and set the parameters, and obtain the optimal topology structure of the cold plate channel through algorithm iterative calculation;

[0009] Solve the thermal elastic equation to output the stress field using the optimal topology structure, calculate the fatigue life spectrum using the modal superposition method, and simultaneously quantify the thermal runaway risk matrix;

[0010] According to the fatigue life map, the thermal runaway risk matrix and the sensitivity field, a dynamic adjustment health degree model of the cold plate channel is established, and a decision is made based on the adjustment result of the health degree model, when the health degree is lower than a threshold value, a flow channel supplement instruction is executed, and an optimized flow channel topology and parameter set are output.

[0011] Preferably, the real-time heat generation power and cooling liquid parameters of the battery pack are obtained, the spatiotemporal thermal load distribution is generated by a transient thermal load equation, the phase field state is output by driving the phase field evolution equation, and the thermal aggregation region coordinate set is identified, which includes:

[0012] The real-time heat generation power, cooling liquid inlet temperature and flow rate of the battery pack under different charging conditions are obtained;

[0013] The spatiotemporal thermal load distribution Q of the battery pack is calculated by a transient thermal load equation load (x,y,z,t);

[0014] Based on the spatiotemporal thermal load distribution, the phase field evolution equation is driven to output the phase field state;

[0015] According to the phase field state, the thermal aggregation region coordinate set is identified, when the phase field state is greater than 0.8, the region is a high-temperature liquid phase region, and is recorded as a thermal aggregation region, so that all coordinate sets satisfying the condition are extracted by traversing the phase field state data.

[0016] Preferably, the thermal aggregation region coordinate set is accepted, the flow channel pressure drop and the material thermal expansion characteristics are combined, the sensitivity field is constructed by coupling the sensitivity function, the high-sensitivity flow channel candidate area is extracted, and the flow channel diameter hierarchical mapping table is generated, which includes:

[0017] A thermocouple array distributed inside the battery pack is used to solve a three-dimensional temperature gradient vector by using a central difference method, which includes X / Y / Z three-direction components, and a convolution operation is performed on the phase field state matrix to extract the change rate of the phase field order parameter in three dimensions of space, and a structured gradient data set is generated, wherein the structured gradient data set includes the position coordinates, temperature gradient vector and phase field gradient vector of each thermal aggregation point;

[0018] The gradient data and real-time system parameters are fused to construct a sensitivity index coupled with multiple physical fields;

[0019] Based on the sensitivity index coupled with multiple physical fields, the high-sensitivity flow channel candidate area is extracted, and then the flow channel diameter hierarchical mapping table is generated, wherein the flow channel diameter hierarchical mapping table includes the flow channel diameters corresponding to different sensitivity regions, so that the flow channel spatial hierarchical configuration is performed;

[0020] The high-sensitivity flow channel candidate area is extracted, and the flow channel diameter hierarchical mapping table is generated, wherein the mapping table includes the flow channel diameters corresponding to different sensitivity regions.

[0021] Preferably, the flow channel diameter grading mapping table defines the objective function and constraint conditions of topology optimization, selects a topology optimization algorithm and sets parameters, and the optimal topology structure of the cold plate channel is obtained through algorithm iteration calculation, which includes:

[0022] According to the diameter mapping table, the path optimization result is solved under the constraints of a minimum bending radius Rmin=5mm and a volume fraction less than or equal to 15%;

[0023] Performing FFT spectrum analysis on the sensitivity field to generate an adaptive pulse;

[0024] Based on the adaptive pulse, a PID controller is constructed, parameters are configured and an instruction stream is output.

[0025] In a second aspect, the application also provides a heavy truck liquid cooling super-charging topology dynamic optimization system, comprising:

[0026] The acquisition module is configured to acquire the real-time heat generation power of the battery pack and the cooling liquid parameters, generate a space-time heat load distribution through a transient heat load equation, drive a phase field evolution equation to output a phase field state, and identify a thermal aggregation region coordinate set;

[0027] The generation module is configured to receive the thermal aggregation region coordinate set, combine flow channel pressure drop and material thermal expansion characteristics, construct a sensitivity field through a coupling sensitivity function, extract a high-sensitivity flow channel candidate area, and generate a flow channel diameter grading mapping table;

[0028] The calculation module is configured to define the objective function and constraint conditions of topology optimization according to the flow channel diameter grading mapping table, select a topology optimization algorithm and set parameters, and obtain the optimal topology structure of the cold plate channel through algorithm iteration calculation;

[0029] The solving module is configured to solve a thermal elastic equation to output a stress field using the optimal topology structure, calculate a fatigue life spectrum using a modal superposition method, and simultaneously quantify a thermal runaway risk matrix;

[0030] The establishment module is configured to establish a dynamic adjustment health degree model of the cold plate channel based on the fatigue life spectrum, the thermal runaway risk matrix, and the sensitivity field, make a decision based on the adjustment result of the health degree model, execute a flow channel supplement instruction when the health degree is lower than a threshold, and output an optimized flow channel topology and parameter set.

[0031] In a third aspect, the application also provides a heavy truck liquid cooling super-charging topology dynamic optimization device, comprising:

[0032] The memory is configured to store a computer program;

[0033] The processor is configured to implement the steps of the heavy truck liquid cooling super-charging topology dynamic optimization method when the computer program is executed.

[0034] In a fourth aspect, the present application also provides a readable storage medium, wherein the readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the above-mentioned heavy truck liquid cooling super-charging topology dynamic optimization method.

[0035] The present application has the following beneficial effects:

[0036] The present application obtains the real-time heating power of the battery pack and the cooling liquid parameters, generates the space-time heat load distribution, and drives the phase field evolution equation to identify the heat accumulation area; combines the flow channel pressure drop and the material thermal expansion characteristics to construct the sensitivity field, extracts the high-sensitivity flow channel candidate area; defines the objective function and the constraint condition of the topology optimization according to the flow channel diameter classification mapping table, and obtains the optimal topology structure of the cold plate channel; uses the optimal topology structure to solve the thermal elastic equation to output the stress field, calculates the fatigue life spectrum, and quantifies the thermal runaway risk matrix; according to the fatigue life spectrum, the thermal runaway risk matrix and the sensitivity field, a dynamic adjustment health degree model of the cold plate channel is established, when the health degree is lower than the threshold, a flow channel supplement instruction is executed, and the optimized flow channel topology and parameter set are output. The method can dynamically adjust the topology structure of the cold plate channel according to the battery heat load, realize efficient thermal management, and improve the battery performance and life.

[0037] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application as set forth in the claims and appended drawings. The purposes and other advantages of the present application can be realized and attained by the structure particularly pointed out in the written description and claims, and the appended drawings. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0039] Figure 1 The heavy truck liquid cooling super-charging topology dynamic optimization method flowchart described in the embodiments of the present application;

[0040] Figure 2 The heavy truck liquid cooling super-charging topology dynamic optimization system structure schematic diagram described in the embodiments of the present application;

[0041] Figure 3 The heavy truck liquid cooling super-charging topology dynamic optimization equipment structure schematic diagram described in the embodiments of the present application.

[0042] As shown in the figure: 701, an acquisition module; 702, a generation module; 703, a calculation module; 704, a solving module; 705, an establishment module; 800, a heavy truck liquid cooling super-charging topology dynamic optimization device; 801, a processor; 802, a memory; 803, a multimedia assembly; 804, an I / O interface; 805, a communication assembly. DETAILED DESCRIPTION

[0043] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the following will be combined with the accompanying drawings of the embodiments of the present application to make a clear and complete description of the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application but not all the embodiments. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application but only represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments in the present application belong to the scope of protection of the present application.

[0044] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second" and the like are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0045] Embodiment 1

[0046] The embodiment provides a heavy truck liquid cooling super-charging topology dynamic optimization method.

[0047] Referring to Figure 1 , the method includes steps S100, S200, S300, S400 and S500.

[0048] S100, acquiring real-time heat generation power of a battery pack and cooling liquid parameters, generating a space-time heat load distribution through a transient heat load equation, driving a phase field evolution equation to output a phase field state, and identifying a heat aggregation region coordinate set.

[0049] It can be understood that the step S100 includes S101, S102, S103 and S104.

[0050] S101, acquiring real-time heat generation power of the battery pack under different charging conditions, cooling liquid inlet temperature and flow rate;

[0051] It should be noted that these data are collected in real time through sensors of a battery management system (BMS) and a cooling system, and the data format is time series data, containing fields such as time stamp, working condition identifier, heat generation power, temperature and flow.

[0052] S102, calculating the space-time thermal load distribution Q of the battery pack by using a transient thermal load equation load (x, y, z, t), and the calculation formula is as follows:

[0053]

[0054] In the formula, Q load is the thermal load of the battery pack at position (x, y, z, t) and time t, P(t) is the heat generation power of the battery pack at time t, is the heat carried away by the cooling liquid;

[0055] S103, driving a phase field evolution equation to output a phase field state based on the space-time thermal load distribution, and the calculation formula is as follows:

[0056]

[0057] In the formula, φ is a phase field order parameter, t is time, M is the speed of phase interface movement, ∈ is a gradient energy coefficient, is the Laplacian of the phase field order parameter φ, H(φ) is a double potential well function, is the derivative of the potential barrier function g(φ) with respect to φ, λ is a temperature-phase field coupling coefficient, T is temperature, and T0 is a reference temperature;

[0058] S104, identifying a thermal aggregation region coordinate set according to the phase field state, when the phase field state is greater than 0.8, the region is a high-temperature liquid phase region, and is recorded as a thermal aggregation region, so as to extract all coordinate sets meeting the condition by traversing the phase field state data.

[0059] It should be noted that the present application can accurately identify the thermal aggregation region coordinate set by acquiring the real-time heat generation power and cooling liquid parameters of the battery pack, generating the space-time thermal load distribution by using the transient thermal load equation, and driving the phase field evolution equation to output the phase field state.

[0060] S200, receiving the thermal aggregation region coordinate set, combining the flow channel pressure drop and the material thermal expansion characteristics, constructing a sensitivity field by using a coupling sensitivity function, extracting a high-sensitivity flow channel candidate area, and generating a flow channel diameter hierarchical mapping table.

[0061] Further, the application combines the flow channel pressure drop and the material thermal expansion characteristics, constructs a sensitivity field by coupling the sensitivity function, and extracts a high-sensitivity flow channel candidate area to generate a flow channel diameter hierarchical mapping table. This step can fully consider the pressure drop characteristics of the flow channel in actual operation and the thermal expansion characteristics of the material, ensure that the optimized flow channel structure can maintain good cooling performance when the thermal load changes, avoid structural damage caused by thermal expansion, and improve the reliability and durability of the system.

[0062] It can be understood that the step S200 includes S201, S202, S203 and S204.

[0063] S201, a three-dimensional temperature gradient vector is solved by using a central difference method through a thermocouple array distributed inside the battery pack, including X / Y / Z three direction components, and a convolution operation is performed on the phase field state matrix to extract the change rate of the phase field order parameter in three dimensions of space, and a structured gradient data set is generated, wherein the structured gradient data set includes the position coordinates of each thermal aggregation point, the temperature gradient vector and the phase field gradient vector;

[0064] S202, the gradient data and real-time system parameters are fused to construct a multi-physical field coupled sensitivity index;

[0065] It should be noted that the dot product of the temperature gradient and the phase field gradient is divided by the product of the lengths of the two (physical nature: when the two vectors are in the same direction, the value tends to 1, indicating efficient heat diffusion; when the two vectors are in opposite directions, the value tends to -1, indicating heat aggregation); then, according to the material thermal elastic parameters (Young's modulus 70GPa, thermal expansion coefficient 23x10 -6 / K), the temperature change rate is converted into the thermal stress change rate (unit: MPa / s), and the absolute value of the ratio of the measured flow channel pressure drop to the maximum allowable pressure drop of the system (80kPa) is taken to reflect the system pressure bearing margin; finally, the above three items are integrated according to the weight coefficients of 7:3:1 (verified by orthogonal test), to generate a full-space sensitivity scalar field.

[0066] S203, based on the multi-physical field coupled sensitivity index, a high-sensitivity flow channel candidate area is extracted, and a flow channel diameter hierarchical mapping table is generated, wherein the flow channel diameter hierarchical mapping table includes the flow channel diameters corresponding to different sensitivity areas, so as to perform flow channel spatial hierarchical configuration;

[0067] It should be noted that the threshold partition rule includes the super-high heat sensitive area (sensitivity ≥ 0.9): implanting needle-shaped microtubules (diameter 2mm), directly penetrating into the intercellular gap to strengthen heat exchange; high heat sensitive area (0.8-0.9): arranging straight main flow channels (diameter 10mm), using the shortest path to connect the inlet and outlet; medium heat sensitive area (0.6-0.8): generating Voronoi network secondary flow channels (diameter 6mm), bionic vein structure to realize global coverage; low heat sensitive area (<0.6): no flow channel is arranged, and the system pressure drop is reduced. Among them, it can be understood that the temperature gradient and the phase field gradient are the core inputs of the heat diffusion efficiency term, and the included angle between the two directions directly determines the value of the term.

[0068] S204, extracting high sensitivity flow channel candidate area, generating flow channel diameter classification mapping table, wherein the mapping table includes the flow channel diameter corresponding to different sensitivity areas.

[0069] It should be noted that S≥0.9 corresponds to the heat runaway core area, and the microtubule penetration type active intervention is adopted; the main flow channel is used to realize the rapid export of heat in the area of 0.8≤S<0.9; the Voronoi subdivision ensures that the flow channel coverage rate is ≥95% in the area of 0.6≤S<0.8.

[0070] S300, according to the flow channel diameter classification mapping table, defining the objective function and constraint conditions of topological optimization, selecting a topological optimization algorithm and setting parameters, and obtaining the optimal topological structure of the cold plate channel through algorithm iteration calculation;

[0071] It should be noted that according to the temperature field distribution data T(x, y, z) obtained in step two, the objective function and constraint conditions of topological optimization are defined, the objective function is to minimize the highest temperature or temperature uniformity of the battery pack, and the constraint conditions include the volume fraction Vf of the cold plate channel, the flow limit of the cooling liquid and the minimum size of the channel; Then select a suitable topological optimization algorithm (such as SIMP method, level set method, etc.), and set the parameters of the algorithm, such as penalty factor, filtering radius and convergence criterion, etc., input the temperature field distribution data, and obtain the optimal topological structure Ω of the cold plate channel through algorithm iteration calculation, and post-process the topological optimization results, including extracting the geometric shape of the cold plate channel, generating the center line and cross-sectional size of the channel, etc. Information is converted into a geometric model that can be used for manufacturing, and at the same time, the performance of the optimized cold plate channel is evaluated, such as calculating its cooling efficiency, pressure drop and temperature uniformity, etc.

[0072] It can be understood that in this step, according to the flow channel diameter classification mapping table, the application defines the objective function and constraint condition of topology optimization, selects the topology optimization algorithm and sets the parameters, and obtains the optimal topology structure of the cold plate channel through algorithm iteration calculation. This optimization process can automatically adjust the topology structure of the cold plate channel according to the heat load distribution and sensitivity field information, so that it can realize efficient thermal management under different working conditions, and significantly improve the adaptability and cooling efficiency of the cooling system.

[0073] It should be noted that the step S300 includes S301, S302 and S303.

[0074] S301, according to the diameter mapping table, the path optimization result is solved under the constraints of minimum bending radius Rmin=5mm and volume fraction less than or equal to 15%, and the calculation formula is as follows:

[0075]

[0076] In the formula, E is the edge set of the flow channel, ||e|| is the edge length, d(e) is the design diameter, and R(e) is the curvature radius;

[0077] S302, FFT spectrum analysis is performed on the sensitivity field to generate an adaptive pulse, and the calculation formula is as follows:

[0078]

[0079] In the formula, Q0 is the reference flow (unit: L / min), Q(t) is the instantaneous flow (unit: L / min), f is the pulse frequency (unit: Hz), t peak is the sensitivity peak duration (unit: s), is the sensitivity change rate (unit: s -1 );

[0080] S303, based on the adaptive pulse, a PID controller is constructed, parameter configuration is selected and instruction flow is output.

[0081] It should be noted that the input of the instruction flow is the output of the topology optimization, the topology optimization solves the structure static configuration problem, the instruction flow realizes dynamic parameter regulation, and the two form a closed loop through real-time temperature / pressure feedback, and finally unified coordination is realized through the self-recovery decision of step five.

[0082] In this step, the flow channel diameters corresponding to different sensitivity regions are listed in the sensitivity field. Larger flow channel diameters are used in high sensitivity regions to enhance cooling effect; smaller flow channel diameters are used in low sensitivity regions to reduce material usage and pressure drop. The minimum bending radius is to ensure smooth flow of the cooling liquid in the flow channel, avoiding increased flow resistance and local overheating due to too small bending radius. In actual application, the flow state (laminar flow or turbulent flow) of the cooling liquid in the flow channel is affected by the bending radius, and smaller bending radius may cause flow separation and vortex, increasing pressure drop and thermal resistance. The volume fraction of less than or equal to 15% is to control the volume ratio of the cooling plate channel in the battery pack, avoiding affecting the energy density and structural strength of the battery pack due to too large flow channel volume.

[0083] In the calculation process, the optimization algorithm (such as genetic algorithm, simulated annealing algorithm, etc.) is used to solve the above optimization problem under the constraint condition, and the optimal flow channel path and diameter distribution are obtained. This process needs to consider the connectivity of the flow channel, the flow characteristics of the cooling liquid and the heat conduction efficiency. This step can reduce material usage and pressure drop while ensuring cooling effect by optimizing the flow channel path and diameter, and improve the thermal management efficiency and structural rationality of the cooling plate channel.

[0084] In this step, through frequency spectrum analysis, the main frequency components in the sensitivity field are identified, which reflect the periodic characteristics of the thermal load change; according to the frequency spectrum analysis result, an adaptive pulse signal is generated for adjusting the flow of the cooling liquid. The frequency and amplitude of the adaptive pulse are adjusted according to the frequency components and amplitudes of the sensitivity field to realize synchronization with the thermal load change. The FFT frequency spectrum analysis of the sensitivity field is needed to extract the main frequency components and corresponding amplitudes; then the frequency f and amplitude of the adaptive pulse are calculated according to these frequency components and amplitudes; finally the adaptive pulse signal is generated. This step can realize synchronization with the thermal load change by adjusting the flow of the cooling liquid through adaptive pulse, improve the response speed and cooling efficiency of the cooling system, and reduce the waste of the cooling liquid. The PID controller in the last step calculates the control instruction according to the input signal and parameter configuration, and outputs it to the actuator (such as flow regulating valve) of the cooling system to realize dynamic adjustment of the flow of the cooling liquid.

[0085] S400, adopt the optimal topology structure, solve the thermal elastic equation to output the stress field, calculate the fatigue life spectrum by using the modal superposition method, and quantify the thermal runaway risk matrix;

[0086] It should be noted that the cold plate channel optimal topology structure Ω obtained according to the above steps, the dynamic adjustment model of the cold plate channel is established, the model includes the geometric parameters (such as diameter, length and bending angle, etc.) of the channel and the flow parameters (such as flow, flow rate and flow direction, etc.) of the cooling liquid, and the thermal load change of the channel under different working conditions is considered; design control algorithm (such as PID control, fuzzy control or model predictive control, etc.), according to the real-time monitored battery pack temperature data and thermal load data, dynamically adjust the geometric parameters of the cold plate channel and the flow parameters of the cooling liquid, the goal of the control algorithm is to minimize the energy consumption and response time of the cooling system under the premise of meeting the cooling demand.

[0087] That is, after adopting the optimal topology structure, the present application solves the thermal elastic equation to output the stress field, and calculates the fatigue life spectrum by using the modal superposition method, and quantifies the thermal runaway risk matrix. This method can comprehensively evaluate the mechanical properties and safety of the optimized cold plate channel under the action of thermal load, provide a scientific basis for subsequent dynamic adjustment, and ensure the stability and safety of the system in long-term operation.

[0088] S500, according to the fatigue life spectrum, the thermal runaway risk matrix and the sensitivity field, a dynamic adjustment health degree model of the cold plate channel is established, and a decision is made based on the adjustment result of the health degree model, when the health degree is lower than the threshold, the flow channel supplement instruction is executed, and the optimized flow channel topology and parameter set are output.

[0089] It should be noted that the dynamic adjustment strategy obtained according to the above steps is simulated and verified, a complete heavy truck liquid cooling supercharging system simulation model is established, including battery pack, cold plate channel, cooling liquid circulation system and control algorithm, etc., the system operation under different charging conditions and extreme environments is simulated, and the cooling performance, energy consumption and reliability of the system are analyzed; and according to the simulation results, the cold plate channel topology structure and dynamic adjustment strategy are optimized, if it is found that the cooling effect is poor or the system energy consumption is too high under certain working conditions, the objective function and constraint condition of topology optimization can be adjusted, and topology optimization is performed again; or optimize the parameters of the control algorithm and improve the dynamic adjustment strategy. The optimized cold plate channel topology structure and dynamic adjustment strategy are applied to the actual heavy truck liquid cooling supercharging system, and field test and long-term operation monitoring are carried out, actual operation data are collected, compared with simulation results, the optimization effect is further verified, and the system is fine-tuned according to the actual operation.

[0090] It can be understood that in this step, a dynamic adjustment health degree model of the cold plate channel is established according to the fatigue life spectrum, the thermal runaway risk matrix and the sensitivity field, and a decision is made based on the adjustment result of the health degree model. When the health degree is lower than the threshold value, the flow channel supplement instruction is executed, and the optimized flow channel topology and parameter set are output. This dynamic adjustment mechanism can monitor the health state of the cold plate channel in real time, and automatically adjust the topology structure when necessary, to ensure that the system is always in the best working state, effectively prolong the service life of the battery pack, reduce the maintenance cost, and improve the overall performance and economic benefit of the heavy truck liquid cooling supercharging system.

[0091] In step four, the fatigue life spectrum of the cold plate channel is obtained by solving the thermoelastic equation and using the modal superposition method. The spectrum reflects the fatigue life of the cold plate channel at different positions, that is, the number of cycles that the material at the position can withstand under the action of cyclic thermal stress. In the above steps, the thermal runaway risk matrix is obtained by analyzing the thermal runaway risk of the battery pack under different working conditions. The matrix quantifies the thermal runaway risk of the battery pack at different positions, and a higher risk area indicates that the battery is more likely to experience thermal runaway at that position, requiring additional cooling measures. In step two, the sensitivity field is constructed by coupling the sensitivity function. The field reflects the sensitivity of the cold plate channel at different positions to changes in thermal load, and a high sensitivity area means that increasing the flow channel or adjusting the flow channel parameters at that position can more effectively improve the thermal management effect.

[0092] Combining the fatigue life spectrum, the thermal runaway risk matrix and the sensitivity field, a dynamic adjustment health degree model of the cold plate channel is established. The model can be a comprehensive evaluation function that weights and combines the above three factors to obtain the health degree value at each position. The determination of the weight coefficient can be based on expert experience, historical data or adjusted through an optimization algorithm. For example, if the fatigue life is crucial to system safety, a larger value of w1 can be assigned; if the thermal runaway risk is the main concern, the weight of w2 is increased. Then, a health degree threshold is set, and when the health degree at a position is lower than the threshold, the cold plate channel at that position is considered to need adjustment. The threshold can be set according to the safety requirements and performance indicators of the system, for example, it can be set to 0.8, indicating that adjustment is needed when the health degree is lower than 80%.

[0093] Based on the evaluation results of the health degree model, a decision is made for the cold plate channel at each location. If the health degree is lower than the threshold, a flow channel supplement instruction is executed; otherwise, the current flow channel structure remains unchanged. The decision-making process can use a simple threshold judgment or combine more complex decision-making algorithms such as fuzzy logic or machine learning methods to improve the accuracy and robustness of the decision-making. The flow channel supplement instruction includes increasing the number of flow channels, adjusting the diameter and position of the flow channels, etc. For example, new flow channels can be added in areas with lower health degree, or the diameter of existing flow channels can be expanded to enhance the cooling effect in that area. The specific content of the supplement instruction can be determined according to the evaluation results of the health degree model and the optimization algorithm. Therefore, after executing the flow channel supplement instruction, the optimized flow channel topology and parameter set are obtained. The parameter set includes the diameter, length, bending angle, cooling liquid flow rate, etc. of the flow channel, which will be used to guide the actual cold plate channel manufacturing and the operation of the cooling system.

[0094] Therefore, this step can timely discover and handle potential problems in the cold plate channel, such as areas with shorter fatigue life or higher risk of thermal runaway, by dynamically adjusting the health degree model, thereby improving the safety of the entire system; through analysis of the fatigue life map and dynamic adjustment of the health degree model, the structure of the cold plate channel can be optimized to reduce damage to the material caused by thermal stress and prolong the service life of the cold plate channel and the battery pack; at the same time, combined with the sensitivity field for dynamic adjustment, it can more accurately respond to changes in thermal load, optimize the flow path and parameters of the cooling liquid, and improve the efficiency and response speed of the thermal management system.

[0095] In summary, the present application uses a phase field evolution model to mark the heat accumulation area in real time, drives the sensitivity field to dynamically grade, implants micro-pipe structures in the ultra-high sensitivity area, and combines with the adaptive pulse cooling technology, which dynamically modulates the flow amplitude according to the sine wave, and the pulse frequency is linked in real time with the sensitivity change rate, to effectively break through the thermal boundary layer and achieve deep homogenization of the battery pack temperature distribution. The measured maximum temperature drop is more than one-quarter, and the maximum temperature difference is compressed to within five degrees Celsius, completely solving the problem of heat accumulation under megawatt fast charging; based on the joint simulation of thermal stress field and vibration mode, the fatigue weak area of the flow channel is accurately predicted; through the health degree function, the fatigue life and thermal runaway risk are fused, and the intelligent self-healing strategy is triggered: redundant flow channels are added to the area with critical life, and micro-pipe networks are encrypted in the high-risk area of thermal runaway; using the sensitivity-driven bionic flow channel grading design, the flow channel layout is simplified in the low sensitivity area, and the fluid resistance distribution is optimized through the leaf vein-like branch structure; at the same time, the amplitude-frequency parameters of pulse cooling are matched in real time with the evolution rate of heat accumulation, avoiding invalid supercooling.

[0096] Example 2:

[0097] As shown in Figure 2 The present embodiment provides a heavy truck liquid cooling super-charging topology dynamic optimization system, as shown inFigure 2 The system comprises:

[0098] The acquisition module 701 is configured to acquire real-time heat generation power of the battery pack and cooling liquid parameters, generate a space-time heat load distribution through a transient heat load equation, drive a phase field evolution equation to output a phase field state, and identify a set of coordinates of a heat accumulation region.

[0099] The generation module 702 is configured to receive the set of coordinates of the heat accumulation region, combine a flow channel pressure drop and material thermal expansion characteristics, construct a sensitivity field through a coupling sensitivity function, extract a high-sensitivity flow channel candidate area, and generate a flow channel diameter hierarchical mapping table.

[0100] The calculation module 703 is configured to define an objective function and constraint conditions of topology optimization according to the flow channel diameter hierarchical mapping table, select a topology optimization algorithm and set parameters, and obtain an optimal topology structure of the cold plate channel through iterative calculation of the algorithm.

[0101] The solving module 704 is configured to solve a thermal elastic equation to output a stress field using the optimal topology structure, calculate a fatigue life spectrum using a modal superposition method, and simultaneously quantify a thermal runaway risk matrix.

[0102] The establishment module 705 is configured to establish a dynamic adjustment health degree model of the cold plate channel according to the fatigue life spectrum, the thermal runaway risk matrix, and the sensitivity field, make a decision based on an adjustment result of the health degree model, execute a flow channel supplement instruction when the health degree is lower than a threshold, and output an optimized flow channel topology and parameter set.

[0103] Specifically, the acquisition module 701 comprises:

[0104] The acquisition unit is configured to acquire real-time heat generation power, cooling liquid inlet temperature, and flow rate of the battery pack under different charging conditions.

[0105] The first calculation unit is configured to calculate a space-time heat load distribution Q(x,y,z,t) of the battery pack using a transient heat load equation, and the calculation formula is as follows: load

[0106]

[0107] In the formula, Q(x,y,z,t) is the heat load of the battery pack at position (x,y,z,t) and time t, P(t) is the heat generation power of the battery pack at time t, load Q is the heat carried away by the cooling liquid.

[0108] The driving unit is configured to drive a phase field evolution equation to output a phase field state based on the space-time heat load distribution, and the calculation formula is as follows:

[0109] ​​

[0110] where φ is the phase field order parameter, t is time, M is the velocity of phase interface movement, ∈ is the gradient energy coefficient, is the Laplacian of the phase field order parameter φ, H(φ) is the double potential well function, is the derivative of the potential barrier function g(φ) with respect to φ, λ is the temperature-phase field coupling coefficient, T is the temperature, and T0 is the reference temperature;

[0111] The judging unit is used to identify the thermal aggregation region coordinate set according to the phase field state. When the phase field state is greater than 0.8, the region is a high-temperature liquid phase region, and is recorded as a thermal aggregation region. Thus, all coordinate sets satisfying the condition are extracted by traversing the phase field state data.

[0112] Specifically, the generating module 702 includes the following modules:

[0113] The first generating unit is used to solve a three-dimensional temperature gradient vector including X / Y / Z three-direction components by using a central difference method through the thermocouple array distributed inside the battery pack, and to extract the change rate of the phase field order parameter in three dimensions of space by performing convolution operation on the phase field state matrix, so as to generate a structured gradient data set, wherein the structured gradient data set includes the position coordinates of each thermal aggregation point, the temperature gradient vector and the phase field gradient vector.

[0114] The first constructing unit is used to fuse the gradient data and real-time system parameters to construct a multi-physical field coupling sensitivity index.

[0115] The extracting unit is used to extract a high-sensitivity flow channel candidate area based on the multi-physical field coupling sensitivity index, and to generate a flow channel diameter hierarchical mapping table, wherein the flow channel diameter hierarchical mapping table includes the flow channel diameters corresponding to different sensitivity areas, so as to perform flow channel spatial hierarchical configuration.

[0116] The second generating unit is used to extract a high-sensitivity flow channel candidate area to generate a flow channel diameter hierarchical mapping table, wherein the mapping table includes the flow channel diameters corresponding to different sensitivity areas.

[0117] Specifically, the calculating module 703 includes the following modules:

[0118] The solving unit is used to solve the path optimization result under the constraints of a minimum bending radius Rmin=5 mm and a volume fraction less than or equal to 15% according to the diameter mapping table, and the calculation formula is as follows:

[0119]

[0120] where E is the flow channel edge set, ||e|| is the edge length, d(e) is the design diameter, and R(e) is the curvature radius.

[0121] Analysis unit: for FFT spectrum analysis of sensitivity field, generating adaptive pulse, whose calculation formula is as follows:

[0122]

[0123] In the formula, Q0 is the reference flow (unit: L / min), Q(t) is the instantaneous flow (unit: L / min), f is the pulse frequency (unit: Hz), t peak is the sensitivity peak duration (unit: s), is the sensitivity rate of change (unit: s -1 );

[0124] Second construction unit: for constructing PID controller based on adaptive pulse, selecting parameter configuration and outputting instruction stream.

[0125] It should be noted that, as for the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment related to the method, and will not be described in detail here.

[0126] Embodiment 3:

[0127] Corresponding to the above method embodiment, a heavy truck liquid cooling super-charging topology dynamic optimization device is also provided in this embodiment, and the heavy truck liquid cooling super-charging topology dynamic optimization device described below can be correspondingly referred to the heavy truck liquid cooling super-charging topology dynamic optimization method described above.

[0128] Figure 3 Fig. 8 is a block diagram of a heavy truck liquid cooling super-charging topology dynamic optimization device 800 according to an example embodiment. As shown in the figure, the heavy truck liquid cooling super-charging topology dynamic optimization device 800 includes a processor 801 and a memory 802. The heavy truck liquid cooling super-charging topology dynamic optimization device 800 also includes one or more of a multimedia component 803, an I / O interface 804, and a communication component 805. Figure 3

[0129] ​The processor 801 is configured to control overall operation of the heavy truck liquid cooling supercharging topology dynamic optimization device 800 to complete all or part of the steps in the heavy truck liquid cooling supercharging topology dynamic optimization method described above. The memory 802 is configured to store various types of data to support the operation of the heavy truck liquid cooling supercharging topology dynamic optimization device 800. These data can include, for example, instructions for operating any application or method on the heavy truck liquid cooling supercharging topology dynamic optimization device 800, and application-related data such as contact data, sent and received messages, pictures, audio, video, and the like. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component 803 can include a screen and an audio component. The screen can be a touch screen, for example, and the audio component is configured to output and / or input audio signals. For example, the audio component can include a microphone configured to receive external audio signals. The received audio signals can be further stored in the memory 802 or transmitted through the communication component 805. The audio component also includes at least one speaker configured to output audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, which can be a keyboard, mouse, or buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is configured to enable wired or wireless communication between the heavy truck liquid cooling supercharging topology dynamic optimization device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, near field communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include a Wi-Fi module, a Bluetooth module or an NFC module.

[0130] In an example embodiment, the heavy truck liquid cooling super-charging topology dynamic optimization device 800 can be implemented by one or more Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor or other electronic elements for executing the heavy truck liquid cooling super-charging topology dynamic optimization method described above.

[0131] In another example embodiment, a computer readable storage medium including program instructions is also provided, which, when executed by a processor, implements the steps of the heavy truck liquid cooling super-charging topology dynamic optimization method described above. For example, the computer readable storage medium can be the memory 802 described above including program instructions, which can be executed by the processor 801 of the heavy truck liquid cooling super-charging topology dynamic optimization device 800 to complete the heavy truck liquid cooling super-charging topology dynamic optimization method described above.

[0132] Embodiment 4:

[0133] Corresponding to the method embodiments described above, in this embodiment, a readable storage medium is also provided, which can be referred to in conjunction with the above-described heavy truck liquid cooling super-charging topology dynamic optimization method.

[0134] The computer program stored on the readable storage medium implements the steps of the heavy truck liquid cooling super-charging topology dynamic optimization method of the method embodiments described above when executed by a processor.

[0135] The readable storage medium can be specifically a U disk, a mobile hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various readable storage media that can store program codes.

[0136] The present application firstly fuses the phase field gradient, the heat stress change rate and the pressure drop constraint into the sensitivity index, solves the core problem of large thermal inertia and response lag in megawatt super-charging through the synergy of bionic hierarchical flow channel and dynamic pulse, and provides a universal thermal management framework for super-fast charging of electric heavy trucks.

[0137] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0138] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0138] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0138] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0138] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0138] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in

Claims

1. A method for dynamic optimization of topology in liquid-cooled supercharging for heavy-duty trucks, characterized in that, include: The system acquires real-time heat generation power and coolant parameters of the battery pack, generates spatiotemporal heat load distribution through transient heat load equation, drives phase field evolution equation to output phase field state, and identifies the coordinate set of heat accumulation region. By taking the coordinate set of the heat accumulation region and combining the pressure drop of the flow channel and the thermal expansion characteristics of the material, a sensitivity field is constructed by coupling the sensitivity function, and high-sensitivity flow channel candidate regions are extracted to generate a flow channel diameter classification mapping table. Based on the channel diameter classification mapping table, the objective function and constraints of topology optimization are defined, the topology optimization algorithm is selected and the parameters are set, and the optimal topology of the cold plate channel is obtained through algorithm iterative calculation. Using the optimal topology, the thermoelastic equation is solved to output the stress field, and the fatigue life spectrum is calculated using the modal superposition method, while the thermal runaway risk matrix is ​​quantified. Based on the fatigue life spectrum, thermal runaway risk matrix and sensitivity field, a dynamic adjustment health model for the cold plate channel is established. Decisions are made based on the adjustment results of the health model. When the health level is lower than the threshold, a channel supplementation command is executed, and the optimized channel topology and parameter set are output.

2. The heavy-duty truck liquid-cooled supercharging topology dynamic optimization method according to claim 1, characterized in that, The process involves acquiring the real-time heat generation power and coolant parameters of the battery pack, generating a spatiotemporal heat load distribution through a transient heat load equation, driving the phase field evolution equation to output the phase field state, and identifying the coordinate set of heat accumulation regions, including: Obtain the real-time heat generation power, coolant inlet temperature and flow rate of the battery pack under different charging conditions; Calculate the spatiotemporal heat load distribution Q of the battery pack using the transient heat load equation. load (x,y,z,t), its calculation formula is as follows: In the formula, Q load Let P(t) be the heat load of the battery pack at position (x,y,z,t) and time t, and let P(t) be the heat generation power of the battery pack at time t. The heat carried away by the coolant; Based on the spatiotemporal heat load distribution, the phase field evolution equation is used to output the phase field state, and its calculation formula is as follows: In the formula, φ is the phase field sequence parameter, t is time, M is the velocity of the phase interface movement, and ∈ is the gradient energy coefficient. Let H(φ) be the Laplace operator for the phase-field order parameter φ, and H(φ) be the double-well function. λ is the derivative of the barrier function g(φ) with respect to φ, λ is the temperature-phase-field coupling coefficient, T is the temperature, and T0 is the reference temperature; The coordinate set of the heat accumulation region is identified based on the phase field state. When the phase field state is greater than 0.8, the region is a high-temperature liquid phase region and is recorded as a heat accumulation region. Thus, by traversing the phase field state data, all coordinate sets that meet the conditions are extracted.

3. The heavy-duty truck liquid-cooled supercharging topology dynamic optimization method according to claim 1, characterized in that, The set of coordinates of the heat accumulation region, combined with the flow channel pressure drop and material thermal expansion characteristics, is used to construct a sensitivity field through a coupled sensitivity function, extract high-sensitivity flow channel candidate regions, and generate a flow channel diameter hierarchical mapping table, which includes: The three-dimensional temperature gradient vector, which includes components in the X, Y, and Z directions, is solved by using a thermocouple array distributed inside the battery pack and the central difference method. The phase field state matrix is ​​convolved to extract the rate of change of the phase field order parameter in the three spatial dimensions, generating a structured gradient dataset. The structured gradient dataset includes the position coordinates of each heat accumulation point, the temperature gradient vector, and the phase field gradient vector. By integrating gradient data with real-time system parameters, a sensitivity index for multi-physics coupling is constructed. Based on the sensitivity index of multi-physics coupling, high-sensitivity flow channel candidate regions are extracted, and then a flow channel diameter hierarchical mapping table is generated. The flow channel diameter hierarchical mapping table includes the flow channel diameters corresponding to different sensitivity regions, thereby performing hierarchical configuration of flow channel space. Extract high-sensitivity flow channel candidate regions and generate a flow channel diameter hierarchical mapping table, which includes the flow channel diameters corresponding to different sensitivity regions.

4. The heavy-duty truck liquid-cooled supercharging topology dynamic optimization method according to claim 1, characterized in that, Based on the flow channel diameter classification mapping table, the objective function and constraints for topology optimization are defined, a topology optimization algorithm is selected and parameters are set, and the optimal topology structure of the cold plate channel is obtained through iterative calculation, including: Based on the diameter mapping table, under the constraints of minimum bending radius Rmin = 5 mm and volume fraction less than or equal to 15%, the path optimization result is solved, and the calculation formula is as follows: In the formula, E is the flow channel edge set, ||e|| is the edge length, d(e) is the design diameter, and R(e) is the radius of curvature; An FFT spectrum analysis of the sensitivity field is performed to generate an adaptive pulse, and the calculation formula is as follows: In the formula, Q0 is the reference flow rate (unit: L / min), Q(t) is the instantaneous flow rate (unit: L / min), f is the pulse frequency (unit: Hz), and t is the instantaneous flow rate. peak The duration of the sensitivity peak (in seconds). Sensitivity change rate (unit: s) -1 ); Based on adaptive pulses, a PID controller is constructed, parameters are selected for configuration, and a command stream is output.

5. A heavy-duty truck liquid-cooled supercharging topology dynamic optimization system, based on the heavy-duty truck liquid-cooled supercharging topology dynamic optimization method according to claim 1, characterized in that, include: Acquisition module: used to acquire the real-time heat generation power and coolant parameters of the battery pack, generate the spatiotemporal heat load distribution through the transient heat load equation, drive the phase field evolution equation to output the phase field state, and identify the coordinate set of heat accumulation regions; The generation module is used to receive the coordinate set of the heat accumulation region, combine the flow channel pressure drop and material thermal expansion characteristics, construct a sensitivity field by coupling sensitivity function, extract high-sensitivity flow channel candidate regions, and generate a flow channel diameter classification mapping table. Calculation module: Based on the flow channel diameter classification mapping table, it defines the objective function and constraints for topology optimization, selects the topology optimization algorithm and sets the parameters, and obtains the optimal topology structure of the cold plate channel through algorithm iteration calculation; The solver module is used to solve the thermoelastic equation and output the stress field using the optimal topology, calculate the fatigue life spectrum using the modal superposition method, and quantify the thermal runaway risk matrix. Establishment Module: This module is used to establish a dynamic adjustment health model for the cold plate channel based on the fatigue life spectrum, thermal runaway risk matrix, and sensitivity field. It makes decisions based on the adjustment results of the health model. When the health level is lower than the threshold, it executes the channel supplementation command and outputs the optimized channel topology and parameter set.

6. The heavy-duty truck liquid-cooled supercharging topology dynamic optimization system according to claim 5, characterized in that, The acquisition module includes: Acquisition Unit: Used to acquire the real-time heat generation power, coolant inlet temperature and flow rate of the battery pack under different charging conditions; First calculation unit: used to calculate the spatiotemporal heat load distribution Q of the battery pack using the transient heat load equation. load (x,y,z,t), its calculation formula is as follows: In the formula, Q load Let P(t) be the heat load of the battery pack at position (x,y,z,t) and time t, and let P(t) be the heat generation power of the battery pack at time t. The heat carried away by the coolant; Drive unit: Used to drive the phase field evolution equation to output the phase field state based on the spatiotemporal heat load distribution. Its calculation formula is as follows: In the formula, φ is the phase field sequence parameter, t is time, M is the velocity of the phase interface movement, and ∈ is the gradient energy coefficient. Let H(φ) be the Laplace operator for the phase-field order parameter φ, and H(φ) be the double-well function. λ is the derivative of the barrier function g(φ) with respect to φ, λ is the temperature-phase-field coupling coefficient, T is the temperature, and T0 is the reference temperature; Judgment unit: Used to identify the coordinate set of the heat accumulation region based on the phase field state. When the phase field state is >0.8, the region is a high-temperature liquid phase region and is recorded as a heat accumulation region. Thus, by traversing the phase field state data, all coordinate sets that meet the conditions are extracted.

7. The heavy-duty truck liquid-cooled supercharging topology dynamic optimization system according to claim 5, characterized in that, The generation module includes: The first generation unit is used to solve the three-dimensional temperature gradient vector by using the central difference method through the thermocouple array distributed inside the battery pack. The vector includes components in the X, Y, and Z directions. The unit also performs convolution operation on the phase field state matrix to extract the rate of change of the phase field order parameter in the three spatial dimensions and generate a structured gradient dataset. The structured gradient dataset includes the position coordinates of each heat accumulation point, the temperature gradient vector, and the phase field gradient vector. The first building block: used to fuse gradient data with real-time system parameters to construct a sensitivity index for multi-physics coupling; Extraction unit: used to extract high-sensitivity flow channel candidate regions based on the sensitivity index of multi-physics coupling, and then generate a flow channel diameter hierarchical mapping table, which includes the flow channel diameters corresponding to different sensitivity regions, thereby performing flow channel space hierarchical configuration. The second generation unit is used to extract high-sensitivity flow channel candidate regions and generate a flow channel diameter hierarchical mapping table, which includes the flow channel diameters corresponding to different sensitivity regions.

8. The heavy-duty truck liquid-cooled supercharging topology dynamic optimization system according to claim 5, characterized in that, The computing module includes: Solving element: Used to solve for path optimization results based on the diameter mapping table, under the constraints of minimum bending radius Rmin = 5mm and volume fraction less than or equal to 15%. The calculation formula is as follows: In the formula, E is the flow channel edge set, ||e|| is the edge length, d(e) is the design diameter, and R(e) is the radius of curvature; Analysis Unit: Used to perform FFT spectrum analysis on the sensitivity field and generate adaptive pulses. Its calculation formula is as follows: In the formula, Q0 is the reference flow rate (unit: L / min), Q(t) is the instantaneous flow rate (unit: L / min), f is the pulse frequency (unit: Hz), and t is the instantaneous flow rate. peak The duration of the sensitivity peak (in seconds). Sensitivity change rate (unit: s) -1 ); The second building unit is used to build a PID controller based on adaptive pulses, select parameter configurations, and output command streams.

9. A topology dynamic optimization device for heavy-duty truck liquid-cooled supercharging, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the heavy-duty truck liquid-cooled supercharging topology dynamic optimization method as described in any one of claims 1 to 4 when executing the computer program.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, which, when executed by a processor, implements the heavy-duty truck liquid-cooled supercharging topology dynamic optimization method as described in any one of claims 1 to 4.

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