Intelligent control method for vehicle-mounted brushless fan

Abstract

The present invention relates to the field of brushless blower technology, and discloses an intelligent control method for a vehicle-mounted brushless blower. The method comprises: performing heat conduction analysis and optimizing configuration on the positive copper sheet and the negative copper sheet of the first double-layer copper sheet structure in the vehicle-mounted brushless blower controller to obtain a second double-layer copper sheet structure; performing bidirectional segmentation of the copper sheet heat flow to obtain heat flow distribution parameters of the upper and lower heat dissipation paths; collecting real-time thermal gradient monitoring data and calculating a three-dimensional thermal gradient matrix; dynamically adjusting the thickness of the thermal conductive mud covering the surface of the second double-layer copper sheet structure to obtain dynamic thickness distribution parameters of the thermal conductive mud; and creating a closed-loop heat dissipation control strategy for the vehicle-mounted brushless blower controller based on the upper and lower heat dissipation path heat flow distribution parameters and the dynamic thickness distribution parameters of the thermal conductive mud. The present invention integrates the upper and lower heat dissipation path heat flow distribution and the dynamic thickness distribution parameters of the thermal conductive mud to establish a complete closed-loop heat dissipation control strategy, thereby improving operational stability and safety.

Description

Technical Field

[0001] The present invention relates to the technical field of brushless fans, and in particular to an intelligent control method for a vehicle-mounted brushless fan. Background Art

[0002] Traditional automotive brushless blower controllers typically use a single-layer copper heat sink structure, relying on an aluminum housing or heat sink for passive heat dissipation during operation. This structure conducts heat generated by the high-frequency switching of the MOSFET to the external environment through a fixed heat dissipation path, providing a relatively simple and direct heat dissipation solution.

[0003] However, when high-frequency MOSFET switching generates large amounts of heat, single-layer heat dissipation systems often lead to uneven heat flow distribution, forming local hotspots, which affect system reliability and service life. Existing heat dissipation technologies focus on increasing the heat dissipation area, adding cooling fans, and using materials with better thermal conductivity. However, these methods are strictly limited within the limited installation space and cannot dynamically adjust the heat dissipation strategy according to actual operating conditions. Furthermore, existing thermal paste application methods typically use a fixed thickness and are evenly applied, which cannot be optimized for heat distribution characteristics in different locations, resulting in wasted heat dissipation resources and insufficient heat dissipation in hot spots.

[0004] Further analysis revealed that traditional cooling systems lack the ability to precisely control and dynamically adjust the heat flow conduction path, making it impossible to intelligently distribute heat flow across multi-layer heat dissipation structures. More critically, existing technologies lack a dynamic adjustment mechanism for the thermal interface based on real-time thermal gradient monitoring, making it impossible to differentiate the thickness of the thermal paste based on the temperature distribution characteristics of different regions. Furthermore, traditional control strategies lack a closed-loop control mechanism that collaboratively optimizes heat flow distribution parameters and thermal interface parameters, making it impossible to maximize heat dissipation efficiency and achieve intelligent thermal management in complex thermal environments. Summary of the Invention

[0005] The present invention provides an intelligent control method for a vehicle-mounted brushless fan. The present invention integrates the heat flux distribution of upper and lower heat dissipation paths and the dynamic thickness distribution parameters of the thermal mud to establish a complete closed-loop heat dissipation control strategy, thereby improving operational stability and safety.

[0006] In a first aspect, the present invention provides an intelligent control method for a vehicle-mounted brushless blower, the intelligent control method for a vehicle-mounted brushless blower comprising:

[0007] Conduct heat conduction analysis and optimize the configuration of the positive and negative copper sheets in the first double-layer copper sheet structure of the on-board brushless fan controller to obtain the second double-layer copper sheet structure.

[0008] Performing bidirectional heat flow segmentation of the copper sheet based on the second double-layer copper sheet structure to obtain heat flow distribution parameters for upper and lower heat dissipation paths;

[0009] Collecting real-time thermal gradient monitoring data of the second double-layer copper sheet structure, and calculating a three-dimensional thermal gradient matrix based on the real-time thermal gradient monitoring data;

[0010] Dynamically adjusting the thickness of the thermal conductive mud covering the surface of the second double-layer copper sheet structure based on the three-dimensional thermal gradient matrix to obtain a dynamic thickness distribution parameter of the thermal conductive mud;

[0011] Based on the heat flow distribution parameters of the upper and lower heat dissipation paths and the dynamic thickness distribution parameters of the thermal paste, a closed-loop heat dissipation control strategy of the on-board brushless fan controller is created.

[0012] Optionally, in a first implementation of the first aspect of the present invention, the heat conduction analysis and optimized configuration of the positive copper sheet and the negative copper sheet of the first double-layer copper sheet structure in the on-board brushless fan controller are performed to obtain a second double-layer copper sheet structure, including:

[0013] Physical parameter measurements were performed on the positive and negative copper sheets of the first double-layer copper sheet structure in the on-board brushless fan controller to obtain the copper sheet thickness and exposed area values.

[0014] Constructing a copper sheet thickness ratio and an exposed area ratio based on the copper sheet thickness value and the exposed area value to obtain a characterization parameter of the thermal conductivity characteristics of the double-layer copper sheet, wherein the copper sheet thickness ratio is defined as the ratio of the thickness of the positive electrode copper sheet to the thickness of the negative electrode copper sheet, and the exposed area ratio is defined as the ratio of the exposed area of ​​the positive electrode copper sheet to the exposed area of ​​the negative electrode copper sheet;

[0015] Conducting thermal conductivity tests on the first double-layer copper sheet structure under different temperature conditions to obtain thermal characteristic evaluation functions of the positive electrode copper sheet and the negative electrode copper sheet;

[0016] Constructing a thermal characteristic mathematical model based on the thermal conductivity characteristic characterization parameters of the double-layer copper sheet and the thermal characteristic evaluation function;

[0017] The copper sheet thickness ratio and the exposed area ratio of the positive electrode copper sheet and the negative electrode copper sheet are optimized according to the thermal characteristic mathematical model to obtain a second double-layer copper sheet structure.

[0018] Optionally, in a second implementation of the first aspect of the present invention, the thickness ratio and exposed area ratio of the positive electrode copper sheet and the negative electrode copper sheet are optimized according to the thermal characteristic mathematical model to obtain a second double-layer copper sheet structure, including:

[0019] Performing heat flow simulation analysis based on the thermal characteristic evaluation function in the thermal characteristic mathematical model to obtain an optimal solution of the thermal characteristic evaluation function;

[0020] Calculating the physical dimensions of the positive electrode copper sheet and the negative electrode copper sheet according to the optimal solution to obtain specific thickness and exposed area values ​​of each copper sheet;

[0021] Based on the specific thickness and exposed area values ​​of each copper sheet, the planar shape and spatial position of the positive copper sheet and the negative copper sheet are optimized to obtain a copper sheet layout plan;

[0022] A heat flow distribution verification test is performed on the copper sheet layout scheme to obtain a second double-layer copper sheet structure.

[0023] Optionally, in a third implementation of the first aspect of the present invention, performing bidirectional copper sheet heat flow segmentation based on the second double-layer copper sheet structure to obtain upper and lower heat dissipation path heat flow distribution parameters includes:

[0024] Performing bidirectional heat flow division on the second double-layer copper sheet structure to obtain a real-time heat flow distribution ratio;

[0025] Analyzing real-time temperature data of the vehicle-mounted brushless fan controller to obtain a temperature-related phase adjustment factor and a temperature-related efficiency coefficient;

[0026] Substituting the temperature-dependent phase adjustment factor and the temperature-dependent efficiency coefficient into the segmented heat flow equation for calculation, to obtain a real-time heat flow distribution value between the upper and lower copper sheets;

[0027] Based on the real-time heat flow distribution value between the upper and lower copper sheets, heat flow distribution is performed on the independent thermal management areas of the second double-layer copper sheet structure to obtain upper and lower heat dissipation path heat flow distribution parameters.

[0028] Optionally, in a fourth implementation of the first aspect of the present invention, analyzing the real-time temperature data of the on-board brushless fan controller to obtain the temperature-related phase adjustment factor and the temperature-related efficiency coefficient includes:

[0029] The temperature sensor array on the second double-layer copper sheet structure collects real-time temperature data of each point on the positive copper sheet and the negative copper sheet;

[0030] Performing regression analysis based on the correlation between the real-time temperature data and the MOSFET switching frequency to obtain a fitting equation for the relationship between temperature and phase;

[0031] Performing data fitting based on the correlation between the real-time temperature data and the heat flow conduction efficiency to obtain a corresponding function between temperature and efficiency coefficient;

[0032] The temperature-phase relationship fitting equation and the corresponding function of the temperature and efficiency coefficient are applied to the segmented heat flow equation to obtain a temperature-dependent phase adjustment factor and a temperature-dependent efficiency coefficient. The temperature-dependent phase adjustment factor controls the phase of heat flow distribution, and the temperature-dependent efficiency coefficient adjusts the efficiency of heat flow distribution.

[0033] Optionally, in a fifth implementation of the first aspect of the present invention, collecting real-time thermal gradient monitoring data of the second double-layer copper sheet structure and calculating a three-dimensional thermal gradient matrix based on the real-time thermal gradient monitoring data includes:

[0034] Collecting real-time thermal gradient monitoring data of the second double-layer copper sheet structure;

[0035] The real-time thermal gradient monitoring data is numerically calculated using a finite difference method to obtain temperature gradient values ​​in each direction, wherein the temperature gradient values ​​in each direction include temperature change rates in the x-direction, the y-direction, and the z-direction;

[0036] The temperature gradient values ​​in each direction are organized into a three-dimensional thermal gradient matrix, and the three-dimensional thermal gradient matrix is ​​used to characterize the direction and intensity of heat transfer in the copper sheet structure.

[0037] Optionally, in a sixth implementation of the first aspect of the present invention, dynamically adjusting the thickness of the thermal conductive mud covering the surface of the second double-layer copper sheet structure based on the three-dimensional thermal gradient matrix to obtain a dynamic thickness distribution parameter of the thermal conductive mud includes:

[0038] Calculating temperature distribution characteristic parameters based on the three-dimensional thermal gradient matrix to obtain temperature difference values ​​in each region, wherein the temperature distribution characteristic parameters include the highest temperature point and the average temperature;

[0039] Calculate the thickness of the thermal conductive mud based on the temperature distribution characteristic parameters to obtain the thickness value of the thermal conductive mud at each point;

[0040] Optimizing the spatial distribution of the thermal paste thickness values ​​at each point to obtain a thermal paste distribution control signal, wherein the thermal paste distribution control signal is used to control a micro piezoelectric ceramic drive device to adjust the precise distribution of the thermal paste;

[0041] The thermal conductive mud distribution control signal is applied to the thermal conductive mud control system on the surface of the second double-layer copper sheet structure to obtain a dynamic thickness distribution parameter of the thermal conductive mud. The dynamic thickness distribution parameter of the thermal conductive mud reduces the thickness of the thermal conductive mud in the high-temperature area to increase the heat dissipation efficiency, and increases the thickness of the thermal conductive mud in the low-temperature area to reduce the heat dissipation efficiency.

[0042] Optionally, in a seventh implementation of the first aspect of the present invention, creating a closed-loop heat dissipation control strategy for the on-board brushless fan controller based on the upper and lower heat dissipation path heat flux distribution parameters and the dynamic thickness distribution parameters of the thermal paste includes:

[0043] Inputting the heat flux distribution parameters of the upper and lower heat dissipation paths and the dynamic thickness distribution parameters of the thermal paste into a hierarchical control architecture to obtain a heat dissipation control instruction set, wherein the hierarchical control architecture includes a temperature acquisition and thermal gradient calculation module, a heat flux distribution and thermal paste control module, and a system operation status management module;

[0044] Control the fan speed and the thermal mud distribution device according to the heat dissipation control instruction set to obtain real-time heat dissipation execution results;

[0045] Calculating the deviation between the real-time heat dissipation execution result and the preset heat dissipation target to obtain a heat dissipation control correction parameter, wherein the heat dissipation control correction parameter is used to dynamically adjust the heat flow distribution ratio and the thickness distribution of the thermal paste;

[0046] A protection strategy judgment is performed based on the heat dissipation control correction parameter to obtain a closed-loop heat dissipation control strategy of the on-board brushless fan controller, wherein the protection strategy judgment includes triggering over-temperature protection when the temperature exceeds 85 degrees Celsius, forcing power off when the temperature exceeds 100 degrees Celsius, and triggering over-current protection when the current exceeds 150% of the rated current.

[0047] In the technical solution provided by the present invention, the uniform distribution of heat flow between the upper and lower heat dissipation paths is achieved through the precise configuration of the thickness ratio and the exposed area ratio of the double-layer copper sheet structure, avoiding the problem of hot spot concentration in traditional single-layer heat dissipation systems and making the temperature distribution more uniform. Based on the copper sheet heat flow bidirectional segmentation algorithm, the heat flow distribution ratio is adjusted in real time according to temperature changes and MOSFET switching frequency, so that the heat dissipation system can intelligently respond to changes in thermal load under different working conditions and adapt to various working conditions. Through the three-dimensional thermal gradient matrix monitoring system, comprehensive monitoring of the temperature of each key position of the double-layer copper sheet structure is achieved, and temperature gradient information in the x, y, and z directions is obtained. The thickness distribution of the thermal conductive mud is dynamically adjusted based on the three-dimensional thermal gradient matrix, so that the thermal conductive mud in the high-temperature area becomes thinner to enhance heat dissipation, and the thermal conductive mud in the low-temperature area becomes thicker to reduce heat dissipation, thereby achieving active management and balanced distribution of heat. The heat flow distribution of the upper and lower heat dissipation paths and the dynamic thickness distribution parameters of the thermal conductive mud are integrated to establish a complete closed-loop heat dissipation control strategy, which includes multiple protection mechanisms such as overtemperature and overcurrent, thereby improving the stability and safety of system operation. The double-layer copper sheet structure features an overlapping design, enabling bidirectional heat dissipation within the limited controller space. This significantly improves space utilization and heat dissipation efficiency compared to traditional single-layer cooling systems. The unique layout of the copper sheet minimizes interference with the control circuitry on the PCB and reduces conducted harassment while ensuring heat dissipation performance, achieving coordinated optimization of thermal management and electromagnetic compatibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0049] Figure 1 A schematic diagram of an embodiment of an intelligent control method for a vehicle-mounted brushless blower according to an embodiment of the present invention;

[0050] like Figure 2 Schematic diagram of the structure of the hierarchical control architecture in the embodiment of the present application. DETAILED DESCRIPTION

[0051] An embodiment of the present invention provides an intelligent control method for a vehicle-mounted brushless blower. The terms "first," "second," "third," "fourth," and so on (if any) in the specification and claims of the present invention and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, products, or apparatus.

[0052] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 An embodiment of the intelligent control method of a vehicle-mounted brushless blower in an embodiment of the present invention includes:

[0053] Step S101: performing heat conduction analysis and optimizing configuration on the positive copper sheet and the negative copper sheet of the first double-layer copper sheet structure in the vehicle-mounted brushless blower controller to obtain a second double-layer copper sheet structure;

[0054] It is understandable that the execution subject of the present invention can be an intelligent control system of a vehicle-mounted brushless blower, or a terminal or a server, which is not limited here. The embodiment of the present invention is described by taking a server as the execution subject as an example.

[0055] Specifically, the thickness of the positive and negative copper sheets was measured using a high-precision thickness gauge and a laser profilometer, while the exposed area of ​​the two layers of copper sheets was evaluated using image recognition and area integration algorithms. Based on the measured data, the copper sheet thickness ratio and exposed area ratio were constructed to obtain the geometric thermal conductivity characteristic parameters of the double-layer copper sheet structure. These two ratios are the core quantitative indicators that determine the heat flow distribution characteristics. The thermal conductivity performance of the first double-layer copper sheet structure was tested in a multi-temperature environment from -20°C to 100°C. The steady-state heat conduction rates of the positive and negative copper sheets at different temperature points were recorded under current excitation. The temperature gradient values ​​were collected using a thermocouple array, and the temperature-related thermal characteristic functions of the positive and negative copper sheets were constructed in combination with the change law of heat flow distribution. Based on the thickness ratio, area ratio and temperature-related thermal characteristic function, a mathematical model for the thermal conduction behavior of the double-layer copper sheet was constructed. The model uses a multivariable heat conduction efficiency function and integrates the coupling relationship between the geometric structure parameters and the material thermal physical parameters.

[0056] Based on the thermal characteristic evaluation function, heat flow simulation analysis was used to simulate a large number of parameter combinations, generating a surface plot of parameter changes and heat flow efficiency responses. Data fitting and extreme value search were used to identify the thickness and area ratios that optimize heat dissipation performance. Substituting the optimal solution into the physical dimension calculation process, combined with the package volume, electrical connection requirements, and manufacturing process conditions, the thickness of the positive copper sheet was determined to be 0.62mm, and the thickness of the negative copper sheet was determined to be 0.80mm. The exposed area of ​​the upper copper sheet was slightly larger than that of the lower copper sheet to enhance thermal radiation efficiency.

[0057] Based on the specific thickness and exposed area of ​​each copper sheet, the planar shape of each copper sheet was meticulously modeled. CAD thermal simulation tools were used to design the copper sheet's contours and edge transition curves. The vertical stacking relationship and horizontal placement of each copper sheet within the spatial structure were determined to avoid localized accumulation and heat flow interference. The copper sheet layout was then tested for heat flow distribution verification. A temperature sensor array was used to collect real-time thermal data and analyze indicators such as thermal gradient, heat flux density, and temperature uniformity. When the test data demonstrated balanced heat flow distribution, no concentrated hot spots, and temperature rise within the specified range, the second double-layer copper sheet structure was determined.

[0058] Step S102: performing bidirectional heat flow segmentation of the copper sheet based on the second double-layer copper sheet structure to obtain heat flow distribution parameters of the upper and lower heat dissipation paths;

[0059] Specifically, a real-time heat flow segmentation mechanism is established based on the second double-layer copper sheet structure. A heat flow monitoring and temperature sensing system dynamically models the heat conduction path within the copper sheet. This continuously monitors the MOSFET's operating status and thermal load changes, acquires real-time temperature distribution data for different areas within the controller, calculates the heat conduction behavior between the upper and lower copper sheets, and determines the current heat flow distribution ratio between the two thermally conductive layers.

[0060] A temperature trend curve is constructed using feedback from the temperature sensor array. Combined with the thermal system response model, a temperature-dependent phase adjustment factor and a temperature-dependent efficiency coefficient are extracted. The former corrects for timing differences in heat flow response caused by temperature changes, while the latter dynamically assesses changes in the thermal conductivity of the copper material at different temperature points, enabling the heat flow control mechanism to proactively adapt to temperature rise trends.

[0061] The temperature-dependent phase adjustment factor and efficiency coefficient are incorporated into the heat flow regulation function, performing real-time segmented calculations to output the heat flow distribution values ​​for the upper and lower copper sheets. This process integrates multi-dimensional parameters such as frequency perturbations, phase offsets, and temperature effects to ensure that the copper sheet system maintains stable conduction efficiency despite thermal load fluctuations. Based on the heat flow distribution values, heat flow control is implemented in independent thermal management zones within the copper sheet structure. Each zone corresponds to the area near key components such as large capacitors, inductors, or ferrite beads, and the heat flow ratio between the upper and lower copper sheets in each zone is dynamically adjusted.

[0062] A high-precision temperature sensing network was constructed, with NTC thermistor arrays installed at key thermal nodes on the positive and negative copper plates to continuously sample temperatures at different spatial locations. The sampling frequency ensured a synchronous response to the high-frequency switching characteristics of the MOSFETs, which ranged from 8.2kHz to 22.7kHz. Real-time temperature data was paired with the MOSFET switching frequency, and a dynamic relationship between temperature changes and thermal pulse frequency was established through time series regression analysis.

[0063] Polynomial fitting or sinusoidal function approximation methods are used to establish a mathematical mapping relationship between temperature and phase change, forming a fitting equation for the temperature-phase relationship, and dynamically correcting the phase delay error in the heat conduction path between the upper and lower copper sheets. A corresponding function between temperature and heat conduction efficiency is also constructed to reflect the nonlinear effect of the copper sheet material's microstructural changes on thermal diffusion capacity as temperature rises. Two fitting functions are applied to the piecewise heat flow equation to calculate the temperature-dependent phase adjustment factor and efficiency coefficient in real time. The former controls the phase offset of heat flow allocation, while the latter dynamically corrects the distribution ratio of the total heat flow between the upper and lower copper sheets.

[0064] Step S103: collecting real-time thermal gradient monitoring data of the second double-layer copper sheet structure, and calculating a three-dimensional thermal gradient matrix based on the real-time thermal gradient monitoring data;

[0065] Specifically, an array of temperature sensors is deployed at key locations within the second double-layer copper sheet structure to achieve high-resolution thermal field sensing. These temperature sensors, based on NTC thermistors, are located at multiple representative locations on the surfaces of the positive and negative copper sheets, covering the edges, center, and proximity of the electromagnetic elements. This forms a multi-node spatial sampling network capable of real-time acquisition of temperature variations at various locations within the copper sheet under different operating conditions. Through a high-frequency sampling mechanism, thermal data with temporal continuity and spatial distribution characteristics is acquired. The temperature data between each sampling point is numerically processed using the finite difference method. This method uses the temperature difference between adjacent points in the temperature field and the difference between their corresponding coordinates as the calculation basis to estimate the temperature change rate in the x, y, and z directions. The x direction corresponds to the horizontal axis of the copper sheet, the y direction corresponds to the lateral direction perpendicular to the axis, and the z direction represents the vertical direction between the upper and lower copper sheets, i.e., the path for heat transfer through the thermal paste layer. During the calculation process, the collected discrete temperature values ​​are subjected to difference quotient processing based on a preset coordinate grid to obtain the temperature gradient values ​​in each direction of the thermal field. After completing the directional gradient calculation, the temperature change rate data in the three directions are summarized into a three-dimensional thermal gradient matrix according to the sampling point organization structure. This matrix is ​​essentially a mathematical tensor with spatial coordinates as indexes and directional temperature gradients as content. It is used to characterize the directionality and intensity characteristics of heat transfer in the copper sheet structure.

[0066] Step S104: dynamically adjust the thickness of the thermal conductive mud covering the surface of the second double-layer copper sheet structure based on the three-dimensional thermal gradient matrix to obtain a dynamic thickness distribution parameter of the thermal conductive mud;

[0067] Specifically, a three-dimensional thermal gradient matrix is ​​used to analyze the thermal distribution of the copper structure's surface. High-frequency sampling of temperature gradient data in the x, y, and z directions accurately restores the directionality and intensity of heat conduction at each monitoring point, extracting the spatial variation characteristics of the overall temperature field. The temperature distribution characteristic parameters are calculated, including locating the coordinates and value of the highest temperature point, calculating the average temperature of all sampling points and the temperature difference between each point, and identifying the distribution pattern of hot spots and redundant heat dissipation areas.

[0068] Based on temperature difference data, combined with the thermal paste's physical properties and thermal resistance adjustment characteristics, a thickness adjustment function is used to calculate the thickness of the thermal paste at each thermal monitoring point. This function uses the base thickness as a starting point and determines the thickness adjustment strategy based on the difference between the temperature at that point and the average temperature. In hot spots, the system automatically reduces the thickness of the thermal paste, allowing heat to flow more quickly through the thermal layer to the air convection zone, improving local heat dissipation efficiency. In cooler areas, the thermal paste thickness is actively increased to slow heat leakage.

[0069] The spatial distribution of the initially calculated thickness values ​​was optimized, with edge smoothing and gradient transition mechanisms introduced to ensure the structural continuity of the thermal paste layer and controllable thermal resistance. The optimized thickness data was encoded into a thermal paste distribution control signal, organized according to the spatial coordinate-thickness relationship, to control a micro-piezoelectric ceramic actuator. This device consists of multiple independently controlled piezoelectric injection valves, positioned at key locations on the copper sheet's surface. The control signals precisely adjust the localized thermal paste injection thickness.

[0070] The thermal paste distribution control signal is fed into the control system in real time, enabling the piezoelectric drive module to form a composite thermal conductive layer with differentiated thermal conductivity on the copper sheet surface, generating a dynamic thickness distribution parameter for the thermal paste. This parameter prioritizes heat dissipation in hot spots, enabling rapid response to thermal load fluctuations. This parameter increases heat flux by thinning the thermal paste in high-temperature areas and achieves thermal field equilibrium by thickening the thermal paste in low-temperature areas.

[0071] Step S105: creating a closed-loop heat dissipation control strategy for the vehicle-mounted brushless fan controller based on the heat flow distribution parameters of the upper and lower heat dissipation paths and the dynamic thickness distribution parameters of the thermal paste.

[0072] Specifically, the system uses heat flux distribution parameters and the dynamic thickness distribution parameters of the thermal paste as inputs and embeds them into the hierarchical control architecture of the onboard brushless blower controller for processing and execution. This architecture consists of three functional modules: a temperature acquisition and thermal gradient calculation module, a heat flux distribution and thermal paste control module, and a system operation status management module. These modules work together to accurately sense and proactively respond to the controller's thermal status.

[0073] The temperature acquisition and thermal gradient calculation module receives real-time signals from multiple temperature sensors within the copper structure, continuously updating the three-dimensional thermal gradient matrix. The heat flux distribution and thermal paste control module generates a cooling control instruction set based on heat flux distribution parameters and thermal paste thickness distribution data. These instructions include PWM signals to control fan speed and execution instructions to drive the piezoelectric ceramic device to adjust the thickness of the thermal paste injection. This instruction set is transmitted to the execution layer in real time, enabling the system to rapidly adjust the cooling path and thermal interface based on transient changes in the heat load.

[0074] The system continuously monitors the effectiveness of thermal control instructions and calculates a thermal control correction parameter by comparing the current thermal state with the preset thermal target. This parameter represents the degree of deviation from the optimal thermal management target. This feedback is used to adjust the heat flux distribution ratio and the thickness of the thermal paste. For example, if the temperature of a certain copper area is higher than the average thermal field, the corresponding fan speed is dynamically increased and the thermal paste thickness is reduced. If a certain area is overcooled, the fan speed is reduced and the thermal paste layer is thickened.

[0075] The system's operational status management module continuously executes protection strategy assessments to ensure rapid response to extreme temperatures or current anomalies. When the temperature in any critical copper area exceeds 85°C, the overtemperature protection mechanism is triggered, increasing airflow. When the temperature exceeds 100°C, a forced power outage is immediately implemented to prevent thermal runaway. When the current exceeds 150% of the rated value, overcurrent protection is triggered, suspending power output and initiating the fault logging and reporting process.

[0076] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0077] Physical parameter measurements were performed on the positive and negative copper sheets of the first double-layer copper sheet structure in the on-board brushless fan controller to obtain the copper sheet thickness and exposed area values.

[0078] Based on the copper sheet thickness values ​​and exposed area values, the copper sheet thickness ratio and exposed area ratio were constructed to obtain the characterization parameters of the thermal conductivity characteristics of the double-layer copper sheet. The copper sheet thickness ratio is defined as the ratio of the positive copper sheet thickness to the negative copper sheet thickness, and the exposed area ratio is defined as the ratio of the positive copper sheet exposed area to the negative copper sheet exposed area.

[0079] The thermal conductivity characteristics of the first double-layer copper sheet structure are tested under different temperature conditions to obtain thermal characteristic evaluation functions of the positive copper sheet and the negative copper sheet;

[0080] A thermal characteristic mathematical model is constructed based on the thermal conductivity characteristic characterization parameters and thermal characteristic evaluation function of the double-layer copper sheet;

[0081] The copper sheet thickness ratio and exposure area ratio of the positive copper sheet and the negative copper sheet are optimized according to the thermal characteristic mathematical model to obtain a second double-layer copper sheet structure.

[0082] Specifically, for the first double-layer copper sheet structure, the geometric shapes of its positive and negative copper sheets are identified and measured. In practice, thickness measurement uses a laser displacement sensor, which has submillimeter resolution and non-contact advantages, and can avoid deformation of the soft copper sheet caused by measuring contact pressure. For example, the measurement of the positive copper sheet yields a set of thickness values, such as 0.62mm for the first positive copper sheet, and 0.62mm for the second, third, and fourth positive copper sheets, respectively, indicating that the layer design has uniform thickness characteristics; while the negative copper sheet is measured to have a thickness of 0.80mm for the first and second negative copper sheets, thus forming a typical thickness asymmetric structure. The ratio of the representative thickness of the positive copper sheet to the representative thickness of the negative copper sheet is calculated to obtain the copper sheet thickness ratio. For example, the ratio of 0.62 to 0.80 is 0.775, which becomes an important structural parameter used in subsequent modeling to represent the difference in heat capacity distribution and conduction efficiency. After completing the thickness measurement, the exposed area is determined. The exposed area refers to the area of ​​the copper sheet that is in direct contact with the air, housing, thermal paste, or other heat transfer media. Its size determines the total amount of heat exchange that the copper sheet can complete per unit time. This parameter is measured using two methods: image processing and coordinate data integration. Taking the image processing method as an example, a horizontal projection image of the copper sheet is obtained under uniform lighting conditions, and the copper sheet boundary is delineated using edge detection and contour extraction algorithms, converting the pixel area into the actual area. If the total exposed area of ​​the positive copper sheet is 76.3cm 2 , and the total exposed area of ​​the negative copper sheet is 60.5cm 2, the calculated exposed area ratio is 1.26, which characterizes the expansion characteristics of the upper copper sheet in the heat exchange surface area. By combining the two parameters of thickness ratio and exposed area ratio, a basic thermal conduction characterization model for the double-layer copper sheet thermal structure is formed. The thermal conductivity characteristics of the first double-layer copper sheet structure are tested under different temperature conditions to clarify the thermal conductivity of copper sheets of different thicknesses and different surface areas under different temperature conditions. The test process is carried out alternately in a constant temperature heating table and a controllable cooling environment, and the temperature nodes are gradually set, for example, from 25°C to 100°C, and the thermal response data is recorded every 10°C. A thermocouple array is used in conjunction with a high-sensitivity infrared thermometer to obtain the temperature distribution of the positive and negative copper sheets under a constant input heat flux, and the temperature rise rate is calculated to obtain its heat transfer capacity per unit length at the current temperature, and establish a thermal characteristic evaluation function that changes with temperature. This function was fitted with measured data. For example, it was found that the positive copper sheet exhibited better thermal conductivity than the negative copper sheet below 60°C, while a reversal of thermal conductivity occurred above 85°C. This temperature-dependent performance crossover demonstrates that in practical applications, the thickness or area of ​​the copper sheet cannot be designed based on a one-size-fits-all approach; the overall thermal response under dynamic conditions must be considered. The above thermal conductivity experimental data was combined with the thickness ratio and area ratio parameters to construct a mathematical model of thermal characteristics. This model simulates and predicts the equilibrium state and ultimate heat conduction capacity of a double-layer copper sheet structure under different thickness and area combinations. It also evaluates the appropriate distribution of heat flow between the positive and negative electrodes under specific thermal power input conditions. The model uses response surface modeling, interpolation fitting, or machine learning regression to generate outputs, such as thermal performance indicators like heat flux distribution balance, minimum temperature gradient, and minimum thermal resistance. This model allows computational simulations to predict the thermal response differences resulting from various thickness and area ratio combinations without actually manufacturing each copper sheet combination, thereby efficiently screening structural parameters. Based on the optimal thermal efficiency solution output by the thermal characteristics mathematical model, the specific physical values ​​corresponding to the optimal thickness ratio and exposed area ratio were applied to the construction of the second double-layer copper sheet structure. For example, the model results showed that when the thickness ratio was 0.78 and the exposed area ratio was 1.26, the heat flux distribution was most balanced and the temperature gradient was minimized. The thickness of the positive copper sheet was selected to be 0.62mm, and that of the negative copper sheet was 0.80mm. At the same time, the total exposed area of ​​the positive copper sheet was set to 76.3cm. 2 、The negative electrode is 60.5cm 2 , that is, to form an optimal heat dissipation structure that meets the triple requirements of structural mechanics, electrical connection and thermal management.

[0083] In a specific embodiment, the step of optimizing the thickness ratio and exposed area ratio of the positive electrode copper sheet and the negative electrode copper sheet according to the thermal characteristics mathematical model to obtain the second double-layer copper sheet structure may specifically include the following steps:

[0084] Conduct heat flow simulation analysis based on the thermal characteristic evaluation function in the thermal characteristic mathematical model to obtain the optimal solution of the thermal characteristic evaluation function;

[0085] The physical dimensions of the positive and negative copper sheets are calculated based on the optimal solution to obtain the specific thickness and exposed area of ​​each sheet.

[0086] Based on the specific thickness and exposed area of ​​each copper sheet, the planar shape and spatial position of the positive and negative copper sheets are optimized to obtain a copper sheet layout plan.

[0087] The copper sheet layout scheme was subjected to heat flow distribution verification testing to obtain the second double-layer copper sheet structure.

[0088] Specifically, a heat flow simulation analysis is performed based on the thermal characteristic evaluation function defined in the thermal characteristic mathematical model. The thermal characteristic evaluation function is a composite parameter function whose input includes parameters such as the thickness ratio of the positive and negative copper sheets, the exposed area ratio, and the trend of temperature-related thermal conductivity. The output is used to evaluate key thermal performance indicators such as the heat flow distribution balance, the degree of temperature gradient minimization, and the heat transfer efficiency of the copper sheet per unit volume. In order to obtain the optimal solution of the function, a parameter space is constructed, the thickness ratio and the area ratio are set as continuous adjustable variables, and multiple representative temperature points are set within the actual thermal load range of the controller. A systematic simulation calculation is performed using a finite element thermal flow simulation tool to generate a multidimensional response surface data set. The response surface is then globally searched using an optimization algorithm to find the function extreme point. In the actual analysis process, for example, when the thickness ratio is 0.78 and the exposed area ratio is 1.26, the heat flow distribution balance coefficient output by the thermal characteristic evaluation function is the largest and the temperature gradient is the smallest. This set of parameters is determined to be the optimal solution under the thermal model. The optimal parameter set was substituted into the physical dimensional design model of the actual copper sheet structure. Based on constraints such as the controller's internal wiring, electromagnetic component placement, and electrical clearance requirements, the thickness ratio and area ratio were converted to the actual geometric dimensions of each copper sheet. For example, if the negative copper sheet thickness is selected to be 0.80 mm, the positive copper sheet thickness should be calculated to be 0.62 mm based on a thickness ratio of 0.78. Furthermore, if the available exposed area of ​​the negative copper sheet is 60.5 square centimeters, the positive copper sheet must be 76.3 square centimeters to meet an area ratio of 1.26. This yielded the target geometric specifications for the upper and lower copper sheets and provided the physical dimensional foundation for constructing the two-layer structure. To enhance the continuity and distribution efficiency of the heat conduction path, the planar shape and spatial position of the positive and negative copper sheets were optimized based on the aforementioned dimensional data. In the planar shape design, the relative position of the copper sheets to key heat sources (such as MOSFETs, inductors, large and small capacitors) was considered to ensure that heat can be directly transferred from the source to the copper sheet structure and rapidly dissipated. For example, the positive copper sheet is divided into four pieces, which are respectively connected to the inductor, large capacitor, small capacitor and both ends of the magnetic bead. This arrangement not only ensures the integrity of the electrical path, but also enhances the heat collection efficiency. The negative copper sheet is divided into two pieces, one of which is connected to the large capacitor and the other to the small capacitor. They are physically designed to be partially curved to avoid spatial interference with the upper magnetic bead assembly, thereby achieving dual coordination of mechanical structure and thermal design. In terms of spatial position design, the upper and lower copper sheets are separated by an insulating plastic layer, and a sufficient overlapping area is set to form a stable heat diffusion surface. The two layers of copper sheets need to be staggered and interwoven in the exposed area to reduce heat accumulation and enhance heat dissipation uniformity. After completing the copper sheet layout plan, its heat flow distribution effect is verified and tested in an actual or simulated environment to confirm whether it meets the thermal optimization goals.Verification is divided into two dimensions: numerical simulation and physical experimentation. In the numerical simulation phase, computational fluid dynamics and thermal conduction simulation software are used to model and analyze the temperature field distribution and heat flow vectors under the new layout to observe whether there are hotspots, heat flow bottlenecks, or areas of gradient anomalies. In the physical experiment phase, multiple temperature sensors are placed on the copper sheet to monitor the thermal field. The collected temperature data is spatially differentiated to infer the actual heat flow path and distribution intensity, and then compared with the theoretical predictions. If the temperature gradient is uniform, the heat diffusion path is clear, and the high-temperature area quickly shifts to the heat dissipation boundary, it indicates that the copper sheet layout scheme has good performance under actual working conditions. If the test results show that the copper sheet thickness distribution is reasonable, the exposed area matches the heat density, the heat flow distribution is balanced and there are no overheating areas, and the comprehensive performance indicators such as spatial structure, electrical layout, and thermal response speed are met, the scheme is confirmed as the second double-layer copper sheet structure.

[0089] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0090] Perform bidirectional heat flow segmentation on the second double-layer copper sheet structure to obtain the real-time heat flow distribution ratio;

[0091] Analyze the real-time temperature data of the vehicle-mounted brushless fan controller to obtain the temperature-related phase adjustment factor and temperature-related efficiency coefficient;

[0092] Substitute the temperature-dependent phase adjustment factor and the temperature-dependent efficiency coefficient into the segmented heat flow equation to calculate the real-time heat flow distribution value between the upper and lower copper sheets;

[0093] Based on the real-time heat flow distribution value between the upper and lower copper sheets, heat flow distribution is performed on the independent thermal management areas of the second double-layer copper sheet structure to obtain the heat flow distribution parameters of the upper and lower heat dissipation paths.

[0094] Specifically, the second double-layer copper sheet structure undergoes bidirectional heat flow partitioning, distributing the total heat flow between the upper positive copper sheet and the lower negative copper sheet based on the current heat load. This partitioning process continuously adjusts as the operating states of the internal components of the on-board brushless blower controller change. To this end, a heat flow distribution response model is constructed within the controller. Real-time data on the system's power state, current fluctuations, and device switching frequency are collected. This data is dynamically correlated with the heat source input, thereby deriving the changing trend of the system's total heat flow per unit time. Furthermore, bidirectional heat flow partitioning must be coupled and regulated based on the physical structure's heat dissipation capacity, the current heat load distribution, and real-time temperature feedback. Therefore, the heat flow allocation ratio—the ratio of the total heat flow allocated to the upper copper sheet to the lower copper sheet—is calculated in real time. This ratio reflects the current priority of the heat dissipation path for heat conduction in different directions and serves as a fundamental variable for subsequent heat flow control. To improve the response accuracy and environmental adaptability of the split control, a temperature feedback mechanism is introduced. A sensor array collects multi-point temperature data from the upper and lower copper sheets, which is then recorded and trended to extract dynamic characteristics of the current thermal environment. By comparing the coupled relationship between temperature data and parameters such as MOSFET switching frequency and load waveform, a dynamic response model between temperature and phase was constructed. From this model, a temperature-dependent phase adjustment factor was extracted, which reflects the relative difference in the thermal response speed of the copper sheet at a specific temperature level. Simultaneously, a temperature-dependent efficiency coefficient was calculated. This coefficient represents the degree to which the actual heat transfer efficiency of the copper sheet material is reduced compared to its theoretical thermal conductivity limit at the current temperature. This coefficient, extracted by the ratio between the measured temperature and the calculated heat conduction path, provides a physical description of the actual heat conduction capacity. The temperature-dependent phase adjustment factor and the temperature-dependent efficiency coefficient were substituted into the piecewise heat flow equation to calculate the real-time heat flow distribution value. In this process, the total heat flow is used as input, and the two dynamic factors are used as parameter correction terms to adjust the heat flow distribution amplitude and response speed between the positive and negative copper sheets to adapt to the changes in thermal conductivity under different temperature field conditions. This ensures that the side with stronger thermal conductivity is preferentially strengthened in high-temperature areas, while the heat flow path is switched promptly in low-temperature areas or areas with sudden heat flow changes. For example, if the temperature continues to rise to above 75°C, the efficiency coefficient indicates that the thermal conductivity of the upper copper sheet is improved under high temperature conditions. At this time, the system automatically shifts the heat flow distribution ratio to the upper layer to accelerate heat dissipation. Conversely, during the low-temperature startup phase, if the lower copper sheet exhibits better thermal conductivity stability, the system adjusts the distribution parameters to tilt toward the lower layer to ensure that initial heat diffusion is not retained. After the total heat flow distribution is completed, it is refined into multiple independent thermal management areas set in the second double-layer copper sheet structure. Taking into account the complexity of the copper sheet structure in planar layout, there are significant differences in the heat load sources, structural connection methods, and power density of surrounding components in different areas. Therefore, independent heat flow distribution is performed according to regional thermal demand, local temperature gradient, and thermal response capability.To achieve this goal, the temperature distribution of each sub-region within the three-dimensional thermal gradient matrix is ​​coupled with a real-time heat flow response model for analysis. The heat load index and heat dissipation capacity coefficient for each region are extracted. Based on this, a regional heat flow allocation function is constructed. This allows the total heat flow to be rationally subdivided into multiple regions within the same layer while maintaining the upper and lower distribution ratios. This ensures that hotspots receive higher heat flow conduction density, while cold areas retain appropriate thermal energy, resulting in an adaptive heat flow equilibrium within the layer. After completing the regional heat flow allocation, the heat flow values ​​for each layer and each block are summarized and output to form the heat flow allocation parameters for the upper and lower heat dissipation paths.

[0095] In a specific embodiment, the step of analyzing the real-time temperature data of the vehicle-mounted brushless fan controller to obtain the temperature-related phase adjustment factor and the temperature-related efficiency coefficient may specifically include the following steps:

[0096] The temperature sensor array on the second double-layer copper sheet structure collects real-time temperature data of each point on the positive copper sheet and the negative copper sheet;

[0097] Based on the correlation between real-time temperature data and MOSFET switching frequency, a regression analysis was performed to obtain a fitting equation for the relationship between temperature and phase.

[0098] Based on the correlation between real-time temperature data and heat conduction efficiency, data fitting is performed to obtain the corresponding function of temperature and efficiency coefficient;

[0099] The temperature-phase relationship fitting equation and the corresponding function of temperature and efficiency coefficient are applied to the segmented heat flow equation to obtain the temperature-dependent phase adjustment factor and the temperature-dependent efficiency coefficient. The temperature-dependent phase adjustment factor controls the phase of heat flow distribution, and the temperature-dependent efficiency coefficient regulates the efficiency of heat flow distribution.

[0100] Specifically, a real-time thermal monitoring system based on spatial multi-point sensing was constructed. This system is attached to the second double-layer copper sheet structure. High-precision temperature sensor arrays are pre-placed in key heat source distribution areas. NTC thermistor sensors are used as sensing units and are installed at several nodes on the positive and negative copper sheets, covering the edges and center of the sheets, as well as locations in direct contact with or thermally coupled to major heat-generating components (such as MOSFETs, inductors, and capacitors). The temperature acquisition system operates at a sampling frequency exceeding 10 Hz, ensuring high temporal resolution of the temperature response over time and a measurement accuracy within ±0.1°C, enabling the reconstruction of the microscopic process of heat diffusion. The temperature data collected by these sensors is transmitted to the main controller via a multi-channel ADC interface, where it is synchronized and paired with the MOSFET switching frequency data in real time to construct a sample set of temperature-frequency data pairs. In practice, the MOSFET switching frequency directly affects the number of on- and off-cycles per unit time, which in turn determines the pulse density of the heat source. This heat pulse density significantly influences the phase propagation characteristics of the thermal wave in the copper sheet. Therefore, based on the frequency and temperature variation patterns under multiple thermal load levels, a regression analysis algorithm was used to model the relationship between the two. In this process, polynomial fitting, exponential decay function fitting, or sinusoidal trend regression were used to construct the most suitable function model based on the variation trend between frequency and temperature rise. For example, when the MOSFET frequency increased from 8kHz to 22kHz, the copper temperature in the corresponding area rose from 45°C to 78°C. However, the temperature rise did not increase linearly, but rather had a certain hysteresis and nonlinear inflection points. Therefore, the use of a first-order linear fit could not fully express its physical process. At this time, a sinusoidal regression function with a temperature correction factor was introduced to more realistically reflect the dynamic offset of the thermal pulse phase during the temperature change process. After completing the regression analysis between temperature and frequency, a fitting equation for temperature and phase change was established. The physical meaning of this equation is that when the system temperature changes, the phase delay of the heat wave propagating along the heat conduction path changes due to changes in the microscopic lattice vibration frequency, thermal expansion behavior, and contact resistance within the copper sheet. This causes a time shift in the heat flux distribution. This shift must be dynamically compensated by a phase adjustment factor to ensure that the heat flux is effectively distributed within the optimal time window. Simultaneously, a second functional model is constructed based on the correlation between real-time temperature data and heat conduction efficiency. The thermal conductivity of the copper sheet at different temperatures is affected by many factors. For example, the thermal diffusivity of the material itself is governed by nonlinear temperature changes. Furthermore, the contact state between the thermal interfaces is prone to expansion and loosening at high temperatures, introducing additional thermal resistance. Therefore, a mapping function between temperature and thermal conductivity efficiency is established to characterize the actual heat transfer capacity of the copper sheet at different operating temperature ranges.In the specific modeling, a combination of simulation and field measurement was used to obtain the thermal output response of the copper sheet at different temperature points under a fixed heat flux input condition. The efficiency reduction coefficient was then determined by comparing the ideal heat flux with the measured heat output. For example, when the surface temperature of the copper sheet was 55°C, its heat flux response was 92% of the theoretical value; however, when the temperature rose to 85°C, the thermal response dropped to 89%, indicating a slight decrease in thermal conductivity within the material. This data was fitted to a function model between temperature and efficiency coefficient, resulting in a slowly varying concave curve whose slope reflects the increasing trend in thermal resistance. The temperature-phase relationship fitting equation and the corresponding function between temperature and efficiency coefficient were applied to the piecewise heat flow equation. This heat flow equation is essentially a multivariable control model that outputs the heat flux distribution ratio between the upper and lower copper sheets at different times based on the total heat flux, the physical parameters of the copper sheet, and a dynamic correction factor. The temperature-dependent phase adjustment factor is used to dynamically adjust the scheduling order of heat flow on the timeline. For example, it shortens the heat flow response delay when the temperature rises and improves the synchronization of heat flow allocation. The temperature-dependent efficiency coefficient is directly used to adjust the spatial distribution ratio of heat flow. For example, in high-temperature areas, more heat flow is directed to the more efficient copper layer to reduce heat accumulation and improve overall heat dissipation capacity. This process ultimately outputs two sets of key variables—the temperature-dependent phase adjustment factor and the temperature-dependent efficiency coefficient. These two sets of parameters respectively give the heat flow control mechanism dynamic adaptability in the time and space domains, enabling it to make the most appropriate response decisions based on the temperature distribution and heat conduction capacity at each moment, thereby improving the real-time, accuracy, and energy efficiency of heat flow distribution.

[0101] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0102] Collecting real-time thermal gradient monitoring data of the second double-layer copper sheet structure;

[0103] The finite difference method is used to perform numerical calculations on the real-time thermal gradient monitoring data to obtain the temperature gradient values ​​in each direction. The temperature gradient values ​​in each direction include the temperature change rates in the x-direction, y-direction, and z-direction.

[0104] The temperature gradient values ​​in each direction are organized into a three-dimensional thermal gradient matrix, which is used to characterize the direction and intensity of heat transfer in the copper sheet structure.

[0105] Specifically, the second double-layer copper sheet structure serves as the physical carrier for heat transfer, and a high-resolution temperature sensor array is built around its spatial distribution characteristics to achieve real-time sensing of thermal field changes. The structural design includes multiple temperature sensors placed on each of the positive and negative copper sheets. Each sensor group covers the edges and center of the sheet, as well as contact areas with key heat source components such as inductors, capacitors, and magnetic beads. The spacing between the sampling points is geometrically calculated to ensure a representative and numerically stable spatial grid structure in the x-direction (horizontal conduction axis), y-direction (lateral direction), and z-direction (vertical interlayer between the upper and lower copper sheets). Real-time thermal gradient monitoring data is collected using high-precision NTC thermistors in conjunction with a multi-channel ADC system. The sampling frequency of each sensor is set between 10Hz and 20Hz to ensure that the system can capture instantaneous temperature trends even under conditions of rapid temperature rise or sudden heat flux caused by high-frequency MOSFET switching. During data acquisition, the control system uses a time synchronization mechanism to ensure comparability of temperature data from different spatial locations at the same moment, laying the foundation for time-domain consistency in subsequent differential calculations. To ensure signal stability and accuracy, the system uses a built-in filtering algorithm to dynamically reduce noise in the raw temperature data, eliminating temperature offsets introduced by environmental fluctuations, electromagnetic interference, or measurement errors, thereby outputting a high-confidence real-time thermal field dataset. The finite difference method is used to numerically calculate the real-time thermal gradient monitoring data. This method, with the finite difference method as the core mathematical tool, calculates the rate of temperature change between any two adjacent sampling points in the thermal field. The finite difference method essentially provides a discrete approximation of the derivative of the continuous variation of temperature in space. In practice, the temperature change ΔT in each direction is differentiated based on the pre-set sensor grid spacing Δx, Δy, and Δz. Specifically, in the x-direction, the temperature difference between two adjacent sensors is divided by their horizontal distance to obtain the x-direction temperature gradient. The same principle is applied to the y and z directions, corresponding to the temperature gradient within the lateral copper sheet and the thermal differential propagation trend between the upper and lower copper sheets, respectively. For example, if the distance between two sensors in the x-direction is 1.2 cm and the temperature difference is 6.6°C, the temperature change rate in the x-direction is 5.5°C / cm. This value reflects the intensity of heat propagation in that direction and indirectly reflects the heat flux distribution in that area. After completing all differential calculations in the three directions, all gradient data are organized according to the arrangement order of the spatial nodes and the directional coordinate system to construct a three-dimensional thermal gradient matrix. This matrix structure is represented as a three-dimensional tensor, in which each row corresponds to a thermal monitoring area, and each column stores the temperature gradient value of the area in the x, y, and z directions, thereby numerically characterizing the directionality and intensity distribution of heat propagation in each area within the entire copper sheet structure. Larger values ​​in the matrix indicate more drastic temperature changes in that direction and higher heat flux density; areas with values ​​close to zero are thermal diffusion equilibrium zones or boundary stability zones.Through the matrix structure, hotspot aggregation areas, thermal resistance mutation areas, and heat flow conduction abnormality areas can be intuitively analyzed. In addition, the three-dimensional thermal gradient matrix has the ability to evolve dynamically. During the continuous update of the thermal field, the matrix content is updated by sliding according to a certain time window to form a thermal gradient time-series evolution sequence, thereby realizing real-time tracking and prediction of changes in the direction of heat flow. The thermal management system uses this sequence to identify rapid temperature change trends and provide early warnings of potential thermal runaway areas. For example, when the temperature gradient in the x-direction of a certain block rises from 3.2°C / cm to 6.8°C / cm in a short period of time, the system determines that the heat accumulation in the area is intensifying, and responds quickly by dynamically adjusting the heat conduction path or fan speed to avoid thermal breakdown or accelerated device aging.

[0106] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0107] Calculate the temperature distribution characteristic parameters based on the three-dimensional thermal gradient matrix to obtain the temperature difference value of each area. The temperature distribution characteristic parameters include the highest temperature point and the average temperature;

[0108] The thickness of the thermal conductive mud is calculated based on the temperature distribution characteristic parameters to obtain the thickness value of the thermal conductive mud at each point;

[0109] The thermal paste thickness values ​​at each point are spatially optimized to obtain a thermal paste distribution control signal, which is used to control the micro piezoelectric ceramic drive device to adjust the precise distribution of the thermal paste.

[0110] The thermal conductive mud distribution control signal is applied to the thermal conductive mud control system on the surface of the second double-layer copper sheet structure to obtain the dynamic thickness distribution parameters of the thermal conductive mud. The dynamic thickness distribution parameters of the thermal conductive mud reduce the thickness of the thermal conductive mud in the high-temperature area to increase the heat dissipation efficiency, and increase the thickness of the thermal conductive mud in the low-temperature area to reduce the heat dissipation efficiency.

[0111] Specifically, a spatial analysis of the three-dimensional thermal gradient matrix is ​​performed to extract characteristic temperature distribution parameters. The average temperature of the overall thermal field is calculated by counting the temperature values ​​of all sampling points in the matrix. The region with the highest temperature value is selected as the highest temperature point in the current system, represented as a combination of spatial coordinates and corresponding temperature values. Furthermore, to improve the adaptability of the control strategy, the temperature difference value of each monitored area is calculated—that is, the deviation of each point's temperature from the average temperature. This temperature difference value, as a thermal equilibrium offset, directly determines the adjustment range of the thermal paste thickness and is a key input to the entire thermal paste control logic. For example, if the average temperature is 64.5°C and the temperature in a certain area reaches 77.3°C, the difference value is 12.8°C, indicating significant heat accumulation in this area, and the thermal paste thickness needs to be reduced to accelerate heat diffusion. After obtaining the temperature difference value for each area, the thermal paste thickness calculation phase begins. The goal is to form a set of thermal paste thickness configurations with differentiated distribution and dynamically adjustable thermal resistance across the entire thermal interface layer. The thickness calculation process utilizes a regulation function based on a deviation-proportional mapping, starting with the base thickness and applying adjustments based on the deviation of each point's temperature from the average temperature. In high-temperature regions, higher temperatures and greater temperature differences correspond to thinner thermal paste thicknesses, reducing interfacial thermal resistance and increasing heat flux. In low-temperature regions, the thickness is appropriately increased to increase local heat capacity, buffer rapid cooling, and prevent heat backflow or interfacial condensation. For example, if the base thickness is 0.22 mm, the system adjusts the thickness proportionally based on the temperature difference, ultimately reducing the thickness to 0.15 mm at high-temperature points and increasing it to 0.30 mm in low-temperature regions. This differentiated distribution effectively achieves dynamic balance within the heat flow path. To ensure smooth physical implementation of the thickness calculation results, spatial distribution optimization is performed to avoid sudden changes or local discontinuities in thickness variations. This spatial distribution optimization employs a two-dimensional smoothing function and a boundary transition control strategy to perform regional interpolation, gradient transition adjustment, and edge release on the calculated thermal paste thickness values, resulting in a continuous and structurally stable thickness distribution. The optimized thickness distribution map is converted into a set of position-thickness mapping instructions, which are encoded into a thermal paste distribution control signal, including the injection coordinates and target thickness corresponding to each key area. The control signal is transmitted in real time to the micro-piezoelectric ceramic drive device, which consists of multiple independently controlled injection units. Each unit accurately injects the thermal paste into the corresponding area, and its micro-adjustment capability can reach 0.01 mm, which can meet the needs of continuously variable thickness. The control system dynamically adjusts the driving voltage and working time of each injection unit according to the received signal, thereby achieving millisecond-level control of the thermal paste injection thickness. The thermal paste distribution control signal is applied to the surface of the second double-layer copper sheet structure, entering the thermal paste control execution stage. During the execution process, the thermal paste is automatically injected into the corresponding area on the surface of the copper sheet, forming a close thermal contact layer with the copper sheet.Because the upper and lower layers of the copper sheet have reserved specific exposed areas and coating channels, the thermal paste can be directly attached to the desired location and remain stable, forming a thermal interface adjustment layer. The thickness of this layer continuously updates as the thermal environment changes, thus forming a dynamic thickness distribution parameter. Through a real-time feedback mechanism, if changes in the heat load cause the original thermal gradient distribution to shift, the system will automatically recalculate the temperature characteristic parameters, update the thickness mapping, and send a control signal to the piezoelectric ceramic module again to complete the secondary distribution correction of the thermal paste, achieving continuous adjustment. The resulting dynamic thickness distribution parameters of the thermal paste functionally achieve real-time adaptive adjustment of the heat flow path. Reducing the thickness in the high-temperature area results in lower interfacial thermal resistance and faster heat conduction speed; while increasing the thickness in the low-temperature area enhances the thermal energy storage capacity and reduces the heat exchange rate, thereby avoiding drastic fluctuations in the overall system heat and improving thermal stability.

[0112] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0113] The heat flux distribution parameters of the upper and lower heat dissipation paths and the dynamic thickness distribution parameters of the thermal paste are input into the hierarchical control architecture to obtain the heat dissipation control instruction set. The hierarchical control architecture includes a temperature acquisition and thermal gradient calculation module, a heat flux distribution and thermal paste control module, and a system operation status management module.

[0114] Control the fan speed and thermal mud distribution device according to the heat dissipation control instruction set to obtain real-time heat dissipation execution results;

[0115] Calculate the deviation between the real-time heat dissipation execution result and the preset heat dissipation target to obtain the heat dissipation control correction parameter, which is used to dynamically adjust the heat flow distribution ratio and the thickness distribution of the thermal paste;

[0116] Based on the heat dissipation control correction parameters, a protection strategy judgment is performed to obtain a closed-loop heat dissipation control strategy for the on-board brushless fan controller. The protection strategy judgment includes triggering over-temperature protection when the temperature exceeds 85 degrees Celsius, forcing power off when the temperature exceeds 100 degrees Celsius, and triggering over-current protection when the current exceeds 150% of the rated current.

[0117] Specifically, the heat flux distribution parameters of the upper and lower heat dissipation paths and the dynamic thickness distribution parameters of the thermal paste are used as the two core input variables of thermal behavior and input into the hierarchical control architecture of the system through the structured data transmission interface to drive the subsequent heat dissipation control logic, such as Figure 2The figure shows a schematic diagram of the hierarchical control architecture in an embodiment of the present application. This hierarchical control architecture consists of three collaborative functional modules: a temperature acquisition and thermal gradient calculation module, a heat flux distribution and thermal paste control module, and a system operation status management module. The temperature acquisition and thermal gradient calculation module acquires real-time temperature data from key areas of the positive and negative copper sheets using a temperature sensor array arranged on the second double-layer copper sheet structure, and calculates a three-dimensional thermal gradient matrix using the finite difference method, thereby dynamically understanding the directionality and intensity of heat propagation within the copper sheet structure. The heat flux distribution and thermal paste control module adjusts and plans the fan speed, thermal paste injection volume, and spatial distribution structure based on the heat flux distribution parameters and the thermal paste thickness distribution parameters, generating an executable heat dissipation control instruction set. The system operation status management module, as the central judgment core of the entire architecture, is responsible for comparing the operation status with preset indicators, determining whether the system is at risk of high temperature, overcurrent, or control deviation exceeding the limit, and triggering protection strategies or corrective actions. Once the upper and lower heat flow parameters and thermal paste thickness data are entered into the system, the controller constructs a comprehensive set of cooling control instructions based on the heat flow trends and thickness compatibility of the thermal interface in different regions. This instruction set includes PWM signals for fan control and spatial thickness adjustment commands for the piezoelectric ceramic injection system. The fan control instructions adjust its speed and operating time based on the total heat flow and the number of hot spots, ensuring that localized high-temperature areas receive strong convection cooling within a short period of time. The thermal paste control signals are distributed graphically to each piezoelectric injection node, ensuring that the thermal layer is appropriately thinned in high-density areas to reduce thermal resistance, while being thickened in low-heat areas to provide a buffer and thermal inertia. As the cooling control instructions are executed at the physical layer, the system collects real-time temperature responses and heat flow trends in each region. By dynamically comparing these feedback results with the preset cooling targets, the system calculates the current cooling control deviation, or cooling control correction parameters. This parameter includes the absolute difference between the actual temperature and the target temperature. It also integrates multi-dimensional indicators such as the temperature rise rate, heat flow response delay, and thermal paste execution error to characterize the heat dissipation offset of the system in its current state. The controller updates the heat flow distribution ratio accordingly, for example, directing more heat flow to the copper layer with higher thermal conductivity efficiency, and adjusting the thickness configuration diagram of the thermal paste to make it more suitable for the new thermal load conditions. When the deviation correction parameter exceeds the allowable range, the system operation status management module immediately enters the protection strategy judgment logic. This logic continuously monitors key temperature values, current values, and their changing trends, and performs hierarchical protection according to the set thresholds.For example, when the temperature in any area exceeds 85 degrees Celsius but has not yet reached the dangerous value, the system automatically enters the over-temperature protection state. At this time, the fan speed is increased, the thickness of the thermal paste is reduced, and the heat dissipation load is redistributed to prevent further heat concentration; when the temperature rises above 100 degrees Celsius, the system judges it as a thermal runaway risk and immediately triggers a forced power-off command to cut off the main power circuit to avoid device damage or functional failure; when the current monitoring module detects that the load current exceeds 150% of the rated current, the system will trigger overcurrent protection and enter power limiting mode or completely interrupt to block the continuous input of abnormal heat sources.

[0118] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent control method for a vehicle-mounted brushless blower, characterized in that: include: The positive copper sheet and the negative copper sheet of the first double-layer copper sheet structure in the vehicle-mounted brushless blower controller are subjected to heat conduction analysis and optimized configuration to obtain a second double-layer copper sheet structure, including: measuring the physical parameters of the positive copper sheet and the negative copper sheet of the first double-layer copper sheet structure in the vehicle-mounted brushless blower controller to obtain the copper sheet thickness value and the exposed area value; constructing the copper sheet thickness ratio and the exposed area ratio based on the copper sheet thickness value and the exposed area value to obtain the double-layer copper sheet heat conduction characteristic characterization parameter, wherein the copper sheet thickness ratio is defined as the ratio of the positive copper sheet thickness to the negative copper sheet thickness, and the exposed area ratio is defined as the ratio of the positive copper sheet exposed area to the negative copper sheet exposed area; the thermal conductivity characteristics of the first double-layer copper sheet structure are measured under different temperature conditions. The method comprises the following steps: testing to obtain thermal characteristic evaluation functions of the positive copper sheet and the negative copper sheet; constructing a thermal characteristic mathematical model based on the thermal conductivity characteristic characterization parameters of the double-layer copper sheet and the thermal characteristic evaluation function; performing a heat flow simulation analysis based on the thermal characteristic evaluation function in the thermal characteristic mathematical model to obtain an optimal solution for the thermal characteristic evaluation function; calculating the physical dimensions of the positive copper sheet and the negative copper sheet based on the optimal solution to obtain specific thickness and exposed area values ​​of each copper sheet; optimizing the planar shape and spatial position of the positive copper sheet and the negative copper sheet based on the specific thickness and exposed area values ​​of each copper sheet to obtain a copper sheet layout scheme; performing a heat flow distribution verification test on the copper sheet layout scheme to obtain a second double-layer copper sheet structure; Performing bidirectional heat flow segmentation of the copper sheet based on the second double-layer copper sheet structure to obtain heat flow distribution parameters for upper and lower heat dissipation paths; Collecting real-time thermal gradient monitoring data of the second double-layer copper sheet structure, and calculating a three-dimensional thermal gradient matrix based on the real-time thermal gradient monitoring data; Dynamically adjusting the thickness of the thermal conductive mud covering the surface of the second double-layer copper sheet structure based on the three-dimensional thermal gradient matrix to obtain a dynamic thickness distribution parameter of the thermal conductive mud; Based on the heat flow distribution parameters of the upper and lower heat dissipation paths and the dynamic thickness distribution parameters of the thermal paste, a closed-loop heat dissipation control strategy of the on-board brushless fan controller is created.

2. The intelligent control method for a vehicle-mounted brushless blower according to claim 1, characterized in that: The performing of bidirectional copper sheet heat flow division based on the second double-layer copper sheet structure to obtain upper and lower heat dissipation path heat flow distribution parameters includes: Performing bidirectional heat flow division on the second double-layer copper sheet structure to obtain a real-time heat flow distribution ratio; Analyzing real-time temperature data of the vehicle-mounted brushless fan controller to obtain a temperature-related phase adjustment factor and a temperature-related efficiency coefficient; Substituting the temperature-dependent phase adjustment factor and the temperature-dependent efficiency coefficient into the segmented heat flow equation for calculation, to obtain a real-time heat flow distribution value between the upper and lower copper sheets; Based on the real-time heat flow distribution value between the upper and lower copper sheets, heat flow distribution is performed on the independent thermal management areas of the second double-layer copper sheet structure to obtain upper and lower heat dissipation path heat flow distribution parameters.

3. The intelligent control method for a vehicle-mounted brushless blower according to claim 2, characterized in that: The real-time temperature data of the vehicle-mounted brushless fan controller is analyzed to obtain a temperature-related phase adjustment factor and a temperature-related efficiency coefficient, including: The temperature sensor array on the second double-layer copper sheet structure collects real-time temperature data of each point on the positive copper sheet and the negative copper sheet; Performing regression analysis based on the correlation between the real-time temperature data and the MOSFET switching frequency to obtain a fitting equation for the relationship between temperature and phase; Performing data fitting based on the correlation between the real-time temperature data and the heat flow conduction efficiency to obtain a corresponding function between temperature and efficiency coefficient; The temperature-phase relationship fitting equation and the corresponding function of the temperature and efficiency coefficient are applied to the segmented heat flow equation to obtain a temperature-dependent phase adjustment factor and a temperature-dependent efficiency coefficient. The temperature-dependent phase adjustment factor controls the phase of heat flow distribution, and the temperature-dependent efficiency coefficient adjusts the efficiency of heat flow distribution.

4. The intelligent control method for a vehicle-mounted brushless blower according to claim 3, characterized in that: The collecting of real-time thermal gradient monitoring data of the second double-layer copper sheet structure and calculating a three-dimensional thermal gradient matrix based on the real-time thermal gradient monitoring data includes: Collecting real-time thermal gradient monitoring data of the second double-layer copper sheet structure; The real-time thermal gradient monitoring data is numerically calculated using a finite difference method to obtain temperature gradient values ​​in each direction, wherein the temperature gradient values ​​in each direction include temperature change rates in the x-direction, the y-direction, and the z-direction; The temperature gradient values ​​in each direction are organized into a three-dimensional thermal gradient matrix, and the three-dimensional thermal gradient matrix is ​​used to characterize the direction and intensity of heat transfer in the copper sheet structure.

5. The intelligent control method for a vehicle-mounted brushless blower according to claim 1, characterized in that: The method of dynamically adjusting the thickness of the thermal conductive mud covering the second double-layer copper sheet structure based on the three-dimensional thermal gradient matrix to obtain a dynamic thickness distribution parameter of the thermal conductive mud includes: Calculating temperature distribution characteristic parameters based on the three-dimensional thermal gradient matrix to obtain temperature difference values ​​in each region, wherein the temperature distribution characteristic parameters include the highest temperature point and the average temperature; Calculate the thickness of the thermal conductive mud based on the temperature distribution characteristic parameters to obtain the thickness value of the thermal conductive mud at each point; Optimizing the spatial distribution of the thermal paste thickness values ​​at each point to obtain a thermal paste distribution control signal, wherein the thermal paste distribution control signal is used to control a micro piezoelectric ceramic drive device to adjust the precise distribution of the thermal paste; The thermal conductive mud distribution control signal is applied to the thermal conductive mud control system on the surface of the second double-layer copper sheet structure to obtain a dynamic thickness distribution parameter of the thermal conductive mud. The dynamic thickness distribution parameter of the thermal conductive mud reduces the thickness of the thermal conductive mud in the high-temperature area to increase the heat dissipation efficiency, and increases the thickness of the thermal conductive mud in the low-temperature area to reduce the heat dissipation efficiency.

6. The intelligent control method for a vehicle-mounted brushless blower according to claim 1, characterized in that: The closed-loop heat dissipation control strategy of the vehicle-mounted brushless fan controller is created based on the upper and lower heat dissipation path heat flow distribution parameters and the dynamic thickness distribution parameters of the thermal paste, including: Inputting the heat flux distribution parameters of the upper and lower heat dissipation paths and the dynamic thickness distribution parameters of the thermal paste into a hierarchical control architecture to obtain a heat dissipation control instruction set, wherein the hierarchical control architecture includes a temperature acquisition and thermal gradient calculation module, a heat flux distribution and thermal paste control module, and a system operation status management module; Control the fan speed and the thermal mud distribution device according to the heat dissipation control instruction set to obtain real-time heat dissipation execution results; Calculating the deviation between the real-time heat dissipation execution result and the preset heat dissipation target to obtain a heat dissipation control correction parameter, wherein the heat dissipation control correction parameter is used to dynamically adjust the heat flow distribution ratio and the thickness distribution of the thermal paste; A protection strategy judgment is performed based on the heat dissipation control correction parameter to obtain a closed-loop heat dissipation control strategy of the on-board brushless fan controller, wherein the protection strategy judgment includes triggering over-temperature protection when the temperature exceeds 85 degrees Celsius, forcing power off when the temperature exceeds 100 degrees Celsius, and triggering over-current protection when the current exceeds 150% of the rated current.

Images