Automobile fan and grille control method, vehicle and medium

Abstract

The application discloses a kind of energy consumption optimization-based automobile fan and grille control method, vehicle and medium, comprising: obtaining the current vehicle speed and heat dissipation demand air volume, based on grille air intake model, fan air volume model, whole vehicle driving power model and fan power consumption model, establish the air volume constraint relationship and total energy consumption target with grille opening degree and fan duty ratio as variable;Eliminate fan duty ratio, obtain single value mapping and total energy consumption target is only about single variable total energy consumption target function of grille opening degree;Judge whether fan full duty ratio air volume meets remaining heat dissipation demand, not satisfied then forced grille full open and fan full duty ratio operation to preferentially guarantee heat dissipation, otherwise iteratively solve in gradient descent mode, obtain energy consumption optimal grille opening degree and matching fan duty ratio and output.The application is reduced by analytic modeling, constraint dimensionality reduction, strict convexity verification and online gradient descent optimization, coordinates grille and fan, effectively reduces whole vehicle energy consumption.

Description

Technical Field

[0001] This application relates to the field of automotive thermal management control technology, and in particular to an automotive fan and grille control method, vehicle, and medium. Background Technology

[0002] With the development of new energy vehicles, active grille shutters (AGS) have become standard equipment to reduce overall vehicle energy consumption. At higher speeds, closing the active grille shutter effectively reduces aerodynamic drag, thus lowering overall vehicle energy consumption; conversely, when the vehicle requires cooling, the grille and fan need to be opened to provide cooling airflow.

[0003] However, when the grille is open, the cold air in front of the front module turns into hot air and flows out through the high-temperature radiator, which takes away heat but also brings energy consumption costs: increasing the grille opening will increase the vehicle's driving resistance, thereby increasing driving energy consumption; while increasing the fan duty cycle will increase the power consumption of the low-voltage electrical system.

[0004] The existing solution prioritizes opening the grille when the vehicle has a cooling airflow requirement; if the airflow requirement is still not met after the grille is fully open, the fan is then activated. This solution only meets the airflow requirement without considering the overall vehicle energy consumption, often resulting in an unoptimal combination of grille opening and fan duty cycle, leading to unnecessary energy waste.

[0005] Therefore, coordinating the grille opening and fan duty cycle to minimize the vehicle's energy consumption while meeting the overall vehicle's cooling airflow requirements is an important issue in the field of automotive thermal management control. Summary of the Invention

[0006] The purpose of this application is to provide an automotive fan and grille control method, vehicle, and medium that can coordinate the opening of the active air intake grille and the duty cycle of the cooling fan while meeting the overall vehicle cooling airflow requirements, thereby minimizing the overall vehicle energy consumption. It also ensures that the obtained control scheme reduces energy consumption in a provably globally optimal manner under the premise of heat dissipation safety, and can be stably and reliably executed online in the vehicle environment.

[0007] To achieve the above objectives: In a first aspect, embodiments of this application provide a method for controlling an automotive fan and grille, including: Obtain the vehicle's current speed and required airflow for cooling; Based on the grille air intake model, fan air volume model, vehicle drive power model, and fan power consumption model, an air volume constraint relationship and a total energy consumption target are established with grille opening and fan duty cycle as variables. The air volume constraint relationship requires that the cooling air volume provided by the grille and fan together meet the heat dissipation demand air volume, and the total energy consumption target is the sum of vehicle drive energy consumption and fan power consumption. The fan duty cycle is eliminated by the air volume constraint relationship to obtain a single-value mapping of the fan duty cycle with respect to the grille opening, and the total energy consumption target is transformed into a single-variable total energy consumption objective function with respect to the grille opening only. Determine whether the air volume provided by the fan when it is running at full duty cycle meets the remaining heat dissipation demand, wherein the remaining heat dissipation demand is the portion of the heat dissipation demand air volume minus the ram air volume that the grille can provide under the current grille opening. If the airflow provided by the fan at full duty cycle is still insufficient to meet the remaining heat dissipation requirements, energy consumption optimization is not performed and the grille is forced to be fully open and the fan to run at full duty cycle, prioritizing heat dissipation; otherwise, the gradient of the single-variable total energy consumption objective function with respect to the grille opening is calculated using gradient descent, and the grille opening is iteratively updated based on the gradient and clamped to the feasible interval until convergence, obtaining the energy-optimal grille opening and the matching fan duty cycle determined by the single-value mapping, and outputting as the energy-optimal control request under the current heat dissipation requirements.

[0008] In one embodiment, the grille air intake model characterizes the increase of grille stamping air intake with increasing vehicle speed and grille opening, and its grille stamping air intake coefficient is calibrated by the cooling system air volume test bench at different vehicle speeds and different grille openings; the fan air volume model characterizes the increase of fan suction air volume with increasing fan duty cycle, and includes a nonlinear compensation term to compensate for nonlinear deviations caused by friction, back pressure and turbulence, and its model coefficients are obtained by fitting the air volume test bench data using the least squares method.

[0009] In one embodiment, the vehicle drive power model represents the vehicle drive power caused by the increased wind resistance as the grille opening increases, and its wind resistance-related parameters are calibrated by wind tunnel tests; the fan power consumption model represents the nonlinear relationship of fan power consumption increasing with the increase of fan duty cycle, and its model coefficients are obtained by fitting the voltage and current data calibrated on the fan test bench using the least squares method.

[0010] In one embodiment, eliminating the fan duty cycle from the airflow constraint relationship includes: The air volume constraint relationship can be written as a quadratic equation in one variable about the fan duty cycle; Solve the quadratic equation in one variable to find the only valid solution within the positive range of fan duty cycle, and obtain the single-valued mapping of fan duty cycle with respect to grille opening. Substituting the single-value mapping into the total energy consumption target yields a univariate total energy consumption objective function that is only related to the grid opening.

[0011] In one embodiment, before optimizing the single-variable total energy consumption objective function based on the gradient, it is verified that the function is a strictly convex function within the feasible interval of the grid opening. Based on the monotonicity and second derivative property of the single-valued mapping, the second derivative of the single-variable total energy consumption objective function is always greater than zero within the feasible interval of the grid opening, thus possessing a strictly convex property. This ensures that the gradient descent optimization stably converges to the globally unique minimum energy consumption point, thereby matching the optimal fan duty cycle.

[0012] In one embodiment, it further includes: During the period after the grille opening converges, the gradient is recalculated and a verification update is performed at a preset period to dynamically optimize when the vehicle speed or heat dissipation requirements change, while keeping the grille opening unchanged during non-verification periods.

[0013] In one embodiment, determining whether the available airflow of the fan operating at full duty cycle meets the remaining heat dissipation requirements includes: The system calculates the air intake volume of the grille stamping corresponding to the current grille opening in real time, and combines it with the air volume required for the overall vehicle cooling to calculate the remaining cooling demand that needs to be compensated by the fan. When the remaining heat dissipation demand exceeds the maximum air supply capacity of the fan under full duty cycle conditions, it is determined that the system cooling capacity cannot meet the heat dissipation demand. Energy consumption optimization is not performed, and the grille is forcibly opened and the fan operates at full duty cycle to prioritize the heat dissipation performance of the entire vehicle.

[0014] In one embodiment, model predictive control, dynamic programming, or lookup table interpolation are used to optimize the single-variable total energy consumption objective function instead of gradient descent.

[0015] Secondly, embodiments of this application provide a vehicle, specifically including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the automotive fan and grille control method as described in the first aspect, or to include the automotive fan and grille control method as described in the first aspect.

[0016] Thirdly, embodiments of this application provide a machine-readable storage medium storing a computer program, wherein when the instructions in the machine-readable storage medium are executed by a processor, the automotive fan and grille control method as described in the first aspect is implemented.

[0017] This application provides a method for controlling an automotive fan and grille, a vehicle, and a medium, including: acquiring the vehicle's current speed and required cooling airflow; establishing four types of analytical models with airflow constraints and a total energy consumption target; eliminating the fan duty cycle to obtain a single-variable total energy consumption objective function; determining whether the fan's full duty cycle airflow meets the remaining cooling requirements; forcing full operation when not meeting the requirements to ensure cooling, otherwise using gradient descent to find the optimal grille opening and matching fan duty cycle. This application transforms the original problem of relying on empirical sequential control and failing to guarantee optimality into a mathematically provable, globally optimal control method that can be stably executed in an in-vehicle environment. This reduces overall vehicle energy consumption in a provably globally optimal manner while ensuring cooling safety, and can flexibly adapt to different vehicle platforms and controller computing power. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the automotive fan and grille control method provided in an embodiment of this application.

[0019] Figure 2 This is a schematic diagram of the vehicle structure provided in an embodiment of this application.

[0020] Processor 410, memory 411, network interface 412, bus system 413. Detailed Implementation

[0021] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0022] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of this application may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.

[0023] It should be understood that although the terms first, second, third, etc., may be used herein to describe various information, such information should not be limited to these terms. These terms are used only to distinguish information of the same type from one another. For example, without departing from the scope of this document, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if," as used herein, can be interpreted as "when," "when," or "in response to determination." Furthermore, as used herein, the singular forms "a," "an," and "the" are intended to also include the plural forms unless the context indicates otherwise. It should be further understood that the terms "comprising," "including," indicate the presence of the stated feature, step, operation, element, component, item, kind, and / or group, but do not exclude the presence, occurrence, or addition of one or more other features, steps, operations, elements, components, items, kinds, and / or groups. The terms "or" and "and / or" as used herein are to be interpreted as inclusive, or mean any one or any combination thereof. Therefore, "A, B, or C" or "A, B, and / or C" means "any one of the following: A; B; C; A and B; A and C; B and C; A, B, and C". Exceptions to this definition will only occur if the combination of elements, functions, steps, or operations is inherently mutually exclusive in some way.

[0024] It should be understood that although the steps in the flowcharts of this application's embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0025] It should be noted that step designations such as S101 and S102 are used in this document for the purpose of more clearly and concisely describing the corresponding content, and do not constitute a substantial limitation on the order. In specific implementation, those skilled in the art may execute S102 first and then S101, etc., but these should all be within the protection scope of this application.

[0026] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0027] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.

[0028] It should be noted that currently, airflow regulation in vehicle front-end cooling modules mainly relies on two types of actuators: active air intake grilles and cooling fans. These two actuators have different impacts on overall vehicle energy consumption: increasing the grille opening increases aerodynamic drag, thus increasing drive energy consumption; increasing the fan duty cycle increases low-voltage electrical power consumption. Existing controls often operate on an empirically-based sequence, opening the grille first and then the fan, under the objective of "sufficient airflow." This approach fails to quantify the conflict and trade-off between the two types of energy consumption, nor does it mathematically prove that the final control point represents the point of minimum global energy consumption. Furthermore, it lacks a reliable safety net for cooling under extreme cooling conditions, leading to significant energy waste and safety hazards in actual operation.

[0029] To address the aforementioned issues, this application proposes a method for controlling automotive fans and grilles. This method allows vehicles to utilize vehicle speed, required cooling airflow, and four types of analytical models. It expresses "meeting cooling airflow requirements" as a constraint and "minimizing energy consumption" as the objective. Through variable dimensionality reduction and rigorous convexity verification, a provably globally unique minimum single-variable optimization problem is obtained. Online gradient descent is used to optimize and output the optimal grille opening and matching fan duty cycle. Under extreme conditions, cooling safety is prioritized and the grille is forced to be fully open, improving the optimality and reliability of overall vehicle energy consumption optimization. This method can be widely applied to thermal management control scenarios for various passenger cars, commercial vehicles, and other models that include active air intake grilles and cooling fans and require optimization of overall vehicle energy consumption.

[0030] like Figure 1 As shown, this application embodiment proposes a method for controlling an automotive fan and grille. This method can be implemented in software and / or hardware, for example, as an onboard thermal management controller. In this embodiment, the method is applied to a vehicle integrating an active grille actuator and a cooling fan actuator. This vehicle can receive vehicle speed signals and cooling demand airflow signals from the vehicle controller or thermal management domain controller, and can process these signals. This application embodiment provides an automotive fan and grille control method, including the following steps: Step S101: Obtain the vehicle's current speed and required airflow for heat dissipation.

[0031] Optionally, the current vehicle speed can refer to the real-time vehicle speed during operation, provided by wheel speed sensors, GPS, or the vehicle controller. Optionally, the required cooling airflow can refer to the minimum cooling airflow required by the radiator to remove heat generated by components such as the battery, motor, and air conditioning system, estimated by the thermal management system based on the current temperature and heat output of each heat-generating component. It is understandable that the required cooling airflow changes dynamically with operating conditions; for example, the required airflow increases when the battery is charging or discharging at high power, the motor is under high load, or the ambient temperature is high.

[0032] In some implementations, step S101, which is to obtain the vehicle's current speed and cooling air volume, also includes: reading the current grille opening as the initial value for subsequent gradient descent iterations, so as to shorten the online optimization convergence time in a hot start manner.

[0033] In the above implementation, by acquiring the two basic inputs of vehicle speed and cooling air volume, and providing the initial value of the current grille opening for subsequent optimization, the entire control method can quickly enter the optimization process with less computing power under actual vehicle operating conditions, thereby balancing real-time performance and optimal energy consumption.

[0034] Step S102: Based on the grille air intake model, fan air volume model, vehicle drive power model, and fan power consumption model, establish an air volume constraint relationship and a total energy consumption target with grille opening and fan duty cycle as variables. The air volume constraint relationship requires that the cooling air volume provided by the grille and fan together meet the heat dissipation demand air volume, and the total energy consumption target is the sum of vehicle drive energy consumption and fan power consumption.

[0035] Optionally, the grille air intake model can be a model representing the relationship between the grille stamping air intake and the increase in vehicle speed and grille opening. Optionally, the fan air volume model can be a model representing the relationship between the fan suction air volume and the increase in fan duty cycle, and includes a nonlinear compensation term to compensate for nonlinear deviations caused by friction, back pressure, and turbulence. Optionally, the vehicle drive power model can be a model representing the relationship between the vehicle drive power caused by the increased wind resistance as the grille opening increases. Optionally, the fan power consumption model can be a nonlinear relationship model representing the increase in fan power consumption as the fan duty cycle increases.

[0036] In some implementations, step S102 involves establishing four types of analytical models and constraints / objectives, specifically including: The air intake volume of the grille increases with increasing vehicle speed v and grille opening x, and is modeled as follows:

[0037] in, The air intake coefficient for grille stamping is obtained by calibrating the cooling system airflow test bench at different vehicle speeds and grille openings, and the average value is taken as the calibration value.

[0038] The fan's suction air volume increases with the fan duty cycle y, and includes a nonlinear compensation term to compensate for nonlinear deviations caused by friction, back pressure, and turbulence, which is modeled as follows:

[0039] in, For ideal linear airflow, Used to compensate for nonlinear deviations caused by friction, back pressure, and turbulence; coefficient , The data was obtained by fitting the air volume test data using the least squares method.

[0040] The cooling airflow provided by the grille and fan together should meet the heat dissipation demand, i.e., the airflow constraint relationship:

[0041] The vehicle's drive power increases with the increase of grille opening (the greater the wind resistance, the higher the drive energy consumption), modeled as follows:

[0042] in, air density, For the frontal area of ​​the entire vehicle, The drag coefficient of the vehicle when the grille is fully closed. The wind resistance increment coefficient is set for the grille; wind resistance-related parameters are calibrated by wind tunnel testing.

[0043] Fan power consumption increases non-linearly with increasing fan duty cycle y, and can be modeled as follows:

[0044] in, , , The voltage and current data calibrated from the fan test bench were obtained by least squares fitting.

[0045] The total energy consumption target is the sum of the vehicle's driving energy consumption and the fan's power consumption:

[0046] In the above implementation, the "satisfying the heat dissipation airflow" is expressed as an equality constraint and the "lowest energy consumption" is expressed as the minimization of the total energy consumption target through four types of analytical models. This transforms the original coordination control problem, which relied on empirical sequential control and could not guarantee the optimality, into a computable constraint optimization problem. At the same time, the model coefficients are calibrated by bench and wind tunnel tests and obtained by least squares fitting, which ensures the model's fit to the real vehicle and makes the subsequent optimization real and effective, rather than based on arbitrary assumptions.

[0047] Step S103: Eliminate the fan duty cycle from the air volume constraint relationship to obtain a single-value mapping of the fan duty cycle with respect to the grille opening, and transform the total energy consumption objective into a single-variable total energy consumption objective function with respect to the grille opening only.

[0048] Optionally, a single-valued mapping can refer to a functional relationship where, given a grille opening x, the fan duty cycle y is uniquely determined by airflow constraints, denoted as y = f(x). Optionally, a single-variable total energy consumption objective function can refer to a total energy consumption function with only the grille opening x as the independent variable. .

[0049] In some implementations, step S103, eliminating the fan duty cycle from the airflow constraint relationship, includes: substituting the airflow constraint into a specific model and writing it as a quadratic equation in one variable about the fan duty cycle y:

[0050] Solve the quadratic equation in one variable to find the unique valid solution (taking the positive root) within the range where the fan duty cycle is positive, and obtain the single-valued mapping of the fan duty cycle with respect to the grille opening:

[0051] Substituting this single-valued mapping into the total energy consumption objective, we obtain the univariate total energy consumption objective function with respect to the grid opening x only:

[0052] In the above implementation, the original bivariate (x, y) coupled constraint optimization problem is transformed into a univariate optimization problem with only grid opening by constraint dimensionality reduction. This not only eliminates the solution complexity caused by multivariate coupling, but also lays a univariate foundation for obtaining the global optimum through strict convexity proof and gradient descent.

[0053] Step S104: Determine whether the air volume provided by the fan when it is running at full duty cycle meets the remaining heat dissipation requirements, wherein the remaining heat dissipation requirements are the heat dissipation air volume minus the ram air volume that the grille can provide under the current grille opening.

[0054] Optionally, the remaining heat dissipation demand can refer to the portion of the required airflow after deducting the airflow that the grille can provide under the current grille opening, i.e., the cooling airflow that needs to be compensated by the fan, denoted as:

[0055] In some implementations, step S104, namely determining whether the available airflow of the fan running at full duty cycle meets the remaining heat dissipation requirements, includes: calculating in real time the grille punching airflow corresponding to the current grille opening. Combined with the overall vehicle cooling requirements, airflow Calculate the remaining cooling demand that needs to be compensated by the fan. When the remaining heat dissipation demand Greater than the maximum air supply capacity under full duty cycle conditions When the system's cooling capacity is insufficient to meet the heat dissipation requirements, it is determined that the system's cooling capacity is inadequate.

[0056] In the above implementation, by calculating the remaining heat dissipation demand and the maximum air supply capacity of the fan before entering the energy consumption optimization process, a judgment basis is provided for the heat dissipation safety in the subsequent step S105, ensuring that heat dissipation safety is the first priority under any extreme heat dissipation conditions, and avoiding sacrificing heat dissipation in pursuit of optimal energy consumption.

[0057] Step S105: When the available airflow of the fan running at full duty cycle is still insufficient to meet the remaining heat dissipation demand, energy consumption optimization is not performed and the grille is forced to be fully open and the fan to run at full duty cycle, prioritizing heat dissipation; otherwise, the gradient of the single-variable total energy consumption objective function with respect to the grille opening is calculated using gradient descent, and the grille opening is iteratively updated based on the gradient and clamped to the feasible interval until convergence, obtaining the energy-optimal grille opening and the matching fan duty cycle determined by single-value mapping, and outputting as the energy-optimal control request under the current heat dissipation demand.

[0058] Alternatively, the gradient can refer to the derivative of the univariate total energy consumption objective function with respect to the grid opening. The symbol indicates the direction of energy consumption increase or decrease as the grille opening changes. Optionally, the energy consumption optimal control request can refer to the optimal grille opening. Matching fan duty cycle determined by single-value mapping The combination is used to drive the coordinated action of the grille actuator and the fan actuator.

[0059] In some implementations, in step S105, before optimizing the single-variable total energy consumption objective function based on the gradient, the function is verified to be a strictly convex function. Specifically, this includes: based on the monotonicity and second derivative property of the single-valued mapping y = f(x), the second derivative of the single-variable total energy consumption objective function is calculated, and it is found that its second derivative is always greater than zero within the feasible interval of the grid opening.

[0060] This univariate function has strict convexity within the feasible interval, ensuring that gradient descent optimization converges stably to the globally unique point of minimum energy consumption, thereby matching the optimal fan duty cycle.

[0061] In some implementations, step S105, which uses gradient descent for optimization, specifically includes: calculating the gradient. When the gradient is greater than zero, increasing x will increase the total power consumption, requiring a decrease in the grid opening; when the gradient is less than zero, increasing x will decrease the total power consumption, requiring an increase in the grid opening. Based on this, a pre-calibrated step size coefficient is used. Scaling the gradient, the grid opening is updated iteratively as follows:

[0062] During the iteration process, hard boundary clamps are applied to the upper and lower limits of the grid opening to restrict the updated grid opening to a feasible range:

[0063] When the gradient approaches zero (less than the preset convergence threshold), the optimal grid opening is determined, updates are stopped, and the grid opening with optimal energy consumption is obtained. ; by single-valued mapping Determine the matching fan duty cycle and output it. and This is a request for optimal energy consumption control under current heat dissipation requirements.

[0064] In the above implementation, when step S104 determines that the heat dissipation capacity is insufficient, the energy consumption optimization is not performed directly and the system is forced to be fully open to prioritize heat dissipation; otherwise, the gradient descent is ensured to converge to the globally unique minimum point through strict convexity verification, rather than just obtaining a local approximate optimum, and the result is guaranteed to fall within the physically executable range by clamping the feasible interval, thereby reducing the vehicle's energy consumption in a provably globally optimal manner while satisfying heat dissipation safety.

[0065] In summary, the above implementation method transforms coordinated control into a computable constrained optimization problem by utilizing four types of analytical models. It then uses variable dimensionality reduction, strict convexity verification, and online gradient descent optimization to obtain the energy-optimal grille opening and matching fan duty cycle. At the same time, it ensures heat dissipation priority by providing a safety net for heat dissipation under extreme conditions. This enables the vehicle to reduce energy consumption in a provably globally optimal manner while meeting heat dissipation requirements, thereby improving the optimality and reliability of thermal management control.

[0066] Based on the same methodological concept as the foregoing embodiments, the foregoing embodiments will be described in detail below through a specific example.

[0067] The specific embodiments of this application aim to construct an active grille and fan coordination control system based on energy consumption optimization and capable of stable on-vehicle execution. The following will provide a detailed description of the system architecture, working principle, key modules, and interaction flow, and will also illustrate the core content to be shown in the accompanying drawings.

[0068] I. System Overall Architecture This system is primarily deployed in vehicles equipped with active grille shutters and cooling fan control hardware. It is an embedded system integrating state perception, modeling optimization, and execution control. Its overall architecture can be divided into three layers: The perception layer consists of the vehicle's existing speed signal sources (wheel speed sensors, GPS) and the thermal management system. It continuously provides the current vehicle speed, the required airflow for heat dissipation, and the current grille opening as initial values ​​for iteration. That is, it executes the above step S101 to obtain the vehicle's current speed and required airflow for heat dissipation.

[0069] The optimization and decision-making layer is the core processing unit, typically integrated into the vehicle's thermal management domain controller or a separate energy consumption optimization module. It runs the modeling, variable dimensionality reduction, strict convexity verification, and gradient descent optimization algorithms of this invention, outputting the optimal grille opening and matching fan duty cycle. Specifically, it executes steps S102 to S105 above, establishing four types of models and constraint objectives, eliminating the fan duty cycle to obtain a single-variable function, determining extreme operating conditions and forcing full opening when the conditions are met; otherwise, it uses gradient descent optimization to output an energy consumption optimal control request.

[0070] Execution layer: This layer consists of the active air intake grille actuator and the cooling fan drive circuit. This layer receives the energy-optimized control request output from the optimization and decision layer, and drives the grille opening and fan duty cycle to coordinate their actions, i.e., executes the output action of step S105 above.

[0071] The information flow of the entire system is as follows: from vehicle speed and heat dissipation demand signals to four types of analytical models to constraint dimensionality reduction and convexity verification to gradient descent optimization to energy consumption optimal control request to coordinated execution of grille and fan.

[0072] II. Detailed Description of Core Module Functions 1. Modeling and Constraints Module Function: Based on calibration coefficients, establish four types of models: grille air intake volume, fan air volume, vehicle drive power and fan power consumption, and form air volume constraints and total energy consumption targets.

[0073] Inputs: vehicle speed, required airflow for heat dissipation, and test bench / wind tunnel calibration coefficients.

[0074] Processing and output: Output the air volume constraint relationship and the total energy consumption target, that is, the model and target established in step S102 above.

[0075] 2. Dimensionality Reduction and Convexity Verification Module Function: Eliminate fan duty cycle from airflow constraint to obtain single-valued mapping and transform the objective into a single-variable function; verify that it is a strictly convex function before optimization to ensure that gradient descent converges to the globally unique minimum.

[0076] Input: air volume constraint, four types of models, and feasible range of grille opening.

[0077] Processing and output: Output the single-variable total energy consumption objective function and the verification conclusion that "the second derivative is always greater than zero, strictly convex, and globally uniquely minimum", which is the convexity verification in steps S103 and S105 above.

[0078] 3. Safety backup and online optimization module Function: First, determine whether the fan's full duty cycle airflow meets the remaining heat dissipation requirements. If not, force it to fully open and exit the optimization process. Otherwise, it iteratively update the grille opening using gradient descent and clamp it to the feasible range until it converges and outputs the optimal control request, and then verifies it again periodically.

[0079] Inputs: Remaining heat dissipation requirements, maximum air supply capacity of the fan at full duty cycle, and gradient of the univariate function.

[0080] Processing and output: Output the energy consumption optimal control request or the forced full-on command under extreme conditions, i.e., the above steps S104 and S105.

[0081] Based on the same inventive concept as the foregoing embodiments, this application provides a vehicle, such as... Figure 2 As shown, the vehicle includes: a processor 410 and a memory 411 storing a computer program; wherein, Figure 2 The processor 410 shown in the diagram does not indicate that there is only one processor 410, but only indicates the positional relationship of the processor 410 relative to other devices. In practical applications, there can be one or more processors 410; similarly, Figure 2 The memory 411 shown in the diagram has the same meaning, that is, it is only used to indicate the positional relationship of memory 411 relative to other devices. In practical applications, there can be one or more memories 411. When the processor 410 runs the computer program, the above-described automotive fan and grille control method is implemented.

[0082] The vehicle may also include at least one network interface 412. Various components in the vehicle are coupled together via a bus system 413. It is understood that the bus system 413 is used to enable communication between these components. In addition to a data bus, the bus system 413 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 2 The general designated all buses as Bus System 413.

[0083] The memory 411 can be volatile or non-volatile, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).The memory 411 described in the embodiments of this application is intended to include, but is not limited to, these and any other suitable types of memory.

[0084] The memory 411 in this embodiment is used to store various types of data to support the operation of the vehicle. Examples of this data include: any computer programs used to operate on the vehicle, such as operating systems and applications; contact data; phonebook data; messages; pictures; videos, etc. The operating system includes various system programs, such as the framework layer, core library layer, driver layer, etc., used to implement various basic services and handle hardware-based tasks. Applications can include various applications, such as media players, browsers, etc., used to implement various application services. Here, the program implementing the method of this embodiment can be included in the application.

[0085] Based on the same inventive concept as the foregoing embodiments, this embodiment also provides a machine-readable storage medium storing a computer program. The machine-readable storage medium can be a magnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM), etc.; it can also be various devices including one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc. When the computer program stored in the machine-readable storage medium is executed by a processor, it implements a method for controlling the automotive fan and grille applied to the aforementioned vehicle. For the specific steps implemented when the computer program is executed by the processor, please refer to [link to relevant documentation]. Figure 1 The description of the illustrated embodiments will not be repeated here.

[0086] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0087] In this document, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, which includes not only the elements listed but also other elements not expressly listed.

[0088] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for controlling an automotive fan and grille, characterized in that, include: Obtain the vehicle's current speed and required airflow for cooling; Based on the grille air intake model, fan air volume model, vehicle drive power model, and fan power consumption model, an air volume constraint relationship and a total energy consumption target are established with grille opening and fan duty cycle as variables. The air volume constraint relationship requires that the cooling air volume provided by the grille and fan together meet the heat dissipation demand air volume, and the total energy consumption target is the sum of vehicle drive energy consumption and fan power consumption. The fan duty cycle is eliminated by the air volume constraint relationship to obtain a single-value mapping of the fan duty cycle with respect to the grille opening, and the total energy consumption target is transformed into a single-variable total energy consumption objective function with respect to the grille opening only. Determine whether the air volume provided by the fan when it is running at full duty cycle meets the remaining heat dissipation demand, wherein the remaining heat dissipation demand is the portion of the heat dissipation demand air volume minus the ram air volume that the grille can provide under the current grille opening. If the airflow provided by the fan at full duty cycle is still insufficient to meet the remaining heat dissipation requirements, energy consumption optimization is not performed and the grille is forced to be fully open and the fan to run at full duty cycle, prioritizing heat dissipation; otherwise, the gradient of the single-variable total energy consumption objective function with respect to the grille opening is calculated using gradient descent, and the grille opening is iteratively updated based on the gradient and clamped to the feasible interval until convergence, obtaining the energy-optimal grille opening and the matching fan duty cycle determined by the single-value mapping, and outputting as the energy-optimal control request under the current heat dissipation requirements.

2. The method according to claim 1, characterized in that, The grille air intake model represents the increase of grille stamping air intake with increasing vehicle speed and grille opening. Its grille stamping air intake coefficient is calibrated by the cooling system air volume test bench at different vehicle speeds and different grille openings. The fan air volume model represents the increase of fan suction air volume with increasing fan duty cycle, and includes a nonlinear compensation term to compensate for nonlinear deviations caused by friction, back pressure and turbulence. Its model coefficients are obtained by fitting the air volume test bench data using the least squares method.

3. The method according to claim 1, characterized in that, The vehicle drive power model represents the vehicle drive power caused by the increased wind resistance as the grille opening increases, and its wind resistance-related parameters are calibrated by wind tunnel tests; the fan power consumption model represents the nonlinear relationship of fan power consumption increasing with the increase of fan duty cycle, and its model coefficients are obtained by fitting the voltage and current data calibrated on the fan test bench using the least squares method.

4. The method according to claim 1, characterized in that, Eliminating the fan duty cycle based on the airflow constraint relationship includes: The air volume constraint relationship can be written as a quadratic equation in one variable about the fan duty cycle; Solve the quadratic equation in one variable to find the only valid solution within the positive range of fan duty cycle, and obtain the single-valued mapping of fan duty cycle with respect to grille opening. Substituting the single-value mapping into the total energy consumption target yields a univariate total energy consumption objective function that is only related to the grid opening.

5. The method according to claim 2, characterized in that, Before optimizing the single-variable total energy consumption objective function based on the gradient, verify that the function is a strictly convex function within the feasible interval of grid opening. Based on the monotonicity and second derivative properties of the single-valued mapping, the second derivative of the single-variable total energy consumption objective function is always greater than zero within the feasible range of grid opening, and has strict convexity properties. This ensures that the gradient descent optimization method stably converges to the globally unique minimum energy consumption point, thereby matching the optimal fan duty cycle.

6. The method according to claim 1, characterized in that, Also includes: During the period after the grille opening converges, the gradient is recalculated and a verification update is performed at a preset period to dynamically optimize when the vehicle speed or heat dissipation requirements change, while keeping the grille opening unchanged during non-verification periods.

7. The method according to claim 1, characterized in that, The determination of whether the airflow provided by the fan when it is running at full duty cycle meets the remaining heat dissipation requirements includes: The system calculates the air intake volume of the grille stamping corresponding to the current grille opening in real time, and combines it with the air volume required for the overall vehicle cooling to calculate the remaining cooling demand that needs to be compensated by the fan. When the remaining heat dissipation demand exceeds the maximum air supply capacity of the fan under full duty cycle conditions, it is determined that the system cooling capacity cannot meet the heat dissipation demand. Energy consumption optimization is not performed, and the grille is forcibly opened and the fan operates at full duty cycle to prioritize the heat dissipation performance of the entire vehicle.

8. The method according to claim 1, characterized in that, The single-variable total energy consumption objective function can be optimized by using any one of model predictive control, dynamic programming, or lookup table interpolation instead of gradient descent.

9. A vehicle, characterized in that, include: A processor and a memory for storing executable instructions; wherein the processor is configured to execute the instructions to implement the automotive fan and grille control method as described in any one of claims 1-8.

10. A machine-readable storage medium, characterized in that, When the instructions in the machine-readable storage medium are executed by a processor, the automotive fan and grille control method as described in any one of claims 1-8 is implemented.

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