A high-power electronic fan control method, device and storage medium

Abstract

The application provides a high-power electronic fan control method, device and storage medium, the method comprising: acquiring real-time temperature of a component to be cooled, vehicle driving state data and predicted road condition data; determining a virtual temperature target value based on the real-time temperature, the vehicle driving state data and the predicted road condition data; determining a target working mode from a plurality of working modes according to the virtual temperature target value; controlling the high-power electronic fan to switch from a current working mode to the target working mode, and in the target working mode, steplessly adjusting the fan motor according to the real-time temperature. By fusing real-time and predicted data to generate a virtual temperature target value, and cooperating with a stepless adjustment mechanism in the mode, the real-time temperature deviation can be quickly and smoothly compensated in the given mode, sufficient time is provided for the power change of the high-power electronic fan, and noise caused by a large power change in a short time and large loss of the high-power fan device are avoided.

Description

Technical Field

[0001] This invention belongs to the field of vehicle heat dissipation management technology, and in particular relates to a high-power electronic fan control method, device and storage medium. Background Technology

[0002] With the development of the new energy vehicle market, vehicle motor power is increasing, and the heat generation power is also increasing. OEMs are gradually transitioning from configuring multiple low-power electric fans to configuring one high-power electric fan, thereby simplifying the vehicle wiring harness, reducing the overall size of the electric fan, and lowering the cost of the electric fan.

[0003] Currently, when a vehicle starts, the speed of the high-power electric fan is usually determined based on the temperature of the component to be cooled (such as the motor). The high-power electric fan can only be controlled to run at a higher power after the component to be cooled has reached a certain temperature. This results in a lag, especially when the component to be cooled is heating up rapidly. The requirement for the high-power electric fan to increase its power in a short period of time may generate noise and cause significant wear and tear on the fan motor, relays, etc. Summary of the Invention

[0004] In view of this, the present invention provides a high-power electronic fan control method, device, equipment and storage medium to solve the problems of lag and equipment wear in the control of high-power electronic fans in the prior art.

[0005] A first aspect of the present invention provides a method for controlling a high-power electric fan, wherein the high-power electric fan is an electric fan with a power greater than a preset threshold used for cooling a vehicle power system, and the high-power electric fan is configured with multiple operating modes in which the power increases sequentially; the method includes:

[0006] Acquire real-time temperature data of the components to be cooled, vehicle driving status data, and predicted road condition data;

[0007] The virtual temperature target value is determined based on the real-time temperature, the vehicle driving status data, and the predicted road condition data.

[0008] Based on the virtual temperature target value, a target operating mode is determined from among the multiple operating modes;

[0009] The high-power electronic fan is controlled to switch from the current working mode to the target working mode, and in the target working mode, the fan motor is steplessly speed-regulated according to the real-time temperature.

[0010] A second aspect of the present invention provides a high-power electronic fan control device, wherein the high-power electronic fan is an electronic fan with a power greater than a preset threshold used for cooling a vehicle power system, and the high-power electronic fan is provided with multiple operating modes in which the power increases sequentially; the device includes:

[0011] The data acquisition module is used to acquire real-time temperature of the components to be cooled, vehicle driving status data, and predicted road condition data.

[0012] The prediction module is used to determine a virtual temperature target value based on the real-time temperature, the vehicle driving status data, and the predicted road condition data;

[0013] The target operating mode determination module is used to determine a target operating mode from a plurality of operating modes based on the virtual temperature target value;

[0014] The execution module is used to control the high-power electronic fan to switch from the current working mode to the target working mode, and in the target working mode, to perform stepless speed regulation of the fan motor according to the real-time temperature.

[0015] A third aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the high-power electronic fan control method as described in the first aspect above.

[0016] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the high-power electronic fan control method as described in the first aspect above.

[0017] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0018] This invention generates a virtual temperature target value by fusing real-time and predicted data, enabling early perception of the heat dissipation requirements of components to be cooled. Based on this virtual temperature target value, mode decisions are made, and with the stepless speed regulation mechanism within the mode, real-time temperature deviations can be quickly and smoothly compensated in a given mode, ensuring the accuracy and smoothness of the control response. This provides sufficient time for power changes in high-power electric fans, avoiding noise caused by large power changes in a short period of time and avoiding significant losses to fan motors, relays, etc. While ensuring a heat dissipation safety margin, it significantly improves the operational stability, energy efficiency, and reliability of the fan system. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of a high-power electronic fan control method provided in an embodiment of the present invention;

[0021] Figure 2 This is a schematic diagram of the structure of a high-power electronic fan provided in an embodiment of the present invention;

[0022] Figure 3 This is a flowchart of a method for determining a virtual temperature target value provided by an embodiment of the present invention;

[0023] Figure 4 This is a schematic diagram of a high-power electronic fan control device provided in an embodiment of the present invention;

[0024] Figure 5 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0025] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the present invention. However, those skilled in the art will recognize that the present application may be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted to avoid unnecessary detail that could obscure the description of the present application.

[0026] The technical solution of the present invention will be illustrated below through specific embodiments.

[0027] Reference Figure 1 The diagram shows a flowchart of a high-power electronic fan control method according to Embodiment 1 of the present invention. The method can be executed by a high-power electronic fan control device, which can be implemented in hardware and / or software and can be configured in an electronic device.

[0028] like Figure 1 As shown, the method may specifically include the following steps:

[0029] S101. Acquire real-time temperature of the component to be cooled, vehicle driving status data, and predicted road condition data.

[0030] The components to be cooled are the core heat-generating parts of the vehicle's powertrain. Their real-time temperatures can include the stator winding temperature of the drive motor, the motor controller temperature, and the battery pack temperature. Once these components heat up, they are cooled by a high-power electric fan.

[0031] In this invention, high power refers to power exceeding a preset threshold (e.g., 1400W). A high-power electric fan is a fan product used to cool a vehicle's power system, consisting primarily of fan blades and components such as a metal support plate.

[0032] A high-powered electric fan (cooling) prevents the car engine from overheating and maintains the optimal operating temperature of the air conditioning system by expelling hot air and promoting efficient heat exchange, ensuring the smooth and reliable operation of the car engine and air conditioning functions.

[0033] For example, the maximum airflow of a 1500W high-power electric fan is usually greater than that of three 800W low-power electric fans (i.e., greater than 12000m³ / h at 100Pa), and the maximum airflow of a 2000W high-power electric fan is usually greater than that of four 800W low-power electric fans (i.e., greater than 16000m³ / h at 100Pa).

[0034] Reference Figure 2 The diagram shows a structural schematic of a high-power electronic fan, which is used in a heat dissipation design, such as... Figure 2 As shown, Figure 2 Image (a) is a front view of a high-powered electric fan. Figure 2 (b) is a rear view of a high-power electronic fan. For example, the structure of a high-power electronic fan may include a fan cover, a rear cover, a wiring harness, a printed circuit board, a rotor, a stator, a housing, fan blades, etc.

[0035] In this embodiment, the high-power electric fan is configured with multiple operating modes that sequentially increase the motor power. The power parameters in these operating modes can be set by technicians based on experience, or they can be generated using an LLM (Large Language Model) from the automotive field based on the operating specifications of the high-power electric fan. This embodiment does not impose any limitations on this. For example, if the rated power of the high-power electric fan is 1500W, and a certain candidate speed setting has 5 operating modes, the motor power of each mode is sequentially 300W, 600W, 900W, 1200W, and 1500W.

[0036] Vehicle driving status data can reflect the driver's real-time intentions and the vehicle's instantaneous load. It is a key input for judging the current rate of heat load generation and predicting short-term trends. Specifically, it includes the current vehicle speed, motor output torque and speed (which can be converted into real-time power), accelerator pedal opening and its rate of change, vehicle acceleration, etc.

[0037] Predictive traffic data primarily originates from in-vehicle navigation or map systems and is used to provide forward-looking information. This enables the system to anticipate upcoming and continuous high-load operating conditions such as hill climbing and high-speed cruising, thereby allowing for early intervention. For example, predictive traffic data includes changes in road gradient, curvature, and elevation along the driving path over a future period (e.g., the next 30-60 seconds), as well as predicted vehicle speed curves that incorporate real-time traffic information.

[0038] Optionally, the predicted traffic data may also include:

[0039] Meteorological data: ambient temperature forecast, solar radiation intensity (different ambient temperatures result in different levels of heat dissipation).

[0040] S102. Determine the virtual temperature target value based on real-time temperature, vehicle driving status data, and predicted road condition data.

[0041] Specifically, a built-in algorithm model, such as a simplified calculation or data-driven model based on vehicle dynamics and thermodynamics, can be used to fuse short-term driving behavior predictions (from driving status) and long-term road load predictions to calculate the expected changes in heat load over a future period.

[0042] The output of this step is a virtual temperature target value, which is not a simple prediction of future temperature, but a temperature setpoint obtained by combining the current measured temperature and the predicted future heat load. At the same time, this virtual temperature target value is the currently applicable temperature setpoint.

[0043] S103. Determine the target operating mode from multiple operating modes based on the virtual temperature target value.

[0044] For example, the working modes may include the following:

[0045] Mode A (Silent Mode): Lowest speed, suitable for parking or very low load, meeting basic ventilation requirements when parked. For example, in this mode, power consumption is <200W, and the speed range is 800-1200 RPM; applicable scenarios: vehicle stationary, equipment temperature <30℃.

[0046] Mode B (Economy Mode): Low to medium speed, corresponding to the typical heat dissipation requirements of low to medium speed driving in urban areas, suitable for driving on flat urban roads. For example, in this mode, power consumption is 200-800W, speed range is 1200-2500 RPM, applicable scenarios: predicted heat load index <0.3, temperature <75℃.

[0047] Mode C (Strong Cooling Mode): Medium to high speed, corresponding to continuous high power output or fast charging scenarios, suitable for high-speed driving or climbing. For example, in this mode, power consumption is 800-2000W, speed range is 2500-4000 RPM, applicable scenarios: predicted heat load index 0.3-0.7, temperature 75-85℃.

[0048] Mode D (Predictive Boost Mode): Based on navigation, the engine speed is increased to near that of Mode C before entering high-load road sections. Suitable for aggressive driving or fast charging. For example, in this mode, power consumption is 2000-3500W, engine speed range is 4000-5500 RPM, and applicable scenarios are: predicted heat load index > 0.7 and temperature > 85℃.

[0049] The target operating mode can be determined by the decision engine. The decision engine outputs the target operating mode through trend evaluation and decision-making. The decision engine does not simply compare the virtual temperature target value T_virtual with a few fixed values, but continuously evaluates the degree of matching between the heat load trend reflected by T_virtual and the current mode.

[0050] Specifically, the decision engine's internal logic will determine whether, based on the current T_virtual level and its change trajectory, maintaining the current mode might lead to insufficient heat dissipation or low energy efficiency; or whether switching to the nearest mode could bring a better balance between heat dissipation and energy consumption.

[0051] Optionally, the decision engine's decision logic incorporates stability constraints, meaning the system has an inertia in its tendency to maintain the existing pattern; however, initiating a pattern switch is more "difficult," requiring the trend indicated by T_virtual to be strong and persistent enough to overcome this state-maintaining inertia. This design directly implements the hysteresis effect, but its implementation may be achieved through internal state variables, time integration, or filtering logic within the algorithm, rather than directly comparing with a set of static thresholds.

[0052] After the above evaluation, the system outputs a specific target operating mode. This output is discrete (e.g., mode A, mode B, mode C).

[0053] S104. Control the high-power electronic fan to switch from the current working mode to the target working mode, and in the target working mode, perform stepless speed regulation of the fan motor according to the real-time temperature.

[0054] This step mainly involves two different levels of execution processes: mode switching control and stepless speed regulation within a mode.

[0055] During mode switching control, when a mode switch is required, the controller does not command a step change in power. Instead, it typically transitions the fan power from the current level to the baseline level corresponding to the target mode according to a preset smooth power adjustment curve. This achieves a smooth transition, avoids impact on the electrical system and mechanical components, and reduces sudden noise changes.

[0056] During stepless speed regulation within a mode, under any defined operating mode (corresponding to a specific power or speed range), the system uses the reference power of that mode as the setpoint, receives real-time measured temperature feedback, and continuously and precisely adjusts the fan speed or power within the range allowed by that mode through closed-loop algorithms such as PID and fuzzy control.

[0057] In this embodiment, discrete mode decisions are decoupled from continuous precise regulation. Mode decisions ensure macroscopic stability and energy efficiency optimization, while stepless speed regulation is responsible for handling real-time, minute temperature fluctuations, ensuring the final control accuracy and response speed, thus balancing system stability (reducing large-scale mode switching) and control precision (achieving stable temperature control).

[0058] This method generates a virtual temperature target value by fusing real-time and predicted data, enabling early perception of the heat dissipation requirements of the components to be cooled. Based on this virtual temperature target value, mode decisions are made, and with the stepless speed regulation mechanism within the mode, real-time temperature deviations can be quickly and smoothly compensated in a given mode, ensuring the accuracy and smoothness of the control response. This provides sufficient time for power changes in high-power electric fans, avoiding noise caused by large power changes in a short period of time and avoiding significant losses to fan motors, relays, etc. While ensuring a heat dissipation safety margin, it significantly improves the operational stability, energy efficiency, and reliability of the fan system.

[0059] Reference Figure 3 The flowchart of a method for determining a virtual temperature target value is shown. The method for determining a virtual temperature target value is a further refinement of S102.

[0060] In an optional embodiment, the predicted road condition data includes predicted vehicle speed curves and meteorological data, such as... Figure 3 As shown, the virtual temperature target value is determined based on real-time temperature, vehicle driving status data, and predicted road condition data, including:

[0061] S301. Determine the change in vehicle power demand within the first preset time period based on vehicle driving status data, and convert the change in vehicle power demand into an increase in vehicle heat load.

[0062] The calculation of vehicle power demand changes is intended to quantify rapid changes in future heat load caused by short-term driver behavior.

[0063] Vehicle driving status data mainly includes the current accelerator pedal opening (throttle depth), pedal opening change rate, current actual vehicle speed, vehicle acceleration, etc. These data directly reflect the driver's real-time operating intentions and the vehicle's transient response.

[0064] Method for predicting changes in vehicle power demand: Based on the above data, using a simplified vehicle dynamics model or a pre-calibrated empirical mapping table, the average power required by the drive motor will change over a relatively short period of time (a first preset time period, such as 10-30 seconds), resulting in the "change in vehicle power demand (ΔP_veh)," expressed in kilowatts (kW). For example, during rapid acceleration, ΔP_veh is a large positive value.

[0065] The vehicle's drive system (motor, electronic control) experiences efficiency losses when outputting power. Almost all of this lost electrical energy is converted into heat energy. The formula for calculating the vehicle's heat load increment is:

[0066] Vehicle heat load increment (Q_veh) = ΔP_veh × (1-η1). Where η1 is the first overall efficiency of the drive system (e.g., 0.93-0.97), and (1-η1) is the power loss coefficient within the first preset time period.

[0067] Q_veh represents the additional thermal power (kW) that will be generated in the near future due to driving behavior, i.e., the vehicle's thermal load.

[0068] In an optional embodiment, the vehicle driving status data includes the accelerator pedal opening, the rate of change of pedal opening, and the vehicle acceleration at the current moment.

[0069] Based on vehicle driving status data, determine the change in vehicle power demand within the first preset time period in the future, and convert the change in vehicle power demand into an increase in vehicle heat load, including:

[0070] Based on at least one of the accelerator pedal opening, the rate of change of pedal opening, and the vehicle acceleration at the current moment, predict the change in vehicle power demand during the first preset time period in the future.

[0071] The change in vehicle power demand is converted into thermal power loss according to a preset first loss ratio to obtain the vehicle thermal load increment.

[0072] S302. Based on the predicted road condition data, predict the additional road load in the second preset time period in the future. The second preset time period is longer than the first preset time period.

[0073] Predicted traffic data: mainly comes from high-precision map data from in-vehicle navigation systems, with the core being the road slope sequence (slope value and corresponding location) on the future path.

[0074] In an optional embodiment, the predicted traffic data further includes vehicle navigation data; predicting the additional road load within a future second preset time period based on the predicted traffic data includes:

[0075] The road gradient sequence for the second preset time period in the future is obtained from the vehicle navigation data. Based on the total vehicle mass, the road gradient sequence and the predicted vehicle speed curve, the additional power required for the vehicle to overcome the gradient during driving is calculated. The additional power of the vehicle is converted into heat power loss according to the preset second loss ratio to obtain the additional road load for the second preset time period in the future.

[0076] Specifically, based on the vehicle's total mass, road gradient sequence, and predicted speed curve, the additional power required for the vehicle to overcome the gradient during operation is calculated, including:

[0077] Based on the road slope sequence and predicted vehicle speed curve, the slope values ​​and predicted vehicle speeds of multiple sampling points within the next second preset time period are determined. For each sampling point, the instantaneous additional power required by the vehicle to overcome the slope at the sampling point is calculated based on the total vehicle mass, the slope value of the sampling point, and the predicted vehicle speed. The instantaneous additional power of each sampling point is integrated to obtain the additional power required by the vehicle to overcome the slope during the vehicle's journey within the next second preset time period.

[0078] Combining the total vehicle mass, road slope angle θ (converted from slope percentage), and the future vehicle speed curve v(t) predicted based on navigation and traffic information, the additional mechanical power required to overcome the slope is calculated using the physics formula: P_grade(t)=m×g×sin(θ(t)) ×v(t).

[0079] By performing time integration or weighted averaging on P_grade(t) within the second preset time period, the overall additional road load (P_road) of the road segment is obtained, in kW.

[0080] This additional mechanical power also needs to be provided by the drive system, and therefore will also generate corresponding efficiency loss heat. The additional road load itself can be regarded as a special power demand change, and its conversion into heat load increment is the same as S301: Q_road=P_road×(1–η2).

[0081] Output: Q_road represents the heat power (kW) that will continue to increase over a long period of time due to specific road conditions ahead, and η2 expresses the power loss coefficient in the second time period in the future.

[0082] The design of the second time period (e.g., 30-90 seconds) being longer than the first time period (e.g., 30 seconds) reflects the hierarchical and scaled prediction of two different heat sources: instantaneous operation and continuous environment.

[0083] The first time period corresponds to driving behavior (rapid acceleration and braking), characterized by rapid changes and low inertia. The corresponding heat load is generated by the instantaneous losses of the motor and electronic control system, and its rate of change is synchronized with the driver's operation, potentially changing drastically within seconds. The affected components (such as IGBT chips and motor windings) have relatively small heat capacity and rapid temperature response. The system needs to respond and compensate quickly at the "second" level. Therefore, the prediction period does not need to be too long; approximately 30 seconds is sufficient to cover a typical rapid acceleration-cruise sequence and provide early warning of impending transient heating.

[0084] The second time period corresponds to the road environment, characterized by slow changes and high inertia. The corresponding heat load is related to the road's geographical attributes (long uphill slopes, continuous high-speed roads). This type of heat load is generated by the "steady-state losses" of vehicles overcoming slopes or continuous high-speed driving, and its changes are slow but persistent. This leads to continuous and cumulative heat generation throughout the entire powertrain system. Affected systems (such as the motor body and coolant circulation system) have large heat capacities and high thermal inertia, resulting in slow temperature rises, but once they rise, they are difficult to suppress quickly. Therefore, the system needs to plan ahead for a longer period (e.g., on the order of ten seconds to minutes). A 30-second prediction may only show the beginning of the slope, while a 90-second prediction can show the entire slope. Longer prediction periods allow the system to identify high-load conditions that will last for tens of seconds or even minutes in advance, thus providing sufficient decision-making and execution time for the early and continuous control of the cooling system.

[0085] Control based on the first time period: responsible for responding to sudden and instantaneous thermal load shocks caused by driver operation, enabling the fan to make rapid fine adjustments to prevent temperature spikes.

[0086] Control based on the second time period: responsible for responding to the trend and continuous increase in heat load caused by the road environment, directing the fans to switch operating modes in advance (such as switching from silent mode to strong cooling mode in advance), and maintaining in this mode until the threat is eliminated.

[0087] The design, where the second time period is longer than the first, is based on in-depth optimization of physical principles, data characteristics, and control requirements. Its beneficial effects include:

[0088] It maximizes the use of information value, restricting the prediction of highly uncertain driving behavior to its effective short time domain, while extending the prediction of highly deterministic road environment to its useful long time domain, making the prediction model more reliable as a whole, providing sufficient advance preparation time to cope with continuous heat load, realizing the advance adjustment of virtual temperature target value and avoiding rapid system response.

[0089] Improve system stability and energy efficiency: By combining slow loop advance planning mode switching with fast loop instantaneous fine-tuning, unnecessary frequent switching of working modes is greatly reduced, making the fan run more smoothly, the overall energy efficiency is higher, and the mechanical and electrical stress is less.

[0090] On the other hand, 1-η1 and 1-η2 are both power loss coefficients, which can be the same or different. Their settings can be determined based on specific road conditions. The heat loss coefficient of a vehicle traveling on flat ground is usually smaller than that when climbing a hill. This is mainly due to the different types and magnitudes of resistance that the vehicle overcomes under different road conditions.

[0091] On a flat road, vehicles primarily overcome rolling resistance and air resistance. When a vehicle travels at a constant, economical speed, the engine or electric motor experiences a stable load, operating within its optimal efficiency range, resulting in the highest energy utilization and relatively low heat loss. For example, a gasoline-powered vehicle traveling at a constant speed of 90 km / h on a flat highway exhibits low fuel consumption per 100 kilometers and high energy efficiency.

[0092] When a vehicle goes uphill, it needs to do extra work to overcome gravity, which leads to a significant increase in the power output of the engine or electric motor and a sharp rise in load. To provide greater driving force, gasoline-powered vehicles will increase the amount of fuel injected, while electric motors will consume more electrical energy, resulting in a higher coefficient of thermal energy loss.

[0093] S303. Determine the amount of natural heat dissipation compensation based on the current vehicle speed, the predicted vehicle speed curve, and meteorological data.

[0094] Current vehicle speed provides the basis for immediate heat dissipation, while the predicted speed curve provides the future trend of heat dissipation capacity. Meteorological data may include ambient temperature, wind speed, and atmospheric pressure (which affects air density), and can be obtained through external sensors or vehicle-to-everything (V2X) services.

[0095] Natural heat dissipation mainly comes from the cooling effect of the wind when the vehicle is in motion. Its heat dissipation power Q_natural is usually proportional to the nth power of the vehicle speed V (n≈1.5-2, depending on the air duct design), the air density ρ (affected by temperature and air pressure), and the temperature difference between the radiator and the environment.

[0096] The natural heat dissipation compensation (C_natural) is a negative heat load value, representing the amount of heat that natural conditions can remove from the device being cooled. It is subtracted in the overall calculation. It can be calculated using an empirical model, for example, C_natural = -[k1×V^n + k2×(T_core - T_a], where k1, k2, and n are calibration parameters, T_a is the ambient temperature, which can be obtained from meteorological data, and T_core - T_a is the temperature difference between the current temperature and the ambient temperature.

[0097] S304. Calculate the comprehensive heat load adjustment based on the vehicle heat load increment, road additional load, and natural heat dissipation compensation.

[0098] In one optional example, the combined heat load adjustment is the sum of the vehicle heat load increment, the road additional load, and the natural heat dissipation compensation.

[0099] In another optional embodiment, the comprehensive heat load adjustment is calculated based on the vehicle heat load increment, road additional load, and natural heat dissipation compensation, including:

[0100] Obtain the preset weights corresponding to the vehicle heat load increment, road additional load, and natural heat dissipation compensation; calculate the weighted sum of the vehicle heat load increment, road additional load, and natural heat dissipation compensation based on the preset weights to obtain the comprehensive heat load adjustment.

[0101] The weights depend on factors such as prediction confidence and current vehicle mode.

[0102] The comprehensive heat load adjustment reflects the net trend of system heat load change over a future period. A positive value indicates that heat generation will exceed natural heat dissipation, which may require a high-power electric fan to work harder, i.e., increase power. A negative value indicates that there is sufficient heat dissipation capacity, which may require a high-power electric fan to reduce power.

[0103] S305. Convert the comprehensive heat load adjustment into a temperature adjustment using a preset thermodynamic model.

[0104] The process by which power causes temperature change is as follows: after mechanical power (or electrical power) loss, a heat load is generated, which leads to temperature change through heat accumulation. Specifically, the heat is converted into temperature change through the system's heat capacity (C).

[0105] Power is converted into heat load, and heat load equals the power loss. Both are measured in watts (W). For example, if a motor consumes 1000W of electrical energy and outputs 850W of mechanical energy, then the 150W of power loss is entirely converted into heat load and needs to be carried away by a fan.

[0106] Thermodynamic models can be simplified models based on the law of conservation of energy. The core parameter is the equivalent heat capacity (C) of the component to be cooled (such as a motor), which represents the amount of heat (unit kJ / ℃) required to raise the temperature of the component by 1 degree Celsius.

[0107] Optional, temperature adjustment amount (ΔT) = (ΔQ_total × Δt_eff) / C.

[0108] ΔQ_total is the total heat load adjustment amount, and Δt_eff is the preset equivalent action time (in seconds), which can be determined through calibration. Δt_eff is not a physical time, but rather an equivalent value that concentrates the future average heat load onto multiple current time periods to achieve the same control effect.

[0109] Optionally, the equivalent action time can be shorter than either the second or first prediction time period. Thermal systems exhibit inertia; heat generated in the future will not immediately and fully translate into a temperature rise. Its impact diffuses and decays over time. For example, heat generated 30 seconds later has a far less impact on the current temperature than the same amount of heat generated immediately now. The equivalent action time reflects this decay. It is essentially an integral over time representation of a "discount factor" or "decay weight." Multiplying the heat load by a shorter time period than the original prediction time period is mathematically equivalent to applying a weighting function that decays exponentially with time to the future heat load and then integrating. A shorter equivalent action time means the model believes the future impact decays faster and is less important for current decisions.

[0110] The purpose of setting the equivalent action time is:

[0111] Preventing overcompensation: If the heat from the entire prediction period (e.g., 60 seconds) is used to adjust the current temperature, the temperature adjustment ΔT will be overestimated. The controller may react too aggressively, causing the fan power to spike, resulting in energy waste, increased noise, and even system overcooling. Then, it may need to urgently reduce power after the prediction window ends, causing oscillations.

[0112] This reflects the uncertainty of prediction: the predicted result may not necessarily occur in reality, and Δt_eff is less than the prediction time period. In terms of control, this is equivalent to reducing the output confidence of the prediction model and improving the robustness of the system.

[0113] Matching actuator response: It also takes time for the fan to increase from its current power to the target power. A discounted, relatively mild ΔT command better matches the dynamic response capability of the fan system, making the power increase process smoother and more controllable.

[0114] The physical meaning of the final output temperature adjustment is: considering future changes in heat load, what temperature value should the current temperature setpoint be adjusted to in order to optimally smooth future temperature fluctuations.

[0115] S306. Calculate the sum of the temperature adjustment amount and the real-time temperature to obtain the virtual temperature target value.

[0116] A virtual temperature target value is not a prediction of a component's temperature at some future point in time, but rather a "target temperature" or "setpoint" calculated at the current moment to guide current control actions. The problem it addresses is: given the current temperature and future predictions, which temperature should be used as the benchmark for fan control to maintain optimal thermal conditions?

[0117] For example, if the current temperature is 75℃, but an uphill climb is predicted (ΔT=+8℃), then T_virtual=83℃. The control system determines that 83℃ is close to the high-power mode switching threshold, and will instruct the fan to increase power in advance. As a result, when the uphill climb actually occurs, the component temperature will only rise slightly rather than surge sharply.

[0118] This embodiment achieves accurate prediction of multi-dimensional heat load through S301-S304, converts heat load into temperature command based on physical laws through S305, and finally generates the core control variable of virtual temperature target value in S306. This enables the heat dissipation control system to have predictive capabilities and can actively and smoothly adjust the heat dissipation intensity. In this way, while ensuring heat dissipation safety, it can minimize the frequency of fan mode switching, reduce power consumption, improve system stability and component life.

[0119] In an optional embodiment, a target operating mode is determined from multiple operating modes based on a virtual temperature target value, including:

[0120] Determine the current power control range corresponding to the current operating mode; compare the virtual temperature target value with the preset hysteresis threshold of the current power control range; determine the target power control range based on the comparison result, and take the operating mode corresponding to the target power control range as the target operating mode;

[0121] The preset hysteresis thresholds include an upshift threshold for triggering a transition to a higher power control range and a downshift threshold for triggering a transition to a lower power control range; for the same power control range, the upshift threshold is higher than the downshift threshold.

[0122] First, determine which power control range (corresponding to an operating mode) the fan is currently operating in. This is the baseline for all subsequent judgments. Then, compare the calculated T_virtual with a pair of hysteresis thresholds specific to the current range. This pair of thresholds includes:

[0123] Upgrade threshold (T_up): A relatively high threshold. Only when T_virtual exceeds this value will the system consider a sufficiently strong temperature rise trend, requiring stronger heat dissipation capabilities, and thus consider entering a higher power consumption range.

[0124] Downgrade threshold (T_down): A relatively low threshold. Only when T_virtual is below this value does the system consider a sufficiently clear cooling trend, indicating that the current heat dissipation capacity may be excessive, and thus consider reverting to a lower power consumption range.

[0125] For the same interval, T_up is forcibly set to be greater than T_down. The difference between the two (T_up - T_down) constitutes a stable fault tolerance band.

[0126] It effectively filters out noise and short-term prediction fluctuations in the T_virtual signal, ensuring that mode switching only occurs when there is a substantial and sustained change in the trend of heat dissipation demand.

[0127] This embodiment significantly reduces the number of times the fan switches between different power levels, directly reducing switching losses in power semiconductor devices and stress cycling in mechanical components, thereby improving the reliability and lifespan of the entire cooling system. It avoids unnecessary power transients caused by frequent mode switching, resulting in smoother fan operation, which helps reduce overall vehicle energy consumption and improve noise and vibration levels.

[0128] In an optional embodiment, controlling a high-power electric fan to switch from its current operating mode to a target operating mode includes:

[0129] Determine the power relationship between the current operating mode and the target operating mode; determine the switching method based on the power relationship, including:

[0130] When switching from high-power operating mode to low-power operating mode, the high-power operating mode must be continuously running for a period of time exceeding the preset minimum stabilization time. Furthermore, the filtered value obtained after the virtual temperature target value is processed by a low-pass filter with a preset time constant is lower than the preset downgrade temperature threshold.

[0131] The process specifically addresses the "switching from high power to low power" direction, setting two corresponding conditions that must be met simultaneously:

[0132] Time requirement (minimum settling time): The system must have been running continuously for at least a preset minimum time (e.g., 90 seconds) in the current high-power operating mode. This mandates a "minimum task cycle." Its purpose is to ensure that the fan's cooling effect in high-power mode is fully utilized, preventing premature termination of the continuous high-intensity cooling state due to a brief, occasional cooling phenomenon. This effectively prevents "speculative" downscaling caused by a temporary improvement in operating conditions, ensuring the integrity of the cooling task.

[0133] Signal conditions (filter threshold comparison): The virtual temperature target value T_virtual must first be processed by a low-pass filter to obtain a smoothed T_filtered, and then T_filtered must be lower than the preset downgrade temperature threshold.

[0134] Low-pass filtering: T_virtual is based on prediction calculations and may contain short-term noise or fluctuations. The role of the low-pass filter is to filter out these high-frequency fluctuations, retaining only the slow, stable trend components. T_filtered no longer reflects the instantaneous prediction value, but rather the trend baseline of T_virtual.

[0135] Threshold comparison: The cooling is considered real and reliable only when the confirmed and stable cooling trend baseline (T_filtered) is also lower than the downgrade threshold.

[0136] Both of the above conditions must be met simultaneously for the system to switch from high-power mode to low-power mode, avoiding the possibility of frequent switching between high and low power modes and ensuring extremely stable macroscopic system performance. Furthermore, it avoids insufficient heat dissipation caused by premature downgrading, thus preventing the system from having to compensate for temperature increases again, even at higher power levels, resulting in better overall energy efficiency. At the same time, the significantly reduced number of power level switching events directly reduces stress and losses on electrical and mechanical components.

[0137] It should be noted that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0138] Reference Figure 4 The diagram illustrates a high-power electronic fan control device according to an embodiment of the present invention. The high-power electronic fan is an electronic fan with a power greater than a preset threshold used for cooling a vehicle's power system. The high-power electronic fan is equipped with multiple operating modes in which the power increases sequentially. The device may specifically include the following modules:

[0139] The data acquisition module 401 is used to acquire the real-time temperature of the component to be cooled, vehicle driving status data, and predicted road condition data.

[0140] Prediction module 402 is used to determine a virtual temperature target value based on the real-time temperature, the vehicle driving status data, and the predicted road condition data;

[0141] The target operating mode determination module 403 is used to determine a target operating mode from a plurality of operating modes based on the virtual temperature target value;

[0142] The execution module 404 is used to control the high-power electronic fan to switch from the current working mode to the target working mode, and in the target working mode, to perform stepless speed regulation of the fan motor according to the real-time temperature.

[0143] Optionally, the predicted road condition data includes predicted vehicle speed curves and meteorological data, and the prediction module 402 includes:

[0144] The first submodule is used to determine the change in vehicle power demand within a future first preset time period based on the vehicle driving status data, and to convert the change in vehicle power demand into an increase in vehicle heat load.

[0145] The second submodule is used to predict the additional road load in a future second preset time period based on the predicted road condition data, where the second preset time period is longer than the first preset time period.

[0146] The third submodule is used to determine the amount of natural heat dissipation compensation based on the current vehicle speed, the predicted vehicle speed curve and meteorological data.

[0147] The fourth submodule is used to calculate the comprehensive heat load adjustment based on the vehicle heat load increment, the road additional load, and the natural heat dissipation compensation.

[0148] The fifth submodule is used to convert the comprehensive heat load adjustment into a temperature adjustment using a preset thermodynamic model;

[0149] The sixth submodule is used to calculate the sum of the temperature adjustment amount and the real-time temperature to obtain the virtual temperature target value.

[0150] Optionally, the vehicle driving status data includes the accelerator pedal opening, the rate of change of pedal opening, and the vehicle acceleration at the current moment.

[0151] The first submodule is used for:

[0152] Based on at least one of the accelerator pedal opening, the rate of change of pedal opening, and the vehicle acceleration at the current moment, predict the change in vehicle power demand during the first preset time period in the future.

[0153] The change in vehicle power demand is converted into thermal power loss according to a preset first loss ratio to obtain the vehicle thermal load increment.

[0154] Optionally, the predicted road condition data further includes vehicle navigation data; the second submodule includes:

[0155] The first unit is used to obtain the road slope sequence for a future second preset time period from the vehicle navigation data;

[0156] The second unit is used to calculate the additional power required by the vehicle to overcome the slope during the vehicle's travel, based on the total vehicle mass, the road gradient sequence, and the predicted vehicle speed curve.

[0157] The third unit is used to convert the vehicle's additional power into thermal power loss according to a preset second loss ratio, so as to obtain the road additional load in the second preset time period in the future.

[0158] Optionally, the second unit is used for:

[0159] Based on the road slope sequence and the predicted vehicle speed curve, determine the slope values ​​and predicted vehicle speeds of multiple sampling points within a future second preset time period;

[0160] For each of the sampling points, the instantaneous additional power required by the vehicle to overcome the slope at the sampling point is calculated based on the total mass of the vehicle, the slope value of the sampling point, and the predicted vehicle speed.

[0161] Integrating the instantaneous additional power at each sampling point yields the additional power required for the vehicle to overcome the gradient during a second preset time period in the future.

[0162] Optionally, the fourth submodule is used for:

[0163] Obtain the preset weights corresponding to the vehicle heat load increment, the road additional load, and the natural heat dissipation compensation;

[0164] The weighted sum of the vehicle heat load increment, the road additional load, and the natural heat dissipation compensation is calculated based on the preset weights to obtain the comprehensive heat load adjustment.

[0165] Optionally, the target operating mode determination module 403 is used for:

[0166] Determine the current power control range corresponding to the current operating mode;

[0167] The virtual temperature target value is compared with the preset hysteresis threshold of the current power control range;

[0168] The target power control range is determined based on the comparison results, and the operating mode corresponding to the target power control range is taken as the target operating mode.

[0169] The preset hysteresis threshold includes an upshift threshold for triggering a transition to a higher power control range and a downshift threshold for triggering a transition to a lower power control range; for the same power control range, the upshift threshold is higher than the downshift threshold.

[0170] Optionally, the execution module 404 is used for:

[0171] Determine the power relationship between the current operating mode and the target operating mode;

[0172] The switching method is determined based on the power magnitude relationship, and the switching method includes:

[0173] When switching from high-power operating mode to low-power operating mode, the high-power operating mode must be continuously running for a period of time exceeding the preset minimum stabilization time. Furthermore, the filtered value obtained after the virtual temperature target value is processed by a low-pass filter with a preset time constant is lower than the preset downgrade temperature threshold.

[0174] The present invention provides a high-power electronic fan control device, which can realize the steps in the aforementioned embodiments of the high-power electronic fan control methods.

[0175] It should be noted that the module division in the various high-power electronic fan control devices provided in the above embodiments is illustrative and only represents one logical functional division. In actual implementation, other division methods may also be used. Furthermore, the functional modules in the various embodiments of this invention can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0176] If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the technical solution of the embodiments of the present invention can be embodied in the form of a computer program product, which is stored in a computer storage medium and includes several instructions to cause an electronic device or processor to execute all or part of the steps of the methods in the various embodiments of the present invention. The aforementioned computer storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0177] Furthermore, the high-power electronic fan control device and the high-power electronic fan control method provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0178] Reference Figure 5 The diagram illustrates an electronic device according to an embodiment of the present invention. Figure 5 As shown, the electronic device in this embodiment of the invention includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the above-described high-power electronic fan control method embodiment. Alternatively, when the processor executes the computer program, it implements the functions of each module in the above-described high-power electronic fan control device embodiment.

[0179] For example, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete this application. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which can be used to describe the execution process of the computer program in the electronic device.

[0180] The electronic device may be a desktop computer, a cloud server, or other computing device. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that... Figure 5 This is merely one example of an electronic device and does not constitute a limitation on the electronic device. It may include more or fewer components than illustrated, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0181] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0182] The memory can be an internal storage unit of the electronic device, such as a hard drive or RAM. Alternatively, it can be an external storage device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. Furthermore, the memory can include both internal and external storage units. The memory is used to store the computer program and other programs and data required by the electronic device. The memory can also be used to temporarily store data that has been output or will be output.

[0183] This invention also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the high-power electronic fan control method as described in the foregoing embodiments.

[0184] This invention also discloses a computer-readable storage medium storing a computer program that, when executed by a processor, implements the high-power electronic fan control method as described in the foregoing embodiments.

[0185] This invention also discloses a computer program product that, when run on a computer, causes the computer to execute the high-power electronic fan control method described in the foregoing embodiments.

[0186] The embodiments described above are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for controlling a high-power electronic fan, characterized in that, The high-power electric fan is an electric fan with a power exceeding a preset threshold, used for cooling the vehicle's power system. The high-power electric fan has multiple operating modes with sequentially increasing power. The method includes: Acquire real-time temperature data of the components to be cooled, vehicle driving status data, and predicted road condition data; The virtual temperature target value is determined based on the real-time temperature, the vehicle driving status data, and the predicted road condition data. Based on the virtual temperature target value, a target operating mode is determined from among the multiple operating modes; The high-power electronic fan is controlled to switch from the current working mode to the target working mode, and in the target working mode, the fan motor is steplessly speed-regulated according to the real-time temperature; The predicted road condition data includes predicted vehicle speed curves and meteorological data. The step of determining a virtual temperature target value based on the real-time temperature, the vehicle driving status data, and the predicted road condition data includes: Based on the vehicle driving status data, determine the change in vehicle power demand within a future first preset time period, and convert the change in vehicle power demand into an increase in vehicle heat load. The road additional load is predicted based on the predicted road condition data for a future second preset time period, where the second preset time period is longer than the first preset time period. The amount of natural heat dissipation compensation is determined based on the current vehicle speed, the predicted vehicle speed curve, and meteorological data. Calculate the comprehensive heat load adjustment based on the vehicle heat load increment, the road additional load, and the natural heat dissipation compensation. The comprehensive heat load adjustment is converted into a temperature adjustment using a preset thermodynamic model. The virtual temperature target value is obtained by summing the temperature adjustment amount and the real-time temperature.

2. The method as described in claim 1, characterized in that, The vehicle driving status data includes the accelerator pedal opening, the rate of change of pedal opening, and the vehicle acceleration at the current moment. The step of determining the change in vehicle power demand within a future first preset time period based on the vehicle driving status data, and converting the change in vehicle power demand into an increase in vehicle heat load, includes: Based on at least one of the accelerator pedal opening, the rate of change of pedal opening, and the vehicle acceleration at the current moment, predict the change in vehicle power demand during the first preset time period in the future. The change in vehicle power demand is converted into thermal power loss according to a preset first loss ratio to obtain the vehicle thermal load increment.

3. The method as described in claim 1, characterized in that, The predicted traffic data also includes vehicle navigation data; the step of predicting the additional road load within a second preset time period based on the predicted traffic data includes: Obtain the road gradient sequence for a second preset time period from the vehicle navigation data; The additional power required to overcome the gradient during vehicle travel is calculated based on the total vehicle mass, the road gradient sequence, and the predicted vehicle speed curve. The vehicle's additional power is converted into thermal power loss according to a preset second loss ratio to obtain the road's additional load in the second preset time period.

4. The method as described in claim 3, characterized in that, The calculation of the additional power required for the vehicle to overcome the gradient during driving, based on the vehicle's total mass, the road gradient sequence, and the predicted speed curve, includes: Based on the road slope sequence and the predicted vehicle speed curve, determine the slope values ​​and predicted vehicle speeds of multiple sampling points within a future second preset time period; For each of the sampling points, the instantaneous additional power required by the vehicle to overcome the slope at the sampling point is calculated based on the total mass of the vehicle, the slope value of the sampling point, and the predicted vehicle speed. Integrating the instantaneous additional power at each sampling point yields the additional power required for the vehicle to overcome the gradient during a second preset time period in the future.

5. The method as described in claim 1, characterized in that, Based on the vehicle heat load increment, the road additional load, and the natural heat dissipation compensation, the comprehensive heat load adjustment is calculated, including: Obtain the preset weights corresponding to the vehicle heat load increment, the road additional load, and the natural heat dissipation compensation; The weighted sum of the vehicle heat load increment, the road additional load, and the natural heat dissipation compensation is calculated based on the preset weights to obtain the comprehensive heat load adjustment.

6. The method according to any one of claims 1-5, characterized in that, The step of determining the target operating mode from multiple operating modes based on the virtual temperature target value includes: Determine the current power control range corresponding to the current operating mode; The virtual temperature target value is compared with the preset hysteresis threshold of the current power control range; The target power control range is determined based on the comparison results, and the operating mode corresponding to the target power control range is taken as the target operating mode. The preset hysteresis threshold includes an upshift threshold for triggering a transition to a higher power control range and a downshift threshold for triggering a transition to a lower power control range; for the same power control range, the upshift threshold is higher than the downshift threshold.

7. The method according to any one of claims 1-5, characterized in that, The control of the high-power electronic fan to switch from the current operating mode to the target operating mode includes: Determine the power relationship between the current operating mode and the target operating mode; The switching method is determined based on the power magnitude relationship, and the switching method includes: When switching from high-power operating mode to low-power operating mode, the high-power operating mode must be continuously running for a period of time exceeding the preset minimum stabilization time. Furthermore, the filtered value obtained after the virtual temperature target value is processed by a low-pass filter with a preset time constant is lower than the preset downgrade temperature threshold.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the high-power electronic fan control method as described in any one of claims 1-7.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the high-power electronic fan control method as described in any one of claims 1-7.

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