Fan control method of new energy vehicle

Abstract

The invention provides a fan control method for a new energy vehicle. The method comprises the steps that the real-time temperature, driving state data and real-time fan noise values of multiple heat sources in the new energy vehicle are obtained; the driving state data comprises a real-time driving speed and a real-time geographic position; according to the real-time geographic position and the real-time fan noise value, the maximum allowable rotating speed of an electronic fan of a heat management system in the new energy vehicle is determined; determining a target rotating speed of the electronic fan according to the real-time temperatures, the real-time running speeds and the maximum allowable rotating speed of the heat sources; and generating a control signal according to the target rotating speed, and controlling the electronic fan according to the control signal. Real-time temperatures, driving state data and real-time fan noise values of a plurality of heat sources are synchronously obtained, the maximum allowable rotating speed is determined in combination with a real-time geographic position, then the real-time temperatures and real-time driving speeds of the heat sources are fused, the target rotating speed is accurately calculated, a control signal is generated, and multi-dimensional dynamic cooperative control is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fan control, in particular to a fan control method for a new energy vehicle. BACKGROUND

[0002] With the continuous deepening of the electrification transformation of commercial vehicles, new energy vehicles such as pure electric heavy trucks are increasingly widely used in the fields of logistics transportation and engineering operation. Their core advantages of zero emission and low operating cost promote the steady increase of their market penetration rate. As a key component of new energy vehicles, the thermal management system is directly related to the working safety of core components such as batteries, motors and electronic controls, the vehicle endurance and the driving comfort. As the core component of the thermal management system, the control strategy of the electronic fan becomes a key factor in determining the efficiency of thermal management. At present, the industry's demand for the working condition adaptability, energy saving and comfort of new energy vehicles continues to upgrade. The traditional electronic fan control strategy has been difficult to meet the dynamic thermal management demand in multiple scenarios, and needs to be optimized and upgraded to meet the industry development trend.

[0003] The existing electronic fan control technology for new energy vehicles mostly uses a rough control logic based on temperature parameters. Some schemes only respond to the temperature signal of the core component. The core defects of such technology are reflected in multiple dimensions: the control dimension is single, there is a lack of multi-parameter coordination mechanism, noise is not included in the effective control dimension, and the head-on wind speed brought by the vehicle speed is not fully considered, making it difficult to achieve a dynamic balance of heat dissipation, energy saving and noise reduction; noise control is missing, to ensure heat dissipation, the fan runs at high speed and produces a lot of noise, or to save energy, the fan is turned off and causes overheating risk; multiple heat source regulation imbalance, for the independent heat sources such as batteries, motors and electronic controls, only the heat dissipation demand of a single component is concerned, and there is a lack of coordinated regulation mechanism, resulting in low thermal management efficiency and difficulty in matching the differentiated heat production intensity of each heat source in different working conditions. SUMMARY

[0004] The present application aims to provide a fan control method for a new energy vehicle to solve the above problems in the prior art, by synchronously acquiring the real-time temperature, driving state data and real-time fan noise value of multiple heat sources, determining the maximum allowable speed in real time according to the real-time geographic position, and then fusing the real-time temperature and real-time driving speed of multiple heat sources to accurately calculate the target speed and generate a control signal, thereby realizing multi-dimensional dynamic coordinated control.

[0005] To achieve the above-mentioned purpose, the technical solutions adopted by the embodiments of the present application are as follows: In a first aspect, the embodiments of the present application provide a fan control method for a new energy vehicle, comprising: Obtaining real-time temperatures of multiple heat sources in a new energy vehicle, driving state data, and a real-time fan noise value; the driving state data includes a real-time driving speed and a real-time geographic position; According to the real-time geographic position and the real-time fan noise value, determining a maximum allowable rotating speed of an electronic fan of a thermal management system in the new energy vehicle; According to the real-time temperatures of the multiple heat sources, the real-time driving speed, and the maximum allowable rotating speed, determining a target rotating speed of the electronic fan; According to the target rotating speed, generating a control signal, and controlling the electronic fan according to the control signal.

[0006] In an optional implementation, the determining of the maximum allowable rotating speed of the electronic fan of the thermal management system in the new energy vehicle according to the real-time geographic position and the real-time fan noise value includes: According to the real-time geographic position, determining a noise sensitivity level of a current scene in which the new energy vehicle is located; According to the real-time fan noise value and the noise sensitivity level of the current scene, determining the maximum allowable rotating speed of the electronic fan.

[0007] In an optional implementation, the determining of the maximum allowable rotating speed of the electronic fan according to the real-time fan noise value and the noise sensitivity level of the current scene includes: Determining a maximum allowable noise limit value of the current scene under the noise sensitivity level; If the real-time fan noise value is greater than or equal to the maximum allowable noise limit value, determining, according to a preset noise-rotating speed characteristic curve, a rotating speed value corresponding to the maximum allowable noise limit value as the maximum allowable rotating speed; If the real-time fan noise value is less than the maximum allowable noise limit value, determining, according to the preset noise-rotating speed characteristic curve, a rotating speed value corresponding to the real-time fan noise value as the maximum allowable rotating speed.

[0008] In an optional implementation, the determining of the target rotating speed of the electronic fan according to the real-time temperatures of the multiple heat sources, the real-time driving speed, and the maximum allowable rotating speed includes: According to the real-time temperatures of the multiple heat sources, determining a comprehensive temperature; According to the comprehensive temperature, performing temperature decision to obtain a corresponding first decision rotating speed; According to the first decision rotating speed and the maximum allowable rotating speed, performing noise constraint to obtain a second decision rotating speed; According to the real-time driving speed, performing vehicle speed energy-saving compensation on the second decision rotating speed to obtain a third decision rotating speed; Determine the target speed according to the comprehensive temperature and the third decision speed.

[0009] In an optional embodiment, the determining of the comprehensive temperature according to the real-time temperatures of the plurality of heat sources comprises: Determine the thermal failure risk of each heat source according to the real-time temperature of each heat source; Determine the temperature weight coefficient of each heat source according to the thermal failure risk of each heat source; Weight the real-time temperatures of the plurality of heat sources according to the temperature weight coefficients of the heat sources to obtain the comprehensive temperature.

[0010] In an optional embodiment, the temperature decision-making according to the comprehensive temperature to obtain the corresponding first decision speed comprises: If the comprehensive temperature is greater than a preset safety temperature threshold and less than or equal to a preset warning temperature threshold, calculate the first decision speed according to the difference between the comprehensive temperature and the preset safety temperature threshold and a preset speed-temperature coefficient.

[0011] In an optional embodiment, the noise constraint according to the first decision speed and the maximum allowable speed to obtain the second decision speed comprises: If the first decision speed is less than or equal to the maximum allowable speed, determine the first decision speed as the second decision speed; If the first decision speed is greater than the maximum allowable speed, determine the maximum allowable speed as the second decision speed.

[0012] In an optional embodiment, the vehicle speed energy-saving compensation of the second decision speed according to the real-time driving speed to obtain the third decision speed comprises: Determine the corresponding air cooling compensation coefficient according to the real-time driving speed; Correct the second decision speed according to the air cooling compensation coefficient to obtain the third decision speed.

[0013] In an optional embodiment, the determining of the corresponding air cooling compensation coefficient according to the real-time driving speed comprises: Determine the air cooling compensation coefficient according to the real-time driving speed and a preset vehicle speed air cooling compensation coefficient characteristic curve; or The driving state data further comprises slope data, load data, state of charge data, and driving trajectory data; determine the working condition category of the new energy vehicle according to the real-time driving speed, the slope data, the load data, the state of charge data, the driving trajectory data, and the real-time geographic location; According to the working condition category, the air cooling compensation coefficient is determined.

[0014] In an optional implementation, the determining the target rotating speed according to the comprehensive temperature and the third decision rotating speed comprises: If the comprehensive temperature is less than or equal to the preset safety temperature threshold, the target rotating speed is determined as zero; If the comprehensive temperature is greater than or equal to a preset danger temperature threshold, the target rotating speed is determined as a rated rotating speed of the electronic fan; If the comprehensive temperature is greater than a preset safety temperature threshold and less than or equal to a preset early warning temperature threshold, the target rotating speed is determined as the third decision rotating speed.

[0015] In a second aspect, the embodiments of the present application further provide a fan control device of a new energy vehicle, comprising: An acquisition module is configured to acquire real-time temperatures of multiple heat sources in the new energy vehicle, driving state data and a real-time fan noise value; the driving state data comprises a real-time driving speed and a real-time geographic position; A determination module is configured to determine a maximum allowable rotating speed of an electronic fan of a thermal management system in the new energy vehicle according to the real-time geographic position and the real-time fan noise value; The determination module is configured to determine a target rotating speed of the electronic fan according to the real-time temperatures of the multiple heat sources, the real-time driving speed and the maximum allowable rotating speed; A control module is configured to generate a control signal according to the target rotating speed, and control the electronic fan according to the control signal.

[0016] In a third aspect, the embodiments of the present application further provide a fan controller, comprising a processor, a storage medium and a bus, the storage medium stores program instructions executable by the processor, when the computer device is running, the processor and the storage medium communicate through the bus, the processor executes the program instructions to execute the steps of the fan control method of the new energy vehicle in any of the first aspect.

[0017] In a fourth aspect, the embodiments of the present application further provide a computer readable storage medium, the storage medium stores a computer program, when the computer program is run by the processor, the steps of the fan control method of the new energy vehicle in any of the first aspect are executed.

[0018] In a fifth aspect, the embodiments of the present application further provide a thermal management system in a new energy vehicle, comprising the fan controller in the third aspect, the fan controller is configured to execute the steps of the fan control method of the new energy vehicle in any of the first aspect.

[0019] The beneficial effects of the present application are: The embodiment of the present application also provides a fan control method of a new energy vehicle, which comprises the following steps: acquiring real-time temperatures of multiple heat sources, driving state data and a real-time fan noise value of the new energy vehicle; the driving state data comprises a real-time driving speed and a real-time geographic position; determining a maximum allowable rotating speed of an electronic fan of a thermal management system in the new energy vehicle according to the real-time geographic position and the real-time fan noise value; determining a target rotating speed of the electronic fan according to the real-time temperatures of the multiple heat sources, the real-time driving speed and the maximum allowable rotating speed; and generating a control signal according to the target rotating speed and controlling the electronic fan according to the control signal. The method of the present application synchronously acquires the real-time temperatures of the multiple heat sources, the driving state data and the real-time fan noise value, determines the maximum allowable rotating speed in combination with the real-time geographic position, and then fuses the real-time temperatures of the multiple heat sources and the real-time driving speed to accurately calculate the target rotating speed and generate the control signal, so as to realize dynamic collaborative control in three dimensions of temperature, speed and noise, guarantee the heat dissipation safety of the multiple heat sources, avoid the noise exceeding the standard, effectively improve the vehicle mileage and driving comfort, and solve the pain points of single control dimension and low thermal management efficiency in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0021] Figure 1 One of the flowcharts of the fan control method of the new energy vehicle provided by the embodiment of the present application; Figure 2 The second flowchart of the fan control method of the new energy vehicle provided by the embodiment of the present application; Figure 3 The third flowchart of the fan control method of the new energy vehicle provided by the embodiment of the present application; Figure 4 The schematic diagram of the preset noise rotating speed characteristic curve provided by the embodiment of the present application; Figure 5 The fourth flowchart of the fan control method of the new energy vehicle provided by the embodiment of the present application; Figure 6 The fifth flowchart of the fan control method of the new energy vehicle provided by the embodiment of the present application; Figure 7 The sixth flowchart of the fan control method of the new energy vehicle provided by the embodiment of the present application; Figure 8 A schematic diagram of a preset vehicle speed air-cooling compensation coefficient characteristic curve provided for an embodiment of this application; Figure 9 A functional module diagram of a fan control device for a new energy vehicle provided in an embodiment of this application; Figure 10 This is a schematic diagram of a fan controller provided in an embodiment of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0023] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0024] In the description of this application, it should be noted that if the terms "upper", "lower", etc. appear to indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship that the product of this application is usually placed in, it is only for the convenience of describing this application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0025] Furthermore, the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Additionally, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] It should be noted that, where there is no conflict, the features in the embodiments of this application can be combined with each other.

[0027] The fan control method for new energy vehicles provided in this application will be explained in detail below with reference to the accompanying drawings and specific examples. The fan control method for new energy vehicles provided in this application can also be implemented by computer equipment through the execution of algorithms or software. The computer equipment can be, for example, a server or a terminal, and the terminal can be a user computer. Figure 1 This is one of the flowcharts illustrating a fan control method for a new energy vehicle provided in an embodiment of this application. Figure 1 As shown, the method includes: S101. Obtain real-time temperature, driving status data, and real-time fan noise values ​​of multiple heat sources in new energy vehicles.

[0028] The driving status data includes: real-time driving speed and real-time geographical location.

[0029] In this embodiment, a temperature sensor array is used to synchronously collect the real-time temperature of multiple heat sources. The heat sources are components in the thermal management system that generate heat during operation, such as battery packs, motor stators, electronic control systems, and cooling modules. The electronic control system includes a motor controller, a DC-DC converter, and an AC-DC converter.

[0030] For example, taking multiple heat sources such as a battery pack, motor stator, and electronic control system as examples, the highest real-time temperature T1 of each battery cell, the real-time temperature T2 of the motor stator, and the highest real-time temperature T3 of the electronic control system are collected by temperature sensors. In addition, the ambient temperature T4 and the inlet / outlet temperature Tin / Tou of the cooling module are also collected by temperature sensors as reference data for the real-time temperature of multiple heat sources. Here, the number of heat sources is not limited.

[0031] The vehicle speed sensor collects the real-time driving speed V (unit: km / h), the GPS module collects the real-time geographical location, and the noise sensor collects the real-time fan noise value Z_env (unit: dB(A)). The collection frequency can be uniformly set to 10Hz to ensure data timestamp consistency.

[0032] S102. Determine the maximum permissible speed of the electronic fan in the thermal management system of new energy vehicles based on real-time geographical location and real-time fan noise value.

[0033] S103. Determine the target speed of the electric fan based on the real-time temperature of multiple heat sources, real-time driving speed, and maximum permissible speed.

[0034] Specifically, the real-time temperature, real-time fan noise value, and real-time driving speed of multiple heat sources are standardized: the 3σ criterion is used to remove outliers (such as sudden changes caused by sensor failure) in the real-time temperature and real-time fan noise values ​​of multiple heat sources; and the real-time driving speed and real-time fan noise value are smoothed by moving average filtering (with a window size of 5 sampling points) to eliminate high-frequency interference.

[0035] The system performs thermal inertia compensation correction on the real-time temperatures of multiple heat sources. For example, it corrects the current real-time temperature value based on the temperature change rate of the previous three sampling cycles to avoid miscontrol caused by instantaneous temperature fluctuations. Finally, it outputs the standardized real-time temperatures of multiple heat sources, real-time fan noise values, and real-time driving speeds.

[0036] Then, based on the real-time geographical location, the maximum permissible noise limit for the current scenario of the new energy vehicle is determined. By comparing the maximum permissible noise limit with the real-time fan noise value, the maximum permissible speed of the electronic fan in the thermal management system of the new energy vehicle is determined.

[0037] The system analyzes and judges the real-time temperature, real-time driving speed, and maximum allowable speed of multiple heat sources to determine the target speed of the electric fan.

[0038] S104. Generate a control signal based on the target rotation speed, and control the electric fan according to the control signal.

[0039] Based on the speed-duty cycle calibration curve of the fan drive motor, the target speed is converted into the corresponding PWM duty cycle signal, i.e., the control signal. The specific formula is: D=D0+(N_final / N0)×(D1-D0), where D0 is the minimum duty cycle for starting (e.g., 10%), D1 is the duty cycle corresponding to the rated speed (e.g., 90%), N_final is the target speed, and N0 is the rated speed of the electronic fan. The frequency of the output PWM signal can be set to 20kHz to ensure smooth fan drive.

[0040] After receiving the control signal, the electric fan is driven to run at the target speed N_final. At the same time, the built-in speed sensor of the electric fan collects the actual speed N_actual in real time and feeds it back through the CAN bus. By comparing the target speed N_actual with the actual speed N_final, if the speed deviation is >5%, the PWM duty cycle is dynamically adjusted for compensation, forming a closed-loop speed control to ensure execution accuracy.

[0041] In summary, this application also provides a fan control method for a new energy vehicle. The method includes: acquiring real-time temperature, driving status data, and real-time fan noise values ​​of multiple heat sources in the new energy vehicle; the driving status data includes real-time driving speed and real-time geographical location; determining the maximum allowable speed of the electronic fan in the thermal management system of the new energy vehicle based on the real-time geographical location and the real-time fan noise value; determining the target speed of the electronic fan based on the real-time temperature, real-time driving speed, and maximum allowable speed of the multiple heat sources; generating a control signal based on the target speed; and controlling the electronic fan based on the control signal. This method, by simultaneously acquiring real-time temperature, driving status data, and real-time fan noise values ​​of multiple heat sources, determining the maximum allowable speed based on the real-time geographical location, and then accurately calculating the target speed and generating a control signal by integrating the real-time temperature and real-time driving speed of multiple heat sources, achieves dynamic coordinated control of temperature, vehicle speed, and noise in three dimensions. This ensures the heat dissipation safety of multiple heat sources, avoids excessive noise, effectively improves the overall vehicle range and driving comfort, and solves the pain points of existing technologies such as single control dimensions and low thermal management efficiency.

[0042] Based on the methods provided in the above embodiments, this application also provides another possible implementation of a fan control method for new energy vehicles. Figure 2 This is a second schematic flowchart illustrating a fan control method for a new energy vehicle provided in an embodiment of this application. Figure 2 As shown, based on real-time geographical location and real-time fan noise levels, the maximum permissible speed of the electronic fan in the thermal management system of new energy vehicles is determined, including: S201. Determine the noise sensitivity level of the current scene where the new energy vehicle is located based on the real-time geographical location.

[0043] In this embodiment, the real-time geographic location collected by the Global Positioning System (GPS) module is matched with a geofence database to determine the current scene of the new energy vehicle and to complete the noise sensitivity level calibration. For example, the noise sensitivity levels may include: Level 1 noise sensitivity level, Level 2 noise sensitivity level, and Level 3 noise sensitivity level. Scenes corresponding to Level 1 noise sensitivity level may include nearby schools, hospitals, residential areas, etc.; scenes corresponding to Level 2 noise sensitivity level may include roadsides, etc.; and scenes corresponding to Level 3 noise sensitivity level may include suburbs, mining areas, etc. Different basic noise thresholds are set for different scenes during the day and night.

[0044] S202. Determine the maximum permissible speed of the electronic fan based on the real-time fan noise value and the noise sensitivity level of the current scene.

[0045] Specifically, the maximum permissible speed of the electronic fan is determined by comparing the real-time fan noise value with the noise sensitivity level of the current scene.

[0046] Optionally, Figure 3 This is the third flowchart illustrating a fan control method for a new energy vehicle provided in this application embodiment. Figure 4 This is a schematic diagram of a preset noise-speed characteristic curve provided in an embodiment of this application. Figure 3 As shown, step S202 specifically includes: S301. Determine the maximum permissible noise limit for the current scene at the noise sensitivity level.

[0047] S302. If the real-time fan noise value is greater than or equal to the maximum permissible noise limit, the speed value corresponding to the maximum permissible noise limit shall be determined as the maximum permissible speed according to the preset noise speed characteristic curve.

[0048] S303. If the real-time fan noise value is less than the maximum permissible noise limit, the speed value corresponding to the real-time fan noise value shall be determined as the maximum permissible speed according to the preset noise speed characteristic curve.

[0049] Specifically, different basic noise thresholds are set for daytime and nighttime noise levels under different noise sensitivity levels. For example, in a Level 1 sensitive scenario, the maximum permissible noise limit during the day is Z=65dB(A), and the maximum permissible noise limit at night is Z=55dB(A); in a Level 2 sensitive scenario, the maximum permissible noise limit during the day is Z=75dB(A), and the maximum permissible noise limit at night is Z=65dB(A). Then, by combining time information, the maximum permissible noise limit for new energy vehicles in the current scenario is determined.

[0050] like Figure 4 As shown, the preset noise speed characteristic curve is obtained in advance through bench testing. The horizontal axis of the preset noise speed characteristic curve is the speed and the vertical axis is the fan noise. The preset noise speed characteristic curve records the noise values ​​corresponding to different noise levels.

[0051] The formula for calculating the fan noise Z is: Z = Z0 + 50 × Where Z0 represents the noise level corresponding to the fan's rated speed, and N represents the current fan speed. This is expressed as the fan's rated speed, thus constructing a preset noise-speed characteristic curve. Then, by comparing the real-time fan noise value with the maximum permissible noise limit, the final maximum permissible speed is determined based on the preset noise-speed characteristic curve.

[0052] For example, if the real-time fan noise value is 85dB(A) and the maximum permissible noise limit is 75dB(A), then the speed value corresponding to 75dB(A) is determined as the maximum permissible speed; if the real-time fan noise value is 65dB(A) and the maximum permissible noise limit is 75dB(A), then the speed value corresponding to 65dB(A) is determined as the maximum permissible speed.

[0053] The method provided in this application determines the scene noise sensitivity level based on real-time geographical location, and then determines the maximum allowable fan speed by combining the real-time fan noise value. Specifically, the maximum allowable noise limit corresponding to the scene noise sensitivity level is first determined, and then the maximum allowable speed is determined by a preset noise speed characteristic curve based on the relationship between the real-time fan noise value and the limit. This ensures the accurate implementation of noise constraints and avoids uncontrolled fan speed due to real-time noise fluctuations. It prevents noise from exceeding the standard and affecting the environment and comfort, and ensures that the fan speed can adapt to the current noise environment. This achieves refined and reliable noise control, and breaks through the limitations of traditional fixed noise thresholds. It realizes the scene-based dynamic adaptation of noise constraints, which can accurately control noise pollution in sensitive areas such as schools and residential areas, and meet heat dissipation needs in ordinary scenarios. It balances noise control and heat dissipation efficiency, and significantly improves the environmental adaptability and comfort of pure electric heavy trucks in diverse scenarios.

[0054] This application also provides another possible implementation of a fan control method for new energy vehicles. Figure 5 This is the fourth flowchart illustrating a fan control method for a new energy vehicle provided in this application embodiment. Figure 5 As shown, the target speed of the electric fan is determined based on the real-time temperature of multiple heat sources, real-time driving speed, and maximum permissible speed, including: S401. Determine the overall temperature based on the real-time temperatures of multiple heat sources.

[0055] S402. Make a temperature decision based on the comprehensive temperature to obtain the corresponding first decision speed.

[0056] In this embodiment, the real-time temperatures of multiple heat sources are weighted and calculated according to their respective weights to obtain a comprehensive temperature. This comprehensive temperature is then compared with multiple preset temperature thresholds to determine the corresponding first decision rotation speed.

[0057] Optionally, if the overall temperature is greater than the preset safe temperature threshold and less than or equal to the preset warning temperature threshold, the first decision speed is calculated based on the difference between the overall temperature and the preset safe temperature threshold, and the preset speed temperature coefficient.

[0058] Specifically, the multiple preset temperature thresholds include a preset safe temperature threshold and a preset warning temperature threshold. The preset safe temperature threshold T_safe can be set to 45℃, and the preset warning temperature threshold T_warn can be set to 55℃.

[0059] If the overall temperature T is greater than 45℃ and less than or equal to 55℃, the first decision speed N_base is calculated based on the difference between the overall temperature and the preset safe temperature threshold, as well as the preset speed temperature coefficient. The specific calculation formula is: N_base=K1×(T-T_safe), where K1 is the preset speed temperature coefficient, for example, set to 50r / (min·℃).

[0060] It should be noted that among the multiple preset temperature thresholds is a preset danger temperature threshold, which can be set to 65℃. The overall temperature T, the preset safe temperature threshold T_safe, and the preset danger temperature threshold T_danger are also compared separately. If the overall temperature T is less than or equal to the preset safe temperature threshold T_safe, the target speed is directly set to zero; if the overall temperature T is greater than or equal to the preset danger temperature threshold T_danger, the emergency cooling mode is triggered, and the target speed is directly set to the rated speed N0 of the electric fan.

[0061] S403. Based on the first decision speed and the maximum allowable speed, noise constraints are applied to obtain the second decision speed.

[0062] Optionally, if the first decision speed is less than or equal to the maximum permissible speed, then the first decision speed is determined as the second decision speed. If the first decision speed is greater than the maximum permissible speed, then the maximum permissible speed is determined as the second decision speed.

[0063] Specifically, the first decision speed N_base and the maximum allowable speed N_max are compared to determine the second decision speed. If N_base ≤ N_max, then N_base is retained as the second decision speed N_temp; if N_base > N_max, then the second decision speed N_temp is set to N_max, and the current temperature status is recorded. If T > T_danger at this time, the rated speed N0 of the electric fan is still given priority, and a high temperature alarm signal is output to the VCU.

[0064] S404. Based on the real-time driving speed, the second decision speed is compensated for with vehicle speed energy saving to obtain the third decision speed.

[0065] Specifically, based on the real-time driving speed, speed-based energy-saving compensation is applied to the second decision speed N_temp to obtain the third decision speed.

[0066] S405. Determine the target speed based on the overall temperature and the third decision speed.

[0067] Specifically, by taking into account the overall temperature, it is determined whether the target speed is the third decision speed.

[0068] Optionally, if the overall temperature is less than or equal to a preset safe temperature threshold, the target speed is determined to be zero; if the overall temperature is greater than or equal to a preset dangerous temperature threshold, the target speed is determined to be the rated speed of the electric fan; if the overall temperature is greater than the preset safe temperature threshold but less than or equal to a preset warning temperature threshold, the target speed is determined to be the third decision speed.

[0069] Specifically, when T > T_danger, temperature has the highest priority, and the target speed N_final = N0 is forced to be output, ignoring noise constraints; when T_safe < T ≤ T_warn, noise constraints take precedence over vehicle speed energy saving, and the target speed is determined as the third decision speed, and it must be ensured that the target speed ≤ N_max; when T ≤ T_safe, vehicle speed energy saving takes precedence, and the target speed N_final = 0 is directly output; at the same time, self-learning optimization is performed based on historical data, and the K1 coefficient is dynamically corrected to improve the control accuracy under different operating conditions.

[0070] The method provided in this application calculates the combined temperature of multiple heat sources, optimizes the rotational speed step by step through temperature decision-making, noise constraints, and vehicle speed energy-saving compensation, and finally determines the target rotational speed by combining the combined temperature. This constructs a hierarchical and progressive rotational speed decision-making mechanism, which not only solves the problem of imbalance in the control of multiple heat sources, but also achieves a dynamic balance between heat dissipation demand, noise control, and energy-saving goals. It avoids insufficient heat dissipation or energy waste caused by a single decision dimension, and improves the intelligence and adaptability of fan control.

[0071] This application also provides another possible implementation of a fan control method for new energy vehicles. Figure 6 This is the fifth flowchart illustrating a fan control method for a new energy vehicle provided in this application embodiment. Figure 6 As shown, the overall temperature is determined based on the real-time temperatures of multiple heat sources, including: S501. Determine the thermal failure risk of each heat source based on its real-time temperature.

[0072] S502. Determine the temperature weighting coefficient of each heat source based on the thermal failure risk of each heat source.

[0073] S503. Based on the temperature weighting coefficient of each heat source, the real-time temperatures of multiple heat sources are weighted to obtain the comprehensive temperature.

[0074] In this embodiment, the temperature range of each heat source is determined based on the real-time temperature of each heat source. The temperature range includes a safe range, a warning range, and a danger range. Based on the temperature range of each heat source, the thermal failure risk of each heat source is determined.

[0075] Based on the thermal failure risk, a weighting coefficient is set to determine the final temperature weighting coefficient of each heat source. For example, the thermal failure risk of the battery pack is 0.5, the thermal failure risk of the motor stator is 0.3, and the thermal failure risk of the electronic control system is 0.2.

[0076] Based on the temperature weighting coefficient of each heat source, the real-time temperatures of multiple heat sources are weighted to obtain the comprehensive temperature. The calculation formula is: T=0.5×T1+0.3×T2+0.2×T3, where T1, T2, and T3 are the real-time temperatures of the battery pack, the motor stator, and the electronic control system, respectively.

[0077] In the method provided in this application embodiment, a temperature weighting coefficient is set according to the thermal failure risk of each heat source, and the real-time temperature of multiple heat sources is weighted to obtain a comprehensive temperature. This breaks through the limitation of independent control of a single heat source, highlights the temperature priority of core components such as batteries and motors, can accurately reflect the overall needs of vehicle thermal management, avoid safety hazards caused by ignoring the thermal failure risk of key components, and improve the overall efficiency and reliability of the thermal management system.

[0078] This application also provides another possible implementation of a fan control method for new energy vehicles. Figure 7 This is the sixth flowchart illustrating a fan control method for a new energy vehicle provided in this application embodiment. Figure 8 This is a schematic diagram of a preset vehicle speed air-cooling compensation coefficient characteristic curve provided in an embodiment of this application. Figure 7 As shown, based on the real-time driving speed, speed-based energy-saving compensation is applied to the second decision speed to obtain the third decision speed, which includes: S601. Determine the corresponding air-cooling compensation coefficient based on the real-time driving speed.

[0079] S602. Based on the air-cooling compensation coefficient, the second decision speed is corrected to obtain the third decision speed.

[0080] In this embodiment, there is a correlation between real-time driving speed and air-cooling compensation coefficient, so the corresponding air-cooling compensation coefficient can be determined based on the real-time driving speed.

[0081] Optionally, the air-cooling compensation coefficient can be determined based on the real-time driving speed and the preset vehicle speed air-cooling compensation coefficient characteristic curve.

[0082] Specifically, such as Figure 8As shown, the characteristic curve of the preset vehicle speed air cooling compensation coefficient is obtained based on CFD simulation and real vehicle calibration correction. There are deviations due to different fans and hardware. The horizontal axis of the characteristic curve of the preset vehicle speed air cooling compensation coefficient is the driving speed, i.e., vehicle speed, and the vertical axis is the air cooling compensation coefficient. The characteristic curve of the preset vehicle speed air cooling compensation coefficient records the air cooling compensation coefficient corresponding to different vehicle speeds.

[0083] Among them, the air-cooling compensation coefficient K is the proportion of the contribution of natural wind brought by vehicle speed to the fan speed correction ratio. The value range is 0≤K≤1.2. K=1 is the baseline compensation, K>1 requires additional speed increase, and K=0 does not require compensation.

[0084] The formula for calculating fan speed is: N = K × N b +N e , where N b The base speed (reference value when the vehicle is parked without natural wind), N e This represents a fixed temperature / load compensation. As vehicle speed increases, the heat dissipation contribution of natural wind increases, the air-cooling compensation coefficient decreases, and consequently, the fan speed decreases. Therefore, based on the preset vehicle speed air-cooling compensation coefficient characteristic curve, the air-cooling compensation coefficient corresponding to the real-time driving speed is determined.

[0085] Alternatively, driving status data also includes: slope data, load data, charging status data, and driving trajectory data. Based on real-time driving speed, slope data, load data, charging status data, driving trajectory data, and real-time geographical location, the operating condition category of the new energy vehicle is determined; based on the operating condition category, the air-cooling compensation coefficient is determined.

[0086] Specifically, based on real-time driving speed, gradient data, load data, charging status data, driving trajectory data, and real-time geographical location, the operating condition category of the new energy vehicle is determined. Operating condition categories include high-speed cruising, low-speed urban driving, heavy-load hill climbing, stationary charging, and idling. Based on the operating condition category, the corresponding air-cooling compensation coefficient is determined. For example, when parking (V=0), the corresponding air-cooling compensation coefficient K=1; when stationary charging / heavy-load operation while parked, the corresponding air-cooling compensation coefficient K=1.2; when low speed (0<V≤30), the corresponding air-cooling compensation coefficient K=1-0.0167V; when medium speed (30<V≤80), the corresponding air-cooling compensation coefficient K=0.92-0.014V; when high speed (V>80), the corresponding air-cooling compensation coefficient K=0; and when high speed and heavy load, the corresponding air-cooling compensation coefficient K=0.1.

[0087] Based on the air-cooling compensation coefficient, the second decision speed is corrected to obtain the third decision speed. Specifically, the calculation expression for the third decision speed is: N_final = N_temp × (1-K). If N_final < 0, then the third decision speed N_final = 0. After correction, the noise value corresponding to the third decision speed N_final is checked again to see if it meets the noise sensitivity level requirements of the current scenario, ensuring that energy saving and noise constraints are both taken into account.

[0088] In the method provided in this application embodiment, the air-cooling compensation coefficient is determined based on the real-time driving speed, and then the second decision speed is corrected to obtain the third decision speed. This fully utilizes the natural air-cooling effect brought by the vehicle speed, reasonably reduces the fan speed to achieve energy saving when driving at high speed, and ensures that the speed meets the heat dissipation requirements under low-speed conditions. This solves the problem of energy waste or insufficient heat dissipation caused by the failure to consider the oncoming wind speed in the prior art, and improves the overall vehicle range and thermal management efficiency.

[0089] The following will continue to explain the fan control device and fan controller for new energy vehicles provided in any of the above embodiments of this application. The specific implementation process and the resulting technical effects are the same as those in the corresponding method embodiments. For the sake of brevity, parts not mentioned in this embodiment can be referred to the corresponding content in the method embodiments.

[0090] Figure 9 This is a functional module diagram of a fan control device for a new energy vehicle provided in an embodiment of this application. Figure 9 As shown, the fan control device 100 of the new energy vehicle includes: The acquisition module 110 is used to acquire real-time temperature, driving status data and real-time fan noise value of multiple heat sources in the new energy vehicle; the driving status data includes: real-time driving speed and real-time geographical location; The determination module 120 is used to determine the maximum allowable speed of the electronic fan in the thermal management system of new energy vehicles based on real-time geographical location and real-time fan noise value. The determination module 120 is also used to determine the target speed of the electric fan based on the real-time temperature of multiple heat sources, the real-time driving speed, and the maximum allowable speed. The control module 130 is used to generate a control signal based on the target rotation speed and to control the electric fan based on the control signal.

[0091] Optionally, the determining module 120 is also used to determine the noise sensitivity level of the current scene where the new energy vehicle is located based on the real-time geographical location; and to determine the maximum allowable speed of the electronic fan based on the real-time fan noise value and the noise sensitivity level of the current scene.

[0092] Optionally, the determining module 120 is further configured to determine the maximum permissible noise limit of the current scene under the noise sensitivity level; if the real-time fan noise value is greater than or equal to the maximum permissible noise limit, the speed value corresponding to the maximum permissible noise limit is determined as the maximum permissible speed according to the preset noise speed characteristic curve; if the real-time fan noise value is less than the maximum permissible noise limit, the speed value corresponding to the real-time fan noise value is determined as the maximum permissible speed according to the preset noise speed characteristic curve.

[0093] Optionally, the determining module 120 is further configured to determine a comprehensive temperature based on the real-time temperatures of multiple heat sources; make a temperature decision based on the comprehensive temperature to obtain a corresponding first decision speed; perform noise constraints based on the first decision speed and the maximum allowable speed to obtain a second decision speed; perform vehicle speed energy-saving compensation on the second decision speed based on the real-time driving speed to obtain a third decision speed; and determine a target speed based on the comprehensive temperature and the third decision speed.

[0094] Optionally, the determining module 120 is also used to determine the thermal failure risk of each heat source based on the real-time temperature of each heat source; determine the temperature weighting coefficient of each heat source based on the thermal failure risk of each heat source; and weight the real-time temperatures of multiple heat sources based on the temperature weighting coefficient of each heat source to obtain a comprehensive temperature.

[0095] Optionally, the determining module 120 is further configured to calculate the first decision speed based on the difference between the comprehensive temperature and the preset safe temperature threshold, and the preset speed temperature coefficient, if the comprehensive temperature is greater than the preset safe temperature threshold and less than or equal to the preset warning temperature threshold.

[0096] Optionally, the determining module 120 is further configured to determine the first decision speed as the second decision speed if the first decision speed is less than or equal to the maximum allowable speed; and to determine the maximum allowable speed as the second decision speed if the first decision speed is greater than the maximum allowable speed.

[0097] Optionally, the determining module 120 is also used to determine the corresponding air-cooling compensation coefficient based on the real-time driving speed; and to correct the second decision speed based on the air-cooling compensation coefficient to obtain the third decision speed.

[0098] Optionally, the determining module 120 is also used to determine the air-cooling compensation coefficient based on the real-time driving speed and the preset vehicle speed air-cooling compensation coefficient characteristic curve; or; the driving status data also includes: slope data, load data, charging status data, driving trajectory data, and the working condition category of the new energy vehicle based on the real-time driving speed, slope data, load data, charging status data, driving trajectory data and real-time geographical location; and the air-cooling compensation coefficient is determined based on the working condition category.

[0099] Optionally, the determining module 120 is further configured to determine the target speed as zero if the overall temperature is less than or equal to a preset safe temperature threshold; determine the target speed as the rated speed of the electric fan if the overall temperature is greater than or equal to a preset dangerous temperature threshold; and determine the target speed as the third decision speed if the overall temperature is greater than the preset safe temperature threshold and less than or equal to a preset warning temperature threshold.

[0100] The above-described device is used to execute the method provided in the foregoing embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0101] These modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more microprocessors, or one or more Field Programmable Gate Arrays (FPGAs). Alternatively, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a system-on-a-chip (SOC).

[0102] Figure 10 This is a schematic diagram of a fan controller provided in an embodiment of this application. This fan controller can be used for fan control in new energy vehicles. Figure 10 As shown, the fan controller includes: a processor 210, a storage medium 220, and a bus 230.

[0103] Storage medium 220 stores machine-readable instructions executable by processor 210. When the fan controller is running, processor 210 communicates with storage medium 220 via bus 230, and processor 210 executes the machine-readable instructions to perform the steps of the above method embodiment. The specific implementation and technical effects are similar and will not be described in detail here.

[0104] Optionally, this application also provides a storage medium 220, on which a computer program is stored. When the computer program is run by a processor, it executes the steps of the above-described method embodiments. The specific implementation and technical effects are similar, and will not be repeated here.

[0105] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0106] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0107] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0108] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of the present invention. The aforementioned 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.

[0109] The above are merely specific embodiments 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 fan control method for a new energy vehicle, characterized in that, include: Acquire real-time temperature, driving status data, and real-time fan noise values ​​of multiple heat sources in new energy vehicles; The driving status data includes: real-time driving speed and real-time geographical location; Based on the real-time geographical location and the real-time fan noise value, determine the maximum allowable speed of the electronic fan in the thermal management system of the new energy vehicle; The target rotational speed of the electronic fan is determined based on the real-time temperatures of the multiple heat sources, the real-time driving speed, and the maximum permissible rotational speed. A control signal is generated based on the target rotation speed, and the electronic fan is controlled based on the control signal.

2. The method according to claim 1, characterized in that, The step of determining the maximum permissible speed of the electronic fan in the thermal management system of the new energy vehicle based on the real-time geographical location and the real-time fan noise value includes: Based on the real-time geographical location, determine the noise sensitivity level of the current scene in which the new energy vehicle is located; The maximum permissible speed of the electronic fan is determined based on the real-time fan noise value and the noise sensitivity level of the current scene.

3. The method according to claim 2, characterized in that, The step of determining the maximum permissible speed of the electronic fan based on the real-time fan noise value and the noise sensitivity level of the current scene includes: Determine the maximum permissible noise limit for the current scene at the noise sensitivity level; If the real-time fan noise value is greater than or equal to the maximum permissible noise limit, the speed value corresponding to the maximum permissible noise limit is determined as the maximum permissible speed according to the preset noise speed characteristic curve; If the real-time fan noise value is less than the maximum permissible noise limit, the speed value corresponding to the real-time fan noise value is determined as the maximum permissible speed based on the preset noise-speed characteristic curve.

4. The method according to claim 1, characterized in that, Determining the target rotational speed of the electronic fan based on the real-time temperatures of the multiple heat sources, the real-time driving speed, and the maximum permissible rotational speed includes: The overall temperature is determined based on the real-time temperatures of the multiple heat sources. A temperature decision is made based on the comprehensive temperature to obtain the corresponding first decision speed. Based on the first decision speed and the maximum allowable speed, a noise constraint is applied to obtain the second decision speed; Based on the real-time driving speed, the second decision speed is compensated for energy saving to obtain the third decision speed. The target speed is determined based on the combined temperature and the third decision speed.

5. The method according to claim 4, characterized in that, The step of determining the overall temperature based on the real-time temperatures of the multiple heat sources includes: Based on the real-time temperature of each heat source, determine the thermal failure risk of each heat source; Based on the thermal failure risk of each heat source, determine the temperature weighting coefficient of each heat source; The combined temperature is obtained by weighting the real-time temperatures of multiple heat sources according to the temperature weighting coefficients of each heat source.

6. The method according to claim 4, characterized in that, The step of making a temperature decision based on the comprehensive temperature to obtain the corresponding first decision speed includes: If the overall temperature is greater than a preset safe temperature threshold and less than or equal to a preset warning temperature threshold, then the first decision speed is calculated based on the difference between the overall temperature and the preset safe temperature threshold, and the preset speed temperature coefficient.

7. The method according to claim 6, characterized in that, The step of obtaining the second decision speed by applying noise constraints based on the first decision speed and the maximum permissible speed includes: If the first decision speed is less than or equal to the maximum allowable speed, then the first decision speed is determined to be the second decision speed; If the first decision speed is greater than the maximum allowable speed, then the maximum allowable speed is determined to be the second decision speed.

8. The method according to claim 4, characterized in that, The step of performing speed-saving compensation on the second decision speed based on the real-time driving speed to obtain the third decision speed includes: Determine the corresponding air-cooling compensation coefficient based on the real-time driving speed; Based on the air-cooling compensation coefficient, the second decision speed is corrected to obtain the third decision speed.

9. The method according to claim 8, characterized in that, The step of determining the corresponding air-cooling compensation coefficient based on the real-time driving speed includes: The air-cooling compensation coefficient is determined based on the real-time driving speed and the preset vehicle speed air-cooling compensation coefficient characteristic curve; or; The driving status data also includes: slope data, load data, charging status data, and driving trajectory data. Based on the real-time driving speed, slope data, load data, charging status data, driving trajectory data, and real-time geographical location, the operating condition category of the new energy vehicle is determined. The air-cooling compensation coefficient is determined based on the operating condition category.

10. The method according to claim 6, characterized in that, Determining the target speed based on the comprehensive temperature and the third decision speed includes: If the overall temperature is less than or equal to the preset safe temperature threshold, then the target rotational speed is determined to be zero. If the overall temperature is greater than or equal to a preset dangerous temperature threshold, then the target speed is determined to be the rated speed of the electronic fan; If the overall temperature is greater than the preset safe temperature threshold and less than or equal to the preset warning temperature threshold, the target speed is determined as the third decision speed.

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